Pan-cancer analysis of whole genomes

MCL1 binds and negatively regulates the transcriptional function of tumor suppressor p73

MCL1, an anti-apoptotic protein that controls chemosensitivity and cell fate through its regulation of intrinsic apoptosis, has been identified as a high-impact target in anti-cancer therapeutic development. With MCL1-specific inhibitors currently in clinical trials, it is imperative that we understand the roles that MCL1 plays in cells, especially when targeting the Bcl-2 homology 3 (BH3) pocket, the central region of MCL1 that mediates apoptotic regulation. Here, we establish that MCL1 has a direct role in controlling p73 transcriptional activity, which modulates target genes associated with DNA damage response, apoptosis, and cell cycle progression. This interaction is mediated through the reverse BH3 (rBH3) motif in the p73 tetramerization domain, which restricts p73 assembly on DNA. Here, we provide a novel mechanism for protein-level regulation of p73 transcriptional activity by MCL1, while also framing a foundation for studying MCL1 inhibitors in combination with platinum-based chemotherapeutics. More broadly, this work expands the role of Bcl-2 family signaling beyond cell fate regulation.

Naturally-Occurring Rare Mutations Cause Mild to Catastrophic Effects in the Multifunctional and Cancer-Associated NQO1 Protein

The functional and pathological implications of the enormous genetic diversity of the human genome are mostly unknown, primarily due to our unability to predict pathogenicity in a high-throughput manner. In this work, we characterized the phenotypic consequences of eight naturally-occurring missense variants on the multifunctional and disease-associated NQO1 protein using biophysical and structural analyses on several protein traits. Mutations found in both exome-sequencing initiatives and in cancer cell lines cause mild to catastrophic effects on NQO1 stability and function. Importantly, some mutations perturb functional features located structurally far from the mutated site. These effects are well rationalized by considering the nature of the mutation, its location in protein structure and the local stability of its environment. Using a set of 22 experimentally characterized mutations in NQO1, we generated experimental scores for pathogenicity that correlate reasonably well with bioinformatic scores derived from a set of commonly used algorithms, although the latter fail to semiquantitatively predict the phenotypic alterations caused by a significant fraction of mutations individually. These results provide insight into the propagation of mutational effects on multifunctional proteins, the implementation of in silico approaches for establishing genotype-phenotype correlations and the molecular determinants underlying loss-of-function in genetic diseases.

Defining the clinical genomic landscape for real-world precision oncology

Through the delivery of large international projects including ICGC and TCGA, knowledge of cancer genomics is reaching saturation point. Enabling this to improve patient outcomes now requires embedding comprehensive genomic profiling into routine oncology practice. Towards this goal, this study defined the biologically and clinically relevant genomic features of adult cancer through detailed curation and analysis of large genomic datasets, accumulated literature and biomarker-driven therapeutics in clinic and development. The characteristics and prevalence of these features were then interrogated in 2348 whole genome sequences, covering 21 solid tumour types, generated by the PCAWG project. This analysis highlights the predominant contribution of copy number alterations and identifies a critical role for disruptive structural variants in the inactivation of clinically important tumour suppressor genes, including PTEN and RB1, which are not currently captured by diagnostic assays. This study defines a set of essential genomic features for the characterisation of common adult cancers.

BRCA1-Associated Protein Is a Potential Prognostic Biomarker and Is Correlated With Immune Infiltration in Liver Hepatocellular Carcinoma: A Pan-Cancer Analysis

Background

BRCA1-associated protein (BRAP) is a critical gene that regulates inflammation-related signaling pathway and affects patients’ prognosis in esophageal squamous cell carcinoma (ESCC). However, its roles in different cancers remain largely unknown.

Methods

BRAP expression in human pan-cancer was analyzed via the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was used to evaluate the association between BRAP expression with mismatch repair (MMR) gene mutation and DNA methyltransferase. We evaluated the influence of BRAP on clinical prognosis by univariate survival analysis. Moreover, the correlation between BRAP and tumor immune infiltration was analyzed via the Tumor Immune Evaluation Resource (TIMER) database. Pearson correlation analysis was used to investigate the correlation between BRAP expression and immune checkpoint genes expression.

Results

BRAP is abnormally overexpressed and significantly correlated with MMR gene mutation level and DNA methyltransferase expression in human pan-cancer. Univariate survival analysis showed that BRAP was significant with patients’ overall survival (OS) in six cancer types, disease-free interval (DFI) in three cancer types, and progression-free interval (PFI) in two cancer types. Remarkably, increased BRAP expression was strongly correlated with patients’ poor prognosis in liver hepatocellular carcinoma (LIHC), whether OS (P < 0.0001, hazard ratio (HR) = 1.1), DFI (P = 0.00099, HR = 1.06), or PFI (P = 0.00025, HR = 1.07). Moreover, a positive relationship was found between BRAP expression and immune infiltrating cells including B cell, CD4 + T cell, CD8 + T cell, dendritic cell, macrophage cell, and neutrophil cell in colon adenocarcinoma (COAD), kidney renal clear cell carcinoma (KIRC), and LIHC. Additionally, BRAP expression showed strong correlations with immune checkpoint genes in LIHC.

Conclusion

BRAP expression is increased in human pan-cancer samples compared with normal tissues. Overexpression of BRAP is correlated with poor prognosis and immune infiltration in multiple cancers, especially in LIHC. These findings suggest that BRAP may be used as a potential molecular biomarker for determining prognosis and immune infiltration in LIHC.

A system for classifying cancers diagnosed in adolescents and young adults

Cancer types in adolescents and young adults form a unique distribution. A system for classifying them is presented.

Cancer therapy shapes the fitness landscape of clonal hematopoiesis

Acquired mutations are pervasive across normal tissues. However, understanding of the processes that drive transformation of certain clones to cancer is limited. Here we study this phenomenon in the context of clonal hematopoiesis (CH) and the development of therapy-related myeloid neoplasms (tMNs). We find that mutations are selected differentially based on exposures. Mutations in ASXL1 are enriched in current or former smokers, whereas cancer therapy with radiation, platinum and topoisomerase II inhibitors preferentially selects for mutations in DNA damage response genes (TP53, PPM1D, CHEK2). Sequential sampling provides definitive evidence that DNA damage response clones outcompete other clones when exposed to certain therapies. Among cases in which CH was previously detected, the CH mutation was present at tMN diagnosis. We identify the molecular characteristics of CH that increase risk of tMN. The increasing implementation of clinical sequencing at diagnosis provides an opportunity to identify patients at risk of tMN for prevention strategies.

