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Supplementary Figures 1-14

Supplementary Tables 1-3

Pancreatic ductal adenocarcinoma (PDA) is an aggressive malignancy characterized by a paucity of tumor-proximal CD8+ T cells and resistance to immunotherapeutic interventions. Cancer-associated mechanisms that elicit CD8+ T-cell exclusion and resistance to immunotherapy are not well-known. Here, using a Kras- and p53-driven model of PDA, we describe a mechanism of action for the protumorigenic cytokine IL35 through STAT3 activation in CD8+ T cells. Distinct from its action on CD4+ T cells, IL35 signaling in gp130+CD8+ T cells activated the transcription factor STAT3, which antagonized intratumoral infiltration and effector function of CD8+ T cells via suppression of CXCR3, CCR5, and IFNγ expression. Inhibition of STAT3 signaling in tumor-educated CD8+ T cells improved PDA growth control upon adoptive transfer to tumor-bearing mice. We showed that activation of STAT3 in CD8+ T cells was driven by B cell– but not regulatory T cell–specific production of IL35. We also demonstrated that B cell–specific deletion of IL35 facilitated CD8+ T-cell activation independently of effector or regulatory CD4+ T cells and was sufficient to phenocopy therapeutic anti-IL35 blockade in overcoming resistance to anti–PD-1 immunotherapy. Finally, we identified a circulating IL35+ B-cell subset in patients with PDA and demonstrated that the presence of IL35+ cells predicted increased occurrence of phosphorylated (p)Stat3+CXCR3−CD8+ T cells in tumors and inversely correlated with a cytotoxic T-cell signature in patients. Together, these data identified B cell–mediated IL35/gp130/STAT3 signaling as an important direct link to CD8+ T-cell exclusion and immunotherapy resistance in PDA.

PDF - 21K, HH3 staining.

PDF file - 118K, GSK-3 inhibition leads to a decrease in TAK1 protein Panc-1 and MiaPaCa-2 cells were treated with GSK-3 inhibitor, AR-A014418 or vehicle control, DMSO in the presence or absence of MG132 for 24 hours. Whole cell extracts were immunoblotted for indicated antibodies.

PDF file - 308K, Left: AR-A014418 inhibits GSK-3 activity in a dose dependent mannerPanc-1 cells were treated with increasing concentrations of GSK-3 inhibitor, AR-A014418, like in Fig.1A. Whole cells extracts were prepared and immunoblotted for specified antibodies. p-Glycogen synthase is a downstream substrate of GSK-3 and thus an indicator of its kinase activity. Right: GSK-3 regulates apoptosis in MiaPaCa-2 cells. . Panc-1 and MiaPaCa-2 cells were transiently transfected with indicated siRNAs for 72 hours. Whole cell extracts were immunoblotted for specified antibodies.

MAF file for copy number variations identified in the cohort

PDF - 609K, pRalA validation.

PDF file - 63K, GSK-3 inhibition inhibits TAK1 mediated signaling. Panc-1 cells were treated with GSK-3 inhibitor, AR-A014418 (30M) for 24 hours and whole cell lysates immunoblotted for indicated antibodies.

PDF file - 181K, Left: Kras regulates TAK1 levels. HKRAS cells were transiently transfected with non-targeting or Kras siRNA for 72 hours. TAK1 was immunoprecitated from whole cell extracts and immunoblotted for specified antibodies. Right: TAK1 inhibition induces apoptosis. MiaPaCa-2 cells were treated with the TAK1 inhibitor, 5Z-7-oxozaenol at indicated concentrations for 24 hours. Whole cells extracts were immunoblotted with specified antibodies.

Treatment history for patients enrolled on the PancSeq protocol.

PDF - 1287K, pRalA changes in tumors.

Supplementary Figure S1: Overview of workflow and data generation. Supplementary Figure S2: Analysis of neoplastic cellularity. Supplementary Figure S3: Recurrent copy number alterations. Supplementary Figure S4: Mutational signature analysis. Supplementary Figure S5: Analysis of PDAC gene expression signatures. Supplementary Figure S6: Clinically relevant alterations in the cohort.

PDF - 112K, MLN8237 structure and additional response data.

Supplementary Experimental Methods

MAF file for mutations identified in the cohort

PDF file - 72K

PDF file - 1842K, GSK-3 inhibition leads to a decrease in TAK1 levels and p100-p52 processing in a statistically significant manner. All data obtained with independent experiments with Panc-1 and MiaPaCa-2, treated with GSK-3 inhibitor for 24 hours (right panel) or transiently transfected with indicated siRNA's for 72 hours (left panel) were quantified by densitometric analyses and is expressed as mean�SD. Statistical evaluation of the differences were calculated using students two-tailed t test and P value < 0.05 was considered significant.

