Showing papers by "Ludmil B. Alexandrov published in 2023"

Topography of mutational signatures in human cancer

Abstract: The somatic mutations found in a cancer genome are imprinted by different mutational processes. Each process exhibits a characteristic mutational signature, which can be affected by the genome architecture. However, the interplay between mutational signatures and topographical genomic features has not been extensively explored. Here, we integrate mutations from 5,120 whole-genome sequenced tumours from 40 cancer types with 516 topographical features from ENCODE to evaluate the effect of nucleosome occupancy, histone modifications, CTCF binding, replication timing, and transcription/replication strand asymmetries on the cancer-specific accumulation of mutations from distinct mutagenic processes. Most mutational signatures are affected by topographical features with signatures of related aetiologies being similarly affected. Certain signatures exhibit periodic behaviours or cancer-type specific enrichments/depletions near topographical features, revealing further information about the processes that imprinted them. Our findings, disseminated via COSMIC, provide a comprehensive online resource for exploring the interactions between mutational signatures and topographical features across human cancer. GRAPHICAL ABSTRACT HIGHLIGHTS Comprehensive topography analysis of mutational signatures encompassing 82,890,857 somatic mutations in 5,120 whole-genome sequenced tumours integrated with 516 tissue-matched topographical features from the ENCODE project. The accumulation of somatic mutations from most mutational signatures is affected by nucleosome occupancy, histone modifications, CTCF binding sites, transcribed regions, or replication strand/timing. Mutational signatures with related aetiologies are consistently characterized by similar genome topographies across tissue types. Topography analysis allows both separating signatures from different aetiologies and understanding the genomic specificity of clustered somatic mutations. A comprehensive online resource, disseminate through the COSMIC signatures database, that allows researchers to explore the interactions between somatic mutational processes and genome architecture within and across cancer types.

Multiomic analysis of malignant pleural mesothelioma identifies molecular axes and specialized tumor profiles driving intertumor heterogeneity

Abstract: Abstract Malignant pleural mesothelioma (MPM) is an aggressive cancer with rising incidence and challenging clinical management. Through a large series of whole-genome sequencing data, integrated with transcriptomic and epigenomic data using multiomics factor analysis, we demonstrate that the current World Health Organization classification only accounts for up to 10% of interpatient molecular differences. Instead, the MESOMICS project paves the way for a morphomolecular classification of MPM based on four dimensions: ploidy, tumor cell morphology, adaptive immune response and CpG island methylator profile. We show that these four dimensions are complementary, capture major interpatient molecular differences and are delimited by extreme phenotypes that—in the case of the interdependent tumor cell morphology and adapted immune response—reflect tumor specialization. These findings unearth the interplay between MPM functional biology and its genomic history, and provide insights into the variations observed in the clinical behavior of patients with MPM.

Germline TP53 mutations undergo copy number gain years prior to tumor diagnosis

Abstract: Li-Fraumeni syndrome (LFS) is a hereditary cancer predisposition syndrome associated with germline TP53 pathogenic variants. Here, we perform whole-genome sequence (WGS) analysis of tumors from 22 patients with TP53 germline pathogenic variants. We observe somatic mutations affecting Wnt, PI3K/AKT signaling, epigenetic modifiers and homologous recombination genes as well as mutational signatures associated with prior chemotherapy. We identify near-ubiquitous early loss of heterozygosity of TP53, with gain of the mutant allele. This occurs earlier in these tumors compared to tumors with somatic TP53 mutations, suggesting the timing of this mark may distinguish germline from somatic TP53 mutations. Phylogenetic trees of tumor evolution, reconstructed from bulk and multi-region WGS, reveal that LFS tumors exhibit comparatively limited heterogeneity. Overall, our study delineates early copy number gains of mutant TP53 as a characteristic mutational process in LFS tumorigenesis, likely arising years prior to tumor diagnosis.

