Transcriptomic Maps of Colorectal Liver Metastasis: Machine Learning of Gene Activation Patterns and Epigenetic Trajectories in Support of Precision Medicine

Abstract

Simple Summary
Liver metastasis is a significant factor contributing to mortality associated with colorectal cancer. Establishing the biological mechanisms of metastasis is crucial for refining diagnostics and identifying therapeutic windows for interventions. Currently, little is known of the processes that govern the development of liver metastases, the role of the tumor microenvironment, the role of epigenetics, and potential treatment-induced shaping effects. Machine learning-based bioinformatics has provided an important methodical option to decipher fine-granular details of the transcriptomic landscape of tumor heterogeneity and the underlying molecular mechanisms. Our molecular portrayal method has potential implications for treatment decisions, which may require personalized diagnostics.

Abstract
The molecular mechanisms of the liver metastasis of colorectal cancer (CRLM) remain poorly understood. Here, we applied machine learning and bioinformatics trajectory inference to analyze a gene expression dataset of CRLM. We studied the co-regulation patterns at the gene level, the potential paths of tumor development, their functional context, and their prognostic relevance. Our analysis confirmed the subtyping of five liver metastasis subtypes (LMS). We provide gene-marker signatures for each LMS, and a comprehensive functional characterization that considers both the hallmarks of cancer and the tumor microenvironment. The ordering of CRLMs along a pseudotime-tree revealed a continuous shift in expression programs, suggesting a developmental relationship between the subtypes. Notably, trajectory inference and personalized analysis discovered a range of epigenetic states that shape and guide metastasis progression. By constructing prognostic maps that divided the expression landscape into regions associated with favorable and unfavorable prognoses, we derived a prognostic expression score. This was associated with critical processes such as epithelial–mesenchymal transition, treatment resistance, and immune evasion. These factors were associated with responses to neoadjuvant treatment and the formation of an immuno-suppressive, mesenchymal state. Our machine learning-based molecular profiling provides an in-depth characterization of CRLM heterogeneity with possible implications for treatment and personalized diagnostics.

Full text

Citation: Ashekyan, O.; Shahbazyan,
N.; Bareghamyan, Y.; Kudryavzeva,
A.; Mandel, D.; Schmidt, M.;
Loeffler-Wirth, H.; Uduman, M.;
Chand, D.; Underwood, D.; et al.
Transcriptomic Maps of Colorectal
Liver Metastasis: Machine Learning
of Gene Activation Patterns and
Epigenetic Trajectories in Support of
Precision Medicine. Cancers 2023,15,
3835. https://doi.org/10.3390/
cancers15153835
Academic Editor: David Wong
Received: 6 July 2023
Revised: 24 July 2023
Accepted: 26 July 2023
Published: 28 July 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
cancers
Article
Transcriptomic Maps of Colorectal Liver Metastasis: Machine
Learning of Gene Activation Patterns and Epigenetic
Trajectories in Support of Precision Medicine
Ohanes Ashekyan 1, † , Nerses Shahbazyan 1, † , Yeva Bareghamyan 1, Anna Kudryavzeva 1, Daria Mandel 1,
Maria Schmidt 2, Henry Loeffler-Wirth 2, Mohamed Uduman 3, Dhan Chand 3, Dennis Underwood 3,
Garo Armen 3, Arsen Arakelyan 4, Lilit Nersisyan 1and Hans Binder 1, 2,*
1Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia;
ohanes.ashekyan@abi.am (O.A.); nerses.shahbazyan@abi.am (N.S.); yeva.bareghamyan@abi.am (Y.B.);
anyakudryavceva804@gmail.com (A.K.); daria.laricheva@abi.am (D.M.); lilit.nersisyan@abi.am (L.N.)
2IZBI, Interdisciplinary Centre for Bioinformatics, Universität Leipzig, Härtelstr. 16–18,
04107 Leipzig, Germany; schmidt@izbi.uni-leipzig.de (M.S.); wirth@izbi.uni-leipzig.de (H.L.-W.)
3Agenus Inc., 3 Forbes Road, Lexington, MA 7305, USA; mohamed.uduman@agenusbio.com (M.U.);
dhan.chand@agenusbio.com (D.C.); dennis.underwood@agenusbio.com (D.U.); armen@agenusbio.com (G.A.)
4Institute of Molecular Biology of the National Academy of Sciences of the Republic of Armenia,
7 Has-Ratyan Str., Yerevan 0014, Armenia; arsen.arakelyan@abi.am
*Correspondence: hans.binder@abi.am
†These authors contributed equally to this work.
Simple Summary:
Liver metastasis is a significant factor contributing to mortality associated with
colorectal cancer. Establishing the biological mechanisms of metastasis is crucial for refining di-
agnostics and identifying therapeutic windows for interventions. Currently, little is known of the
processes that govern the development of liver metastases, the role of the tumor microenvironment,
the role of epigenetics, and potential treatment-induced shaping effects. Machine learning-based
bioinformatics has provided an important methodical option to decipher fine-granular details of the
transcriptomic landscape of tumor heterogeneity and the underlying molecular mechanisms. Our
molecular portrayal method has potential implications for treatment decisions, which may require
personalized diagnostics.
Abstract:
The molecular mechanisms of the liver metastasis of colorectal cancer (CRLM) remain
poorly understood. Here, we applied machine learning and bioinformatics trajectory inference to
analyze a gene expression dataset of CRLM. We studied the co-regulation patterns at the gene level,
the potential paths of tumor development, their functional context, and their prognostic relevance.
Our analysis confirmed the subtyping of five liver metastasis subtypes (LMS). We provide gene-
marker signatures for each LMS, and a comprehensive functional characterization that considers
both the hallmarks of cancer and the tumor microenvironment. The ordering of CRLMs along a
pseudotime-tree revealed a continuous shift in expression programs, suggesting a developmental
relationship between the subtypes. Notably, trajectory inference and personalized analysis discov-
ered a range of epigenetic states that shape and guide metastasis progression. By constructing
prognostic maps that divided the expression landscape into regions associated with favorable and
unfavorable prognoses, we derived a prognostic expression score. This was associated with critical
processes such as epithelial–mesenchymal transition, treatment resistance, and immune evasion.
These factors were associated with responses to neoadjuvant treatment and the formation of an
immuno-suppressive, mesenchymal state. Our machine learning-based molecular profiling provides
an in-depth characterization of CRLM heterogeneity with possible implications for treatment and
personalized diagnostics.
Cancers 2023,15, 3835. https://doi.org/10.3390/cancers15153835 https://www.mdpi.com/journal/cancers

Cancers 2023,15, 3835 2 of 31
Keywords:
genomics analysis of liver metastases; gene expression; molecular mechanisms; tumor
heterogeneity; prognostic score; treatment resistance; self-organizing map machine learning
1. Introduction
Liver metastasis, linked with poor prognosis, is a common occurrence in various
cancers, including colorectal cancer (CRC), pancreatic cancer, breast cancer, melanoma, and
lung cancer. In CRC, the liver is the primary site of metastasis owing to the anatomical and
vascular connections between the colorectal regions and the liver [
1
]. Such metastases pose
a significant challenge for clinical intervention and represent a major cause of CRC-related
mortality. However, the molecular mechanisms that govern the molecular heterogeneity
and tumor development in liver metastasis remain poorly understood.
The tumor microenvironment (TME) significantly impacts the pathophysiology of
cancer cells metastasizing to the liver. It includes liver sinusoidal endothelial cells, Kupffer
cells, hepatic stellate cells, and parenchymal hepatocytes, as well as infiltrating stromal
and immune cells [
2
]. Interactions with the TME facilitate cancer cells to overcome the
tumor stroma, settle, and to colonize. Interestingly, colorectal cancer liver metastases
(CRLM) show high genetic concordance in key lesions, mutations, and copy number
variations (CNV) with primary CRC suggesting the liver microenvironment has a limited
influence on the mutation pattern of CRLM cells, which remain largely genetically primed
by their primary CRC origin [
3
]. This correlation of molecular characteristics between
primary and metastatic tumors is further supported by the functional and transcriptional
studies on CRLM [
3
–
7
]. However, genetic lesions and adaptive interactions with the TME,
while necessary, are not sufficient for cancer initiation and progression. As a third factor,
epigenetic regulation, including chromatin remodeling associated and driven by a large
set of histone- and DNA-modifying mechanisms, are essential for cancer clones to acquire
the plasticity necessary for adaptive cell fate changes towards evolutionary fitness in an
epigenetic landscape [
8
,
9
]. Currently, little is known of the evolutionary processes that
govern CRLM, the role of epigenetics, and potential treatment-induced shaping effects.
Establishing the biological mechanisms of metastasis is crucial for refining diagnostics and
identifying therapeutic windows for interventions.
Molecular subtyping has emerged as an important concept to decipher cancer het-
erogeneity in both primary and metastatic tumors [
10
]. While correlations between the
molecular subtypes of primary CRC, metastatic propensity, and responses to therapy have
been noted [
6
], it is not clear to what degree these CRC characteristics are maintained
after distant spread. Recently, Moosavi et al. developed a metastasis-oriented subtyping
framework through the transcriptomic analysis of patients with CRLMs [
5
]. Their de novo
liver metastases subtypes (LMS) recapitulated epithelial-like and mesenchymal-like tumors,
with the latter showing a strong immune and stromal component.
In this study, we leveraged machine learning-based bioinformatics to analyze the
transcriptomes of 283 CRLM [
5
], to characterize the molecular landscape with single-tumor
resolution, and to deduce cues for CRLM development under neoadjuvant treatment. We
further evaluated and extended the LMS framework, and identified subtype-specific gene
signatures, explored their functional relevance to cancer progression, and suggest possi-
ble developmental paths under epigenetic control using pseudotime inference through
in-depth transcriptomic analysis. Our approach utilizes a high-resolution molecular cartog-
raphy and portrayal method previously applied to various cancer types [
11
–
14
], treatment
resistance [
15
], and modes of epigenetic regulation [
16
,
17
]. In parallel, we have estab-
lished, and made available, an interactive web platform for more detailed insights into our
analyses. Through the personalized portrayal of gene expression data and the creation
of prognostic maps for CRLM, we open new avenues for personalized diagnostics and
treatment decision-making.

Cancers 2023,15, 3835 3 of 31
2. Materials and Methods
2.1. Gene Expression Data of Colon Liver Metastases (CRLM)
Microarray-based gene expression data (GeneChip Human Transcriptome Array 2.0;
HTA2.0) of 283 hepatic resections of liver metastases (CRLM) of primary colorectal (CRC)
tumors were taken from GEO-database under accession numbers GSE159216 (see also [
5
])
covering the expression of, in total, more than 67,000 probe sets. We used the classification
of CRLM-samples into five liver metastasis subtypes (LMS1-LMS5) and an “unclassified”
group, all taken from the original paper [
5
]. The latter class showed an expression signature
of healthy liver tissue and therefore it was assigned as liver-like (LIV, see Section 3). We
used preprocessed gene expression data, as described in [5].
2.2. SOM Portrayal and Downstream Functional Analysis
The gene expression data were transformed to log10-scale, then quantile normalized
and centralized (with respect to the mean log-expression of the gene averaged over all
CRLM studied). Throughout the paper, we use the terms “over-“ and “under-“expression
for positive and negative values (larger/smaller than the mean value, respectively). Ex-
pression profiles denote the vector of expression data of each transcript across all CRLM-
samples. Expression profiles were clustered using self-organizing map (SOM) machine
learning. This method transforms the ~67,000 transcript-centric profiles into 2500 meta-
gene profiles of reduced dimensionality and visualizes their expression in each CRLM
sample as two-dimensional quadratic images with 50
×
50 metagene resolution [
18
]. Meta-
genes were colored according to their expression for each CRLM from dark blue (low
expression) to maroon (high expression), thereby providing a portrait of their gene ex-
pression state. An alternative “coastline” color scale was applied to better resolve subtle
expression changes for over (red) and under (blue) expression regions. The SOM clustered
similar profiles of co-expressed genes together, appearing as spot-like regions in the por-
traits [
18
]. Subtype-specific mean portraits were generated by averaging the metagene
landscapes of all CRLM belonging to the same class. Difference portraits between subtypes
were calculated as the differences between the respective mean portraits. Details of SOM
training and parametrization have been previously described [
18
,
19
]. Bioinformatics down-
stream analyses, including class discovery and sample diversity analysis, feature selection
from metagenes and spots, and biological function-mining using gene sets analysis, were
performed as described in [
18
,
20
]. All downstream methods were implemented in the
R-package ‘oposSOM’ [
21
] and applied for analysis. Gene sets implemented specifically
for this study are provided in Supplementary Table S2. Single-cell RNAseq data from
Che et al. [
22
] (GSE178318) on CRLM were re-analyzed to extract cell-specific marker genes.
2.3. Prognostic Maps
For prognostic maps, the hazard ratio (HR) relative to the mean overall survival (OS)
of all patients was calculated for each metagene-pixel on the map. This was done by
selecting patients with a centralized metagene expression greater than zero or one standard
deviation and coloring the pixel in an HR-scale from red (indicating inferior prognosis) to
blue, as described in [11].
2.4. oposSOM Browser of Livermet-Transcriptomes
The results of transcriptome analyses of CRLM presented in this publication can be
interactively explored for further details using the oposSOM browser [
23
], available via the
IZBI-portal (www.izbi.de, accessed on 27 July 2023; https://apps.health-atlas.de/opossom-
browser/?dataset=15, accessed on 27 July 2023). For more information refer to the data
availability statement below and Supplementary Figure S1.