PDF file - 3743K, TAK1 inhibition alters cell cycle regulation. Panc-1 cells were treated with TAK1 inhibitor, 5Z-7-oxozaenol or vehicle control, DMSO for 24 hours. Cells were fixed and stained with Propidium Iodide and analyzed by Flow cytometry.

PDF file - 113K, TAK1 inhibition decreases cell proliferation in Kras+ cells. Indicated cell lines were seeded in E-plate-16 (Roche) at 3000 cells/well/100μl. 24hours after plating, cells were treated with TAK1 inhibitor 5Z-7-oxozaenol (0.625μM), or vehicle control, DMSO. Cell impedance was measured every 2 hours for the entire course of experiment using RTCA.

PDF file - 263K, Kras regulates GSK-3α mRNA levels. HKRAS cells were transiently transfected with non-targeting control or KRAS siRNA. RNA was isolated, reverse transcribed to cDNA and real time RT-PCR performed for Kras and GSK-3α. Gus B was used as endogenous control.

Table S4

Purpose: To perform real-time whole genome sequencing (WGS) and RNA sequencing (RNASeq) of advanced pancreatic ductal adenocarcinoma (PDAC) to identify predictive mutational and transcriptional features for better treatment selection.Experimental Design: Patients with advanced PDAC were prospectively recruited prior to first-line combination chemotherapy. Fresh tumor tissue was acquired by image-guided percutaneous core biopsy for WGS and RNASeq. Laser capture microdissection was performed for all cases. Primary endpoint was feasibility to report WGS results prior to first disease assessment CT scan at 8 weeks. The main secondary endpoint was discovery of patient subsets with predictive mutational and transcriptional signatures.Results: Sixty-three patients underwent a tumor biopsy between December 2015 and June 2017. WGS and RNASeq were successful in 62 (98%) and 60 (95%), respectively. Genomic results were reported at a median of 35 days (range, 19–52 days) from biopsy, meeting the primary feasibility endpoint. Objective responses to first-line chemotherapy were significantly better in patients with the classical PDAC RNA subtype compared with those with the basal-like subtype (P = 0.004). The best progression-free survival was observed in those with classical subtype treated with m-FOLFIRINOX. GATA6 expression in tumor measured by RNA in situ hybridization was found to be a robust surrogate biomarker for differentiating classical and basal-like PDAC subtypes. Potentially actionable genetic alterations were found in 30% of patients.Conclusions: Prospective genomic profiling of advanced PDAC is feasible, and our early data indicate that chemotherapy response differs among patients with different genomic/transcriptomic subtypes. Clin Cancer Res; 24(6); 1344–54. ©2017 AACR.

Supplementary Methods

Purpose:Molecular subtyping for pancreatic cancer has made substantial progress in recent years, facilitating the optimization of existing therapeutic approaches to improve clinical outcomes in pancreatic cancer. With advances in treatment combinations and choices, it is becoming increasingly important to determine ways to place patients on the best therapies upfront. Although various molecular subtyping systems for pancreatic cancer have been proposed, consensus regarding proposed subtypes, as well as their relative clinical utility, remains largely unknown and presents a natural barrier to wider clinical adoption.Experimental Design:We assess three major subtype classification schemas in the context of results from two clinical trials and by meta-analysis of publicly available expression data to assess statistical criteria of subtype robustness and overall clinical relevance. We then developed a single-sample classifier (SSC) using penalized logistic regression based on the most robust and replicable schema.Results:We demonstrate that a tumor-intrinsic two-subtype schema is most robust, replicable, and clinically relevant. We developed Purity Independent Subtyping of Tumors (PurIST), a SSC with robust and highly replicable performance on a wide range of platforms and sample types. We show that PurIST subtypes have meaningful associations with patient prognosis and have significant implications for treatment response to FOLIFIRNOX.Conclusions:The flexibility and utility of PurIST on low-input samples such as tumor biopsies allows it to be used at the time of diagnosis to facilitate the choice of effective therapies for patients with pancreatic ductal adenocarcinoma and should be considered in the context of future clinical trials.