Visualizing and exploring patterns of large mutational events with SigProfilerMatrixGenerator

Abstract: Background All cancers harbor somatic mutations in their genomes. In principle, mutations affecting between one and fifty base pairs are generally classified as small mutational events. Conversely, large mutational events affect more than fifty base pairs, and, in most cases, they encompass copy-number and structural variants affecting many thousands of base pairs. Prior studies have demonstrated that examining patterns of somatic mutations can be leveraged to provide both biological and clinical insights, thus, resulting in an extensive repertoire of tools for evaluating small mutational events. Recently, classification schemas for examining large-scale mutational events have emerged and shown their utility across the spectrum of human cancers. However, there has been no standard bioinformatics tool that allows visualizing and exploring these large-scale mutational events Results Here, we present a new version of SigProfilerMatrixGenerator that now delivers integrated capabilities for examining large mutational events. The tool provides support for examining copy-number variants and structural variants under two previously developed classification schemas and it supports data from numerous algorithms and data modalities. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. Conclusions The new version of SigProfilerMatrixGenerator provides the first standardized bioinformatics tool for optimized exploration and visualization of two previously developed classification schemas for copy number and structural variants. The tool is freely available at https://github.com/AlexandrovLab/SigProfilerMatrixGenerator with an extensive documentation at https://osf.io/s93d5/wiki/home/.

Comprehensive analysis of mutational signatures reveals distinct patterns and molecular processes across 27 pediatric cancers

Abstract: Analysis of mutational signatures can reveal underlying molecular mechanisms of the processes that have imprinted the somatic mutations found in cancer genomes. Here, we analyze single base substitutions and small insertions and deletions in pediatric cancers encompassing 785 whole-genome sequenced tumors from 27 molecularly defined cancer subtypes. We identified only a small number of mutational signatures active in pediatric cancers, compared with previously analyzed adult cancers. Further, we report a significant difference in the proportion of pediatric tumors showing homologous recombination repair defect signatures compared with previous analyses. In pediatric leukemias, we identified an indel signature, not previously reported, characterized by long insertions in nonrepeat regions, affecting mainly intronic and intergenic regions, but also exons of known cancer genes. We provide a systematic overview of COSMIC v.3 mutational signatures active across pediatric cancers, which is highly relevant for understanding tumor biology and enabling future research in defining biomarkers of treatment response.

Unraveling the impact of a germline heterozygous POLD1 frameshift variant in serrated polyposis syndrome

Abstract: Serrated polyposis syndrome (SPS) is one of the most frequent polyposis syndromes characterized by an increased risk for developing colorectal cancer (CRC). Although SPS etiology has been mainly associated with environmental factors, germline predisposition to SPS could also be relevant for cases with familial aggregation or a family history of SPS/CRC. After whole-exome sequencing of 39 SPS patients from 16 families, we identified a heterozygous germline frameshift variant in the POLD1 gene (c.1941delG, p.(Lys648fs*46)) in a patient with SPS and CRC. Tumor presented an ultra-hypermutated phenotype and microsatellite instability. The POLD1 germline variant segregated in three additional SPS-affected family members. We attempted to create yeast and cellular models for this variant but were no viable. Alternatively, we generated patient-derived organoids (PDOs) from healthy rectal tissue of the index case, as well as from a control donor. Then, we challenged PDOs with a DNA-damaging agent to induce replication stress. No significant differences were observed in the DNA damage response between control and POLD1-Lys648fs PDOs, nor specific mutational signatures were observed. Our results do not support the pathogenicity of the analyzed POLD1 frameshift variant. One possible explanation is that haplosufficiency of the wild-type allele may be compensating for the absence of expression of the frameshift allele. Overall, future work is required to elucidate if functional consequences could be derived from POLD1 alterations different from missense variants in their proofreading domain. To our knowledge, our study presents the first organoid model for germline POLD1 variants and establishes the basis for its use as a model for disease in SPS, CRC and other malignancies.

Deep learning predicts HRD and platinum response from histology slides in breast and ovarian cancer

Abstract: Breast and ovarian cancers harboring homologous recombination deficiencies (HRD) can benefit from platinum-based chemotherapies and PARP inhibitors. Standard diagnostic tests for detecting HRD utilize molecular profiling, which is not universally available especially for medically underserved populations. Here, we trained a deep learning approach for predicting genomically derived HRD scores from routinely sampled hematoxylin and eosin (H&E)-stained histopathological slides. For breast cancer, the approach was externally validated on three independent cohorts and allowed predicting patients' response to platinum treatment. Using transfer learning, we demonstrated the method's clinical applicability to H&E-images from high-grade ovarian tumors. Importantly, our deep learning approach outperformed existing genomic HRD biomarkers in predicting response to platinum-based therapies across multiple cohorts, providing a complementary approach for detecting HRD in patients across diverse socioeconomic groups.

