SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution

Clonality, Heterogeneity, and Evolution of Synchronous Bilateral Ovarian Cancer

Synchronous bilateral ovarian cancer (SBOC) represents a relatively frequent occurrence and clinically relevant diagnostic dilemma. Delineation of its clonal architecture, genetic heterogeneity, and evolutionary trajectories may have important implications for prognosis and management of patients with SBOC. Here, we describe the results of next-generation whole-exome or whole-genome sequencing of specimens from 12 SBOC cases and report that bilateral tumors from each individual display a comparable number of genomic abnormalities and similar mutational signatures of single-nucleotide variations. Clonality indices based on tumor-specific alterations supported monoclonal origins of SBOC. Each of the ovarian lesions was nevertheless oligoclonal, with inferred metastatic tumors harboring more subclones than their primary counterparts. The phylogenetic structure of SBOC indicated that most cancer cell dissemination occurred early, when the primary carcinoma was still relatively small (<100 million cells). Accordingly, the mutation spectra and mutational signatures of somatic variants exhibited pronounced spatiotemporal differences in each patient. Overall, these findings suggest that SBOCs are clonally related and form through pelvic spread rather than independent multifocal oncogenesis. Metastatic dissemination is often an early event, with dynamic mutational processes leading to divergent evolution and intratumor and intertumor heterogeneity, ultimately contributing substantially to phenotypic plasticity and diverse clinical course in SBOC. Cancer Res; 77(23); 6551-61. ©2017 AACR.

Tumor Heterogeneity Predicts Metastatic Potential in Colorectal Cancer

Purpose: Tumors continuously evolve to maintain growth; secondary mutations facilitate this process, resulting in high tumor heterogeneity. In this study, we compared mutations in paired primary and metastatic colorectal cancer tumor samples to determine whether tumor heterogeneity can predict tumor metastasis.Experimental Design: Somatic variations in 46 pairs of matched primary-liver metastatic tumors and 42 primary tumors without metastasis were analyzed by whole-exome sequencing. Tumor clonality was estimated from single-nucleotide and copy-number variations. The correlation between clinical parameters of patients and clonal heterogeneity in liver metastasis was evaluated.Results: Tumor heterogeneity across colorectal cancer samples was highly variable; however, a high degree of tumor heterogeneity was associated with a worse disease-free survival. Highly heterogeneous primary colorectal cancer was correlated with a higher rate of liver metastasis. Recurrent somatic mutations in APC, TP53, and KRAS were frequently detected in highly heterogeneous colorectal cancer. The variant allele frequency of these mutations was high, while somatic mutations in other genes such as PIK3CA and NOTCH1 were low. The number and distribution of primary colorectal cancer subclones were preserved in metastatic tumors.Conclusions: Heterogeneity of primary colorectal cancer tumors can predict the potential for liver metastasis and thus, clinical outcome of patients. Clin Cancer Res; 23(23); 7209-16. ©2017 AACR.

Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing

In multiple myeloma malignant plasma cells expand within the bone marrow. Since this site is well-perfused, a rapid dissemination of "fitter" clones may be anticipated. However, an imbalanced distribution of multiple myeloma is frequently observed in medical imaging. Here, we perform multi-region sequencing, including iliac crest and radiology-guided focal lesion specimens from 51 patients to gain insight into the spatial clonal architecture. We demonstrate spatial genomic heterogeneity in more than 75% of patients, including inactivation of CDKN2C and TP53, and mutations affecting mitogen-activated protein kinase genes. We show that the extent of spatial heterogeneity is positively associated with the size of biopsied focal lesions consistent with regional outgrowth of advanced clones. The results support a model for multiple myeloma progression with clonal sweeps in the early phase and regional evolution in advanced disease. We suggest that multi-region investigations are critical to understanding intra-patient heterogeneity and the evolutionary processes in multiple myeloma.In multiple myeloma, malignant cells expand within bone marrow. Here, the authors use multi-region sequencing in patient samples to analyse spatial clonal architecture and heterogeneity, providing novel insight into multiple myeloma progression and evolution.

Whole-Exome Sequencing of Metaplastic Breast Carcinoma Indicates Monoclonality with Associated Ductal Carcinoma Component

Purpose: Although most human cancers display a single histology, there are unusual cases where two or more distinct tissue types present within a primary tumor. One such example is metaplastic breast carcinoma, a rare but aggressive cancer with a heterogeneous histology, including squamous, chondroid, and spindle cells. Metaplastic carcinomas often contain an admixed conventional ductal invasive or in situ mammary carcinoma component, and are typically triple-negative for estrogen receptor, progesterone receptor, and HER-2 amplification/overexpression. An unanswered question is the origin of metaplastic breast cancers. While they may arise independently from their ductal components, their close juxtaposition favors a model that postulates a shared origin, either as two derivatives from the same primary cancer or one histology as an outgrowth of the other. Understanding the mechanism of development of these tumors may inform clinical decisions.Experimental Design: We performed exome sequencing for paired metaplastic and adjacent conventional invasive ductal carcinomas in 8 patients and created a pipeline to identify somatic variants and predict their functional impact, without having normal tissue. We then determined the genetic relationships between the histologically distinct compartments.Results: In each case, the tumor components have nearly identical landscapes of somatic mutation, implying that the differing histologies do not derive from genetic clonal divergence.Conclusions: A shared origin for tumors with differing histologies suggests that epigenetic or noncoding changes may mediate the metaplastic phenotype and that alternative therapeutic approaches, including epigenetic therapies, may be required for metaplastic breast cancers. Clin Cancer Res; 23(16); 4875-84. ©2017 AACR.

Principles of Reconstructing the Subclonal Architecture of Cancers

Most cancers evolve from a single founder cell through a series of clonal expansions that are driven by somatic mutations. These clonal expansions can lead to several coexisting subclones sharing subsets of mutations. Analysis of massively parallel sequencing data can infer a tumor's subclonal composition through the identification of populations of cells with shared mutations. We describe the principles that underlie subclonal reconstruction through single nucleotide variants (SNVs) or copy number alterations (CNAs) from bulk or single-cell sequencing. These principles include estimating the fraction of tumor cells for SNVs and CNAs, performing clustering of SNVs from single- and multisample cases, and single-cell sequencing. The application of subclonal reconstruction methods is providing key insights into tumor evolution, identifying subclonal driver mutations, patterns of parallel evolution and differences in mutational signatures between cellular populations, and characterizing the mechanisms of therapy resistance, spread, and metastasis.

Clonal composition of human ovarian cancer based on copy number analysis reveals a reciprocal relation with oncogenic mutation status

Intratumoral heterogeneity of cancer cells remains largely unexplored. Here we investigated the composition of ovarian cancer and its biological relevance. A whole-genome single nucleotide polymorphism array was applied to detect the clonal composition of 24 formalin-fixed, paraffin-embedded samples of human ovarian cancer. Genome-wide segmentation data consisting of the log2 ratio (log2R) and B allele frequency (BAF) were used to calculate an estimate of the clonal composition number (CC number) for each tumor. Somatic mutation profiles of cancer-related genes were also determined for the same 24 samples by next-generation sequencing. The CC number was estimated successfully for 23 of the 24 cancer samples. The mean ± SD value for the CC number was 1.7 ± 1.1 (range of 0-4). A somatic mutation in at least one gene was identified in 22 of the 24 ovarian cancer samples, with the mutations including those in the oncogenes KRAS (29.2%), PIK3CA (12.5%), BRAF (8.3%), FGFR2 (4.2%), and JAK2 (4.2%) as well as those in the tumor suppressor genes TP53 (54.2%), FBXW7 (8.3%), PTEN (4.2%), and RB1 (4.2%). Tumors with one or more oncogenic mutations had a significantly lower CC number than did those without such a mutation (1.0 ± 0.8 versus 2.3 ± 0.9, P = 0.0027), suggesting that cancers with driver oncogene mutations are less heterogeneous than those with other mutations. Our results thus reveal a reciprocal relation between oncogenic mutation status and clonal composition in ovarian cancer using the established method for the estimation of the CC number.