Table S5

Supplementary Methods, Tables and Figures Supplementary Table 1: Summary of whole genome sequencing results including performance metrics and time to reporting (N=62). Supplementary Table 2: Summary of GATA6 RNA in situ results and its correlation with RNA subtype and histologic grade. Supplementary Figure 1: WGS Performance Characteristics. Supplementary Figure 2: Both COMP-0037 and COMP-0057 had germline BRCA2 p.S1982fs mutations but only COMP-0037 had a second hit (somatic LOH of wild type allele) and showed hallmarks of double strand break repair deficiency. Supplementary Figure 3: BRCA2 mutation status of COMP-0057. Supplementary Figure 4: Subtypes of advanced PDAC by Moffitt tumour classification Supplementary Figure 5: Correlation between Moffitt tumor RNA subtypes and GATA6 gene expression. Supplementary Figure 6: Correlation between GATA6 expression measured by RNASeq and RNA in situ hybridization. Supplementary Figure 7: Differentially expressed pathways between Moffitt's basal like and classical RNA subtypes Supplementary Figure 8: CDK4 amplification identified in COMP-0008 (A) and CDK6 amplification identified in COMP-0010 (B) validated by fluorescence in-situ hybridization.

Supplementary Figures

Table S2

Supplementary Tables and Methods.

Table S1

Table S6

ECT2 is an activator of RHO GTPases that is essential for cytokinesis. In addition, ECT2 was identified as an oncoprotein when expressed ectopically in NIH/3T3 fibroblasts. However, oncogenic activation of ECT2 resulted from N-terminal truncation, and such truncated ECT2 proteins have not been found in patients with cancer. In this study, we observed elevated expression of full-length ECT2 protein in preneoplastic colon adenomas, driven by increased ECT2 mRNA abundance and associated with APC tumor-suppressor loss. Elevated ECT2 levels were detected in the cytoplasm and nucleus of colorectal cancer tissue, suggesting cytoplasmic mislocalization as one mechanism of early oncogenic ECT2 activation. Importantly, elevated nuclear ECT2 correlated with poorly differentiated tumors, and a low cytoplasmic:nuclear ratio of ECT2 protein correlated with poor patient survival, suggesting that nuclear and cytoplasmic ECT2 play distinct roles in colorectal cancer. Depletion of ECT2 reduced anchorage-independent cancer cell growth and invasion independent of its function in cytokinesis, and loss of Ect2 extended survival in a KrasG12D Apc-null colon cancer mouse model. Expression of ECT2 variants with impaired nuclear localization or guanine nucleotide exchange catalytic activity failed to restore cancer cell growth or invasion, indicating that active, nuclear ECT2 is required to support tumor progression. Nuclear ECT2 promoted ribosomal DNA transcription and ribosome biogenesis in colorectal cancer. These results support a driver role for both cytoplasmic and nuclear ECT2 overexpression in colorectal cancer and emphasize the critical role of precise subcellular localization in dictating ECT2 function in neoplastic cells.Significance:ECT2 overexpression and mislocalization support its role as a driver in colon cancer that is independent from its function in normal cell cytokinesis.

Supplementary Figures S1-S4

Table S3

Supplementary Figures 1-3 from Activation and Involvement of Ral GTPases in Colorectal Cancer

Protein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Correspondingly, their central role in signaling implicates kinome dysfunction as a common driver of cancer, where numerous kinases have been identified as having a causal or modulating role in cancer development and progression. Driven by their importance, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Given the growing importance of kinase-targeted therapies, linking the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC50). We then built computational models on this data set that achieve a high degree of prediction accuracy (R2of 0.7 and RMSE of 0.9), and were able to identify a set of well-characterized and understudied kinases that significantly affect cell responses. Further, we validated these models experimentally by testing predicted effects in breast cancer cell lines, and extended the model scope by performing additional validation in patient-derived pancreatic cancer cell lines. Overall, these results demonstrate that broad quantification of kinome inhibition state is highly predictive of downstream cellular phenotypes.

