Biology subjects

Pich, O.

Publications and source records attributed to Pich, O..

11 recordsLinked to original sources

bioRxiv2025

Genetic background sets the trajectory of cancer evolution

Human cancers are heterogeneous. Their genomes evolve from genetically diverse germlines in complex and dynamic environments, including exposure to potential carcinogens. This heterogeneity of humans, our environmental exposures, and subsequent tumours makes it challenging to understand the extent to which cancer evolution is predictable. Addressing this limitation, we re-ran early tumour evolution hundreds of times in diverse, inbred mouse strains, capturing genetic variation comparable to and beyond that found in human populations. The sex, environment, and carcinogenic exposures were all controlled and tumours comprehensively profiled with whole genome and transcriptome sequencing. Within a strain, there was a high degree of consistency in the mutational landscape, a limited range of driver mutations, and all strains converged on the acquisition of a MAPK activating mutation with similar transcriptional disruption of that pathway. Despite these similarities in the phenotypic state of tumours, different strains took markedly divergent paths to reach that state. This included pronounced biases in the precise driver mutations, the strain specific occurrence of whole genome duplication, and differences in subclonal selection that reflected both cancer susceptibility and tumour growth rate. These results show that interactions between the germline genome and the environment are highly deterministic for the trajectory of tumour genome evolution, and even modest genetic divergence can substantially alter selection pressures during cancer development, influencing both cancer risk and the biology of the tumour that develops.

genomics↗

bioRxiv2023

Genomic evolution of non-small cell lung cancer patient-derived xenograft models

Patient-derived xenograft (PDX) models of cancer, developed through injection of patient tumour cells into immunocompromised mice, have been widely adopted in preclinical studies, as well as in precision oncology approaches. However, the extent to which PDX models represent the underlying genetic diversity of a patients tumour and the extent of on-going genomic evolution in PDX models are incompletely understood, particularly in the context of heterogeneous cancers such as non-small cell lung cancer (NSCLC). To investigate the depiction of intratumour heterogeneity by PDX models, we derived 47 new subcutaneous multi-region PDX models from 22 patients with primary NSCLC enrolled in the clinical longitudinal cohort study TRACERx. By analysing whole exome sequencing data from primary tumours and PDX models, we find that PDX establishment creates a genomic bottleneck, with 76% of PDX models being derived from a single primary tumour subclone. Despite this, multiple primary tumour subclones were capable of PDX establishment in regional PDX models, indicating that PDX libraries derived from multiple tumour regions can capture intratumour heterogeneity. Acquisition of somatic mutations continued during PDX model expansion, and was associated with APOBEC- or mismatch repair deficiency-induced mutational signatures in a subset of models. Overall, while NSCLC PDX models retain truncal genomic alterations, the absence of subclonal heterogeneity representative of the primary tumour is a major limitation. Our results emphasise the importance of characterising and monitoring intratumour heterogeneity in the context of pre-clinical cancer studies.

cancer biology↗

bioRxiv2022

Strand-resolved mutagenicity of DNA damage and repair

DNA base damage is a major source of oncogenic mutations1. Such damage can produce strand-phased mutation patterns and multiallelic variation through the process of lesion segregation2. Here, we exploited these properties to reveal how strand-asymmetric processes, such as replication and transcription, shape DNA damage and repair. Despite distinct mechanisms of leading and lagging strand replication3,4, we observe identical fidelity and damage tolerance for both strands. For small DNA adducts, our results support a model in which the same translesion polymerase is recruited on-the-fly to both replication strands, starkly contrasting the strand asymmetric tolerance of bulky adducts5. We find that DNA damage tolerance is also common during transcription, where RNA-polymerases frequently bypass lesions without triggering repair. At multiple genomic scales, we show the pattern of DNA damage induced mutations is largely shaped by the influence of DNA accessibility on repair efficiency, rather than gradients of DNA damage. Finally, we reveal specific genomic conditions that can corrupt the fidelity of nucleotide excision repair and actively drive oncogenic mutagenesis. These results provide insight into how strand-asymmetric mechanisms underlie the formation, tolerance, and repair of DNA damage, thereby shaping cancer genome evolution.

