Universal Patterns of Selection in Cancer and Somatic Tissues

Genomic evidence supports a clonal diaspora model for metastases of esophageal adenocarcinoma

The poor outcomes in esophageal adenocarcinoma (EAC) prompted us to interrogate the pattern and timing of metastatic spread. Whole-genome sequencing and phylogenetic analysis of 388 samples across 18 individuals with EAC showed, in 90% of patients, that multiple subclones from the primary tumor spread very rapidly from the primary site to form multiple metastases, including lymph nodes and distant tissues-a mode of dissemination that we term 'clonal diaspora'. Metastatic subclones at autopsy were present in tissue and blood samples from earlier time points. These findings have implications for our understanding and clinical evaluation of EAC.

Large-scale analysis of acquired chromosomal alterations in non-tumor samples from patients with cancer

Mosaicism, the presence of subpopulations of cells bearing somatic mutations, is associated with disease and aging and has been detected in diverse tissues, including apparently normal cells adjacent to tumors. To analyze mosaicism on a large scale, we surveyed haplotype-specific somatic copy number alterations (sCNAs) in 1,708 normal-appearing adjacent-to-tumor (NAT) tissue samples from 27 cancer sites and in 7,149 blood samples from The Cancer Genome Atlas. We find substantial variation across tissues in the rate, burden and types of sCNAs, including those spanning entire chromosome arms. We document matching sCNAs in the NAT tissue and the adjacent tumor, suggesting a shared clonal origin, as well as instances in which both NAT tissue and tumor tissue harbor a gain of the same oncogene arising in parallel from distinct parental haplotypes. These results shed light on pan-tissue mutations characteristic of field cancerization, the presence of oncogenic processes adjacent to cancer cells.

HotSpotAnnotations-a database for hotspot mutations and annotations in cancer

Hotspots, recurrently mutated DNA positions in cancer, are thought to be oncogenic drivers because random chance is unlikely and the knowledge of clear examples of oncogenic hotspots in genes like BRAF, IDH1, KRAS and NRAS among many other genes. Hotspots are attractive because provide opportunities for biomedical research and novel treatments. Nevertheless, recent evidence, such as DNA hairpins for APOBEC3A, suggests that a considerable fraction of hotspots seem to be passengers rather than drivers. To document hotspots, the database HotSpotsAnnotations is proposed. For this, a statistical model was implemented to detect putative hotspots, which was applied to TCGA cancer datasets covering 33 cancer types, 10 182 patients and 3 175 929 mutations. Then, genes and hotspots were annotated by two published methods (APOBEC3A hairpins and dN/dS ratio) that may inform and warn researchers about possible false functional hotspots. Moreover, manual annotation from users can be added and shared. From the 23 198 detected as possible hotspots, 4435 were selected after false discovery rate correction and minimum mutation count. From these, 305 were annotated as likely for APOBEC3A whereas 442 were annotated as unlikely. To date, this is the first database dedicated to annotating hotspots for possible false functional hotspots.

Revisiting the tumorigenesis timeline with a data-driven generative model

Recently, investigators have shown that only a few driver gene mutational events appear to be needed for cancer to occur. However, the reason that some mutational events precede others in the same cancer and the explanation for tissue-specific differences in this timing, remain mysterious. We here combine mathematical modeling with epidemiologic studies and sequencing data to address these questions. We suggest that the first driver event in cancers generally occurred at early ages and provide estimates for the fitness of different types of drivers during tumor evolution, showing how they vary with the tissue of origin.

Cancer is driven by the sequential accumulation of genetic and epigenetic changes in oncogenes and tumor suppressor genes. The timing of these events is not well understood. Moreover, it is currently unknown why the same driver gene change appears as an early event in some cancer types and as a later event, or not at all, in others. These questions have become even more topical with the recent progress brought by genome-wide sequencing studies of cancer. Focusing on mutational events, we provide a mathematical model of the full process of tumor evolution that includes different types of fitness advantages for driver genes and carrying-capacity considerations. The model is able to recapitulate a substantial proportion of the observed cancer incidence in several cancer types (colorectal, pancreatic, and leukemia) and inherited conditions (Lynch and familial adenomatous polyposis), by changing only 2 tissue-specific parameters: the number of stem cells in a tissue and its cell division frequency. The model sheds light on the evolutionary dynamics of cancer by suggesting a generalized early onset of tumorigenesis followed by slow mutational waves, in contrast to previous conclusions. Formulas and estimates are provided for the fitness increases induced by driver mutations, often much larger than previously described, and highly tissue dependent. Our results suggest a mechanistic explanation for why the selective fitness advantage introduced by specific driver genes is tissue dependent.

Extensive subclonal mutational diversity in human colorectal cancer and its significance

Human colorectal cancers (CRCs) contain both clonal and subclonal mutations. Clonal driver mutations are positively selected, present in most cells, and drive malignant progression. Subclonal mutations are randomly dispersed throughout the genome, providing a vast reservoir of mutant cells that can expand, repopulate the tumor, and result in the rapid emergence of resistance, as well as being a major contributor to tumor heterogeneity. Here, we apply duplex sequencing (DS) methodology to quantify subclonal mutations in CRC tumor with unprecedented depth (104) and accuracy (<10-7). We measured mutation frequencies in genes encoding replicative DNA polymerases and in genes frequently mutated in CRC, and found an unexpectedly high effective mutation rate, 7.1 × 10-7. The curve of subclonal mutation accumulation as a function of sequencing depth, using DNA obtained from 5 different tumors, is in accord with a neutral model of tumor evolution. We present a theoretical approach to model neutral evolution independent of the infinite-sites assumption (which states that a particular mutation arises only in one tumor cell at any given time). Our analysis indicates that the infinite-sites assumption is not applicable once the number of tumor cells exceeds the reciprocal of the mutation rate, a circumstance relevant to even the smallest clinically diagnosable tumor. Our methods allow accurate estimation of the total mutation burden in clinical cancers. Our results indicate that no DNA locus is wild type in every malignant cell within a tumor at the time of diagnosis (probability of all cells being wild type, 10-308).

The somatic mutation landscape of the human body

BACKGROUND

Somatic mutations in healthy tissues contribute to aging, neurodegeneration, and cancer initiation, yet they remain largely uncharacterized.

RESULTS

To gain a better understanding of the genome-wide distribution and functional impact of somatic mutations, we leverage the genomic information contained in the transcriptome to uniformly call somatic mutations from over 7500 tissue samples, representing 36 distinct tissues. This catalog, containing over 280,000 mutations, reveals a wide diversity of tissue-specific mutation profiles associated with gene expression levels and chromatin states. For example, lung samples with low expression of the mismatch-repair gene MLH1 show a mutation signature of deficient mismatch repair. In addition, we find pervasive negative selection acting on missense and nonsense mutations, except for mutations previously observed in cancer samples, which are under positive selection and are highly enriched in many healthy tissues.

