MutSignatures: an R package for extraction and analysis of cancer mutational signatures

Effect of C-to-T transition at CpG sites on tumor suppressor genes in tumor development in cattle evaluated by somatic mutation analysis in enzootic bovine leukosis

Oncogenic transformation of normal cells is caused by mutations and chromosomal abnormalities in cancer-related genes. Enzootic bovine leukosis (EBL) is a malignant B-cell lymphoma caused by bovine leukemia virus (BLV) infection in cattle. Although a small fraction of BLV-infected cattle develops EBL after a long latent period, the mechanisms for oncogenesis in EBL cattle remain largely unknown. In this study, we analyzed the types and patterns of somatic mutations in cancer cells from 36 EBL cases, targeting 21 cancer-related genes. Various somatic mutations were identified in eight genes, TP53, KMT2D, CREBBP, KRAS, PTEN, NOTCH1, MYD88, and CARD11. In addition, TP53 gene was found to be mutated in 69.4% of EBL cases, with most being biallelic mutations. In some cases, associations were observed between the ages at which cattle had developed EBL and somatic mutation patterns; young onset of EBL possibly occurs due to high impact mutations affecting protein translation and biallelic mutations. Furthermore, nucleotide substitution patterns indicated that cytosine at CpG sites tended to be converted to thymine in many EBL cases, which was considered to be the result of spontaneous deamination of 5-methylcytosine. These results demonstrate how somatic mutations have occurred in cancer cells leading to EBL development, thereby explaining its pathogenic mechanism. These findings will contribute to a better understanding and future elucidation of disease progression in BLV infection.IMPORTANCEEnzootic bovine leukosis (EBL) is a malignant and lethal disease in cattle. Currently, there are no effective vaccines or therapeutic methods against bovine leukemia virus (BLV) infection, resulting in severe economic losses in livestock industry. This study provides a renewed hypothesis to explain the general mechanisms of EBL onset by combining the previous finding that several integration sites of BLV provirus can affect the increase in survival and proliferation of infected cells. We demonstrate that two additional random events are necessary for oncogenic transformation in infected cell clones, elucidating the reason why only few infected cattle develop EBL. Further exploration of somatic mutation and BLV integration sites could support this hypothesis more firmly, potentially contributing to the development of novel control methods for EBL onset.

The cytidine deaminase APOBEC3C has unique sequence and genome feature preferences

APOBEC proteins are cytidine deaminases that restrict the replication of viruses and transposable elements. Several members of the APOBEC3 family, APOBEC3A, APOBEC3B, and APOBEC3H-I, can access the nucleus and cause what is thought to be indiscriminate deamination of the genome, resulting in mutagenesis and genome instability. Although APOBEC3C is also present in the nucleus, the full scope of its deamination target preferences is unknown. By expressing human APOBEC3C in a yeast model system, I have defined the APOBEC3C mutation signature, as well as the preferred genome features of APOBEC3C targets. The APOBEC3C mutation signature is distinct from those of the known cancer genome mutators APOBEC3A and APOBEC3B. APOBEC3C produces DNA strand-coordinated mutation clusters, and APOBEC3C mutations are enriched near the transcription start sites of active genes. Surprisingly, APOBEC3C lacks the bias for the lagging strand of DNA replication that is seen for APOBEC3A and APOBEC3B. The unique preferences of APOBEC3C constitute a mutation profile that will be useful in defining sites of APOBEC3C mutagenesis in human genomes.

Diffsig: Associating Risk Factors with Mutational Signatures

BACKGROUND

Somatic mutational signatures elucidate molecular vulnerabilities to therapy, and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors.

METHODS

Here, we develop a statistical model, Diffsig, for estimating the association of one or more continuous or categorical risk factors with DNA mutational signatures. Diffsig takes into account the uncertainty associated with assigning signatures to samples as well as multiple risk factors' simultaneous effect on observed DNA mutations.

RESULTS

We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. In simulation, our model was capable of accurately estimating expected associations in a variety of contexts.

CONCLUSIONS

Diffsig allows researchers to quantify and perform inference on the associations of risk factors with mutational signatures.

IMPACT

We expect Diffsig to provide more robust associations of risk factors with signatures to lead to better understanding of the tumor development process and improved models of tumorigenesis.