genomics↗

bioRxiv2020

The evolution of hematopoietic cells under cancer therapy

Chemotherapies may influence the evolution of somatic tissues through the introduction of genetic variation in cells and by changing the selective pressures they face. However, the contributions of chemotherapeutic agents to the mutation burden of healthy cells and to clonal expansions in somatic tissues are not clear. Here, we exploit the mutational footprint of some chemotherapies to explore their influence on the evolution of hematopoietic cells. Cells of Acute Myeloid Leukemia (AML) secondary to treatment with platinum-based drugs showed a clear mutational footprint of these drugs, indicating that healthy blood cells received chemotherapy mutations. In contrast, no trace of 5-fluorouracil (5-FU) mutational signature was found in AML secondary to exposure to 5-FU, suggesting that cells establishing the AML were quiescent during treatment. We used the platinum-based mutational signature as a barcode to precisely time clonal expansions with respect to the moment of exposure to the drug. The enrichment for clonal mutations among treatment-related mutations in all platinum-treated AMLs shows that these secondary neoplasms begin their clonal expansion after the start of the cytotoxic treatment. In contrast, the absence of detectable platinum-related mutations in healthy blood samples with clonal hematopoiesis is consistent with a clonal expansion that predates the exposure to the cytotoxic agent, which favours particular pre-existing clones.

cancer biology↗

bioRxiv2020

Discovering the drivers of clonal hematopoiesis

Mutations in genes that confer a selective advantage to hematopoietic stem cells (HSCs) in certain conditions drive clonal hematopoiesis (CH). While some CH drivers have been identified experimentally or through epidemiological studies, the compendium of all genes able to drive CH upon mutations in HSCs is far from complete. We propose that identifying signals of positive selection in blood somatic mutations may be an effective way to identify CH driver genes, similarly as done to identify cancer genes. Using a reverse somatic variant calling approach, we repurposed whole-genome and whole-exome blood/tumor paired samples of more than 12,000 donors from two large cancer genomics cohorts to identify blood somatic mutations. The application of IntOGen, a robust driver discovery pipeline, to blood somatic mutations across both cohorts, and more than 24,000 targeted sequenced samples yielded a list of close to 70 genes with signals of positive selection in CH, available at http://www.intogen.org/ch. This approach recovers all known CH genes, and discovers novel candidates. Generating this compendium is an essential step to understand the molecular mechanisms of CH and to accurately detect individuals with CH to ascertain their risk to develop related diseases.

genomics↗

bioRxiv2020

Mutational dynamics and transmission properties of SARS-CoV-2 superspreading events in Austria

Superspreading events shape the COVID-19 pandemic. Here we provide a national-scale analysis of SARS-CoV-2 outbreaks in Austria, a country that played a major role for virus transmission across Europe and beyond. Capitalizing on a national epidemiological surveillance system, we performed deep whole-genome sequencing of virus isolates from 576 samples to cover major Austrian SARS-CoV-2 clusters. Our data chart a map of early viral spreading in Europe, including the path from low-frequency mutations to fixation. Detailed epidemiological surveys enabled us to calculate the effective SARS-CoV-2 population bottlenecks during transmission and unveil time-resolved intra-patient viral quasispecies dynamics. This study demonstrates the power of integrating deep viral genome sequencing and epidemiological data to better understand how SARS-CoV-2 spreads through populations. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

microbiology↗

bioRxiv2020

In silico saturation mutagenesis of cancer genes

Extensive bioinformatics analysis of datasets of tumor somatic mutations data have revealed the presence of some 500-600 cancer driver genes. The identification of all potential driver mutations affecting cancer genes is essential to implement precision cancer medicine and to understand the interplay of mutation probability and selection in tumor development. Here, we present an in silico saturation mutagenesis approach to identify all driver mutations in 568 cancer genes across 66 tumor types. For most cancer genes the mutation probability across tissues --underpinned by active mutational processes-- influences which driver variants have been observed, although this differs significantly between tumor suppressor and oncogenes. The role of selection is apparent in some of the latter, the observed and unobserved driver mutations of which are equally likely to occur. The number of potential driver mutations in a cancer gene roughly determines how many mutations are available for detection across newly sequenced tumors.

