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We performed phylogenetic analysis of high-grade serous ovarian cancers (68 samples from seven patients), identifying constituent clones and quantifying their relative abundances at multiple intraperitoneal sites. Through whole-genome and single-nucleus sequencing, we identified evolutionary features including mutation loss, convergence of the structural genome and temporal activation of mutational processes that patterned clonal progression. We then determined the precise clonal mixtures comprising each tumor sample. The majority of sites were clonally pure or composed of clones from a single phylogenetic clade. However, each patient contained at least one site composed of polyphyletic clones. Five patients exhibited monoclonal and unidirectional seeding from the ovary to intraperitoneal sites, and two patients demonstrated polyclonal spread and reseeding. Our findings indicate that at least two distinct modes of intraperitoneal spread operate in clonal dissemination and highlight the distribution of migratory potential over clonal populations comprising high-grade serous ovarian cancers.

DICER1 plays a critical role in microRNA (miRNA) biogenesis. Recurrent somatic 'hotspot' mutations at the four metal-binding sites within the RNase IIIb domain of DICER1 were identified in ovarian sex cord-stromal tumours and have since been described in other paediatric tumours. In this study, we screened the RNase IIIb domain of DICER1 in 290 endometrial tumours and identified six cases with hotspot mutations, including two cases affected by an atypical G1809R mutation directly adjacent to a metal-binding site. Using Illumina and Sanger targeted resequencing, we observed and validated biallelic DICER1 mutations in several cases with hotspot mutations. Through in vitro DICER1 cleavage assays, small RNA deep sequencing and real-time PCR, we demonstrated that mutations adding a positively charged side chain to residue 1809 have similar detrimental effects on 5p miRNA production to mutations at the metal-binding sites. As expected, 5p miRNAs were globally reduced in tumours and cell lines with hotspot mutations. Pathway analysis of gene expression profiles indicated that genes de-repressed due to loss of 5p miRNAs are strongly associated with pathways regulating the cell cycle. Using a Dicer1-null mouse cell line model, we found that expression of DICER1 hotspot mutants promoted cell proliferation, whereas wild-type (WT) DICER1 inhibited cell proliferation. Furthermore, targets of let-7 family miRNAs are enriched among the up-regulated genes, suggesting that loss of let-7 may be impacting downstream pathways. Our results reveal that DICER1 hotspot mutations are implicated in common malignancies and may constitute a unique oncogenic pathway.

CDC-like kinase phosphorylation of serine/arginine-rich proteins is central to RNA splicing reactions. Yet, the genomic network of CDC-like kinase-dependent RNA processing events remains poorly defined. Here, we explore the connectivity of genomic CDC-like kinase splicing functions by applying graduated, short-exposure, pharmacological CDC-like kinase inhibition using a novel small molecule (T3) with very high potency, selectivity, and cell-based stability. Using RNA-Seq, we define CDC-like kinase-responsive alternative splicing events, the large majority of which monotonically increase or decrease with increasing CDC-like kinase inhibition. We show that distinct RNA-binding motifs are associated with T3 response in skipped exons. Unexpectedly, we observe dose-dependent conjoined gene transcription, which is associated with motif enrichment in the last and second exons of upstream and downstream partners, respectively. siRNA knockdown of CLK2-associated genes significantly increases conjoined gene formation. Collectively, our results reveal an unexpected role for CDC-like kinase in conjoined gene formation, via regulation of 3′-end processing and associated splicing factors., The phosphorylation of serine/arginine-rich proteins by CDC-like kinase is a central regulatory mechanism for RNA splicing reactions. Here, the authors synthesize a novel small molecule CLK inhibitor and map CLK-responsive alternative splicing events and discover an effect on conjoined gene transcription.

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.