CONCLUSIONS

These findings reveal fundamental patterns of tissue-specific somatic evolution and shed light on aging and the earliest stages of tumorigenesis.

Few SINEs of life: Alu elements have little evidence for biological relevance despite elevated translation

Transposable elements colonize genomes and with time may end up being incorporated into functional regions. SINE Alu elements, which appeared in the primate lineage, are ubiquitous in the human genome and more than a thousand overlap annotated coding exons. Although almost all Alu-derived coding exons appear to be in alternative transcripts, they have been incorporated into the main coding transcript in at least 11 genes. The extent to which Alu regions are incorporated into functional proteins is unclear, but we detected reliable peptide evidence to support the translation to protein of 33 Alu-derived exons. All but one of the Alu elements for which we detected peptides were frame-preserving and there was proportionally seven times more peptide evidence for Alu elements as for other primate exons. Despite this strong evidence for translation to protein we found no evidence of selection, either from cross species alignments or human population variation data, among these Alu-derived exons. Overall, our results confirm that SINE Alu elements have contributed to the expansion of the human proteome, and this contribution appears to be stronger than might be expected over such a relatively short evolutionary timeframe. Despite this, the biological relevance of these modifications remains open to question.

Somatic inflammatory gene mutations in human ulcerative colitis epithelium

With ageing, normal human tissues experience an expansion of somatic clones that carry cancer mutations^1-7. However, whether such clonal expansion exists in the non-neoplastic intestine remains unknown. Here, using whole-exome sequencing data from 76 clonal human colon organoids, we identify a unique pattern of somatic mutagenesis in the inflamed epithelium of patients with ulcerative colitis. The affected epithelium accumulates somatic mutations in multiple genes that are related to IL-17 signalling-including NFKBIZ, ZC3H12A and PIGR, which are genes that are rarely affected in colon cancer. Targeted sequencing validates the pervasive spread of mutations that are related to IL-17 signalling. Unbiased CRISPR-based knockout screening in colon organoids reveals that the mutations confer resistance to the pro-apoptotic response that is induced by IL-17A. Some of these genetic mutations are known to exacerbate experimental colitis in mice^8-11, and somatic mutagenesis in human colon epithelium may be causally linked to the inflammatory process. Our findings highlight a genetic landscape that adapts to a hostile microenvironment, and demonstrate its potential contribution to the pathogenesis of ulcerative colitis.

WITER: a powerful method for estimation of cancer-driver genes using a weighted iterative regression modelling background mutation counts

Genomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modelling distribution of background mutation counts, existing statistical methods are often underpowered to discriminate cancer-driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER, http://grass.cgs.hku.hk/limx/witer or KGGSeq,http://grass.cgs.hku.hk/limx/kggseq/), to detect cancer-driver genes showing an excess of somatic mutations. By fitting the distribution of background mutation counts properly, this approach works well even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in most tested cancers. Applying this approach, we estimated 229 driver genes in 26 different types of cancers. In silico validation confirmed 78% of predicted genes as likely known drivers and many other genes as very likely new drivers for corresponding cancers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects and rescue of more genes missed by alternative tools in moderate or small samples.

Using somatic variant richness to mine signals from rare variants in the cancer genome

To date, the vast preponderance of somatic variants observed in the cancer genome have been rare variants, and it is common in practice to encounter in a new tumor variants that have not been observed previously. Here we focus on probability estimation for encountering such hitherto unseen variants. We draw upon statistical methodology that has been developed in other fields of study, notably in species estimation in ecology, and word frequency estimation in computational linguistics. Analysis of whole-exome and targeted panel sequencing data sets reveal substantial variability in variant "richness" between genes that could be harnessed for clinically relevant problems. We quantify the variant-tissue association and show a strong gene-specific, lineage-dependent pattern of encountering new variants. This variability is largely determined by the proportion of observed variants that are rare. Our findings suggest that variants that occur at very low frequencies can harbor important signals that are clinically consequential.

Lack of detectable neoantigen depletion signals in the untreated cancer genome

Somatic mutations can result in the formation of neoantigens, immunogenic peptides that are presented on the tumor cell surface by HLA molecules. These mutations are expected to be under negative selection pressure, but the extent of the resulting neoantigen depletion remains unclear. On the basis of HLA affinity predictions, we annotated the human genome for its translatability to HLA binding peptides and screened for reduced single nucleotide substitution rates in large genomic data sets from untreated cancers. Apparent neoantigen depletion signals become negligible when taking into consideration trinucleotide-based mutational signatures, owing to lack of power or to efficient immune evasion mechanisms that are active early during tumor evolution.

Longitudinal molecular trajectories of diffuse glioma in adults

The evolutionary processes that drive universal therapeutic resistance in adult patients with diffuse glioma remain unclear1,2. Here we analysed temporally separated DNA-sequencing data and matched clinical annotation from 222 adult patients with glioma. By analysing mutations and copy numbers across the three major subtypes of diffuse glioma, we found that driver genes detected at the initial stage of disease were retained at recurrence, whereas there was little evidence of recurrence-specific gene alterations. Treatment with alkylating agents resulted in a hypermutator phenotype at different rates across the glioma subtypes, and hypermutation was not associated with differences in overall survival. Acquired aneuploidy was frequently detected in recurrent gliomas and was characterized by IDH mutation but without co-deletion of chromosome arms 1p/19q, and further converged with acquired alterations in the cell cycle and poor outcomes. The clonal architecture of each tumour remained similar over time, but the presence of subclonal selection was associated with decreased survival. Finally, there were no differences in the levels of immunoediting between initial and recurrent gliomas. Collectively, our results suggest that the strongest selective pressures occur during early glioma development and that current therapies shape this evolution in a largely stochastic manner.

