Passenger Hotspot Mutations in Cancer

Mutations in cancer-relevant genes are ubiquitous in histologically normal endometrial tissue

OBJECTIVE

Endometrial cancer (EndoCA) is the most common gynecologic cancer and incidence and mortality rate continue to increase. Despite well-characterized knowledge of EndoCA-defining mutations, no effective diagnostic or screening tests exist. To lay the foundation for testing development, our study focused on defining the prevalence of somatic mutations present in non-cancerous uterine tissue.

METHODS

We obtained ≥8 uterine samplings, including separate endometrial and myometrial layers, from each of 22 women undergoing hysterectomy for non-cancer conditions. We ultra-deep sequenced (>2000× coverage) samples using a 125 cancer-relevant gene panel.

RESULTS

All women harbored complex mutation patterns. In total, 308 somatic mutations were identified with mutant allele frequencies ranging up to 96.0%. These encompassed 56 unique mutations from 24 genes. The majority of samples possessed predicted functional cancer mutations but curiously no growth advantage over non-functional mutations was detected. Functional mutations were enriched with increasing patient age (p = 0.045) and BMI (p = 0.0007) and in endometrial versus myometrial layers (68% vs 39%, p = 0.0002). Finally, while the somatic mutation landscape shared similar mutation prevalence in key TCGA-defined EndoCA genes, notably PIK3CA, significant differences were identified, including NOTCH1 (77% vs 10%), PTEN (9% vs 61%), TP53 (0% vs 37%) and CTNNB1 (0% vs 26%).

CONCLUSIONS

An important caveat for future liquid biopsy/DNA-based cancer diagnostics is the repertoire of shared and distinct mutation profiles between histologically unremarkable and EndoCA tissues. The lack of selection pressure between functional and non-functional mutations in histologically unremarkable uterine tissue may offer a glimpse into an unrecognized EndoCA protective mechanism.

Non-hypermutator cancers access driver mutations through reversals in germline mutational bias

Cancer is an evolutionary disease driven by mutations in asexually-reproducing somatic cells. In asexual microbes, bias reversals in the mutation spectrum can speed adaptation by increasing access to previously undersampled beneficial mutations. By analyzing tumors from 20 tissues, along with normal tissue and the germline, we demonstrate this effect in cancer. Non-hypermutated tumors reverse the germline mutation bias and have consistent spectra across tissues. These spectra changes carry the signature of hypoxia, and they facilitate positive selection in cancer genes. Hypermutated and non-hypermutated tumors thus acquire driver mutations differently: hypermutated tumors by higher mutation rates and non-hypermutated tumors by changing the mutation spectrum to reverse the germline mutation bias.

How CRISPR Is Revolutionizing the Generation of New Models for Cancer Research

Cancers arise through acquisition of mutations in genes that regulate core biological processes like cell proliferation and cell death. Decades of cancer research have led to the identification of genes and mutations causally involved in disease development and evolution, yet defining their precise function across different cancer types and how they influence therapy responses has been challenging. Mouse models have helped define the in vivo function of cancer-associated alterations, and genome-editing approaches using CRISPR have dramatically accelerated the pace at which these models are developed and studied. Here, we highlight how CRISPR technologies have impacted the development and use of mouse models for cancer research and discuss the many ways in which these rapidly evolving platforms will continue to transform our understanding of this disease.

Hotspot propensity across mutational processes

The sparsity of mutations observed across tumours hinders our ability to study mutation rate variability at nucleotide resolution. To circumvent this, here we investigated the propensity of mutational processes to form mutational hotspots as a readout of their mutation rate variability at single base resolution. Mutational signatures 1 and 17 have the highest hotspot propensity (5-78 times higher than other processes). After accounting for trinucleotide mutational probabilities, sequence composition and mutational heterogeneity at 10 Kbp, most (94-95%) signature 17 hotspots remain unexplained, suggesting a significant role of local genomic features. For signature 1, the inclusion of genome-wide distribution of methylated CpG sites into models can explain most (80-100%) of the hotspot propensity. There is an increased hotspot propensity of signature 1 in normal tissues and de novo germline mutations. We demonstrate that hotspot propensity is a useful readout to assess the accuracy of mutation rate models at nucleotide resolution. This new approach and the findings derived from it open up new avenues for a range of somatic and germline studies investigating and modelling mutagenesis.

A Mouse-Specific Model to Detect Genes under Selection in Tumors

The mouse is a widely used model organism in cancer research. However, no computational methods exist to identify cancer driver genes in mice due to a lack of labeled training data. To address this knowledge gap, we adapted the GUST (Genes Under Selection in Tumors) model, originally trained on human exomes, to mouse exomes via transfer learning. The resulting tool, called GUST-mouse, can estimate long-term and short-term evolutionary selection in mouse tumors, and distinguish between oncogenes, tumor suppressor genes, and passenger genes using high-throughput sequencing data. We applied GUST-mouse to analyze 65 exomes of mouse primary breast cancer models and 17 exomes of mouse leukemia models. Comparing the predictions between cancer types and between human and mouse tumors revealed common and unique driver genes. The GUST-mouse method is available as an open-source R package on github.

Reuniting philosophy and science to advance cancer research

Cancers rely on multiple, heterogeneous processes at different scales, pertaining to many biomedical fields. Therefore, understanding cancer is necessarily an interdisciplinary task that requires placing specialised experimental and clinical research into a broader conceptual, theoretical, and methodological framework. Without such a framework, oncology will collect piecemeal results, with scant dialogue between the different scientific communities studying cancer. We argue that one important way forward in service of a more successful dialogue is through greater integration of applied sciences (experimental and clinical) with conceptual and theoretical approaches, informed by philosophical methods. By way of illustration, we explore six central themes: (i) the role of mutations in cancer; (ii) the clonal evolution of cancer cells; (iii) the relationship between cancer and multicellularity; (iv) the tumour microenvironment; (v) the immune system; and (vi) stem cells. In each case, we examine open questions in the scientific literature through a philosophical methodology and show the benefit of such a synergy for the scientific and medical understanding of cancer.