Tumor phylogeny inference using tree-constrained importance sampling

MOTIVATION

A tumor arises from an evolutionary process that can be modeled as a phylogenetic tree. However, reconstructing this tree is challenging as most cancer sequencing uses bulk tumor tissue containing heterogeneous mixtures of cells.

RESULTS

We introduce P robabilistic A lgorithm for S omatic Tr ee I nference (PASTRI), a new algorithm for bulk-tumor sequencing data that clusters somatic mutations into clones and infers a phylogenetic tree that describes the evolutionary history of the tumor. PASTRI uses an importance sampling algorithm that combines a probabilistic model of DNA sequencing data with a enumeration algorithm based on the combinatorial constraints defined by the underlying phylogenetic tree. As a result, tree inference is fast, accurate and robust to noise. We demonstrate on simulated data that PASTRI outperforms other cancer phylogeny algorithms in terms of runtime and accuracy. On real data from a chronic lymphocytic leukemia (CLL) patient, we show that a simple linear phylogeny better explains the data the complex branching phylogeny that was previously reported. PASTRI provides a robust approach for phylogenetic tree inference from mixed samples.

AVAILABILITY AND IMPLEMENTATION

Software is available at compbio.cs.brown.edu/software.

CONTACT

braphael@princeton.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mutational Landscapes of Smoking-Related Cancers in Caucasians and African Americans: Precision Oncology Perspectives at Wake Forest Baptist Comprehensive Cancer Center

Background: Cancers related to tobacco use and African-American ancestry are under-characterized by genomics. This gap in precision oncology research represents a major challenge in the health disparities in the United States. Methods: The Precision Oncology trial at the Wake Forest Baptist Comprehensive Cancer Center enrolled 431 cancer patients from March 2015 to May 2016. The composition of these patients consists of a high representation of tobacco-related cancers (e.g., lung, colorectal, and bladder) and African-American ancestry (13.5%). Tumors were sequenced to identify mutations to gain insight into genetic alterations associated with smoking and/or African-American ancestry. Results: Tobacco-related cancers exhibit a high mutational load. These tumors are characterized by high-frequency mutations in TP53, DNA damage repair genes (BRCA2 and ATM), and chromatin remodeling genes (the lysine methyltransferases KMT2D or MLL2, and KMT2C or MLL3). These tobacco-related cancers also exhibit augmented tumor heterogeneities. Smoking related genetic mutations were validated by The Cancer Genome Atlas dataset that includes 2,821 cases with known smoking status. The Wake Forest and The Cancer Genome Atlas cohorts (431 and 7,991 cases, respectively) revealed a significantly increased mutation rate in the TP53 gene in the African-American subgroup studied. Both cohorts also revealed 5 genes (e.g. CDK8) significantly amplified in the African-American population. Conclusions: These results provide strong evidence that tobacco is a major cause of genomic instability and heterogeneity in cancer. TP53 mutations and key oncogene amplifications emerge as key factors contributing to cancer outcome disparities among different racial/ethnic groups.

Between-region genetic divergence reflects the mode and tempo of tumor evolution

Given the implications of tumor dynamics for precision medicine, there is a need to systematically characterize the mode of evolution across diverse solid tumor types. In particular, methods to infer the role of natural selection within established human tumors are lacking. By simulating spatial tumor growth under different evolutionary modes and examining patterns of between-region subclonal genetic divergence from multiregion sequencing (MRS) data, we demonstrate that it is feasible to distinguish tumors driven by strong positive subclonal selection from those evolving neutrally or under weak selection, as the latter fail to dramatically alter subclonal composition. We developed a classifier based on measures of between-region subclonal genetic divergence and projected patient data into model space, finding different modes of evolution both within and between solid tumor types. Our findings have broad implications for how human tumors progress, how they accumulate intratumoral heterogeneity, and ultimately how they may be more effectively treated.

Intratumoral heterogeneity and subclonal diversification of early breast cancer

Heterogeneity has long been recognized as a feature of some primary breast cancers manifesting as mixed histopathological subtypes or variable expression of the therapeutic targets ER, PgR and HER2. The recent emergence of next generation sequencing (NGS) technologies has revolutionized our understanding of the extent and nature of subclonal diversification. Careful examination of primary breast cancers often reveals multiple genomically distinct subclones that may contain driver alterations that follow spatial patterns of segregation. Subclonality is of clinical relevance as it forms the substrate of selection and can give rise to aggressive clinical features such as invasiveness, metastasis and treatment resistance. However, spatial and temporal intra-tumoral heterogeneity pose fundamental challenges to representative sampling and consequently the feasibility of a personalized medicine approach. Fundamental clinical and biological questions are starting to be addressed by applying NGS to the study of intra-tumoral heterogeneity and the insights that it provides should be used to better inform the prospective design of clinico-genomics trials.

Computational Methods for Characterizing Cancer Mutational Heterogeneity

Advances in DNA sequencing technologies have allowed the characterization of somatic mutations in a large number of cancer genomes at an unprecedented level of detail, revealing the extreme genetic heterogeneity of cancer at two different levels: inter-tumor, with different patients of the same cancer type presenting different collections of somatic mutations, and intra-tumor, with different clones coexisting within the same tumor. Both inter-tumor and intra-tumor heterogeneity have crucial implications for clinical practices. Here, we review computational methods that use somatic alterations measured through next-generation DNA sequencing technologies for characterizing tumor heterogeneity and its association with clinical variables. We first review computational methods for studying inter-tumor heterogeneity, focusing on methods that attempt to summarize cancer heterogeneity by discovering pathways that are commonly mutated across different patients of the same cancer type. We then review computational methods for characterizing intra-tumor heterogeneity using information from bulk sequencing data or from single cell sequencing data. Finally, we present some of the recent computational methodologies that have been proposed to identify and assess the association between inter- or intra-tumor heterogeneity with clinical variables.

Multilevel genomics of colorectal cancers with microsatellite instability-clinical impact of JAK1 mutations and consensus molecular subtype 1

BACKGROUND

Approximately 15% of primary colorectal cancers have DNA mismatch repair deficiency, causing a complex genome with thousands of small mutations-the microsatellite instability (MSI) phenotype. We investigated molecular heterogeneity and tumor immunogenicity in relation to clinical endpoints within this distinct subtype of colorectal cancers.

METHODS

A total of 333 primary MSI+ colorectal tumors from multiple cohorts were analyzed by multilevel genomics and computational modeling-including mutation profiling, clonality modeling, and neoantigen prediction in a subset of the tumors, as well as gene expression profiling for consensus molecular subtypes (CMS) and immune cell infiltration.

