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Transcription phenotypes of pancreatic cancer are driven by genomic events during tumor evolution

An Author Correction to this article was published on 12 February 2020

This article has been updated


Pancreatic adenocarcinoma presents as a spectrum of a highly aggressive disease in patients. The basis of this disease heterogeneity has proved difficult to resolve due to poor tumor cellularity and extensive genomic instability. To address this, a dataset of whole genomes and transcriptomes was generated from purified epithelium of primary and metastatic tumors. Transcriptome analysis demonstrated that molecular subtypes are a product of a gene expression continuum driven by a mixture of intratumoral subpopulations, which was confirmed by single-cell analysis. Integrated whole-genome analysis uncovered that molecular subtypes are linked to specific copy number aberrations in genes such as mutant KRAS and GATA6. By mapping tumor genetic histories, tetraploidization emerged as a key mutational process behind these events. Taken together, these data support the premise that the constellation of genomic aberrations in the tumor gives rise to the molecular subtype, and that disease heterogeneity is due to ongoing genomic instability during progression.

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Fig. 1: Molecular classification of the disease cohort.
Fig. 2: Tracking Basal- and Classical-related signatures with scRNA-seq.
Fig. 3: DNA copy number analysis of molecular subtypes.
Fig. 4: Evolution of mutant KRAS amplifications.
Fig. 5: Switch in patient subtype linked to copy number changes in mutant KRAS.
Fig. 6: Genomic evolution of the molecular subtypes of pancreatic cancer.

Data availability

Raw data are free available from EGA under accession code EGAS00001002543.

Code availability

No unique code was developed for this study. R scripts or functions used are indicated in the relevant Results, figure legends or Methods.

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We thank all members of the Notta Laboratory and PanCuRx program at the Ontario Institute for Cancer Research (OICR) for their critical review of the manuscript, and the Genomics Technology Program at OICR for technical support. We also thank F. Real and B. Stanger for intellectual input and critical discussions regarding the manuscript. We also acknowledge the participation of patients and their families, and the POG and PanGen teams. This study was conducted with the support of OICR (PanCuRx Translational Research Initiative) through funding provided by the Government of Ontario, the Wallace McCain Centre for Pancreatic Cancer supported by the Princess Margaret Cancer Foundation, the Terry Fox Research Institute, the Canadian Cancer Society Research Institute and the Pancreatic Cancer Canada Foundation. The study was also supported by charitable donations from the Canadian Friends of the Hebrew University (A. U. Soyka). S.G. is the recipient of an Investigator Award from OICR. G.Z. is a clinical research scholar of the Fonds de Recherche en Santé du Québec. F.N. is supported by the Gattuso-Slaight Personalized Cancer Medicine Fund, funding from the OICR, the Canadian Institutes of Health Research (no. 388785) and the Cancer Research Society (no. 23383). G.Z. is a Clinician-Scientist of the Fonds de la Recherche en Santé du Québec. P.J.C. is a Wellcome Trust Senior Clinical Fellow and L.D.S. and S.G. are recipients of Senior or Clinician-Scientist Awards from OICR.

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Authors and Affiliations



Data analysis and interpretation were done by M.C.-S.-Y., J.C.K., S.G. and F.N. Samples were processed by K.N., E.F.F., J.M., J.M.W., S.-B.L. and D.C. M.C.-S.-Y., J.C.K., G.W.W., K.N., R.E.D., G.H.J., A.Z., P.M.K., V.S., B.H.-K. and L.D.S. were responsible for genomics. S.-B.L., D.C., I.L. and J.M.S.B. were responsible for LCM. Single-cell RNA-seq was done by M.C.-S.-Y. and K.N. S.K., R.C. and S.E.F. were responsible for pathology. Sample acquisition and clinical annotation were done by G.M.O., A.A.C., A.D., R.C.G., J.M.W., A.B., G.Z., J.J.K. and S.G. Data were validated by H.T., F.E.M.F., J.M.K., J.T.T., D.J.R., D.F.S., S.J.M.J, M.A.M., J.L. and D.A.T. The manuscript was written by M.C.-S.-Y., J.C.K. and F.N. The manuscript was edited by E.F.F., H.T., F.E.M.F., P.J.C. and D.A.T. The project was led and supervised by S.G. and F.N.

