Research Article

Cancer Biology

Molecular portraits of colorectal cancer morphological regions

RECETOX, Faculty of Science, Masarykova Univerzita, Czech Republic
Central European Institute of Technology, Masarykova Univerzita, Czech Republic
Masaryk Memorial Cancer Institute, Czech Republic
Faculty of Medicine, Masarykova Univerzita, Czech Republic
Central European Institute of Technology, Department of Biology, Faculty of Medicine, Masarykova Univerzita, Czech Republic
Department of Pharmacology and Toxicology, Faculty of Pharmacy, Masarykova Univerzita, Czech Republic
Institute of Pathology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Germany
Berlin Institute of Health, Germany
German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Germany
Faculty of Medicine, Digestive Oncology Unit, Katholieke Universiteit Leuven, Belgium

Nov 13, 2023

https://doi.org/10.7554/eLife.86655.3

Open access
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Figures
Tables
Additional files

6 figures, 1 table and 14 additional files

Figures

Figure 1

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Morphological patterns and their distribution in the dataset.

(A) The six CRC morphological patterns of interest (morphotypes). *Left*: example of an original annotation used for macrodissection and RNA extraction. Note that the original annotations in the image are not identical to the ones used in the main text. Here, A-SE stands for serrated (SE) in the text, B-DE for desmoplastic (DE) in the text, C-MUC for mucinous (MU) in the text, and D-ST for solid/trabecular (TB) in the text, respectively. Also, N indicates a tumor-adjacent normal epithelial region and S a supportive stroma region, respectively. *Right*: examples of morphotypes – complex tubular (CT), desmoplastic (DE), mucinous (MU), papillary (PP), serrated (SE), and solid/trabecular (TB). (B) Morphotype distribution per case (unique tumor) and intersections thereof: some cases had several morphotypes profiled.

Figure 2 with 3 supplements

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CRC morphotypes: in silico decomposition of the cellular admixture.

(A) Boxplots of the tumor purity (epithelial content – ESTIMATE method) in each tumor morphotype and the two non-tumor regions, ordered by increasing median values. (B) Signatures specific to colon crypt compartments and major cell types estimated from gene expression data in terms of normalized enrichment scores (NES): only statistically significant scores are shown. (C) Immune cell fractions (and unassigned fractions) inferred from gene expression data using quanTIseq method. (D) Types of cancer-associated fibroblasts (CAFs) as estimated from gene expression using the signatures from Khaliq et al., 2022; Kieffer et al., 2020.

Figure 2—figure supplement 1

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Epithelial signatures from Pelka et al., 2021.

Only statistically significant scores (NES) are shown.

Figure 2—figure supplement 2

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Immune signatures from Pelka et al., 2021.

Only statistically significant scores (NES) are shown.

Figure 2—figure supplement 3

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Stromal signatures from Pelka et al., 2021.

Only statistically significant scores (NES) are shown.

Figure 3 with 2 supplements

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Top differentially expressed genes and hallmark pathways.

(A) GSEA scores for hallmark pathways in the six morphotypes and two non-tumoral regions. Only pathways with statistically significant scores are shown. (B) Principal component analysis of hallmark pathways: the median profiles of the six morphotypes (CT: complex tubular, DE: desmoplastic, MU: mucinous, PP: papillary, SE: serrated, and TB: solid/trabecular) and the two non-tumoral regions (NR: tumor-adjacent normal and ST: supportive stroma) are projected onto the space defined by first two principal components (74% of the total variance). The top pathways contributing to the principal axes are shown as well. See also Figure 3—figure supplement 1. (C) Heatmap of top 5 up- and down-regulated genes for each of the six morphotypes.

Figure 3—figure supplement 1

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Principal component analysis of hallmark pathways GSEA scores: loadings for the first two principal components, i.e., contribution of pathways to the first two axes.

Figure 3—figure supplement 2

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Hallmark pathways differential activation between pairs of morphotypes.

Here we compare the results from GSEA applied to differentially expressed genes between pairs of morphotypes originating from all cases to results of GSEA applied to differentially expressed genes between pairs of morphotypes originating from the same section (tumor; i.e., matched pairs of morphotypes). All results are shown, including the statistically not significant ones. First, four columns correspond to pairs of morphotypes from all cases, while the last four, to matched pairs of morphotypes.

Figure 4 with 1 supplement

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Intra-tumoral heterogeneity and the morphotypes (for all core samples, including those unassigned by the classifiers).