Cancers 2023,15, 3835 4 of 31
3. Results
3.1. SOM Portrayal Deciphers Liver Metastases Subtypes (LMS)
The self-organizing maps (SOM) algorithm was applied to transcriptomic data, gener-
ating individual SOM portraits of each of the 283 CRLM samples (Figures 1a and S2). These
portraits were group-sorted according to their previous classification [
5
] into five subtypes
LMS1–LMS5 and one “unclassified” group, LIV, which strongly resembled the transcrip-
tional characteristics of healthy liver tissue. Mean portraits of each subtype indicate the
specific expression patterns of spot-like clusters of overexpressed and under-expressed
genes, using a red-to-blue color scale, respectively (Figure 1b).
Cancers2023,15,xFORPEERREVIEW4of34
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
3.Results
3.1.SOMPortrayalDeciphersLiverMetastasesSubtypes(LMS)
Theself-organizingmaps(SOM)algorithmwasappliedtotranscriptomicdata,gen-
eratingindividualSOMportraitsofeachofthe283CRLMsamples(Figures1aandS2).
Theseportraitsweregroup-sortedaccordingtotheirpreviousclassification[5]intofive
subtypesLMS1–LMS5andone“unclassified”group,LIV,whichstronglyresembledthe
transcriptionalcharacteristicsofhealthylivertissue.Meanportraitsofeachsubtypeindi-
catethespecificexpressionpaernsofspot-likeclustersofoverexpressedandunder-ex-
pressedgenes,usingared-to-bluecolorscale,respectively(Figure1b).
Thedistributionofspotnumbersrevealsthatthepersonalizedportraitsmostfre-
quentlyexpressonetothreesuchspot-clusters(Figure1c).LMS3andLMS4displaythe
broadestdistributionsofspotnumbersand,consequently,themostheterogeneousex-
pressionpaerns.Themostvariablegenesacrossthedatasetsarelocatedinsixmajorspot
modulesofthegenescoregulatedacrossthesubtypesassignedwithcapitalleersA–F
(seethesummaryandvariancemapsFigure1d).Eachmapiscomposedof2500pixels,
alsoreferredtoasmetagenes,eachcontainingupto200genes(seethepopulationmapin
Figure1d).Keygenesharboringsomaticmutationsand/orcopynumbervariations(CNV)
inCRCandCRLM[24–26]arelocatedinornearthespots(Figure1d,partbelow),which
includescanonicalcoreCRCdrivers(combinationsofAPC,KRAS,TP53,orSMAD4),
whichcancombinewithoneadditionalcandidatemetastasisdriver(TCF7L2,AMER1,or
PTPRT)[27].
SimilarityplotsoftheCRLMusingindependentcomponentanalysis(ICA,Figure
1e)andamultidimensionalnetworkpresentation(Figure1e)revealtheintermixingofthe
CRLMofnearlyallLMS,exceptforLMS5andLIV(seealsoSupplementaryFigureS3).
Thisisalsoreflectedinthesilhoueeplotthatcomparesintra-versusinter-clustersimi-
laritiesofthesamples,where,exceptLMS5andLIV,alargefractionoftheCRLMshow
highpreferencesforinter-clustersimilarities.This,overall,confirmstheheterogeneous
transcriptionalpaernsofCRLMandtheirstratificationintofiveLMSandaLIVgroup
enrichedinhealthylivertissue.

Figure1.SOMportrayalofLMSsubtypes.(a)IndividualgeneexpressionSOMportraitsofthe283
livermetastases(CRLM)samplestakenfrom[5](seeSupplementaryFigureS2foranenlarged
Figure 1.
SOM portrayal of LMS subtypes. (
a
) Individual gene expression SOM portraits of the
283 liver metastases (CRLM) samples taken from [
5
] (see Supplementary Figure S2 for an enlarged
portrait gallery). (
b
) Mean portraits of each of the subtypes LMS 1-5 and LIV (CRLM strongly
contaminated with liver tissue). N assigns the respective sample size. (
c
) Distribution of spot
numbers in the individual portraits of each subtype. (
d
) Supporting maps summarize the major
overexpression spots containing highly variant genes across all the portraits (assigned with capital
letters A–F; the variance of gene expression across the map; the color-coded distribution of the
number of genes in each of the 2500 pixels of the map called metagenes; and the locations of key
CRC- and CRLM-related genes on the SOM portraits. (
e
) Sample similarity relations are visualized
using Independent Component Analysis (ICA) and a multidimensional network. The silhouette plot
estimates the similarity of each sample to its own subtype cluster through silhouette scores, and
indicates the most similar subtype with a color-code bar below.
The distribution of spot numbers reveals that the personalized portraits most fre-
quently express one to three such spot-clusters (Figure 1c). LMS3 and LMS4 display the
broadest distributions of spot numbers and, consequently, the most heterogeneous expres-
sion patterns. The most variable genes across the datasets are located in six major spot
modules of the genes coregulated across the subtypes assigned with capital letters A–F
(see the summary and variance maps Figure 1d). Each map is composed of 2500 pixels,
also referred to as metagenes, each containing up to 200 genes (see the population map

Cancers 2023,15, 3835 5 of 31
in Figure 1d). Key genes harboring somatic mutations and/or copy number variations
(CNV) in CRC and CRLM [
24
–
26
] are located in or near the spots (Figure 1d, part below),
which includes canonical core CRC drivers (combinations of APC,KRAS,TP53, or SMAD4),
which can combine with one additional candidate metastasis driver (TCF7L2,AMER1, or
PTPRT) [27].
Similarity plots of the CRLM using independent component analysis (ICA, Figure 1e)
and a multidimensional network presentation (Figure 1e) reveal the intermixing of the
CRLM of nearly all LMS, except for LMS5 and LIV (see also Supplementary Figure S3).
This is also reflected in the silhouette plot that compares intra- versus inter-cluster simi-
larities of the samples, where, except LMS5 and LIV, a large fraction of the CRLM show
high preferences for inter-cluster similarities. This, overall, confirms the heterogeneous
transcriptional patterns of CRLM and their stratification into five LMS and a LIV group
enriched in healthy liver tissue.
3.2. LMS Are Governed by Six Major Modules of Co-Regulated Genes
The spots A–F taken from the mean LMS portraits represent modules containing
about 200 (spot A) to 623 (spot C) co-expressed genes (Figure 2a, lists of the spot genes are
provided in Supplementary Table S2). Their expression profiles reveal the overexpression
of spot A in LMS1, spots B and D in LMS3, spot C in LMS4, spot E in LMS5, and the
under-expression of spots A and C in LMS5 (Figures 2b and S3). Their functional context
was established by gene set analysis using an array of gene sets implemented in oposSOM
(see Figure 2a, Supplementary Table S1 and next subsection). The spot profiles show
the expression of the spot modules as bar plots, where the samples within each subtype
are ordered with the increasing expression values of the spot E that shows inflammatory
characteristics (Figure 2b). The negative slope of the expression values of spot D, associated
with upper crypt genes and chromosomal instability (CIN) anticorrelates with spot E.
Conversely, the expression of spot A (epithelium) positively and negatively correlates with
the inflammatory characteristics, respectively, suggesting an overlay of various immune-
related functions in LMS1 and LMS5. Overall, the distinct spot profile indicates complex co-
expression patterns governing a multitude of cellular functions, which are addressed below.
Spot F exhibits the expression characteristics of healthy liver tissue, with high values
in the LIV group and in about 35% of the CRLM samples across all LMS, suggesting
contamination with liver tissue (Figure 2b, part below). These samples are prone to LMS
misclassification, as most of them show a negative silhouette score and preference for
the LIV-group (Figure 1d). Liver (ALB) and intestine gene marker genes (KRT20) [
5
] are
located in spots F and C, respectively, defining a major transcriptomic axis between liver
and CRLM.
The receiver operating characteristic (ROC) curves indicate a high classification power
for the overexpression of spot A for LMS1 (AUC = 0.95), of spot B (0.86) and D (0.81) for
LMS3, of spot C for LMS4 (0.76), and of spot E for LMS5 (0.98) (Figure 2c). LMS2 exhibits
mixed characteristics, combining moderate AUCs for spots A, B, and C. Notably, all the
nine genes of the mini classifier distinguishing between LMS1 and other subtypes [
5
] are
located in spot A. Spot C (cycling genes) is a specific under-expression marker for LMS5
(AUC = 0.11), implying that cell-cycle activity in LMS5 is distinctly reduced compared with
the other LMS. ROCs of spot F reflect no preference for any LMS, thus supporting the view
that contaminations with healthy liver tissue are distributed over all LMS.
The combined classification power of these spots is demonstrated using ternary dia-
grams of their expressions (Figure 2d). A ternary combination of spots A, D, and E provides
the best resolution, with LMS1 distributing to the spot A corner, while LMS3 and LMS5
distribute to the D and E corners, respectively, and LMS4 to the middle. LMS2 could not be
specifically classified with single spot markers. Other combinations of spots in the ternary
diagrams show a depleted population of the B-corner, especially in combination with spots
A and D.

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spotsprovidetranscriptomicmarkersignaturesofthesubtypeswheretriplecombinations
ofepithelial-(spotA),CIN(spotD),andmesenchymal/inflammation(spotE)bestresolve
thesubtypesLMS1,LMS2-4,andLMS5,respectively.

Figure2.Expressionprofilesofthespot-modules.(a)Theheatmapshowssample-specificexpres-
sionofeachspot,groupedbysubtypes.HierarchicalclusteringofsamplesaccordingtospotFex-
pressionisshownbelowalongwiththeexpressionofthespotFthatreflectslowerandhigherliver
cellcontentinLIVlowandLIVhigh,respectively.Thepercentageofsamplesclassifiedintohighand
lowspotFexpressionprofilesisshownbelow.Genesetanalysisassociatesspotgeneswithbiolog-
icalfunction(seeSupplementaryTab leS1andtext).(b)Barplotsofspotexpressionprofilesare
sortedwithincreasingexpressionofspotEineachsubtypetobeervisualizeco-expressionofthe
otherspotswithinflammatoryresponse.(c)Receiveroperatingcharacteristiccurves(ROC)ofsub-
typeclassificationpowerofeachspotexpression.ROCcurvesbelowthediagonalline(AUC<0.5)
meanthatunderexpression(lowsensitivity)isabeerpredictorthanoverexpression(highsensi-
tivity).AUCvaluesarelistedinthetablebelow.(d)Ternarydiagramscombineexpressionofse-
lectedspottuples.
3.3.FunctionalandTMEContextandRelationtoPrimaryCRCCharacteristics
TofurthercharacterizethefunctionalcontextoftheCRLMsubtypes,weanalyzeda
varietyofgenesetsimplementedintheoposSOMsoftwarepackagewithheatmaps(Fig-
ures3andS4),withsingle-setprofilesandgenesetmapsfordetailedinspection(Supple-
mentaryFigureS5),andspotenrichment.Forcomparisonwithapriorindependentgene
expressionstudyonCRLM,weconsideredtwenty-sixgenemodules(GM1-26)identified
bytheauthors[4]andcomputedtheirGSZscoresinoursamples(Figure3a).Thegene
setsrelatedtoendothelialcharacteristics,angiogenesis,andinnateimmunitywereupreg-
ulatedinLMS5,thoserelatedtocellcycle—inLMS1-4,thoserelatedtoinflammationand
Figure 2.
Expression profiles of the spot-modules. (
a
) The heatmap shows sample-specific expression
of each spot, grouped by subtypes. Hierarchical clustering of samples according to spot F expression
is shown below along with the expression of the spot F that reflects lower and higher liver cell
content in LIVlow and LIVhigh, respectively. The percentage of samples classified into high and low
spot F expression profiles is shown below. Gene set analysis associates spot genes with biological
function (see Supplementary Table S1 and text). (
b
) Barplots of spot expression profiles are sorted
with increasing expression of spot E in each subtype to better visualize co-expression of the other
spots with inflammatory response. (
c
) Receiver operating characteristic curves (ROC) of subtype
classification power of each spot expression. ROC curves below the diagonal line (AUC < 0.5) mean
that underexpression (low sensitivity) is a better predictor than overexpression (high sensitivity).
AUC values are listed in the table below. (
d
) Ternary diagrams combine expression of selected
spot tuples.
Overall, the six overexpression spots provide transcriptomic marker signatures of
four (LMS1, LMS3, LMS4, LMS5) of the five subtypes. Triple combinations of spots A
(epithelium), D (CIN), and E (endothelium/mesenchyme and inflammation) best resolve
subtypes LMS1, LMS3, LMS 4, and LMS5, respectively. Therefore, the six overexpression
spots provide transcriptomic marker signatures of the subtypes where triple combinations
of epithelial- (spot A), CIN (spot D), and mesenchymal/inflammation (spot E) best resolve
the subtypes LMS1, LMS2-4, and LMS5, respectively.
3.3. Functional and TME Context and Relation to Primary CRC Characteristics
To further characterize the functional context of the CRLM subtypes, we analyzed
a variety of gene sets implemented in the oposSOM software package with heatmaps
(
Figures 3and S4
), with single-set profiles and gene set maps for detailed inspection (Sup-

