Cancer Immunogenomics

  • Eduard Porta

Summary

Our laboratory uses computational approaches to study the interaction between genetic variants in cancer genomes and multiple aspects of cancer, ranging from the immune response against tumors to the susceptibility of cancer cells to different treatments. In recent years we have developed several computational tools to identify which mutations are driving the transformation of healthy cells into malignant ones. To that end, we combined data from protein structures with the mutations of over 10.000 cancer genomes. We have also shown that there is a relationship between which mutations drive oncogenesis in a tumor and how the patient’s immune system responds against it. More recently, we have focused our efforts into deciphering the role of inherited genetic variations into cancer predisposition and immuno-oncology. In the near future, our main goal is to understand how these three things, inherited variants, acquired mutations and the immune system, interact with each other in cancer.

Research

Over the last two decades there has been an explosion of, essentially, three types of big data in cancer research. We started by collecting germline genotypes from cancer patients and matching controls during the Genome Wide Association Studies era that started around 2005. Nowadays, public databases such as dbGaP store hundreds of thousands of germline genotypes of cancer patients, and we have identified over 2000 germline variants linked to some form of cancer. Similarly, since around 2010, there has been an exponential growth in the amount of publicly available somatic tumor genomes, which have provided exceptional insights into tumor evolution and oncogenic mechanisms driving cancer growth. Finally, over the last five years, thanks to the development of single cell sequencing technologies and medical breakthroughs such as immune checkpoint inhibitors, there is a growing appreciation for the role of the amount and composition of the cells in the tumor microenvironment.

So far, most of us have been studying these three different aspects of tumor immunobiology separately. For example, in my case I dedicated the majority of my career into studying the somatic cancer genome, whereas others did the same for germline variants or the tumor microenvironment. However, it is now evident that these three different aspects of cancer are inextricably interwoven and need to be studied together. We know, among others, that the immune system and the somatic genome interact with each other. For example, as explained above, I described how tumors with some somatic driver mutations tend to have certain types of immune cells in their tumor microenvironment. The germline and somatic genomes also interact with each other, as evidenced by the higher prevalence of specific driver mutations depending on the germline genetic background of the cancer patient. Finally, we have just submitted a manuscript describing how the immune response against cancer cells can be predicted using germline data, showing a connection between these two elements. In summary, it is time to move beyond the study of these elements on their own and start to understand this “Cancer Trialogue”. 

Trialogue

Awards

La Caixa Junior Leader (2018)

Beatriu de Pinos fellowship (2017)

People

NamePosition
Eduard PortaEduard Porta 1Group Leader
Victoria Ruiz SerraLogo greenPhD Student

Selected publications

Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, Colaprico A, Wendl MC, Kim J, Reardon B, Ng PK, Jeong KJ, Cao S, Wang Z, Gao J, Gao Q, Wang F, Liu EM, Mularoni L, Rubio-Perez C, Nagarajan N, Cortés-Ciriano I, Zhou DC, Liang WW, Hess JM, Yellapantula VD, Tamborero D, Gonzalez-Perez A, Suphavilai C, Ko JY, Khurana E, Park PJ, Van Allen EM, Liang H, Lawrence MS, Godzik A, Lopez-Bigas N, Stuart J, Wheeler D, Getz G, Chen K, Lazar AJ, Mills GB, Karchin R, Ding L

Comprehensive Characterization of Cancer Driver Genes and Mutations.

Cell 5 Apr 2018, 173 (2) 371-385.e18.
Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%-85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors.
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Ding L, Bailey MH, Porta-Pardo E, Thorsson V, Colaprico A, Bertrand D, Gibbs DL, Weerasinghe A, Huang KL, Tokheim C, Cortés-Ciriano I, Jayasinghe R, Chen F, Yu L, Sun S, Olsen C, Kim J, Taylor AM, Cherniack AD, Akbani R, Suphavilai C, Nagarajan N, Stuart JM, Mills GB, Wyczalkowski MA, Vincent BG, Hutter CM, Zenklusen JC, Hoadley KA, Wendl MC, Shmulevich L, Lazar AJ, Wheeler DA, Getz G

Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics.

Cell 5 Apr 2018, 173 (2) 305-320.e10.
The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing.
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Porta-Pardo E, Kamburov A, Tamborero D, Pons T, Grases D, Valencia A, Lopez-Bigas N, Getz G, Godzik A

Comparison of algorithms for the detection of cancer drivers at subgene resolution.

Nat. Methods Aug 2017, 14 (8) 782-788. Epub 17 Jul 2017
Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally, most algorithms for cancer-driver identification look for genes that have more mutations than expected from the average background mutation rate. However, there is now a wide variety of methods that look for nonrandom distribution of mutations within proteins as a signal for the driving role of mutations in cancer. Here we classify and review such subgene-resolution algorithms, compare their findings on four distinct cancer data sets from The Cancer Genome Atlas and discuss how predictions from these algorithms can be interpreted in the emerging paradigms that challenge the simple dichotomy between driver and passenger genes.
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Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, Porta-Pardo E, Gao GF, Plaisier CL, Eddy JA, Ziv E, Culhane AC, Paull EO, Sivakumar IKA, Gentles AJ, Malhotra R, Farshidfar F, Colaprico A, Parker JS, Mose LE, Vo NS, Liu J, Liu Y, Rader J, Dhankani V, Reynolds SM, Bowlby R, Califano A, Cherniack AD, Anastassiou D, Bedognetti D, Mokrab Y, Newman AM, Rao A, Chen K, Krasnitz A, Hu H, Malta TM, Noushmehr H, Pedamallu CS, Bullman S, Ojesina AI, Lamb A, Zhou W, Shen H, Choueiri TK, Weinstein JN, Guinney J, Saltz J, Holt RA, Rabkin CS, Lazar AJ, Serody JS, Demicco EG, Disis ML, Vincent BG, Shmulevich I

The Immune Landscape of Cancer.

Immunity 17 Apr 2018, 48 (4) 812-830.e14. Epub 5 Apr 2018
We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.
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Porta-Pardo E, Garcia-Alonso L, Hrabe T, Dopazo J, Godzik A

A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces.

PLoS Comput. Biol. Oct 2015, 11 (10) e1004518. Epub 20 Oct 2015
Despite their importance in maintaining the integrity of all cellular pathways, the role of mutations on protein-protein interaction (PPI) interfaces as cancer drivers has not been systematically studied. Here we analyzed the mutation patterns of the PPI interfaces from 10,028 proteins in a pan-cancer cohort of 5,989 tumors from 23 projects of The Cancer Genome Atlas (TCGA) to find interfaces enriched in somatic missense mutations. To that end we use e-Driver, an algorithm to analyze the mutation distribution of specific protein functional regions. We identified 103 PPI interfaces enriched in somatic cancer mutations. 32 of these interfaces are found in proteins coded by known cancer driver genes. The remaining 71 interfaces are found in proteins that have not been previously identified as cancer drivers even that, in most cases, there is an extensive literature suggesting they play an important role in cancer. Finally, we integrate these findings with clinical information to show how tumors apparently driven by the same gene have different behaviors, including patient outcomes, depending on which specific interfaces are mutated.
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