Background: Pancreatic ductal adenocarcinoma (PDAC) demonstrates variable prognosis and response to first-line FOLFIRINOX therapy depending on molecular subtype; patients with classical tumors showed a greater response compared to basal-like tumors. Identifying molecular subtype may inform treatment approaches, but current subtyping schemas rely on tumor biopsies which are invasive and can be technically challenging. Blood based epigenetic assays are commercially available, and identification of subtype-associated epigenetic modifications may provide a path toward blood-based PDAC molecular subtyping. We analyzed methylation levels and developed a subtype classifier using methylation data on PDAC tumors. Using this classifier, we then developed a methylation-specific qPCR assay to subtype PDAC tumors by analyzing methylation at particular sites in tumor DNA which may now be further adapted to more facile liquid biopsy approaches. Methods: Using methylation data (n=150) from TCGA PAAD, we applied a random forest (RF) model followed by a k-Top Scoring Pairs (k-TSP) algorithm to develop a methylation site-based classifier that allows prediction of subtype by comparing methylation levels between paired sites. The classifier was validated on two independent datasets, including ICGC (n=82) and patient-derived xenografts (PDXs) (n=21). We designed methylation-specific qPCR primers for these sites and validated their specificity with fully methylated or fully unmethylated DNA. Using genomic DNA extracted from 10 PDAC PDXs of known molecular subtype, we obtained methylation levels at sites identified by the classifier via a SYBR Green-based qPCR assay. We then used our classifier to predict subtype of the PDX tumors. Results: We validated our RF-kTSP methylation subtype classifier on two independent cohorts and predicted subtype using 20 methylation sites. In ICGC, we found balanced accuracy=0.94, AUROC=0.98, AU-PR=0.89. In PDX PDAC tumors, we found balanced accuracy=1, AUROC=1, AU-PR=1. We designed 14/20 primers for qPCR and confirmed specificity for methylated DNA. Using 14/20 methylation sites, we achieved 80% correct subtype prediction of 10 PDX tumors (5/5 basal-like, 3/5 classical). Conclusion: We have developed a methylation-based classifier that accurately and replicably predicts PDAC subtype. We continue to develop a qPCR assay for PDAC subtype that so far has 80% accuracy using 14/20 probes. We are completing validation of the remaining 6 primers and correlating methylation levels at each site with those obtained from EPIC array methylation analysis. The long-term goal is to apply our complete set of primers in a digital droplet PCR assay that will be amenable for subtyping using circulating tumor DNA (ctDNA). Citation Format: Zachary Schrank, Des Weighill, Hannah Thel, Hannah Trembath, Ashley Morrison, Jen Jen Yeh. Development of a methylation-based classifier to identify pancreatic adenocarcinoma subtype [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 979.

Elevated levels of Mucin-16 (MUC16) in conjunction with a high expression of truncated O-glycans is implicated in playing crucial roles in the malignancy of pancreatic ductal adenocarcinoma (PDAC). However, the mechanisms by which such aberrant glycoforms present on MUC16 itself promote an increased disease burden in PDAC are yet to be elucidated. This study demonstrates that the CRISPR/Cas9-mediated genetic deletion of MUC16 in PDAC cells decreases tumor cell migration. We found that MUC16 enhances tumor malignancy by activating the integrin-linked kinase and focal adhesion kinase (ILK/FAK)-signaling axis. These findings are especially noteworthy in truncated O-glycan (Tn and STn antigen)-expressing PDAC cells. Activation of these oncogenic-signaling pathways resulted in part from interactions between MUC16 and integrin complexes (α4β1), which showed a stronger association with aberrant glycoforms of MUC16. Using a monoclonal antibody to functionally hinder MUC16 significantly reduced the migratory cascades in our model. Together, these findings suggest that truncated O-glycan containing MUC16 exacerbates malignancy in PDAC by activating FAK signaling through specific interactions with α4 and β1 integrin complexes on cancer cell membranes. Targeting these aberrant glycoforms of MUC16 can aid in the development of a novel platform to study and treat metastatic pancreatic cancer.

We have previously identified two tumor specific molecular subtypes in pancreatic ductal adenocarcinoma (PDAC), basal-like and classical. Although basal-like tumors are found in less than 20% of patients, patients with basal-like tumors have a worse prognosis and are largely resistant to FOLFIRINOX chemotherapy. To identify targetable basal-like subtype specific vulnerabilities in PDAC, we utilized Multiplexed kinase Inhibitor Beads and Mass Spectrometry (MIB-MS) to profile the kinome in patient derived xenograft (PDX) models that recapitulate the tumor subtypes found in patients. MIB-MS results show that kinase expression is significantly associated with molecular subtype. Notably, basal-like tumors show increased expression of receptor tyrosine kinases (RTKs) including EGFR, MET, and IGF1R and MAP kinase members MEK2 and BRAF indicating activation of the MAP kinase signaling cascade. These RTKs including EGFR are also differentially expressed in primary patient tumors suggesting the true efficacy of EGFR and other kinase inhibitors in basal-like tumors may have been masked by the larger proportion of patients (>80%) with unresponsive classical tumors. Furthermore, inhibition of MAP kinase signaling with the MEK inhibitor trametinib results in kinome reprogramming exclusive to classical tumors. MIB-MS profiling reveals upregulation of TAOK3 and MEK3/6 indicating activation of p38 MAP kinase signaling as a classical subtype specific compensatory mechanism against MEK inhibition. Overall, these results present actionable basal-like subtype specific kinase targets by defining a novel subtype specific kinome in PDAC. Precision approaches in clinical trials are needed to determine if new and previously thought to be disappointing kinase inhibitors may be efficacious in patients with basal-like tumors. Citation Format: Yi Xu, Ashley B. Morrison, Silvia G. Herrera, Michael P. East, Gary L. Johnson, Jen Jen Yeh. Proteomic profiling reveals subtype specific kinase expression in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr PR011.