Abstract 1166: APOBEC deaminases compete with tobacco smoking mutagenesis and affect age at onset of lung cancer

Abstract: APOBEC enzymes are part of the innate immunity and are responsible for restricting viruses and retroelements by deaminating cytosine residues. Most solid tumors harbor different levels of somatic mutations attributed to the off-target activities of APOBEC3A (A3A) and/or APOBEC3B (A3B). However, how APOBEC3A/B interact with exogenous mutagenic processes in shaping tumor development is largely unknown. Here, by combining deep whole-genome sequencing with multi-omics profiling of 309 lung cancers from smokers with detailed tobacco smoking information, we identify two subtypes defined by low (LAS) and high (HAS) APOBEC mutagenesis. LAS are enriched for A3B-like mutagenesis and KRAS mutations, whereas HAS for A3A-like mutagenesis and TP53 mutations. Unlike APOBEC3A, APOBEC3B expression is strongly associated with an upregulation of the base excision repair pathway. Hypermutation by unrepaired A3A and tobacco smoking mutagenesis combined with TP53-induced genomic instability can trigger senescence, apoptosis, and cell regeneration, as indicated by telomere shortening, high expression of pulmonary healing signaling pathway and stemness markers in HAS. The expected association of tobacco smoking exposure with genomic/epigenomic changes are not observed in HAS, a plausible consequence of frequent cell senescence or apoptosis. HAS tumors have slower clonal expansion and older age at onset compared to LAS, particularly in heavy smokers, consistent with high proportions of newly generated, unmutated cells in HAS. These findings show how heterogeneity in mutational burden across competing mutational processes and cell types contributes to tumor development, with important clinical implications. Citation Format: Tongwu Zhang, Jian Sang, Phuc H. Hoang, Wei Zhao, Jennifer Rosenbaum, Leszek J. Klimczak, John McElderry, Alyssa Klein, Christopher Wirth, Erik N. Bergstrom, Marcos Díaz-Gay, Raviteja Vangara, Amy Hutchinson, Scott M. Lawrence, Nathan Cole, Bin Zhu, Teresa M. Przytycka, Jianxin Shi, Neil E. Caporaso, Robert Homer, Angela C. Pesatori, Dario Consonni, Stephen J. Chanock, David C. Wedge, Dmitry A. Gordenin, Ludmil B. Alexandrov, Reuben S. Harris, Maria Teresa Landi. APOBEC deaminases compete with tobacco smoking mutagenesis and affect age at onset of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1166.

Arsenic is a potent co-mutagen of ultraviolet light

Abstract: Environmental co-exposures are widespread and are major contributors to carcinogenic mechanisms. Two well-established environmental agents causing skin cancer are ultraviolet radiation (UVR) and arsenic. Arsenic is a known co-carcinogen that enhances UVR’s carcinogenicity. However, the mechanisms of arsenic co-carcinogenesis are not well understood. In this study, we utilized primary human keratinocytes and a hairless mouse model to investigate the carcinogenic and mutagenic properties of co-exposure to arsenic and UVR. In vitro and in vivo exposures revealed that, on its own, arsenic is neither mutagenic nor carcinogenic. However, in combination with UVR, arsenic exposure has a synergistic effect leading to an accelerated mouse skin carcinogenesis as well as to more than 2-fold enrichment of UVR mutational burden. Notably, mutational signature ID13, previously found only in UVR-associated human skin cancers, was observed exclusively in mouse skin tumors and cell lines jointly exposed to arsenic and UVR. This signature was not observed in any model system exposed purely to arsenic or purely to UVR, making ID13 the first co-exposure signature to be reported using controlled experimental conditions. Analysis of existing genomics data from basal cell carcinomas and melanomas revealed that only a subset of human skin cancers harbor ID13 and, consistent with our experimental observations, these cancers exhibited an elevated UVR mutagenesis. Our results provide the first report of a unique mutational signature caused by a co-exposure to two environmental carcinogens and the first comprehensive evidence that arsenic is a potent co-mutagen and co-carcinogen of UVR. Importantly, our findings suggest that a large proportion of human skin cancers are not formed purely due to UVR exposure but rather due to a co-exposure of UVR and other co-mutagens such as arsenic.