Radiation Drives the Evolution of Orthotopic Xenografts Initiated from Glioblastoma Stem-like Cells

A consequence of the intratumor heterogeneity (ITH) of glioblastoma (GBM) is the susceptibility to treatment-driven evolution. To determine the potential of radiotherapy to influence GBM evolution, we used orthotopic xenografts initiated from CD133+ GBM stem-like cells (GSC). Toward this end, orthotopic xenografts grown in nude mice were exposed to a fractionated radiation protocol, which resulted in a significant increase in animal survival. Brain tumors from control and irradiated mice were then collected at morbidity and compared in terms of growth pattern, clonal diversity, and genomic architecture. In mice that received fractionated radiation, tumors were less invasive, with more clearly demarcated borders and tumor core hypercellularity as compared with controls, suggesting a fundamental change in tumor biology. Viral integration site analysis indicated a reduction in clonal diversity in the irradiated tumors, implying a decrease in ITH. Changes in clonal diversity were not detected after irradiation of GSCs in vitro, suggesting that the radiation-induced reduction in ITH was dependent on the brain microenvironment. Whole-exome sequencing revealed differences in mutation patterns between control and irradiated tumors, which included modifications in the presence and clonality of driver mutations associated with GBM. Moreover, changes in the distribution of mutations as a function of subpopulation size between control and irradiated tumors were consistent with subclone expansion and contraction, that is, subpopulation evolution. Taken together, these results indicate that radiation drives the evolution of the GSC-initiated orthotopic xenografts and suggest that radiation-driven evolution may have therapeutic implications for recurrent GBM. SIGNIFICANCE: Radiation drives the evolution of glioblastoma orthotopic xenografts; when translated to the clinic, this may have therapeutic implications for recurrent tumors.

Identification and ranking of recurrent neo-epitopes in cancer

Background

Immune escape is one of the hallmarks of cancer and several new treatment approaches attempt to modulate and restore the immune system’s capability to target cancer cells. At the heart of the immune recognition process lies antigen presentation from somatic mutations. These neo-epitopes are emerging as attractive targets for cancer immunotherapy and new strategies for rapid identification of relevant candidates have become a priority.

Methods

We carefully screen TCGA data sets for recurrent somatic amino acid exchanges and apply MHC class I binding predictions.

Results

We propose a method for in silico selection and prioritization of candidates which have a high potential for neo-antigen generation and are likely to appear in multiple patients. While the percentage of patients carrying a specific neo-epitope and HLA-type combination is relatively small, the sheer number of new patients leads to surprisingly high reoccurence numbers. We identify 769 epitopes which are expected to occur in 77629 patients per year.

Conclusion

While our candidate list will definitely contain false positives, the results provide an objective order for wet-lab testing of reusable neo-epitopes. Thus recurrent neo-epitopes may be suitable to supplement existing personalized T cell treatment approaches with precision treatment options.

The genomic landscape of metastatic castration-resistant prostate cancers reveals multiple distinct genotypes with potential clinical impact

Metastatic castration-resistant prostate cancer (mCRPC) has a highly complex genomic landscape. With the recent development of novel treatments, accurate stratification strategies are needed. Here we present the whole-genome sequencing (WGS) analysis of fresh-frozen metastatic biopsies from 197 mCRPC patients. Using unsupervised clustering based on genomic features, we define eight distinct genomic clusters. We observe potentially clinically relevant genotypes, including microsatellite instability (MSI), homologous recombination deficiency (HRD) enriched with genomic deletions and BRCA2 aberrations, a tandem duplication genotype associated with CDK12-/- and a chromothripsis-enriched subgroup. Our data suggests that stratification on WGS characteristics may improve identification of MSI, CDK12-/- and HRD patients. From WGS and ChIP-seq data, we show the potential relevance of recurrent alterations in non-coding regions identified with WGS and highlight the central role of AR signaling in tumor progression. These data underline the potential value of using WGS to accurately stratify mCRPC patients into clinically actionable subgroups.

Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

BACKGROUND

Systematic interrogation of single-nucleotide variants (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single-cell level. While SNV detection from abundant single-cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed.

RESULTS

Here, we perform a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2, and VarScan2, using both simulation and scRNA-seq datasets, and identify multiple elements influencing their performance. While the specificities are generally high, with sensitivities exceeding 90% for most tools when calling homozygous SNVs in high-confident coding regions with sufficient read depths, such sensitivities dramatically decrease when calling SNVs with low read depths, low variant allele frequencies, or in specific genomic contexts. SAMtools shows the highest sensitivity in most cases especially with low supporting reads, despite the relatively low specificity in introns or high-identity regions. Strelka2 shows consistently good performance when sufficient supporting reads are provided, while FreeBayes shows good performance in the cases of high variant allele frequencies.

CONCLUSIONS

We recommend SAMtools, Strelka2, FreeBayes, or CTAT, depending on the specific conditions of usage. Our study provides the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.

Contrasting the impact of cytotoxic and cytostatic drug therapies on tumour progression

A tumour grows when the total division (birth) rate of its cells exceeds their total mortality (death) rate. The capability for uncontrolled growth within the host tissue is acquired via the accumulation of driver mutations which enable the tumour to progress through various hallmarks of cancer. We present a mathematical model of the penultimate stage in such a progression. We assume the tumour has reached the limit of its present growth potential due to cell competition that either results in total birth rate reduction or death rate increase. The tumour can then progress to the final stage by either seeding a metastasis or acquiring a driver mutation. We influence the ensuing evolutionary dynamics by cytotoxic (increasing death rate) or cytostatic (decreasing birth rate) therapy while keeping the effect of the therapy on net growth reduction constant. Comparing the treatments head to head we derive conditions for choosing optimal therapy. We quantify how the choice and the related gain of optimal therapy depends on driver mutation, metastasis, intrinsic cell birth and death rates, and the details of cell competition. We show that detailed understanding of the cell population dynamics could be exploited in choosing the right mode of treatment with substantial therapy gains.

Cells and organisms evolve to better survive in their environments and to adapt to new challenges. Such dynamics manifest in a particularly problematic way with the evolution of drug resistance, which is increasingly recognized as a key challenge for global health. Thus, developing therapy paradigms that factor in evolutionary dynamics is an important goal. Using a minimal mathematical model of a cancer cell population we contrast cytotoxic (increasing death rate) and cytostatic (decreasing birth rate) treatments while keeping the effect of the therapy on the net growth reduction constant. We then quantify how the choice and the related gain of optimal therapy depends on driver mutation, metastasis, intrinsic cell birth and death rates and the details of cell competition. Most importantly, we identify specific cell population dynamics under which a certain treatment could be significantly better than the alternative.

Somatic Mutations and Genome Stability Maintenance in Clonal Coral Colonies

One challenge for multicellular organisms is maintaining genome stability in the face of mutagens across long life spans. Imperfect genome maintenance leads to mutation accumulation in somatic cells, which is associated with tumors and senescence in vertebrates. Colonial reef-building corals are often large, can live for hundreds of years, rarely develop recognizable tumors, and are thought to convert somatic cells into gamete producers, so they are a pivotal group in which to understand long-term genome maintenance. To measure rates and patterns of somatic mutations, we analyzed transcriptomes from 17 to 22 branches from each of four Acropora hyacinthus colonies, determined putative single nucleotide variants, and verified them with Sanger resequencing. Unlike for human skin carcinomas, there is no signature of mutations caused by UV damage, indicating either higher efficiency of repair than in vertebrates, or strong sunscreen protection in these shallow water tropical animals. The somatic mutation frequency per nucleotide in A. hyacinthus is on the same order of magnitude (10−7) as noncancerous human somatic cells, and accumulation of mutations with age is similar. Loss of heterozygosity variants outnumber gain of heterozygosity mutations ∼2:1. Although the mutation frequency is similar in mammals and corals, the preponderance of loss of heterozygosity changes and potential selection may reduce the frequency of deleterious mutations in colonial animals like corals. This may limit the deleterious effects of somatic mutations on the coral organism as well as potential offspring.

