Bioinformatics, 38(8), 2022, 2374–2376 https://doi.org/10.1093/bioinformatics/btac111 Advance Access Publication Date: 18 February 2022 Applications Note Data and text mining PCIG: a web-based application to explore immune– genomics interactions across cancer types Anna Pedrola1,*, Sebastià Franch-Expósito2, Sara Lahoz3, Roger Esteban-Fabró4, Rodrigo Dienstmann1, Laia Bassaganyas5,* and Jordi Camps 3,6,* 1 Oncology Data Science (ODysSey) Group, Vall d’Hebron Institute of Oncology, Barcelona 08035, Spain, 2Marie-Josee and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA, 3Translational Colorectal Cancer Genomics, Gastrointestinal and Pancreatic Oncology Team, IDIBAPS, Hospital Clınic de Barcelona, CIBEREHD, Barcelona 08036, Spain, 4Liver Cancer Translational Research Group, Liver Unit, IDIBAPS, Hospital Clınic de Barcelona, CIBEREHD, Barcelona 08036, Spain, 5Department of Medical Genetics, University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK and 6 Department of Cell Biology, Physiology and Immunology, Universitat Autònoma de Barcelona, Bellaterra 08193, Spain *To whom correspondence should be addressed. Associate Editor: Anthony Mathelier Received on August 12, 2021; revised on December 30, 2021; editorial decision on January 19, 2022; accepted on February 16, 2022 Abstract Motivation: Genomic alterations can modulate the tumor immunophenotype depending on their nature and tissue of origin. Although this immune–genomic interaction may shape disease progression and response to immunotherapy, the factors governing such dynamics and the influence of each tissue-specific context remain poorly understood. Results: Here, we have developed the PanCancer ImmunoGenomics (PCIG) tool, a web-based resource that provides researchers with the opportunity to mine immunome–genome relationships across several cancer types using data from the Pan-Cancer Analysis of Whole-Genomes (PCAWG) study, which comprises >2,600 samples spanning across 20 different cancer primary sites. PCIG yields an integrative analysis of the crosstalk between somatic genom- ic alterations and different immune features, thus helping to understand immune response-related processes. Availability and implementation: PCIG is freely available at https://pcig.vhio.net and is supported by all major web browsers. PCIG was developed with Django, which is a Python-based free and open-source framework, and it uses SQL Server as a relational database management system. The code is freely available for download at GitHub https://github.com/AnnaPG/PCIG and in its online supplementary material. Contact: annapedrolagomez@gmail.com or laia.bassaganyas@gmail.com or JCAMPS@clinic.cat Supplementary information: Supplementary data are available at Bioinformatics online. 1 Introduction also influence the pattern of immune infiltrates (Varn et al., 2017). This complex and dynamic interplay between the cancer genomic Cancer genomes play key roles in determining tumor immune fea- landscape and the tumor immune infiltration still remains poorly tures, hence having important implications on disease progression understood. and response to immunotherapy (Thorsson et al., 2018). Somatic To overcome the limited analysis of whole-exome sequencing genomic alterations may enhance anti-tumor immune activity by data, which can hinder the complete view of genomic alterations enabling the differentiation between self and non-self (tumor) and complexity, the recently published Pan-Cancer Analysis of through neoantigen presentation, but they promote immune evasion Whole Genomes (PCAWG) project (https://dcc.icgc.org/pcawg) in later stages of the disease (Litchfield et al., 2021; Mizuno et al., includes whole-genome sequencing (WGS) data for more than 2,600 2021). In fact, the nature of genomic events can affect the genome– samples from The Cancer Genome Atlas (TCGA) and the immune interaction. Overall, the mutational burden is generally International Cancer Genome Consortium (ICGC), spanning up to associated with an activated tumor immunome environment and 20 different cancer types. Importantly, for a subset of 1,300 sam- better responses to immunotherapy, whereas high burdens of copy- ples, PCAWG also incorporates whole-transcriptomic data analyzed number alterations (CNAs) often correlate with immune depletion by RNAseq, providing an exceptional opportunity to comprehen- and immunotherapy resistance (Davoli et al., 2017; Tamborero sively investigate relationships between genomic alterations and the et al., 2018). Moreover, the cancer type and the tissue of origin may immune system in primary tumors from different origins (ICGC/ C The Author(s) 2022. Published by Oxford University Press. V 2374 This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com PCIG: exploring immune–genomics interactions 2375 TCGA Pan-Cancer Analysis of Whole Genomes Consortium, 2020). Nevertheless, WGS data analysis can be costly and time-consuming, limiting its feasibility for rapid and comprehensive exploration. Here, to