Abstract
Co-occurrent KRAS and TP53 mutations define a majority of patients with pancreatic ductal adenocarcinoma (PDAC) and define its pro-metastatic proclivity. Here, we demonstrate that KRAS-TP53 co-alteration is associated with worse survival compared with either KRAS-alone or TP53-alone altered PDAC in 245 patients with metastatic disease treated at a tertiary referral cancer center, and validate this observation in two independent molecularly annotated datasets. Compared with non-TP53 mutated KRAS-altered tumors, KRAS-TP53 co-alteration engenders disproportionately innate immune-enriched and CD8+ T-cell-excluded immune signatures. Leveraging in silico, in vitro, and in vivo models of human and murine PDAC, we discover a novel intersection between KRAS-TP53 co-altered transcriptomes, TP63-defined squamous trans-differentiation, and myeloid-cell migration into the tumor microenvironment. Comparison of single-cell transcriptomes between KRAS-TP53 co-altered and KRAS-altered/TP53WT tumors revealed cancer cell-autonomous transcriptional programs that orchestrate innate immune trafficking and function. Moreover, we uncover granulocyte-derived inflammasome activation and TNF signaling as putative paracrine mediators of innate immunoregulatory transcriptional programs in KRAS-TP53 co-altered PDAC. Immune subtyping of KRAS-TP53 co-altered PDAC reveals conflation of intratumor heterogeneity with progenitor-like stemness properties. Coalescing these distinct molecular characteristics into a KRAS-TP53 co-altered “immunoregulatory program” predicts chemoresistance in metastatic PDAC patients enrolled in the COMPASS trial, as well as worse overall survival.
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Data availability
The authors declare that the data supporting the findings of this study, and information for retrieval of all external sources of data, are available within the manuscript. The detailed mutational information of tumor samples in the UMiami cohort (n = 245) was collated from commercial vendors in the UMiami Patient Atlas, and putative oncogenic mutations are provided in Table S1.
References
Waddell N, Pajic M, Patch AM, Chang DK, Kassahn KS, Bailey P, et al. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature. 2015;518:495–501.
Balachandran VP, Beatty GL, Dougan SK. Broadening the Impact of Immunotherapy to Pancreatic Cancer: Challenges and Opportunities. Gastroenterology. 2019;156:2056–72.
Datta J, Narayan RR, Goldman DA, Chatila WK, Gonen M, Strong J, et al. Distinct genomic profiles are associated with conversion to resection and survival in patients with initially unresectable colorectal liver metastases treated with systemic and hepatic artery chemotherapy. Ann Surg. 2020. https://doi.org/10.1097/SLA.0000000000004613.
Datta J, Smith JJ, Chatila WK, McAuliffe JC, Kandoth C, Vakiani E, et al. Coaltered Ras/B-raf and TP53 is associated with extremes of survivorship and distinct patterns of metastasis in patients with metastatic colorectal cancer. Clin Cancer Res. 2020;26:1077–85.
Smith JJ, Chatila WK, Sanchez-Vega F, Datta J, Connell LC, Szeglin BC, et al. Genomic stratification beyond Ras/B-Raf in colorectal liver metastasis patients treated with hepatic arterial infusion. Cancer Med. 2019;8:6538–48.
Dosch AR, Mehra S, Merchant NB, Datta J. Deciphering high risk molecular alterations in gastrointestinal malignancy utilizing an extreme outlier strategy. Oncoscience. 2020;7:26–9.
Dosch AR, Chatila WK, Ban Y, Bianchi A, Deshpande NU, De Castro Silva I, et al. Ras-p53 genomic cooperativity as a model to investigate mechanisms of innate immune regulation in gastrointestinal cancers. Oncotarget. 2022;12:2104–2110.
Hingorani SR, Wang L, Multani AS, Combs C, Deramaudt TB, Hruban RH, et al. Trp53R172H and KrasG12D cooperate to promote chromosomal instability and widely metastatic pancreatic ductal adenocarcinoma in mice. Cancer Cell. 2005;7:469–83.
Bailey JM, Hendley AM, Lafaro KJ, Pruski MA, Jones NC, Alsina J, et al. p53 mutations cooperate with oncogenic Kras to promote adenocarcinoma from pancreatic ductal cells. Oncogene. 2016;35:4282–8.
Kim MP, Li X, Deng J, Zhang Y, Dai B, Allton KL, et al. Oncogenic KRAS recruits an expansive transcriptional network through mutant p53 to drive pancreatic cancer metastasis. Cancer Discov. 2021;11:2094–111.