Genomic profiling of newly diagnosed glioblastoma patients and its potential for clinical utility - a prospective, translational study

Glioblastoma (GBM) is an incurable brain tumor for which new treatment strategies are urgently needed. Next-generation sequencing of GBM has most often been performed retrospectively and on archival tissue from both diagnostic and relapse surgeries with limited knowledge of clinical information, including treatment given. We sought to investigate the genomic composition prospectively in treatment-naïve patients, searched for possible targetable aberrations, and investigated for prognostic and/or predictive factors. A total of 108 newly diagnosed GBM patients were included. Clinical information, progression-free survival, and overall survival (OS) were noted. Tissues were analyzed by whole-exome sequencing, single nucleotide polymorphism (SNP) and transcriptome arrays, and RNA sequencing; assessed for mutations, fusions, tumor mutational burden (TMB), and chromosomal instability (CI); and classified into GBM subgroups. Each genomic report was discussed at a multidisciplinary tumor board meeting to evaluate for matching trials. From 111 consecutive patients, 97.3% accepted inclusion in this study. Eighty-six (77%) were treated with radiation therapy/temozolomide (TMZ) and adjuvant TMZ. One NTRK2 and three FGFR3-TACC3 fusions were identified. Copy number alterations in GRB2 and SMYD4 were significantly correlated with worse median OS together with known clinical variables like age, performance status, steroid dose, and O6-methyl-guanine-DNA-methyl-transferase status. Patients with CI-median or TMB-high had significantly worse median OS compared to CI-low/high or TMB-low/median. In conclusion, performing genomic profiling at diagnosis enables evaluation of genomic-driven therapy at the first progression. Furthermore, TMB-high or CI-median patients had worse median OS, which can support the possibility of offering experimental treatment already at the first line for this group.

DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features

Data-driven discovery of cancer driver genes, including tumor suppressor genes (TSGs) and oncogenes (OGs), is imperative for cancer prevention, diagnosis, and treatment. Although epigenetic alterations are important for tumor initiation and progression, most known driver genes were identified based on genetic alterations alone. Here, we developed an algorithm, DORGE (Discovery of Oncogenes and tumor suppressoR genes using Genetic and Epigenetic features), to identify TSGs and OGs by integrating comprehensive genetic and epigenetic data. DORGE identified histone modifications as strong predictors for TSGs, and it found missense mutations, super enhancers, and methylation differences as strong predictors for OGs. We extensively validated DORGE-predicted cancer driver genes using independent functional genomics data. We also found that DORGE-predicted dual-functional genes (both TSGs and OGs) are enriched at hubs in protein-protein interaction and drug-gene networks. Overall, our study has deepened the understanding of epigenetic mechanisms in tumorigenesis and revealed previously undetected cancer driver genes.

Revealing the impact of structural variants in multiple myeloma

The landscape of structural variants (SVs) in multiple myeloma remains poorly understood. Here, we performed comprehensive analysis of SVs in a large cohort of 752 multiple myeloma patients by low coverage long-insert whole genome sequencing. We identified 68 SV hotspots involving 17 new candidate driver genes, including the therapeutic targets BCMA (TNFRSF17), SLAMF and MCL1. Catastrophic complex rearrangements termed chromothripsis were present in 24% of patients and independently associated with poor clinical outcomes. Templated insertions were the second most frequent complex event (19%), mostly involved in super-enhancer hijacking and activation of oncogenes such as CCND1 and MYC. Importantly, in 31% of patients two or more seemingly independent putative driver events were caused by a single structural event, demonstrating that the complex genomic landscape of multiple myeloma can be acquired through few key events during tumor evolutionary history. Overall, this study reveals the critical role of SVs in multiple myeloma pathogenesis.

Whole genome, transcriptome and methylome profiling enhances actionable target discovery in high-risk pediatric cancer

The Zero Childhood Cancer Program is a precision medicine program to benefit children with poor-outcome, rare, relapsed or refractory cancer. Using tumor and germline whole genome sequencing (WGS) and RNA sequencing (RNAseq) across 252 tumors from high-risk pediatric patients with cancer, we identified 968 reportable molecular aberrations (39.9% in WGS and RNAseq, 35.1% in WGS only and 25.0% in RNAseq only). Of these patients, 93.7% had at least one germline or somatic aberration, 71.4% had therapeutic targets and 5.2% had a change in diagnosis. WGS identified pathogenic cancer-predisposing variants in 16.2% of patients. In 76 central nervous system tumors, methylome analysis confirmed diagnosis in 71.1% of patients and contributed to a change of diagnosis in two patients (2.6%). To date, 43 patients have received a recommended therapy, 38 of whom could be evaluated, with 31% showing objective evidence of clinical benefit. Comprehensive molecular profiling resolved the molecular basis of virtually all high-risk cancers, leading to clinical benefit in some patients.

Single-cell analysis of copy-number alterations in serous ovarian cancer reveals substantial heterogeneity in both low- and high-grade tumors

Unusually high aneuploidy is a hallmark of epithelial serous ovarian cancer (SOC). Previous analyses have focused on aneuploidy on average across all tumor cells. With the expansion of single-cell sequencing technologies, however, an analysis of copy number heterogeneity cell-to-cell is now technically feasible. Here, we describe an analysis of single-cell RNA sequencing (scRNA-seq) data to infer arm-level aneuploidy in individual serous ovarian cancer cells. By first clustering high-quality sequenced epithelial versus non-epithelial cells, high-confidence tumor cell populations were identified. InferCNV was used to predict segmented copy-number alterations (CNAs), which were then used to determine arm-level aneuploidy at the single-cell level. Control comparisons of normal cells to normal cells showed zero arm-level aneuploidy, whereas a median of four aneuploid events were detectable in cancer cells. A heterogeneity analysis of high-grade tumor cells compared to low-grade tumor cells showed similar levels of cell-to-cell variation between cancer grades. Metastatic tumors potentially showed selection pressure with reduced cell-to-cell variation compared to cells from primary tumors. Minor cell populations with CNAs similar to metastatic cells were identified within the matched primary tumors. Taken together, these results provide a minimum estimate for single-cell aneuploidy in serous ovarian cancer and demonstrate the utility of single-cell sequencing for CNA analysis.

Regulation of Canonical Oncogenic Signaling Pathways in Cancer via DNA Methylation

Disruption of signaling pathways that plays a role in the normal development and cellular homeostasis may lead to the dysregulation of cellular signaling and bring about the onset of different diseases, including cancer. In addition to genetic aberrations, DNA methylation also acts as an epigenetic modifier to drive the onset and progression of cancer by mediating the reversible transcription of related genes. Although the role of DNA methylation as an alternative driver of carcinogenesis has been well-established, the global effects of DNA methylation on oncogenic signaling pathways and the presentation of cancer is only emerging. In this article, we introduced a differential methylation parsing pipeline (MethylMine) which mined for epigenetic biomarkers based on feature selection. This pipeline was used to mine for biomarkers, which presented a substantial difference in methylation between the tumor and the matching normal tissue samples. Combined with the Data Integration Analysis for Biomarker discovery (DIABLO) framework for machine learning and multi-omic analysis, we revisited the TCGA DNA methylation and RNA-Seq datasets for breast, colorectal, lung, and prostate cancer, and identified differentially methylated genes within the NRF2-KEAP1/PI3K oncogenic pathway, which regulates the expression of cytoprotective genes, that serve as potential therapeutic targets to treat different cancers.

