Pan-cancer whole-genome comparison of primary and metastatic solid tumours

Cancer genomics; DNA damage and repair; MetastasisGenòmica del càncer; Dany i reparació de l'ADN, MetàstasiGenómica del cáncer; Daño y reparación del ADN; MetástasisMetastatic cancer remains an almost inevitably lethal disease1,2,3. A better understanding of disease progression and response to therapies therefore remains of utmost importance. Here we characterize the genomic differences between early-stage untreated primary tumours and late-stage treated metastatic tumours using a harmonized pan-cancer analysis (or reanalysis) of two unpaired primary4 and metastatic5 cohorts of 7,108 whole-genome-sequenced tumours. Metastatic tumours in general have a lower intratumour heterogeneity and a conserved karyotype, displaying only a modest increase in mutations, although frequencies of structural variants are elevated overall. Furthermore, highly variable tumour-specific contributions of mutational footprints of endogenous (for example, SBS1 and APOBEC) and exogenous mutational processes (for example, platinum treatment) are present. The majority of cancer types had either moderate genomic differences (for example, lung adenocarcinoma) or highly consistent genomic portraits (for example, ovarian serous carcinoma) when comparing early-stage and late-stage disease. Breast, prostate, thyroid and kidney renal clear cell carcinomas and pancreatic neuroendocrine tumours are clear exceptions to the rule, displaying an extensive transformation of their genomic landscape in advanced stages. Exposure to treatment further scars the tumour genome and introduces an evolutionary bottleneck that selects for known therapy-resistant drivers in approximately half of treated patients. Our data showcase the potential of pan-cancer whole-genome analysis to identify distinctive features of late-stage tumours and provide a valuable resource to further investigate the biological basis of cancer and resistance to therapies

Mutational impact of culturing human pluripotent and adult stem cells

Genetic changes acquired during in vitro culture pose a potential risk for the successful application of stem cells in regenerative medicine. To assess mutation accumulation risks induced by culturing, we determined genetic aberrations in individual human induced pluripotent stem cells (iPS cells) and adult stem cells (ASCs) by whole genome sequencing analyses. Individual iPS cells, intestinal ASCs and liver ASCs accumulated 3.5±0.5, 7.2±1.0 and 8.4±3.6 base substitutions per population doubling, respectively. The annual in vitro mutation accumulation rate of ASCs adds up to ∼1600 base pair substitutions, which is ∼40-fold higher than the in vivo rate of ∼40 base pair substitutions per year. Mutational analysis revealed a distinct in vitro induced mutational signature that is irrespective of stem cell type and distinct from the in vivo mutational signature. This in vitro signature is characterized by C to A changes that have previously been linked to oxidative stress conditions. Additionally, we observed stem cell-specific mutational signatures and differences in transcriptional strand bias, indicating differential activity of DNA repair mechanisms between stem cell types in culture. We demonstrate that the empirically defined mutation rates, spectra, and genomic distribution enable risk assessment by modelling the accumulation of specific oncogenic mutations during typical in vitro expansion, manipulation or screening experiments using human stem cells. Taken together, we have here for the first time accurately quantified and characterized in vitro mutation accumulation in human iPS cells and ASCs in a direct comparison. These results provide insights for further optimization of culture conditions for safe in vivo utilization of these cell types for regenerative purposes

Distinct Genomic Profiles Are Associated with Treatment Response and Survival in Ovarian Cancer

SIMPLE SUMMARY: In most patients with ovarian cancer, their disease eventually becomes resistant to chemotherapy. The timing and type of treatment given are therefore highly important. Currently, treatment choice is mainly based on the subtype of cancer (from a histological point of view), prior response to chemotherapy, and the time it takes for the disease to recur. In this study, we combined complete genome data of the tumor with clinical data to better understand treatment responses. In total, 132 tumor samples were included, all from patients with disease that had spread beyond the primary location. By clustering the samples based on genetic characteristics, we have identified subgroups with distinct response rates and survival outcomes. We suggest that in the future, this data can be used to make more informed treatment choices for individuals with ovarian cancer. ABSTRACT: The majority of patients with ovarian cancer ultimately develop recurrent chemotherapy-resistant disease. Treatment stratification is mainly based on histological subtype and stage, prior response to platinum-based chemotherapy, and time to recurrent disease. Here, we integrated clinical treatment, treatment response, and survival data with whole-genome sequencing profiles of 132 solid tumor biopsies of metastatic epithelial ovarian cancer to explore genome-informed stratification opportunities. Samples from primary and recurrent disease harbored comparable numbers of single nucleotide variants and structural variants. Mutational signatures represented platinum exposure, homologous recombination deficiency, and aging. Unsupervised hierarchical clustering based on genomic input data identified specific ovarian cancer subgroups, characterized by homologous recombination deficiency, genome stability, and duplications. The clusters exhibited distinct response rates and survival probabilities which could thus potentially be used for genome-informed therapy stratification for more personalized ovarian cancer treatment

Homologous recombination deficiency scar: mutations and beyond—implications for precision oncology

Homologous recombination deficiency (HRD) is a prevalent in approximately 17% of tumors and is associated with enhanced sensitivity to anticancer therapies inducing double-strand DNA breaks. Accurate detection of HRD would therefore allow improved patient selection and outcome of conventional and targeted anticancer therapies. However, current clinical assessment of HRD mainly relies on determining germline BRCA1/2 mutational status and is insufficient for adequate patient stratification as mechanisms of HRD occurrence extend beyond functional BRCA1/2 loss. HRD, regardless of BRCA1/2 status, is associated with specific forms of genomic and mutational signatures termed HRD scar. Detection of this HRD scar might therefore be a more reliable biomarker for HRD. This review discusses and compares different methods of assessing HRD and HRD scar, their advances into the clinic, and their potential implications for precision oncology

Learning mutational signatures and their multidimensional genomic properties with TensorSignatures

Currently available tools for the analysis of mutational signatures do not make use of all possible genomic properties aside from mutation patterns. Here the authors present TensorSignatures, an efficient framework that jointly infers mutational signatures and their genomic determinants

Portrait of a cancer: mutational signature analyses for cancer diagnostic

Background: In the past decade, systematic and comprehensive analyses of cancer genomes have identified cancer driver genes and revealed unprecedented insight into the molecular mechanisms underlying the initiation and progression of cancer. These studies illustrate that although every cancer has a unique genetic make-up, there are only a limited number of mechanisms that shape the mutational landscapes of cancer genomes, as reflected by characteristic computationally-derived mutational signatures. Importantly, the molecular mechanisms underlying specific signatures can now be dissected and coupled to treatment strategies. Systematic characterization of mutational signatures in a cancer patient's genome may thus be a promising new tool for molecular tumor diagnosis and classification. Results: In this review, we describe the status of mutational signature analysis in cancer genomes and discuss the opportunities and relevance, as well as future challenges, for further implementation of mutational signatures in clinical tumor diagnostics and therapy guidance. Conclusions: Scientific studies have illustrated the potential of mutational signature analysis in cancer research. As such, we believe that the implementation of mutational signature analysis within the diagnostic workflow will improve cancer diagnosis in the future