Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer.

High-grade serous ovarian cancer (HGSC) exhibits extensive malignant clonal diversity with widespread but non-random patterns of disease dissemination. We investigated whether local immune microenvironment factors shape tumor progression properties at the interface of tumor-infiltrating lymphocytes (TILs) and cancer cells. Through multi-region study of 212 samples from 38 patients with whole-genome sequencing, immunohistochemistry, histologic image analysis, gene expression profiling, and T and B cell receptor sequencing, we identified three immunologic subtypes across samples and extensive within-patient diversity. Epithelial CD8+ TILs negatively associated with malignant diversity, reflecting immunological pruning of tumor clones inferred by neoantigen depletion, HLA I loss of heterozygosity, and spatial tracking between T cell and tumor clones. In addition, combinatorial prognostic effects of mutational processes and immune properties were observed, illuminating how specific genomic aberration types associate with immune response and impact survival. We conclude that within-patient spatial immune microenvironment variation shapes intraperitoneal malignant spread, provoking new evolutionary perspectives on HGSC clonal dispersion.

Harmonized molecular classification; assessment of a single-test ProMisE NGS tool.

OBJECTIVES: Despite recommendations for integrating molecular classification of endometrial cancers (EC) into pathology reporting and clinical management, uptake is inconsistent. To assign ProMisE subtype, all molecular components must be available (POLE mutation status, mismatch repair (MMR) and p53 immunohistochemistry (IHC)) and often these are assessed at different stages of care and/or at different centres resulting in delays in treatment. We assessed a single-test DNA-based targeted next generation sequencing (NGS) molecular classifier (ProMisE NGS), comparing concordance and prognostic value to the original ProMisE classifier. METHODS: DNA was extracted from formalin-fixed paraffin embedded (FFPE) ECs that had previously undergone ProMisE molecular classification (POLE sequencing, IHC for p53 and MMR). DNA was sequenced using the clinically validated Imagia Canexia Health Find It™ amplicon-based NGS gene panel assay to assess for pathogenic POLE mutations (unchanged from original ProMisE), TP53 mutations (in lieu of p53 IHC), and microsatellite instability (MSI) (in lieu of MMR IHC),with the same order of segregation as original ProMisE used for subtype assignment. Molecular subtype assignment of both classifiers was compared by concordance metrics and Kaplan-Meier survival statistics. RESULTS: The new DNA-based NGS molecular classifier (ProMisE NGS) was used to determine the molecular subtype in 164 ECs previously classified with ProMisE. 159/164 cases were concordant with a kappa statistic of 0.96 and an overall accuracy of 0.97. Prognostic differences in progression-free, disease-specific and overall survival between the four molecular subtypes were observed for the new NGS classifier, recapitulating the survival curves of the original ProMisE classifier. ProMisE NGS was 100% concordant between matched biopsy and hysterectomy samples. CONCLUSION: ProMisE NGS is feasible on standard FFPE material, demonstrates high concordance with the original ProMisE classifier and maintains prognostic value in EC. This test has the potential to facilitate implementation of molecular classification of EC at the time of first diagnosis.

The utility of color normalization for AI-based diagnosis of hematoxylin and eosin-stained pathology images.

The color variation of hematoxylin and eosin (H&E)-stained tissues has presented a challenge for applications of artificial intelligence (AI) in digital pathology. Many color normalization algorithms have been developed in recent years in order to reduce the color variation between H&E images. However, previous efforts in benchmarking these algorithms have produced conflicting results and none have sufficiently assessed the efficacy of the various color normalization methods for improving diagnostic performance of AI systems. In this study, we systematically investigated eight color normalization algorithms for AI-based classification of H&E-stained histopathology slides, in the context of using images both from one center and from multiple centers. Our results show that color normalization does not consistently improve classification performance when both training and testing data are from a single center. However, using four multi-center datasets of two cancer types (ovarian and pleural) and objective functions, we show that color normalization can significantly improve the classification accuracy of images from external datasets (ovarian cancer: 0.25 AUC increase, p = 1.6 e-05; pleural cancer: 0.21 AUC increase, p = 1.4 e-10). Furthermore, we introduce a novel augmentation strategy by mixing color-normalized images using three easily accessible algorithms that consistently improves the diagnosis of test images from external centers, even when the individual normalization methods had varied results. We anticipate our study to be a starting point for reliable use of color normalization to improve AI-based, digital pathology-empowered diagnosis of cancers sourced from multiple centers. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Deep learning-based histotype diagnosis of ovarian carcinoma whole-slide pathology images.