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

Tumors are made of evolving and heterogeneous populations of cells which arise from successive appearance and expansion of subclonal populations, following acquisition of mutations conferring them a selective advantage. Those subclonal populations can be sensitive or resistant to different treatments, and provide information about tumor aetiology and future evolution. Hence, it is important to be able to assess the level of heterogeneity of tumors with high reliability for clinical applications. In the past few years, a large number of methods have been proposed to estimate intra-tumor heterogeneity from whole exome sequencing (WES) data, but the accuracy and robustness of these methods on real data remains elusive. Here we systematically apply and compare 6 computational methods to estimate tumor heterogeneity on 1,697 WES samples from the cancer genome atlas (TCGA) covering 3 cancer types (breast invasive carcinoma, bladder urothelial carcinoma, and head and neck squamous cell carcinoma), and two distinct input mutation sets. We observe significant differences between the estimates produced by different methods, and identify several likely confounding factors in heterogeneity assessment for the different methods. We further show that the prognostic value of tumor heterogeneity for survival prediction is limited in those datasets, and find no evidence that it improves over prognosis based on other clinical variables. In conclusion, heterogeneity inference from WES data on a single sample, and its use in cancer prognosis, should be considered with caution. Other approaches to assess intra-tumoral heterogeneity such as those based on multiple samples may be preferable for clinical applications.

Applications and analysis of targeted genomic sequencing in cancer studies

Next Generation Sequencing (NGS) has dramatically improved the flexibility and outcomes of cancer research and clinical trials, providing highly sensitive and accurate high-throughput platforms for large-scale genomic testing. In contrast to whole-genome (WGS) or whole-exome sequencing (WES), targeted genomic sequencing (TS) focuses on a panel of genes or targets known to have strong associations with pathogenesis of disease and/or clinical relevance, offering greater sequencing depth with reduced costs and data burden. This allows targeted sequencing to identify low frequency variants in targeted regions with high confidence, thus suitable for profiling low-quality and fragmented clinical DNA samples. As a result, TS has been widely used in clinical research and trials for patient stratification and the development of targeted therapeutics. However, its transition to routine clinical use has been slow. Many technical and analytical obstacles still remain and need to be discussed and addressed before large-scale and cross-centre implementation. Gold-standard and state-of-the-art procedures and pipelines are urgently needed to accelerate this transition. In this review we first present how TS is conducted in cancer research, including various target enrichment platforms, the construction of target panels, and selected research and clinical studies utilising TS to profile clinical samples. We then present a generalised analytical workflow for TS data discussing important parameters and filters in detail, aiming to provide the best practices of TS usage and analyses.

Evolution and Impact of Subclonal Mutations in Papillary Thyroid Cancer

Unlike many cancers, the pattern of tumor evolution in papillary thyroid cancer (PTC) and its potential role in relapse have not been elucidated. In this study, multi-region whole-exome sequencing (WES) was performed on early-stage PTC tumors (n = 257 tumor regions) from 79 individuals, including 17 who had developed relapse, to understand the temporal and spatial framework within which subclonal mutations catalyze tumor evolution and its potential clinical relevance. Paired primary-relapse tumor tissues were also available for a subset of individuals. The resulting catalog of variants was analyzed to explore evolutionary histories, define clonal and subclonal events, and assess the relationship between intra-tumor heterogeneity and relapse-free survival. The multi-region WES approach was key in correctly classifying subclonal mutations, 40% of which would have otherwise been erroneously considered clonal. We observed both linear and branching evolution patterns in our PTC cohort. A higher burden of subclonal mutations was significantly associated with increased risk of relapse. We conclude that relapse in PTC, while generally rare, does not follow a predictable evolutionary path and that subclonal mutation burden may serve as a prognostic factor. Larger studies utilizing multi-region sequencing in relapsed PTC case subjects with matching primary tissues are needed to confirm these observations.

Recurrent somatic mutations reveal new insights into consequences of mutagenic processes in cancer

The sheer size of the human genome makes it improbable that identical somatic mutations at the exact same position are observed in multiple tumours solely by chance. The scarcity of cancer driver mutations also precludes positive selection as the sole explanation. Therefore, recurrent mutations may be highly informative of characteristics of mutational processes. To explore the potential, we use recurrence as a starting point to cluster >2,500 whole genomes of a pan-cancer cohort. We describe each genome with 13 recurrence-based and 29 general mutational features. Using principal component analysis we reduce the dimensionality and create independent features. We apply hierarchical clustering to the first 18 principal components followed by k-means clustering. We show that the resulting 16 clusters capture clinically relevant cancer phenotypes. High levels of recurrent substitutions separate the clusters that we link to UV-light exposure and deregulated activity of POLE from the one representing defective mismatch repair, which shows high levels of recurrent insertions/deletions. Recurrence of both mutation types characterizes cancer genomes with somatic hypermutation of immunoglobulin genes and the cluster of genomes exposed to gastric acid. Low levels of recurrence are observed for the cluster where tobacco-smoke exposure induces mutagenesis and the one linked to increased activity of cytidine deaminases. Notably, the majority of substitutions are recurrent in a single tumour type, while recurrent insertions/deletions point to shared processes between tumour types. Recurrence also reveals susceptible sequence motifs, including TT[C>A]TTT and AAC[T>G]T for the POLE and 'gastric-acid exposure' clusters, respectively. Moreover, we refine knowledge of mutagenesis, including increased C/G deletion levels in general for lung tumours and specifically in midsize homopolymer sequence contexts for microsatellite instable tumours. Our findings are an important step towards the development of a generic cancer diagnostic test for clinical practice based on whole-genome sequencing that could replace multiple diagnostics currently in use.