A Retrospective Review and Comprehensive Tumour Profiling of Advanced Non-Melanomatous Cutaneous Spindle Cell Neoplasms Treated with Immune-Checkpoint Inhibitors

Non-melanomatous cutaneous spindle cell neoplasms are a rare group of malignancies that present a diagnostic challenge, and for which there is a lack of consensus on how to best manage patients with advanced disease and only limited reports of immune-checkpoint inhibitor (ICI) responses. In this study, we performed a single-center retrospective review of treatment outcomes for all advanced non-melanomatous cutaneous spindle cell neoplasms treated with ICIs. Blinded histopathology reviews occurred to confirm each diagnosis. Comprehensive tumour profiling included whole exome sequencing for tumour mutational burden (TMB) and ultraviolet(UV) signatures, and immunohistochemistry for immune-cell infiltration (CD4/CD3/CD8/CD103/CD20) and immune-checkpoint expression (PD-L1/LAG3/TIGIT). Seven patients were identified. The objective response rate was 86% (6/7) with five complete responses (CR). Responses were durable with two patients in CR > 30 months after ICI commencement. All patients had high TMB and UV signatures. One patient had PD-L1 100% (combined positive score) with abundant immune-cell infiltration and LAG3 expression. In advanced non-melanomatous cutaneous spindle cell neoplasms, excellent responses to ICIs with durable disease control were observed. ICIs are worthy of further exploration in these patients. UV signatures and high TMB could be used to help select patients for treatment.

Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy

Characterization of the diverse malignant and stromal cell states that make up soft tissue sarcomas and their correlation with patient outcomes has proven difficult using fixed clinical specimens. Here, we employed EcoTyper, a machine-learning framework, to identify the fundamental cell states and cellular ecosystems that make up sarcomas on a large scale using bulk transcriptomes with clinical annotations. We identified and validated 23 sarcoma-specific, transcriptionally defined cell states, many of which were highly prognostic of patient outcomes across independent datasets. We discovered three conserved cellular communities or ecotypes associated with underlying genomic alterations and distinct clinical outcomes. We show that one ecotype defined by tumor-associated macrophages and epithelial-like malignant cells predicts response to immune-checkpoint inhibition but not chemotherapy and validate our findings in an independent cohort. Our results may enable identification of patients with soft tissue sarcomas who could benefit from immunotherapy and help develop new therapeutic strategies.

Genomic characterization of peritoneal lavage cytology-positive gastric cancer

Objective

Positive peritoneal lavege cytology (CY1) gastric cancer is featured by dismal prognosis, with high risks of peritoneal metastasis. However, there is a lack of evidence on pathogenic mechanism and signature of CY1 and there is a continuous debate on CY1 therapy. Therefore, exploring the mechanism of CY1 is crucial for treatment strategies and targets for CY1 gastric cancer.

Methods

In order to figure out specific driver genes and marker genes of CY1 gastric cancer, and ultimately offer clues for potential marker and risk assessment of CY1, 17 cytology-positive gastric cancer patients and 31 matched cytology-negative gastric cancer patients were enrolled in this study. The enrollment criteria were based on the results of diagnostic laparoscopy staging and cytology inspection of exfoliated cells. Whole exome sequencing was then performed on tumor samples to evaluate genomic characterization of cytology-positive gastric cancer.

Results

Least absolute shrinkage and selection operator (LASSO) algorithm identified 43 cytology-positive marker genes, while MutSigCV identified 42 cytology-positive specific driver genes. CD3G and CDKL2 were both driver and marker genes of CY1. Regarding mutational signatures, driver gene mutation and tumor subclone architecture, no significant differences were observed between CY1 and negative peritoneal lavege cytology (CY0).

Conclusions

There might not be distinct differences between CY1 and CY0, and CY1 might represent the progression of CY0 gastric cancer rather than constituting an independent subtype. This genomic analysis will thus provide key molecular insights into CY1, which may have a direct effect on treatment recommendations for CY1 and CY0 patients, and provides opportunities for genome-guided clinical trials and drug development.

Evaluating topography of mutational signatures with SigProfilerTopography

The mutations found in a cancer genome are shaped by diverse processes, each displaying a characteristic mutational signature that may be influenced by the genome's architecture. While prior analyses have evaluated the effect of topographical genomic features on mutational signatures, there has been no computational tool that can comprehensively examine this interplay. Here, we present SigProfilerTopography, a Python package that allows evaluating the effect of chromatin organization, histone modifications, transcription factor binding, DNA replication, and DNA transcription on the activities of different mutational processes. SigProfilerTopography elucidates the unique topographical characteristics of mutational signatures, unveiling their underlying biological and molecular mechanisms.