Corresponding authors

Correspondence to Steven Gallinger or Faiyaz Notta.

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Extended data

Extended Data Fig. 1 Survival and tumour response data in resectable and advanced disease based on previous subtyping schemes.

a, Survival analysis for resectable (left column) and advanced (right column) patients based on previous Moffitt et al14, Collisson et al15, and Bailey et al16 classification schemes. P-values are from the two-sided log-rank test. b, Waterfall plot of best tumour response assessed by RECIST 1.1 criteria for 66 pancreatic cancer patients on the COMPASS trial based on previous classification schemes. P-values are from the two-sided Wilcoxon rank-sum test.

Extended Data Fig. 2 Gene expression analysis on Hybrid tumours demonstrating that they land on a bene expression continuum.

a, Only 8 genes were differentially expressed when Hybrids were compared to the rest of the cohort (shown in heatmap). These 8 genes were essentially restricted to a subset of Classical-B tumours. Differentially expressed genes were defined as genes with an adjusted p-value < = 0.05 (two-sided Wald test followed by FDR correction) and absolute log2(fold change (FC)) > = 1. b, Summary table of the number differentially expressed genes identified in hybrids versus each of the other subtypes. Differentially expressed genes were defined as genes with an adjusted p-value < = 0.05 (two-sided Wald test followed by FDR correction) and absolute log2(FC) > = 1. c, Heatmap of upregulated genes in (b) ordered by subtype. The left panel depicts which comparison each gene was upregulated in. d, Venn diagram showing overlap of upregulated genes identified in (b). No genes were commonly upregulated in Hybrids when compared to all other subtypes. e, Boxplots showing the EMT scores across subtypes (Basal-likeA = 27, Basal-likeB = 24, Hybrid = 57, Classical-like-A = 103 and Classical-like-B = 36). p-values are from the two-sided Wilcoxon rank-sum test. Box whisker plots represent median value and the first and third quartiles. The whiskers represent the most extreme point no further than 1.5*IQR.

Extended Data Fig. 3 Survival and chemotherapy response in subtypes from Fig. 1.

a, Survival analysis comparing Basal-like-B and Hybrid tumours to three previous classifications in resectable patients (Basal-like, Moffitt14; quasi-mesenchymal, Collisson15; and squamous, Bailey16). b, Survival analysis for resectable patients based on 5 disease subtypes (Stage I/II; p = 0.0004, two-sided log-rank test). Number of patients in each subtype is shown. c, Survival analysis for advanced patients comparing Basal-like-A (n = 14) versus rest (n = 66) (Stage III/IV; p = 0.12, two-sided log-rank test). d, RECIST waterfall plot for the 66 advanced patients based on 5 disease subtypes. Patients were treated with modified FOLFIRINOX or Gemcitabline/nab-paclitaxel based chemotherapy. P-value is from the two-sided Wilcoxon rank-sum test of Basal-like-A tumours versus rest. e and f, RECIST waterfall (p-value from two-sided Wilcoxon rank-sum test) and survival plots (p-value from two-sided log-rank test) for advanced patients who received e) Modified FOLFIRINOX or f) Gemcitabine-based treatment.

Extended Data Fig. 4 Single cell RNA-seq analysis on patient tumours.

