Machine learning-based tissue of origin classification for cancer of unknown primary diagnostics using genome-wide mutation features

Cancers of unknown primary (CUP) origin account for ∼3% of all cancer diagnoses, whereby the tumor tissue of origin (TOO) cannot be determined. Using a uniformly processed dataset encompassing 6756 whole-genome sequenced primary and metastatic tumors, we develop Cancer of Unknown Primary Location Resolver (CUPLR), a random forest TOO classifier that employs 511 features based on simple and complex somatic driver and passenger mutations. CUPLR distinguishes 35 cancer (sub)types with ∼90% recall and ∼90% precision based on cross-validation and test set predictions. We find that structural variant derived features increase the performance and utility for classifying specific cancer types. With CUPLR, we could determine the TOO for 82/141 (58%) of CUP patients. Although CUPLR is based on machine learning, it provides a human interpretable graphical report with detailed feature explanations. The comprehensive output of CUPLR complements existing histopathological procedures and can enable improved diagnostics for CUP patients

Common anti-cancer therapies induce somatic mutations in stem cells of healthy tissue

Genome-wide mutation analyses have revealed that specific anti-cancer drugs are highly mutagenic to cancer cells, but the mutational impact of anti-cancer therapies on normal cells is not known. Here, we examine genome-wide somatic mutation patterns in 42 healthy adult stem cells (ASCs) of the colon or the liver from 14 cancer patients (mean of 3.2 ASC per donor) that received systemic chemotherapy and/or local radiotherapy. The platinum-based chemo-drug Oxaliplatin induces on average 535 ± 260 mutations in colon ASC, while 5-FU shows a complete mutagenic absence in most, but not all colon ASCs. In contrast with the colon, normal liver ASCs escape mutagenesis from systemic treatment with Oxaliplatin and 5-FU. Thus, while chemotherapies are highly effective at killing cancer cells, their systemic use also increases the mutational burden of long-lived normal stem cells responsible for tissue renewal thereby increasing the risk for developing second cancers

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

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

Metastatic cancer remains an almost inevitably lethal disease 1-3. 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 primary 4 and metastatic 5 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

The genome-wide mutational consequences of DNA hypomethylation

DNA methylation is important for establishing and maintaining cell identity and for genomic stability. This is achieved by regulating the accessibility of regulatory and transcriptional elements and the compaction of subtelomeric, centromeric, and other inactive genomic regions. Carcinogenesis is accompanied by a global loss in DNA methylation, which facilitates the transformation of cells. Cancer hypomethylation may also cause genomic instability, for example through interference with the protective function of telomeres and centromeres. However, understanding the role(s) of hypomethylation in tumor evolution is incomplete because the precise mutational consequences of global hypomethylation have thus far not been systematically assessed. Here we made genome-wide inventories of all possible genetic variation that accumulates in single cells upon the long-term global hypomethylation by CRISPR interference-mediated conditional knockdown of DNMT1. Depletion of DNMT1 resulted in a genomewide reduction in DNA methylation. The degree of DNA methylation loss was similar to that observed in many cancer types. Hypomethylated cells showed reduced proliferation rates, increased transcription of genes, reactivation of the inactive X-chromosome and abnormal nuclear morphologies. Prolonged hypomethylation was accompanied by increased chromosomal instability. However, there was no increase in mutational burden, enrichment for certain mutational signatures or accumulation of structural variation to the genome. In conclusion, the primary consequence of hypomethylation is genomic instability, which in cancer leads to increased tumor heterogeneity and thereby fuels cancer evolution

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

MutationalPatterns: the one stop shop for the analysis of mutational processes

BACKGROUND: The collective of somatic mutations in a genome represents a record of mutational processes that have been operative in a cell. These processes can be investigated by extracting relevant mutational patterns from sequencing data. RESULTS: Here, we present the next version of MutationalPatterns, an R/Bioconductor package, which allows in-depth mutational analysis of catalogues of single and double base substitutions as well as small insertions and deletions. Major features of the package include the possibility to perform regional mutation spectra analyses and the possibility to detect strand asymmetry phenomena, such as lesion segregation. On top of this, the package also contains functions to determine how likely it is that a signature can cause damaging mutations (i.e., mutations that affect protein function). This updated package supports stricter signature refitting on known signatures in order to prevent overfitting. Using simulated mutation matrices containing varied signature contributions, we showed that reliable refitting can be achieved even when only 50 mutations are present per signature. Additionally, we incorporated bootstrapped signature refitting to assess the robustness of the signature analyses. Finally, we applied the package on genome mutation data of cell lines in which we deleted specific DNA repair processes and on large cancer datasets, to show how the package can be used to generate novel biological insights. CONCLUSIONS: This novel version of MutationalPatterns allows for more comprehensive analyses and visualization of mutational patterns in order to study the underlying processes. Ultimately, in-depth mutational analyses may contribute to improved biological insights in mechanisms of mutation accumulation as well as aid cancer diagnostics. MutationalPatterns is freely available at http://bioconductor.org/packages/MutationalPatterns

EXO1 protects BRCA1-deficient cells against toxic DNA lesions

Inactivating mutations in the BRCA1 and BRCA2 genes impair DNA double-strand break (DSB) repair by homologous recombination (HR), leading to chromosomal instability and cancer. Importantly, BRCA1/2 deficiency also causes therapeutically targetable vulnerabilities. Here, we identify the dependency on the end resection factor EXO1 as a key vulnerability of BRCA1-deficient cells. EXO1 deficiency generates poly(ADP-ribose)-decorated DNA lesions during S phase that associate with unresolved DSBs and genomic instability in BRCA1-deficient but not in wild-type or BRCA2-deficient cells. Our data indicate that BRCA1/EXO1 double-deficient cells accumulate DSBs due to impaired repair by single-strand annealing (SSA) on top of their HR defect. In contrast, BRCA2-deficient cells retain SSA activity in the absence of EXO1 and hence tolerate EXO1 loss. Consistent with a dependency on EXO1-mediated SSA, we find that BRCA1-mutated tumors show elevated EXO1 expression and increased SSA-associated genomic scars compared with BRCA1-proficient tumors. Overall, our findings uncover EXO1 as a promising therapeutic target for BRCA1-deficient tumors