Misannotated Multi-Nucleotide Variants in Public Cancer Genomics Datasets Lead to Inaccurate Mutation Calls with Significant Implications

Although next-generation sequencing is widely used in cancer to profile tumors and detect variants, most somatic variant callers used in these pipelines identify variants at the lowest possible granularity, single-nucleotide variants (SNV). As a result, multiple adjacent SNVs are called individually instead of as a multi-nucleotide variants (MNV). With this approach, the amino acid change from the individual SNV within a codon could be different from the amino acid change based on the MNV that results from combining SNV, leading to incorrect conclusions about the downstream effects of the variants. Here, we analyzed 10,383 variant call files (VCF) from the Cancer Genome Atlas (TCGA) and found 12,141 incorrectly annotated MNVs. Analysis of seven commonly mutated genes from 178 studies in cBioPortal revealed that MNVs were consistently missed in 20 of these studies, whereas they were correctly annotated in 15 more recent studies. At the BRAF V600 locus, the most common example of MNV, several public datasets reported separate BRAF V600E and BRAF V600M variants instead of a single merged V600K variant. VCFs from the TCGA Mutect2 caller were used to develop a solution to merge SNV to MNV. Our custom script used the phasing information from the SNV VCF and determined whether SNVs were at the same codon and needed to be merged into MNV before variant annotation. This study shows that institutions performing NGS sequencing for cancer genomics should incorporate the step of merging MNV as a best practice in their pipelines. SIGNIFICANCE: Identification of incorrect mutation calls in TCGA, including clinically relevant BRAF V600 and KRAS G12, will influence research and potentially clinical decisions.

Globally altered epigenetic landscape and delayed osteogenic differentiation in H3.3-G34W-mutant giant cell tumor of bone

The neoplastic stromal cells of giant cell tumor of bone (GCTB) carry a mutation in H3F3A, leading to a mutant histone variant, H3.3-G34W, as a sole recurrent genetic alteration. We show that in patient-derived stromal cells H3.3-G34W is incorporated into the chromatin and associates with massive epigenetic alterations on the DNA methylation, chromatin accessibility and histone modification level, that can be partially recapitulated in an orthogonal cell line system by the introduction of H3.3-G34W. These epigenetic alterations affect mainly heterochromatic and bivalent regions and provide possible explanations for the genomic instability, as well as the osteolytic phenotype of GCTB. The mutation occurs in differentiating mesenchymal stem cells and associates with an impaired osteogenic differentiation. We propose that the observed epigenetic alterations reflect distinct differentiation stages of H3.3 WT and H3.3 MUT stromal cells and add to H3.3-G34W-associated changes.

The histone variant mutation H3.3-G34W occurs in the majority of giant cell tumor of bone (GCTB). By profiling patient-derived GCTB tumor cells, the authors show that this mutation associates with epigenetic alterations in heterochromatic and bivalent regions that contribute to an impaired osteogenic differentiation and the osteolytic phenotype of GCTB.

Alerting the immune system to DNA damage: micronuclei as mediators

Healthy cells experience thousands of DNA lesions per day during normal cellular metabolism, and ionizing radiation and chemotherapeutic drugs rely on DNA damage to kill cancer cells. In response to such lesions, the DNA damage response (DDR) activates cell-cycle checkpoints, initiates DNA repair mechanisms, or promotes the clearance of irreparable cells. Work over the past decade has revealed broader influences of the DDR, involving inflammatory gene expression following unresolved DNA damage, and immune surveillance of damaged or mutated cells. Subcellular structures called micronuclei, containing broken fragments of DNA or whole chromosomes that have been isolated away from the rest of the genome, are now recognized as one mediator of DDR-associated immune recognition. Micronuclei can initiate pro-inflammatory signaling cascades, or massively degrade to invoke distinct forms of genomic instability. In this mini-review, we aim to provide an overview of the current evidence linking the DDR to activation of the immune response through micronuclei formation, identifying key areas of interest, open questions, and emerging implications.

Integration of Online Omics-Data Resources for Cancer Research

The manifestations of cancerous phenotypes necessitate alterations at different levels of information-flow from genome to proteome. The molecular alterations at different information processing levels serve as the basis for the cancer phenotype to emerge. To understand the underlying mechanisms that drive the acquisition of cancer hallmarks it is required to interrogate cancer cells using multiple levels of information flow represented by different omics - such as genomics, epigenomics, transcriptomics, and proteomics. The advantage of multi-omics data integration comes with a trade-off in the form of an added layer of complexity originating from inherently diverse types of omics-datasets that may pose a challenge to integrate the omics-data in a biologically meaningful manner. The plethora of cancer-specific online omics-data resources, if able to be integrated efficiently and systematically, may facilitate the generation of new biological insights for cancer research. In this review, we provide a comprehensive overview of the online single- and multi-omics resources that are dedicated to cancer. We catalog various online omics-data resources such as The Cancer Genome Atlas (TCGA) along with various TCGA-associated data portals and tools for multi-omics analysis and visualization, the International Cancer Genome Consortium (ICGC), Catalogue of Somatic Mutations in Cancer (COSMIC), The Pathology Atlas, Gene Expression Omnibus (GEO), and PRoteomics IDEntifications (PRIDE). By comparing the strengths and limitations of the respective online resources, we aim to highlight the current biological and technological challenges and possible strategies to overcome these challenges. We outline the available schemes for the integration of the multi-omics dimensions for stratifying cancer patients and biomarker prediction based on the integrated molecular-signatures of cancer. Finally, we propose the multi-omics driven systems-biology approaches to realize the potential of precision onco-medicine as the future of cancer research. We believe this systematic review will encourage scientists and clinicians worldwide to utilize the online resources to explore and integrate the available omics datasets that may provide a window of opportunity to generate new biological insights and contribute to the advancement of the field of cancer research.

Integrating pathology, chromosomal instability and mutations for risk stratification in early-stage endometrioid endometrial carcinoma

Background

Risk stratifications for endometrial carcinoma (EC) depend on histopathology and molecular pathology. Histopathological risk stratification lacks reproducibility, neglects heterogeneity and contributes little to surgical procedures. Existing molecular stratification is useless in patients with specific pathological or molecular characteristics and cannot guide postoperative adjuvant radiotherapies. Chromosomal instability (CIN), the numerical and structural alterations of chromosomes resulting from ongoing errors of chromosome segregation, is an intrinsic biological mechanism for the evolution of different prognostic factors of histopathology and molecular pathology and may be applicable to the risk stratification of EC.