Tea and Its Components Prevent Cancer: A Review of the Redox-Related Mechanism

Cancer is a worldwide epidemic and represents a major threat to human health and survival. Reactive oxygen species (ROS) play a dual role in cancer cells, which includes both promoting and inhibiting carcinogenesis. Tea remains one of the most prevalent beverages consumed due in part to its anti- or pro-oxidative properties. The active compounds in tea, particularly tea polyphenols, can directly or indirectly scavenge ROS to reduce oncogenesis and cancerometastasis. Interestingly, the excessive levels of ROS induced by consuming tea could induce programmed cell death (PCD) or non-PCD of cancer cells. On the basis of illustrating the relationship between ROS and cancer, the current review discusses the composition and efficacy of tea including the redox-relative (including anti-oxidative and pro-oxidative activity) mechanisms and their role along with other components in preventing and treating cancer. This information will highlight the basis for the clinical utilization of tea extracts in the prevention or treatment of cancer in the future.

Pan-cancer whole-genome analyses of metastatic solid tumours

Metastatic cancer is a major cause of death and is associated with poor treatment efficacy. A better understanding of the characteristics of late-stage cancer is required to help adapt personalized treatments, reduce overtreatment and improve outcomes. Here we describe the largest, to our knowledge, pan-cancer study of metastatic solid tumour genomes, including whole-genome sequencing data for 2,520 pairs of tumour and normal tissue, analysed at median depths of 106× and 38×, respectively, and surveying more than 70 million somatic variants. The characteristic mutations of metastatic lesions varied widely, with mutations that reflect those of the primary tumour types, and with high rates of whole-genome duplication events (56%). Individual metastatic lesions were relatively homogeneous, with the vast majority (96%) of driver mutations being clonal and up to 80% of tumour-suppressor genes being inactivated bi-allelically by different mutational mechanisms. Although metastatic tumour genomes showed similar mutational landscape and driver genes to primary tumours, we find characteristics that could contribute to responsiveness to therapy or resistance in individual patients. We implement an approach for the review of clinically relevant associations and their potential for actionability. For 62% of patients, we identify genetic variants that may be used to stratify patients towards therapies that either have been approved or are in clinical trials. This demonstrates the importance of comprehensive genomic tumour profiling for precision medicine in cancer.

ncdDetect2: improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation

Motivation

Understanding the mutational processes that act during cancer development is a key topic of cancer biology. Nevertheless, much remains to be learned, as a complex interplay of processes with dependencies on a range of genomic features creates highly heterogeneous cancer genomes. Accurate driver detection relies on unbiased models of the mutation rate that also capture rate variation from uncharacterized sources.

Results

Here, we analyse patterns of observed-to-expected mutation counts across 505 whole cancer genomes, and find that genomic features missing from our mutation-rate model likely operate on a megabase length scale. We extend our site-specific model of the mutation rate to include the additional variance from these sources, which leads to robust significance evaluation of candidate cancer drivers. We thus present ncdDetect v.2, with greatly improved cancer driver detection specificity. Finally, we show that ranking candidates by their posterior mean value of their effect sizes offers an equivalent and more computationally efficient alternative to ranking by their P-values.

Availability and implementation

ncdDetect v.2 is implemented as an R-package and is freely available at http://github.com/TobiasMadsen/ncdDetect2

Supplementary information

Supplementary data are available at Bioinformatics online.

Unraveling the Heterogeneous Mutational Signature of Spontaneously Developing Tumors in MLH1-/- Mice

Mismatch repair deficient (MMR-D) tumors exemplify the prototypic hypermutator phenotype. Owing to the high mutation rates, plenty of neo-antigens are present on the tumor cells' surface, ideally shared among different cancer types. The MLH1 knock out mouse represents a preclinical model that resembles features of the human MMR-D counterpart. While these mice develop neoplasias in a sequential twin-peaked manner (lymphomas > gastrointestinal tumors (GIT)) we aimed at identification of underlying molecular mechanisms. Using whole-genome sequencing, we focused on (I) shared and (II) mutually exclusive mutations and describe the process of ongoing mutational events in tumor-derived cell cultures. The landscape of MLH1^-/- tumors is heterogeneous with only a few shared mutations being detectable among different tumor entities (ARID1A and IDH2). With respect to coding microsatellite analysis of MMR-D-related target genes, partial overlap was detectable, yet recognizing shared antigens. The present study is the first reporting results of a comparison between spontaneously developing tumors in MMR-D driven tumorigenesis. Additionally to identifying ARID1A as potential causative mutation hotspot, this comprehensive characterization of the mutational landscape may be a good starting point to refine therapeutic concepts.

The landscape of somatic mutation in normal colorectal epithelial cells

The colorectal adenoma-carcinoma sequence has provided a paradigmatic framework for understanding the successive somatic genetic changes and consequent clonal expansions that lead to cancer1. However, our understanding of the earliest phases of colorectal neoplastic changes-which may occur in morphologically normal tissue-is comparatively limited, as for most cancer types. Here we use whole-genome sequencing to analyse hundreds of normal crypts from 42 individuals. Signatures of multiple mutational processes were revealed; some of these were ubiquitous and continuous, whereas others were only found in some individuals, in some crypts or during certain periods of life. Probable driver mutations were present in around 1% of normal colorectal crypts in middle-aged individuals, indicating that adenomas and carcinomas are rare outcomes of a pervasive process of neoplastic change across morphologically normal colorectal epithelium. Colorectal cancers exhibit substantially increased mutational burdens relative to normal cells. Sequencing normal colorectal cells provides quantitative insights into the genomic and clonal evolution of cancer.

Somatic mutations and clonal dynamics in healthy and cirrhotic human liver

The most common causes of chronic liver disease are excess alcohol intake, viral hepatitis and non-alcoholic fatty liver disease, with the clinical spectrum ranging in severity from hepatic inflammation to cirrhosis, liver failure or hepatocellular carcinoma (HCC). The genome of HCC exhibits diverse mutational signatures, resulting in recurrent mutations across more than 30 cancer genes1-7. Stem cells from normal livers have a low mutational burden and limited diversity of signatures8, which suggests that the complexity of HCC arises during the progression to chronic liver disease and subsequent malignant transformation. Here, by sequencing whole genomes of 482 microdissections of 100-500 hepatocytes from 5 normal and 9 cirrhotic livers, we show that cirrhotic liver has a higher mutational burden than normal liver. Although rare in normal hepatocytes, structural variants, including chromothripsis, were prominent in cirrhosis. Driver mutations, such as point mutations and structural variants, affected 1-5% of clones. Clonal expansions of millimetres in diameter occurred in cirrhosis, with clones sequestered by the bands of fibrosis that surround regenerative nodules. Some mutational signatures were universal and equally active in both non-malignant hepatocytes and HCCs; some were substantially more active in HCCs than chronic liver disease; and others-arising from exogenous exposures-were present in a subset of patients. The activity of exogenous signatures between adjacent cirrhotic nodules varied by up to tenfold within each patient, as a result of clone-specific and microenvironmental forces. Synchronous HCCs exhibited the same mutational signatures as background cirrhotic liver, but with higher burden. Somatic mutations chronicle the exposures, toxicity, regeneration and clonal structure of liver tissue as it progresses from health to disease.