The relapsed acute lymphoblastic leukemia network (ReALLNet): a multidisciplinary project from the spanish society of pediatric hematology and oncology (SEHOP)

Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer, with survival rates exceeding 85%. However, 15% of patients will relapse; consequently, their survival rates decrease to below 50%. Therefore, several research and innovation studies are focusing on pediatric relapsed or refractory ALL (R/R ALL). Driven by this context and following the European strategic plan to implement precision medicine equitably, the Relapsed ALL Network (ReALLNet) was launched under the umbrella of SEHOP in 2021, aiming to connect bedside patient care with expert groups in R/R ALL in an interdisciplinary and multicentric network. To achieve this objective, a board consisting of experts in diagnosis, management, preclinical research, and clinical trials has been established. The requirements of treatment centers have been evaluated, and the available oncogenomic and functional study resources have been assessed and organized. A shipping platform has been developed to process samples requiring study derivation, and an integrated diagnostic committee has been established to report results. These biological data, as well as patient outcomes, are collected in a national registry. Additionally, samples from all patients are stored in a biobank. This comprehensive repository of data and samples is expected to foster an environment where preclinical researchers and data scientists can seek to meet the complex needs of this challenging population. This proof of concept aims to demonstrate that a network-based organization, such as that embodied by ReALLNet, provides the ideal niche for the equitable and efficient implementation of "what's next" in the management of children with R/R ALL.

Pan-cancer proteogenomics connects oncogenic drivers to functional states

Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.

Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation

Post-translational modifications (PTMs) play key roles in regulating cell signaling and physiology in both normal and cancer cells. Advances in mass spectrometry enable high-throughput, accurate, and sensitive measurement of PTM levels to better understand their role, prevalence, and crosstalk. Here, we analyze the largest collection of proteogenomics data from 1,110 patients with PTM profiles across 11 cancer types (10 from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium [CPTAC]). Our study reveals pan-cancer patterns of changes in protein acetylation and phosphorylation involved in hallmark cancer processes. These patterns revealed subsets of tumors, from different cancer types, including those with dysregulated DNA repair driven by phosphorylation, altered metabolic regulation associated with immune response driven by acetylation, affected kinase specificity by crosstalk between acetylation and phosphorylation, and modified histone regulation. Overall, this resource highlights the rich biology governed by PTMs and exposes potential new therapeutic avenues.

Sequence dependencies and mutation rates of localized mutational processes in cancer

BACKGROUND

Cancer mutations accumulate through replication errors and DNA damage coupled with incomplete repair. Individual mutational processes often show nucleotide sequence and functional region preferences. As a result, some sequence contexts mutate at much higher rates than others, with additional variation found between functional regions. Mutational hotspots, with recurrent mutations across cancer samples, represent genomic positions with elevated mutation rates, often caused by highly localized mutational processes.

METHODS

We count the 11-mer genomic sequences across the genome, and using the PCAWG set of 2583 pan-cancer whole genomes, we associate 11-mers with mutational signatures, hotspots of single nucleotide variants, and specific genomic regions. We evaluate the mutation rates of individual and combined sets of 11-mers and derive mutational sequence motifs.

RESULTS

We show that hotspots generally identify highly mutable sequence contexts. Using these, we show that some mutational signatures are enriched in hotspot sequence contexts, corresponding to well-defined sequence preferences for the underlying localized mutational processes. This includes signature 17b (of unknown etiology) and signatures 62 (POLE deficiency), 7a (UV), and 72 (linked to lymphomas). In some cases, the mutation rate and sequence preference increase further when focusing on certain genomic regions, such as signature 62 in transcribed regions, where the mutation rate is increased up to 9-folds over cancer type and mutational signature average.

CONCLUSIONS

We summarize our findings in a catalog of localized mutational processes, their sequence preferences, and their estimated mutation rates.

Integrative identification of non-coding regulatory regions driving metastatic prostate cancer

Large-scale sequencing efforts of thousands of tumor samples have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of germline and somatic variants occur within non-coding portions of the genome. These genomic regions do not directly encode for specific proteins, but can play key roles in cancer progression, for example by driving aberrant gene expression control. Here, we designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Application of this approach to whole-genome sequencing (WGS) data from a large cohort of metastatic castration-resistant prostate cancer (mCRPC) revealed a large set of recurrently mutated regions. We used (i) in silico prioritization of functional non-coding mutations, (ii) massively parallel reporter assays, and (iii) in vivo CRISPR-interference (CRISPRi) screens in xenografted mice to systematically identify and validate driver regulatory regions that drive mCRPC. We discovered that one of these enhancer regions, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. We found that both SF3A1 and CCDC157 are promoters of tumor growth in xenograft models of prostate cancer. We nominated a number of transcription factors, including SOX6, to be responsible for higher expression of SF3A1 and CCDC157. Collectively, we have established and confirmed an integrative computational and experimental approach that enables the systematic detection of non-coding regulatory regions that drive the progression of human cancers.

MutBLESS: A tool to identify disease-prone sites in cancer using deep learning

Understanding the molecular basis and impact of mutations at different stages of cancer are long-standing challenges in cancer biology. Identification of driver mutations from experiments is expensive and time intensive. In the present study, we collected the data for experimentally known driver mutations in 22 different cancer types and classified them into six categories: breast cancer (BRCA), acute myeloid leukaemia (LAML), endometrial carcinoma (EC), stomach cancer (STAD), skin cancer (SKCM), and other cancer types which contains 5747 disease prone and 5514 neutral sites in 516 proteins. The analysis of amino acid distribution along mutant sites revealed that the motifs AAA and LR are preferred in disease-prone sites whereas QPP and QF are dominant in neutral sites. Further, we developed a method using deep neural networks to predict disease-prone sites with amino acid sequence-based features such as physicochemical properties, secondary structure, tri-peptide motifs and conservation scores. We obtained an average AUC of 0.97 in five cancer types BRCA, LAML, EC, STAD and SKCM in a test dataset and 0.72 in all other cancer types together. Our method showed excellent performance for identifying cancer-specific mutations with an average sensitivity, specificity, and accuracy of 96.56 %, 97.39 %, and 97.64 %, respectively. We developed a web server for identifying cancer-prone sites, and it is available at https://web.iitm.ac.in/bioinfo2/MutBLESS/index.html. We suggest that our method can serve as an effective method to identify disease-prone sites and assist to develop therapeutic strategies.

