Periodic Reporting for period 4 - HYPER-INSIGHT (Hypermutated tumors: insight into genome maintenance and cancer vulnerabilities provided by an extreme burden of somatic mutations)
Reporting period: 2022-08-01 to 2024-01-31
DNA is the molecule of heredity: it encodes the genetic information that is necessary for cells to survive, grow, divide and differentiate into tissues that make up organisms. When this information stored in DNA is changed, this is referred to as mutation. In humans and other animals, mutations that happen in germline cells can lead to disease phenotypes in the offspring, while mutations in somatic cells can cause tumors (or, they may potentially contribute to aging of tissues). In many cancers, and potentially in some healthy cells, a large number of mutations can accumulate because a DNA repair system has failed, which is a common risk factor for cancer. The HYPER-INSIGHT project is interested in properties of cancer cells after they accumulate a large number of mutations (“hypermutation”). This can help us understand how the human cells copy and repair DNA, with implications for cancer research. Additionally, cells undergoing hypermutation might develop a dependency on certain genes to help them cope with deficient DNA repair; we have searched for such genes, which could be exploited therapeutically, to selectively target cancer cells while sparing healthy tissues. The conclusions of the HYPER-INSIGHT project have significantly advanced our understanding of the landscape of somatic mutations in cancer. Through large-scale genomic analyses and development of novel statistical methodologies, we have generated insights into the organization of DNA replication and repair processes across chromosomes, the impact of positive and negative selection on somatic mutations and copy number alterations, and the identification of cancer vulnerabilities stemming from the mutator phenotypes of tumors. Our conclusions underscore the potential for harnessing the knowledge of mutagenesis in cancer to develop more effective and personalized therapies. The HYPER-INSIGHT project has opened new avenues for the global research efforts to reduce the burden of cancer on society and improve patient outcomes.
The HYPER-INSIGHT project was focused on elucidating the complex landscape of somatic mutations in human cancers, by using a combination of large-scale genomic analyses, development of innovative computational tools, and validation by experiment. Our work has encompassed three main objectives: (1) understanding the regional organization of DNA replication and repair processes across the human genome; (2) assessing the impact of negative and positive selection on somatic mutations and copy-number alterations in cancer; and (3) identifying cancer vulnerabilities associated with mutational phenotypes by a combination of experimental and computational techniques. In the project, we have made substantial progress towards these objectives, as evidenced in various high-impact scientific publications resulting from our research. We have identified rare germline variants that influence somatic mutational processes, developed novel methods for detecting interactions between copy number alterations and point mutations, and characterized the prevalence and causes of TP53-loss phenocopying events in human tumors. We have also uncovered the role of DNA mismatch repair in promoting APOBEC3-mediated diffuse hypermutation and identified HMCES as a synthetic lethal target in APOBEC3A-expressing cancers. We identified patterns in mutation risk in across thousands of cancer genomes that result from rapid cell cycling and loss of tumor suppressor genes TP53 and RB1. Finally, we established that mutational signatures of various sorts constitute robust markers describing drug response in cells from different tumor types. In addition to these findings, we have developed methodologies and resources that have been widely adopted. These include NMDetective, a tool that can predict the impact of nonsense-mediated mRNA decay (NMD) mechanism on disease-causing nonsense mutations, and a framework for matching cancer cell lines – an important experimental model system -- with cancer type and subtype of origin. The results of the HYPER-INSIGHT project have been disseminated through publications in high visibility journals, presentations at international conferences, and code and datasets were routinely released via Github and FigShare or supplementary material in papers, respectively. Our findings have opened avenues for future research and collaboration towards development of more effective and personalized cancer therapies.
In the HYPER-INSIGHT project, we aimed to exceed the state-of-the-art in computational cancer genomics methods for large-scale analysis of mutational data. Our goal is to further the understanding of biological mechanisms of DNA copying and repair in human cells, and also to propose further therapeutic vulnerabilities of cancerous cells.
