Optimizing cancer genome sequencing and analysis

Development and validation of a targeted next generation DNA sequencing panel outperforming whole exome sequencing for the identification of clinically relevant genetic variants

Next generation sequencing (NGS) technologies have revolutionized our approach to genomic research. The use of whole genome sequencing (WGS), whole exome sequencing (WES), transcriptome profiling, and targeted DNA sequencing has exponentially improved our understanding of the human genome and the genetic complexities underlying malignancy. Yet, WGS and WES clinical applications remain limited due to high costs and the large volume of data generated. When utilized to address biological questions in basic science studies, targeted sequencing panels have proven extremely valuable due to reduced costs and higher sequencing depth. However, the routine application of targeted sequencing to the clinical setting is limited to a few cancer subtypes. Some highly aggressive tumor types, like type 2 endometrial cancer (EC), could greatly benefit from routine genomic analysis using targeted sequencing. To explore the potential utility of a mid size panel (~150 genes) in the clinical setting, we developed and validated a custom panel against WGS, WES, and another commercially available targeted panel. Our results indicate that a mid size custom designed panel is as efficient as WGS and WES in mapping variants of biological and clinical relevance, rendering higher coverage, at a lower cost, with fewer variants of uncertain significance. Because of the much higher sequencing depth that could be achieved, our results demonstrate that targeted sequencing outperformed WGS and WES in the mapping of pathogenic variants in a breast cancer case, as well as a case of mixed serous and high-grade endometrioid EC, the most aggressive EC subtype.

Visualizing Clonal Evolution in Cancer

Rapid and inexpensive single-cell sequencing is driving new visualizations of cancer instability and evolution. Krzywinski discusses how to present clone evolution plots in order to visualize temporal, phylogenetic, and spatial aspects of a tumor in a single static image.

Melorheostosis: Exome sequencing of an associated dermatosis implicates postzygotic mosaicism of mutated KRAS

Melorheostosis (MEL) is the rare sporadic dysostosis characterized by monostotic or polyostotic osteosclerosis and hyperostosis often distributed in a sclerotomal pattern. The prevailing hypothesis for MEL invokes postzygotic mosaicism. Sometimes scleroderma-like skin changes, considered a representation of the pathogenetic process of MEL, overlie the bony changes, and sometimes MEL becomes malignant. Osteopoikilosis (OPK) is the autosomal dominant skeletal dysplasia that features symmetrically distributed punctate osteosclerosis due to heterozygous loss-of-function mutation within LEMD3. Rarely, radiographic findings of MEL occur in OPK. However, germline mutation of LEMD3 does not explain sporadic MEL. To explore if mosaicism underlies MEL, we studied a boy with polyostotic MEL and characteristic overlying scleroderma-like skin, a few bony lesions consistent with OPK, and a large epidermal nevus known to usually harbor a HRAS, FGFR3, or PIK3CA gene mutation. Exome sequencing was performed to ~100× average read depth for his two dermatoses, two areas of normal skin, and peripheral blood leukocytes. As expected for non-malignant tissues, the patient's mutation burden in his normal skin and leukocytes was low. He, his mother, and his maternal grandfather carried a heterozygous, germline, in-frame, 24-base-pair deletion in LEMD3. Radiographs of the patient and his mother revealed bony foci consistent with OPK, but she showed no MEL. For the patient, somatic variant analysis, using four algorithms to compare all 20 possible pairwise combinations of his five DNA samples, identified only one high-confidence mutation, heterozygous KRAS Q61H (NM_033360.3:c.183A>C, NP_203524.1:p.Gln61His), in both his dermatoses but absent in his normal skin and blood. Thus, sparing our patient biopsy of his MEL bone, we identified a heterozygous somatic KRAS mutation in his scleroderma-like dermatosis considered a surrogate for MEL. This implicates postzygotic mosaicism of mutated KRAS, perhaps facilitated by germline LEMD3 haploinsufficiency, causing his MEL.