Landscape of Genetic Alterations Underlying Hallmark Signature Changes in Cancer Reveals TP53 Aneuploidy-driven Metabolic Reprogramming

The hallmark signatures based on gene expression capture core cancer processes. Through a pan-cancer analysis, we describe the overview of hallmark signatures across tumor types/subtypes and reveal significant relationships between these signatures and genetic alterations. TP53 mutation exerts diverse changes, including increased proliferation and glycolysis, which are closely mimicked by widespread copy-number alterations. Hallmark signature and copy-number clustering identify a cluster of squamous tumors and basal-like breast and bladder cancers with elevated proliferation signatures, frequent TP53 mutation, and high aneuploidy. In these basal-like/squamous TP53-mutated tumors, a specific and consistent spectrum of copy-number alterations is preferentially selected prior to whole-genome duplication. Within Trp53-null breast cancer mouse models, these copy-number alterations spontaneously occur and recapitulate the hallmark signature changes observed in the human condition. Together, our analysis reveals intertumor and intratumor heterogeneity of the hallmark signatures, uncovering an oncogenic program induced by TP53 mutation and select aneuploidy events to drive a worsened prognosis.

Significance

Our data demonstrate that TP53 mutation and a resultant selected pattern of aneuploidies cause an aggressive transcriptional program including upregulation of glycolysis signature with prognostic implications. Importantly, basal-like breast cancer demonstrates genetic and/or phenotypic changes closely related to squamous tumors including 5q deletion that reveal alterations that could offer therapeutic options across tumor types regardless of tissue of origin.

Mitochondrial DNA is a major source of driver mutations in cancer

Mitochondrial DNA (mtDNA) mutations are among the most common genetic events in all tumors and directly impact metabolic homeostasis. Despite the central role mitochondria play in energy metabolism and cellular physiology, the role of mutations in the mitochondrial genomes of tumors has been contentious. Until recently, genomic and functional studies of mtDNA variants were impeded by a lack of adequate tumor mtDNA sequencing data and available methods for mitochondrial genome engineering. These barriers and a conceptual fog surrounding the functional impact of mtDNA mutations in tumors have begun to lift, revealing a path to understanding the role of this essential metabolic genome in cancer initiation and progression. Here we discuss the history, recent developments, and challenges that remain for mitochondrial oncogenetics as the impact of a major new class of cancer-associated mutations is unveiled.

Examining clustered somatic mutations with SigProfilerClusters

MOTIVATION

Clustered mutations are found in the human germline as well as in the genomes of cancer and normal somatic cells. Clustered events can be imprinted by a multitude of mutational processes, and they have been implicated in both cancer evolution and development disorders. Existing tools for identifying clustered mutations have been optimized for a particular subtype of clustered event and, in most cases, relied on a predefined inter-mutational distance (IMD) cutoff combined with a piecewise linear regression analysis.

RESULTS

Here, we present SigProfilerClusters, an automated tool for detecting all types of clustered mutations by calculating a sample-dependent IMD threshold using a simulated background model that takes into account extended sequence context, transcriptional strand asymmetries and regional mutation densities. SigProfilerClusters disentangles all types of clustered events from non-clustered mutations and annotates each clustered event into an established subclass, including the widely used classes of doublet-base substitutions, multi-base substitutions, omikli and kataegis. SigProfilerClusters outputs non-clustered mutations and clustered events using standard data formats as well as provides multiple visualizations for exploring the distributions and patterns of clustered mutations across the genome.

AVAILABILITY AND IMPLEMENTATION

SigProfilerClusters is supported across most operating systems and made freely available at https://github.com/AlexandrovLab/SigProfilerClusters with an extensive documentation located at https://osf.io/qpmzw/wiki/home/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational analysis of cancer genome sequencing data

Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.

Genome-wide analysis of somatic noncoding mutation patterns in cancer

We established a genome-wide compendium of somatic mutation events in 3949 whole cancer genomes representing 19 tumor types. Protein-coding events captured well-established drivers. Noncoding events near tissue-specific genes, such as ALB in the liver or KLK3 in the prostate, characterized localized passenger mutation patterns and may reflect tumor-cell-of-origin imprinting. Noncoding events in regulatory promoter and enhancer regions frequently involved cancer-relevant genes such as BCL6, FGFR2, RAD51B, SMC6, TERT, and XBP1 and represent possible drivers. Unlike most noncoding regulatory events, XBP1 mutations primarily accumulated outside the gene's promoter, and we validated their effect on gene expression using CRISPR-interference screening and luciferase reporter assays. Broadly, our study provides a blueprint for capturing mutation events across the entire genome to guide advances in biological discovery, therapies, and diagnostics.

Biallelic mutations in cancer genomes reveal local mutational determinants

The infinite sites model of molecular evolution posits that every position in the genome is mutated at most once1. By restricting the number of possible mutation histories, haplotypes and alleles, it forms a cornerstone of tumor phylogenetic analysis2 and is often implied when calling, phasing and interpreting variants3,4 or studying the mutational landscape as a whole5. Here we identify 18,295 biallelic mutations, where the same base is mutated independently on both parental copies, in 559 (21%) bulk sequencing samples from the Pan-Cancer Analysis of Whole Genomes study. Biallelic mutations reveal ultraviolet light damage hotspots at E26 transformation-specific (ETS) and nuclear factor of activated T cells (NFAT) binding sites, and hypermutable motifs in POLE-mutant and other cancers. We formulate recommendations for variant calling and provide frameworks to model and detect biallelic mutations. These results highlight the need for accurate models of mutation rates and tumor evolution, as well as their inference from sequencing data.

Mapping clustered mutations in cancer reveals APOBEC3 mutagenesis of ecDNA

Clustered somatic mutations are common in cancer genomes and previous analyses reveal several types of clustered single-base substitutions, which include doublet- and multi-base substitutions1-5, diffuse hypermutation termed omikli6, and longer strand-coordinated events termed kataegis3,7-9. Here we provide a comprehensive characterization of clustered substitutions and clustered small insertions and deletions (indels) across 2,583 whole-genome-sequenced cancers from 30 types of cancer10. Clustered mutations were highly enriched in driver genes and associated with differential gene expression and changes in overall survival. Several distinct mutational processes gave rise to clustered indels, including signatures that were enriched in tobacco smokers and homologous-recombination-deficient cancers. Doublet-base substitutions were caused by at least 12 mutational processes, whereas most multi-base substitutions were generated by either tobacco smoking or exposure to ultraviolet light. Omikli events, which have previously been attributed to APOBEC3 activity6, accounted for a large proportion of clustered substitutions; however, only 16.2% of omikli matched APOBEC3 patterns. Kataegis was generated by multiple mutational processes, and 76.1% of all kataegic events exhibited mutational patterns that are associated with the activation-induced deaminase (AID) and APOBEC3 family of deaminases. Co-occurrence of APOBEC3 kataegis and extrachromosomal DNA (ecDNA), termed kyklonas (Greek for cyclone), was found in 31% of samples with ecDNA. Multiple distinct kyklonic events were observed on most mutated ecDNA. ecDNA containing known cancer genes exhibited both positive selection and kyklonic hypermutation. Our results reveal the diversity of clustered mutational processes in human cancer and the role of APOBEC3 in recurrently mutating and fuelling the evolution of ecDNA.