Ovarian carcinoma has the highest mortality of all female reproductive cancers and current treatment has become histotype-specific. Pathologists diagnose five common histotypes by microscopic examination, however, histotype determination is not straightforward, with only moderate interobserver agreement between general pathologists (Cohen's kappa 0.54-0.67). We hypothesized that machine learning (ML)-based image classification models may be able to recognize ovarian carcinoma histotype sufficiently well that they could aid pathologists in diagnosis. We trained four different artificial intelligence (AI) algorithms based on deep convolutional neural networks to automatically classify hematoxylin and eosin-stained whole slide images. Performance was assessed through cross-validation on the training set (948 slides corresponding to 485 patients), and on an independent test set of 60 patients from another institution. The best-performing model achieved a diagnostic concordance of 81.38% (Cohen's kappa of 0.7378) in our training set, and 80.97% concordance (Cohen's kappa 0.7547) on the external dataset. Eight cases misclassified by ML in the external set were reviewed by two subspecialty pathologists blinded to the diagnoses, molecular and immunophenotype data, and ML-based predictions. Interestingly, in 4 of 8 cases from the external dataset, the expert review pathologists rendered diagnoses, based on blind review of the whole section slides classified by AI, that were in agreement with AI rather than the integrated reference diagnosis. The performance characteristics of our classifiers indicate potential for improved diagnostic performance if used as an adjunct to conventional histopathology.

Deep-learning based classification distinguishes sarcomatoid malignant mesotheliomas from benign spindle cell mesothelial proliferations.

Sarcomatoid mesothelioma is an aggressive malignancy that can be challenging to distinguish from benign spindle cell mesothelial proliferations based on biopsy, and this distinction is crucial to patient treatment and prognosis. A novel deep learning based classifier may be able to aid pathologists in making this critical diagnostic distinction. SpindleMesoNET was trained on cases of malignant sarcomatoid mesothelioma and benign spindle cell mesothelial proliferations. Performance was assessed through cross-validation on the training set, on an independent set of challenging cases referred for expert opinion ('referral' test set), and on an externally stained set from outside institutions ('externally stained' test set). SpindleMesoNET predicted the benign or malignant status of cases with AUC's of 0.932, 0.925, and 0.989 on the cross-validation, referral and external test sets, respectively. The accuracy of SpindleMesoNET on the referral set cases (92.5%) was comparable to the average accuracy of 3 experienced pathologists on the same slide set (91.7%). We conclude that SpindleMesoNET can accurately distinguish sarcomatoid mesothelioma from benign spindle cell mesothelial proliferations. A deep learning system of this type holds potential for future use as an ancillary test in diagnostic pathology.

Synthesis of diagnostic quality cancer pathology images by generative adversarial networks.

Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to synthesize medical images. In this study, we evaluated the efficacy of generative adversarial networks to synthesize high-resolution pathology images of 10 histological types of cancer, including five cancer types from The Cancer Genome Atlas and the five major histological subtypes of ovarian carcinoma. The quality of these images was assessed using a comprehensive survey of board-certified pathologists (n = 9) and pathology trainees (n = 6). Our results show that the real and synthetic images are classified by histotype with comparable accuracies and the synthetic images are visually indistinguishable from real images. Furthermore, we trained deep convolutional neural networks to diagnose the different cancer types and determined that the synthetic images perform as well as additional real images when used to supplement a small training set. These findings have important applications in proficiency testing of medical practitioners and quality assurance in clinical laboratories. Furthermore, training of computer-aided diagnostic systems can benefit from synthetic images where labeled datasets are limited (e.g. rare cancers). We have created a publicly available website where clinicians and researchers can attempt questions from the image survey (http://gan.aimlab.ca/). © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution.

