Temporal single-cell atlas of non-neuronal retinal cells reveals dynamic, coordinated multicellular responses to central nervous system injury.

Non-neuronal cells are key to the complex cellular interplay that follows central nervous system insult. To understand this interplay, we generated a single-cell atlas of immune, glial and retinal pigment epithelial cells from adult mouse retina before and at multiple time points after axonal transection. We identified rare subsets in naive retina, including interferon (IFN)-response glia and border-associated macrophages, and delineated injury-induced changes in cell composition, expression programs and interactions. Computational analysis charted a three-phase multicellular inflammatory cascade after injury. In the early phase, retinal macroglia and microglia were reactivated, providing chemotactic signals concurrent with infiltration of CCR2+ monocytes from the circulation. These cells differentiated into macrophages in the intermediate phase, while an IFN-response program, likely driven by microglia-derived type I IFN, was activated across resident glia. The late phase indicated inflammatory resolution. Our findings provide a framework to decipher cellular circuitry, spatial relationships and molecular interactions following tissue injury.

Disruption of the IL-33-ST2-AKT signaling axis impairs neurodevelopment by inhibiting microglial metabolic adaptation and phagocytic function.

To accommodate the changing needs of the developing brain, microglia must undergo substantial morphological, phenotypic, and functional reprogramming. Here, we examined whether cellular metabolism regulates microglial function during neurodevelopment. Microglial mitochondria bioenergetics correlated with and were functionally coupled to phagocytic activity in the developing brain. Transcriptional profiling of microglia with diverse metabolic profiles revealed an activation signature wherein the interleukin (IL)-33 signaling axis is associated with phagocytic activity. Genetic perturbation of IL-33 or its receptor ST2 led to microglial dystrophy, impaired synaptic function, and behavioral abnormalities. Conditional deletion of Il33 from astrocytes or Il1rl1, encoding ST2, in microglia increased susceptibility to seizures. Mechanistically, IL-33 promoted mitochondrial activity and phagocytosis in an AKT-dependent manner. Mitochondrial metabolism and AKT activity were temporally regulated in vivo. Thus, a microglia-astrocyte circuit mediated by the IL-33-ST2-AKT signaling axis supports microglial metabolic adaptation and phagocytic function during early development, with implications for neurodevelopmental and neuropsychiatric disorders.

Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces.

Single-cell RNA-Seq (scRNA-seq) is invaluable for studying biological systems. Dimensionality reduction is a crucial step in interpreting the relation between cells in scRNA-seq data. However, current dimensionality reduction methods are often confounded by multiple simultaneous technical and biological variability, result in "crowding" of cells in the center of the latent space, or inadequately capture temporal relationships. Here, we introduce scPhere, a scalable deep generative model to embed cells into low-dimensional hyperspherical or hyperbolic spaces to accurately represent scRNA-seq data. ScPhere addresses multi-level, complex batch factors, facilitates the interactive visualization of large datasets, resolves cell crowding, and uncovers temporal trajectories. We demonstrate scPhere on nine large datasets in complex tissue from human patients or animal development. Our results show how scPhere facilitates the interpretation of scRNA-seq data by generating batch-invariant embeddings to map data from new individuals, identifies cell types affected by biological variables, infers cells' spatial positions in pre-defined biological specimens, and highlights complex cellular relations.

Single-Cell Transcriptome Analysis Reveals Disease-Defining T-cell Subsets in the Tumor Microenvironment of Classic Hodgkin Lymphoma.