OUP accepted manuscript

Activation-induced deaminase (AID) is a DNA-cytosine deaminase that mediates maturation of antibodies through somatic hypermutation and class-switch recombination. While it causes mutations in immunoglobulin heavy and light chain genes and strand breaks in the switch regions of the immunoglobulin heavy chain gene, it largely avoids causing such damage in the rest of the genome. To help understand targeting by human AID, we expressed it in repair-deficient Escherichia coli and mapped the created uracils in the genomic DNA using uracil pull-down and sequencing, UPD-seq. We found that both AID and the human APOBEC3A preferentially target tRNA genes and transcription start sites, but do not show preference for highly transcribed genes. Unlike A3A, AID did not show a strong replicative strand bias or a preference for hairpin loops. Overlapping uracilation peaks between these enzymes contained binding sites for a protein, FIS, that helps create topological domains in the E. coli genome. To confirm whether these findings were relevant to B cells, we examined mutations from lymphoma and leukemia genomes within AID-preferred sequences. These mutations also lacked replicative strand bias or a hairpin loop preference. We propose here a model for how AID avoids causing mutations in the single-stranded DNA found within replication forks.

Lynch Syndrome and MSI-H Cancers: From Mechanisms to "Off-The-Shelf" Cancer Vaccines

Defective DNA mismatch repair (dMMR) is associated with many cancer types including colon, gastric, endometrial, ovarian, hepatobiliary tract, urinary tract, brain and skin cancers. Lynch syndrome - a hereditary cause of dMMR - confers increased lifetime risk of malignancy in different organs and tissues. These Lynch syndrome pathogenic alleles are widely present in humans at a 1:320 population frequency of a single allele and associated with an up to 80% risk of developing microsatellite unstable cancer (microsatellite instability - high, or MSI-H). Advanced MSI-H tumors can be effectively treated with checkpoint inhibitors (CPI), however, that has led to response rates of only 30-60% despite their high tumor mutational burden and favorable immune gene signatures in the tumor microenvironment (TME). We and others have characterized a subset of MSI-H associated highly recurrent frameshift mutations that yield shared immunogenic neoantigens. These frameshifts might serve as targets for off-the-shelf cancer vaccine designs. In this review we discuss the current state of research around MSI-H cancer vaccine development, its application to MSI-H and Lynch syndrome cancer patients and the utility of MSI-H as a biomarker for CPI therapy. We also summarize the tumor intrinsic mechanisms underlying the high occurrence rates of certain frameshifts in MSI-H. Finally, we provide an overview of pivotal clinical trials investigating MSI-H as a biomarker for CPI therapy and MSI-H vaccines. Overall, this review aims to inform the development of novel research paradigms and therapeutics.

Temporal evolution of master regulator Crp identifies pyrimidines as catabolite modulator factors

The evolution of microorganisms often involves changes of unclear relevance, such as transient phenotypes and sequential development of multiple adaptive mutations in hotspot genes. Previously, we showed that ageing colonies of an E. coli mutant unable to produce cAMP when grown on maltose, accumulated mutations in the crp gene (encoding a global transcription factor) and in genes involved in pyrimidine metabolism such as cmk; combined mutations in both crp and cmk enabled fermentation of maltose (which usually requires cAMP-mediated Crp activation for catabolic pathway expression). Here, we study the sequential generation of hotspot mutations in those genes, and uncover a regulatory role of pyrimidine nucleosides in carbon catabolism. Cytidine binds to the cytidine regulator CytR, modifies the expression of sigma factor 32 (RpoH), and thereby impacts global gene expression. In addition, cytidine binds and activates a Crp mutant directly, thus modulating catabolic pathway expression, and could be the catabolite modulating factor whose existence was suggested by Jacques Monod and colleagues in 1976. Therefore, transcription factor Crp appears to work in concert with CytR and RpoH, serving a dual role in sensing both carbon availability and metabolic flux towards DNA and RNA. Our findings show how certain alterations in metabolite concentrations (associated with colony ageing and/or due to mutations in metabolic or regulatory genes) can drive the evolution in non-growing cells.

Microbial evolution often involves transient phenotypes and sequential development of multiple mutations of unclear relevance. Here, the authors show that the evolution of non-growing E. coli cells can be driven by alterations in pyrimidine nucleoside levels associated with colony ageing and/or due to mutations in metabolic or regulatory genes.

Genome-wide mapping of genomic DNA damage: methods and implications

Exposures from the external and internal environments lead to the modification of genomic DNA, which is implicated in the cause of numerous diseases, including cancer, cardiovascular, pulmonary and neurodegenerative diseases, together with ageing. However, the precise mechanism(s) linking the presence of damage, to impact upon cellular function and pathogenesis, is far from clear. Genomic location of specific forms of damage is likely to be highly informative in understanding this process, as the impact of downstream events (e.g. mutation, microsatellite instability, altered methylation and gene expression) on cellular function will be positional—events at key locations will have the greatest impact. However, until recently, methods for assessing DNA damage determined the totality of damage in the genomic location, with no positional information. The technique of “mapping DNA adductomics” describes the molecular approaches that map a variety of forms of DNA damage, to specific locations across the nuclear and mitochondrial genomes. We propose that integrated comparison of this information with other genome-wide data, such as mutational hotspots for specific genotoxins, tumour-specific mutation patterns and chromatin organisation and transcriptional activity in non-cancerous lesions (such as nevi), pre-cancerous conditions (such as polyps) and tumours, will improve our understanding of how environmental toxins lead to cancer. Adopting an analogous approach for non-cancer diseases, including the development of genome-wide assays for other cellular outcomes of DNA damage, will improve our understanding of the role of DNA damage in pathogenesis more generally.