Cancers 2023,15, 3835 7 of 31
plementary Figure S5), and spot enrichment. For comparison with a prior independent
gene expression study on CRLM, we considered twenty-six gene modules (GM1-26) iden-
tified by the authors [
4
] and computed their GSZ scores in our samples (Figure 3a). The
gene sets related to endothelial characteristics, angiogenesis, and innate immunity were
upregulated in LMS5, those related to cell cycle—in LMS1-4, those related to inflammation
and neoantigens found in microsatellite instable (MSI) CRC in LMS1, and those specific to
liver-tissue spread across all LMS.
Next, we analyzed the expression signatures of primary CRC subtypes in our LMS-
stratified CRLM data (Figures 3b–d and S5a). Signatures of four consensus molecular
subtypes (CMS1-4) of CRC [
24
] indicate associations between the subtypes of the primary
tumors and of liver metastases, namely CMS1 (immune-activated) with LMS1 (and to a
less degree with LMS5), CMS2 (canonical, WNT-signaling) with LMS2-4, CMS3 (metabolic)
with LMS4 (and partly LMS1), and CMS4 (mesenchymal) with LMS5 (Figure 3b, see
also [
5
]). This indicates the resemblance of transcriptional programs between LMS and
CMS subtypes, which is further supported with signatures of another gene-interaction-
perturbation-network-based subtyping (GINS1-6) [28] (Figure 3c).
To gain further insight into the immunogenetic properties of LMS, we assessed signa-
tures associated with immune microenvironmental subtypes (IM1-5). These subtypes con-
sider the activation patterns of the MHCII receptor HLA-DR and of the immune checkpoint
inhibitors (ICI) PD1 and ICOS related to T-cell states (Figure 3d) [
3
]. According to these
patterns, the inflamed IM2 type, which resembles LMS1, is characterized by deactivated
ICIs. In contrast, the LMS5-resembling IM1 and IM5 exhibit a T-cell-exhausted immune
environment. This is regulated by the hypoxia-marker SLCA1 with inferior survival risk
and treatment resistance [3].
Next, we considered a series of signatures characterizing the tumor microenvironment
(TME, Figure 3e–h). A pan-cancer classification of TME-types [
29
] shows that LMS1
corresponds to a fibrotic TME-type, LMS2–LMS4 to an immune depleted and proliferative
TME-type, and LMS5 to an immune-enriched and fibrotic TME-type (Figure 3e). For a
closer look, we mapped immunogenicity-related genes in solid cancers to the CRLM [
30
]
(Figures 3f and S4). Accordingly, the expression of MHC class II, and both immune-inhibitor
and -stimulator genes, upregulate in LMS5, while MHC class I shows slight activity in both
LMS1 and LMS5. To obtain a more specific picture, we next studied signatures extracted
from the single-cell transcriptomics data of CRLM [
22
] (Figures 3g and S6). It shows that
endothelial cell activity in LMS5 is accompanied by upregulated expression from a series of
immune cells such as B-, natural killer (NK)-, myeloid, plasma-, and plasmacytoid dendritic
(pDC) cells. It is reported that, e.g., pDCs, promote the recruitment of regulatory T cells
(Tregs) into the tumor microenvironment, leading to immune suppression and promoting
tumor growth [
31
]. Contrarily, epithelial and T cells are activated in LMS1 and, partly,
LMS4. Cell-type deconvolution refines this result in terms of the fraction of immune cells
in the TME (Figure 3h) [
32
]. Indeed, CD8+ T- and NK-cell invasion is most prominent in
LMS1, while regulatory T and B cells are enriched in LMS5. Interestingly, the frequencies
of tumor-suppressive M1- and of tumor-promoting M2-macrophages are high and low,
respectively, which suggests a pivotal role of macrophage polarization in CRLM in analogy
to primary CRC [
33
–
35
]. We found similar activity profiles across the LMS using specific
marker sets for M2-like (SPP1+ and MRC1 + CCL18) macrophages identified in CRLM and
possibly originating from liver-intrinsic Kupffer cells [7].
Finally, we were interested in signatures related to cancer hallmarks and copy num-
ber variations along the chromosomes and chromatin states associated with genetics and
epigenetics, respectively (Figures 3i–k and S5c–e). Cancer hallmark signatures [
36
] reveal
functional associations in a broader context (Figure 3i), which generally reflect subtype-
specific activations of the hallmark programs. For example, the epithelial–mesenchymal
transition (EMT) signature is activated in LMS5, while being downregulated in LMS1, corre-
sponding to the mesenchymal and epithelial nature of these subtypes. Gene expression and
copy number variations (CNV) are typically linked via a dose–response relationship [
37
].

Cancers 2023,15, 3835 8 of 31
We found that the genes on chromosomes 7 and 13, both showing CNV gains in primary
CRC and CRLM [
38
], accumulate in spot D and are upregulated in LMS3 (and, partly
LMS4), while the genes on, e.g., chromosome 18 showing CNV-losses in CRC and CRLM,
downregulate in these LMS (Figure 3j, see red and blue boxes, respectively). Overall, we
observed the systematic over- and under-expression of genes along chromosomes known
for their CNV gains and losses in CRC and CRLM: they accumulate in the CIN-related
LMS3 (and partly LMS4) and thus govern the genomic regulation of these LMS.
We were also interested in the footprints of (epi-)genetic effects in the CRLM tran-
scriptomes. Gene sets for different chromatin states in colonic tissue were taken from [
39
]
and mapped to the CRLM transcriptomes (Figures 3k and S5e, see also [
40
]). Genes with
active (TssA) and with repressed and poised (ReprPC and TssP, respectively) promoters
were upregulated in a virtually exclusive fashion in each of the LMS. The percentage of
CRLM with highly expressed genes with TssA-promoters increased from 7% in LMS1 to
48% in LMS4, meaning that the fraction of open chromatin was markedly larger in the
proliferative LMS4 compared with epithelial LMS1. In turn, closed chromatin associated
genes with repressed and poised promoters had the biggest fractions in LMS1 and LMS5
with epithelial and endothelial characteristics, respectively.
In summary, gene set analysis specifies the context of the LMS in terms of cellular
programs related to epithelial- (LMS1), endothelial/mesenchymal- (LMS5), proliferative
and virtually immune desert TME (LMS3 and 4), and immune cell composition, as well as
chromatin states and CNV. These mechanisms markedly resemble molecular mechanisms
observed in CRC-subtypes, suggesting that CRLM partly preserves the characteristics of
the primary tumors. Pathway activities can be browsed interactively using the oposSOM
web tool (see above). As worked example, activity patterns of the WNT-pathway for each
LMS is provided in Supplementary Figure S7 showing specific activation in LMS2.
Cancers2023,15,xFORPEERREVIEW8of34


knownfortheirCNVgainsandlossesinCRCandCRLM:theyaccumulateintheCIN-
relatedLMS3(andpartlyLMS4)andthusgovernthegenomicregulationoftheseLMS.
Wewerealsointerestedinthefootprintsof(epi-)geneticeffectsintheCRLMtran-
scriptomes.Genesetsfordifferentchromatinstatesincolonictissueweretakenfrom[39]
andmappedtotheCRLMtranscriptomes(Figures3kandS5e,seealso[40]).Geneswith
active(TssA)andwithrepressedandpoised(ReprPCandTss P,respectively)promoters
wereupregulatedinavirtuallyexclusivefashionineachoftheLMS.Thepercentageof
CRLMwithhighlyexpressedgeneswithTssA-promotersincreasedfrom7%inLMS1to
48%inLMS4,meaningthatthefractionofopenchromatinwasmarkedlylargerinthe
proliferativeLMS4comparedwithepithelialLMS1.Inturn,closedchromatinassociated
geneswithrepressedandpoisedpromotershadthebiggestfractionsinLMS1andLMS5
withepithelialandendothelialcharacteristics,respectively.
Insummary,genesetanalysisspecifiesthecontextoftheLMSintermsofcellular
programsrelatedtoepithelial-(LMS1),endothelial/mesenchymal-(LMS5),proliferative
andvirtuallyimmunedesertTME(LMS3and4),andimmunecellcomposition,aswellas
chromatinstatesandCNV.Thesemechanismsmarkedlyresemblemolecularmechanisms
observedinCRC-subtypes,suggestingthatCRLMpartlypreservesthecharacteristicsof
theprimarytumors.PathwayactivitiescanbebrowsedinteractivelyusingtheoposSOM
webtool(seeabove).Asworkedexample,activitypaernsoftheWNT-pathwayforeach
LMSisprovidedinSupplementaryFigureS7showingspecificactivationinLMS2.
 
Figure3.FunctionalcharacteristicsofCRLMusinggenesetZscores(GSZ)profilinginCRLMof
varioussignatures:(a)Expressionofgenemodules(GM)extractedfromanindependentstudy[4]
confirmLMSandspotclustersinthisstudy.(b)ConsensusmolecularsubtypesofCRC(CMS1-4)
[24]resembleCRLMexpression.(c)Thereispartialconcordancebetweenthegeneinteractionper-
turbationnetworksubtypes(GINS1-6)signatures[28]andtheLMSexpressionpaerns.(d)Immune
microenvironmenttypes(IM1-5)relatetheLMStospecificactivationpaernsofimmunecheck-
pointinhibitorsandHLA‐DRB[3].(e)Tumormicroenvironment(TME)PanCancersignaturestaken
from[29]assigntheLMStodifferentTME-subtypes.(f)Immunogenicitygenesinsolidcancerswere
mostlyupregulatedinLMS5[30].(g)Cellspecificexpressionmarkersextractedfromasingle-cell
RNAanalysisofCRLM[22]activateintwoclustersinLMS1andLMS5,respectively.(i)Hallmark
signaturesofcancer[36]confirmthefunctionalassignmentofLMSandspots.(j)Genesonchromo-
somesshowdoseresponseeffectsofcopynumbergains(redframe)andlosses(blue)typically
Figure 3.
Functional characteristics of CRLM using gene set Z scores (GSZ) profiling in CRLM of
various signatures: (
a
) Expression of gene modules (GM) extracted from an independent study [
4
]
confirm LMS and spot clusters in this study. (
b
) Consensus molecular subtypes of CRC (CMS1-
4) [
24
] resemble CRLM expression. (
c
) There is partial concordance between the gene interaction
perturbation network subtypes (GINS1-6) signatures [
28
] and the LMS expression patterns. (
d
) Immune

Cancers 2023,15, 3835 9 of 31
microenvironment types (IM1-5) relate the LMS to specific activation patterns of immune check-
point inhibitors and HLA-DRB [
3
]. (
e
) Tumor microenvironment (TME) PanCancer signatures taken
from [
29
] assign the LMS to different TME-subtypes. (
f
) Immunogenicity genes in solid cancers were
mostly upregulated in LMS5 [
30
]. (
g
) Cell specific expression markers extracted from a single-cell
RNA analysis of CRLM [
22
] activate in two clusters in LMS1 and LMS5, respectively. (
i
) Hallmark
signatures of cancer [
36
] confirm the functional assignment of LMS and spots. (
j
) Genes on chro-
mosomes show dose response effects of copy number gains (red frame) and losses (blue) typically
observed in CRC and CRLM [
41
]. (
k
) Epigenetic signatures identify two distinct patterns across all
LMS, one related to open, actively transcribed genes, the other to repressed and poised ones [
42
].
(
h
) Cell-deconvolution of CRLM transcriptomics data [
43
] reveals LMS-specific fractions of selected
cell types. Gene sets implemented in this study are given in Supplementary Table S3.
3.4. Trajectory Inference Indicates Continuous Alterations of Cellular States along Developmental
Paths and Gradual Changes of the TME
We performed trajectory analysis on the SOM portraits using the Monocle method [
44
],
which sorts CRLM in low-dimensional space, an approach usually applied for inferring
developmental paths in single-cell RNAseq settings. The application of this method to bulk
expression data enables the extraction of potential paths of cancer progression [
13
]. The
obtained tree divides into three major segments (Seg1–3) where Seg1 further subdivides
into six subsegments (Seg1.1–1.6, Figure 4a). The mean portraits of the (sub)segments reveal
systematic alterations of the expression patterns. To better characterize these changes, we
visualize spot expression as well as the accumulation of the different LMS along the tree
(Figure 4b,c, respectively). LMS2, LMS5 (partly), and LMS3 samples accumulate near
the ends of Seg1, Seg2, and Seg3, respectively, paralleled by the high expression of the
respective marker spots A, D, and E, respectively. LMS4 CRLM distributes over wider
ranges of the tree and the tumors of LMS5 also accumulate in Seg1.3, which is also described
with the expression of spot E.
To better understand expression changes along the three paths connecting different
tip-points we generated spot profiles along the segments (Figure 4d). They reveal that
the tree sorts the CRLM according to gradually changing expression levels, e.g., of the
linearly increasing spot D expression from Seg1.1 to Seg2 opposed by its decay along
Seg3, particularly associated with the accumulation of LMS5 (Figure 4d, middle column,
Movie S1, a movie of the CRLM-portraits along the trajectory from Seg1.1 to Seg3 is pro-
vided in the supplementary file). Hence, tree analysis shows that the activity levels of
biological characteristics such as epithelium (spot A), proliferation (spot C), CIN (spot
D), and endothelium/inflammation (spot E) changes continuously and not abruptly be-
tween the CRLM, which possibly reflects developmental dynamics rather than static states.
Interestingly, these continuous changes are overlaid by the typical differences between
the subtype expression, e.g., of the high expression of spot A in LMS1 and of the lower
expression in LMS2-4 along Seg1 and Seg2.
Note that these expression differences associate with the overall survival hazard
ratio (HR) and thus with prognosis, which also changes smoothly along the trajectories.
Interestingly, CRLM samples of LMS1 split into two subgroups described with a high and
low spot A expression and HR values. A series of individual portraits along the trajectory
Seg1.1 towards Seg3 reveals that the expression patterns dynamically shift towards spot E,
via the lower right part of the map (plasticity plateau, see next subsection), or rather, via its
upper edge (Figure 4d, part below). Hence, the trajectories refer to (pseudo-) dynamically
changing expression patterns beyond the spots, which overall illustrates the diversity of
the transition states linking the states at the tips of the tree, which can be interpreted as
archetypic expression states (Supplementary Figure S8).