Hodgkin lymphoma is characterized by an extensively dominant tumor microenvironment (TME) composed of different types of noncancerous immune cells with rare malignant cells. Characterization of the cellular components and their spatial relationship is crucial to understanding cross-talk and therapeutic targeting in the TME. We performed single-cell RNA sequencing of more than 127,000 cells from 22 Hodgkin lymphoma tissue specimens and 5 reactive lymph nodes, profiling for the first time the phenotype of the Hodgkin lymphoma-specific immune microenvironment at single-cell resolution. Single-cell expression profiling identified a novel Hodgkin lymphoma-associated subset of T cells with prominent expression of the inhibitory receptor LAG3, and functional analyses established this LAG3+ T-cell population as a mediator of immunosuppression. Multiplexed spatial assessment of immune cells in the microenvironment also revealed increased LAG3+ T cells in the direct vicinity of MHC class II-deficient tumor cells. Our findings provide novel insights into TME biology and suggest new approaches to immune-checkpoint targeting in Hodgkin lymphoma. SIGNIFICANCE: We provide detailed functional and spatial characteristics of immune cells in classic Hodgkin lymphoma at single-cell resolution. Specifically, we identified a regulatory T-cell-like immunosuppressive subset of LAG3+ T cells contributing to the immune-escape phenotype. Our insights aid in the development of novel biomarkers and combination treatment strategies targeting immune checkpoints.See related commentary by Fisher and Oh, p. 342.This article is highlighted in the In This Issue feature, p. 327.

densityCut: an efficient and versatile topological approach for automatic clustering of biological data.

MOTIVATION: Many biological data processing problems can be formalized as clustering problems to partition data points into sensible and biologically interpretable groups. RESULTS: This article introduces densityCut, a novel density-based clustering algorithm, which is both time- and space-efficient and proceeds as follows: densityCut first roughly estimates the densities of data points from a K-nearest neighbour graph and then refines the densities via a random walk. A cluster consists of points falling into the basin of attraction of an estimated mode of the underlining density function. A post-processing step merges clusters and generates a hierarchical cluster tree. The number of clusters is selected from the most stable clustering in the hierarchical cluster tree. Experimental results on ten synthetic benchmark datasets and two microarray gene expression datasets demonstrate that densityCut performs better than state-of-the-art algorithms for clustering biological datasets. For applications, we focus on the recent cancer mutation clustering and single cell data analyses, namely to cluster variant allele frequencies of somatic mutations to reveal clonal architectures of individual tumours, to cluster single-cell gene expression data to uncover cell population compositions, and to cluster single-cell mass cytometry data to detect communities of cells of the same functional states or types. densityCut performs better than competing algorithms and is scalable to large datasets. AVAILABILITY AND IMPLEMENTATION: Data and the densityCut R package is available from https://bitbucket.org/jerry00/densitycut_dev CONTACT: : condon@cs.ubc.ca or sshah@bccrc.ca or jiaruid@cs.ubc.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of the epigenetic reader CBX2 as a potential drug target in advanced prostate cancer.

BACKGROUND: While localized prostate cancer (PCa) can be effectively cured, metastatic disease inevitably progresses to a lethal state called castration-resistant prostate cancer (CRPC). Emerging evidence suggests that aberrant epigenetic repression by the polycomb group (PcG) complexes fuels PCa progression, providing novel therapeutic opportunities. RESULTS: In the search for potential epigenetic drivers of CRPC, we analyzed the molecular profile of PcG members in patient-derived xenografts and clinical samples. Overall, our results identify the PcG protein and methyl-lysine reader CBX2 as a potential therapeutic target in advanced PCa. We report that CBX2 was recurrently up-regulated in metastatic CRPC and that elevated CBX2 expression was correlated with poor clinical outcome in PCa cohorts. Furthermore, CBX2 depletion abrogated cell viability and induced caspase 3-mediated apoptosis in metastatic PCa cell lines. Mechanistically explaining this phenotype, microarray analysis in CBX2-depleted cells revealed that CBX2 controls the expression of many key regulators of cell proliferation and metastasis. CONCLUSIONS: Taken together, this study provides the first evidence that CBX2 inhibition induces cancer cell death, positioning CBX2 as an attractive drug target in lethal CRPC.

Systematic analysis of somatic mutations impacting gene expression in 12 tumour types.