RESULTS

Novel, frequent frameshift mutations in four cancer-critical genes were identified by deep exome sequencing, including in CRTC1, BCL9, JAK1, and PTCH1. JAK1 loss-of-function mutations were validated with an overall frequency of 20% in Norwegian and British patients, and mutated tumors had up-regulation of transcriptional signatures associated with resistance to anti-PD-1 treatment. Clonality analyses revealed a high level of intra-tumor heterogeneity; however, this was not associated with disease progression. Among the MSI+ tumors, the total mutation load correlated with the number of predicted neoantigens (P = 4 × 10-5), but not with immune cell infiltration-this was dependent on the CMS class; MSI+ tumors in CMS1 were highly immunogenic compared to MSI+ tumors in CMS2-4. Both JAK1 mutations and CMS1 were favorable prognostic factors (hazard ratios 0.2 [0.05-0.9] and 0.4 [0.2-0.9], respectively, P = 0.03 and 0.02).

CONCLUSIONS

Multilevel genomic analyses of MSI+ colorectal cancer revealed molecular heterogeneity with clinical relevance, including tumor immunogenicity and a favorable patient outcome associated with JAK1 mutations and the transcriptomic subgroup CMS1, emphasizing the potential for prognostic stratification of this clinically important subtype. See related research highlight by Samstein and Chan 10.1186/s13073-017-0438-9.

Circulating mutational portrait of cancer: manifestation of aggressive clonal events in both early and late stages

BACKGROUND

Solid tumors residing in tissues and organs leave footprints in circulation through circulating tumor cells (CTCs) and circulating tumor DNAs (ctDNA). Characterization of the ctDNA portraits and comparison with tumor DNA mutational portraits may reveal clinically actionable information on solid tumors that is traditionally achieved through more invasive approaches.

METHODS

We isolated ctDNAs from plasma of patients of 103 lung cancer and 74 other solid tumors of different tissue origins. Deep sequencing using the Guardant360 test was performed to identify mutations in 73 clinically actionable genes, and the results were associated with clinical characteristics of the patient. The mutation profiles of 37 lung cancer cases with paired ctDNA and tumor genomic DNA sequencing were used to evaluate clonal representation of tumor in circulation. Five lung cancer cases with longitudinal ctDNA sampling were monitored for cancer progression or response to treatments.

RESULTS

Mutations in TP53, EGFR, and KRAS genes are most prevalent in our cohort. Mutation rates of ctDNA are similar in early (I and II) and late stage (III and IV) cancers. Mutation in DNA repair genes BRCA1, BRCA2, and ATM are found in 18.1% (32/177) of cases. Patients with higher mutation rates had significantly higher mortality rates. Lung cancer of never smokers exhibited significantly higher ctDNA mutation rates as well as higher EGFR and ERBB2 mutations than ever smokers. Comparative analysis of ctDNA and tumor DNA mutation data from the same patients showed that key driver mutations could be detected in plasma even when they were present at a minor clonal population in the tumor. Mutations of key genes found in the tumor tissue could remain in circulation even after frontline radiotherapy and chemotherapy suggesting these mutations represented resistance mechanisms. Longitudinal sampling of five lung cancer cases showed distinct changes in ctDNA mutation portraits that are consistent with cancer progression or response to EGFR drug treatment.

CONCLUSIONS

This study demonstrates that ctDNA mutation rates in the key tumor-associated genes are clinical parameters relevant to smoking status and mortality. Mutations in ctDNA may serve as an early detection tool for cancer. This study quantitatively confirms the hypothesis that ctDNAs in circulation is the result of dissemination of aggressive tumor clones and survival of resistant clones. This study supports the use of ctDNA profiling as a less-invasive approach to monitor cancer progression and selection of appropriate drugs during cancer evolution.

phyC: Clustering cancer evolutionary trees

Multi-regional sequencing provides new opportunities to investigate genetic heterogeneity within or between common tumors from an evolutionary perspective. Several state-of-the-art methods have been proposed for reconstructing cancer evolutionary trees based on multi-regional sequencing data to develop models of cancer evolution. However, there have been few studies on comparisons of a set of cancer evolutionary trees. We propose a clustering method (phyC) for cancer evolutionary trees, in which sub-groups of the trees are identified based on topology and edge length attributes. For interpretation, we also propose a method for evaluating the sub-clonal diversity of trees in the clusters, which provides insight into the acceleration of sub-clonal expansion. Simulation showed that the proposed method can detect true clusters with sufficient accuracy. Application of the method to actual multi-regional sequencing data of clear cell renal carcinoma and non-small cell lung cancer allowed for the detection of clusters related to cancer type or phenotype. phyC is implemented with R(≥3.2.2) and is available from https://github.com/ymatts/phyC.

Elucidating the differences between cancer evolutionary patterns among patients is valuable in personalized medicine, since therapeutic response mostly depends on cancer evolution process. Recently, computational methods have been extensively studied to reconstruct a cancer evolutionary pattern within a patient, which is visualized as a so-called “cancer evolutionary tree” constructed from multi-regional sequencing data. However, there have been few studies on comparisons of a set of cancer evolutionary trees to better understand the relationship between a set of cancer evolutionary patterns and patient phenotypes. Given a set of tree objects for multiple patients, we propose an unsupervised learning approach to identify subgroups of patients through clustering the respective cancer evolutionary trees. Using this approach, we effectively identified the patterns of different evolutionary modes in a simulation analysis, and also successfully detected the phenotype-related and cancer type-related subgroups to characterize tree structures within subgroups using actual datasets. We believe that the value and impact of our work will grow as more and more datasets for the cancer evolution of patients become available.

eTumorType, An Algorithm of Discriminating Cancer Types for Circulating Tumor Cells or Cell-free DNAs in Blood

With the technology development on detecting circulating tumor cells (CTCs) and cell-free DNAs (cfDNAs) in blood, serum, and plasma, non-invasive diagnosis of cancer becomes promising. A few studies reported good correlations between signals from tumor tissues and CTCs or cfDNAs, making it possible to detect cancers using CTCs and cfDNAs. However, the detection cannot tell which cancer types the person has. To meet these challenges, we developed an algorithm, eTumorType, to identify cancer types based on copy number variations (CNVs) of the cancer founding clone. eTumorType integrates cancer hallmark concepts and a few computational techniques such as stochastic gradient boosting, voting, centroid, and leading patterns. eTumorType has been trained and validated on a large dataset including 18 common cancer types and 5327 tumor samples. eTumorType produced high accuracies (0.86-0.96) and high recall rates (0.79-0.92) for predicting colon, brain, prostate, and kidney cancers. In addition, relatively high accuracies (0.78-0.92) and recall rates (0.58-0.95) have also been achieved for predicting ovarian, breast luminal, lung, endometrial, stomach, head and neck, leukemia, and skin cancers. These results suggest that eTumorType could be used for non-invasive diagnosis to determine cancer types based on CNVs of CTCs and cfDNAs.