Human cancers, including breast cancers, comprise clones differing in mutation content. Clones evolve dynamically in space and time following principles of Darwinian evolution, underpinning important emergent features such as drug resistance and metastasis. Human breast cancer xenoengraftment is used as a means of capturing and studying tumour biology, and breast tumour xenografts are generally assumed to be reasonable models of the originating tumours. However, the consequences and reproducibility of engraftment and propagation on the genomic clonal architecture of tumours have not been systematically examined at single-cell resolution. Here we show, using deep-genome and single-cell sequencing methods, the clonal dynamics of initial engraftment and subsequent serial propagation of primary and metastatic human breast cancers in immunodeficient mice. In all 15 cases examined, clonal selection on engraftment was observed in both primary and metastatic breast tumours, varying in degree from extreme selective engraftment of minor (<5% of starting population) clones to moderate, polyclonal engraftment. Furthermore, ongoing clonal dynamics during serial passaging is a feature of tumours experiencing modest initial selection. Through single-cell sequencing, we show that major mutation clusters estimated from tumour population sequencing relate predictably to the most abundant clonal genotypes, even in clonally complex and rapidly evolving cases. Finally, we show that similar clonal expansion patterns can emerge in independent grafts of the same starting tumour population, indicating that genomic aberrations can be reproducible determinants of evolutionary trajectories. Our results show that measurement of genomically defined clonal population dynamics will be highly informative for functional studies using patient-derived breast cancer xenoengraftment.

Regulation of pH by Carbonic Anhydrase 9 Mediates Survival of Pancreatic Cancer Cells With Activated KRAS in Response to Hypoxia.

BACKGROUND & AIMS: Most pancreatic ductal adenocarcinomas (PDACs) express an activated form of KRAS, become hypoxic and dysplastic, and are refractory to chemo and radiation therapies. To survive in the hypoxic environment, PDAC cells upregulate enzymes and transporters involved in pH regulation, including the extracellular facing carbonic anhydrase 9 (CA9). We evaluated the effect of blocking CA9, in combination with administration of gemcitabine, in mouse models of pancreatic cancer. METHODS: We knocked down expression of KRAS in human (PK-8 and PK-1) PDAC cells with small hairpin RNAs. Human and mouse (KrasG12D/Pdx1-Cre/Tp53/RosaYFP) PDAC cells were incubated with inhibitors of MEK (trametinib) or extracellular signal-regulated kinase (ERK), and some cells were cultured under hypoxic conditions. We measured levels and stability of the hypoxia-inducible factor 1 subunit alpha (HIF1A), endothelial PAS domain 1 protein (EPAS1, also called HIF2A), CA9, solute carrier family 16 member 4 (SLC16A4, also called MCT4), and SLC2A1 (also called GLUT1) by immunoblot analyses. We analyzed intracellular pH (pHi) and extracellular metabolic flux. We knocked down expression of CA9 in PDAC cells, or inhibited CA9 with SLC-0111, incubated them with gemcitabine, and assessed pHi, metabolic flux, and cytotoxicity under normoxic and hypoxic conditions. Cells were also injected into either immune-compromised or immune-competent mice and growth of xenograft tumors was assessed. Tumor fragments derived from patients with PDAC were surgically ligated to the pancreas of mice and the growth of tumors was assessed. We performed tissue microarray analyses of 205 human PDAC samples to measure levels of CA9 and associated expression of genes that regulate hypoxia with outcomes of patients using the Cancer Genome Atlas database. RESULTS: Under hypoxic conditions, PDAC cells had increased levels of HIF1A and HIF2A, upregulated expression of CA9, and activated glycolysis. Knockdown of KRAS in PDAC cells, or incubation with trametinib, reduced the posttranscriptional stabilization of HIF1A and HIF2A, upregulation of CA9, pHi, and glycolysis in response to hypoxia. CA9 was expressed by 66% of PDAC samples analyzed; high expression of genes associated with metabolic adaptation to hypoxia, including CA9, correlated with significantly reduced survival times of patients. Knockdown or pharmacologic inhibition of CA9 in PDAC cells significantly reduced pHi in cells under hypoxic conditions, decreased gemcitabine-induced glycolysis, and increased their sensitivity to gemcitabine. PDAC cells with knockdown of CA9 formed smaller xenograft tumors in mice, and injection of gemcitabine inhibited tumor growth and significantly increased survival times of mice. In mice with xenograft tumors grown from human PDAC cells, oral administration of SLC-0111 and injection of gemcitabine increased intratumor acidosis and increased cell death. These tumors, and tumors grown from PDAC patient-derived tumor fragments, grew more slowly than xenograft tumors in mice given control agents, resulting in longer survival times. In KrasG12D/Pdx1-Cre/Tp53/RosaYFP genetically modified mice, oral administration of SLC-0111 and injection of gemcitabine reduced numbers of B cells in tumors. CONCLUSIONS: In response to hypoxia, PDAC cells that express activated KRAS increase expression of CA9, via stabilization of HIF1A and HIF2A, to regulate pH and glycolysis. Disruption of this pathway slows growth of PDAC xenograft tumors in mice and might be developed for treatment of pancreatic cancer.