We present a novel hierarchical Bayes statistical model, xseq, to systematically quantify the impact of somatic mutations on expression profiles. We establish the theoretical framework and robust inference characteristics of the method using computational benchmarking. We then use xseq to analyse thousands of tumour data sets available through The Cancer Genome Atlas, to systematically quantify somatic mutations impacting expression profiles. We identify 30 novel cis-effect tumour suppressor gene candidates, enriched in loss-of-function mutations and biallelic inactivation. Analysis of trans-effects of mutations and copy number alterations with xseq identifies mutations in 150 genes impacting expression networks, with 89 novel predictions. We reveal two important novel characteristics of mutation impact on expression: (1) patients harbouring known driver mutations exhibit different downstream gene expression consequences; (2) expression patterns for some mutations are stable across tumour types. These results have critical implications for identification and interpretation of mutations with consequent impact on transcription in cancer.

Cis-regulatory somatic mutations and gene-expression alteration in B-cell lymphomas.

BACKGROUND: With the rapid increase of whole-genome sequencing of human cancers, an important opportunity to analyze and characterize somatic mutations lying within cis-regulatory regions has emerged. A focus on protein-coding regions to identify nonsense or missense mutations disruptive to protein structure and/or function has led to important insights; however, the impact on gene expression of mutations lying within cis-regulatory regions remains under-explored. We analyzed somatic mutations from 84 matched tumor-normal whole genomes from B-cell lymphomas with accompanying gene expression measurements to elucidate the extent to which these cancers are disrupted by cis-regulatory mutations. RESULTS: We characterize mutations overlapping a high quality set of well-annotated transcription factor binding sites (TFBSs), covering a similar portion of the genome as protein-coding exons. Our results indicate that cis-regulatory mutations overlapping predicted TFBSs are enriched in promoter regions of genes involved in apoptosis or growth/proliferation. By integrating gene expression data with mutation data, our computational approach culminates with identification of cis-regulatory mutations most likely to participate in dysregulation of the gene expression program. The impact can be measured along with protein-coding mutations to highlight key mutations disrupting gene expression and pathways in cancer. CONCLUSIONS: Our study yields specific genes with disrupted expression triggered by genomic mutations in either the coding or the regulatory space. It implies that mutated regulatory components of the genome contribute substantially to cancer pathways. Our analyses demonstrate that identifying genomically altered cis-regulatory elements coupled with analysis of gene expression data will augment biological interpretation of mutational landscapes of cancers.

An RCOR1 loss-associated gene expression signature identifies a prognostically significant DLBCL subgroup.

Effective treatment of diffuse large B-cell lymphoma (DLBCL) is plagued by heterogeneous responses to standard therapy, and molecular mechanisms underlying unfavorable outcomes in lymphoma patients remain elusive. Here, we profiled 148 genomes with 91 matching transcriptomes in a DLBCL cohort treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP) to uncover molecular subgroups linked to treatment failure. Systematic integration of high-resolution genotyping arrays and RNA sequencing data revealed novel deletions in RCOR1 to be associated with unfavorable progression-free survival (P = .001). Integration of expression data from the clinical samples with data from RCOR1 knockdowns in the lymphoma cell lines KM-H2 and Raji yielded an RCOR1 loss-associated gene signature comprising 233 genes. This signature identified a subgroup of patients with unfavorable overall survival (P = .023). The prognostic significance of the 233-gene signature for overall survival was reproduced in an independent cohort comprising 195 R-CHOP-treated patients (P = .039). Additionally, we discovered that within the International Prognostic Index low-risk group, the gene signature provides additional prognostic value that was independent of the cell-of-origin phenotype. We present a novel and reproducible molecular subgroup of DLBCL that impacts risk-stratification of R-CHOP-treated DLBCL patients and reveals a possible new avenue for therapeutic intervention strategies.

Intratumoral heterogeneity in a minority of ovarian low-grade serous carcinomas.