Comprehensive Genomic and Transcriptomic Analysis of Sclerosing Pneumocytoma

Sclerosing pneumocytoma is a rare and distinct lung neoplasm whose histogenesis and molecular alterations are the subject of ongoing research. Our recent study revealed that AKT1 internal tandem duplications (ITD), point mutations, and short indels were present in almost all tested sclerosing pneumocytomas, suggesting that AKT1 mutations are a major driving oncogenic event in this tumor. Although the pathogenic role of AKT1 point mutations is well established, the significance of AKT1 ITD in oncogenesis remains largely unexplored. We conducted comprehensive genomic and transcriptomic analyses of sclerosing pneumocytoma to address this knowledge gap. RNA-sequencing data from 23 tumors and whole-exome sequencing data from 44 tumors were used to obtain insights into their genetic and transcriptomic profiles. Our analysis revealed a high degree of genetic and transcriptomic similarity between tumors carrying AKT1 ITD and those with AKT1 point mutations. Mutational signature analysis revealed COSMIC signatures 1 and 5 as the prevailing signatures of sclerosing pneumocytoma, associated with the spontaneous deamination of 5-methylcytosine and an unknown etiology, respectively. RNA-sequencing data analysis revealed that the sclerosing pneumocytoma gene expression profile is characterized by activation of the PI3K/AKT/mTOR pathway, which exhibits significant similarity between tumors harboring AKT1 ITD and those with AKT1 point mutations. Notably, an upregulation of SOX9, a transcription factor known for its involvement in fetal lung development, was observed in sclerosing pneumocytoma. Specifically, SOX9 expression was prominent in the round cell component, whereas it was relatively lower in the surface cell component of the tumor. To the best of our knowledge, this is the first comprehensive investigation of the genomic and transcriptomic characteristics of sclerosing pneumocytoma. Results of the present study provide insights into the molecular attributes of sclerosing pneumocytoma and a basis for future studies of this enigmatic tumor.

Mutational topography reflects clinical neuroblastoma heterogeneity

Neuroblastoma is a pediatric solid tumor characterized by strong clinical heterogeneity. Although clinical risk-defining genomic alterations exist in neuroblastomas, the mutational processes involved in their generation remain largely unclear. By examining the topography and mutational signatures derived from all variant classes, we identified co-occurring mutational footprints, which we termed mutational scenarios. We demonstrate that clinical neuroblastoma heterogeneity is associated with differences in the mutational processes driving these scenarios, linking risk-defining pathognomonic variants to distinct molecular processes. Whereas high-risk MYCN-amplified neuroblastomas were characterized by signs of replication slippage and stress, homologous recombination-associated signatures defined high-risk non-MYCN-amplified patients. Non-high-risk neuroblastomas were marked by footprints of chromosome mis-segregation and TOP1 mutational activity. Furthermore, analysis of subclonal mutations uncovered differential activity of these processes through neuroblastoma evolution. Thus, clinical heterogeneity of neuroblastoma patients can be linked to differences in the mutational processes that are active in their tumors.

Growth Dynamics of Ductal Carcinoma in Situ Recapitulate Normal Breast Development

Ductal carcinoma in situ (DCIS) and invasive breast cancer share many morphologic, proteomic, and genomic alterations. Yet in contrast to invasive cancer, many DCIS tumors do not progress and may remain indolent over decades. To better understand the heterogenous nature of this disease, we reconstructed the growth dynamics of 18 DCIS tumors based on the geo-spatial distribution of their somatic mutations. The somatic mutation topographies revealed that DCIS is multiclonal and consists of spatially discontinuous subclonal lesions. Here we show that this pattern of spread is consistent with a new 'Comet' model of DCIS tumorigenesis, whereby multiple subclones arise early and nucleate the buds of the growing tumor. The discontinuous, multiclonal growth of the Comet model is analogous to the branching morphogenesis of normal breast development that governs the rapid expansion of the mammary epithelium during puberty. The branching morphogenesis-like dynamics of the proposed Comet model diverges from the canonical model of clonal evolution, and better explains observed genomic spatial data. Importantly, the Comet model allows for the clinically relevant scenario of extensive DCIS spread, without being subjected to the selective pressures of subclone competition that promote the emergence of increasingly invasive phenotypes. As such, the normal cell movement inferred during DCIS growth provides a new explanation for the limited risk of progression in DCIS and adds biologic rationale for ongoing clinical efforts to reduce DCIS overtreatment.