a, A negative selection workflow was used to enrich for tumour epithelial cells prior to scRNA-seq on the 10X platform. data acquisition and processing. b and c, In b, UMAP plot of single-cell clusters from patient 95373 (n = 2252 cells) after negative selection. In c, lineage markers highlighting epithelium (EPCAM, KRT19), immune cells (PTPRC, CD33) and fibroblasts (THY1, ACTA2) are shown. This analysis was repeated on 14 additional samples. d and e, After identification of epithelial cells (n = 2121 cells) (above), we then sought to identify the various types of epithelial cells in the pancreas. In d, UMAP plot highlighting epithelial cell clusters in patient 97727. In e, epithelial cell clusters are then marked with lineage markers for the exocrine (SPP1, CFTR) and endocrine (GCG, INS) cells. This analysis was repeated on 14 additional samples. f, UMAP plot of patient 85948 (n = 2235 cells) showing single cell clusters. Cell types are identified on the plot. g, Single-cell RNA-seq data were searched for reads carrying the KRAS mutation. Mutation-positive cells are only present in malignant cell clusters (from f). h, Heatmap of inferred single-cell CNV ( from one case to validate cells classified as malignant epithelium are aneuploidy. Top track shows the copy number profile from normal epithelium. Bottom track bottom track shows the copy number alterations in the tumour. i, Stacked bar plot of the 4 tumour signature scores for each identified single cell cluster from 15 samples. Clusters are grouped into three types; Basal clusters contain > 75% Sig. 2 and 10, Classical clusters contain > 75% Sig. 1 and 6, and Mixed clusters consist of the rest expressing mixtures of Basal and Classical signatures. Asterisk denotes one cluster lacking expression of the 4 signatures.

Extended Data Fig. 5 Genome analyses on transcription subtypes.

a, Tumours with homologous recombination defects (HRD, yellow) and mismatch repair (MMR, blue) deficiency are highlighted. HRD and MMR was inferred from using specific genomic criteria outlines in9 (ex. indels, SVs and SNV load, mutational signatures [Sig. 3], somatic and germline mutations in genes in the HRD/MMR pathway—ex. BRCA1, BRCA2, PALB2 or MLH1, MSH2, MSH6, PMS1, PMS2). b, Basic genomic analyses of the subtypes. Box whisker plots represent medians + /- 95% confidence intervals. SNV—single nucleotide variants; SV—structural variants; BA—Basal-like-A (n = 23); BB—Basal-like-B (n = 23); Hy—hybrid (n = 52); CA—Classical-A (n = 94); Classical-B (n = 33). Box whisker plots shown median and first/third quartiles with all points. c, Frequency of statistically significant mutated genes found by MutSigCV in the WGS cohort (n = 303)49. d, Log10 p-values of recurrently mutated genes identified by MutSigCV. p-values were obtained from a two tailed Fisher’s exact tests for the presence of mutations in the identified genes between Basal-like-A/B (n = 46) vs Classical-A/B (n = 127) and between Basal-like-A (n = 23) vs rest (n = 202). e, Significantly mutated gene networks detected by HOTNET2 in the WGS cohort (n = 303). Red to blue scale depicts significance of that mutated gene in the network (Red—hot; Blue—cold). p-values calculated by the algorithm50. f, Oncoprint of genes found in HOTNET2 networks (shown in e) based on subtypes (n = 225).

Extended Data Fig. 6 Pathway and GATA6 expression analysis of our cohort.

a, Enriched pathways identified by GSEA of Basal-like-A/B versus Classical-A/B tumours. b, Frequency of genetically intact SMAD4 allele (left panel), TP53 mutation (middle panel) and complete loss of CDKN2A (right panel) in the subtypes. P-values are from two-sided Fisher’s exact test (n = 225 cohort; individual n values shown in the plot). c, GATA6 amplifications (normalized for ploidy) from Fig. 3d separated based on primary (n = 170) and metastatic (n = 58) tumours. p-values are from the Kruskal-Wallis test. d, GATA6 expression in primary and metastatic tumours according to subtypes. Same tumours as above. p-values are from the Kruskal-Wallis test. Box whisker plots shown median and first/third quartiles with all points. Primary (n = 170); mets (n = 58). e, Correlation of GATA6 expression and copy number. To account for ploidy related changes, the copy number of GATA6 was normalized to the ploidy. Two-sided Spearman correlation coefficient, p-values and the number of tumours in each subtype are shown on the plot. f, Representative (of n = 3 patients) RNAish images for GATA6 expression of adjacent exocrine tissue (duct/acinar cells) from a patient where normal parenchyma was present.