PhyloOncology: Understanding cancer through phylogenetic analysis

Despite decades of research and an enormity of resultant data, cancer remains a significant public health problem. New tools and fresh perspectives are needed to obtain fundamental insights, to develop better prognostic and predictive tools, and to identify improved therapeutic interventions. With increasingly common genome-scale data, one suite of algorithms and concepts with potential to shed light on cancer biology is phylogenetics, a scientific discipline used in diverse fields. From grouping subsets of cancer samples to tracing subclonal evolution during cancer progression and metastasis, the use of phylogenetics is a powerful systems biology approach. Well-developed phylogenetic applications provide fast, robust approaches to analyze high-dimensional, heterogeneous cancer data sets. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

The evolution of tumour phylogenetics: principles and practice

Rapid advances in high-throughput sequencing and a growing realization of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogenetic studies of tumour progression. These studies have yielded not only new insights but also a plethora of experimental approaches, sometimes reaching conflicting or poorly supported conclusions. Here, we consider this body of work in light of the key computational principles underpinning phylogenetic inference, with the goal of providing practical guidance on the design and analysis of scientifically rigorous tumour phylogeny studies. We survey the range of methods and tools available to the researcher, their key applications, and the various unsolved problems, closing with a perspective on the prospects and broader implications of this field.

Advances in understanding tumour evolution through single-cell sequencing

The mutational heterogeneity observed within tumours poses additional challenges to the development of effective cancer treatments. A thorough understanding of a tumour's subclonal composition and its mutational history is essential to open up the design of treatments tailored to individual patients. Comparative studies on a large number of tumours permit the identification of mutational patterns which may refine forecasts of cancer progression, response to treatment and metastatic potential. The composition of tumours is shaped by evolutionary processes. Recent advances in next-generation sequencing offer the possibility to analyse the evolutionary history and accompanying heterogeneity of tumours at an unprecedented resolution, by sequencing single cells. New computational challenges arise when moving from bulk to single-cell sequencing data, leading to the development of novel modelling frameworks. In this review, we present the state of the art methods for understanding the phylogeny encoded in bulk or single-cell sequencing data, and highlight future directions for developing more comprehensive and informative pictures of tumour evolution. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

A population genetics perspective on the determinants of intra-tumor heterogeneity

Cancer results from the acquisition of somatic alterations in a microevolutionary process that typically occurs over many years, much of which is occult. Understanding the evolutionary dynamics that are operative at different stages of progression in individual tumors might inform the earlier detection, diagnosis, and treatment of cancer. Although these processes cannot be directly observed, the resultant spatiotemporal patterns of genetic variation amongst tumor cells encode their evolutionary histories. Such intra-tumor heterogeneity is pervasive not only at the genomic level, but also at the transcriptomic, phenotypic, and cellular levels. Given the implications for precision medicine, the accurate quantification of heterogeneity within and between tumors has become a major focus of current research. In this review, we provide a population genetics perspective on the determinants of intra-tumor heterogeneity and approaches to quantify genetic diversity. We summarize evidence for different modes of evolution based on recent cancer genome sequencing studies and discuss emerging evolutionary strategies to therapeutically exploit tumor heterogeneity. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors

Colorectal carcinoma represents a heterogeneous entity, with only a fraction of the tumours responding to available therapies, requiring a better molecular understanding of the disease in precision oncology. To address this challenge, the OncoTrack consortium recruited 106 CRC patients (stages I–IV) and developed a pre-clinical platform generating a compendium of drug sensitivity data totalling >4,000 assays testing 16 clinical drugs on patient-derived in vivo and in vitro models. This large biobank of 106 tumours, 35 organoids and 59 xenografts, with extensive omics data comparing donor tumours and derived models provides a resource for advancing our understanding of CRC. Models recapitulate many of the genetic and transcriptomic features of the donors, but defined less complex molecular sub-groups because of the loss of human stroma. Linking molecular profiles with drug sensitivity patterns identifies novel biomarkers, including a signature outperforming RAS/RAF mutations in predicting sensitivity to the EGFR inhibitor cetuximab.

The heterogeneity of colorectal cancer has important clinical and therapeutic implications. Here the authors analysed the responses of a large biobank of organoids and xenografts derived from colorectal patients to a panel of clinically relevant therapeutic agents to identify genes signatures associated with drug response.

Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future

Intratumor heterogeneity, which fosters tumor evolution, is a key challenge in cancer medicine. Here, we review data and technologies that have revealed intra-tumor heterogeneity across cancer types and the dynamics, constraints, and contingencies inherent to tumor evolution. We emphasize the importance of macro-evolutionary leaps, often involving large-scale chromosomal alterations, in driving tumor evolution and metastasis and consider the role of the tumor microenvironment in engendering heterogeneity and drug resistance. We suggest that bold approaches to drug development, harnessing the adaptive properties of the immune-microenvironment while limiting those of the tumor, combined with advances in clinical trial-design, will improve patient outcome.

Reconstructing metastatic seeding patterns of human cancers

Reconstructing the evolutionary history of metastases is critical for understanding their basic biological principles and has profound clinical implications. Genome-wide sequencing data has enabled modern phylogenomic methods to accurately dissect subclones and their phylogenies from noisy and impure bulk tumour samples at unprecedented depth. However, existing methods are not designed to infer metastatic seeding patterns. Here we develop a tool, called Treeomics, to reconstruct the phylogeny of metastases and map subclones to their anatomic locations. Treeomics infers comprehensive seeding patterns for pancreatic, ovarian, and prostate cancers. Moreover, Treeomics correctly disambiguates true seeding patterns from sequencing artifacts; 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumour heterogeneity among distinct samples. In silico benchmarking on simulated tumour phylogenies across a wide range of sample purities (15–95%) and sequencing depths (25-800 × ) demonstrates the accuracy of Treeomics compared with existing methods.

Tumours frequently metastasize to multiple anatomical sites and understanding how these different metastases evolve may be important for therapy. Here, the authors develop a method—Treeomics—that can construct phylogenies from multiple metastases from next-generation sequencing data.

Tumor evolution: Linear, branching, neutral or punctuated?

Intratumor heterogeneity has been widely reported in human cancers, but our knowledge of how this genetic diversity emerges over time remains limited. A central challenge in studying tumor evolution is the difficulty in collecting longitudinal samples from cancer patients. Consequently, most studies have inferred tumor evolution from single time-point samples, providing very indirect information. These data have led to several competing models of tumor evolution: linear, branching, neutral and punctuated. Each model makes different assumptions regarding the timing of mutations and selection of clones, and therefore has different implications for the diagnosis and therapeutic treatment of cancer patients. Furthermore, emerging evidence suggests that models may change during tumor progression or operate concurrently for different classes of mutations. Finally, we discuss data that supports the theory that most human tumors evolve from a single cell in the normal tissue. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Use of application containers and workflows for genomic data analysis

Background:

The rapid acquisition of biological data and development of computationally intensive analyses has led to a need for novel approaches to software deployment. In particular, the complexity of common analytic tools for genomics makes them difficult to deploy and decreases the reproducibility of computational experiments.

Methods:

Recent technologies that allow for application virtualization, such as Docker, allow developers and bioinformaticians to isolate these applications and deploy secure, scalable platforms that have the potential to dramatically increase the efficiency of big data processing.

Results:

While limitations exist, this study demonstrates a successful implementation of a pipeline with several discrete software applications for the analysis of next-generation sequencing (NGS) data.

Conclusions:

With this approach, we significantly reduced the amount of time needed to perform clonal analysis from NGS data in acute myeloid leukemia.