Background Follicular lymphoma (FL) is an indolent, yet incurable B cell malignancy. A subset of patients experience an increased mortality rate driven by two distinct clinical end points: histological transformation and early progression after immunochemotherapy. The nature of tumor clonal dynamics leading to these clinical end points is poorly understood, and previously determined genetic alterations do not explain the majority of transformed cases or accurately predict early progressive disease. We contend that detailed knowledge of the expansion patterns of specific cell populations plus their associated mutations would provide insight into therapeutic strategies and disease biology over the time course of FL clinical histories. Methods and Findings Using a combination of whole genome sequencing, targeted deep sequencing, and digital droplet PCR on matched diagnostic and relapse specimens, we deciphered the constituent clonal populations in 15 transformation cases and 6 progression cases, and measured the change in clonal population abundance over time. We observed widely divergent patterns of clonal dynamics in transformed cases relative to progressed cases. Transformation specimens were generally composed of clones that were rare or absent in diagnostic specimens, consistent with dramatic clonal expansions that came to dominate the transformation specimens. This pattern was independent of time to transformation and treatment modality. By contrast, early progression specimens were composed of clones that were already present in the diagnostic specimens and exhibited only moderate clonal dynamics, even in the presence of immunochemotherapy. Analysis of somatic mutations impacting 94 genes was undertaken in an extension cohort consisting of 395 samples from 277 patients in order to decipher disrupted biology in the two clinical end points. We found 12 genes that were more commonly mutated in transformed samples than in the preceding FL tumors, including TP53, B2M, CCND3, GNA13, S1PR2, and P2RY8. Moreover, ten genes were more commonly mutated in diagnostic specimens of patients with early progression, including TP53, BTG1, MKI67, and XBP1. Conclusions Our results illuminate contrasting modes of evolution shaping the clinical histories of transformation and progression. They have implications for interpretation of evolutionary dynamics in the context of treatment-induced selective pressures, and indicate that transformation and progression will require different clinical management strategies., Sohrab Shah and colleagues explore the evolutionary histories that shape clinical and transformation dynamics in follicular lymphoma, Author Summary Why Was This Study Done? Follicular lymphoma (FL) is a largely incurable malignancy in which early progression and transformation have consistently been linked to lymphoma-related mortality. We contended that detailed characterization of clonal dynamics would reveal fundamental biological properties with implications for future patient management strategies relating to both transformation and progression. We also sought to identify recurrent gene mutations associated with transformation and/or early progression in a large patient cohort. What Did the Researchers Do and Find? Using whole genome sequencing, deep allelic sampling by amplicon sequencing, and digital droplet PCR, we found dramatic clonal expansions in transformed disease, whereby dominant clones in transformation samples emerged from extremely low prevalence clones or from clones that were not detected in the diagnostic samples. The dynamics of disease progression during treatment in the absence of transformation showed markedly different characteristics, with much of the clonal architecture preserved from diagnostic to relapse specimens. Targeted capture-based sequencing in a large extension cohort then established genetic variants associated with transformation and early progression in the broader patient population. What Do These Findings Mean? Taken together, our findings illuminate previously undescribed patterns of clonal expansion underpinning FL clinical histories suggesting that contrasting management strategies will be necessary across the FL patient population. We uncovered novel associations of gene mutations with early progression that could inform future prognostic assay development.

Author(s): Taghiyar, M Jafar; Rosner, Jamie; Grewal, Diljot; Grande, Bruno M; Aniba, Radhouane; Grewal, Jasleen; Boutros, Paul C; Morin, Ryan D; Bashashati, Ali; Shah, Sohrab P | Abstract: BackgroundThe field of next-generation sequencing informatics has matured to a point where algorithmic advances in sequence alignment and individual feature detection methods have stabilized. Practical and robust implementation of complex analytical workflows (where such tools are structured into "best practices" for automated analysis of next-generation sequencing datasets) still requires significant programming investment and expertise.ResultsWe present Kronos, a software platform for facilitating the development and execution of modular, auditable, and distributable bioinformatics workflows. Kronos obviates the need for explicit coding of workflows by compiling a text configuration file into executable Python applications. Making analysis modules would still require programming. The framework of each workflow includes a run manager to execute the encoded workflows locally (or on a cluster or cloud), parallelize tasks, and log all runtime events. The resulting workflows are highly modular and configurable by construction, facilitating flexible and extensible meta-applications that can be modified easily through configuration file editing. The workflows are fully encoded for ease of distribution and can be instantiated on external systems, a step toward reproducible research and comparative analyses. We introduce a framework for building Kronos components that function as shareable, modular nodes in Kronos workflows.ConclusionsThe Kronos platform provides a standard framework for developers to implement custom tools, reuse existing tools, and contribute to the community at large. Kronos is shipped with both Docker and Amazon Web Services Machine Images. It is free, open source, and available through the Python Package Index and at https://github.com/jtaghiyar/kronos.