Maddalena M, Mallel G, Nataraj NB, Shreberk-Shaked M, Hassin O, Mukherjee S, et al. TP53 missense mutations in PDAC are associated with enhanced fibrosis and an immunosuppressive microenvironment. Proc Natl Acad Sci USA. 2021;118:e2025631118.
Miao YR, Zhang Q, Lei Q, Luo M, Xie GY, Wang H, et al. ImmuCellAI: a unique method for comprehensive T-cell subsets abundance prediction and its application in cancer immunotherapy. Adv Sci. 2020;7:1902880.
Bronte V, Brandau S, Chen SH, Colombo MP, Frey AB, Greten TF, et al. Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards. Nat Commun. 2016;7:12150.
Aguirre AJ, Bardeesy N, Sinha M, Lopez L, Tuveson DA, Horner J, et al. Activated Kras and Ink4a/Arf deficiency cooperate to produce metastatic pancreatic ductal adenocarcinoma. Genes Dev. 2003;17:3112–6.
Bardeesy N, Aguirre AJ, Chu GC, Cheng KH, Lopez LV, Hezel AF, et al. Both p16(Ink4a) and the p19(Arf)-p53 pathway constrain progression of pancreatic adenocarcinoma in the mouse. Proc Natl Acad Sci USA 2006;103:5947–52.
Izeradjene K, Combs C, Best M, Gopinathan A, Wagner A, Grady WM, et al. Kras(G12D) and Smad4/Dpc4 haploinsufficiency cooperate to induce mucinous cystic neoplasms and invasive adenocarcinoma of the pancreas. Cancer Cell. 2007;11:229–43.
Cancer Genome Atlas Research Network. Electronic address aadhe, Cancer Genome Atlas Research Network. Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell. 2017;32:185–203.e113.
Somerville TD, Biffi G, Dassler-Plenker J, Hur SK, He XY, Vance KE, et al. Squamous trans-differentiation of pancreatic cancer cells promotes stromal inflammation. Elife. 2020;9:e53381. https://doi.org/10.7554/eLife.53381.
Bailey P, Chang DK, Nones K, Johns AL, Patch AM, Gingras MC, et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature. 2016;531:47–52.
Cancer Genome Atlas Research N. Comprehensive genomic characterization of squamous cell lung cancers. Nature. 2012;489:519–25.
O’Kane GM, Grunwald BT, Jang GH, Masoomian M, Picardo S, Grant RC, et al. GATA6 expression distinguishes classical and basal-like subtypes in advanced pancreatic cancer. Clin Cancer Res. 2020;26:4901–10.
Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, et al. The immune landscape of cancer. Immunity. 2018;48:812–830.e814.
Nirmal AJ, Regan T, Shih BB, Hume DA, Sims AH, Freeman TC. Immune cell gene signatures for profiling the microenvironment of solid tumors. Cancer Immunol Res. 2018;6:1388–400.
Hosein AN, Huang H, Wang Z, Parmar K, Du W, Huang J, et al. Cellular heterogeneity during mouse pancreatic ductaladenocarcinoma progression at single-cell resolution. JCI Insight. 2019;5:e129212. https://doi.org/10.1172/jci.insight.129212.
Li J, Byrne KT, Yan F, Yamazoe T, Chen Z, Baslan T, et al. Tumor cell-intrinsic factors underlie heterogeneity of immune cell infiltration and response to immunotherapy. Immunity. 2018;49:178–193.e177.
Pan Y, Lu F, Fei Q, Yu X, Xiong P, Yu X, et al. Single-cell RNA sequencing reveals compartmental remodeling of tumor-infiltrating immune cells induced by anti-CD47 targeting in pancreatic cancer. J Hematol Oncol. 2019;12:124.
La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H, Petukhov V, et al. RNA velocity of single cells. Nature. 2018;560:494–8.
Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381–6.
Kato H, Yoshioka F, Yokochi K, Tanaka C, Koike S, Matsunaga S, et al. Echocardiographic evaluation in congenital heart disease. Jpn Circ J. 1979;43:343–56.
Oh JY, Kang MS, An BK, Song EA, Kwon JH, Kwon YK. Occurrence of purulent arthritis broilers vertically infected with Salmonella enterica serovar Enteritidis in Korea. Poult Sci. 2010;89:2116–22.
Kuhn DJ, Dou QP. The role of interleukin-2 receptor alpha in cancer. Front Biosci. 2005;10:1462–74.
Rose D. Selected ongoing clinical trials. South Med J. 2002;95:621–3.
Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN, et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell. 2018;173:338–354.e315.