Genetic alterations and their clinical implications in gastric cancer peritoneal carcinomatosis revealed by whole-exome sequencing of malignant ascites

Peritoneal carcinomatosis accompanied by malignant ascites is a major cause of death of advanced gastric cancer (GC). To comprehensively characterize the underlying genomic events involved in GC peritoneal carcinomatosis, we analyzed whole-exome sequences of normal gastric tissues, primary tumors, and malignant ascites from eight GC patients. We identified a unique mutational signature biased toward C-to-A substitutions in malignant ascites. In contrast, the patients who received treatment of adjuvant chemotherapy showed a high rate of C-to-T substitutions along with hypermutation in malignant ascites. Comparative analysis revealed several candidate mutations for GC peritoneal carcinomatosis: recurrent mutations in COL4A6, INTS2, and PTPN13; mutations in druggable genes including TEP1, PRKCD, BRAF, ERBB4, PIK3CA, HDAC9, FYN, FASN, BIRC2, FLT3, ROCK1, CD22, and PIK3C2B; and mutations in metastasis-associated genes including TNFSF12, L1CAM, DIAPH3, ROCK1, TGFBR1, MYO9B, NR4A1, and RHOA. Notably, gene ontology analysis revealed the significant enrichment of mutations in the Rho-ROCK signaling pathway-associated biological processes in malignant ascites. At least four of the eight patients acquired somatic mutations in the Rho-ROCK pathway components, suggesting the possible relevance of this pathway to GC peritoneal carcinomatosis. These results provide a genome-wide molecular understanding of GC peritoneal carcinomatosis and its clinical implications, thereby facilitating the development of effective therapeutics.

Acquired RAS or EGFR mutations and duration of response to EGFR blockade in colorectal cancer

Blockade of the epidermal growth factor receptor (EGFR) with the monoclonal antibodies cetuximab or panitumumab is effective in a subset of colorectal cancers (CRCs), but the emergence of resistance limits the efficacy of these therapeutic agents. At relapse, the majority of patients develop RAS mutations, while a subset acquires EGFR extracellular domain (ECD) mutations. Here we find that patients who experience greater and longer responses to EGFR blockade preferentially develop EGFR ECD mutations, while RAS mutations emerge more frequently in patients with smaller tumour shrinkage and shorter progression-free survival. In circulating cell-free tumour DNA of patients treated with anti-EGFR antibodies, RAS mutations emerge earlier than EGFR ECD variants. Subclonal RAS but not EGFR ECD mutations are present in CRC samples obtained before exposure to EGFR blockade. These data indicate that clonal evolution of drug-resistant cells is associated with the clinical outcome of CRC patients treated with anti-EGFR antibodies.

Evolutionary scalpels for dissecting tumor ecosystems

Amidst the growing literature on cancer genomics and intratumor heterogeneity, essential principles in evolutionary biology recur time and time again. Here we use these principles to guide the reader through major advances in cancer research, highlighting issues of "hit hard, hit early" treatment strategies, drug resistance, and metastasis. We distinguish between two frameworks for understanding heterogeneous tumors, both of which can inform treatment strategies: (1) The tumor as diverse ecosystem, a Darwinian population of sometimes-competing, sometimes-cooperating cells; (2) The tumor as tightly integrated, self-regulating organ, which may hijack developmental signals to restore functional heterogeneity after treatment. While the first framework dominates literature on cancer evolution, the second framework enjoys support as well. Throughout this review, we illustrate how mathematical models inform understanding of tumor progression and treatment outcomes. Connecting models to genomic data faces computational and technical hurdles, but high-throughput single-cell technologies show promise to clear these hurdles. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Clonal haemopoiesis and therapy-related myeloid malignancies in elderly patients: a proof-of-concept, case-control study

BACKGROUND

Clonal haemopoiesis of indeterminate potential (CHIP) is an age-associated genetic event linked to increased risk of primary haematological malignancies and increased all-cause mortality, but the prevalence of CHIP in patients who develop therapy-related myeloid neoplasms is unknown. We did this study to investigate whether chemotherapy-treated patients with cancer who have CHIP are at increased risk of developing therapy-related myeloid neoplasms.

METHODS

We did a nested, case-control, proof-of-concept study to compare the prevalence of CHIP between patients with cancer who later developed therapy-related myeloid neoplasms (cases) and patients who did not develop these neoplasms (controls). We identified cases from our internal biorepository of 123 357 patients who consented to participate in the Total Cancer Care biobanking protocol at Moffitt Cancer Center (Tampa, FL, USA) between Jan 1, 2006, and June 1, 2016. We included all individuals who were diagnosed with a primary malignancy, were treated with chemotherapy, subsequently developed a therapy-related myeloid neoplasm, and were 70 years or older at either diagnosis. For inclusion in this study, individuals must have had a peripheral blood or mononuclear cell sample collected before the diagnosis of therapy-related myeloid neoplasm. Controls were individuals who were diagnosed with a primary malignancy at age 70 years or older and were treated with chemotherapy but did not develop therapy-related myeloid neoplasms. Controls were matched to cases in at least a 4:1 ratio on the basis of sex, primary tumour type, age at diagnosis, smoking status, chemotherapy drug class, and duration of follow-up. We used sequential targeted and whole-exome sequencing and described clonal evolution in cases for whom paired CHIP and therapy-related myeloid neoplasm samples were available. The primary endpoint of this study was the development of therapy-related myeloid neoplasm and the primary exposure was CHIP.

FINDINGS

We identified 13 cases and 56 case-matched controls. The prevalence of CHIP in all patients (23 [33%] of 69 patients) was higher than has previously been reported in elderly individuals without cancer (about 10%). Cases had a significantly higher prevalence of CHIP than did matched controls (eight [62%] of 13 cases vs 15 [27%] of 56 controls, p=0·024; odds ratio 5·75, 95% CI 1·52-25·09, p=0·013). The most commonly mutated genes in cases with CHIP were TET2 (three [38%] of eight patients) and TP53(three [38%] of eight patients), whereas controls most often had TET2 mutations (six [40%] of 15 patients). In most (four [67%] of six patients) cases for whom paired CHIP and therapy-related myeloid neoplasm samples were available, the mean allele frequency of CHIP mutations had expanded by the time of the therapy-related myeloid neoplasm diagnosis. However, a subset of paired samples (two [33%] of six patients) had CHIP mutations that decreased in allele frequency, giving way to expansion of a distinct mutant clone.

INTERPRETATION

Patients with cancer who have CHIP are at increased risk of developing therapy-related myeloid neoplasms. The distribution of CHIP-related gene mutations differs between individuals with therapy-related myeloid neoplasm and those without, suggesting that mutation-specific differences might exist in therapy-related myeloid neoplasm risk.

FUNDING

Moffitt Cancer Center.

Tumor Evolution in Two Patients with Basal-like Breast Cancer: A Retrospective Genomics Study of Multiple Metastases

BACKGROUND

Metastasis is the main cause of cancer patient deaths and remains a poorly characterized process. It is still unclear when in tumor progression the ability to metastasize arises and whether this ability is inherent to the primary tumor or is acquired well after primary tumor formation. Next-generation sequencing and analytical methods to define clonal heterogeneity provide a means for identifying genetic events and the temporal relationships between these events in the primary and metastatic tumors within an individual.