examine genome–immunome interactions across a wide spectrum of cancer types, we exploited PCAWG data (https:// pcawg.xenahubs.net) to create the PanCancer ImmunoGenomics (PCIG) tool, a web-accessible resource that can be used as a launch- ing platform for clinical and translational research studies to deepen in the exploration of cancer immunogenomics. To this end, we sur- veyed somatic non-synonymous mutations, CNAs, complex struc- tural variations (SVs), as well as gene expression and clinical classification. Moreover, we created an array of integrative analyses with additional estimated variables, such as the tumor immune com- position, the expression of a chemokine-gene signature, the presence of chromothripsis, deletions encompassing the human leukocyte antigen locus, and the levels of broad and focal CNAs. The goal of PCIG is to provide researchers with a fast and easy-to-use tool to visualize the relationships between cancer genomes and immune- related phenotypes to better understand tumor immunogenicity. 2 Implementation PCIG is a web-based interface to explore and visualize the integra- tion of multiple genomic, transcriptomic and immunological fea- tures in different cancer types. This tool was created by using the Python-based open-source Django framework. An extended explan- ation of the PCIG pipeline for data mining and analysis, the genomic and transcriptomic datasets from PCAWG used here, and a descrip- tion of additional estimated variables are detailed in Supplementary Material. PCIG also provides an extensive User Guide, explaining details for each analysis and different plots presented on the website. A diagram showing the main parameters and flowchart of the ana- Fig. 1. Schematic diagram of PCIG and examples of its performance. (A) Flowchart lysis performed by PCIG is depicted in Figure 1A. Briefly, PCIG presenting the data sources, types, and analytical tools used by PCIG. (B) Detailed explores relationships between numerous immune-genomic parame- summary of the analyses PCIG performs under each tab on the website. (C) ters (Fig. 1A) from 2,658 samples across 40 different cancer types clas- Correlation plots between ImmuneScore (source: ESTIMATE) and BCS (top) or sified based on the primary site (Fig. 1B). Specifically, PCIG employs FCS (bottom) (source: CNApp) using the COAD-US cohort. (D) Correlation plot WGS data to quantify CNA scores and the tumor mutational load per between levels of MHC expression (source: Immunophenoscore) and BCS (top) or sample, and considers transcriptomic profiles associated with stromal FCS (bottom) (source: CNApp) using the OV-US cohort. (E) Correlation plot be- and immune-related genes to perform correlation and integrative anal- tween ImmuneScore (source: ESTIMATE) and the mutational load (source: https:// pcawg.xenahubs.net) using the SKCM-US cohort. yses to assess the dynamics between these tumor features, and with tumor baseline clinical characteristics (Supplementary Material). Three main sections are deployed upon selection of primary site 3 Results and discussion and cancer type: Summary, Genomics and Immuno-Genomics. In To exemplify the analytical applicability of PCIG, we explored five summary, the main clinical and molecular characteristics are detailed datasets: colon adenocarcinoma (COAD-US, n ¼ 44), head and neck for the selected subset of tumor samples, along with the genomic squamous-cell carcinoma (HNSC-US, n ¼ 44), lung adenocarcinoma profiling at the subcytoband level and corresponding parameters ana- (n ¼ 38), ovarian cancer (OV-US, n ¼ 42) and skin cancer (SKCM- lyzed using PCIG’s pipeline. Genomics section presents results from the correlative analysis between different genomic variables obtained US, n ¼ 37). Analysis of genomic imbalances showed that ovarian by WGS data (2,600 samples), including the number of non- tumors displayed the highest BCS and FCS values (Supplementary synonymous mutations, broad and focal CNA scores (BCS and FCS, Fig. S1A and B), suggesting gross and chromosome-specific aneu- respectively; Franch-Expósito et al., 2020), and the presence of chro- ploidies. In contrast, the highest values of mutational load were mothripsis events, indicative of complex SVs (Cortes-Ciriano et al., observed in colon cancer, probably due to the presence of POLE- 2020). Finally, established correlation analyses between genomic vari- mutated or mismatch repair deficient tumors in the COAD-US data- ables and different tumor immune metrics computationally derived set (Supplementary Fig. S2). In agreement with previous reports of from transcriptomic data (1,300 samples) are depicted in the depleted CD8þ lymphocytic activity in highly aneuploid tumors Immuno-Genomics section, including (i) global level of immune and (Bassaganyas et al., 2020; Davoli et al., 2017), we observed a signifi- stromal cell infiltrates by ESTIMATE (i.e. ImmuneScore and cant negative correlation between ImmuneScore and BCS or FCS in StromalScore; Yoshihara et al., 2013), (ii) quantification of