Cancers 2023,15, 3835 10 of 31
Cancers2023,15,xFORPEERREVIEW11of34


 
Figure4.TreetrajectoryanalysisofCRLMheterogeneityusingmonocle[44].(a)Themonocle-tree
dividesintotwomajorsegments(Seg1–3)whereSeg1furthersplitsintosixsubsegments(1.1–6).
MeanexpressionportraitsaveragedovertheCRLMportraitsalongthesegmentsrevealchanging
expressionpaerns.(b)SamplesalongthetreearecoloredbyspotexpressionA–F.Ellipsesindicate
areasofhighexpressionoftherespectivespot.(c)DifferentLMSaccumulateindifferentsegments
asmarkedbytheellipsesandbytheoverlapcoefficients(OC=overlap(LMS,segment)/(min_size
(LMS,segment)betweenLMSandsegments.(d)Profilesalongdifferentpathsrevealsmooth
changesinspotexpression.Thelogexpressionprofilesofspotsandhazardratios(HR)usesorted
CRLMalongtheabscissa.Pseudotime(PT)-scaledplots“virtually”compressthedataalongthe
subsegments1.1and1.3(SupplementaryFigureS8a).SeriesofindividualCRLMportraitsfrom
LMS1andLMS5indicatetheirpseudo-dynamicstowardsspotE(partlybelowandseealsoarrows
intheportraits;forportraitsofothersubtypesseeSupplementaryFigureS8b).Theblueandred
dashedlinesalongtheHRscaerplotvisualizeLMS1HR-highand-lowsubgroups.(e)Sankey
river-flowdiagrambetweensubtypeandsegmentstratificationofCRLMs.OCvaluesgreaterthan
0.5showthatLMS3and,partly,LMS4flowtoSeg2,andLMS5toSeg3andSeg1.3.Theirdifference
portraitsresembletheepithelialpaernsofSeg1.1tooverlaywithSeg3paerns.Forinterpretation
offunctionalcharacteristicsseeSupplementaryFigureS5andnextsubsection.(f)Comparisonof
plasmaandendothelialcellsignatures(Figure3e)intermsofbiplotsandtheirdifferenceprofile
indicatethatbothsignaturescorrelatebutcombinewithdifferentamplitudesintheCRLM(redand
bluearrowsandframes).
Figure 4.
Tree trajectory analysis of CRLM heterogeneity using monocle [
44
]. (
a
) The monocle-tree
divides into two major segments (Seg1–3) where Seg1 further splits into six subsegments (1.1–6).
Mean expression portraits averaged over the CRLM portraits along the segments reveal changing
expression patterns. (
b
) Samples along the tree are colored by spot expression A–F. Ellipses indicate
areas of high expression of the respective spot. (
c
) Different LMS accumulate in different segments
as marked by the ellipses and by the overlap coefficients (OC = overlap (LMS, segment)/(min_size
(LMS, segment) between LMS and segments. (
d
) Profiles along different paths reveal smooth changes
in spot expression. The log expression profiles of spots and hazard ratios (HR) use sorted CRLM
along the abscissa. Pseudotime (PT)-scaled plots “virtually” compress the data along the subsegments
1.1 and 1.3 (Supplementary Figure S8a). Series of individual CRLM portraits from LMS1 and LMS5
indicate their pseudo-dynamics towards spot E (partly below and see also arrows in the portraits; for
portraits of other subtypes see Supplementary Figure S8b). The blue and red dashed lines along the
HR scatterplot visualize LMS1 HR-high and -low subgroups. (
e
) Sankey river-flow diagram between
subtype and segment stratification of CRLMs. OC values greater than 0.5 show that LMS3 and, partly,
LMS4 flow to Seg2, and LMS5 to Seg3 and Seg1.3. Their difference portraits resemble the epithelial
patterns of Seg1.1 to overlay with Seg3 patterns. For interpretation of functional characteristics
see Supplementary Figure S5 and next subsection. (
f
) Comparison of plasma and endothelial cell
signatures (Figure 3e) in terms of biplots and their difference profile indicate that both signatures
correlate but combine with different amplitudes in the CRLM (red and blue arrows and frames).

Cancers 2023,15, 3835 11 of 31
The river plot visualizes the distribution of LMS between the segments (Figure 4e).
A comparison of the mean portraits on both sides of the plots indicates more diverse
and partly different spot patterns, particularly along Seg1, which suggests a fine granular
activation of cellular programs in the CRLM not resolved in the LMS strata. One finds
CRLM dominated by LMS1 in Seg1.1 and that LMS4 distributes broadly over Seg1.1, 1.2,
1.4–1.6, and 1.3, all of which show footprints of oxidative phosphorylation (oxphos) and
proliferation. To better understand the bimodal split of LMS5 between Seg3 and Seg1.3 we
calculated the difference portrait between them. It reveals that Seg1.3 can be understood
as a superposition of the transcriptional programs of Seg3 and Seg1.2, i.e., of endothelial,
epithelial, and proliferative gene functions, i.e., as a mixture of LMS3 and, to a lesser degree,
of LMS1, which reflects a transition state between both LMS.
Moreover, Seg1.3 and Seg3 exhibit minor differences in the position of spot E in the
right upper corner of the map, which also becomes evident in the difference map (see red
and blue arrow in Figure 4e). Spot E is associated with the TME, which contains endothe-
lial/CAF and inflammatory cells. The biplot of both compounds (where we selected plasma
cells (PC) as a proxy of the immune compound) shows an overall linear relation between
them with maximal values in LMS5 (Figure 4f, left part). The individual CRLM values
considerably scatter with large deviations into positive (CAF dominance, red dashed frame
and arrow) and negative (PC dominance, blue dashed frame and arrow) directions. These
variations reflect relative PC-dominance in LMS1, partly in LMS4, and CAF dominance in
LMS5 and partly in LMS3 (difference plot in Figure 4f, right part).
When considering the marker genes of the other immune cells, we observe that B
cells behave similarly to PC while mast cells more resemble CAFs, which overall reflects
a CRLM-specific change in the cell communities of the TME (Supplementary Figure S9).
Notably, the LMS5 tumors in Seg1.3 and Seg3 refer to a more endothelium/CAF-enriched
and -depleted TME, respectively (Supplementary Figure S9).
In conclusion, we observe a modified ordering of CRLMS along the monocle-tree,
revealing continuously changing expression levels between the LMS, rather than clear-cut
expression differences in most cases. This suggests developmental relationships among the
tumors. Their ordering is governed by epithelial LMS1, CIN-affected LMS3, and immune
cell-enriched LMS5 accumulating near the ends of the three major branches of the tree.
These can be interpreted as archetypic expression states. LMS5 further splits into substrata
enriched more with immune cells or CAFs, where the latter subgroup shares endothelial
functions with LMS1 and also proliferative activity with LMS4.
3.5. High-Resolution Expression Cartography Deciphers an Interplay between Genetic and
Epigenetic Regulation of Metastasis
Next, we sought to better understand the fine-grained details of the expression land-
scape that became visible along the monocle tree. The LMS-averaged mean portraits
provided a transcriptomic landscape with six major overexpression spot modules A–F, each
associating with a specific functional context and a specific differential expression in virtu-
ally all of the subtypes (Figure 5a and also Figures 1and 2). The oposSOM software offers
an alternative segmentation of the expression landscape, such as through a personalized
summary map. This approach leverages the individual expression portraits of CRLM, thus
enabling the detection of less frequent and subtler overexpression patterns independent of
LMS classification (see Supplementary Figure S10 for details). This method reveals a richer,
more structured overexpression landscape with additional details. These include a “plas-
ticity plateau” in the right lower corner as well as an “RNA-protein molecular processing
ridge” of modules assigned to functions such as RNA-processing, de-ubiquitination, and
proteasome in the middle of the map (Figure 5b). The former plateau is associated with
the epigenetic mechanisms of cellular reprogramming into plastic states, such as poised
and repressed gene promoters in colonic tissue, targets of PRC2 (polycomb repressive
complex 2) and of the CpG island methylation phenotype (CIMP) [
45
–
47
], the genes re-
sponsive for thretionin treatment via rhodopsin receptors [
48
], and gene signatures related

Cancers 2023,15, 3835 12 of 31
to keratinization [
49
,
50
] (Supplementary Figure S5). The “processing ridge” shows distinct
expression modules associating with CRC- and CRLM-related mechanisms, such as protein
degradation and de-ubiquitination [
51
–
53
], RNA-processing, guanyl-exchange [
54
], and
ribosomal assembly [55], as well as the adaptation of the Golgi apparatus in cancer [56].
Weighted-topology overlap (wto-) networks [
57
] between the spots reveal anticor-
relation between spot E and the other spots on the one hand and between the plasticity
plateau and the proliferative spot C on the other, i.e., overall between the left and right
side of the map (see the wto-correlation maps in Figure 5a,b). This reflects the antagonism
between the inflammatory and endothelial contexts (spot E and LMS5) and the epithelial
and proliferative functions (spot A–D). Indeed, biplots of the gene set activities “cell-cycle”
versus “PRC2-targets” reflect a negative slope between LMS5 and the other LMS while the
correlation of “cell cycle” versus “translation” and “oxphos” is positive (Figure 5c, left part).
Profiles and maps of these gene sets provide further interesting details (Figure 5c, right
part): genes of the set “cell cycle” accumulate in spot C and genes of the sets translation
and oxphos accumulate in the three characteristic areas, including spot B (see also Supple-
mentary Figure S5). All these functions associate with low expression in LMS5 and high
expression in the other LMS, however with subtle mutual differences, e.g., the moderate
downregulation of oxphos activity in LMS3.
The genes EZH2 and SUZ12, both encoding components of the PRC2 complex, also
locate near spot C, while, in contrast, the PRC2-target genes accumulate in characteristic
areas in the right part of the map near spots E, partly A, and in the “plasticity plateau”.
They are associated with upregulation in LMS5 and partly in LMS1. Genes activated in the
CIMP (CpG island methylator phenotype) accumulate in similar regions, thus suggesting
the promoter hypomethylation of PRC2-targets. Similar gene accumulation patterns in the
right part of the map were found for repressed and poised promoters in the colon as well
as so-called low-expression transcription factors (low-TF) which associate with virtually
deactivated chromatin states in a wider sense (Figures 5c and S5e) [
58
]. It was recently
reported that the downregulation of EZH2, the catalytic subunit of PRC2, results in increased
DNA replication initiation through the loss of repressive histone methylation marks from
bivalent, poised promoters retaining the ability to be activated as differentiation proceeds [
59
].
It involves thousands of genomic loci of DNA replication origins scattered throughout the
genome that associate with open euchromatin regions and the regulation of pluripotency
and differentiation-related genes. In contrast, so-called high expression-TF genes and active
promoter states accumulate in the left part of the map including spots B–D, in activated
cycling and metabolically active states. Importantly, key mutated genes in CRC and CRLM
also locate in the left high-expression part of the map in support of their driver function for
transcriptome programs (Figure 1c). The bimodal expression of repressed/poised versus
active/transcribed chromatin states, evident in the heatmap in Figure 3k, thus transforms into
virtually left-to-right antagonisms of gene expression in the SOM landscape.
Comparing the averaged expression portraits along the tree segments (Figure 4a) with
the personalized landscape (Figure 5b) enables the inference of a trajectory in the gene-
expression landscape [
60
]. Accordingly, it links expression modules related to epithelial
functions (Seg1) with intestine-developmental (Seg2) and inflammatory and mesenchyme
(Seg3) functions. Moreover, it suggests an additional path through the plasticity plateau
towards the LMS5 tumors accumulating in Seg1.3 (Figure 5a). Interestingly, this “epigenetic”
path includes a region enriched in G-protein-coupled receptors (Figure 5c), which play a
crucial role as signaling transducers in the development of CRC [
61
] in promoting EMT [
62
],
where particular G-protein-coupled receptors (GPCRs) such as ADGRF5 [
63
] and ARRB1
and ARRB2 [
64
] were identified as EMT- and poor survival markers in CRC. These genes
locate near spot E and upregulate in LMS5. The GPCR LGR5 as an exception locates
in the high expression side of the landscapes in spot C, upregulated in highly cycling
CRLM, which agrees with the role of LGR5 as a marker for proliferative stem-like cells in
CRC [
65
,
66
]. In summary, high-resolution cartography of the CRLM transcriptomes reveals
a network of cellular programs governed by genetic lesions (mutations and CNVs) and by