METHODS AND FINDINGS

We performed DNA whole genome and mRNA sequencing on two primary tumors, each with either four or five distinct tissue site-specific metastases, from two individuals with triple-negative/basal-like breast cancers. As evidenced by their case histories, each patient had an aggressive disease course with abbreviated survival. In each patient, the overall gene expression signatures, DNA copy number patterns, and somatic mutation patterns were highly similar across each primary tumor and its associated metastases. Almost every mutation found in the primary was found in a metastasis (for the two patients, 52/54 and 75/75). Many of these mutations were found in every tumor (11/54 and 65/75, respectively). In addition, each metastasis had fewer metastatic-specific events and shared at least 50% of its somatic mutation repertoire with the primary tumor, and all samples from each patient grouped together by gene expression clustering analysis. TP53 was the only mutated gene in common between both patients and was present in every tumor in this study. Strikingly, each metastasis resulted from multiclonal seeding instead of from a single cell of origin, and few of the new mutations, present only in the metastases, were expressed in mRNAs. Because of the clinical differences between these two patients and the small sample size of our study, the generalizability of these findings will need to be further examined in larger cohorts of patients.

CONCLUSIONS

Our findings suggest that multiclonal seeding may be common amongst basal-like breast cancers. In these two patients, mutations and DNA copy number changes in the primary tumors appear to have had a biologic impact on metastatic potential, whereas mutations arising in the metastases were much more likely to be passengers.

Predictors of Chemosensitivity in Triple Negative Breast Cancer: An Integrated Genomic Analysis

BACKGROUND

Triple negative breast cancer (TNBC) is a highly heterogeneous and aggressive disease, and although no effective targeted therapies are available to date, about one-third of patients with TNBC achieve pathologic complete response (pCR) from standard-of-care anthracycline/taxane (ACT) chemotherapy. The heterogeneity of these tumors, however, has hindered the discovery of effective biomarkers to identify such patients.

METHODS AND FINDINGS

We performed whole exome sequencing on 29 TNBC cases from the MD Anderson Cancer Center (MDACC) selected because they had either pCR (n = 18) or extensive residual disease (n = 11) after neoadjuvant chemotherapy, with cases from The Cancer Genome Atlas (TCGA; n = 144) and METABRIC (n = 278) cohorts serving as validation cohorts. Our analysis revealed that mutations in the AR- and FOXA1-regulated networks, in which BRCA1 plays a key role, are associated with significantly higher sensitivity to ACT chemotherapy in the MDACC cohort (pCR rate of 94.1% compared to 16.6% in tumors without mutations in AR/FOXA1 pathway, adjusted p = 0.02) and significantly better survival outcome in the TCGA TNBC cohort (log-rank test, p = 0.05). Combined analysis of DNA sequencing, DNA methylation, and RNA sequencing identified tumors of a distinct BRCA-deficient (BRCA-D) TNBC subtype characterized by low levels of wild-type BRCA1/2 expression. Patients with functionally BRCA-D tumors had significantly better survival with standard-of-care chemotherapy than patients whose tumors were not BRCA-D (log-rank test, p = 0.021), and they had significantly higher mutation burden (p < 0.001) and presented clonal neoantigens that were associated with increased immune cell activity. A transcriptional signature of BRCA-D TNBC tumors was independently validated to be significantly associated with improved survival in the METABRIC dataset (log-rank test, p = 0.009). As a retrospective study, limitations include the small size and potential selection bias in the discovery cohort.

CONCLUSIONS

The comprehensive molecular analysis presented in this study directly links BRCA deficiency with increased clonal mutation burden and significantly enhanced chemosensitivity in TNBC and suggests that functional RNA-based BRCA deficiency needs to be further examined in TNBC.

Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures

BACKGROUND

Tumor-infiltrating immune cells have been linked to prognosis and response to immunotherapy; however, the levels of distinct immune cell subsets and the signals that draw them into a tumor, such as the expression of antigen presenting machinery genes, remain poorly characterized. Here, we employ a gene expression-based computational method to profile the infiltration levels of 24 immune cell populations in 19 cancer types.

RESULTS

We compare cancer types using an immune infiltration score and a T cell infiltration score and find that clear cell renal cell carcinoma (ccRCC) is among the highest for both scores. Using immune infiltration profiles as well as transcriptomic and proteomic datasets, we characterize three groups of ccRCC tumors: T cell enriched, heterogeneously infiltrated, and non-infiltrated. We observe that the immunogenicity of ccRCC tumors cannot be explained by mutation load or neo-antigen load, but is highly correlated with MHC class I antigen presenting machinery expression (APM). We explore the prognostic value of distinct T cell subsets and show in two cohorts that Th17 cells and CD8+ T/Treg ratio are associated with improved survival, whereas Th2 cells and Tregs are associated with negative outcomes. Investigation of the association of immune infiltration patterns with the subclonal architecture of tumors shows that both APM and T cell levels are negatively associated with subclone number.

CONCLUSIONS

Our analysis sheds light on the immune infiltration patterns of 19 human cancers and unravels mRNA signatures with prognostic utility and immunotherapeutic biomarker potential in ccRCC.

Impact of mutational profiles on response of primary oestrogen receptor-positive breast cancers to oestrogen deprivation

Pre-surgical studies allow study of the relationship between mutations and response of oestrogen receptor-positive (ER+) breast cancer to aromatase inhibitors (AIs) but have been limited to small biopsies. Here in phase I of this study, we perform exome sequencing on baseline, surgical core-cuts and blood from 60 patients (40 AI treated, 20 controls). In poor responders (based on Ki67 change), we find significantly more somatic mutations than good responders. Subclones exclusive to baseline or surgical cores occur in ∼30% of tumours. In phase II, we combine targeted sequencing on another 28 treated patients with phase I. We find six genes frequently mutated: PIK3CA, TP53, CDH1, MLL3, ABCA13 and FLG with 71% concordance between paired cores. TP53 mutations are associated with poor response. We conclude that multiple biopsies are essential for confident mutational profiling of ER+ breast cancer and TP53 mutations are associated with resistance to oestrogen deprivation therapy.

Visualizing tumor evolution with the fishplot package for R

BACKGROUND

Massively-parallel sequencing at depth is now enabling tumor heterogeneity and evolution to be characterized in unprecedented detail. Tracking these changes in clonal architecture often provides insight into therapeutic response and resistance. In complex cases involving multiple timepoints, standard visualizations, such as scatterplots, can be difficult to interpret. Current data visualization methods are also typically manual and laborious, and often only approximate subclonal fractions.

RESULTS

We have developed an R package that accurately and intuitively displays changes in clonal structure over time. It requires simple input data and produces illustrative and easy-to-interpret graphs suitable for diagnosis, presentation, and publication.

CONCLUSIONS

The simplicity, power, and flexibility of this tool make it valuable for visualizing tumor evolution, and it has potential utility in both research and clinical settings. The fishplot package is available at https://github.com/chrisamiller/fishplot .