Current Perspectives in Cancer Immunotherapy

Different immunotherapeutic approaches have proved to be of significant clinical value to many patients with different types of advanced cancer. However, we need more precise immunotherapies and predictive biomarkers to increase the successful response rates. The advent of next generation sequencing technologies and their applications in immuno-oncology has helped us tremendously towards this aim. We are now moving towards the realization of personalized medicine, thus, significantly increasing our expectations for a more successful management of the disease. Here, we discuss the current immunotherapeutic approaches against cancer, including immune checkpoint blockade with an emphasis on anti-PD-L1 and anti-CTLA-4 monoclonal antibodies. We also analyze a growing list of other co-inhibitory and co-stimulatory markers and emphasize the mechanism of action of the principal pathway for each of these, as well as on drugs that either have been FDA-approved or are under clinical investigation. We further discuss recent advances in other immunotherapies, including cytokine therapy, adoptive cell transfer therapy and therapeutic vaccines. We finally discuss the modulation of gut microbiota composition and response to immunotherapy, as well as how tumor-intrinsic factors and immunological processes influence the mutational and epigenetic landscape of progressing tumors and response to immunotherapy but also how immunotherapeutic intervention influences the landscape of cancer neoepitopes and tumor immunoediting.

Effect Sizes of Somatic Mutations in Cancer

A major goal of cancer biology is determination of the relative importance of the genetic alterations that confer selective advantage to cancer cells. Tumor sequence surveys have frequently ranked the importance of substitutions to cancer growth by P value or a false-discovery conversion thereof. However, P values are thresholds for belief, not metrics of effect. Their frequent misuse as metrics of effect has often been vociferously decried, even in cases when the only attributable mistake was omission of effect sizes. Here, we propose an appropriate ranking-the cancer effect size, which is the selection intensity for somatic variants in cancer cell lineages. The selection intensity is a metric of the survival and reproductive advantage conferred by mutations in somatic tissue. Thus, they are of fundamental importance to oncology, and have immediate relevance to ongoing decision making in precision medicine tumor boards, to the selection and design of clinical trials, to the targeted development of pharmaceuticals, and to basic research prioritization. Within this commentary, we first discuss the scope of current methods that rank confidence in the overrepresentation of specific mutated genes in cancer genomes. Then we bring to bear recent advances that draw upon an understanding of the development of cancer as an evolutionary process to estimate the effect size of somatic variants leading to cancer. We demonstrate the estimation of the effect sizes of all recurrent single nucleotide variants in 22 cancer types, quantifying relative importance within and between driver genes.

Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours

For cancers, such as common solid tumours, variants in the genome give a selective growth advantage to certain cells. It has recently been argued that the mean count of coding single nucleotide variants acting as disease-drivers in common solid tumours is frequently small in size, but significantly variable by cancer type (hypermutation is excluded from this study). In this paper we investigate this proposal through the use of integrative machine-learning-based classifiers we have proposed recently for predicting the disease-driver status of single nucleotide variants (SNVs) in the human cancer genome. We find that predicted driver counts are compatible with this proposal, have similar variabilities by cancer type and, to a certain extent, the drivers are identifiable by these machine learning methods. We further discuss predicted driver counts stratified by stage of disease and driver counts in non-coding regions of the cancer genome, in addition to driver-genes.

Passenger Hotspot Mutations in Cancer

Current statistical models for assessing hotspot significance do not properly account for variation in site-specific mutability, thereby yielding many false-positives. We thus (i) detail a Log-normal-Poisson (LNP) background model that accounts for this variability in a manner consistent with models of mutagenesis; (ii) use it to show that passenger hotspots arise from all common mutational processes; and (iii) apply it to a ∼10,000-patient cohort to nominate driver hotspots with far fewer false-positives compared with conventional methods. Overall, we show that many cancer hotspot mutations recurring at the same genomic site across multiple tumors are actually passenger events, recurring at inherently mutable genomic sites under no positive selection.

Radiation Biology and Its Role in the Canadian Radiation Protection Framework

The linear no-threshold (linear-non-threshold) model is a dose-response model that has long served as the foundation of the international radiation protection framework, which includes the Canadian regulatory framework. Its purpose is to inform the choice of appropriate dose limits and subsequent as low as reasonably achievable requirements, social and economic factors taken into account. The linear no-threshold model assumes that the risk of developing cancer increases proportionately with increasing radiation dose. The linear no-threshold model has historically been applied by extrapolating the risk of cancer at high doses (>1,000 mSv) down to low doses in a linear manner. As the health effects of radiation exposure at low doses remain ambiguous, reducing uncertainties found in cancer risk dose-response models can be achieved through in vitro and animal-based studies. The purpose of this critical review is to analyze whether the linear no-threshold model is still applicable for use by modern nuclear regulators for radiation protection purposes, or if there is sufficient scientific evidence supporting an alternate model from which to derive regulatory dose limits.

Rare, functional, somatic variants in gene families linked to cancer genes: GPCR signaling as a paradigm

Oncodriver genes are usually identified when mutations recur in multiple tumours. Different drivers often converge in the activation or repression of key cancer-relevant pathways. However, as many pathways contain multiple members of the same gene family, individual mutations might be overlooked, as each family member would necessarily have a lower mutation frequency and thus not identified as significant in any one-gene-at-a-time analysis. Here, we looked for mutated, functional sequence positions in gene families that were mutually exclusive (in patients) with another gene in the same pathway, which identified both known and new candidate oncodrivers. For instance, many inactivating mutations in multiple G-protein (particularly Gi/o) coupled receptors, are mutually exclusive with Gαs oncogenic activating mutations, both of which ultimately enhance cAMP signalling. By integrating transcriptomics and interaction data, we show that the Gs pathway is upregulated in multiple cancer types, even those lacking known GNAS activating mutations. This suggests that cancer cells may develop alternative strategies to activate adenylate cyclase signalling in multiple cancer types. Our study provides a mechanistic interpretation for several rare somatic mutations in multi-gene oncodrivers, and offers possible explanations for known and potential off-label cancer treatments, suggesting new therapeutic opportunities.