Cancers 2023,15, 3835 13 of 31
epigenetic chromatin remodeling leading to the antagonistic activation of epithelial-like
proliferative cancer cells or inflammatory, mesenchyme-like cells. This provides rationales
for the sequential activation of transcriptional programs and the ordering of CRLM along
the segments of the monocle-tree.
Cancers2023,15,xFORPEERREVIEW14of34
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

Figure5.TranscriptomiclandscapeofCRLM.(a)LMSlandscapeexpressespeaksA–Fupregulated
inasubtype-specificfashion.Blueregionsassignareasof,onaverage,downregulatedgenomicpro-
gramsineachofthesubtypes.CMS-resemblanceisshowninthesmallmap.Theweighted-topology
overlap(WTO)networkshowscorrelationsbetweenspotsrevealingthatSpotEoverallanticorre-
lateswiththeotherspots.(b).The”personalized”landscapeconsidersupregulatedspotsofindi-
vidualsamples,independentofLMSsubtyping.Itrevealedamoregranularexpressionlandscape
and,particularly,a“plasticityplateau”relatedtovariousepigeneticmechanismspossiblypromot-
ingcellularreprogramming.TheWTOnetworkshowsassociationbetweentheprograms,mostly
demonstratinganticorrelationoftheplasticityplateauwiththeproliferativeprogramsnearspotC.
(c)Biplotsofcellcycleexpressionsignature[67]withPRC2targets[68]indicateoverallnegative
correlationsandpositivecorrelationswithtranslationandoxidativephosphorylation(oxphos)[36].
Thegenesofthelaerfunctionalitiesaccumulateinregionsalongtheleftedgeofthemap(red
circles)whilePRC2targetsoccupyspecificareasintherightpart,which,notably,agreewithareas
occupiedbymethylatedgenesoftheCRC-CIMPtype[69]andalsoG-protein-coupledreceptors
(ReactomeGPCR-signaling).
3.6.PortrayalofClinical,Mutational,andTelomereMaintenanceCharacteristics
Next,wecharacterizedtheCRLMafterstratificationintothedifferentLMSwithre-
specttoselectedclinicalitemsandmutations,aswellastelomeremaintenance(TM)
Figure 5.
Transcriptomic landscape of CRLM. (
a
) LMS landscape expresses peaks A–F upregulated in a
subtype-specific fashion. Blue regions assign areas of, on average, downregulated genomic programs in
each of the subtypes. CMS-resemblance is shown in the small map. The weighted-topology overlap
(WTO) network shows correlations between spots revealing that Spot E overall anticorrelates with the
other spots. (
b
). The ”personalized” landscape considers upregulated spots of individual samples,
independent of LMS subtyping. It revealed a more granular expression landscape and, particularly, a
“plasticity plateau” related to various epigenetic mechanisms possibly promoting cellular reprogram-
ming. The WTO network shows association between the programs, mostly demonstrating anticorrelation
of the plasticity plateau with the proliferative programs near spot C. (
c
) Biplots of cell cycle expression
signature [
67
] with PRC2 targets [
68
] indicate overall negative correlations and positive correlations
with translation and oxidative phosphorylation (oxphos) [
36
]. The genes of the latter functionalities
accumulate in regions along the left edge of the map (red circles) while PRC2 targets occupy specific
areas in the right part, which, notably, agree with areas occupied by methylated genes of the CRC-CIMP
type [69] and also G-protein-coupled receptors (Reactome GPCR-signaling).

Cancers 2023,15, 3835 14 of 31
3.6. Portrayal of Clinical, Mutational, and Telomere Maintenance Characteristics
Next, we characterized the CRLM after stratification into the different LMS with
respect to selected clinical items and mutations, as well as telomere maintenance (TM)
pathway activities by means of SOM portrayal and overall survival (OS) curves (Figure 6).
LMS1 has an inferior prognosis compared with the other LMS2-5 with a virtually identical
prognosis (see also [
5
]). These OS-relations partially align with the comparable CMS,
namely the inferior prognosis of CMS1 CRC compared with CMS2-4 [
70
]. However,
this deviates from other studies that reported a poorer prognosis for LMS5-resembling
CMS4 [
24
,
28
], possibly due to the early stage, non-metastatic nature of these CRC cohorts.
Furthermore, males, compared with females, showed a higher incidence (67% versus 33%)
as well as an inferior prognosis. This sexual bimorphism is in-line with previous reports on
CRC [
71
–
73
] and liver metastases possibly due to the regulation of the TME [
74
]. The sex-
stratified portraits indicate the increased expression of spot D (CIN) in women and of spot
B (PRC2, metabolism) in men together with an increased expression level of the plasticity
plateau. Sex-specific biomarkers for CRC taken from [
75
], located in spot D (CLDN1,
ANAPC7), are upregulated for women, and in/near spot B (ESM1,GUCA2A,VWA2) are
upregulated for men, which further supports the analogy between CRC and CRLM.
Cancers2023,15,xFORPEERREVIEW16of34
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 
Figure6.Subtypes,selectedclinicalcharacteristics,somaticmutations,andtelomeremaintenance
pathwayactivation.Therespectivecaseswereshowninsamplespaceassimilaritynets,asmean
portraitsaveragedovertherespectivesamples,andintermsofprognosisusingoverallsurvival
(OS)curves.ThetableliststhepercentagesofCRLMintherespectivestrata(dataweretakenfrom
[5]).Genelocationsaremappedascrossesintheportraits.Sexualmarkergenesweretakenfrom
[75].TheredarrowsindicateupregulatedspotE(inflammation).Analysisoftelomere-maintenance
mechanismsincludesviolinplotsofthepathwaysignalflow(PSF)activityoftelomerase(T)and
alternative(A)telomeremaintenanceusingTMpathwaysfrom[76],abiplotofT-andA-TMPSF
valuesandtheirstratificationaccordingtothefourquadrants(a,t…lowvalues,A,T…highvalues),
similarityplot,meanportraits,andOS-curves.
3.7.PrognosticMapsProvideanHR‐ScorealongtheEMT‐AxisRelatedtoTreatment
Weusedthesurvivaldatafromthecohorttogenerateprognosticmaps,whichdis-
playthehazardratio(HR)foreachpixel(metagene)inaredtobluecolorscale.TheHR
activestatuswasestimatedbyapplyingtwothresholdseingsforoverexpression:either
largerthanthemeanoronestandarddeviationawayfromthemeanofmetageneexpres-
sion(Figure7a,band[11]fordetails).Inferiorprognosis(indicatedbyredareasinthe
map)isassociatedwiththeupregulationofgenesinandaroundspotAandtheprocessing
ridgeinthemiddleofthemap.Theseoverexpressionstatesarepredominatelyfoundin
LMS1.Goodprognosis(blueareas)isfoundaroundspotE,associatingwithLMS5,and
alsospotB,C,andDupregulatedinLMS2-4.Adetailedcomparisonoftheprognostic
mapswiththeoverexpressionmapsrevealsthatmetagenesofmaximumandminimum
HR(HRmaxandHRmin,respectively;forlistsofgenesseeSupplementaryTable S4)
slightlydeviatefromtheoverexpressionspot-areasofmaximumexpression,particularly
ofspotAandE,respectively.Eachofthosemetagenescontainabout80transcripts(red
andbluetrianglesinFigure7a).TheirprofilesupregulateinLMS1(maxHR)andLMS5
(minHR),respectively(Figure7b).Wedefinedaprognosticscorebycalculatingthelog-
expressiondifference(HR=maxHR−minHR),rankedtheCRLMwithincreasingHR-
score,andcalculatedtheOS-survivalcurvesforthehigh-andlow-riskgroups(topand
Figure 6.
Subtypes, selected clinical characteristics, somatic mutations, and telomere maintenance
pathway activation. The respective cases were shown in sample space as similarity nets, as mean
portraits averaged over the respective samples, and in terms of prognosis using overall survival (OS)
curves. The table lists the percentages of CRLM in the respective strata (data were taken from [
5
]).
Gene locations are mapped as crosses in the portraits. Sexual marker genes were taken from [
75
].
The red arrows indicate upregulated spot E (inflammation). Analysis of telomere-maintenance
mechanisms includes violin plots of the pathway signal flow (PSF) activity of telomerase (T) and
alternative (A) telomere maintenance using TM pathways from [
76
], a biplot of T- and A-TM PSF
values and their stratification according to the four quadrants (a, t
. . .
low values, A, T
. . .
high values),
similarity plot, mean portraits, and OS-curves.

Cancers 2023,15, 3835 15 of 31
Extrahepatic disease accompanying CRLM and synchronous metastasis (within 6 months
of CRC diagnosis) are both associated with an average upregulation of spot E (inflamma-
tion). KRAS and TP53 somatic mutations associate with the expression changes of spot
B, which harbors both genes in its neighborhood, acting in opposite directions, however,
namely in up- and downregulation, respectively, which is in accordance with the major
functions of KRAS as oncogene and of TP53 as a tumor suppressor. These mutations, as
well as that of NRAS, on average activate spot E and thus the endothelial immune respon-
sive LMS5 and, on the other hand, associate with the downregulation of proliferation and
CIN characteristics (spot C and D, respectively) and of the epithelial signature (spot A)
for mutated KRAS. The mean LMS5/spot E characteristics of CRLM with mutated KRAS
and/or TP53 align with the increased mutation frequencies of these genes along a hypoxia
axis that associates with the activation of spot E [3].
Finally, we compared the pathway signal flow (PSF) activation values of the telomere
maintenance pathways via telomerase-dependent (TEL) and alternative (ALT) mechanisms
(Figure 6below, [
76
,
77
]). The ALT-versus-TEL activity biplot shows that the double-low
quadrant “at” (a
. . .
ALT-low, t
. . .
TEL-low) enriches LMS5 with the marked upregulation
of spot E and downregulated proliferation of spot C. The “At” quadrant (A
. . .
ALT-high,
TEL-low) associates with activated proliferation (spot C), while the “aT”-quadrant up-
regulates the plasticity plateau. Poor and better prognoses associate with “at” and “At”
quadrants, respectively, i.e., with the vertical ALT-axis. Interestingly, the high-ALT “At”
quadrant and the high TEL quadrant “aT” associate with the downregulation of ATRX
and the upregulation of TERT, respectively (see gene locations marked by crosses in the
At and aT portraits), which are also known to switch-on ALT and TEL TM in other cancer
types, such as gliomas [
78
]. In summary, the stratification of CRLM according to selected
clinical items, the mutations of key genes as well as TM, reveals a dualism of expression
between LMS5-like and non-LMS5-like patterns with activated spot E versus spot A and C,
respectively, which reflects the overall antagonism between mesenchyme/inflammatory
and proliferative/metabolic cellular functions where, e.g., mutated (TP53,KRAS) CRLM
are more prone to the former states.
3.7. Prognostic Maps Provide an HR-Score along the EMT-Axis Related to Treatment
We used the survival data from the cohort to generate prognostic maps, which display
the hazard ratio (HR) for each pixel (metagene) in a red to blue color scale. The HR active
status was estimated by applying two threshold settings for overexpression: either larger
than the mean or one standard deviation away from the mean of metagene expression
(Figure 7a,b and [
11
] for details). Inferior prognosis (indicated by red areas in the map) is
associated with the upregulation of genes in and around spot A and the processing ridge
in the middle of the map. These overexpression states are predominately found in LMS1.
Good prognosis (blue areas) is found around spot E, associating with LMS5, and also spot
B, C, and D upregulated in LMS2-4. A detailed comparison of the prognostic maps with
the overexpression maps reveals that metagenes of maximum and minimum HR (HRmax
and HRmin, respectively; for lists of genes see Supplementary Table S4) slightly deviate
from the overexpression spot-areas of maximum expression, particularly of spot A and E,
respectively. Each of those metagenes contain about 80 transcripts (red and blue triangles
in Figure 7a). Their profiles upregulate in LMS1 (maxHR) and LMS5 (minHR), respectively
(Figure 7b). We defined a prognostic score by calculating the log-expression difference
(
∆
HR = maxHR
−
minHR), ranked the CRLM with increasing
∆
HR-score, and calculated
the OS-survival curves for the high- and low-risk groups (top and bottom 50% or 25% of
CRLM), which resulted in OS-curves resembling those between LMS1 and the other three
subtypes (compare with Figure 6).
Substituting the genes taken from the maxHR and minHR metagenes by genes of
spot A and spot E results in similar OS-curves, indicating that limiting the prognosis of
CRLM patients is well reflected by the subtypes LMS1 (most inferior) and LMS5 (better).
Key genes frequently mutated and/or deregulated in CRC and CRLM, including APC,