Immunogenomics of Hypermutated Glioblastoma: A Patient with Germline POLE Deficiency Treated with Checkpoint Blockade Immunotherapy

We present the case of a patient with a left frontal glioblastoma with primitive neuroectodermal tumor features and hypermutated genotype in the setting of a POLE germline alteration. During standard-of-care chemoradiation, the patient developed a cervical spine metastasis and was subsequently treated with pembrolizumab. Shortly thereafter, the patient developed an additional metastatic spinal lesion. Using whole-exome DNA sequencing and clonal analysis, we report changes in the subclonal architecture throughout treatment. Furthermore, a persistently high neoantigen load was observed within all tumors. Interestingly, following initiation of pembrolizumab, brisk lymphocyte infiltration was observed in the subsequently resected metastatic spinal lesion and an objective radiographic response was noted in a progressive intracranial lesion, suggestive of active central nervous system (CNS) immunosurveillance following checkpoint blockade therapy.

SIGNIFICANCE

It is unclear whether hypermutated glioblastomas are susceptible to checkpoint blockade in adults. Herein, we provide proof of principle that glioblastomas with DNA-repair defects treated with checkpoint blockade may result in CNS immune activation, leading to clinically and immunologically significant responses. These patients may represent a genomically stratified group for whom immunotherapy could be considered. Cancer Discov; 6(11); 1230-6. ©2016 AACR.See related commentary by Snyder and Wolchok, p. 1210This article is highlighted in the In This Issue feature, p. 1197.

Genome-wide mutation profiles of colorectal tumors and associated liver metastases at the exome and transcriptome levels

To characterize the mutation profiles of colorectal cancer (CRC) primary tumors (PTs) and liver metastases (CLMs), we performed both whole-exome and RNA sequencing. Ten significantly mutated genes, including BMI1, CARD11, and NRG1, were found in 34 CRCs with CLMs. We defined three mutation classes (Class 1 to 3) based on the absence or presence of mutations during liver metastasis. Most mutations were classified into Class 1 (shared between PTs and CLMs), suggesting the common clonal origin of PTs and CLMs. Class 1 was more strongly associated with the clinical characteristics of advanced cancer and was more frequently superimposed with chromosomal deletions in CLMs than Class 2 (PT-specific). The integration of exome and RNA sequencing revealed that variant-allele frequencies (VAFs) of mutations in the transcriptome tended to have stronger functional implications than those in the exome. For instance, VAFs of the TP53 and APC mutations in the transcriptome significantly correlated with the expression level of their target genes. Additionally, mutations with high functional impact were enriched with high VAFs in the CLM transcriptomes. We identified 11 mutation-associated splicing events in the CRC transcriptomes. Thus, the integration of the exome and the transcriptome may elucidate the underlying molecular events responsible for CLMs.

Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing

Cancer is a disease driven by evolutionary selection on somatic genetic and epigenetic alterations. Here, we propose Canopy, a method for inferring the evolutionary phylogeny of a tumor using both somatic copy number alterations and single-nucleotide alterations from one or more samples derived from a single patient. Canopy is applied to bulk sequencing datasets of both longitudinal and spatial experimental designs and to a transplantable metastasis model derived from human cancer cell line MDA-MB-231. Canopy successfully identifies cell populations and infers phylogenies that are in concordance with existing knowledge and ground truth. Through simulations, we explore the effects of key parameters on deconvolution accuracy and compare against existing methods. Canopy is an open-source R package available at https://cran.r-project.org/web/packages/Canopy/.

Aromatase inhibition remodels the clonal architecture of estrogen-receptor-positive breast cancers

Resistance to oestrogen-deprivation therapy is common in oestrogen-receptor-positive (ER+) breast cancer. To better understand the contributions of tumour heterogeneity and evolution to resistance, here we perform comprehensive genomic characterization of 22 primary tumours sampled before and after 4 months of neoadjuvant aromatase inhibitor (NAI) treatment. Comparing whole-genome sequencing of tumour/normal pairs from the two time points, with coincident tumour RNA sequencing, reveals widespread spatial and temporal heterogeneity, with marked remodelling of the clonal landscape in response to NAI. Two cases have genomic evidence of two independent tumours, most obviously an ER− ‘collision tumour', which was only detected after NAI treatment of baseline ER+ disease. Many mutations are newly detected or enriched post treatment, including two ligand-binding domain mutations in ESR1. The observed clonal complexity of the ER+ breast cancer genome suggests that precision medicine approaches based on genomic analysis of a single specimen are likely insufficient to capture all clinically significant information.

Aromatase inhibitors are used to treat oestrogen-receptor-positive breast cancer. Here, the authors use genomic approaches to analyse tumours before and after neo-adjuvant treatment and find that treatment alters the clonal landscape of the tumours.

Allele-Specific Quantification of Structural Variations in Cancer Genomes

Aneuploidy and structural variations (SVs) generate cancer genomes containing a mixture of rearranged genomic segments with extensive somatic copy number alterations. However, existing methods can identify either SVs or allele-specific copy number alterations, but not both simultaneously, which provides a limited view of cancer genome structure. Here we introduce Weaver, an algorithm for the quantification and analysis of allele-specific copy numbers of SVs. Weaver uses a Markov Random Field to estimate joint probabilities of allele-specific copy number of SVs and their inter-connectivity based on paired-end whole-genome sequencing data. Weaver also predicts the timing of SVs relative to chromosome amplifications. We demonstrate the accuracy of Weaver using simulations and findings from whole-genome Optical Mapping. We apply Weaver to generate allele-specific copy numbers of SVs for MCF-7 and HeLa cell lines, and identify recurrent SV patterns in 44 TCGA ovarian cancer whole-genome sequencing datasets. Our approach provides a more complete assessment of the complex genomic architectures inherent to many cancer genomes.

Population Genomics of Reduced Vancomycin Susceptibility in Staphylococcus aureus

The increased prevalence of vancomycin-intermediate Staphylococcus aureus (VISA) is an emerging health care threat. Genome-based comparative methods hold great promise to uncover the genetic basis of the VISA phenotype, which remains obscure. S. aureus isolates were collected from a single individual that presented with recurrent staphylococcal bacteremia at three time points, and the isolates showed successively reduced levels of vancomycin susceptibility. A population genomic approach was taken to compare patient S. aureus isolates with decreasing vancomycin susceptibility across the three time points. To do this, patient isolates were sequenced to high coverage (~500×), and sequence reads were used to model site-specific allelic variation within and between isolate populations. Population genetic methods were then applied to evaluate the overall levels of variation across the three time points and to identify individual variants that show anomalous levels of allelic change between populations. A successive reduction in the overall levels of population genomic variation was observed across the three time points, consistent with a population bottleneck resulting from antibiotic treatment. Despite this overall reduction in variation, a number of individual mutations were swept to high frequency in the VISA population. These mutations were implicated as potentially involved in the VISA phenotype and interrogated with respect to their functional roles. This approach allowed us to identify a number of mutations previously implicated in VISA along with allelic changes within a novel class of genes, encoding LPXTG motif-containing cell-wall-anchoring proteins, which shed light on a novel mechanistic aspect of vancomycin resistance. IMPORTANCE The emergence and spread of antibiotic resistance among bacterial pathogens are two of the gravest threats to public health facing the world today. We report the development and application of a novel population genomic technique aimed at uncovering the evolutionary dynamics and genetic determinants of antibiotic resistance in Staphylococcus aureus. This method was applied to S. aureus cultures isolated from a single patient who showed decreased susceptibility to the vancomycin antibiotic over time. Our approach relies on the increased resolution afforded by next-generation genome-sequencing technology, and it allowed us to discover a number of S. aureus mutations, in both known and novel gene targets, which appear to have evolved under adaptive pressure to evade vancomycin mechanisms of action. The approach we lay out in this work can be applied to resistance to any number of antibiotics across numerous species of bacterial pathogens.