Mutational signature assignment heterogeneity is widespread and can be addressed by ensemble approaches

Single-base substitution (SBS) mutational signatures have become standard practice in cancer genomics. In lieu of de novo signature extraction, reference signature assignment allows users to estimate the activities of pre-established SBS signatures within individual malignancies. Several tools have been developed for this purpose, each with differing methodologies. However, due to a lack of standardization, there may be inter-tool variability in signature assignment. We deeply characterized three assignment strategies and five SBS signature assignment tools. We observed that assignment strategy choice can significantly influence results and interpretations. Despite varying recommendations by tools, Refit performed best by reducing overfitting and maximizing reconstruction of the original mutational spectra. Even after uniform application of Refit, tools varied remarkably in signature assignments both qualitatively (Jaccard index = 0.38-0.83) and quantitatively (Kendall tau-b = 0.18-0.76). This phenomenon was exacerbated for 'flat' signatures such as the homologous recombination deficiency signature SBS3. An ensemble approach (EnsembleFit), which leverages output from all five tools, increased SBS3 assignment accuracy in BRCA1/2-deficient breast carcinomas. After generating synthetic mutational profiles for thousands of pan-cancer tumors, EnsembleFit reduced signature activity assignment error 15.9-24.7% on average using Catalogue of Somatic Mutations In Cancer and non-standard reference signature sets. We have also released the EnsembleFit web portal (https://www.ensemblefit.pittlabgenomics.com) for users to generate or download ensemble-based SBS signature assignments using any strategy and combination of tools. Overall, we show that signature assignment heterogeneity across tools and strategies is non-negligible and propose a viable, ensemble solution.

Donor-Derived Follicular Lymphoma After Kidney Transplantation: A Case Report

Donor-derived follicular lymphoma after kidney transplant revealed by genomic profiling.

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.

Why loss of Y? A pan-cancer genome analysis of tumors with loss of Y chromosome

Loss of the Y chromosome (LoY) is frequently observed in somatic cells of elderly men. However, LoY is highly increased in tumor tissue and correlates with an overall worse prognosis. The underlying causes and downstream effects of LoY are widely unknown. Therefore, we analyzed genomic and transcriptomic data of 13 cancer types (2375 patients) and classified tumors of male patients according to loss or retain of the Y chromosome (LoY or RoY, average LoY fraction: 0.46). The frequencies of LoY ranged from almost absence (glioblastoma, glioma, thyroid carcinoma) to 77% (kidney renal papillary cell carcinoma). Genomic instability, aneuploidy, and mutation burden were enriched in LoY tumors. In addition, we found more frequently in LoY tumors the gate keeping tumor suppressor gene TP53 mutated in three cancer types (colon adenocarcinoma, head and neck squamous carcinoma, lung adenocarcinoma) and oncogenes MET, CDK6, KRAS, and EGFR amplified in multiple cancer types. On the transcriptomic level, we observed MMP13, known to be involved in invasion, to be up-regulated in LoY of three adenocarcinomas and down-regulation of the tumor suppressor gene GPC5 in LoY of three cancer types. Furthermore, we found enrichment of a smoking-related mutation signature in LoY tumors of head and neck and lung cancer. Strikingly, we observed a correlation between cancer type-specific sex bias in incidence rates and frequencies of LoY, in line with the hypothesis that LoY increases cancer risk in males. Overall, LoY is a frequent phenomenon in cancer that is enriched in genomically unstable tumors. It correlates with genomic features beyond the Y chromosome and might contribute to higher incidence rates in males.

Diffsig: Associating Risk Factors With Mutational Signatures

Somatic mutational signatures elucidate molecular vulnerabilities to therapy and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors. Here we present Diffsig, a model and R package for estimating the association of risk factors with mutational signatures, suggesting etiologies for the pre-defined mutational signatures. Diffsig is a Bayesian Dirichlet-multinomial hierarchical model that allows testing of any type of risk factor while taking into account the uncertainty associated with samples with a low number of observations. In simulation, we found that our method can accurately estimate risk factor-mutational signal associations. We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. Diffsig is implemented as an R package available at: https://github.com/jennprk/diffsig.

Deep genomic analysis of malignant peripheral nerve sheath tumor cell lines challenges current malignant peripheral nerve sheath tumor diagnosis

Malignant peripheral nerve sheath tumors (MPNSTs) are soft-tissue sarcomas of the peripheral nervous system that develop either sporadically or in the context of neurofibromatosis type 1 (NF1). MPNST diagnosis can be challenging and treatment outcomes are poor. We present here a resource consisting of the genomic characterization of 9 widely used human MPNST cell lines for their use in translational research. NF1-related cell lines recapitulated primary MPNST copy number profiles, exhibited NF1, CDKN2A, and SUZ12/EED tumor suppressor gene (TSG) inactivation, and presented no gain-of-function mutations. In contrast, sporadic cell lines collectively displayed different TSG inactivation patterns and presented kinase-activating mutations, fusion genes, altered mutational frequencies and COSMIC signatures, and different methylome-based classifications. Cell lines re-classified as melanomas and other sarcomas exhibited a different drug-treatment response. Deep genomic analysis, methylome-based classification, and cell-identity marker expression, challenged the identity of common MPNST cell lines, opening an opportunity to revise MPNST differential diagnosis.