Cancers 2023,15, 3835 16 of 31
FBXW7,SMAD4,PIK3CA,WNT5A, and ATRX, are found adjacent to the minHR metagene,
suggesting that their upregulation is associated with better prognosis (compare Figure 7a,
left map and Figure 1c). Further, the minHR area enriches genes of the set “Golgi cis-
cisternae” (gene ontology (GO) cellular component) playing a role in sustaining invasion
and metastasis such as the intracellular signaling platform, lipid biosynthesis, protein se-
cretion, the formation of extracellular vesicles, and the overall supporting of the adaptation
of cancer cells to acquire a mesenchymal-like migratory phenotype via extracellular matrix
remodeling ([
56
] and references cited therein). The maxHR regions are enriched in genes of
the epithelial functional context and bad prognosis in cholangiocarcinoma [
79
] as well as
nasopharyngeal- [
80
] carcinomas (Supplementary Figure S5). A plot along the
∆
HR score
(after averaging and smoothing) indicates that HR increases, on average, only at larger
values of the score, which are enriched with LMS1 cases (Figure 7d).
The expression profiles of gene signatures related to neoadjuvant chemotherapy and
treatment resistance in CRC and CRLM show activation in LMS5 and partly in LMS1
suggesting a treatment- and metastasis-driven shift of function towards an immuno-
suppressive phenotype (Figure 7e, left part) [
6
]. A detailed inspection of the signature
profiles and their gene maps reveals close similarity with those of the hallmark set “hy-
poxia” which was recently identified as a driving process in CRC liver metastasis and
chemotherapy-treatment resistance (Figure 7e, right part) [
3
]. The hypoxia signature genes
distribute over spots E and A, where the latter hosts the key regulator SLC2A, which
promotes immunosuppression via M2-macrophage enrichment combined with immune
escape via specific CD4+ T-cell exhaustion [
3
] (Figure 7e, right part). The virtually identi-
cal profiles and maps of the hallmark genes “epithelial-mesenchymal transition” (EMT)
and “angiogenesis” reflect the close relations between these functions: both blood vessel
formation (angiogenesis) [
81
] as well as senescence in a secretory cell phenotype [
82
] fa-
cilitate metastasis and treatment resistance. The respective marker genes accumulate in
and around spot E together with immuno-suppressors and hallmark “EMT” genes thus
indicating co-regulation. Interestingly, the PIEZO1 gene, coding a mechano-sensitive sensor
membrane-channel protein also locates in the endothelial/CAF-rich left region of spot E.
The mechano-sensitivity of tumor and of TME-cells such as macrophages is important
for their ability to transduce mechanical forces into biochemical signals associated with
signaling pathways involved in cancer metastasis and EMT, such as angiogenesis, cell
migration, intravasation, and proliferation [
83
–
85
], as well as cytokinesis and endosome
trafficking [
86
]. The proper processing of mechanical stress is an essential function to
master the changing cell architecture and extracellular-matrix (ECM) organization along
the EMT-path [87].
In conclusion, the prognostic maps divide the expression landscape into regions of
better and worse prognosis, which are associated with the functional context of the affected
spots assigned above. These maps enable the extraction of a prognostic expression score
along the EMT-axis between spots A and E and their adjacent regions. Treatment resistance,
immune evasion, and a series of EMT-related processes such as hypoxia, angiogenesis,
senescence, and micro-mechanic sensitivity associate with neoadjuvant treatment and the
formation of an immuno-suppressive, mesenchymal state of CRLMS.
3.8. Intra-Patient Heterogeneity of Metastasis
More than one CRLM samples were collected from nearly fifty patients at different
time-points, [
5
] providing a set of multiple CRLM (mCRLM) which enables a glimpse of the
intra-patient mCRLM heterogeneity. A river flow plot links the intra-patient mCRLM with
their LMS and segment classes revealing no unique assignment in many cases (
Figure 8a
).
For an alternative perspective, we applied the continuous
∆
HR prognostic score for het-
erogeneity analysis in terms of a mean-value ranked plot of the mCRLM (Figure 8b). Its
slope and vertical spread closely resemble the respective plot of the individual CRLM
(compare with Figure 7c). The standard deviation of
∆
HR for each mCRLM overall forms

Cancers 2023,15, 3835 17 of 31
a relatively narrow range of intra-patient variability (see box-heights or separate plot in
Figure 8b below).
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 
Figure7.Prognosticmapsandscoresandtheirrelationtotreatmentandtheepithelial–mesenchy-
maltransition(EMT).(a)Theprognosticmapcolorcodesassociationsbetweenmetageneexpression
andthehazardratio(HR)relativetotheoverallsurvivalaveragedoverallCRLMinred(highex-
pression,highHR)toblue(highexpression,lowHR).Theleftmapreferstoalowerandtheright
maptoahigherexpressionthreshold.MetagenesofmaximumandminimumHR(HRmaxand
HRmin,respectively)areindicatedbyredandbluetrianglesintheleftmap.(b)Profilesofthe
HRminandHRmaxmetageneexpression.Theoverallsurvival(OS)-curvesofpatientswithacti-
vatedandnon-activatedexpressionarevirtuallymirrorsymmetricalforHRminandHRmaxre-
gardinggoodandinferiorprognoses.(c)ProfilesofgenesfromHRmaxandHRminmetagenesare
combinedintoaHRscore.PositiveandnegativeHRvalueswerestronglyenrichedwithinLMS1
andLMS5CRLMs.A25-percentileselectionalongthescoreprovidesslightlymorediscriminative
OScurvesthanthe50-percentileselectionintogoodandbadprognosisCRLM.Usinggenesofspots
AandEforthescore(spot)providessimilarresults.(d)TheHR-versus-(HR-)scoreplotreveals,
onaverage,anon-linearrelationwhereHRincreasesvirtuallyonlyatlargerscorevaluesatin-
creasedLMS1content.(e)Genesetanalysissupportsthefunctionalinterpretationofneoadjuvant
chemotherapytreatmentresistanceandbadprognosisintermsofEMT:theheatmapconsiders
genespromotingmetastasisviaanangiogenicmechanism[88],“jackpot”genesoftreatmentre-
sistancedrivenbyepigenetics[89],genesupregulatedafterFOLFIRItreatment[90],andgenesup-
ordownregulatedafterchemotherapyofCRLMpatientsandinvitro5-Fluorouracil(5-FU)[6].x–y
biplotsofthetreatmenteffect(x—downregulatedgenes,y—upregulatedgenes)showashiftto-
wardsimmunosuppressiveLMS5.Theup-profilesandgenemapsresemblethoseofthehallmark
hypoxia[36],promotingviciousandimmuno-suppressivemetastasis[3].HallmarkgenesofEMT-
andangiogenesisaccumulateinspotEtogetherwithimmunecheckpointinhibitors[81]andsenes-
cencegeneswhichfacilitateresistanceagainstimmunotherapy[82].
3.8.Intra‐PatientHeterogeneityofMetastasis
MorethanoneCRLMsampleswerecollectedfromnearlyfiftypatientsatdifferent
time-points,[5]providingasetofmultipleCRLM(mCRLM)whichenablesaglimpseof
theintra-patientmCRLMheterogeneity.Ariverflowplotlinkstheintra-patientmCRLM
withtheirLMSandsegmentclassesrevealingnouniqueassignmentinmanycases
Figure 7.
Prognostic maps and scores and their relation to treatment and the epithelial–mesenchymal
transition (EMT). (
a
) The prognostic map color codes associations between metagene expression and
the hazard ratio (HR) relative to the overall survival averaged over all CRLM in red (high expression,
high HR) to blue (high expression, low HR). The left map refers to a lower and the right map to
a higher expression threshold. Metagenes of maximum and minimum HR (HRmax and HRmin,
respectively) are indicated by red and blue triangles in the left map. (
b
) Profiles of the HRmin
and HRmax metagene expression. The overall survival (OS)-curves of patients with activated and
non-activated expression are virtually mirror symmetrical for HRmin and HRmax regarding good
and inferior prognoses. (
c
) Profiles of genes from HRmax and HRmin metagenes are combined into
a
∆
HR score. Positive and negative
∆
HR values were strongly enriched within LMS1 and LMS5
CRLMs. A 25-percentile selection along the score provides slightly more discriminative OS curves
than the 50-percentile selection into good and bad prognosis CRLM. Using genes of spots A and E for
the score (
∆
spot) provides similar results. (
d
) The HR-versus-(
∆
HR-)score plot reveals, on average,
a non-linear relation where HR increases virtually only at larger score values at increased LMS1
content. (
e
) Gene set analysis supports the functional interpretation of neoadjuvant chemotherapy
treatment resistance and bad prognosis in terms of EMT: the heatmap considers genes promoting
metastasis via an angiogenic mechanism [
88
], “jackpot” genes of treatment resistance driven by epi-
genetics [
89
], genes upregulated after FOLFIRI treatment [
90
], and genes up- or downregulated after
chemotherapy of CRLM patients and
in vitro
5-Fluorouracil (5-FU) [
6
]. x–y biplots of the treatment
effect (
x—downregulated
genes, y—upregulated genes) show a shift towards immunosuppressive
LMS5. The up-profiles and gene maps resemble those of the hallmark hypoxia [
36
], promoting vicious
and immuno-suppressive metastasis [
3
]. Hallmark genes of EMT- and angiogenesis accumulate
in spot E together with immune checkpoint inhibitors [
81
] and senescence genes which facilitate
resistance against immunotherapy [82].

Cancers 2023,15, 3835 18 of 31
Cancers2023,15,xFORPEERREVIEW20of34


 
Figure8.Intra-patientheterogeneityofCRLM:(a)FlowdiagramofmultipleCRLM(mCRLM)from
thesamepatienttowardsLMSandsegmentclassificationstotheleftandtotheright,respectively.
(b)MeanHRscoreofthemCRLMaveragedperpatientandrankedfromthelefttotheright.The
boxesrefertothestandarddeviation(SD)aroundthemedianperCRLMandenlargedplotofthe
standarddeviation.(c)SOMportraitsofthemCRLMperpatientsortedbypart(b)(seepatientno.
andarrows).ThecolorbarsassigntheLMS.
4.Discussion
4.1.SubtypingStratifiesCRLMalongCancerHallmarksandTMEEducation
WereanalyzedtranscriptomedataoflivermetastasesusingSOMportrayal,ama-
chinelearningmethodthatgeneratesindividualimagesofeachtumorsample,reduces
thedimensionsofthetranscriptome-wideexpressiondata,andenablesstraightforward
andintuitivedownstreamanalysis[21].ThepreviouslyidentifiedfiveLMS[5]wereused
toprovidesubtype-specificSOMportraitsrevealingsixmajormodulesofco-regulated
genes.Theserelatedtomolecularhallmarksofcancer,namelyavoidingimmunedestruc-
tion(spotA),tumor-promotinginflammationandangiogenesis(spotE),sustainingpro-
liferation(spotC),deregulatingcellularenergetics,metabolicactivityandDNArepair
(spotB),andCIN-relatedgenomicinstability(spotD)(Figure9)[91].Thespot-genespro-
viderobustsignaturesforclassifyingtheLMS,meetingatrade-offcriterionofbalancing
theinterpretabilityandspecificityofmolecularmarkers[92].
ThefunctionalcharacteristicsoftheLMSalignwithknownmolecularCRCsubtypes,
suggestingthatmetastasis,atleastpartly,maintainscharacteristicsofprimarytumors,
whichcouldinformsubtype-targetedtherapyforbothmetastaticandnon-metastatictu-
mors.Ahighconcordanceofgenetickeylesions,mutations,andCNVbetweenprimary
andmetastatictumorssupportsthisobservation.Thissuggeststhatthereisminimalim-
pactofthelivermicroenvironmentonCRLMmutationpaerns,butratherneutral
Figure 8.
Intra-patient heterogeneity of CRLM: (
a
) Flow diagram of multiple CRLM (mCRLM) from
the same patient towards LMS and segment classifications to the left and to the right, respectively.
(
b
) Mean
∆
HR score of the mCRLM averaged per patient and ranked from the left to the right. The
boxes refer to the standard deviation (SD) around the median per CRLM and enlarged plot of the
standard deviation. (
c
) SOM portraits of the mCRLM per patient sorted by part (
b
) (see patient no.
and arrows). The color bars assign the LMS.
The overall inter-patient heterogeneity of the prognostic score exceeds the intra-patient
heterogeneity by roughly one order of magnitude, considering the change in
∆
HR across
its linear range of the plot (
∆∆
HR ~1) as a measure of the former and the mean SD (~0.1)
as a rough estimate of the latter effect. About 50% of the mCRLM from the same patient
belong to different subtypes [
5
], a result that suggests a relatively large intra-patient het-
erogeneity in contrast to our estimation. Inspection of the individual portraits (with more
than three mCRLM per patient) reveals that part of the mCRLM of the same patient with
different LMS memberships show very similar expression patterns (see, e.g., pat. 467,
141, 61, 47, Figure 8b). Moreover, the distinction between LMS2–LMS3 is relatively uncer-
tain, which then also applies to the heterogeneity estimation based on LMS-membership
(Figures 1d and 2c).
On the other hand, the SD increases towards the end of the
∆
HR scale. Selected
portraits from these regions (e.g., pat. 503 from the left and pat. 89, 16 from the right
side) show relatively diverse patterns despite their unique LMS assignment, suggesting a
relatively high variability of the underlying transcriptional states, particularly in the low-
expression epigenetic effects. Notably, the portraits of the right, high inferior prognosis side
reveal an enrichment of CRLM with activated “plasticity plateau” referring to high (red)
∆
HR areas in the prognostic map (Figure 7a). In summary, the transcriptomes of mCRLM
from the same patient are relatively homogeneous based on the prognostic
∆
HR score and