Clinical cancer genomic profiling

Technological innovation and rapid reduction in sequencing costs have enabled the genomic profiling of hundreds of cancer-associated genes as a component of routine cancer care. Tumour genomic profiling can refine cancer subtype classification, identify which patients are most likely to benefit from systemic therapies and screen for germline variants that influence heritable cancer risk. Here, we discuss ongoing efforts to enhance the clinical utility of tumour genomic profiling by integrating tumour and germline analyses, characterizing allelic context and identifying mutational signatures that influence therapy response. We also discuss the potential clinical utility of more comprehensive whole-genome and whole-transcriptome sequencing and ultra-sensitive cell-free DNA profiling platforms, which allow for minimally invasive, serial analyses of tumour-derived DNA in blood.

Functional annotation of noncoding mutations in cancer

Recurrent regulatory mutations affecting transcription factor binding sites in 2,500 cancer samples.

In a cancer genome, the noncoding sequence contains the vast majority of somatic mutations. While very few are expected to be cancer drivers, those affecting regulatory elements have the potential to have downstream effects on gene regulation that may contribute to cancer progression. To prioritize regulatory mutations, we screened somatic mutations in the Pan-Cancer Analysis of Whole Genomes cohort of 2,515 cancer genomes on individual bases to assess their potential regulatory roles in their respective cancer types. We found a highly significant enrichment of regulatory mutations associated with the deamination signature overlapping a CpG site in the CCAAT/Enhancer Binding Protein β recognition sites in many cancer types. Overall, 5,749 mutated regulatory elements were identified in 1,844 tumor samples from 39 cohorts containing 11,962 candidate regulatory mutations. Our analysis indicated 20 or more regulatory mutations in 5.5% of the samples, and an overall average of six per tumor. Several recurrent elements were identified, and major cancer-related pathways were significantly enriched for genes nearby the mutated regulatory elements. Our results provide a detailed view of the role of regulatory elements in cancer genomes.

Targeted doxorubicin delivery and release within breast cancer environment using PEGylated chitosan nanoparticles labeled with monoclonal antibodies

Breast cancer has been one of the top chronic and life-threatening diseases worldwide. Nano-drug therapeutic systems have proved their efficacy as a selective treatment compared to the traditional ones that are associated with serious adverse effects. Here, biodegradable chitosan nanoparticles (CSNPs) were synthesized to provide selective and sustained release of doxorubicin (DOX) within the breast tumor microenvironment. CSNPs surface was modified using Polyethylene glycol (PEG) to enhance their blood circulation timing. To provide high drug selectivity, CSNPs functionalized with two different types of breast cancer-specific monoclonal antibodies (mAb); anti-human mammaglobin (Anti-hMAM) and anti-human epidermal growth factor (Anti-HER2). Anti-hMAM PEGylated DOX loaded CSNPs and Anti-HER2 PEGylated DOX loaded CSNPs nano-formulations were the most cytotoxic against MCF-7 cancer cells than L-929 normal cells compared to free DOX. Finally, we believe that dose-dependent system toxicity of freely ingested DOX can be managed with such targeted nano-formulated drug delivery platforms.

Most Commonly Mutated Genes in High-Grade Serous Ovarian Carcinoma Are Nonessential for Ovarian Surface Epithelial Stem Cell Transformation

High-grade serous ovarian carcinoma (HGSOC) is the fifth leading cause of cancer-related deaths of women in the United States. Disease-associated mutations have been identified by the Cancer Genome Atlas Research Network. However, aside from mutations in TP53 or the RB1 pathway that are common in HGSOC, the contributions of mutation combinations are unclear. Here, we report CRISPR mutagenesis of 20 putative HGSOC driver genes to identify combinatorial disruptions of genes that transform either ovarian surface epithelium stem cells (OSE-SCs) or non-stem cells (OSE-NSs). Our results support the OSE-SC theory of HGSOC initiation and suggest that most commonly mutated genes in HGSOC have no effect on OSE-SC transformation initiation. Our results indicate that disruption of TP53 and PTEN, combined with RB1 disruption, constitutes a core set of mutations driving efficient transformation in vitro. The combined data may contribute to more accurate modeling of HGSOC development.

The landscape and driver potential of site-specific hotspots across cancer genomes

Large sets of whole cancer genomes make it possible to study mutation hotspots genome-wide. Here we detect, categorize, and characterize site-specific hotspots using 2279 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes project and provide a resource of annotated hotspots genome-wide. We investigate the excess of hotspots in both protein-coding and gene regulatory regions and develop measures of positive selection and functional impact for individual hotspots. Using cancer allele fractions, expression aberrations, mutational signatures, and a variety of genomic features, such as potential gain or loss of transcription factor binding sites, we annotate and prioritize all highly mutated hotspots. Genome-wide we find more high-frequency SNV and indel hotspots than expected given mutational background models. Protein-coding regions are generally enriched for SNV hotspots compared to other regions. Gene regulatory hotspots show enrichment of potential same-patient second-hit missense mutations, consistent with enrichment of hotspot driver mutations compared to singletons. For protein-coding regions, splice-sites, promoters, and enhancers, we see an excess of hotspots associated with cancer genes. Interestingly, missense hotspot mutations in tumor suppressors are associated with elevated expression, suggesting localized amino-acid changes with functional impact. For individual non-coding hotspots, only a small number show clear signs of positive selection, including known sites in the TERT promoter and the 5' UTR of TP53. Most of the new candidates have few mutations and limited driver evidence. However, a hotspot in an enhancer of the oncogene POU2AF1, which may create a transcription factor binding site, presents multiple lines of driver-consistent evidence.

To portray clonal evolution in blood cancer, count your stem cells

Clonal evolution, the process of expansion and diversification of mutated cells, plays an important role in cancer development, resistance, and relapse. Although clonal evolution is most often conceived of as driven by natural selection, recent studies uncovered that neutral evolution shapes clonal evolution in a significant proportion of solid cancers. In hematological malignancies, the interplay between neutral evolution and natural selection is also disputed. Because natural selection selects cells with a greater fitness, providing a growth advantage to some cells relative to others, the architecture of clonal evolution serves as indirect evidence to distinguish natural selection from neutral evolution and has been associated with different prognoses for the patient. Linear architecture, when the new mutant clone grows within the previous one, is characteristic of hematological malignancies and is typically interpreted as being driven by natural selection. Here, we discuss the role of natural selection and neutral evolution in the production of linear clonal architectures in hematological malignancies. Although it is tempting to attribute linear evolution to natural selection, we argue that a lower number of contributing stem cells accompanied by genetic drift can also result in a linear pattern of evolution, as illustrated by simulations of clonal evolution in hematopoietic stem cells. The number of stem cells contributing to long-term clonal evolution is not known in the pathological context, and we advocate that estimating these numbers in the context of cancer and aging is crucial to parsing out neutral evolution from natural selection, 2 processes that require different therapeutic strategies.

