Download Supplemental Figures 1 to 11

Supplementary Materials for:
Immune cytolytic activity stratifies distinct molecular subsets of human pancreatic
cancer
Authors: David Balli1†, Andrew J. Rech1†, Ben Z. Stanger1-5‡*, and Robert H. Vonderheide1,2,5‡*
Correspondence to: [email protected]; [email protected]
This PDF file includes:
Figs. S1 to S11
Supplementary Materials:
Supplementary Figure 1. Cumulative distribution of cytolytic index between PAAD and
STAD cohorts. Cumulative density (left) and cumulative frequency (right) of the cytolytic index
between PDA (PAAD) and stomach adenocarcinoma (STAD) cohorts in TCGA. PDA has a
much smaller distribution in comparison with STAD (Kolmogorov-Smirnov test).
Supplementary Figure 2. Enrichment of selected immune related gene sets in cytolytichigh PDA tumors. Cytolytic-high tumors show increase enrichment of gene sets from
activated, cytolytic CD8+ T cell populations (37, 38).
Supplementary Figure 3. Enrichment of immune related gene sets in cytolytic-high PDA
tumors. Gene set variation analysis (GSVA) of known immune related gene sets show
statistically significant increase in tumors identified as cytolytic-high based on expression GZMA
and PRF1.
Supplementary Figure 4. Cytolytic index associates strongly with expression of Immunome
gene sets. (A) GSVA scores for gene sets comprising 28 immune cell types identified
previously (Immunome, (40)). Cytolytic high and low tumors are designated by orange and dark
green boxes, respectively. (B) GSVA of gene sets related to Tight junctions, Arginine/Proline
Metabolism, Cholesterol/Steroid synthesis, and glucose metabolism from the REACTOME (R)
and KEGG (K) databases. (C) GSVA of gene sets related to Cholesterol/Steroid synthesis, and
glucose metabolism from the REACTOME and KEGG databases.
ES = -0.40
NES = -1.97
FDR = 0.00
Hot
CYT High
Myc Target Genes
enrichment score (ES)
enrichment score (ES)
Genes up-regulated in PDA SCNA
Cold
CYT Low
ES = -0.33
NES = -1.62
FDR = 0.00
Hot
CYT High
Cold
CYT Low
Figure S5.
Supplementary Figure 5. Expression of copy number alteration-associated and MYC
target genes are statistically enriched in cytolytic-low PDA tumors. (A) Gene set enrichment
analysis (GSEA) of genes whose expression in pancreatic tumor correlated with copy number
gains (42). (B) GSEA of known MYC target genes (43). ES = Enrichment Score, NES =
Normalized Enrichment Score, FDR = False discovery rate.
Supplementary Figure 6. Tumor cellularity/purity estimates do not correlate with
mutational load. Tumor purity estimates for TCGA PAAD calculated by ABSOLUTE do not
correlate with total mutation count (top left), total copy number events (top right), type I
neoepitopes (bottom left), and type II neoepitopes (bottom right)
Supplementary Figure 7. Tumor purity estimates for PDA, stomach adenocarcinoma
(STAD) and lung adenocarcinoma (LUAD). Distribution of ABSOLUTE cellularity estimates
between pancreatic ductal adenocarcinoma, stomach adenocarcinoma and lung adenocarcinoma.
PDA has comparable cellularity to STAD and LUAD cohorts in TCGA.
Figure S8. No difference in tumor cellularity or purity between cytolytic subtypes in
TCGA PAAD cohort. Cellularity estimates were obtained using two bioinformatics approaches
(ABSOLUTE and Sequenza) to determine if cellularity differed between cytolytic subsets in
PDA cohort. P-values were calculated using Kruskal-Wallis test.
Figure S9. Cytolytic subtypes associate with neoepitope load in stomach (STAD) and lung
adenocarcinoma (LUAD). Predicted MHC Class I neoepitope load was calculated from
stomach and lung adenocarcinoma samples in TCGA and stratified into CYT High (top decile)
and CYT low (bottom quartile). Total mutations, predicted class I neoepitopes, and number of
mutations generating 1 or more predicted neoepitopes were significantly higher in CYT high
tumors in both STAD and LUAD samples.
Figure S10. Neoepitope burden does not differ across PDA subtypes. (A) Hierarchical
clustering of TCGA PAAD dataset using subtype classifiers from Moffitt et al., 2015 (left) and
Bailey et al., 2016 (right) and association with cytolytic subtypes. (B) Mutation and neoepitope
load as a function of Moffitt subtype classification of TCGA PAAD. Statistical enrichment of
total mutation count between Classical and Normal Stroma (one-way ANOVA, Tukey’s post hoc
test for multiple comparisons). (C) Same B except using Bailey subtype classification of TCGA
PAAD.
Figure S11. Correlation plots of individual Immune suppression index versus cytolytic
index. Pairwise correlation between Cytolytic index (log-average of GZMA and PRF1) versus
individual genes of Immune suppression index (log-average of PD1, PDL1, PDL2, IDO1, IDO2,
CTLA4, A2AR, TIGIT, LAG3, VTCN1, TIM3, VISTA).