The coming 15 years in gynaecological pathology: digitisation, artificial intelligence, and new technologies.

Surgical pathology forms the cornerstone of modern oncological medicine, owing to the wealth of clinically relevant information that can be obtained from tissue morphology. Although several ancillary testing modalities have been added to surgical pathology, the way in which we view and interpret tissue morphology has remained largely unchanged since the inception of our profession. In this review, we discuss new technological advances that promise to transform the way in which we access tissue morphology and how we use it to guide patient care.

Integrated structural variation and point mutation signatures in cancer genomes using correlated topic models.

Mutation signatures in cancer genomes reflect endogenous and exogenous mutational processes, offering insights into tumour etiology, features for prognostic and biologic stratification and vulnerabilities to be exploited therapeutically. We present a novel machine learning formalism for improved signature inference, based on multi-modal correlated topic models (MMCTM) which can at once infer signatures from both single nucleotide and structural variation counts derived from cancer genome sequencing data. We exemplify the utility of our approach on two hormone driven, DNA repair deficient cancers: breast and ovary (n = 755 samples total). We show how introducing correlated structure both within and between modes of mutation can increase accuracy of signature discovery, particularly in the context of sparse data. Our study emphasizes the importance of integrating multiple mutation modes for signature discovery and patient stratification, and provides a statistical modeling framework to incorporate additional features of interest for future studies.

Correction: TERT promoter mutation in adult granulosa cell tumor of the ovary.

The original version of this Article omitted the author Hannah van Meurs from the Department of Gynecology, Center for Gynecologic Oncology Amsterdam, Academic Medical Center, 1100 DD Amsterdam, The Netherlands. This has been corrected in both the PDF and HTML versions of the article.

Genetic profiling of MYC and BCL2 in diffuse large B-cell lymphoma determines cell-of-origin-specific clinical impact.