An extended APOBEC3A mutation signature in cancer

APOBEC mutagenesis, a major driver of cancer evolution, is known for targeting TpC sites in DNA. Recently, we showed that APOBEC3A (A3A) targets DNA hairpin loops. Here, we show that DNA secondary structure is in fact an orthogonal influence on A3A substrate optimality and, surprisingly, can override the TpC sequence preference. VpC (non-TpC) sites in optimal hairpins can outperform TpC sites as mutational hotspots. This expanded understanding of APOBEC mutagenesis illuminates the genomic Twin Paradox, a puzzling pattern of closely spaced mutation hotspots in cancer genomes, in which one is a canonical TpC site but the other is a VpC site, and double mutants are seen only in trans, suggesting a two-hit driver event. Our results clarify this paradox, revealing that both hotspots in these twins are optimal A3A substrates. Our findings reshape the notion of a mutation signature, highlighting the additive roles played by DNA sequence and DNA structure.

Naturally occurring hotspot cancer mutations in Gα13 promote oncogenic signaling

Heterotrimeric G-proteins are signaling switches broadly divided into four families based on the sequence and functional similarity of their Gα subunits: Gs, Gi/o, Gq/11, and G12/13 Artificial mutations that activate Gα subunits of each of these families have long been known to induce oncogenic transformation in experimental systems. With the advent of next-generation sequencing, activating hotspot mutations in Gs, Gi/o, or Gq/11 proteins have also been identified in patient tumor samples. In contrast, patient tumor-associated G12/13 mutations characterized to date lead to inactivation rather than activation. By using bioinformatic pathway analysis and signaling assays, here we identified cancer-associated hotspot mutations in Arg-200 of Gα13 (encoded by GNA13) as potent activators of oncogenic signaling. First, we found that components of a G12/13-dependent signaling cascade that culminates in activation of the Hippo pathway effectors YAP and TAZ is frequently altered in bladder cancer. Up-regulation of this signaling cascade correlates with increased YAP/TAZ activation transcriptional signatures in this cancer type. Among the G12/13 pathway alterations were mutations in Arg-200 of Gα13, which we validated to promote YAP/TAZ-dependent (TEAD) and MRTF-A/B-dependent (SRE.L) transcriptional activity. We further showed that this mechanism relies on the same RhoGEF-RhoGTPase cascade components that are up-regulated in bladder cancers. Moreover, Gα13 Arg-200 mutants induced oncogenic transformation in vitro as determined by focus formation assays. In summary, our findings on Gα13 mutants establish that naturally occurring hotspot mutations in Gα subunits of any of the four families of heterotrimeric G-proteins are putative cancer drivers.

Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles

As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.

Cancer-associated mutations in endometriosis: shedding light on the pathogenesis and pathophysiology

BACKGROUND

Endometriosis is a benign gynaecological disease. Thus, it came as a complete surprise when it was reported recently that the majority of deep endometriosis lesions harbour somatic mutations and a sizeable portion of them contain known cancer-associated mutations (CAMs). Four more studies have since been published, all demonstrating the existence of CAMs in different subtypes of endometriosis. While the field is still evolving, the confirmation of CAMs has raised many questions that were previously overlooked.

OBJECTIVE AND RATIONALE

A comprehensive overview of CAMs in endometriosis has been produced. In addition, with the recently emerged understanding of the natural history of endometriotic lesions as well as CAMs in normal and apparently healthy tissues, this review attempts to address the following questions: Why has there been such a wild discrepancy in reported mutation frequencies? Why does ectopic endometrium have a higher mutation rate than that of eutopic endometrium? Would the presence of CAMs in endometriotic lesions increase the risk of cancer to the bearers? Why do endometriotic epithelial cells have much higher mutation frequencies than their stromal counterpart? What clinical implications, if any, do the CAMs have for the bearers? Do these CAMs tell us anything about the pathogenesis and/or pathophysiology of endometriosis?

SEARCH METHODS

The PubMed database was searched, from its inception to September 2019, for all papers in English using the term 'endometriosis and CAM', 'endometriosis and cancer-driver mutation', 'somatic mutations', 'fibrosis', 'fibrosis and epigenetic', 'CAMs and tumorigenesis', 'somatic mutation and normal tissues', 'oestrogen receptor and fibrosis', 'oxidative stress and fibrosis', 'ARID1A mutation', and 'Kirsten rat sarcoma mutation and therapeutics'. All retrieved papers were read and, when relevant, incorporated into the review results.

OUTCOMES

Seven papers that identified CAMs in endometriosis using various sequencing methods were retrieved, and their results were somewhat different. Yet, it is apparent that those using microdissection techniques and more accurate sequencing methods found more CAMs, echoing recent discoveries that apparently healthy tissues also harbour CAMs as a result of the replicative aging process. Hence endometriotic lesions, irrespective of subtype, if left intact, would generate CAMs as part of replicative aging, oxidative stress and perhaps other factors yet to be identified and, in some rare cases, develop cancer. The published data still are unable to paint a clear picture on pathogenesis of endometriosis. However, since endometriotic epithelial cells have a higher turnover than their stromal counterpart due to cyclic bleeding, and since the endometriotic stromal component can be formed by refresh influx of mesenchymal cells through epithelial-mesenchymal transition, endothelial-mesenchymal transition, mesothelial-mesenchymal transition and other processes as well as recruitment of bone-marrow-derived stem cells and outflow due to smooth muscle metaplasia, endometriotic epithelial cells have much higher mutation frequencies than their stromal counterpart. The epithelial and stromal cellular components develop in a dependent and co-evolving manner. Genes involved in CAMs are likely to be active players in lesional fibrogenesis, and hyperestrogenism and oxidative stress are likely drivers of both CAMs and fibrogenesis. Finally, endometriotic lesions harbouring CAMs would conceivably be more refractory to medical treatment, due, in no small part, to their high fibrotic content and reduced vascularity and cellularity.