BACKGROUND: Ovarian low-grade serous carcinoma (LGSC) has fewer mutations than ovarian high-grade serous carcinoma (HGSC) and a less aggressive clinical course. However, an overwhelming majority of LGSC patients do not respond to conventional chemotherapy resulting in a poor long-term prognosis comparable to women diagnosed with HGSC. KRAS and BRAF mutations are common in LGSC, leading to clinical trials targeting the MAPK pathway. We assessed the stability of targetable somatic mutations over space and/or time in LGSC, with a view to inform stratified treatment strategies and clinical trial design. METHODS: Eleven LGSC cases with primary and recurrent paired samples were identified (stage IIB-IV). Tumor DNA was isolated from 1-4 formalin-fixed paraffin-embedded tumor blocks from both the primary and recurrence (n = 37 tumor and n = 7 normal samples). Mutational analysis was performed using the Ion Torrent AmpliSeqTM Cancer Panel, with targeted validation using Fluidigm-MiSeq, Sanger sequencing and/or Raindance Raindrop digital PCR. RESULTS: KRAS (3/11), BRAF (2/11) and/or NRAS (1/11) mutations were identified in five unique cases. A novel, non-synonymous mutation in SMAD4 was observed in one case. No somatic mutations were detected in the remaining six cases. In two cases with a single matched primary and recurrent sample, two KRAS hotspot mutations (G12V, G12R) were both stable over time. In three cases with multiple samplings from both the primary and recurrent surgery some mutations (NRAS Q61R, BRAF V600E, SMAD4 R361G) were stable across all samples, while others (KRAS G12V, BRAF G469V) were unstable. CONCLUSIONS: Overall, the majority of cases with detectable somatic mutations showed mutational stability over space and time while one of five cases showed both temporal and spatial mutational instability in presumed drivers of disease. Investigation of additional cases is required to confirm whether mutational heterogeneity in a minority of LGSC is a general phenomenon that should be factored into the design of clinical trials and stratified treatment for this patient population.

TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data.

The evolution of cancer genomes within a single tumor creates mixed cell populations with divergent somatic mutational landscapes. Inference of tumor subpopulations has been disproportionately focused on the assessment of somatic point mutations, whereas computational methods targeting evolutionary dynamics of copy number alterations (CNA) and loss of heterozygosity (LOH) in whole-genome sequencing data remain underdeveloped. We present a novel probabilistic model, TITAN, to infer CNA and LOH events while accounting for mixtures of cell populations, thereby estimating the proportion of cells harboring each event. We evaluate TITAN on idealized mixtures, simulating clonal populations from whole-genome sequences taken from genomically heterogeneous ovarian tumor sites collected from the same patient. In addition, we show in 23 whole genomes of breast tumors that the inference of CNA and LOH using TITAN critically informs population structure and the nature of the evolving cancer genome. Finally, we experimentally validated subclonal predictions using fluorescence in situ hybridization (FISH) and single-cell sequencing from an ovarian cancer patient sample, thereby recapitulating the key modeling assumptions of TITAN.

Ovarian and endometrial endometrioid carcinomas have distinct CTNNB1 and PTEN mutation profiles.

Ovarian endometrioid carcinomas and endometrial endometrioid carcinomas share many histological and molecular alterations. These similarities are likely due to a common endometrial epithelial precursor cell of origin, with most ovarian endometrioid carcinomas arising from endometriosis. To directly compare the mutation profiles of two morphologically similar tumor types, endometrial endometrioid carcinomas (n=307) and ovarian endometrioid carcinomas (n=33), we performed select exon capture sequencing on a panel of genes: ARID1A, PTEN, PIK3CA, KRAS, CTNNB1, PPP2R1A, TP53. We found that PTEN mutations are more frequent in low-grade endometrial endometrioid carcinomas (67%) compared with low-grade ovarian endometrioid carcinomas (17%) (P<0.0001). By contrast, CTNNB1 mutations are significantly different in low-grade ovarian endometrioid carcinomas (53%) compared with low-grade endometrial endometrioid carcinomas (28%) (P<0.0057). This difference in CTNNB1 mutation frequency may be reflective of the distinct microenvironments; the epithelial cells lining an endometriotic cyst within the ovary are exposed to a highly oxidative environment that promotes tumorigenesis. Understanding the distinct mutation patterns found in the PI3K and Wnt pathways of ovarian and endometrial endometrioid carcinomas may provide future opportunities for stratifying patients for targeted therapeutics.

Distinct evolutionary trajectories of primary high-grade serous ovarian cancers revealed through spatial mutational profiling.