DICER1 plays a critical role in microRNA (miRNA) biogenesis. Recurrent somatic 'hotspot' mutations at the four metal-binding sites within the RNase IIIb domain of DICER1 were identified in ovarian sex cord-stromal tumours and have since been described in other paediatric tumours. In this study, we screened the RNase IIIb domain of DICER1 in 290 endometrial tumours and identified six cases with hotspot mutations, including two cases affected by an atypical G1809R mutation directly adjacent to a metal-binding site. Using Illumina and Sanger targeted resequencing, we observed and validated biallelic DICER1 mutations in several cases with hotspot mutations. Through in vitro DICER1 cleavage assays, small RNA deep sequencing and real-time PCR, we demonstrated that mutations adding a positively charged side chain to residue 1809 have similar detrimental effects on 5p miRNA production to mutations at the metal-binding sites. As expected, 5p miRNAs were globally reduced in tumours and cell lines with hotspot mutations. Pathway analysis of gene expression profiles indicated that genes de-repressed due to loss of 5p miRNAs are strongly associated with pathways regulating the cell cycle. Using a Dicer1-null mouse cell line model, we found that expression of DICER1 hotspot mutants promoted cell proliferation, whereas wild-type (WT) DICER1 inhibited cell proliferation. Furthermore, targets of let-7 family miRNAs are enriched among the up-regulated genes, suggesting that loss of let-7 may be impacting downstream pathways. Our results reveal that DICER1 hotspot mutations are implicated in common malignancies and may constitute a unique oncogenic pathway.