Cancers 2023,15, 3835 19 of 31
its variability. mCRLM of inferior prognosis is variant regarding their epigenetic context
showing either the activation of spot A (epithelium) and/or of the plasticity plateau.
4. Discussion
4.1. Subtyping Stratifies CRLM along Cancer Hallmarks and TME Education
We reanalyzed transcriptome data of liver metastases using SOM portrayal, a machine
learning method that generates individual images of each tumor sample, reduces the
dimensions of the transcriptome-wide expression data, and enables straightforward and
intuitive downstream analysis [
21
]. The previously identified five LMS [
5
] were used to
provide subtype-specific SOM portraits revealing six major modules of co-regulated genes.
These related to molecular hallmarks of cancer, namely avoiding immune destruction (spot
A), tumor-promoting inflammation and angiogenesis (spot E), sustaining proliferation
(spot C), deregulating cellular energetics, metabolic activity and DNA repair (spot B),
and CIN-related genomic instability (spot D) (Figure 9) [
91
]. The spot-genes provide
robust signatures for classifying the LMS, meeting a trade-off criterion of balancing the
interpretability and specificity of molecular markers [92].
The functional characteristics of the LMS align with known molecular CRC subtypes,
suggesting that metastasis, at least partly, maintains characteristics of primary tumors,
which could inform subtype-targeted therapy for both metastatic and non-metastatic tu-
mors. A high concordance of genetic key lesions, mutations, and CNV between primary
and metastatic tumors supports this observation. This suggests that there is minimal impact
of the liver microenvironment on CRLM mutation patterns, but rather neutral evolution
mechanisms of early metastasis due to the high fitness of the metastatic clones [
3
,
27
]. On
the other hand, the liver facilitates metastatic expansion from other organs because of its
complex immune system that dampens immunity to cancer-related neoantigens, promotes
immune editing, and changes TME-characteristics of the metastatic compartment com-
pared with the primary tumors [
7
,
93
,
94
] thus offering a tumor-supporting (pre-)metastatic
niche [
95
]. Interestingly, a recent PanCancer metastasis study on 4000 metastatic tumors
across thirty-two cancer types taken from the cancer genome atlas (TCGA) reported four
major molecular groups of metastasis (s1-4) [
10
] partly associating with the function of our
spot modules, namely proliferation, DNA repair and CIN (spot C and D) in s1, metabolism
(spot B and C) in s2, and inflammation and immunity (spot E) in s4. The epigenetic subtype
s3 can be related to the ”plasticity plateau” and, partly, spot B-accumulating PRC2 targets
observed across all LMS with elevated expression. Extensive drug testing in cell lines has
suggested therapeutic options for s1 (e.g., targeting MYC) and s3 (e.g., targeting histone
acetylation and methylation as well as TERT and EZH2). Potential treatment options for
primary CRC were recently reviewed [
96
] and include immune and adjuvant therapy de-
pending on the CIMP-status for CMS1 (and possibly LMS1), targeted treatment to MAPK-
and WNT-pathways, MYC-expression or glycolysis metabolism for CMS2 and/or CMS3
(and possibly LMS2-4), TGFbeta, and also cancer stemness inhibitors for CMS4 (and possibly
LMS5). We also studied telomere maintenance pathways in CRLM and found decreased
activity in both TEL and ALT TM in LMS5, but increased ALT, which directly correlates
with the upregulation of spot C (proliferation, stemness). Changes in TM activity are a
hallmark of cancer modulation in CRC [
77
] and are suggested as markers of response
to therapy [97].
Our analysis identified factors driving CRLM characteristics towards the immune-
suppressive spot-module E, namely accompanying extrahepatic disease(s), synchronous
metastasis, and TP53,NRAS, and KRAS mutations, where the frequency of these mutations
is reported to increase in immuno-suppressive immunotypes expressing spot E in our
data [
3
]. Moreover, neoadjuvant chemotherapy also induces a shift of CRLM and of derived
tumor cell cultures towards a mesenchymal phenotype [
5
,
6
], which is supported by the
LMS5-resemblance of the extracted expression signature. However, it is important to note
that cancer therapies, particularly platinum-based/5-FU therapies, can cause significant
changes in the tumor genome, and introduce an evolutionary bottleneck that selects for

Cancers 2023,15, 3835 20 of 31
known therapy-resistant drivers [
98
] and induces mutational burdens exceeding aging-
related mutations accumulating in the same time-span by several orders of magnitude [
99
].
However, the impact of this mutational “toxicity” on tumor development is not entirely
clear. Early CRC drivers were found to be enriched in CRLM, which can harbor private
mutations (PTPRT, and to a lesser degree, AMER1 and TCLF1), also suggesting stringent
evolutionary selection mechanisms for the part of the CRLM [
27
] that is possibly governed
by treatment [25].
Cancers2023,15,xFORPEERREVIEW23of34


 
Figure9.Aholisticviewoncolorectalcancerlivermetastasis:molecularheterogeneity,tumormi-
croenvironment,geneticandepigeneticmodesofregulation,prognosis,tumordevelopment,and
possibletreatmentoptions.Seediscussion.
4.2.TrajectoryInferenceandPersonalizedAnalysisDiscoveraHiddenUniverseofContinuous
EpigeneticStatesShapingMetastasis
LMS1-5subtypeswereidentifiedbyclassdiscoveryusingnon-negativematrixfac-
torizationfollowedbyclusternumberoptimizationbasedongeneswithhighspecificex-
pressionlevelsacrossthesubtypes[5].Consequently,theLMS-specificspotmarkerswere
biasedtowardshighlyvariantgenes,whichcanunintentionallyneglectlesservariantgene
clusters.Ourpersonalizedlandscape-approachconsidersindividualportraitsbeyondthe
LMS-strataandthereforeextractsadditionalfeaturesappearingasweaklyvariantspot
Figure 9.
A holistic view on colorectal cancer liver metastasis: molecular heterogeneity, tumor
microenvironment, genetic and epigenetic modes of regulation, prognosis, tumor development, and
possible treatment options. See discussion.

Cancers 2023,15, 3835 21 of 31
Genetic alterations and chromosomal instability are necessary but insufficient for
cancer initiation, progression, and metastasis. Instead, tumors, and particularly CRLM,
are rather complex ecosystems involving cancer cells and the TME [
94
]. Our analysis
suggests an interaction between the TME and stroma-mediated immune suppression and
treatment resistance during CRLM development. Using a series of TME-related signatures,
as well as the single-cell deconvolution of bulk expression data, we found indications of an
immunogenic pro-tumoral and tumor-suppressive TME in LMS1 and LMS5, respectively,
and of predominantly immune desert properties in LMS2-4. However, part of the CRLM of
the immune-desert subtypes show a medium-level expression of spot E, thus also reflecting
moderate inflammatory, immuno-suppressive characteristics in these LMS. We found that
spot E overlays signatures of cancer-associated fibroblasts, of the stroma and of different
immune cells such as B-, plasma, pDC, and mast cells reflecting the overall considerable het-
erogeneity and changing composition of the TME-cell communities not explicitly resolved
in the LMS stratification. This suggests the education of the TME towards stroma-mediated
immune suppression and treatment resistance upon CRLM development [
100
,
101
]. Further,
spot E co-expresses with a series of immune checkpoint inhibitors such as CTLA4,PDL1,
and TIM3, whose increased expression associates with T-cell exhaustion, macrophage re-
education from M2 towards M1, and decaying CD8+ expression in agreement with [
3
,
7
,
102
].
The central role of tumor-associated macrophages (TAMs) in the TME is substantiated
by the evolutionary consequences TMA impose through positive or negative selective
pressure exerted by/on tumor cells [
35
]. TAM complexity is paving a possibly way for
the design of novel TAM-targeting therapies to harness their anti-tumoral potential [
103
],
e.g., through CCR5-inhibitor treatment reprogramming TAMs toward a pro-inflammatory
phenotype in metastatic CRC patients [
104
]. Overall, transcriptomic portrayal confirms
the previous subtyping of CRLM, deepens the understanding of their functional context,
provides marker signatures for the LMS, and delineates the remodeling of the TME from
an immuno-active to an immuno-suppressive state.
4.2. Trajectory Inference and Personalized Analysis Discover a Hidden Universe of Continuous
Epigenetic States Shaping Metastasis
LMS 1-5 subtypes were identified by class discovery using non-negative matrix fac-
torization followed by cluster number optimization based on genes with high specific
expression levels across the subtypes [
5
]. Consequently, the LMS-specific spot markers
were biased towards highly variant genes, which can unintentionally neglect lesser variant
gene clusters. Our personalized landscape-approach considers individual portraits beyond
the LMS-strata and therefore extracts additional features appearing as weakly variant
spot modules not detected in the LMS-centered analysis, akin to a “dark matter” of gene
expression (in analogy to the not-visible matter in the universe).
Notably, our approach reveals the activation of regulatory programs with epigenetic
impact such as PRC2-targetted genes with poised and repressed promoters in a healthy
colon which change their promoter status in CRLM through chromatin remodeling [
17
].
We found that these epigenetic modes antagonistically regulate compared with a part of the
high-expression programs including, e.g., genes with active promoters governing functions
such as cell cycle, DNA repair, and oxphos metabolism. The activation of such epigenetic
modes is associated with the ability of cancer cells to acquire plasticity regarding their fates.
It enables them to transit between high-expression states and low-expression states, such
as along the epithelial–mesenchymal transition axis taking place between LMS1 and LMS5.
We observed such epigenetic states in all subtypes, which suggests that all of them are
prone to fate decisions via plasticity-driven changes in their state.
The balancing between active and proliferative genes on the left, and repressed, plastic
chromatin states on the right enables evolutionary adaptation to the TME and drives not
only the adenoma-to-carcinoma transition of CRC but also promotes metastasis, e.g., via
the EMT [
45
]. Components of the PRC2 such as EZH2 and SUZ12 (located in spot C) can
facilitate these transitions and promote CRC stem-like cells (as indicated by the stemness