High-grade serous ovarian cancer (HGSC) is characterized by poor outcome, often attributed to the emergence of treatment-resistant subclones. We sought to measure the degree of genomic diversity within primary, untreated HGSCs to examine the natural state of tumour evolution prior to therapy. We performed exome sequencing, copy number analysis, targeted amplicon deep sequencing and gene expression profiling on 31 spatially and temporally separated HGSC tumour specimens (six patients), including ovarian masses, distant metastases and fallopian tube lesions. We found widespread intratumoural variation in mutation, copy number and gene expression profiles, with key driver alterations in genes present in only a subset of samples (eg PIK3CA, CTNNB1, NF1). On average, only 51.5% of mutations were present in every sample of a given case (range 10.2-91.4%), with TP53 as the only somatic mutation consistently present in all samples. Complex segmental aneuploidies, such as whole-genome doubling, were present in a subset of samples from the same individual, with divergent copy number changes segregating independently of point mutation acquisition. Reconstruction of evolutionary histories showed one patient with mixed HGSC and endometrioid histology, with common aetiologic origin in the fallopian tube and subsequent selection of different driver mutations in the histologically distinct samples. In this patient, we observed mixed cell populations in the early fallopian tube lesion, indicating that diversity arises at early stages of tumourigenesis. Our results revealed that HGSCs exhibit highly individual evolutionary trajectories and diverse genomic tapestries prior to therapy, exposing an essential biological characteristic to inform future design of personalized therapeutic solutions and investigation of drug-resistance mechanisms.

Mutational and structural analysis of diffuse large B-cell lymphoma using whole-genome sequencing.

Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous cancer composed of at least 2 molecular subtypes that differ in gene expression and distribution of mutations. Recently, application of genome/exome sequencing and RNA-seq to DLBCL has revealed numerous genes that are recurrent targets of somatic point mutation in this disease. Here we provide a whole-genome-sequencing-based perspective of DLBCL mutational complexity by characterizing 40 de novo DLBCL cases and 13 DLBCL cell lines and combining these data with DNA copy number analysis and RNA-seq from an extended cohort of 96 cases. Our analysis identified widespread genomic rearrangements including evidence for chromothripsis as well as the presence of known and novel fusion transcripts. We uncovered new gene targets of recurrent somatic point mutations and genes that are targeted by focal somatic deletions in this disease. We highlight the recurrence of germinal center B-cell-restricted mutations affecting genes that encode the S1P receptor and 2 small GTPases (GNA13 and GNAI2) that together converge on regulation of B-cell homing. We further analyzed our data to approximate the relative temporal order in which some recurrent mutations were acquired and demonstrate that ongoing acquisition of mutations and intratumoral clonal heterogeneity are common features of DLBCL. This study further improves our understanding of the processes and pathways involved in lymphomagenesis, and some of the pathways mutated here may indicate new avenues for therapeutic intervention.

A robust hidden semi-Markov model with application to aCGH data processing.

Hidden semi-Markov models are effective at modelling sequences with succession of homogenous zones by choosing appropriate state duration distributions. To compensate for model mis-specification and provide protection against outliers, we design a robust hidden semi-Markov model with Student's t mixture models as the emission distributions. The proposed approach is used to model array based comparative genomic hybridization data. Experiments conducted on the benchmark data from the Coriell cell lines, and glioblastoma multiforme data illustrate the reliability of the technique.

Mutation discovery in regions of segmental cancer genome amplifications with CoNAn-SNV: a mixture model for next generation sequencing of tumors.