Introduction: Follicular lymphoma (FL) remains a significant clinical burden as it is an incurable disease and most patients will eventually suffer from disease progression. Two clinical events are associated with poor outcomes for patients with FL: (1) histological transformation (TFL) of their original FL into a high-grade, aggressive lymphoma subtype (2-3% of patients per year) and (2) early disease progression (PFL) where patients experience treatment failure within 2 years of receiving therapy (20% of patients). Despite recent high-throughput sequencing studies, the nature of tumor clonal dynamics leading to TFL or PFL is poorly understood and it is unknown if similar, or contrasting, modes of selection underpin these FL clinical events. Materials & Methods:We assembled a study cohort consisting of 21 patients: 15 experiencing TFL and 6 PFL. For each TFL and PFL patient, we obtained primary biopsies (T1; taken at the time of the initial FL diagnosis), biopsies at transformation/progression (T2) and matched normal samples. We performed whole genome sequencing on each specimen and identified single point mutations and copy number alterations using MutationSeq and TITAN, respectively. We compared T1 to T2 somatic mutation profiles and identified mutations associated with extinction of T1 clones and expansion of T2 clones. To validate these patterns, we selected 192 positions from each patient for deep-targeted sequencing validation (~10733X) in their T1, T2, and normal samples. We applied a statistical model (PyClone) to estimate cancer cell fraction (CCF) of each validated mutation. These CCF estimates were used to construct clonal phylogenies (Citup) and infer clonal dynamic patterns during their evolutionary histories. The Wright-Fisher model of genetic drift was used to model tumor evolution. Results: Temporal analysis of mutational burden revealed that mutational burden was significantly higher in T2 (8162 mutations ± 2146) than in T1 (6373 ± 2630) tumors for both TFL and PFL patients (Wilcox P < 0.001). This was independent of time interval between sampling (Spearman R2 = 0.029, P = 0.456). Mutation variant allelic fraction (VAF) distributions revealed that all distributions showed evidence of shared clonal ancestry between T1 and T2 tumors accompanied by substantial numbers of T1 and T2-specific mutations. We selected ≥ 192 mutations per patient from these distributions and performed deep-targeted amplicon sequencing, validating 96.3% of mutations and acquiring precise VAFs to infer clonal dynamics. In 13 of 15 TFL patients (87%), we observed dramatic clonal dynamics, characteristic of T2 tumors dominated by clones (or phylogenetic lineages) that were absent or extremely rare in T1 tumors (< 1% CCF). Digital droplet PCR was used to confirm the existence of both scenarios (confirming a clone as rare as 2 out of approximately 105 cells). Tumor evolution modeling demonstrated that this mode of evolution was driven through positive selection for mutations that confer fitness advantages and not by genetic drift. In contrast, PFL patients exhibited markedly different patterns of clonal dynamics compared to TFL patients. 4 of 6 PFL patients (67%) harbored readily detectable clones at T1, which expanded to full clonal prevalence during treatment with immuno-chemotherapy. Tumor evolution modeling demonstrated that this mode of evolution could be explained under neutral evolutionary dynamics (drift). Conclusions: We have shown that histological transformation and early progression manifest through divergent modes of tumor evolution. As the transformation phenotype may arise after diagnosis, more frequent monitoring of these patients will be required to determine the exact timing of the evolutionary inflection point that elicits transformation. In comparison, prediction of early treatment resistance should be achievable through comprehensive characterization of the genetic and clonal composition at diagnosis; this would ultimately identify patients who may benefit from upfront alternative therapies without the need to first endure predictable early treatment failure. Disclosures Sehn: roche/genentech: Consultancy, Honoraria; amgen: Consultancy, Honoraria; seattle genetics: Consultancy, Honoraria; abbvie: Consultancy, Honoraria; TG therapeutics: Consultancy, Honoraria; celgene: Consultancy, Honoraria; lundbeck: Consultancy, Honoraria; janssen: Consultancy, Honoraria. Connors:Millennium Takeda: Research Funding; Seattle Genetics: Research Funding; F Hoffmann-La Roche: Research Funding; Bristol Myers Squib: Research Funding; NanoString Technologies: Research Funding. Scott:Janssen: Consultancy; Celgene: Consultancy; Roche: Honoraria; BC Cancer Agency: Patents & Royalties: Inventor on a patent licensed to NanoString Technologies.

Deep-genome and single-cell sequencing analyses of patient-derived breast cancer xenografts reveal extensive, dynamic and reproducible changes in intra-tumoral mutational clonal composition on engraftment and serial propagation. Xenograft transplantation of primary human cancer cells into mice provides valuable models in which to study mechanisms underlying tumorigenesis, drug response and resistance. This study demonstrates that clonal evolution resembling that seen in human tumours also occurs on engraftment and during subsequent passaging of breast tumours in immunodeficient mice. In addition, similar clonal expansion patterns emerge in independent grafts of the same starting tumour population, indicating that genomic aberrations can be reproducible determinants of evolutionary trajectories. These findings suggest that patient-derived xenografts may be useful for studying patient-specific tumour characteristics such as the response to drugs tailored to specific genomic alterations. Human cancers, including breast cancers, comprise clones differing in mutation content. Clones evolve dynamically in space and time following principles of Darwinian evolution1,2, underpinning important emergent features such as drug resistance and metastasis3,4,5,6,7. 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 tumours8,9,10. 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 (

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.

