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In cancer, evolutionary forces select for clones that evade the immune system. Here we analyzed >10,000 primary tumors and 356 immune-checkpoint-treated metastases using immune dN/dS, the ratio of nonsynonymous to synonymous mutations in the immunopeptidome, to measure immune selection in cohorts and individuals. We classified tumors as immune edited when antigenic mutations were removed by negative selection and immune escaped when antigenicity was covered up by aberrant immune modulation. Only in immune-edited tumors was immune predation linked to CD8 T cell infiltration. Immune-escaped metastases experienced the best response to immunotherapy, whereas immune-edited patients did not benefit, suggesting a preexisting resistance mechanism. Similarly, in a longitudinal cohort, nivolumab treatment removes neoantigens exclusively in the immunopeptidome of nonimmune-edited patients, the group with the best overall survival response. Our work uses dN/dS to differentiate between immune-edited and immune-escaped tumors, measuring potential antigenicity and ultimately helping predict response to treatment.

Background High-grade serous ovarian cancer (HGSOC) is characterized by extensive intratumoral heterogeneity, which is associated with drug resistance. Our aim was to study the effects of drugs used in the treatment of HGSOC - carboplatin (C), paclitaxel (P) and olaparib (O) - on clonal dynamics in experimental models. Methods We tracked clonal evolution in PEO1, a BRCA2-mutant HGSOC cell line model, by transfecting an initial population of 1.714x106 cells with a barcode (BC) library (CloneTrackerXP). Barcoded populations of cells (18.73x106 cells/flask) were grown in HYPERflasks till confluence, and then replicates were exposed to C (3163 nM) for 5 weeks, P (6.32 nM) for 4 weeks, and O (28467 nM) for 5 weeks (as single agents), reducing the cell population to < 10% (controls kept till cell confluence in the absence of drugs). After that, drug pressure was removed, allowing cells to regrow fully. NGS was used to quantify BCs in the final cell populations and, to get information on the temporal dynamics, also in dead floating cells collected from the weekly culture media change. Heterogeneity of the cell populations was estimated using the Shannon diversity index (SDI). Results The SDI in the controls was 6.306 (95% Bootstrap CI 6.304-6.310) for C and 6.192 (95% Bootstrap CI 6.190-6.197) for P and O. After exposure to C, P and O, the SDI was reduced to 3.208, 2.520 and 4.753 (95% Bootstrap CI 3.209-3.211, 2.517-2.523, 4.750-4.759) respectively, using aggregated replicates (pairwise 2-sided Hutcheson t-test: p < 2.2e-16 pre-drug exposure vs C, P and O). The number of unique BCs in controls vs C, P and O were 1799 vs 545, 1463 vs 288 and 1463 vs 1270, respectively, demonstrating reduction of heterogeneity and change in frequency of clones following drug selection pressure. Cell populations exposed to C and O share some similar clone selection (approximately 60%). This is important as, although the degree of heterogeneity caused by individual drugs are different (C and P lead to less heterogeneous populations than O), there is some concordance between the remaining clones determined by DNA damaging/DNA damage response inhibitors compared to clone selection following exposure to antimicrotubule agents. The selection pattern of BCs among replicates of the different drugs was similar with conservation of BCs with the highest fitness, demonstrating that the initial conditions are determinants of selection under drug exposure. Conclusion Our work, for the first time, quantifies and shows that heterogeneity followed by O therapy is greater than by C and P. Furthermore, the emergent population of cells post-selection pressure was more similar in the C and O groups when compared to P-exposed cells, suggesting correlation in the mechanism of action of drugs and drug resistance/tolerance. Our results also indicate that evolutionary dynamics are largely deterministic and, therefore, can be predictable. Citation Format: Alvaro H. Ingles Russo Garces, Salvatore Milite, Javier Fernandez-Mateos, Bingjie Chen, Lisa Pickard, Adam Stewart, Sara Diaz Sanchez, Rachel Lau, Erica Oliveira, Susana Banerjee, Andrea Sottoriva, Udai Banerji. A study of evolutionary dynamics induced by carboplatin, olaparib and paclitaxel in high-grade serous ovarian cancer cell line models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3790.

Cancer is a global health issue that places enormous demands on healthcare systems. Basic research, the development of targeted treatments, and the utility of DNA sequencing in clinical settings, have been significantly improved with the introduction of whole genome sequencing. However the broad applications of this technology come with complications. To date there has been very little standardisation in how data quality is assessed, leading to inconsistencies in analyses and disparate conclusions. Manual checking and complex consensus calling strategies often do not scale to large sample numbers, which leads to procedural bottlenecks. To address this issue, we present a quality control method that integrates somatic point mutations, allele-specific copy numbers, and tumour purity into a single quantitative score. We demonstrate its power via simulations, and on 𝑛 = 2778 whole-genomes from PCAWG, on 𝑛 = 10 multi-region whole-genomes of two colorectal cancers and on 𝑛 = 48 whole-exomes from TCGA. Our approach significantly improves the generation of cancer mutation data, providing visualisations for cross-referencing with other analyses. The method is fully automated and designed to be compatible with any bioinformatic pipeline, and can automatise tool parameterization paving the way for fast computational assessment of data quality in the era of whole genome sequencing.

MotivationCancers are composed by several heterogeneous subpopulations, each one harbouring different genetic and epigenetic somatic alterations that contribute to disease onset and therapy response. In recent years, copy number alterations leading to tumour aneuploidy have been identified as potential key drivers of such populations, but the definition of the precise makeup of cancer subclones from sequencing assays remains challenging. In the end, little is known about the mapping between complex copy number alterations and their effect on cancer phenotypes.ResultsWe introduce CONGAS, a Bayesian probabilistic method to phase bulk DNA and single-cell RNA measurements from independent assays. CONGAS jointly identifies clusters of single cells with subclonal copy number alterations, and differences in RNA expression. The model builds statistical priors leveraging bulk DNA sequencing data, does not require a normal reference and scales fast thanks to a GPU backend and variational inference. We test CONGAS on both simulated and real data, and find that it can determine the tumour subclonal composition at the single-cell level together with clone-specific RNA phenotypes in tumour data generated from both 10x and Smart-Seq assays.AvailabilityCONGAS is available as 2 packages: CONGAS (https://github.com/caravagnalab/congas), which implements the model in Python, and RCONGAS (https://caravagnalab.github.io/rcongas/), which provides R functions to process inputs, outputs, and run CONGAS fits. The analysis of real data and scripts to generate figures of this paper are available via RCONGAS; code associated to simulations is available at https://github.com/caravagnalab/rcongas_test.Contactgcaravagna@units.itSupplementary informationSupplementary data are available at Bioinformatics online.

Stage I epithelial ovarian cancer (EOC) represents about 10% of all EOCs. It is characterized by a complex histopathological and molecular heterogeneity, and it is composed of five main histological subtypes (mucinous, endometrioid, clear cell and high, and low grade serous), which have peculiar genetic, molecular, and clinical characteristics. As it occurs less frequently than advanced-stage EOC, its molecular features have not been thoroughly investigated. In this study, using in silico approaches and gene expression data, on a multicentric cohort composed of 208 snap-frozen tumor biopsies, we explored the subtype-specific molecular alterations that regulate tumor aggressiveness in stage I EOC. We found that single genes rather than pathways are responsible for histotype specificities and that a cAMP-PKA-CREB1 signaling axis seems to play a central role in histotype differentiation. Moreover, our results indicate that immune response seems to be, at least in part, involved in histotype differences, as a higher immune-reactive behavior of serous and mucinous samples was observed with respect to other histotypes.