Reconstructing mutational lineages in breast cancer by multi-patient-targeted single-cell DNA sequencing

Single-cell DNA sequencing (scDNA-seq) methods are powerful tools for profiling mutations in cancer cells; however, most genomic regions sequenced in single cells are non-informative. To overcome this issue, we developed a multi-patient-targeted (MPT) scDNA-seq method. MPT involves first performing bulk exome sequencing across a cohort of cancer patients to identify somatic mutations, which are then pooled together to develop a single custom targeted panel for high-throughput scDNA-seq using a microfluidics platform. We applied MPT to profile 330 mutations across 23,500 cells from 5 patients with triple negative-breast cancer (TNBC), which showed that 3 tumors were monoclonal and 2 tumors were polyclonal. From these data, we reconstructed mutational lineages and identified early mutational and copy-number events, including early TP53 mutations that occurred in all five patients. Collectively, our data suggest that MPT can overcome a major technical obstacle for studying tumor evolution using scDNA-seq by profiling information-rich mutation sites.

Analysis of Mutational Signatures Using the mutSignatures R Library

Accumulation of somatic mutations is a hallmark of cancer. Defects in DNA metabolism and DNA repair and exposure to mutagens may result in characteristic nonrandom profiles of DNA mutations, also known as mutational signatures. Resolving mutational signatures can help identifying genetic instability processes active in human cancer samples, and there is an expectation that this information might be exploited in the future for drug discovery and personalized treatment.Here we show how to analyze bladder cancer mutation data using mutSignatures, an open-source R-based computational framework aimed at investigating DNA mutational signatures. We illustrate the typical steps of a mutational signature analysis. We start by importing and pre-processing mutation data from a list of Variant Call Format (VCF) files. Next, we show how to perform de novo mutational signature extraction and how to determine activity of previously resolved mutational signatures, including Catalogue of Somatic Mutations In Cancer (COSMIC) signatures. Finally, we provide insights into parameter selection, algorithm tuning, and data visualization.Overall, the chapter guides the reader through all steps of a mutational signature analysis using R and mutSignatures, a software that may help gathering insights into genetic instability and cancer biology.

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.

Mathematical Modeling of Impacts of Patient Differences on COVID-19 Lung Fibrosis Outcomes

Patient-specific premorbidity, age, and sex are significant heterogeneous factors that influence the severe manifestation of lung diseases, including COVID-19 fibrosis. The renin-angiotensin system (RAS) plays a prominent role in regulating effects of these factors. Recent evidence suggests that patient-specific alteration of RAS homeostasis with premorbidity and the expression level of angiotensin converting enzyme 2 (ACE2), depending on age and sex, is correlated with lung fibrosis. However, conflicting evidence suggests decreases, increases, or no changes in RAS after SARS-CoV-2 infection. In addition, detailed mechanisms connecting the patient-specific conditions before infection to infection-induced fibrosis are still unknown. Here, a mathematical model is developed to quantify the systemic contribution of heterogeneous factors of RAS in the progression of lung fibrosis. Three submodels are connected-a RAS model, an agent-based COVID-19 in-host immune response model, and a fibrosis model-to investigate the effects of patient-group-specific factors in the systemic alteration of RAS and collagen deposition in the lung. The model results indicate cell death due to inflammatory response as a major contributor to the reduction of ACE and ACE2, whereas there are no significant changes in ACE2 dynamics due to viral-bound internalization of ACE2. Reduction of ACE reduces the homeostasis of RAS including angiotensin II (ANGII), while the decrease in ACE2 increases ANGII and results in severe lung injury and fibrosis. The model explains possible mechanisms for conflicting evidence of RAS alterations in previously published studies. Also, the results show that ACE2 variations with age and sex significantly alter RAS peptides and lead to fibrosis with around 20% additional collagen deposition from systemic RAS with slight variations depending on age and sex. This model may find further applications in patient-specific calibrations of tissue models for acute and chronic lung diseases to develop personalized treatments.