Gillet JP, Calcagno AM, Varma S, Davidson B, Bunkholt Elstrand M, Ganapathi R, et al. Multidrug resistance-linked gene signature predicts overall survival of patients with primary ovarian serous carcinoma. Clin Cancer Res. 2012;18:3197–206.
Tomkiewicz C, Hans S, Mucchielli MH, Agier N, Delacroix H, Marisa L, et al. A head and neck cancer tumor response-specific gene signature for cisplatin, 5-fluorouracil induction chemotherapy fails with added taxanes. PLoS One. 2012;7:e47170.
Tavassoly I, Hu Y, Zhao S, Mariottini C, Boran A, Chen Y, et al. Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses. Mol Oncol. 2019;13:1725–43.
Rogan PK. Multigene signatures of responses to chemotherapy derived by biochemically-inspired machine learning. Mol Genet Metab. 2019;128:45–52.
Cui Y, Guo G. Immunomodulatory function of the tumor suppressor p53 in host immune response and the tumor microenvironment. Int J Mol Sci. 2016;17:1942. https://doi.org/10.3390/ijms17111942.
Hamarsheh S, Gross O, Brummer T, Zeiser R. Immune modulatory effects of oncogenic KRAS in cancer. Nat Commun. 2020;11:5439.
Blagih J, Zani F, Chakravarty P, Hennequart M, Pilley S, Hobor S, et al. Cancer-specific loss of p53 leads to a modulation of myeloid and T cell responses. Cell Rep. 2020;30:481–496.e486.
Hamarsheh S, Zeiser R. NLRP3 inflammasome activation in cancer: a double-edged sword. Front Immunol. 2020;11:1444.
Mahdavi Sharif P, Jabbari P, Razi S, Keshavarz-Fathi M, Rezaei N. Importance of TNF-alpha and its alterations in the development of cancers. Cytokine. 2020;130:155066.
McGranahan N, Swanton C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell. 2017;168:613–28.
Mukherjee A, Huynh V, Gaines K, Reh WA, Vasquez KM. Targeting the high-mobility group box 3 protein sensitizes chemoresistant ovarian cancer cells to cisplatin. Cancer Res. 2019;79:3185–91.
Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, et al. Comprehensive characterization of cancer driver genes and mutations. Cell. 2018;173:371–385.e318.
Chakravarty D, Gao J, Phillips SM, Kundra R, Zhang H, Wang J, et al. OncoKB: a precision oncology knowledge base. JCO Precis Oncol. 2017;2017:PO.17.00011. https://doi.org/10.1200/PO.17.00011.
Goldman MJ, Craft B, Hastie M, Repecka K, McDade F, Kamath A, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38:675–8.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1523.
Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27:1739–40.
Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2016;44:e71.
Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One. 2010;5:e13984.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.
Gu Z, Gu L, Eils R, Schlesner M, Brors B. circlize Implements and enhances circular visualization in R. Bioinformatics. 2014;30:2811–2.
Kramer A, Green J, Pollard J Jr., Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics. 2014;30:523–30.
Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–7.
Fan J, Salathia N, Liu R, Kaeser GE, Yung YC, Herman JL, et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat Methods. 2016;13:241–4.
Aung KL, Fischer SE, Denroche RE, Jang GH, Dodd A, Creighton S, et al. Genomics-driven precision medicine for advanced pancreatic cancer: early results from the COMPASS trial. Clin Cancer Res. 2018;24:1344–54.
Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009;462:108–12.
Acknowledgements
This work was supported by KL2 career development grant from Miami CTSI under NIH Award UL1TR002736, Stanley Glaser Foundation, American College of Surgeons Franklin Martin Career Development Award, and Association for Academic Surgery Joel J. Roslyn Faculty Award (to JD); NIH R01 CA161976 (to NBM); and NCI/NIH Award P30CA240139 (to JD and NBM).
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JD designed the study; JD, NBM provided funding; JD, YB, AB, IDCS, NUD, LLC, SM, SS, CR, XS, XD, AC, PS, ARD performed the experiments; JD, AP, PJH, NSN, JMW, JJK, XC, KVK, NBM provided access to clinical/murine samples; JD, YB, AB, CR wrote the manuscript; all authors reviewed/edited the manuscript.
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Datta, J., Bianchi, A., De Castro Silva, I. et al. Distinct mechanisms of innate and adaptive immune regulation underlie poor oncologic outcomes associated with KRAS-TP53 co-alteration in pancreatic cancer. Oncogene 41, 3640–3654 (2022). https://doi.org/10.1038/s41388-022-02368-w
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DOI: https://doi.org/10.1038/s41388-022-02368-w
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