Next generation sequencing has now enabled a cost-effective enumeration of the full mutational complement of a tumor genome-in particular single nucleotide variants (SNVs). Most current computational and statistical models for analyzing next generation sequencing data, however, do not account for cancer-specific biological properties, including somatic segmental copy number alterations (CNAs)-which require special treatment of the data. Here we present CoNAn-SNV (Copy Number Annotated SNV): a novel algorithm for the inference of single nucleotide variants (SNVs) that overlap copy number alterations. The method is based on modelling the notion that genomic regions of segmental duplication and amplification induce an extended genotype space where a subset of genotypes will exhibit heavily skewed allelic distributions in SNVs (and therefore render them undetectable by methods that assume diploidy). We introduce the concept of modelling allelic counts from sequencing data using a panel of Binomial mixture models where the number of mixtures for a given locus in the genome is informed by a discrete copy number state given as input. We applied CoNAn-SNV to a previously published whole genome shotgun data set obtained from a lobular breast cancer and show that it is able to discover 21 experimentally revalidated somatic non-synonymous mutations in a lobular breast cancer genome that were not detected using copy number insensitive SNV detection algorithms. Importantly, ROC analysis shows that the increased sensitivity of CoNAn-SNV does not result in disproportionate loss of specificity. This was also supported by analysis of a recently published lymphoma genome with a relatively quiescent karyotype, where CoNAn-SNV showed similar results to other callers except in regions of copy number gain where increased sensitivity was conferred. Our results indicate that in genomically unstable tumors, copy number annotation for SNV detection will be critical to fully characterize the mutational landscape of cancer genomes.

Use of mutation profiles to refine the classification of endometrial carcinomas.

The classification of endometrial carcinomas is based on pathological assessment of tumour cell type; the different cell types (endometrioid, serous, carcinosarcoma, mixed, undifferentiated, and clear cell) are associated with distinct molecular alterations. This current classification system for high-grade subtypes, in particular the distinction between high-grade endometrioid (EEC-3) and serous carcinomas (ESC), is limited in its reproducibility and prognostic abilities. Therefore, a search for specific molecular classifiers to improve endometrial carcinoma subclassification is warranted. We performed target enrichment sequencing on 393 endometrial carcinomas from two large cohorts, sequencing exons from the following nine genes: ARID1A, PPP2R1A, PTEN, PIK3CA, KRAS, CTNNB1, TP53, BRAF, and PPP2R5C. Based on this gene panel, each endometrial carcinoma subtype shows a distinct mutation profile. EEC-3s have significantly different frequencies of PTEN and TP53 mutations when compared to low-grade endometrioid carcinomas. ESCs and EEC-3s are distinct subtypes with significantly different frequencies of mutations in PTEN, ARID1A, PPP2R1A, TP53, and CTNNB1. From the mutation profiles, we were able to identify subtype outliers, ie cases diagnosed morphologically as one subtype but with a mutation profile suggestive of a different subtype. Careful review of these diagnostically challenging cases suggested that the original morphological classification was incorrect in most instances. The molecular profile of carcinosarcomas suggests two distinct mutation profiles for these tumours: endometrioid-type (PTEN, PIK3CA, ARID1A, KRAS mutations) and serous-type (TP53 and PPP2R1A mutations). While this nine-gene panel does not allow for a purely molecularly based classification of endometrial carcinoma, it may prove useful as an adjunct to morphological classification and serve as an aid in the classification of problematic cases. If used in practice, it may lead to improved diagnostic reproducibility and may also serve to stratify patients for targeted therapeutics.

Integrative analysis of genome-wide loss of heterozygosity and monoallelic expression at nucleotide resolution reveals disrupted pathways in triple-negative breast cancer.

Loss of heterozygosity (LOH) and copy number alteration (CNA) feature prominently in the somatic genomic landscape of tumors. As such, karyotypic aberrations in cancer genomes have been studied extensively to discover novel oncogenes and tumor-suppressor genes. Advances in sequencing technology have enabled the cost-effective detection of tumor genome and transcriptome mutation events at single-base-pair resolution; however, computational methods for predicting segmental regions of LOH in this context are not yet fully explored. Consequently, whole transcriptome, nucleotide-level resolution analysis of monoallelic expression patterns associated with LOH has not yet been undertaken in cancer. We developed a novel approach for inference of LOH from paired tumor/normal sequence data and applied it to a cohort of 23 triple-negative breast cancer (TNBC) genomes. Following extensive benchmarking experiments, we describe the nucleotide-resolution landscape of LOH in TNBC and assess the consequent effect of LOH on the transcriptomes of these tumors using RNA-seq-derived measurements of allele-specific expression. We show that the majority of monoallelic expression in the transcriptomes of triple-negative breast cancer can be explained by genomic regions of LOH and establish an upper bound for monoallelic expression that may be explained by other tumor-specific modifications such as epigenetics or mutations. Monoallelically expressed genes associated with LOH reveal that cell cycle, homologous recombination and actin-cytoskeletal functions are putatively disrupted by LOH in TNBC. Finally, we show how inference of LOH can be used to interpret allele frequencies of somatic mutations and postulate on temporal ordering of mutations in the evolutionary history of these tumors.