Results

By analyzing CIN25 and CIN70, two reliable gene expression signatures for CIN, we found that EC with unfavorable prognostic factors of histopathology or molecular pathology had serious CIN. However, the POLE mutant, as a favorable prognostic factor, had elevated CIN signatures, and the CTNNB1 mutant, as an unfavorable prognostic factor, had decreased CIN signatures. Only if these two mutations were excluded were CIN signatures strongly prognostic for outcomes in different adjuvant radiotherapy subgroups. Integrating pathology, CIN signatures and POLE/CTNNB1 mutation stratified stageIendometrioid EC into four groups with improved risk prognostication and treatment recommendations.

Conclusions

We revealed the possibility of integrating histopathology and molecular pathology by CIN for risk stratification in early-stage EC. Our integrated risk model deserves further improvement and validation.

Global view of human protein glycosylation pathways and functions

Glycosylation is the most abundant and diverse form of post-translational modification of proteins that is common to all eukaryotic cells. Enzymatic glycosylation of proteins involves a complex metabolic network and different types of glycosylation pathways that orchestrate enormous amplification of the proteome in producing diversity of proteoforms and its biological functions. The tremendous structural diversity of glycans attached to proteins poses analytical challenges that limit exploration of specific functions of glycosylation. Major advances in quantitative transcriptomics, proteomics and nuclease-based gene editing are now opening new global ways to explore protein glycosylation through analysing and targeting enzymes involved in glycosylation processes. In silico models predicting cellular glycosylation capacities and glycosylation outcomes are emerging, and refined maps of the glycosylation pathways facilitate genetic approaches to address functions of the vast glycoproteome. These approaches apply commonly available cell biology tools, and we predict that use of (single-cell) transcriptomics, genetic screens, genetic engineering of cellular glycosylation capacities and custom design of glycoprotein therapeutics are advancements that will ignite wider integration of glycosylation in general cell biology.

Molecular tumor classification using DNA methylome analysis

Tumor classifiers based on molecular patterns promise to define and reliably classify tumor entities. The high tissue- and cell type-specificity of DNA methylation, as well as its high stability, makes DNA methylation an ideal choice for the development of tumor classifiers. Herein, we review existing tumor classifiers using DNA methylome analysis and will provide an overview on their emerging impact on cancer classification, the detection of novel cancer subentities and patient stratification with a focus on brain tumors, sarcomas and hematopoietic malignancies. Furthermore, we provide an outlook on the enormous potential of DNA methylome analysis to complement classical histopathological and genetic diagnostics, including the emerging field of epigenomic analysis in liquid biopsies.

Untangling a complex web: Computational analyses of tumor molecular profiles to decode driver mechanisms

Genome-scale studies focusing on molecular profiling of cancers across tissue types have revealed a plethora of aberrations across the genomic, transcriptomic, and epigenomic scales. The significant molecular heterogeneity across individual tumors even within the same tissue context complicates decoding the key etiologic mechanisms of this disease. Furthermore, it is increasingly likely that biologic mechanisms underlying the pathobiology of cancer involve multiple molecular entities interacting across functional scales. This has motivated the development of computational approaches that integrate molecular measurements with prior biological knowledge in increasingly intricate ways to enable the discovery of driver genomic aberrations across cancers. Here, we review diverse methodological approaches that have powered significant advances in our understanding of the genomic underpinnings of cancer at the cohort and at the individual tumor scales. We outline the key advances and challenges in the computational discovery of cancer mechanisms while motivating the development of systems biology approaches to comprehensively decode the biologic drivers of this complex disease.

Prediction of genome-wide effects of single nucleotide variants on transcription factor binding

Single nucleotide variants (SNVs) located in transcriptional regulatory regions can result in gene expression changes that lead to adaptive or detrimental phenotypic outcomes. Here, we predict gain or loss of binding sites for 741 transcription factors (TFs) across the human genome. We calculated ‘gainability’ and ‘disruptability’ scores for each TF that represent the likelihood of binding sites being created or disrupted, respectively. We found that functional cis-eQTL SNVs are more likely to alter TF binding sites than rare SNVs in the human population. In addition, we show that cancer somatic mutations have different effects on TF binding sites from different TF families on a cancer-type basis. Finally, we discuss the relationship between these results and cancer mutational signatures. Altogether, we provide a blueprint to study the impact of SNVs derived from genetic variation or disease association on TF binding to gene regulatory regions.

Systems Biology and Experimental Model Systems of Cancer

Over the past decade, we have witnessed an increasing number of large-scale studies that have provided multi-omics data by high-throughput sequencing approaches. This has particularly helped with identifying key (epi)genetic alterations in cancers. Importantly, aberrations that lead to the activation of signaling networks through the disruption of normal cellular homeostasis is seen both in cancer cells and also in the neighboring tumor microenvironment. Cancer systems biology approaches have enabled the efficient integration of experimental data with computational algorithms and the implementation of actionable targeted therapies, as the exceptions, for the treatment of cancer. Comprehensive multi-omics data obtained through the sequencing of tumor samples and experimental model systems will be important in implementing novel cancer systems biology approaches and increasing their efficacy for tailoring novel personalized treatment modalities in cancer. In this review, we discuss emerging cancer systems biology approaches based on multi-omics data derived from bulk and single-cell genomics studies in addition to existing experimental model systems that play a critical role in understanding (epi)genetic heterogeneity and therapy resistance in cancer.

A high-stringency blueprint of the human proteome

The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP's tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases.

Whole-genome sequencing of acral melanoma reveals genomic complexity and diversity

To increase understanding of the genomic landscape of acral melanoma, a rare form of melanoma occurring on palms, soles or nail beds, whole genome sequencing of 87 tumors with matching transcriptome sequencing for 63 tumors was performed. Here we report that mutational signature analysis reveals a subset of tumors, mostly subungual, with an ultraviolet radiation signature. Significantly mutated genes are BRAF, NRAS, NF1, NOTCH2, PTEN and TYRP1. Mutations and amplification of KIT are also common. Structural rearrangement and copy number signatures show that whole genome duplication, aneuploidy and complex rearrangements are common. Complex rearrangements occur recurrently and are associated with amplification of TERT, CDK4, MDM2, CCND1, PAK1 and GAB2, indicating potential therapeutic options.

Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design

Machine learning that generates biological hypotheses has transformative potential, but most learning algorithms are susceptible to pathological failure when exploring regimes beyond the training data distribution. A solution to address this issue is to quantify prediction uncertainty so that algorithms can gracefully handle novel phenomena that confound standard methods. Here, we demonstrate the broad utility of robust uncertainty prediction in biological discovery. By leveraging Gaussian process-based uncertainty prediction on modern pre-trained features, we train a model on just 72 compounds to make predictions over a 10,833-compound library, identifying and experimentally validating compounds with nanomolar affinity for diverse kinases and whole-cell growth inhibition of Mycobacterium tuberculosis. Uncertainty facilitates a tight iterative loop between computation and experimentation and generalizes across biological domains as diverse as protein engineering and single-cell transcriptomics. More broadly, our work demonstrates that uncertainty should play a key role in the increasing adoption of machine learning algorithms into the experimental lifecycle.

[Genetic instability, a factor limiting the efficiency of targeted therapies in solid oncology]

Genomic instability is one of the main properties of tumour development, promoting first the acquisition of genetic alterations and thus carcinogenesis. Then, the chronic and anarchic proliferation of cancer cells also supports and contributes to this instability allowing a continuous evolution of the tumour. The accumulation of mutations resulting from that instability contributes to tumour heterogeneity that occurs in a specific environment. The resulting diversity of oncogenic drivers further complicates the characterization of the origin of cancer cells dysfunction and consequently therapeutic decision. However, the consideration of the molecular context in oncology has initiated the development of targeted therapies. Based on the concept of oncogene addiction and synthetic lethality, these new drugs require the characterization and identification of specific tumour biomarkers. Targeted therapies have thus considerably optimized patient management, improving efficiency and quality of life while limiting the side effects observed with conventional chemotherapies. However, despite significant clinical benefits, some major limitations to their administration remain. The study of the current issues related to these new therapeutic molecules is becoming crucial for patient management towards an improvement of personalized medicine.

Pan-neuroblastoma analysis reveals age- and signature-associated driver alterations

Neuroblastoma is a pediatric malignancy with heterogeneous clinical outcomes. To better understand neuroblastoma pathogenesis, here we analyze whole-genome, whole-exome and/or transcriptome data from 702 neuroblastoma samples. Forty percent of samples harbor at least one recurrent driver gene alteration and most aberrations, including MYCN, ATRX, and TERT alterations, differ in frequency by age. MYCN alterations occur at median 2.3 years of age, TERT at 3.8 years, and ATRX at 5.6 years. COSMIC mutational signature 18, previously associated with reactive oxygen species, is the most common cause of driver point mutations in neuroblastoma, including most ALK and Ras-activating variants. Signature 18 appears early and is continuous throughout disease evolution. Signature 18 is enriched in neuroblastomas with MYCN amplification, 17q gain, and increased expression of mitochondrial ribosome and electron transport-associated genes. Recurrent FGFR1 variants in six patients, and ALK N-terminal structural alterations in five samples, identify additional patients potentially amenable to precision therapy.

AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes

The current challenge in cancer research is to increase the resolution of driver prediction from gene-level to mutation-level, which is more closely aligned with the goal of precision cancer medicine. Improved methods to distinguish drivers from passengers are urgently needed to dig out driver mutations from increasing exome sequencing studies. Here, we developed an ensemble method, AI-Driver (AI-based driver classifier, https://github.com/hatchetProject/AI-Driver), to predict the driver status of somatic missense mutations based on 23 pathogenicity features. AI-Driver has the best overall performance compared with any individual tool and two cancer-specific driver predicting methods. We demonstrate the superior and stable performance of our model using four independent benchmarks. We provide pre-computed AI-Driver scores for all possible human missense variants (http://aidriver.maolab.org/) to identify driver mutations in the sea of somatic mutations discovered by personal cancer sequencing. We believe that AI-Driver together with pre-computed database will play vital important roles in the human cancer studies, such as identification of driver mutation in personal cancer genomes, discovery of targeting sites for cancer therapeutic treatments and prediction of tumor biomarkers for early diagnosis by liquid biopsy.

Circulating Tumor DNA Testing Opens New Perspectives in Melanoma Management

Malignant melanoma accounts for about 1% of all skin cancers, but it causes most of the skin cancer-related deaths. Circulating tumor DNA (ctDNA) testing is emerging as a relevant tool for the diagnosis and monitoring of cancer. The availability of highly sensitive techniques, including next generation sequencing (NGS)-based panels, has increased the fields of application of ctDNA testing. While ctDNA-based tests for the early detection of melanoma are not available yet, perioperative ctDNA analysis in patients with surgically resectable melanoma offers relevant prognostic information: i) the detection of ctDNA before surgery correlates with the extent and the aggressiveness of the disease; ii) ctDNA testing after surgery/adjuvant therapy identifies minimal residual disease; iii) testing ctDNA during the follow-up can detect a tumor recurrence, anticipating clinical/radiological progression. In patients with advanced melanoma, several studies have demonstrated that the analysis of ctDNA can better depict tumor heterogeneity and provides relevant prognostic information. In addition, ctDNA testing during treatment allows assessing the response to systemic therapy and identifying resistance mechanisms. Although validation in prospective clinical trials is needed for most of these approaches, ctDNA testing opens up new scenarios in the management of melanoma patients that could lead to improvements in the diagnosis and therapy of this disease.

Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology

Data-independent acquisition mass spectrometry (DIA-MS) is a next generation proteomic methodology that generates permanent digital proteome maps offering highly reproducible retrospective analysis of cellular and tissue specimens. The adoption of this technology has ushered a new wave of oncology studies across a wide range of applications including its use in molecular classification, oncogenic pathway analysis, drug and biomarker discovery and unravelling mechanisms of therapy response and resistance. In this review, we provide an overview of the experimental workflows commonly used in DIA-MS, including its current strengths and limitations versus conventional data-dependent acquisition mass spectrometry (DDA-MS). We further summarise a number of key studies to illustrate the power of this technology when applied to different facets of oncology. Finally we offer a perspective of the latest innovations in DIA-MS technology and machine learning-based algorithms necessary for driving the development of high-throughput, in-depth and reproducible proteomic assays that are compatible with clinical diagnostic workflows, which will ultimately enable the delivery of precision cancer medicine to achieve optimal patient outcomes.