WIDER IMPLICATIONS

The accumulating data on CAMs in endometriosis have shed new light on the pathogenesis and pathophysiology of endometriosis. They also suggest new challenges in management. The distinct yet co-evolving developmental trajectories of endometriotic stroma and epithelium underscore the importance of the lesional microenvironment and ever-changing cellular identity. Mutational profiling of normal endometrium from women of different ages and reproductive history is needed in order to gain a deeper understanding of the pathogenesis. Moreover, one area that has conspicuously received scant attention is the epigenetic landscape of ectopic, eutopic and normal endometrium.

Computational methods for detecting cancer hotspots

Cancer mutations that are recurrently observed among patients are known as hotspots. Hotspots are highly relevant because they are, presumably, likely functional. Known hotspots in BRAF, PIK3CA, TP53, KRAS, IDH1 support this idea. However, hundreds of hotspots have never been validated experimentally. The detection of hotspots nevertheless is challenging because background mutations obscure their statistical and computational identification. Although several algorithms have been applied to identify hotspots, they have not been reviewed before. Thus, in this mini-review, we summarize more than 40 computational methods applied to detect cancer hotspots in coding and non-coding DNA. We first organize the methods in cluster-based, 3D, position-specific, and miscellaneous to provide a general overview. Then, we describe their embed procedures, implementations, variations, and differences. Finally, we discuss some advantages, provide some ideas for future developments, and mention opportunities such as application to viral integrations, translocations, and epigenetics.

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.

Leveraging Systematic Functional Analysis to Benchmark an In Silico Framework Distinguishes Driver from Passenger MEK Mutants in Cancer

Despite significant advances in cancer precision medicine, a significant hurdle to its broader adoption remains the multitude of variants of unknown significance identified by clinical tumor sequencing and the lack of biologically validated methods to distinguish between functional and benign variants. Here we used functional data on MAP2K1 and MAP2K2 mutations generated in real-time within a co-clinical trial framework to benchmark the predictive value of a three-part in silico methodology. Our computational approach to variant classification incorporated hotspot analysis, three-dimensional molecular dynamics simulation, and sequence paralogy. In silico prediction accurately distinguished functional from benign MAP2K1 and MAP2K2 mutants, yet drug sensitivity varied widely among activating mutant alleles. These results suggest that multifaceted in silico modeling can inform patient accrual to MEK/ERK inhibitor clinical trials, but computational methods need to be paired with laboratory- and clinic-based efforts designed to unravel variabilities in drug response. SIGNIFICANCE: Leveraging prospective functional characterization of MEK1/2 mutants, it was found that hotspot analysis, molecular dynamics simulation, and sequence paralogy are complementary tools that can robustly prioritize variants for biologic, therapeutic, and clinical validation.See related commentary by Whitehead and Sebolt-Leopold, p. 4042.

Pan-cancer multi-omics analysis and orthogonal experimental assessment of epigenetic driver genes

The recent identification of recurrently mutated epigenetic regulator genes (ERGs) supports their critical role in tumorigenesis. We conducted a pan-cancer analysis integrating (epi)genome, transcriptome, and DNA methylome alterations in a curated list of 426 ERGs across 33 cancer types, comprising 10,845 tumor and 730 normal tissues. We found that, in addition to mutations, copy number alterations in ERGs were more frequent than previously anticipated and tightly linked to expression aberrations. Novel bioinformatics approaches, integrating the strengths of various driver prediction and multi-omics algorithms, and an orthogonal in vitro screen (CRISPR-Cas9) targeting all ERGs revealed genes with driver roles within and across malignancies and shared driver mechanisms operating across multiple cancer types and hallmarks. This is the largest and most comprehensive analysis thus far; it is also the first experimental effort to specifically identify ERG drivers (epidrivers) and characterize their deregulation and functional impact in oncogenic processes.

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.

Somatic selection distinguishes oncogenes and tumor suppressor genes

Motivation

Functions of cancer driver genes vary substantially across tissues and organs. Distinguishing passenger genes, oncogenes (OGs) and tumor-suppressor genes (TSGs) for each cancer type is critical for understanding tumor biology and identifying clinically actionable targets. Although many computational tools are available to predict putative cancer driver genes, resources for context-aware classifications of OGs and TSGs are limited.

Results

We show that the direction and magnitude of somatic selection of protein-coding mutations are significantly different for passenger genes, OGs and TSGs. Based on these patterns, we develop a new method (genes under selection in tumors) to discover OGs and TSGs in a cancer-type specific manner. Genes under selection in tumors shows a high accuracy (92%) when evaluated via strict cross-validations. Its application to 10 172 tumor exomes found known and novel cancer drivers with high tissue-specificities. In 11 out of 13 OGs shared among multiple cancer types, we found functional domains selectively engaged in different cancers, suggesting differences in disease mechanisms.

Availability and implementation

An R implementation of the GUST algorithm is available at https://github.com/liliulab/gust. A database with pre-computed results is available at https://liliulab.shinyapps.io/gust.

Supplementary information

Supplementary data are available at Bioinformatics online.

Modeling and analysis of site-specific mutations in cancer identifies known plus putative novel hotspots and bias due to contextual sequences

In cancer, recurrently mutated sites in DNA and proteins, called hotspots, are thought to be raised by positive selection and therefore important due to its potential functional impact. Although recent evidence for APOBEC enzymatic activity have shown that specific types of sequences are likely to be false, the identification of putative hotspots is important to confirm either its functional role or its mechanistic bias. In this work, an algorithm and a statistical model is presented to detect hotspots. The model consists of a beta-binomial component plus fixed effects that efficiently fits the distribution of mutated sites. The algorithm employs an optimal stepwise approach to find the model parameters. Simulations show that the proposed algorithmic model is highly accurate for common hotspots. The approach has been applied to TCGA mutational data from 33 cancer types. The results show that well-known cancer hotspots are easily detected. Besides, novel hotspots are also detected. An analysis of the sequence context of detected hotspots show a preference for TCG sites that may be related to APOBEC or other unknown mechanistic biases. The detected hotspots are available online in http://bioinformatica.mty.itesm.mx/HotSpotsAnnotations.