The clonal and mutational evolution spectrum of primary triple-negative breast cancers.

Primary triple-negative breast cancers (TNBCs), a tumour type defined by lack of oestrogen receptor, progesterone receptor and ERBB2 gene amplification, represent approximately 16% of all breast cancers. Here we show in 104 TNBC cases that at the time of diagnosis these cancers exhibit a wide and continuous spectrum of genomic evolution, with some having only a handful of coding somatic aberrations in a few pathways, whereas others contain hundreds of coding somatic mutations. High-throughput RNA sequencing (RNA-seq) revealed that only approximately 36% of mutations are expressed. Using deep re-sequencing measurements of allelic abundance for 2,414 somatic mutations, we determine for the first time-to our knowledge-in an epithelial tumour subtype, the relative abundance of clonal frequencies among cases representative of the population. We show that TNBCs vary widely in their clonal frequencies at the time of diagnosis, with the basal subtype of TNBC showing more variation than non-basal TNBC. Although p53 (also known as TP53), PIK3CA and PTEN somatic mutations seem to be clonally dominant compared to other genes, in some tumours their clonal frequencies are incompatible with founder status. Mutations in cytoskeletal, cell shape and motility proteins occurred at lower clonal frequencies, suggesting that they occurred later during tumour progression. Taken together, our results show that understanding the biology and therapeutic responses of patients with TNBC will require the determination of individual tumour clonal genotypes.

BRCA1 and BRCA2 mutations correlate with TP53 abnormalities and presence of immune cell infiltrates in ovarian high-grade serous carcinoma.

We characterized BRCA1 and BRCA2 status (mutation/methylation) in a consecutive series of cases of ovarian carcinoma in order to identify differences in clinicopathological features, molecular characteristics, and outcome between the pelvic high-grade serous cancers with (i) germline or somatic mutations in BRCA1 or BRCA2, (ii) methylation of BRCA1, and (iii) normal BRCA1 or BRCA2. In all, 131 women were identified prospectively, who were undergoing surgical staging and agreed to germline testing for BRCA1 and BRCA2 mutations. Histopathology, germline and somatic BRCA1 or BRCA2 mutations, BRCA1 methylation, and BRCA1 and BRCA2 mRNA expression levels distinguished four subgroups. In all, 103 cases were high-grade serous carcinoma and of these 31 (30%) had germline or somatic BRCA1 or BRCA2 mutations (20% BRCA1 and 10% BRCA2) (group 1), 21 (20%) had methylation of BRCA1 (group 2), and in 51 (50%) there was no BRCA loss (group 3). Group 4 consisted of 28 cases of non-high-grade serous, none of which had BRCA loss. BRCA1 and BRCA2 mRNA expression levels correlated with designated group (P=0.0008). Among high-grade serous carcinomas, there were no differences between groups 1-3 with respect to stage, ascites, CA125 level, platinum sensitivity, cytoreduction rate, neoadjuvant chemotherapy, or survival. Tumors with BRCA1 or BRCA2 mutations had increased immune infiltrates (CD20 and TIA-1) compared with high-grade serous without mutations (P=0.034, 0.027). TP53 expression differed between groups (P<0.0001), with abnormal TP53 expression in 49/50 tumors from groups 1 and 2. Wild-type TP53 expression was associated with worse outcome in high-grade serous (P<0.001). BRCA loss (mutation/methylation) is a common event in the pelvic high-grade serous (50%). TP53 abnormalities and increased immune cell infiltrates are significantly more common in high-grade serous with germline and somatic mutations in BRCA1 or BRCA2, compared with tumors lacking BRCA abnormalities.