cancer biology↗

bioRxiv2019

Pervasive lesion segregation shapes cancer genome evolution

Cancers arise through the acquisition of oncogenic mutations and grow through clonal expansion1, 2. Here we reveal that most mutagenic DNA lesions are not resolved as mutations within a single cell-cycle. Instead, DNA lesions segregate unrepaired into daughter cells for multiple cell generations, resulting in the chromosome-scale phasing of subsequent mutations. We characterise this process in mutagen-induced mouse liver tumours and show that DNA replication across persisting lesions can generate multiple alternative alleles in successive cell divisions, thereby increasing both multi-allelic and combinatorial genetic diversity. The phasing of lesions enables the accurate measurement of strand biased repair processes, the quantification of oncogenic selection, and the fine mapping of sister chromatid exchange events. Finally, we demonstrate that lesion segregation is a unifying property of exogenous mutagens, including UV light and chemotherapy agents in human cells and tumours, which has profound implications for the evolution and adaptation of cancer genomes.

cancer biology↗

bioRxiv2019

The mutational footprints of cancer therapies

Some cancer therapies damage DNA and cause mutations both in cancer and healthy cells of the patient1. These therapy-induced mutations may underlie some of the long-term and late side effects of the treatment, such as mental disabilities, organ toxicities and secondary neoplasms. Currently we ignore the mutation pattern and burden caused by different cancer treatments. Here we identify mutational signatures, or footprints of six widely-used anti-cancer therapies with the study of whole-genomes from more than 3500 metastatic tumors originated in different organs. These include previously known and new mutational signatures generated by platinum-based drugs, and a novel signature of treatment with nucleoside metabolic inhibitors. Exploiting these mutational footprints, we estimate the contribution of different treatments to the mutation burden of tumors and their risk of causing coding and likely driver mutations in the genome. In summary, the mutational footprints identified here open a window to precisely appraise the mutational risk of different cancer therapies to understand their late side effects.

genomics↗

bioRxiv2017

Discovery and characterization of coding and non-coding driver mutations in more than 2,500 whole cancer genomes

Discovery of cancer drivers has traditionally focused on the identification of protein-coding genes. Here we present a comprehensive analysis of putative cancer driver mutations in both protein-coding and non-coding genomic regions across >2,500 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We developed a statistically rigorous strategy for combining significance levels from multiple driver discovery methods and demonstrate that the integrated results overcome limitations of individual methods. We combined this strategy with careful filtering and applied it to protein-coding genes, promoters, untranslated regions (UTRs), distal enhancers and non-coding RNAs. These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types. Novel recurrent events were found in the promoters or 5UTRs of TP53, RFTN1, RNF34, and MTG2, in the 3UTRs of NFKBIZ and TOB1, and in the non-coding RNA RMRP. We provide evidence that the previously reported non-coding RNAs NEAT1 and MALAT1 may be subject to a localized mutational process. Perhaps the most striking finding is the relative paucity of point mutations driving cancer in non-coding genes and regulatory elements. Though we have limited power to discover infrequent non-coding drivers in individual cohorts, combined analysis of promoters of known cancer genes show little excess of mutations beyond TERT.

genomics↗

bioRxiv2017

The whole-genome panorama of cancer drivers

The advance of personalized cancer medicine requires the accurate identification of the mutations driving each patients tumor. However, to date, we have only been able to obtain partial insights into the contribution of genomic events to tumor development. Here, we design a comprehensive approach to identify the driver mutations in each patients tumor and obtain a whole-genome panorama of driver events across more than 2,500 tumors from 37 types of cancer. This panorama includes coding and non-coding point mutations, copy number alterations and other genomic rearrangements of somatic origin, and potentially predisposing germline variants. We demonstrate that genomic events are at the root of virtually all tumors, with each carrying on average 4.6 driver events. Most individual tumors harbor a unique combination of drivers, and we uncover the most frequent co-occurring driver events. Half of all cancer genes are affected by several types of driver mutations. In summary, the panorama described here provides answers to fundamental questions in cancer genomics and bridges the gap between cancer genomics and personalized cancer medicine.

cancer biology↗