NFE2L2 Is a Potential Prognostic Biomarker and Is Correlated with Immune Infiltration in Brain Lower Grade Glioma: A Pan-Cancer Analysis

Nuclear factor, erythroid 2 like 2 (NFE2L2, NRF2) is a transcription factor that regulates various antioxidant enzymes. It plays a vital physiological role in regulating oxidative stress and inflammatory response. However, the roles of NFE2L2 in human cancers are still unclear. Our study is aimed at analyzing the prognostic value of NFE2L2 in pan-cancer and at revealing the relationship between NFE2L2 expression and tumor immunity. The present study revealed that NFE2L2 was abnormally expressed and significantly correlated with mismatch repair (MMR) gene mutation levels and DNA methyltransferase expression in human pan-cancer. In particular, pan-cancer survival analysis indicated that NFE2L2 expression was associated with adverse outcomes-overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI)-in adrenocortical carcinoma (ACC), brain lower grade glioma (LGG), and pancreatic adenocarcinoma (PAAD) patients. A positive relationship was also found between NFE2L2 expression and immune infiltration, including B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells, especially in breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), kidney renal clear cell carcinoma (KIRC), LGG, liver hepatocellular carcinoma (LIHC), and prostate adenocarcinoma (PRAD). Additionally, NFE2L2 expression was positively correlated with the immune score and the expression of immune checkpoint markers in LGG. In conclusion, these results indicate that transcription factor NFE2L2 is a potential prognostic biomarker and is correlated with immune infiltration in LGG.

Framework for quality assessment of whole genome cancer sequences

Bringing together cancer genomes from different projects increases power and allows the investigation of pan-cancer, molecular mechanisms. However, working with whole genomes sequenced over several years in different sequencing centres requires a framework to compare the quality of these sequences. We used the Pan-Cancer Analysis of Whole Genomes cohort as a test case to construct such a framework. This cohort contains whole cancer genomes of 2832 donors from 18 sequencing centres. We developed a non-redundant set of five quality control (QC) measurements to establish a star rating system. These QC measures reflect known differences in sequencing protocol and provide a guide to downstream analyses and allow for exclusion of samples of poor quality. We have found that this is an effective framework of quality measures. The implementation of the framework is available at: https://dockstore.org/containers/quay.io/jwerner_dkfz/pancanqc:1.2.2.

Working with cancer genomes from multiple projects can increase investigative power, but quality of sequences can vary. Here, the authors present a framework for comparing whole genome sequencing quality to help researchers guide downstream analyses and exclude poor quality samples.

Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator

BACKGROUND

Performing a statistical test requires a null hypothesis. In cancer genomics, a key challenge is the fast generation of accurate somatic mutational landscapes that can be used as a realistic null hypothesis for making biological discoveries.

RESULTS

Here we present SigProfilerSimulator, a powerful tool that is capable of simulating the mutational landscapes of thousands of cancer genomes at different resolutions within seconds. Applying SigProfilerSimulator to 2144 whole-genome sequenced cancers reveals: (i) that most doublet base substitutions are not due to two adjacent single base substitutions but likely occur as single genomic events; (ii) that an extended sequencing context of ± 2 bp is required to more completely capture the patterns of substitution mutational signatures in human cancer; (iii) information on false-positive discovery rate of commonly used bioinformatics tools for detecting driver genes.

CONCLUSIONS

SigProfilerSimulator's breadth of features allows one to construct a tailored null hypothesis and use it for evaluating the accuracy of other bioinformatics tools or for downstream statistical analysis for biological discoveries. SigProfilerSimulator is freely available at https://github.com/AlexandrovLab/SigProfilerSimulator with an extensive documentation at https://osf.io/usxjz/wiki/home/ .

Drivers underpinning the malignant transformation of giant cell tumour of bone

The rare benign giant cell tumour of bone (GCTB) is defined by an almost unique mutation in the H3.3 family of histone genes H3‐3A or H3‐3B; however, the same mutation is occasionally found in primary malignant bone tumours which share many features with the benign variant. Moreover, lung metastases can occur despite the absence of malignant histological features in either the primary or metastatic lesions. Herein we investigated the genetic events of 17 GCTBs including benign and malignant variants and the methylation profiles of 122 bone tumour samples including GCTBs. Benign GCTBs possessed few somatic alterations and no other known drivers besides the H3.3 mutation, whereas all malignant tumours harboured at least one additional driver mutation and exhibited genomic features resembling osteosarcomas, including high mutational burden, additional driver event(s), and a high degree of aneuploidy. The H3.3 mutation was found to predate the development of aneuploidy. In contrast to osteosarcomas, malignant H3.3‐mutated tumours were enriched for a variety of alterations involving TERT, other than amplification, suggesting telomere dysfunction in the transformation of benign to malignant GCTB. DNA sequencing of the benign metastasising GCTB revealed no additional driver alterations; polyclonal seeding in the lung was identified, implying that the metastatic lesions represent an embolic event. Unsupervised clustering of DNA methylation profiles revealed that malignant H3.3‐mutated tumours are distinct from their benign counterpart, and other bone tumours. Differential methylation analysis identified CCND1, encoding cyclin D1, as a plausible cancer driver gene in these tumours because hypermethylation of the CCND1 promoter was specific for GCTBs. We report here the genomic and methylation patterns underlying the rare clinical phenomena of benign metastasising and malignant transformation of GCTB and show how the combination of genomic and epigenomic findings could potentially distinguish benign from malignant GCTBs, thereby predicting aggressive behaviour in challenging diagnostic cases. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Trajectory and uniqueness of mutational signatures in yeast mutators

The acquisition of mutations plays critical roles in adaptation, evolution, senescence, and tumorigenesis. Massive genome sequencing has allowed extraction of specific features of many mutational landscapes but it remains difficult to retrospectively determine the mechanistic origin(s), selective forces, and trajectories of transient or persistent mutations and genome rearrangements. Here, we conducted a prospective reciprocal approach to inactivate 13 single or multiple evolutionary conserved genes involved in distinct genome maintenance processes and characterize de novo mutations in 274 diploid Saccharomyces cerevisiae mutation accumulation lines. This approach revealed the diversity, complexity, and ultimate uniqueness of mutational landscapes, differently composed of base substitutions, small insertions/deletions (InDels), structural variants, and/or ploidy variations. Several landscapes parallel the repertoire of mutational signatures in human cancers while others are either novel or composites of subsignatures resulting from distinct DNA damage lesions. Notably, the increase of base substitutions in the homologous recombination-deficient Rad51 mutant, specifically dependent on the Polζ translesion polymerase, yields COSMIC signature 3 observed in BRCA1/BRCA2-mutant breast cancer tumors. Furthermore, "mutome" analyses in highly polymorphic diploids and single-cell bottleneck lineages revealed a diverse spectrum of loss-of-heterozygosity (LOH) signatures characterized by interstitial and terminal chromosomal events resulting from interhomolog mitotic cross-overs. Following the appearance of heterozygous mutations, the strong stimulation of LOHs in the rad27/FEN1 and tsa1/PRDX1 backgrounds leads to fixation of homozygous mutations or their loss along the lineage. Overall, these mutomes and their trajectories provide a mechanistic framework to understand the origin and dynamics of genome variations that accumulate during clonal evolution.