Primary triple negative breast cancers (TNBC) represent approximately 16% of all breast cancers1 and are a tumour type defined by exclusion, for which comprehensive landscapes of somatic mutation have not been determined. Here we show in 104 early TNBC cases, that at the time of diagnosis these cancers exhibit a wide and continuous spectrum of genomic evolution, with some exhibiting only a handful of somatic aberrations in a few pathways, whereas others contain hundreds of somatic events and multiple pathways implicated. Integration with matched whole transcriptome sequence data revealed that only ~36% of mutations are expressed. By examining single nucleotide variant (SNV) allelic abundance derived from deep re-sequencing (median >20,000 fold) measurements in 2414 somatic mutations, we determine for the first time in an epithelial tumour, the relative abundance of clonal genotypes among cases in the population. We show that TNBC vary widely and continuously in their clonal frequencies at the time of diagnosis, with basal subtype TNBC2,3 exhibiting more variation than non-basal TNBC. Although p53 and PIK3CA/PTEN somatic mutations appear clonally dominant compared with other pathways, in some tumours their clonal frequencies are incompatible with founder status. Mutations in cytoskeletal and cell shape/motility proteins occurred at lower clonal frequencies, suggesting they occurred later during tumour progression. Taken together our results show that future attempts to dissect the biology and therapeutic responses of TNBC will require the determination of individual tumour clonal genotypes.

Alternation in genes associated with microRNA (miRNA) biogenesis pathway may lead to miRNA dysregulation, and is implicated in a variety of human malignancies. Previously our group identified recurrent somatic “hotspot” mutations (E1705, D1709, D1810, E1813) in a critical miRNA-processing gene, DICER1, in rare sex cord-stromal tumors. During miRNA biogenesis, the two RNase III domains of DICER1 form an intramolecular dimer, which leads to the cleavage of the precursor miRNA (pre-miRNA) hairpin and generate mature 5p and 3p miRNAs from 5’ and 3’ arms of the precursor hairpin respectively. Studies have shown that the hotspot mutations in the RNase IIIb metal binding domain could impair DICER1's ability to generate mature 5p miRNAs, leading to global loss of 5p miRNAs. Recently, in collaboration with The Cancer Genome Atlas (TCGA), we identified DICER1 hotspot mutations in a small subset of endometrial cancer from TCGA cohort (6/248) as well as our own tumor bank (6/307), suggesting disruption of DICER1 is implicated in a common malignancy. We also found an additional recurrent mutation G1809R and demonstrated that it has similar detrimental effects on miRNA biogenesis as hotspot mutations through deep sequencing and realtime PCR. Using Illumina Miseq targeted resequencing and Sanger sequencing, we observed biallelic DICER1 mutations in RNase IIIb domain in some but not all cases. miRNA deep sequencing confirmed that 5p miRNAs are decreased in both cell line models and endometrial tumors with hotspot mutations. Bioinformatic analysis of RNA sequencing profiles from TCGA dataset predicted hotspot DICER1 mutations to have greater functional impact than non-hotspot DICER1 mutations on gene expression. The oncogenic properties of DICER1 hotspot mutations are currently under investigation. Citation Format: Jiamin Chen, Yemin Wang, Melissa McConechy, Michael Anglesio, Janine Senz, Winnie Yang, Jamie Rosner, Andy Chu, Grace Cheng, Gregg Morin, David Huntsman. Recurrent DICER1 hotspot mutations in endometrial cancer and their impact on microRNA biogenesis. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 544. doi:10.1158/1538-7445.AM2014-544

Simultaneous interrogation of tumor genomes and transcriptomes is underway in unprecedented global efforts. Yet, despite the essential need to separate driver mutations modulating gene expression networks from transcriptionally inert passenger mutations, robust computational methods to ascertain the impact of individual mutations on transcriptional networks are underdeveloped. We introduce a novel computational framework, DriverNet, to identify likely driver mutations by virtue of their effect on mRNA expression networks. Application to four cancer datasets reveals the prevalence of rare candidate driver mutations associated with disrupted transcriptional networks and a simultaneous modulation of oncogenic and metabolic networks, induced by copy number co-modification of adjacent oncogenic and metabolic drivers. DriverNet is available on Bioconductor or at http://compbio.bccrc.ca/software/drivernet/ .