On measuring selection in cancer from subclonal mutation frequencies

Recently available cancer sequencing data have revealed a complex view of the cancer genome containing a multitude of mutations, including drivers responsible for cancer progression and neutral passengers. Measuring selection in cancer and distinguishing drivers from passengers have important implications for development of novel treatment strategies. It has recently been argued that a third of cancers are evolving neutrally, as their mutational frequency spectrum follows a 1/f power law expected from neutral evolution in a particular intermediate frequency range. We study a stochastic model of cancer evolution and derive a formula for the probability distribution of the cancer cell frequency of a subclonal driver, demonstrating that driver frequency is biased towards 0 and 1. We show that it is difficult to capture a driver mutation at an intermediate frequency, and thus the calling of neutrality due to a lack of such driver will significantly overestimate the number of neutrally evolving tumors. Our approach provides quantification of the validity of the 1/f statistic across the entire range of relevant parameter values. We also show that our conclusions remain valid for non-exponential models: spatial 3d model and sigmoidal growth, relevant for early- and late stages of cancer growth.

Genomic landscape and chronological reconstruction of driver events in multiple myeloma

The multiple myeloma (MM) genome is heterogeneous and evolves through preclinical and post-diagnosis phases. Here we report a catalog and hierarchy of driver lesions using sequences from 67 MM genomes serially collected from 30 patients together with public exome datasets. Bayesian clustering defines at least 7 genomic subgroups with distinct sets of co-operating events. Focusing on whole genome sequencing data, complex structural events emerge as major drivers, including chromothripsis and a novel replication-based mechanism of templated insertions, which typically occur early. Hyperdiploidy also occurs early, with individual trisomies often acquired in different chronological windows during evolution, and with a preferred order of acquisition. Conversely, positively selected point mutations, whole genome duplication and chromoplexy events occur in later disease phases. Thus, initiating driver events, drawn from a limited repertoire of structural and numerical chromosomal changes, shape preferred trajectories of evolution that are biologically relevant but heterogeneous across patients.

Chronic iron exposure and c-Myc/H-ras-mediated transformation in fallopian tube cells alter the expression of EVI1, amplified at 3q26.2 in ovarian cancer

Mechanisms underlying the pathogenesis of high-grade serous epithelial ovarian cancers (HGSOC) are not yet well defined although key precursor cells have been identified (including fimbriated fallopian tube epithelium, FTSECs). Since iron is elevated in endometriotic cysts and the pelvic cavity, it is suggested that this source of redox-active iron may contribute to ovarian cancer pathogenesis. Specifically, sources of nontransferrin-bound iron (NTBI) within the pelvic cavity could arise from ovulation, retrograde menstruation, follicular fluid, or iron overload conditions (i.e., hemochromatosis). Herein, we investigated the cellular response of p53-inactivated and telomerase-expressing (immortalized) FTSECs (Pax8⁺/FoxJ1⁻) to NTBI (presented as ferric ammonium citrate (FAC), supplemented in media for >2 months) in order to assess its ability to promote the transition to a tumor-like phenotype; this cellular response was compared with immortalized FTSECs transformed with H-Ras^V12A and c-Myc^T58A. Both approaches resulted in increased cell numbers and expression of the oncogenic transcriptional regulator, ecotropic virus integration site 1 (EVI1, a gene most frequently amplified at 3q26.2 in HGSOC, represented by multiple variants), along with other oncogenic gene products. In contrast to the transformed cells, FAC-exposed FTSECs elicited elevated migratory capacity (and epithelial–mesenchymal transition mRNA profile) along with increased expression of DNA damage response proteins (i.e., FANCD2) and hTERT mRNA relative to controls. Interestingly, in FAC-exposed FTSECs, EVI1 siRNA attenuated hTERT mRNA expression, whereas siRNAs targeting β-catenin and BMI1 (both elevated with chronic iron exposure) reduced Myc and Cyclin D1 proteins. Collectively, our novel findings provide strong foundational evidence for potential iron-induced initiation events, including EVI1 alterations, in the pathogenesis of HGSOC, warranting further in depth investigations. Thus, these findings will substantially advance our understanding of the contribution of iron enriched within the pelvic cavity, which may identify patients at risk of developing this deadly disease.

KH-Type Splicing Regulatory Protein Controls Colorectal Cancer Cell Growth and Modulates the Tumor Microenvironment

KH-type splicing regulatory protein (KHSRP) is a multifunctional nucleic acid binding protein implicated in key aspects of cancer cell biology: inflammation and cell-fate determination. However, the role KHSRP plays in colorectal cancer (CRC) tumorigenesis remains largely unknown. Using a combination of in silico analysis of large data sets, ex vivo analysis of protein expression in patients, and mechanistic studies using in vitro models of CRC, we investigated the oncogenic role of KHSRP. We demonstrated KHSRP expression in the epithelial and stromal compartments of both primary and metastatic tumors. Elevated expression was found in tumor versus matched normal tissue, and these findings were validated in larger independent cohorts in silico. KHSRP expression was a prognostic indicator of worse overall survival (hazard ratio, 3.74; 95% CI, 1.43-22.97; P = 0.0138). Mechanistic data in CRC cell line models supported a role of KHSRP in driving epithelial cell proliferation in both a primary and metastatic setting, through control of the G₁/S transition. In addition, KHSRP promoted a proangiogenic extracellular environment by regulating the secretion of oncogenic proteins involved in diverse cellular processes, such as migration and response to cellular stress. Our study provides novel mechanistic insight into the tumor-promoting effects of KHSRP in CRC.

Proteomic and genomic signatures of repeat instability in cancer and adjacent normal tissues

Repetitive sequences are hotspots of evolution at multiple levels. However, due to difficulties involved in their assembly and analysis, the role of repeats in tumor evolution is poorly understood. We developed a rigorous motif-based methodology to quantify variations in the repeat content, beyond microsatellites, in proteomes and genomes directly from proteomic and genomic raw data. This method was applied to a wide range of tumors and normal tissues. We identify high similarity between repeat instability patterns in tumors and their patient-matched adjacent normal tissues. Nonetheless, tumor-specific signatures both in protein expression and in the genome strongly correlate with cancer progression and robustly predict the tumorigenic state. In a patient, the hierarchy of genomic repeat instability signatures accurately reconstructs tumor evolution, with primary tumors differentiated from metastases. We observe an inverse relationship between repeat instability and point mutation load within and across patients independent of other somatic aberrations. Thus, repeat instability is a distinct, transient, and compensatory adaptive mechanism in tumor evolution and a potential signal for early detection.

Somatic evolution and global expansion of an ancient transmissible cancer lineage

The canine transmissible venereal tumor (CTVT) is a cancer lineage that arose several millennia ago and survives by "metastasizing" between hosts through cell transfer. The somatic mutations in this cancer record its phylogeography and evolutionary history. We constructed a time-resolved phylogeny from 546 CTVT exomes and describe the lineage's worldwide expansion. Examining variation in mutational exposure, we identify a highly context-specific mutational process that operated early in the cancer's evolution but subsequently vanished, correlate ultraviolet-light mutagenesis with tumor latitude, and describe tumors with heritable hyperactivity of an endogenous mutational process. CTVT displays little evidence of ongoing positive selection, and negative selection is detectable only in essential genes. We illustrate how long-lived clonal organisms capture changing mutagenic environments, and reveal that neutral genetic drift is the dominant feature of long-term cancer evolution.