Cancers 2023,15, 3835 22 of 31
marker LGR5 in spot C, see below) via repressive interactions with their targets (located in
the plasticity plateau) in concert with changes to histone and DNA methylation in CIMP
genes. Notably, the bimodality between active and repressed chromatin states and their
association with proliferative and immunogenic states was described for other tumors
such as lymphomas [
92
] and gliomas [
11
]. In a more general sense, the interplay between
high-expression-genetic and low-expression-epigenetic states, as identified in the CRLM
expression landscape, is a key condition for the reorganization of transcriptional programs
that facilitate tumor development via the emergence of new attractor states better adapted
to the changing TME [
8
]. Epigenetic mechanisms also affect antitumor and protumor
immunity and the TME immune and CAF cell composition, thus facilitating immune state
transitions during tumor development [
103
]. An interesting argument supporting the role
of epigenetics in metastasis comes from the result that mutational patterns of passenger
mutations in metastatic tumors outperform those of driver mutations in predicting their
primary tumor of origin, presumably because the regional density of somatic passenger
mutations reflects chromatin accessibility to DNA-repair complexes, which in turn relates
to the epigenetic state of the cancer cell [105].
Trajectory inference revealed possible developmental paths from epithelial (as seen in
LMS1) to mesenchymal (in LMS5) characteristics, which could lead to enhanced metastatic
fitness through an interplay of genetic and epigenetic factors [
106
]. These trajectories are
governed by continuous alterations in spot expressions suggesting that the LMS subtypes
are “connected” through a continuum of intermediate states. This continuum combines the
“archetypic” LMS characteristics and epigenetic regulatory modes such as the plasticity
plateau with the LMS spots in diverse ways and with varying degrees of influence. Further,
the transition to metastasis is thought to be facilitated by the presence of proliferative
CRC cancer stem cells (CSCs). These CSCs constitute presumably a small population of
highly tumorigenic cells, possessing pluripotency and self-renewal properties that drive
metastasis and treatment resistance. This process is sustained by a dynamic TME [
107
].
Complex interactions between the TME and CSC not only maintain stemness but also fuels
tumor evolution into aggressive, invasive, migratory phenotypes [100], a process thought
to be under the influence of epigenetic control [
108
]. The CSC marker, LGR5, also a marker
for normal intestinal stem cells, suggests that the stem-cell program in the colon could
be conserved in CRC [
106
,
109
] and CRLM. Additional factors such as CRC-typical CIN
(e.g., in spot D), which boost metastasis, amplify oncogenic signaling, leading to more
aggressive cancer clones, punctuated tumor evolution [
110
], and immunoediting in the
TME context [111].
The oscillation between hallmark-related archetypic genetic and plastic epigenetic
states suggests that high-transcription activity stabilizes the genetic states, while low-
transcription promotes transitions in response to developmental or environmental cues [
112
].
Open chromatin states, associated with high expression, may inhibit differentiation and pro-
mote tumor growth via proliferative, metabolic, and CIN hallmarks, primarily in LMS2-4.
Conversely, permissive or “plastic” states may allow cell fate transitions, providing a fitness
advantage along the EMT-trajectory from Seg1.1 (mainly LMS1) to Seg3 (mainly LMS5)
of the monocle tree. We recently showed in melanoma cell lines that targeted treatment
can shift cell physiology from high-transcription to plastic, low-transcription epigenetic
states [
15
]. In summary, combining trajectory inference with Self-Organizing Map (SOM)
portrayal reveals a continuum of transcriptomic states that divides into archetypic hallmark-
states and intermediate transition states driven by epigenetic plasticity and characterized
by diverse combinations of these characteristics.
4.3. Towards Precision Diagnostics and Treatment Decisions of CRLM
This dual continuous and plastic characteristic of CRLM heterogeneity necessitates a
fine granular diagnostic scheme that surpasses the somewhat generalized LMS stratification.
Our portrayal method provides unique molecular images of each tumor, enabling per-
sonalized diagnosis through the intuitive visual interpretation of spot patterns, which are

Cancers 2023,15, 3835 23 of 31
interpretable in terms of activated cellular programs. Hence, these personalized portraits
can be evaluated by experts in a diagnostic pipeline to assess the specific transcriptional
state of a CRLM sample. Such “manual” inspection of molecular portraits could be au-
tomated, for example, through neuronal network machine learning, in future stages of
technical development (see [
92
] for a proof of principle application to another cancer type).
Moreover, transcriptomic maps can be transformed into prognostic landscapes visualiz-
ing the association between gene expression, cellular programs, and their corresponding
HR. To adequately account for the continuous nature of CRLM heterogeneity within the
transcriptomic landscape, we applied a one-dimensional HR-score along the EMT axis as a
proof of principle solution that links the best and worst HR metagenes in the landscape.
This scoring method can be extended to multidimensional molecular coordinates, as identi-
fied by the monocle-tree analysis or by combining different spots for prognostics within a
coordinate system of continuous scores [113].
Another implication of the continuous developmental trajectories of CRLM, which
are governed by epigenetics, is that treatment will require molecular surveillance and
adaptation to evolving states. The dynamic cellular composition and functional character-
istics of the immune landscape along the trajectory of cancer development are expected
to impact therapeutic efficacy and clinical outcomes. Targeting epigenetic modifiers to
remodel the tumor-immune microenvironment holds great potential as an integral part of
anticancer regimens [103].
The so-called oligometastatic hypothesis provides an alternative perspective on the
development and treatment of metastasis, suggesting that the early and aggressive treat-
ment of metastases may prevent the further spread of cancer and even achieve long-term
remission [
104
]. When applied to CRLM, it shows that a canonical, proliferative subtype
resembling LMS3-4, with an elevated CIN-signature score [
114
] of genes accumulating in
and around spot C is associated with increased metastatic propensity, and inferior overall
survival [
115
–
117
] (Supplementary Figure S5d). Surprisingly, a specific subgroup of these
CRLM, also with a high CIN-score, exhibits the opposite effect. They demonstrate a thera-
peutic vulnerability to DNA-damaging therapies, leading to improved treatment responses
and low clinical risk with a 10-year survival rate of 95% after resection [
116
]. However,
the challenge remains how to differentiate high-CIN-score tumors likely to respond to
DNA-damaging therapies from those unlikely to respond. Our results show that CRLM
with an upregulated CIN-score (spot C, proliferation up) widely distributed over LMS1–
LMS4 with a broad spectrum of functional and genomic characteristics associated with
activated proliferation. This provides a basis for further studies to distinguish responders
from non-responders to DNA-damaging therapy.
4.4. Limitations and Open Points
Our analysis has several key limitations. Firstly, it does not directly consider patient-
paired CRLM and primary CRC, making it unclear how the molecular characteristics of the
primary CRC are maintained after distant spread. While the mapping of CRC signatures
suggests strong parallels, this remains an area requiring further clarification. On the other
hand, the analysis of multiple CRLM based on the metagene HR-score suggests small
intra-patient variability that indirectly implies a relatively homogeneous metastatic spread.
Secondly, our analysis is based on a cross-sectional cohort of bulk tumor samples
without longitudinal follow-up data on tumor development over time. However, the
observed cross-sectional heterogeneity can be interpreted in terms of a series of devel-
opmental CRLM states existing in the different patients along the pseudotime scale,
which simulates developmental dynamics [
44
]. Alternatively, it could reflect the intra-
tumoral CRLM heterogeneity of microscopic lesions all co-existing at the same stage of
tumor progression.
Both interpretations suggest that heterogeneous micro-lesions may change their com-
position along developmental trajectories of metastasis. This view is supported by spatial
transcriptomics of CRLM, which show that cellular neighborhood archetypes reflecting

Cancers 2023,15, 3835 24 of 31
different stages of tumor progression and resembling our LMS-types are active simulta-
neously in different spatial microregions of the same tumor [
118
]. Other studies have
identified distinct modes of micro-vessel vascularization, specific metabolic alterations, and
aWNT-signaling signature with a prognostic impact co-existing in the same CRLM [
119
].
Interestingly, a recent spatial transcriptomics study of synchronous resections of primary
CRC and matched CRLM suggests that the stromal-versus-inflammatory balance of the
invasive edge of CRLM samples impacts prognosis. Specifically, it reported that an increase
in LMS5-like features (increased Tregs and stromal composition) compared with inflam-
matory ones is associated with a worse prognosis [
120
]. Another study along this line
suggested that observable histopathological growth patterns (HGPs) can be interpreted
in terms of molecular mechanisms [
121
]. These patterns differentiate between LMS5-like
lesions (desmoplastic HGP, enriched in EMT, angiogenesis, stroma, and immune signa-
tures) and lesions resembling LMS3-4 (replacement HGP, enriched in metabolism, cell
cycle, and DNA damage repair signatures), as well as combinations of both. This implies
that microscopic imaging in combination with spatial transcriptomics can support molec-
ular diagnostics. Together, this data suggests that the heterogeneity of liver metastases
appears to distribute on a microscopic scale, both spatially and temporally. This implies
that different LMS-states and cellular TME-communities coexist in the same tumor and
change their composition upon development, usually towards immune-suppressive and
treatment-resistant states. Further investigation into these dynamics is necessary to fully
understand their implications for treatment and prognosis.
5. Conclusions
Machine learning using an omics portrayal of CRLM provides a comprehensive and
detailed understanding of the molecular heterogeneity underlying this complex disease.
Our study revealed a shift from treatment-sensitive to treatment-resistant tumors that are
guided by genetic, epigenetic, and microenvironmental factors. Our findings encourage
further studies to better understand the micro-spatial heterogeneity of the CRLM, the
underlying epigenetic mechanisms, the changing cellular communities in the TME inter-
acting with the tumor cells, and also possible genetic determinants. Understanding this
heterogeneity is crucial for tailoring effective treatment strategies, where precision medicine
is one promising approach. It must involve the genomic profiling of individual tumors in
follow-up settings to identify specific alterations and vulnerabilities in space and time to
tackle the diverse manifestations of CRLM and provide better control of disease progres-
sion. This information can guide the selection of targeted therapies, immunotherapies, or
combined treatments.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/cancers15153835/s1, Figure S1: Browsing the livermets-dataset
using the oposSOM-browser; Figure S2. Gallery of SOM portraits in standard and coastline scales;
Figure S3: Sorting the portraits; Figure S4: Heatmaps of the gene set categories gene ontology
biological process (GO BP) and immunome (taken from [4]); Figure S5: Detailed functional analysis
using gene set maps and profiles; Figure S6: Single cell analysis of CRLM data for cell marker gene
extraction and mapping to CRLM data; Figure S7. Pathway activity analysis of WNT-signaling;
Figure S8: Pseudo-dynamics analysis of CRLM development; Figure S9: Heterogeneity of CAF and
immune cell expression across the LMS; Figure S10. Comparison of different segmentation methods
for visualizing the expression land-scape, extracting expression spot-modules and characterizing
them in terms of spot implications and mutual correlations between them; Table S1. Spot module
characteristics; Table S2: is provided as excel sheet. It contains lists of spot genes A-F; Table S3:
Collection of gene sets specifically implemented in oposSOM in this publication; Table S4: is provided
as supplementary excel sheet. List of genes with maximum and minimum HR, list of delta-HR ranked
CRLM. See also Figure 7c. References [122–130] are cited in the supplementary materials. Movie S1:
Tree-trajectory-Seg1-to-Seg3.

Cancers 2023,15, 3835 25 of 31
Author Contributions:
Conception: H.B., A.A., L.N., G.A., D.C. and D.U.; methodology: H.B., A.A.,
M.S., O.A. and N.S.; software: H.L.-W. and M.S.; analysis and interpretation: all authors. All authors
have read and agreed to the published version of the manuscript.
Funding:
A.A.’s contribution to this work was supported by the Science Committee of Armenia
(project number 21AG-1F021).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
An interactive online browser is provided, which provides different
views on the CRLM-data set: https://apps.health-atlas.de/opossom-browser/?dataset=15 and
www.izbi.de (see also Supplementary Figure S1).
Acknowledgments:
The Armenian Bioinformatics Institute (ABI) thanks Agenus Inc. very much
for their support in establishing Cancer Bioinformatics in Armenia. O.A., N.S., and A.K. appreciate
the support of a stipendium paid by Agenus Inc. (Lexington, MA, USA). We acknowledge support
from the German Research Foundation (DFG) and Universität Leipzig within the program of Open
Access Publishing.
Conflicts of Interest:
O.A., N.S., and A.K. were supported by a stipendium paid by Agenus Inc
(Lexington, MA, USA) for their scientific studies. G.A., D.U., D.C., and M.U. were from Agenus
Inc (Lexington, MA), a biotech company with a focus on immunotherapy of cancer, which supports
bioinformatics research in the frame of its support-program for young scientists in Armenia. The
other authors declare no conflict of interest.
Abbreviations
ALT Alternative lengthening of telomeres
AUC Area Under the ROC Curve
CAF Cancer-associated fibroblast
CIN Chromosomal instability
CRC Colorectal cancer
CRLM Colorectal liver metastases
CMS Consensus molecular subtyping
CNV Copy number variations
CIMP CPG island methylation phenotype
EPC Endothelial progenitor cell
ECM Extracellular matrix
FU Fluorouracil
GM Gene module
GO Gene ontology
GINS Gene-interaction-perturbation-network-based subtyping
GPCR G-protein coupled receptor
HR Hazard ratio
HepSC Hepatic stellate cell
HGP Histopathological growth patterns
ICI Immune checkpoint inhibitor
ICA Independent component analysis
KEGG Kyoto Encyclopedia of Genes and Genomes
LIV Liver
LM Liver metastases
LMS Liver metastasis subtype
LSEC Liver sinusoidal endothelial cell
MSI Microsatellite instable
NK Natural killer
OS Overall survival
OC lOverlap coefficient
PSF Pathway signal flow

Cancers 2023,15, 3835 26 of 31
PC Plasma cells
pDC Plasmacytoid dendritic cells
PRC2 Polycomb repressive complex 2
PCA Principal component analysis
PT Pseudotime
ROC Receiver operator characteristic
SOM Self-organizing map
scRNAseq Single cell RNA sequencing
TAF Tumor associated macrophages
TEL Telomerase
TM Telomere maintenance
TCGA The cancer genome atlas
TF Transcription factor
TME Tumor microenvironment
UMAP Uniform manifold approximation and projection for dimension
reduction
UMI Unique molecular identifier
WTO Weighted topological overlap
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