Extensive heterogeneity in somatic mutation and selection in the human bladder

The extent of somatic mutation and clonal selection in the human bladder remains unknown. We sequenced 2097 bladder microbiopsies from 20 individuals using targeted (n = 1914 microbiopsies), whole-exome (n = 655), and whole-genome (n = 88) sequencing. We found widespread positive selection in 17 genes. Chromatin remodeling genes were frequently mutated, whereas mutations were absent in several major bladder cancer genes. There was extensive interindividual variation in selection, with different driver genes dominating the clonal landscape across individuals. Mutational signatures were heterogeneous across clones and individuals, which suggests differential exposure to mutagens in the urine. Evidence of APOBEC mutagenesis was found in 22% of the microbiopsies. Sequencing multiple microbiopsies from five patients with bladder cancer enabled comparisons with cancer-free individuals and across histological features. This study reveals a rich landscape of mutational processes and selection in normal urothelium with large heterogeneity across clones and individuals.

Progression and dormancy in metastatic thyroid cancer: concepts and clinical implications

Distant metastasis classically has been defined as a late-stage event in cancer progression. However, it has become clear that metastases also may occur early in the "lifetime" of a cancer and that they may remain stable at distant sites. This stability of metastatic cancer deposits has been termed "metastatic dormancy" or, as we term it, "metastatic progression dormancy" as the progression either may reflect growth of already existing metastases or new cancer spread. Biologically, dormancy is the presence of nongrowing, static metastatic cells that survive over time. Clinically, dormancy is defined by stability in tumor markers, imaging, and clinical course. Metastatic well-differentiated thyroid cancer offers an excellent tumor type to understand these processes for several reasons: (1) primary therapy often includes removal of the entire gland with ablation of residual normal tissue thereby removing one source for new metastases; (2) the presence of a sensitive biochemical and radiographic monitoring tests enabling monitoring of metastasis throughout the progression process; and (3) its tendency toward prolonged clinical dormancy that can last for years or decades be followed by progression. This latter factor provides opportunities to define therapeutic targets and/or markers of progression. In this review, we will discuss concepts of metastatic progression dormancy and the factors that drive both long-term stability and loss of dormancy with a focus on thyroid cancer.

Sarcoma and the 100,000 Genomes Project: our experience and changes to practice

The largest whole genome sequencing (WGS) endeavour involving cancer and rare diseases was initiated in the UK in 2015 and ran for 5 years. Despite its rarity, sarcoma ranked third overall among the number of patients' samples sent for sequencing. Herein, we recount the lessons learned by a specialist sarcoma centre that recruited close to 1000 patients to the project, so that we and others may learn from our experience. WGS data was generated from 597 patients, but samples from the remaining approximately 400 patients were not sequenced. This was largely accounted for by unsuitability due to extensive necrosis, secondary to neoadjuvant radiotherapy or chemotherapy, or being placed in formalin. The number of informative genomes produced was reduced further by a PCR amplification step. We showed that this loss of genomic data could be mitigated by sequencing whole genomes from needle core biopsies. Storage of resection specimens at 4 °C for up to 96 h overcame the challenge of freezing tissue out of hours including weekends. Removing access to formalin increased compliance to these storage arrangements. With over 70 different sarcoma subtypes described, WGS was a useful tool for refining diagnoses and identifying novel alterations. Genomes from 350 of the cohort of 597 patients were analysed in this study. Overall, diagnoses were modified for 3% of patients following review of the WGS findings. Continued refinement of the variant-calling bioinformatic pipelines is required as not all alterations were identified when validated against histology and standard of care diagnostic tests. Further research is necessary to evaluate the impact of germline mutations in patients with sarcoma, and sarcomas with evidence of hypermutation. Despite 50% of the WGS exhibiting domain 1 alterations, the number of patients with sarcoma who were eligible for clinical trials remains small, highlighting the need to revaluate clinical trial design.

Repeat expansions confer WRN dependence in microsatellite-unstable cancers

The RecQ DNA helicase WRN is a synthetic lethal target for cancer cells with microsatellite instability (MSI), a form of genetic hypermutability that arises from impaired mismatch repair1-4. Depletion of WRN induces widespread DNA double-strand breaks in MSI cells, leading to cell cycle arrest and/or apoptosis. However, the mechanism by which WRN protects MSI-associated cancers from double-strand breaks remains unclear. Here we show that TA-dinucleotide repeats are highly unstable in MSI cells and undergo large-scale expansions, distinct from previously described insertion or deletion mutations of a few nucleotides5. Expanded TA repeats form non-B DNA secondary structures that stall replication forks, activate the ATR checkpoint kinase, and require unwinding by the WRN helicase. In the absence of WRN, the expanded TA-dinucleotide repeats are susceptible to cleavage by the MUS81 nuclease, leading to massive chromosome shattering. These findings identify a distinct biomarker that underlies the synthetic lethal dependence on WRN, and support the development of therapeutic agents that target WRN for MSI-associated cancers.

Infantile fibrosarcoma-like tumor driven by novel RBPMS-MET fusion consolidated with cabozantinib

Infantile fibrosarcoma (IFS) is nearly universally driven by gene fusions involving the NTRK family. ETV6-NTRK3 fusions account for ∼85% of alterations; the remainder are attributed to NTRK-variant fusions. Rarely, other genomic aberrations have been described in association with tumors identified as IFS or IFS-like. We describe the utility of genomic characterization of an IFS-like tumor. We also describe the successful treatment combination of VAC (vincristine, actinomycin, cyclophosphamide) with tyrosine kinase inhibitor (TKI) maintenance in this entity. This patient presented at birth with a right facial mass, enlarging at 1 mo to 4.9 × 4.5 × 6.3 cm. Biopsy demonstrated hypercellular fascicles of spindle cells with patchy positivity for smooth muscle actin (SMA) and negativity for S100, desmin, myogenin, and MyoD1. Targeted RNA sequencing identified a novel RBPMS-MET fusion with confirmed absence of ETV6-NTRK3, and the patient was diagnosed with an IFS-like tumor. A positron emission tomography (PET) scan was negative for metastatic disease. VAC was given for a duration of 10 mo. Resection at 13 mo of age demonstrated positive margins. Cabozantinib, a MET-targeting TKI, was initiated. The patient tolerated cabozantinib well and has no evidence of disease at 24 mo of age. We describe a novel RBPMS-MET driver fusion in association with a locally aggressive IFS-like tumor. MET functions as an oncogene and, when associated with the RNA binding protein RBPMS, forms an in-frame fusion product that retains the MET kinase domain. This fusion is associated with aberrant cell signaling pathway expression and subsequent malignancy. We describe treatment with cabozantinib in a patient with an IFS-like neoplasm.