Extended Data Fig. 7 Tumour SV, ploidy and KRAS imbalance criteria.

a, Frequency of mutant KRAS imbalance in primary and metastatic disease according to subtype. Imbalance was defined as [wildtype allele copy number + 1]. b, Ploidy distribution in primary versus metastatic disease (Stage IV) based on cohort (i) and molecular subtype (ii). p-values are from two-sided Fisher’s exact test. Pa—primary (n = 148); Met—metastatic (n = 56). c, Structural variant (SV) burden in primary versus metastatic disease—broken down by cohort (i) and by molecular subtype (ii) BA—Basal-like-A; BB—Basal-like-B; Hy—hybrid; CA—Classical-A; Classical-B. Only tumours with a KRAS mutation were analyzed here. p-values are from the two-sided Wilcoxon rank-sum test. Box whisker plots shown median and first/third quartiles with all points. Cohort numbers same as b. d, i. Copy number plot of the mutant KRAS allele only in tumours with an imbalance. There were two distinct clusters and a weaker third cluster with fewer tumours. This supports that there are at least two distinct copy number imbalance categories (red arrows). The two distinct categories of KRAS imbalance. This rationale was used to develop the three-tiered system (Major, minor and balanced) shown in ii. ii. Defining three different states of copy number imbalance in mutant KRAS. The degree of imbalance is plotted as Mutant CN (x-axis) vs. Mutant CN/Total CN (y-axis). CN - copy number.

Extended Data Fig. 8 KRAS imbalances in tumour subtypes according to disease stage.

a, Mutant imbalance plots for KRAS for the subtypes according to clinical stage (Top panel: Stage IV; bottom panel: Stage I-III primary). In left bottom panel Basal-like-B only values are shown in brackets. See Supplementary Fig. 8f. for information on Basal-like-A tumours. b, Survival analysis for advanced patients stratified based on degree of mutant KRAS imbalance (Stage III/IV; p = 0.13, two-sided log-rank test). c, Box whisker plots comparing the number of structural variants to the distinct categories of mutant KRAS imbalance in primary (major = 9, minor = 62 and balanced = 117) and metastatic samples (major = 21, minor = 36 and balanced = 23). P-values are from the two-sided Wilcoxon rank-sum test between balanced and major. Box whisker plots represent median value and the first and third quartiles. The whiskers represent the most extreme point no further than 1.5*IQR.

Extended Data Fig. 9 Survival and tumour response analysis based on tumour ploidy and structural variants.

a, RECIST waterfall plots of tumour response based on ploidy of the tumour. P-values are from the two-sided Wilcoxon rank-sum test. b, RECIST waterfall plots of tumour response based on structural variant categories identified by Waddell et al.1. P-values are from the two-sided Wilcoxon rank-sum test. c, Survival analysis based on tumour ploidy or structural variants segregated by clinical tumour stage. P-values are from the two-sided log-rank test. Numbers of tumours and statistical test used are shown on the plots.

Extended Data Fig. 10 Analysis of two patients which demonstrated a change in tumour subtype with disease progression.

a, Positions of Compass_0003 and Compass_0064 tumours on the consensus clustering dendrogram of the molecular subtypes. b, RNAish for GATA6 on liver biopsy from Compass_0003. Lack of GATA6 expression confirms Basal-like phenotype. c, Genomic analysis of Compass_0003 and Compass_0064. Bar plots show numbers of SNVs and indels. Circos plots show genome-wide SVs. d, Copy number and germline SNP plots for chr12 from diagnosis (top panel) and liver metastasis (bottom panel). A unique duplication event that amplified mutant KRAS is found only in metastasis. e, Integrative genomics viewer (IGV) image of genomic region showing the paired-end reads (green) that denote the duplication event from (d) in liver metastasis of Compass_0003 (bottom panel). The same duplication is absent at diagnosis (top panel) but is present in the primary tumour-derived xenograft (PDX; middle panel). f, Celluloid plot of the PDX generated from the primary tumour of Compass_0003. g, Copy number and germline SNP plots of chromosome 12 showing KRAS status at diagnosis (top panel) and progression (bottom panel) of Compass_0064.

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Chan-Seng-Yue, M., Kim, J.C., Wilson, G.W. et al. Transcription phenotypes of pancreatic cancer are driven by genomic events during tumor evolution. Nat Genet 52, 231–240 (2020).

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