Detailed modeling of positive selection improves detection of cancer driver genes

Identifying driver genes from somatic mutations is a central problem in cancer biology. Existing methods, however, either lack explicit statistical models, or use models based on simplistic assumptions. Here, we present driverMAPS (Model-based Analysis of Positive Selection), a model-based approach to driver gene identification. This method explicitly models positive selection at the single-base level, as well as highly heterogeneous background mutational processes. In particular, the selection model captures elevated mutation rates in functionally important sites using multiple external annotations, and spatial clustering of mutations. Simulations under realistic evolutionary models demonstrate the increased power of driverMAPS over current approaches. Applying driverMAPS to TCGA data of 20 tumor types, we identified 159 new potential driver genes, including the mRNA methyltransferase METTL3-METTL14. We experimentally validated METTL3 as a tumor suppressor gene in bladder cancer, providing support to the important role mRNA modification plays in tumorigenesis.

Finding driver genes sheds lights on the biological mechanisms propelling the development of a tumour, and can suggest therapeutic strategies. Here, the authors develop driverMAPS, a model-based approach to identify driver genes, and apply it to TCGA datasets.

Somatic and Germline Mutation Periodicity Follow the Orientation of the DNA Minor Groove around Nucleosomes

Mutation rates along the genome are highly variable and influenced by several chromatin features. Here, we addressed how nucleosomes, the most pervasive chromatin structure in eukaryotes, affect the generation of mutations. We discovered that within nucleosomes, the somatic mutation rate across several tumor cohorts exhibits a strong 10 base pair (bp) periodicity. This periodic pattern tracks the alternation of the DNA minor groove facing toward and away from the histones. The strength and phase of the mutation rate periodicity are determined by the mutational processes active in tumors. We uncovered similar periodic patterns in the genetic variation among human and Arabidopsis populations, also detectable in their divergence from close species, indicating that the same principles underlie germline and somatic mutation rates. We propose that differential DNA damage and repair processes dependent on the minor groove orientation in nucleosome-bound DNA contribute to the 10-bp periodicity in AT/CG content in eukaryotic genomes.

Relating evolutionary selection and mutant clonal dynamics in normal epithelia

Cancer develops from mutated cells in normal tissues. Whether somatic mutations alter normal cell dynamics is key to understanding cancer risk and guiding interventions to reduce it. An analysis of the first incomplete moment of size distributions of clones carrying cancer-associated mutations in normal human eyelid skin gives a good fit with neutral drift, arguing mutations do not affect cell fate. However, this suggestion conflicts with genetic evidence in the same dataset that argues for strong positive selection of a subset of mutations. This implies cells carrying these mutations have a competitive advantage over normal cells, leading to large clonal expansions within the tissue. In the normal epithelium, clone growth is constrained by the limited size of the proliferating compartment and competition with surrounding cells. We show that if these factors are taken into account, the first incomplete moment of the clone size distribution is unable to exclude non-neutral behaviour. Furthermore, experimental factors can make a non-neutral clone size distribution appear neutral. We validate these principles with a new experimental dataset showing that when experiments are appropriately designed, the first incomplete moment can be a useful indicator of non-neutral competition. Finally, we discuss the complex relationship between mutant clone sizes and genetic selection.

Quantifying immune-based counterselection of somatic mutations

Somatic mutations in protein-coding regions can generate ‘neoantigens’ causing developing cancers to be eliminated by the immune system. Quantitative estimates of the strength of this counterselection phenomenon have been lacking. We quantified the extent to which somatic mutations are depleted in peptides that are predicted to be displayed by major histocompatibility complex (MHC) class I proteins. The extent of this depletion depended on expression level of the neoantigenic gene, and on whether the patient had one or two MHC-encoding alleles that can display the peptide, suggesting MHC-encoding alleles are incompletely dominant. This study provides an initial quantitative understanding of counter-selection of identifiable subclasses of neoantigenic somatic variation.

Cancer immunotherapy and personalized cancer vaccines depend on clearance of cancer and pre-cancer cells by the immune system. However, little is known about the strength of this phenomenon as it acts on the cell populations which give rise to tumors. Here we provide an initial quantitative estimate of the fraction of neo-antigen-containing cells in this population that are cleared by the MHC class I-dependent immune system. The impacts of both neo-antigenic gene expression and the number of neo-antigen-displaying MHC alleles on this clearance phenomenon were examined. A more complete understanding of immune clearance of neoantigenic cells and how this phenomenon varies between patients and cancers, has the potential to guide immunotherapy and cancer vaccines.

Whole-genome landscape of mucosal melanoma reveals diverse drivers and therapeutic targets

Knowledge of key drivers and therapeutic targets in mucosal melanoma is limited due to the paucity of comprehensive mutation data on this rare tumor type. To better understand the genomic landscape of mucosal melanoma, here we describe whole genome sequencing analysis of 67 tumors and validation of driver gene mutations by exome sequencing of 45 tumors. Tumors have a low point mutation burden and high numbers of structural variants, including recurrent structural rearrangements targeting TERT, CDK4 and MDM2. Significantly mutated genes are NRAS, BRAF, NF1, KIT, SF3B1, TP53, SPRED1, ATRX, HLA-A and CHD8. SF3B1 mutations occur more commonly in female genital and anorectal melanomas and CTNNB1 mutations implicate a role for WNT signaling defects in the genesis of some mucosal melanomas. TERT aberrations and ATRX mutations are associated with alterations in telomere length. Mutation profiles of the majority of mucosal melanomas suggest potential susceptibility to CDK4/6 and/or MEK inhibitors.

Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma

The identification of cancer-promoting genetic alterations is challenging particularly in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we describe a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously. Analysing 261 EACs from the OCCAMS Consortium, we discover helper genes that, alongside well-known drivers, promote cancer. We confirm the robustness of our approach in 107 additional EACs. Unlike recurrent alterations of known drivers, these cancer helper genes are rare or patient-specific. However, they converge towards perturbations of well-known cancer processes. Recurrence of the same process perturbations, rather than individual genes, divides EACs into six clusters differing in their molecular and clinical features. Experimentally mimicking the alterations of predicted helper genes in cancer and pre-cancer cells validates their contribution to disease progression, while reverting their alterations reveals EAC acquired dependencies that can be exploited in therapy.