TGFβR-SMAD3 Signaling Induces Resistance to PARP Inhibitors in the Bone Marrow Microenvironment

Synthetic lethality triggered by PARP inhibitor (PARPi) yields promising therapeutic results. Unfortunately, tumor cells acquire PARPi resistance, which is usually associated with the restoration of homologous recombination, loss of PARP1 expression, and/or loss of DNA double-strand break (DSB) end resection regulation. Here, we identify a constitutive mechanism of resistance to PARPi. We report that the bone marrow microenvironment (BMM) facilitates DSB repair activity in leukemia cells to protect them against PARPi-mediated synthetic lethality. This effect depends on the hypoxia-induced overexpression of transforming growth factor beta receptor (TGFβR) kinase on malignant cells, which is activated by bone marrow stromal cells-derived transforming growth factor beta 1 (TGF-β1). Genetic and/or pharmacological targeting of the TGF-β1-TGFβR kinase axis results in the restoration of the sensitivity of malignant cells to PARPi in BMM and prolongs the survival of leukemia-bearing mice. Our finding may lead to the therapeutic application of the TGFβR inhibitor in patients receiving PARPis.

Molecular origins of APOBEC-associated mutations in cancer

The APOBEC family of cytidine deaminases has been proposed to represent a major enzymatic source of mutations in cancer. Here, we summarize available evidence that links APOBEC deaminases to cancer mutagenesis. We also highlight newly identified human cell models of APOBEC mutagenesis, including cancer cell lines with suspected endogenous APOBEC activity and a cell system of telomere crisis-associated mutations. Finally, we draw on recent data to propose potential causes of APOBEC misregulation in cancer, including the instigating factors, the relevant mutator(s), and the mechanisms underlying generation of the genome-dispersed and clustered APOBEC-induced mutations.

A compendium of mutational cancer driver genes

A fundamental goal in cancer research is to understand the mechanisms of cell transformation. This is key to developing more efficient cancer detection methods and therapeutic approaches. One milestone towards this objective is the identification of all the genes with mutations capable of driving tumours. Since the 1970s, the list of cancer genes has been growing steadily. Because cancer driver genes are under positive selection in tumorigenesis, their observed patterns of somatic mutations across tumours in a cohort deviate from those expected from neutral mutagenesis. These deviations, which constitute signals of positive selection, may be detected by carefully designed bioinformatics methods, which have become the state of the art in the identification of driver genes. A systematic approach combining several of these signals could lead to a compendium of mutational cancer genes. In this Review, we present the Integrative OncoGenomics (IntOGen) pipeline, an implementation of such an approach to obtain the compendium of mutational cancer drivers. Its application to somatic mutations of more than 28,000 tumours of 66 cancer types reveals 568 cancer genes and points towards their mechanisms of tumorigenesis. The application of this approach to the ever-growing datasets of somatic tumour mutations will support the continuous refinement of our knowledge of the genetic basis of cancer.

Parallelized Latent Dirichlet Allocation Provides a Novel Interpretability of Mutation Signatures in Cancer Genomes

Mutation signatures are defined as the distribution of specific mutations such as activity of AID/APOBEC family proteins. Previous studies have reported numerous signatures, using matrix factorization methods for mutation catalogs. Different mutation signatures are active in different tumor types; hence, signature activity varies greatly among tumor types and becomes sparse. Because of this, many previous methods require dividing mutation catalogs for each tumor type. Here, we propose parallelized latent Dirichlet allocation (PLDA), a novel Bayesian model to simultaneously predict mutation signatures with all mutation catalogs. PLDA is an extended model of latent Dirichlet allocation (LDA), which is one of the methods used for signature prediction. It has parallelized hyperparameters of Dirichlet distributions for LDA, and they represent the sparsity of signature activities for each tumor type, thus facilitating simultaneous analyses. First, we conducted a simulation experiment to compare PLDA with previous methods (including SigProfiler and SignatureAnalyzer) using artificial data and confirmed that PLDA could predict signature structures as accurately as previous methods without searching for the optimal hyperparameters. Next, we applied PLDA to PCAWG (Pan-Cancer Analysis of Whole Genomes) mutation catalogs and obtained a signature set different from the one predicted by SigProfiler. Further, we have shown that the mutation spectrum represented by the predicted signature with PLDA provides a novel interpretability through post-analyses.

Characteristics of mutational signatures of unknown etiology

Although not all somatic mutations are cancer drivers, their mutational signatures, i.e. the patterns of genomic alterations at a genome-wide scale, provide insights into past exposure to mutagens, DNA damage and repair processes. Computational deconvolution of somatic mutation patterns and expert curation pan-cancer studies have identified a number of mutational signatures associated with point mutations, dinucleotide substitutions, insertions and deletions, and rearrangements, and have established etiologies for a subset of these signatures. However, the mechanisms underlying nearly one-third of all mutational signatures are not yet understood. The signatures with established etiology and those with hitherto unknown origin appear to have some differences in strand bias, GC content and nucleotide context diversity. It is possible that some of the hitherto ‘unknown’ signatures predominantly occur outside gene regions. While nucleotide contexts might be adequate to establish etiologies of some mutational signatures, in other cases additional features, such as broader (epi)genomic contexts, including chromatin, replication timing, processivity and local mutational patterns, may help fully understand the underlying DNA damage and repair processes. Nonetheless, remarkable progress in characterization of mutational signatures has provided fundamental insights into the biology of cancer, informed disease etiology and opened up new opportunities for cancer prevention, risk management, and therapeutic decision making.

An Epigenetic Mechanism Underlying Chromosome 17p Deletion-Driven Tumorigenesis

Chromosome copy-number variations are a hallmark of cancer. Among them, the prevalent chromosome 17p deletions are associated with poor prognosis and can promote tumorigenesis more than TP53 loss. Here, we use multiple functional genetic strategies and identify a new 17p tumor suppressor gene (TSG), plant homeodomain finger protein 23 (PHF23). Its deficiency impairs B-cell differentiation and promotes immature B-lymphoblastic malignancy. Mechanistically, we demonstrate that PHF23, an H3K4me3 reader, directly binds the SIN3-HDAC complex through its N-terminus and represses its deacetylation activity on H3K27ac. Thus, the PHF23-SIN3-HDAC (PSH) complex coordinates these two major active histone markers for the activation of downstream TSGs and differentiation-related genes. Furthermore, dysregulation of the PSH complex is essential for the development and maintenance of PHF23-deficient and 17p-deleted tumors. Hence, our study reveals a novel epigenetic regulatory mechanism that contributes to the pathology of 17p-deleted cancers and suggests a susceptibility in this disease. SIGNIFICANCE: We identify PHF23, encoding an H3K4me3 reader, as a new TSG on chromosome 17p, which is frequently deleted in human cancers. Mechanistically, PHF23 forms a previously unreported histone-modifying complex, the PSH complex, which regulates gene activation through a synergistic link between H3K4me3 and H3K27ac.This article is highlighted in the In This Issue feature, p. 1.