Integrated analysis of cervical squamous cell carcinoma cohorts from three continents reveals conserved subtypes of prognostic significance

Human papillomavirus (HPV)-associated cervical cancer is a leading cause of cancer deaths in women. Here we present an integrated multi-omic analysis of 643 cervical squamous cell carcinomas (CSCC, the most common histological variant of cervical cancer), representing patient populations from the USA, Europe and Sub-Saharan Africa and identify two CSCC subtypes (C1 and C2) with differing prognosis. C1 and C2 tumours can be driven by either of the two most common HPV types in cervical cancer (16 and 18) and while HPV16 and HPV18 are overrepresented among C1 and C2 tumours respectively, the prognostic difference between groups is not due to HPV type. C2 tumours, which comprise approximately 20% of CSCCs across these cohorts, display distinct genomic alterations, including loss or mutation of the STK11 tumour suppressor gene, increased expression of several immune checkpoint genes and differences in the tumour immune microenvironment that may explain the shorter survival associated with this group. In conclusion, we identify two therapy-relevant CSCC subtypes that share the same defining characteristics across three geographically diverse cohorts.

Human papillomavirus (HPV) is a known cause of cervical cancer. Here, the authors perform a multi-omic analysis using published cervical squamous cell carcinoma cohorts from the USA, Europe, and SubSaharan Africa and identify two cervical squamous cell carcinoma subtypes that display prognostic differences.

A phase II study of talazoparib monotherapy in patients with wild-type BRCA1 and BRCA2 with a mutation in other homologous recombination genes

Talazoparib, a PARP inhibitor, is active in germline BRCA1 and BRCA2 (gBRCA1/2)-mutant advanced breast cancer, but its activity beyond gBRCA1/2 is poorly understood. We conducted Talazoparib Beyond BRCA ( NCT02401347 ), an open-label phase II trial, to evaluate talazoparib in patients with pretreated advanced HER2-negative breast cancer (n = 13) or other solid tumors (n = 7) with mutations in homologous recombination (HR) pathway genes other than BRCA1 and BRCA2. In patients with breast cancer, four patients had a Response Evaluation Criteria in Solid Tumors (RECIST) partial response (overall response rate, 31%), and three additional patients had stable disease of ≥6 months (clinical benefit rate, 54%). All patients with germline mutations in PALB2 (gPALB2; encoding partner and localizer of BRCA2) had treatment-associated tumor regression. Tumor or plasma circulating tumor DNA (ctDNA) HR deficiency (HRD) scores were correlated with treatment outcomes and were increased in all gPALB2 tumors. In addition, a gPALB2-associated mutational signature was associated with tumor response. Thus, talazoparib has been demonstrated to have efficacy in patients with advanced breast cancer who have gPALB2 mutations, showing activity in the context of HR pathway gene mutations beyond gBRCA1/2.

Case Report: Malignant Brain Tumors in Siblings With MSH6 Mutations

Background

Familial brain tumor incidences are low. Identifying the genetic alterations of familial brain tumors can help better understand the pathogenesis and make therapy regimens for these tumors.

Case Presentation

An elder female and a younger male were diagnosed with brain tumors at the age of 10 and 5, respectively. Whole-genome sequencing analysis of the two patients’ blood, primary brain tumor tissues, and their parents’ blood samples was performed, which revealed that the two tumor samples harbored extremely high somatic mutation loads. Additionally, we observed pigmentation on the male patient’s skin.

Conclusion

Germline, biallelic mutation of MSH6—a gene related to DNA mismatch repair whose defect will result in constitutional mismatch repair deficiency (CMMRD)—is causal for the brain tumors of these two siblings.

A clinically annotated post-mortem approach to study multi-organ somatic mutational clonality in normal tissues

Recent research on normal human tissues identified omnipresent clones of cells, driven by somatic mutations known to be responsible for carcinogenesis (e.g., in TP53 or NOTCH1). These new insights are fundamentally changing current tumor evolution models, with broad oncological implications. Most studies are based on surgical remnant tissues, which are not available for many organs and rarely in a pan-organ setting (multiple organs from the same individual). Here, we describe an approach based on clinically annotated post-mortem tissues, derived from whole-body donors that are routinely used for educational purposes at human anatomy units. We validated this post-mortem approach using UV-exposed and unexposed epidermal skin tissues and confirm the presence of positively selected NOTCH1/2-, TP53- and FAT1-driven clones. No selection signals were detected in a set of immune genes or housekeeping genes. Additionally, we provide the first evidence for smoking-induced clonal changes in oral epithelia, likely underlying the origin of head and neck carcinogenesis. In conclusion, the whole-body donor-based approach provides a nearly unlimited healthy tissue resource to study mutational clonality and gain fundamental mutagenic insights in the presumed earliest stages of tumor evolution.