Abstract 3130: Extrachromosomal DNA in the cancerous transformation of Barrett’s esophagus

Abstract: Oncogene amplification on extrachromosomal DNA (ecDNA) drives rapid tumor evolution, treatment resistance, and poor outcomes for patients. Computational tools can detect ecDNA in whole-genome sequencing (WGS) data from biopsies. However, the lack of longitudinal studies tracking patients from pre-cancer to cancer have made it difficult to determine how ecDNAs contribute to the malignant transformation. Two independent surveillance studies of Barrett’s esophagus (BE) patients enabled us to study this transition, as BE is the precursor lesion of esophageal adenocarcinoma (EAC). The studies included a longitudinal case-control study and an independent, cross-sectional surveillance cohort, both with histological correlatives, providing an unprecedented opportunity to study the role of ecDNA in the transition of BE to EAC. We analyzed WGS data from 206 patients in a cross-sectional surveillance cohort from Cambridge University UK, with biopsy-validated BE, including 42 patients who never developed high-grade dysplasia (HGD) or EAC during multi-year follow-ups, 25 patients with HGD, 51 patients with early-stage EAC, and 88 patients with late-stage EAC. ecDNA frequency increased between early-stage EAC (24%) and late-stage EAC (43%), suggesting continual ecDNA formation during cancer progression. We additionally analyzed WGS and histology data collected from multi-regional sampling of BE and EAC tissue at two time points from 80 BE patients in an observational case-control study at the Fred Hutchinson Cancer Center (FHCC), where 40 of the patients developed BE-associated EAC during the study and 40 did not. Among FHCC patients, 33% who developed EAC had at least one esophageal biopsy with ecDNA prior to, or at EAC diagnosis. In biopsies collected before cancer diagnosis, ecDNA presence in the biopsy was enriched among patients who later developed EAC compared to patients who did not. When linked to histology data, ecDNA-positive biopsies associated with worsened histological states before cancer diagnosis (p=0.015), and also at cancer diagnosis (p=0.037), with the pre-cancer timepoint being on average 2.1 years prior to cancer diagnosis. The ecDNAs had diverse collections of oncogenes and immunomodulatory genes, and showed higher copy number states than other focal amplification types. Furthermore, ecDNAs showed increasing copy number and structural complexity in more advanced disease stages highlighting their positive selection and ongoing structural evolution. In both FHCC and Cambridge cohorts, we found an association of TP53 loss with ecDNA formation demonstrating the role of prior genomic instability to form ecDNA. In total, our findings illustrate that oncogenic ecDNA can develop early in the malignant transformation, and that they are tied to worsening patient disease status. Citation Format: Jens Luebeck, Alvin W. Ng, Patricia C. Galipeau, Xiaohong Li, Annalise Katz-Summercorn, Hoon Kim, Sriganesh Jammula, Yudou He, Roel Verhaak, Carlo C. Maley, Ludmil B. Alexandrov, Rebecca C. Fitzgerald, Thomas G. Paulson, Howard Y. Chang, Sihan Wu, Vineet Bafna, Paul S. Mischel. Extrachromosomal DNA in the cancerous transformation of Barrett’s esophagus [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3130.

Comprehensive repertoire of the chromosomal alteration and mutational signatures across 16 cancer types from 10,983 cancer patients.

Abstract: Whole genome sequencing (WGS) allows exploration of the complete compendium of oncogenic processes generating characteristic patterns of mutations. Mutational signatures provide clues to tumour aetiology and highlight potentially targetable pathway defects. Here, alongside single base substitution (SBS), double base substitution (DBS), small insertions and deletions (ID) and copy number aberration (CN) signatures covered by the Catalogue of Somatic Mutations in Cancer (COSMIC) we report signatures from an additional mutation type, structural variations (SV), all extracted de-novo from WGS in 10,983 patients across 16 tumour types recruited to the 100,000 genomes project. Across the five mutation classes we report 137 signatures, with 29 signatures new to COSMIC, including the first COSMIC SV signature reference set. We relate the signatures to clinical outcomes and likely response to therapy, demonstrating the role of signature analysis in delivering the vision of precision oncology.

Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment

Abstract: Analysis of mutational signatures is a powerful approach for understanding the mutagenic processes that have shaped the evolution of a cancer genome. Here we present SigProfilerAssignment, a desktop and an online computational framework for assigning all types of mutational signatures to individual samples. SigProfilerAssignment is the first tool that allows both analysis of copy-number signatures and probabilistic assignment of signatures to individual somatic mutations. As its computational engine, the tool uses a custom implementation of the forward stagewise algorithm for sparse regression and nonnegative least squares for numerical optimization. Analysis of 2,700 synthetic cancer genomes with and without noise demonstrates that SigProfilerAssignment outperforms four commonly used approaches for assigning mutational signatures. SigProfilerAssignment is freely available at https://github.com/AlexandrovLab/SigProfilerAssignment with a web implementation at https://cancer.sanger.ac.uk/signatures/assignment/.