The clinical significance of MYC and BCL2 genetic alterations in diffuse large B-cell lymphoma (DLBCL), apart from translocations, has not been comprehensively investigated using high-resolution genetic assays. In this study, we profiled MYC and BCL2 genetic alterations using next-generation sequencing and high-resolution SNP array in 347 de novo DLBCL cases treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) at the British Columbia Cancer Agency. Cell-of-origin (COO) subtype was determined by Lymph2Cx digital gene expression profiling. We showed that the incidence of MYC/BCL2 genetic alterations and their clinical significance were largely dependent on COO subtypes. It is noteworthy that the presence of BCL2 gain/amplification is significantly associated with poor outcome in activated B-cell-like and BCL2 translocation with poor outcome in germinal center B-cell subtypes, respectively. Both have prognostic significance independent of MYC/BCL2 dual expression and the International Prognostic Index (IPI). Furthermore, the combination of BCL2 genetic alterations with IPI identifies markedly worse prognostic groups within individual COO subtypes. Thus, high-resolution genomic assays identify extremely poor prognostic groups within each COO subtype on the basis of BCL2 genetic status in this large, uniformly R-CHOP-treated population-based cohort of DLBCL. These results suggest COO subtype-specific biomarkers based on BCL2 genetic alterations can be used to risk-stratify patients with DLBCL treated with immunochemotherapy.

Molecular profiling and molecular classification of endometrioid ovarian carcinomas.

OBJECTIVE: Endometrioid ovarian carcinomas (EOCs) comprise 5-10% of all ovarian cancers and commonly co-occur with synchronous endometrioid endometrial cancer (EEC). We sought to examine the molecular characteristics of pure EOCs in patients without concomitant EEC. METHODS: EOCs and matched normal samples were subjected to massively parallel sequencing targeting 341-468 cancer-related genes (n = 8) or whole-genome sequencing (n = 28). Mutational frequencies of EOCs were compared to those of high-grade serous ovarian cancers (HGSOCs; n = 224) and EECs (n = 186) from The Cancer Genome Atlas, and synchronous EOCs (n = 23). RESULTS: EOCs were heterogeneous, frequently harboring KRAS, PIK3CA, PTEN, CTNNB1, ARID1A and TP53 mutations. EOCs were distinct from HGSOCs at the mutational level, less frequently harboring TP53 but more frequently displaying KRAS, PIK3CA, PIK3R1, PTEN and CTNNB1 mutations. Compared to synchronous EOCs and pure EECs, pure EOCs less frequently harbored PTEN, PIK3R1 and ARID1A mutations. Akin to EECs, EOCs could be stratified into the four molecular subtypes: 3% POLE (ultramutated), 19% MSI (hypermutated), 17% copy-number high (serous-like) and 61% copy-number low (endometrioid). In addition to microsatellite instability, a subset of EOCs harbored potentially targetable mutations, including AKT1 and ERBB2 hotspot mutations. EOCs of MSI (hypermutated) subtype uniformly displayed a good outcome. CONCLUSIONS: EOCs are heterogeneous at the genomic level and harbor targetable genetic alterations. Despite the similarities in the repertoire of somatic mutations between pure EOCs, synchronous EOCs and EECs, the frequencies of mutations affecting known driver genes differ. Further studies are required to define the impact of the molecular subtypes on the outcome and treatment of EOC patients.

CDK12 regulates alternative last exon mRNA splicing and promotes breast cancer cell invasion.

CDK12 (cyclin-dependent kinase 12) is a regulatory kinase with evolutionarily conserved roles in modulating transcription elongation. Recent tumor genome studies of breast and ovarian cancers highlighted recurrent CDK12 mutations, which have been shown to disrupt DNA repair in cell-based assays. In breast cancers, CDK12 is also frequently co-amplified with the HER2 (ERBB2) oncogene. The mechanisms underlying functions of CDK12 in general and in cancer remain poorly defined. Based on global analysis of mRNA transcripts in normal and breast cancer cell lines with and without CDK12 amplification, we demonstrate that CDK12 primarily regulates alternative last exon (ALE) splicing, a specialized subtype of alternative mRNA splicing, that is both gene- and cell type-specific. These are unusual properties for spliceosome regulatory factors, which typically regulate multiple forms of alternative splicing in a global manner. In breast cancer cells, regulation by CDK12 modulates ALE splicing of the DNA damage response activator ATM and a DNAJB6 isoform that influences cell invasion and tumorigenesis in xenografts. We found that there is a direct correlation between CDK12 levels, DNAJB6 isoform levels and the migration capacity and invasiveness of breast tumor cells. This suggests that CDK12 gene amplification can contribute to the pathogenesis of the cancer.