Only cases with at least two distinct morphotypes present are shown. (A) Left: CMS assignment for tumors represented by multiple regions. Right: CMS assignment per morphotype (and two non-tumoral patterns). (B) Left: iCMS assignment for tumors represented by multiple regions. Right: iCMS assignment per morphotype (and two non-tumoral patterns). (C) Differences between paired signatures: morphotypes vs whole tumor (each signature was normalized to [0,1] prior to computing the differences). Only four (morphotype, whole tumor) pairs were represented enough in the data. (D) Boxplots for the ten (normalized) signatures across morphotypes. The ‘Eschrich’ and ‘Jorissen’ signatures vary significantly (Kruskal-Wallis’s test) across morphotypes. For equivalent plots for all samples, including non-core, see Figure 4—figure supplement 1.

Figure 4—figure supplement 1

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Molecular subtypes and morphotypes in all samples, including non-core samples.

Note that the sets of samples in A and C are the same as in Figure 4, as only core samples also had at least two distinct morphological regions.

Figure 5 with 2 supplements

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Intra-tumoral heterogeneity case study.

For the same case, different CMS labels are assigned to regions and whole tumor profile. The hallmark pathways show various levels of activation (as computed by GSVA) within same section. The relative change in prognostic scores indicate potential underestimation of risk for some signatures, while others appear to be stable across tumor. See also Figure 5—figure supplements 1 and 2. Note that in the pathology section image, the original annotations were preserved, and they are not identical to the ones used in the main text. Here, MUC stands for mucinous (MU) in the text. Also, N indicates a tumor-adjacent normal epithelial region and S a supportive stroma region, respectively.

Figure 5—figure supplement 1

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Intra-tumoral heterogeneity additional case study.

Figure 5—figure supplement 2

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Figure 6 with 1 supplement

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Normalized enrichment scores from GSEA for selected resistance signatures (from C2 section of MSigDB).

Only significant scores are shown.

Figure 6—figure supplement 1

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Resistance scores (GSVA) per patient and morphotype for cases where the whole–tumor prediction is contradicted by some regional score.

Tables

Table 1

Results of comparison of each morphotype (and the two non-tumoral regions) with the average profile.

The table shows the top 20 up- and down- regulated genes and significantly activated hallmark pathways and processes (as result of GSEA). The genes not significant after p-value adjustment (at FDR = 0.15) have their symbols greyed. See also Supplementary files 5–6.