Firstly, we were interested in using mutational patterns observed in tumors to be able to measure the DNA repair capacity and also DNA replication programs of tumor cells. This has implications for understanding how errors occur during DNA copying, and therefore also for occurrence of heritable diseases and cancer (that often result from such errors). We have identified germline variants in DNA repair and replication and chromatin modifying genes that influence somatic mutational processes (Vali-Pour et al 2022 Nature Comms). Furthermore, we characterized the redistribution of mutation risk across chromosomes in response to cell cycle gene alterations like RB1 and TP53 (Salvadores & Supek 2024 Nature Cancer)
Secondly, we were interested in learning more about evolutionary pressures on cancer genes. We addressed this by examining genetic interactions that affect selection on DNA mutations and copy number changes in cancers. This may reveal new mechanisms of carcinogenesis and/or potentially vulnerabilities (conditionally-essential genes) that are important for tumoral cells (Besedina & Supek 2024).
Thirdly, we are continuing our experimentation on various models of hypermutating cells examined on cancer cell lines, such as to be able to support a variety of predictions from computational cancer genomics with experimental data, adding further support to the multitude of evidence we will collect about hypermutating cells. Our discovery of HMCES gene as a synthetic lethal target in APOBEC3A-expressing lung cancer cells (Biayna et al. 2021 PLOS Biol), as well as a identifying particular pattern of clustered mutagenesis by APOBEC3A in cancer genomes (Mas-Ponte & Supek 2020 Nature Genet) are a prime example of how the integration of computational and experimental approaches can uncover novel cancer vulnerabilities. Further, our large scale analysis of cancer cell line genomes for mutational signatures identified widespread links to drug sensitivity, suggesting mutation patterns as a very promising marker for investigating patient stratification in the future (Levatic et al. 2022 Nature Comms).
In addition to these core lines of research, we have continued to develop novel methodologies and resources for the cancer genomics community, building on our successes with tools such as NMDetective (Lindeboom et al. 2019 Nature Genetics) and our framework for reclassifying cancer cell lines by tumor type and subtype (Salvadores et al 2020 Sci Adv). Finally, we devised methodologies for understanding toxicity of CRISPR/Cas9 damage to DNA, and how its toxicity can be avoided by design of CRISPR libraries (Alvarez et al 2022 Nature Comms).
Firstly, we were interested in using mutational patterns observed in tumors to be able to measure the DNA repair capacity and also DNA replication programs of tumor cells. This has implications for understanding how errors occur during DNA copying, and therefore also for occurrence of heritable diseases and cancer (that often result from such errors). We have identified germline variants in DNA repair and replication and chromatin modifying genes that influence somatic mutational processes (Vali-Pour et al 2022 Nature Comms). Furthermore, we characterized the redistribution of mutation risk across chromosomes in response to cell cycle gene alterations like RB1 and TP53 (Salvadores & Supek 2024 Nature Cancer)
Secondly, we were interested in learning more about evolutionary pressures on cancer genes. We addressed this by examining genetic interactions that affect selection on DNA mutations and copy number changes in cancers. This may reveal new mechanisms of carcinogenesis and/or potentially vulnerabilities (conditionally-essential genes) that are important for tumoral cells (Besedina & Supek 2024).
Thirdly, we are continuing our experimentation on various models of hypermutating cells examined on cancer cell lines, such as to be able to support a variety of predictions from computational cancer genomics with experimental data, adding further support to the multitude of evidence we will collect about hypermutating cells. Our discovery of HMCES gene as a synthetic lethal target in APOBEC3A-expressing lung cancer cells (Biayna et al. 2021 PLOS Biol), as well as a identifying particular pattern of clustered mutagenesis by APOBEC3A in cancer genomes (Mas-Ponte & Supek 2020 Nature Genet) are a prime example of how the integration of computational and experimental approaches can uncover novel cancer vulnerabilities. Further, our large scale analysis of cancer cell line genomes for mutational signatures identified widespread links to drug sensitivity, suggesting mutation patterns as a very promising marker for investigating patient stratification in the future (Levatic et al. 2022 Nature Comms).
In addition to these core lines of research, we have continued to develop novel methodologies and resources for the cancer genomics community, building on our successes with tools such as NMDetective (Lindeboom et al. 2019 Nature Genetics) and our framework for reclassifying cancer cell lines by tumor type and subtype (Salvadores et al 2020 Sci Adv). Finally, we devised methodologies for understanding toxicity of CRISPR/Cas9 damage to DNA, and how its toxicity can be avoided by design of CRISPR libraries (Alvarez et al 2022 Nature Comms).