Exome sequencing of hepatocellular carcinoma in lemurs identifies potential cancer drivers: A pilot study

Background and objectives

Hepatocellular carcinoma occurs frequently in prosimians, but the cause of these liver cancers in this group is unknown. Characterizing the genetic changes associated with hepatocellular carcinoma in prosimians may point to possible causes, treatments and methods of prevention, aiding conservation efforts that are particularly crucial to the survival of endangered lemurs. Although genomic studies of cancer in non-human primates have been hampered by a lack of tools, recent studies have demonstrated the efficacy of using human exome capture reagents across primates.

Methodology

In this proof-of-principle study, we applied human exome capture reagents to tumor-normal pairs from five lemurs with hepatocellular carcinoma to characterize the mutational landscape of this disease in lemurs.

Results

Several genes implicated in human hepatocellular carcinoma, including ARID1A, TP53 and CTNNB1, were mutated in multiple lemurs, and analysis of cancer driver genes mutated in these samples identified enrichment of genes involved with TP53 degradation and regulation. In addition to these similarities with human hepatocellular carcinoma, we also noted unique features, including six genes that contain mutations in all five lemurs. Interestingly, these genes are infrequently mutated in human hepatocellular carcinoma, suggesting potential differences in the etiology and/or progression of this cancer in lemurs and humans.

Conclusions and implications

Collectively, this pilot study suggests that human exome capture reagents are a promising tool for genomic studies of cancer in lemurs and other non-human primates.

Lay Summary

Hepatocellular carcinoma occurs frequently in prosimians, but the cause of these liver cancers is unknown. In this proof-of-principle study, we applied human DNA sequencing tools to tumor-normal pairs from five lemurs with hepatocellular carcinoma and compared the lemur mutation profiles to those of human hepatocellular carcinomas.

High burden of clonal hematopoiesis in first responders exposed to the World Trade Center disaster

The terrorist attacks on the World Trade Center (WTC) created an unprecedented environmental exposure to aerosolized dust, gases and potential carcinogens. Clonal hematopoiesis (CH) is defined as the acquisition of somatic mutations in blood cells and is associated with smoking and exposure to genotoxic stimuli. Here we show that deep targeted sequencing of blood samples identified a significantly higher proportion of WTC-exposed first responders with CH (10%; 48 out of 481) when compared with non-WTC-exposed firefighters (6.7%; 17 out of 255; odds ratio, 3.14; 95% confidence interval, 1.64-6.03; P = 0.0006) after controlling for age, sex and race/ethnicity. The frequency of somatic mutations in WTC-exposed first responders showed an age-related increase and predominantly affected DNMT3A, TET2 and other CH-associated genes. Exposure of lymphoblastoid cells to WTC particulate matter led to dysregulation of DNA replication at common fragile sites in vitro. Moreover, mice treated with WTC particulate matter developed an increased burden of mutations in hematopoietic stem and progenitor cell compartments. In summary, the high burden of CH in WTC-exposed first responders provides a rationale for enhanced screening and preventative efforts in this population.

Accuracy of mutational signature software on correlated signatures

Mutational signatures are characteristic patterns of mutations generated by exogenous mutagens or by endogenous mutational processes. Mutational signatures are important for research into DNA damage and repair, aging, cancer biology, genetic toxicology, and epidemiology. Unsupervised learning can infer mutational signatures from the somatic mutations in large numbers of tumors, and separating correlated signatures is a notable challenge for this task. To investigate which methods can best meet this challenge, we assessed 18 computational methods for inferring mutational signatures on 20 synthetic data sets that incorporated varying degrees of correlated activity of two common mutational signatures. Performance varied widely, and four methods noticeably outperformed the others: hdp (based on hierarchical Dirichlet processes), SigProExtractor (based on multiple non-negative matrix factorizations over resampled data), TCSM (based on an approach used in document topic analysis), and mutSpec.NMF (also based on non-negative matrix factorization). The results underscored the complexities of mutational signature extraction, including the importance and difficulty of determining the correct number of signatures and the importance of hyperparameters. Our findings indicate directions for improvement of the software and show a need for care when interpreting results from any of these methods, including the need for assessing sensitivity of the results to input parameters.

Aberrant somatic hypermutation of CCND1 generates non-coding drivers of mantle cell lymphomagenesis

Aberrant somatic hypermutation (aSHM) can target proto-oncogenes and drive oncogenesis. In mantle cell lymphoma (MCL), CCND1 is targeted by aSHM in the non-nodal subtype (nnMCL), giving rise to exon1 encoded mutant proteins like E36K, Y44D, and C47S that contribute to lymphomagenesis by virtue of their increased protein stability and nuclear localization. However, the vast majority of somatic variants generated by aSHM are found in the first intron of CCND1 but their significance for mantle cell lymphomagenesis is unknown. We performed whole-genome and whole-transcriptome sequencing in 84 MCL patients to explore the contribution of non-coding somatic variants created by aSHM to lymphomagenesis. We show that non-coding variants are enriched in a MCL specific manner in transcription factor-binding sites, that non-coding variants are associated with increased CCND1 mRNA expression, and that coding variants in the first exon of CCND1 are more often synonymous or cause benign amino acid changes than in other types of lymphomas carrying a t(11;14) translocation. Therefore, the increased frequency of somatic variants due to aSHM might be a consequence of selection pressure manifested at the transcriptional level rather than being a mere mechanistic consequence of misguided activation-induced cytidine deaminase (AID) activity.