Distinct evolution and dynamics of epigenetic and genetic heterogeneity in acute myeloid leukemia

Genetic heterogeneity contributes to clinical outcome and progression of most tumors, but little is known about allelic diversity for epigenetic compartments, and almost no data exist for acute myeloid leukemia (AML). We examined epigenetic heterogeneity as assessed by cytosine methylation within defined genomic loci with four CpGs (epialleles), somatic mutations, and transcriptomes of AML patient samples at serial time points. We observed that epigenetic allele burden is linked to inferior outcome and varies considerably during disease progression. Epigenetic and genetic allelic burden and patterning followed different patterns and kinetics during disease progression. We observed a subset of AMLs with high epiallele and low somatic mutation burden at diagnosis, a subset with high somatic mutation and lower epiallele burdens at diagnosis, and a subset with a mixed profile, suggesting distinct modes of tumor heterogeneity. Genes linked to promoter-associated epiallele shifts during tumor progression showed increased single-cell transcriptional variance and differential expression, suggesting functional impact on gene regulation. Thus, genetic and epigenetic heterogeneity can occur with distinct kinetics likely to affect the biological and clinical features of tumors.

Algorithmic methods to infer the evolutionary trajectories in cancer progression

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next-generation sequencing data and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent work on the "selective advantage" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular, and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications because it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations, and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression as well as to suggest novel experimentally verifiable hypotheses.

Mutational hierarchies in myelodysplastic syndromes dynamically adapt and evolve upon therapy response and failure

Clonal evolution is believed to be a main driver for progression of various types of cancer and implicated in facilitating resistance to drugs. However, the hierarchical organization of malignant clones in the hematopoiesis of myelodysplastic syndromes (MDS) and its impact on response to drug therapy remain poorly understood. Using high-throughput sequencing of patient and xenografted cells, we evaluated the intratumoral heterogeneity (n= 54) and reconstructed mutational trajectories (n = 39) in patients suffering from MDS (n = 52) and chronic myelomonocytic leukemia-1 (n = 2). We identified linear and also branching evolution paths and confirmed on a patient-specific level that somatic mutations in epigenetic regulators and RNA splicing genes frequently constitute isolated disease-initiating events. Using high-throughput exome- and/or deep-sequencing, we analyzed 103 chronologically acquired samples from 22 patients covering a cumulative observation time of 75 years MDS disease progression. Our data revealed highly dynamic shaping of complex oligoclonal architectures, specifically upon treatment with lenalidomide and other drugs. Despite initial clinical response to treatment, patients' marrow persistently remained clonal with rapid outgrowth of founder-, sub-, or even fully independent clones, indicating an increased dynamic rate of clonal turnover. The emergence and disappearance of specific clones frequently correlated with changes of clinical parameters, highlighting their distinct and far-reaching functional properties. Intriguingly, increasingly complex mutational trajectories are frequently accompanied by clinical progression during the course of disease. These data substantiate a need for regular broad molecular monitoring to guide clinical treatment decisions in MDS.

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.

Comprehensive mutational analysis of primary and relapse acute promyelocytic leukemia

Acute promyelocytic leukemia (APL) is a subtype of myeloid leukemia characterized by differentiation block at the promyelocyte stage. Besides the presence of chromosomal rearrangement t(15;17), leading to the formation of PML-RARA (promyelocytic leukemia-retinoic acid receptor alpha) fusion, other genetic alterations have also been implicated in APL. Here, we performed comprehensive mutational analysis of primary and relapse APL to identify somatic alterations, which cooperate with PML-RARA in the pathogenesis of APL. We explored the mutational landscape using whole-exome (n=12) and subsequent targeted sequencing of 398 genes in 153 primary and 69 relapse APL. Both primary and relapse APL harbored an average of eight non-silent somatic mutations per exome. We observed recurrent alterations of FLT3, WT1, NRAS and KRAS in the newly diagnosed APL, whereas mutations in other genes commonly mutated in myeloid leukemia were rarely detected. The molecular signature of APL relapse was characterized by emergence of frequent mutations in PML and RARA genes. Our sequencing data also demonstrates incidence of loss-of-function mutations in previously unidentified genes, ARID1B and ARID1A, both of which encode for key components of the SWI/SNF complex. We show that knockdown of ARID1B in APL cell line, NB4, results in large-scale activation of gene expression and reduced in vitro differentiation potential.

Mass concentration in a nonlocal model of clonal selection

Self-renewal is a constitutive property of stem cells. Testing the cancer stem cell hypothesis requires investigation of the impact of self-renewal on cancer expansion. To better understand this impact, we propose a mathematical model describing the dynamics of a continuum of cell clones structured by the self-renewal potential. The model is an extension of the finite multi-compartment models of interactions between normal and cancer cells in acute leukemias. It takes a form of a system of integro-differential equations with a nonlinear and nonlocal coupling which describes regulatory feedback loops of cell proliferation and differentiation. We show that this coupling leads to mass concentration in points corresponding to the maxima of the self-renewal potential and the solutions of the model tend asymptotically to Dirac measures multiplied by positive constants. Furthermore, using a Lyapunov function constructed for the finite dimensional counterpart of the model, we prove that the total mass of the solution converges to a globally stable equilibrium. Additionally, we show stability of the model in the space of positive Radon measures equipped with the flat metric (bounded Lipschitz distance). Analytical results are illustrated by numerical simulations.

Whole exome sequencing identifies lncRNA GAS8-AS1 and LPAR4 as novel papillary thyroid carcinoma driver alternations

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. However, we know little of mutational spectrum in the Chinese population. Thus, here we report the identification of somatic mutations for Chinese PTC using 402 tumor-normal pairs (Discovery: 91 pairs via exome sequencing; validation: 311 pairs via Sanger sequencing). We observed three distinct mutational signatures, evidently different from the two mutational signatures among Caucasian PTCs. Ten significantly mutated genes were identified, most previously uncharacterized. Notably, we found that long non-coding RNA (lncRNA) GAS8-AS1 is the secondary most frequently altered gene and acts as a novel tumor suppressor in PTC. As a mutation hotspot, the c.713A>G/714T>C dinucleotide substitution was found among 89.1% patients with GAS8-AS1 mutations and associated with advanced PTC disease (P = 0.009). Interestingly, the wild-type lncRNA GAS8-AS1 (A713T714) showed consistently higher capability to inhibit cancer cell growth compared to the mutated lncRNA (G713C714). Further studies also elucidated the oncogene nature of the G protein-coupled receptor LPAR4 and its c.872T>G (p.Ile291Ser) mutation in PTC malignant transformation. The BRAF c.1799T>A (p.Val600Glu) substitution was present in 59.0% Chinese PTCs, more frequently observed in patients with lymph node metastasis (P = 1.6 × 10(-4)). Together our study defines a exome mutational spectrum of PTC in the Chinese population and highlights lncRNA GAS8-AS1 and LPAR4 as potential diagnostics and therapeutic targets.