Morph	Top 20 up-regulated genes (compared to mean)	Top 20 down-regulated genes (compared to mean)	Hallmark pathways with high score	Active processes (based on the active hallmark pathways)
MU	ARF4, MUC2, SULF1, FNDC1, LOXL1, LGALS1, ANTXR1, BGN, COL12A1, PALLD, MEG8, DKK3, ACVR1, GPX8, CALD1, FBN1, MLLT11, CSRP2, TUSC3, GREM1	TIMD4, PRELID3BP3, EREG, KDM4A, CCDC175, TDP2, CHMP1B2P, ACE2, NLRP7, UGT2A3, SLC26A3, A1CF, TSPAN6, CLDN10, TMIGD1, BMP5, MS4A12, FAM3B, CLCA4, MEP1A	EMT, TNF a signaling via NFKB, Complement, IL2 STAT5 signaling, hypoxia, inflammatory response, KRAS signaling, UV response, myogenesis, coagulation, apical junction, allograft rejection, IL6 JAK STAT3 signaling, interferon gamma response, apoptosis, TGF-beta signaling, angiogenesis, hedgehog signaling, estrogen response early, NOTCH signaling, WNT beta catenin signaling, cholesterol homeostasis	Inflammation, neoangiogenesis, increased metastatic potential, apoptosis, development
DE	OLFML2B, INHBA, LUM, SULF1, PTPN14, PRDM6, SPOCK1, RDX, EDNRA, COL12A1, CTHRC1, PRRX1, LGALS1, COPZ2, COL10A1, TNFAIP6, IGFL1P1, ST6GAL2, FAP, BGN	SLC17A4, ANPEP, DEFA5, RAP1GAP, MRAP2, ADH1C, TRIQK, REG1A, SLC4A4, UGT2B15, REG4, SEMA6A, L1TD1, MS4A12, SI, SPINK4, CLCA4, MUC2, CLCA1, CA1	EMT, TNF a signaling via NFKB, Complement, IL2 STAT5 signaling, hypoxia, inflammatory response, KRAS signaling, UV response, myogenesis, coagulation, apical junction, apoptosis, TGF-beta signaling, angiogenesis, hedgehog signaling, estrogen response early	Inflammation, neoangiogenesis, increased metastatic potential, apoptosis
PP	PTPRD, KNDC1, MIMT1, UPK3B, MPZ, MMP15, CYP4F12, SNORD4A, SNAR-C3, TMTC4, LRCOL1, GATA5, SNAR-E, EPHA7, IPO4, SNAR-I, CASC21, NUTF2, SNAR-B2, RPL31P50	IGKV3-11, IGHV4-39, ANPEP, OR4F8P, HEPACAM2, ADAM28, CPS1, TMIGD1, NPY6R, ITLN1, SI, ADH1C, CAV1, MMP2, FDCSP, CLU, REG1A, RSPO3, PAX8-AS1, PALMD	MYC targets V1, MYC targets V2, E2F targets, KRAS signaling DOWN, WNT beta catenin signaling,
SE	PPAN-P2RY11, TUBB4BP7, JADE3, PFDN6, CLDN2, YAF2, BOLL, SLAMF9, SLC12A2, CCDC175, GRIN2B, TUBB3P2, GAPDHP71, RPS2P25, MAT1A, NOX1, SNORD12C, SMAD6, MECOM, EXTL2	IGKV2D-29, MYLK, TAGLN, CNTNAP3P2, GLI3, CPXM2, NR3C1, CNN1, PECAM1, COLEC12, IGKV4-1, IGKV2D-30, DPYD, CLU, TSHZ2, ADH1B, IL10RA, PDE7B, ABCA8, CDC42SE2	MYC targets V1, MYC targets V2, E2F targets, G2M checkpoint,
CT	TMEM97, RPL13, CLDN1, TFDP1, CKS2, CDCA7, TPX2, ANLN, RAD54B, KRT18, HSPH1, CCT6A, PLK1, TMEM97P2, CSE1L, MIPEP, SNORA71D, SNORA71C, PTTG1, PLBD1	CR2, OGN, SNORD114-21, SLC30A10, CLCA4, SNORD114-12, DCLK1, FAT4, CPA3, ADH1B, SLC26A2, SNORD114-20, SFRP1, ZG16, FGF7, SNORD113-1, ABCA8, B4GALNT2, MS4A12, CA1	MYC targets V1, MYC targets V2, E2F targets, G2M checkpoint, MTORC1 signaling, unfolded protein response, Glycolysis, oxidative phosphorylation, fatty acid metabolism, protein secretion	Proliferation, Catabolism, oxidative stress, cell cycle disruption
TB	CKAP2, HSP90AA1, PPP3CA, REEP4, MSH6, TOP2A, HSPE1, PPP2R5C, TBCA, VRK2, NIFK, TXNL4A, MNAT1, ERI1, XPO1, VTRNA1-2, ANP32A, ARF6, RNF2, EIF4A1P7	FLJ22763, TMEM236, NPY6R, IGKV3D-20, IGKV2D-30, OLFM4, SELENBP1, LRRC19, CDHR1, IGHA1, SNORD123, SLC26A3, CXCL14, SLC3A1, SEMA5A, MS4A12, IGHA2, CLCA4, NXPE4, NXPE1	MYC targets V1, MYC targets V2, E2F targets, G2M checkpoint, MTORC1 signaling, unfolded protein response, Glycolysis, oxidative phosphorylation, fatty acid metabolism, protein secretion, cholesterol homeostasis,	Inflammation, catabolism, apoptosis, oxidative stress, proliferation, cell cycle disruption
NR	PIGR, SLC26A3, ADH1B, NXPE1, IGHA2, CLCA1, JCHAIN, IGHA1, FCGBP, IGK, NXPE4, SLC9A2, MUC2, NR3C2, TMEM236, MS4A12, FABP1, IGLC3, IGKV1D-39, LRRC19	TACSTD2, FAM83D, ASPN, CXCL11, CTHRC1, SLC39A6, IFNE, SULF1, HSPH1, ELFN1-AS1, THBS2, CLDN1, SIM2, SLC22A3, SPARC, FN1, AHNAK2, COL11A1, SPP1, INHBA	Heme metabolism, bile acid metabolism, xenobiotic metabolism, fatty acid metabolism
ST	SFRP2, ADH1B, EMCN, STEAP4, ADAMTS1, ABI3BP, SPARCL1, DCN, PTGDS, PALMD, NOVA1, SLIT3, OGN, SERPINF1, RSPO3, CPA3, FBLN5, C3, EFEMP1, PBX3	FRK, AADACP1, CKS2, HOOK1, CLDN1, ANLN, S100P, UGT8, MACC1, EXPH5, CYP3A5, OCIAD2, SLC12A2, GK, EVADR, TMC5, REG4, TFF1, TCN1, CXCL8	EMT, TNF a signaling via NFKB, Complement, IL2 STAT5 signaling, hypoxia, inflammatory response, KRAS signaling, UV response, myogenesis, coagulation, apical junction, allograft rejection, IL6 JAK STAT3 signaling, interferon gamma response	Inflammation, neoangiogenesis, increased metastatic potential