MSA: reproducible mutational signature attribution with confidence based on simulations

BACKGROUND

Mutational signatures proved to be a useful tool for identifying patterns of mutations in genomes, often providing valuable insights about mutagenic processes or normal DNA damage. De novo extraction of signatures is commonly performed using Non-Negative Matrix Factorisation methods, however, accurate attribution of these signatures to individual samples is a distinct problem requiring uncertainty estimation, particularly in noisy scenarios or when the acting signatures have similar shapes. Whilst many packages for signature attribution exist, a few provide accuracy measures, and most are not easily reproducible nor scalable in high-performance computing environments.

RESULTS

We present Mutational Signature Attribution (MSA), a reproducible pipeline designed to assign signatures of different mutation types on a single-sample basis, using Non-Negative Least Squares method with optimisation based on configurable simulations. Parametric bootstrap is proposed as a way to measure statistical uncertainties of signature attribution. Supported mutation types include single and doublet base substitutions, indels and structural variants. Results are validated using simulations with reference COSMIC signatures, as well as randomly generated signatures.

CONCLUSIONS

MSA is a tool for optimised mutational signature attribution based on simulations, providing confidence intervals using parametric bootstrap. It comprises a set of Python scripts unified in a single Nextflow pipeline with containerisation for cross-platform reproducibility and scalability in high-performance computing environments. The tool is publicly available from https://gitlab.com/s.senkin/MSA .

Mutational signatures: emerging concepts, caveats and clinical applications

Whole-genome sequencing has brought the cancer genomics community into new territory. Thanks to the sheer power provided by the thousands of mutations present in each patient's cancer, we have been able to discern generic patterns of mutations, termed 'mutational signatures', that arise during tumorigenesis. These mutational signatures provide new insights into the causes of individual cancers, revealing both endogenous and exogenous factors that have influenced cancer development. This Review brings readers up to date in a field that is expanding in computational, experimental and clinical directions. We focus on recent conceptual advances, underscoring some of the caveats associated with using the mutational signature frameworks and highlighting the latest experimental insights. We conclude by bringing attention to areas that are likely to see advancements in clinical applications.

Mutation analysis links angioimmunoblastic T-cell lymphoma to clonal hematopoiesis and smoking

Background

Although advance has been made in understanding the pathogenesis of mature T-cell neoplasms, the initiation and progression of angioimmunoblastic T-cell lymphoma (AITL) and peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS), remain poorly understood. A subset of AITL/PTCL-NOS patients develop concomitant hematologic neoplasms (CHN), and a biomarker to predict this risk is lacking.

Methods

We generated and analyzed the mutation profiles through 537-gene targeted sequencing of the primary tumors and matched bone marrow/peripheral blood samples in 25 patients with AITL and two with PTCL-NOS.

Results

Clonal hematopoiesis (CH)-associated genomic alterations, found in 70.4% of the AITL/PTCL-NOS patients, were shared among CH and T-cell lymphoma, as well as concomitant myeloid neoplasms or diffuse large B-cell lymphoma (DLBCL) that developed before or after AITL. Aberrant AID/APOBEC activity-associated and tobacco smoking-associated mutational signatures were respectively enriched in the early CH-associated mutations and late non-CH-associated mutations during AITL/PTCL-NOS development. Moreover, analysis showed that the presence of CH harboring ≥2 pathogenic TET2 variants with ≥15% of allele burden conferred higher risk for CHN (p=0.0006, hazard ratio = 14.01, positive predictive value = 88.9%, negative predictive value = 92.1%).

Conclusions

We provided genetic evidence that AITL/PTCL-NOS, CH, and CHN can frequently arise from common mutated hematopoietic precursor clones. Our data also suggests smoking exposure as a potential risk factor for AITL/PTCL-NOS progression. These findings provide insights into the cell origin and etiology of AITL and PTCL-NOS and provide a novel stratification biomarker for CHN risk in AITL patients.

Funding

R01 grant (CA194547) from the National Cancer Institute to WT.