HER family in cancer progression: From discovery to 2020 and beyond

The human epidermal growth factor receptor (HER) family of receptor tyrosine kinases (RTKs) are among the first layer of molecules that receive, interpret, and transduce signals leading to distinct cancer cell phenotypes. Since the discovery of the tooth-lid factor-later characterized as the epidermal growth factor (EGF)-and its high-affinity binding EGF receptor, HER kinases have emerged as one of the commonly upregulated or hyperactivated or mutated kinases in epithelial tumors, thus allowing HER1-3 family members to regulate several hallmarks of cancer development and progression. Each member of the HER family exhibits shared and unique structural features to engage multiple receptor activation modes, leading to a range of overlapping and distinct phenotypes. EGFR, the founding HER family member, provided the roadmap for the development of the cell surface RTK-directed targeted cancer therapy by serving as a prototype/precursor for the currently used HER-directed cancer drugs. We herein provide a brief account of the discoveries, defining moments, and historical context of the HER family and guidepost advances in basic, translational, and clinical research that solidified a prominent position of the HER family in cancer research and treatment. We also discuss the significance of HER3 pseudokinase in cancer biology; its unique structural features that drive transregulation among HER1-3, leading to a superior proximal signaling response; and potential role of HER3 as a shared effector of acquired therapeutic resistance against diverse oncology drugs. Finally, we also narrate some of the current drawbacks of HER-directed therapies and provide insights into postulated advances in HER biology with extensive implications of these therapies in cancer research and treatment.

Phase and context shape the function of composite oncogenic mutations

Cancers develop as a result of driver mutations^1,2 that lead to clonal outgrowth and the evolution of disease^3,4. The discovery and functional characterization of individual driver mutations are central aims of cancer research, and have elucidated myriad phenotypes⁵ and therapeutic vulnerabilities⁶. However, the serial genetic evolution of mutant cancer genes^7,8 and the allelic context in which they arise is poorly understood in both common and rare cancer genes and tumour types. Here we find that nearly one in four human tumours contains a composite mutation of a cancer-associated gene, defined as two or more nonsynonymous somatic mutations in the same gene and tumour. Composite mutations are enriched in specific genes, have an elevated rate of use of less-common hotspot mutations acquired in a chronology driven in part by oncogenic fitness, and arise in an allelic configuration that reflects context-specific selective pressures. cis-acting composite mutations are hypermorphic in some genes in which dosage effects predominate (such as TERT), whereas they lead to selection of function in other genes (such as TP53). Collectively, composite mutations are driver alterations that arise from context- and allele-specific selective pressures that are dependent in part on gene and mutation function, and which lead to complex-often neomorphic-functions of biological and therapeutic importance.

Functional characterization of MCAK/Kif2C cancer mutations using high-throughput microscopic analysis

The microtubule (MT)-depolymerizing activity of MCAK/Kif2C can be quantified by expressing the motor in cultured cells and measuring tubulin fluorescence levels after enough hours have passed to allow tubulin autoregulation to proceed. This method allows us to score the impact of point mutations within the motor domain. We found that, despite their distinctly different activities, many mutations that impact transport kinesins also impair MCAK/Kif2C's depolymerizing activity. We improved our workflow using CellProfiler to significantly speed up the imaging and analysis of transfected cells. This allowed us to rapidly interrogate a number of MCAK/Kif2C motor domain mutations documented in the cancer database cBioPortal. We found that a large proportion of these mutations adversely impact the motor. Using green fluorescent protein-FKBP-MCAK CRISPR cells we found that one deleterious hot-spot mutation increased chromosome instability in a wild-type (WT) background, suggesting that such mutants have the potential to promote tumor karyotype evolution. We also found that increasing WT MCAK/Kif2C protein levels over that of endogenous MCAK/Kif2C similarly increased chromosome instability. Thus, endogenous MCAK/Kif2C activity in normal cells is tuned to a mean level to achieve maximal suppression of chromosome instability.

The paradox of cancer genes in non-malignant conditions: implications for precision medicine

Next-generation sequencing has enabled patient selection for targeted drugs, some of which have shown remarkable efficacy in cancers that have the cognate molecular signatures. Intriguingly, rapidly emerging data indicate that altered genes representing oncogenic drivers can also be found in sporadic non-malignant conditions, some of which have negligible and/or low potential for transformation to cancer. For instance, activating KRAS mutations are discerned in endometriosis and in brain arteriovenous malformations, inactivating TP53 tumor suppressor mutations in rheumatoid arthritis synovium, and AKT, MAPK, and AMPK pathway gene alterations in the brains of Alzheimer's disease patients. Furthermore, these types of alterations may also characterize hereditary conditions that result in diverse disabilities and that are associated with a range of lifetime susceptibility to the development of cancer, varying from near universal to no elevated risk. Very recently, the repurposing of targeted cancer drugs for non-malignant conditions that are associated with these genomic alterations has yielded therapeutic successes. For instance, the phenotypic manifestations of CLOVES syndrome, which is characterized by tissue overgrowth and complex vascular anomalies that result from the activation of PIK3CA mutations, can be ameliorated by the PIK3CA inhibitor alpelisib, which was developed and approved for breast cancer. In this review, we discuss the profound implications of finding molecular alterations in non-malignant conditions that are indistinguishable from those driving cancers, with respect to our understanding of the genomic basis of medicine, the potential confounding effects in early cancer detection that relies on sensitive blood tests for oncogenic mutations, and the possibility of reverse repurposing drugs that are used in oncology in order to ameliorate non-malignant illnesses and/or to prevent the emergence of cancer.

Identification of cancer driver genes based on nucleotide context

Cancer genomes contain large numbers of somatic mutations, but few of these mutations drive tumor development. Current approaches identify driver genes based on mutational recurrence, or they approximate the functional consequences of nonsynonymous mutations using bioinformatic scores. While passenger mutations are enriched in characteristic nucleotide contexts, driver mutations occur in functional positions, which are not necessarily surrounded by a particular nucleotide context. We observed that mutations in contexts that deviate from the characteristic contexts around passenger mutations provide a signal in favor of driver genes. We therefore developed a method that combines this feature with the signals traditionally used for driver gene identification. We applied our method to whole-exome sequencing data from 11,873 tumor-normal pairs and identified 460 driver genes that clustered into 21 cancer-related pathways. Our study provides a resource of driver genes across 28 tumor types with additional driver genes identified based on mutations in unusual nucleotide contexts.