the four the majority of cancer types, especially affecting the COAD-US, main determinants of tumor immunogenicity by Immunophenoscore HNSC-US and OV-US datasets (Fig. 1C and Supplementary Fig. (i.e. major histocompatibility complex [MHC]-related antigen proc- S3A and B). Likewise, tumors with high BCS or FCS such as OV-US essing genes; checkpoints; effector cells; suppressor cells [SC]; exhibited decreased expression of antigen-presenting MHC-related Charoentong et al., 2017) and (iii) tumor inflammation assessment machinery (Fig. 1D and Supplementary Fig. S4), confirming that through a 12-chemokine gene signature, also associated with the pres- highly complex genomic tumors bear cold immunophenotypes. ence of tertiary lymphoid structures, suggestive of a good prognosis in Conversely, the presence of a high mutational load observed in skin several cancers (Sautès-Fridman et al., 2019). melanoma and colon adenocarcinoma appeared to be significantly PCIG provides high comprehensive plots that can be down- associated with more active immunophenotype profiles (Fig. 1E and loaded together with their associated processed data for further ana- Supplementary Fig. S5). lysis. Because of differences in data sources for each cancer type and In summary, PCIG correlates cancer genomic traits and immune- the limited number of cases in some cohorts, some analyses may re- related phenotypes, thus helping the interpretation of tumor im- quire the use of validation datasets. munogenicity. In this sense, our analysis of five different cancer 2376 A.Pedrola et al. types included in PCAWG further suggests that the tissue of origin References and the genomic landscape have an impact on the tumor immune in- Bassaganyas,L. et al. (2020) Copy-number alteration burden differentially filtrate. Altogether, PCIG assists in the processing and visualization impacts immune profiles and molecular features of hepatocellular carcin- of large datasets, facilitates exhaustive immune-genomic analyses oma. Clin. Cancer Res., 26, 6350–6361. for hypotheses generation, and displays very complex data in an Charoentong,P. et al. (2017) Pan-cancer immunogenomic analyses reveal easy and comprehensible manner. genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep., 18, 248–262. Cortes-Ciriano,I. et al.; PCAWG Consortium. (2020) Comprehensive analysis Acknowledgements of chromothripsis in 2,658 human cancers using whole-genome sequencing. Nat. Genet., 52, 331–341. The authors would like to thank the Vall d’Hebron Institute of Oncology IT Davoli,T. et al. (2017) Tumor aneuploidy correlates with markers of immune for technical support. The study has been developed in part at the Centre evasion and with reduced response to immunotherapy. Science, 355, Esther Koplowitz from IDIBAPS/CERCA Programme/Generalitat de eaaf8399. Catalunya in Barcelona, Spain. Franch-Expósito,S. et al. (2020) CNApp, a tool for the quantification of copy number alterations and integrative analysis revealing clinical implications. Elife, 9, 1–22. Funding ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. (2020) Pan-cancer analysis of whole genomes. Nature, 578, 82–93. This work was supported by the Instituto de Salud Carlos III and co-funded Litchfield,K. et al. (2021) Meta-analysis of tumor- and T cell-intrinsic mecha- by the European Regional Development Fund (ERDF) [CPII18/00026, nisms of sensitization to checkpoint inhibition. Cell, 184, 596–614.e14. PI17/01304], the CIBEREHD and CIBERNED programs from Instituto de Mizuno,S. et al. (2021) Immunogenomic pan-cancer landscape reveals im- Salud Carlos III, the Agència de Gestió d’Ajuts Universitaris i de Recerca, mune escape mechanisms and immunoediting histories. Sci. Rep., 11, 15713 Generalitat de Catalunya [2017 SGR 1035], and Fundación Cientıfica de la Sautès-Fridman,C. et al. (2019) Tertiary lymphoid structures in the era of can- Asociación Espa~ nola Contra el Cáncer [GCB13131592CAST]. S.L. cer immunotherapy. Nat. Rev. Cancer, 19, 307–325. obtained a PFIS grant from Instituto de Salud Carlos III and co-funded by Tamborero,D. et al. (2018) A Pan-cancer landscape of interactions between the European Regional Development Fund (ERDF) [FI18/00221]. R.E.F. is solid tumors and infiltrating immune cell populations. Clin. Cancer Res., supported by a doctoral training grant from MICINN/MINECO [BES- 24, 3717–3728. 2017-081286] and a mobility grant from Fundació Universitària Agustı Thorsson,V. et al.; Cancer Genome Atlas Research Network. (2018) The im- Pedro i Pons. This article is based upon work from COST Action mune landscape of cancer. Immunity, 48, 812–830.e14. [CA17118] and supported by COST (European Cooperation in Science and Varn,F.S. et al. (2017) Systematic pan-cancer analysis reveals immune cell Technology). interactions in the tumor microenvironment. Cancer Res., 77, 1271–1282. Yoshihara,K. et al. (2013) Inferring tumour purity and stromal and immune Conflict of Interest: none declared. cell admixture from expression data. Nat. Commun., 4, 2612.