Robust high-performance nanoliter-volume single-cell multiple displacement amplification on planar substrates.

The genomes of large numbers of single cells must be sequenced to further understanding of the biological significance of genomic heterogeneity in complex systems. Whole genome amplification (WGA) of single cells is generally the first step in such studies, but is prone to nonuniformity that can compromise genomic measurement accuracy. Despite recent advances, robust performance in high-throughput single-cell WGA remains elusive. Here, we introduce droplet multiple displacement amplification (MDA), a method that uses commercially available liquid dispensing to perform high-throughput single-cell MDA in nanoliter volumes. The performance of droplet MDA is characterized using a large dataset of 129 normal diploid cells, and is shown to exceed previously reported single-cell WGA methods in amplification uniformity, genome coverage, and/or robustness. We achieve up to 80% coverage of a single-cell genome at 5× sequencing depth, and demonstrate excellent single-nucleotide variant (SNV) detection using targeted sequencing of droplet MDA product to achieve a median allelic dropout of 15%, and using whole genome sequencing to achieve false and true positive rates of 9.66 × 10(-6) and 68.8%, respectively, in a G1-phase cell. We further show that droplet MDA allows for the detection of copy number variants (CNVs) as small as 30 kb in single cells of an ovarian cancer cell line and as small as 9 Mb in two high-grade serous ovarian cancer samples using only 0.02× depth. Droplet MDA provides an accessible and scalable method for performing robust and accurate CNV and SNV measurements on large numbers of single cells.

TERT promoter mutation in adult granulosa cell tumor of the ovary.

The telomerase reverse transcriptase (TERT) gene is highly expressed in stem cells and silenced upon differentiation. Cancer cells can attain immortality by activating TERT to maintain telomere length and telomerase activity, which is a crucial step of tumorigenesis. Two somatic mutations in the TERT promoter (C228T; C250T) have been identified as gain-of-function mutations that promote transcriptional activation of TERT in multiple cancers, such as melanoma and glioblastoma. A recent study investigating TERT promoter mutations in ovarian carcinomas found C228T and C250T mutations in 15.9% of clear cell carcinomas. However, it is unknown whether these mutations are frequent in other ovarian cancer subtypes, in particular, sex cord-stromal tumors including adult granulosa cell tumors. We performed whole-genome sequencing on ten adult granulosa cell tumors with matched normal blood and identified a TERT C228T promoter mutation in 50% of tumors. We found that adult granulosa cell tumors with mutated TERT promoter have increased expression of TERT mRNA and exhibited significantly longer telomeres compared to those with wild-type TERT promoter. Extension cohort analysis using allelic discrimination revealed the TERT C228T mutation in 51 of 229 primary adult granulosa cell tumors (22%), 24 of 58 recurrent adult granulosa cell tumors (41%), and 1 of 22 other sex cord-stromal tumors (5%). There was a significant difference in overall survival between patients with TERT C228T promoter mutation in the primary tumors and those without it (p = 0.00253, log-rank test). In seven adult granulosa cell tumors, we found the TERT C228T mutation present in recurrent tumors and absent in the corresponding primary tumor. Our data suggest that TERT C228T promoter mutations may have an important role in progression of adult granulosa cell tumors.

The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups.

The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ~40% of genes, with the landscape dominated by cis- and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA­RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the 'CNA-devoid' subgroup and a basal-specific chromosome 5 deletion-associated mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome.

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.

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.

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.