Comprehensive discovery of noncoding RNAs in acute myeloid leukemia cell transcriptomes

To detect diverse and novel RNA species comprehensively, we compared deep small RNA and RNA sequencing (RNA-seq) methods applied to a primary acute myeloid leukemia (AML) sample. We were able to discover previously unannotated small RNAs using deep sequencing of a library method using broader insert size selection. We analyzed the long noncoding RNA (lncRNA) landscape in AML by comparing deep sequencing from multiple RNA-seq library construction methods for the sample that we studied and then integrating RNA-seq data from 179 AML cases. This identified lncRNAs that are completely novel, differentially expressed, and associated with specific AML subtypes. Our study revealed the complexity of the noncoding RNA transcriptome through a combined strategy of strand-specific small RNA and total RNA-seq. This dataset will serve as an invaluable resource for future RNA-based analyses.

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.

Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma

Objective:

To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each.

Methods:

Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs.

Results:

More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts.

Conclusions:

The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible.

ClinicalTrials.gov identifier:

NCT02725684.

Coming of age: ten years of next-generation sequencing technologies

Since the completion of the human genome project in 2003, extraordinary progress has been made in genome sequencing technologies, which has led to a decreased cost per megabase and an increase in the number and diversity of sequenced genomes. An astonishing complexity of genome architecture has been revealed, bringing these sequencing technologies to even greater advancements. Some approaches maximize the number of bases sequenced in the least amount of time, generating a wealth of data that can be used to understand increasingly complex phenotypes. Alternatively, other approaches now aim to sequence longer contiguous pieces of DNA, which are essential for resolving structurally complex regions. These and other strategies are providing researchers and clinicians a variety of tools to probe genomes in greater depth, leading to an enhanced understanding of how genome sequence variants underlie phenotype and disease.

Cancer Immunogenomics: Computational Neoantigen Identification and Vaccine Design

The application of modern high-throughput genomics to the study of cancer genomes has exploded in the past few years, yielding unanticipated insights into the myriad and complex combinations of genomic alterations that lead to the development of cancers. Coincident with these genomic approaches have been computational analyses that are capable of multiplex evaluations of genomic data toward specific therapeutic end points. One such approach is called “immunogenomics” and is now being developed to interpret protein-altering changes in cancer cells in the context of predicted preferential binding of these altered peptides by the patient’s immune molecules, specifically human leukocyte antigen (HLA) class I and II proteins. One goal of immunogenomics is to identify those cancer-specific alterations that are likely to elicit an immune response that is highly specific to the patient’s cancer cells following stimulation by a personalized vaccine. The elements of such an approach are outlined herein and constitute an emerging therapeutic option for cancer patients.

Measurable residual disease testing in acute myeloid leukaemia

There is considerable interest in developing techniques to detect and/or quantify remaining leukaemia cells termed measurable or, less precisely, minimal residual disease (MRD) in persons with acute myeloid leukaemia (AML) in complete remission defined by cytomorphological criteria. An important reason for AML MRD-testing is the possibility of estimating the likelihood (and timing) of leukaemia relapse. A perfect MRD-test would precisely quantify leukaemia cells biologically able and likely to cause leukaemia relapse within a defined interval. AML is genetically diverse and there is currently no uniform approach to detecting such cells. Several technologies focused on immune phenotype or cytogenetic and/or molecular abnormalities have been developed, each with advantages and disadvantages. Many studies report a positive MRD-test at diverse time points during AML therapy identifies persons with a higher risk of leukaemia relapse compared with those with a negative MRD-test even after adjusting for other prognostic and predictive variables. No MRD-test in AML has perfect sensitivity and specificity for relapse prediction at the cohort- or subject levels and there are substantial rates of false-positive and -negative tests. Despite these limitations, correlations between MRD-test results and relapse risk have generated interest in MRD-test result-directed therapy interventions. However, convincing proof that a specific intervention will reduce relapse risk in persons with a positive MRD-test is lacking and needs testing in randomized trials. Routine clinical use of MRD-testing requires further refinements and standardization/harmonization of assay platforms and results reporting. Such data are needed to determine whether results of MRD-testing can be used as a surrogate end point in AML therapy trials. This could make drug-testing more efficient and accelerate regulatory approvals. Although MRD-testing in AML has advanced substantially, much remains to be done.

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.

High-grade serous ovarian cancer: the clone wars

BACKGROUND

The last 5 years' studies using next-generation sequencing provided evidences that many types of solid tumors present spatial and temporal genetic heterogeneity and are composed of multiple populations of genetically distinct subclones that evolve over time following a pattern of branched evolution. The evolutionary nature of cancer has been proposed as the major contributor to drug resistance and treatment failure. In this review, we present the current state of knowledge about the clonal evolution of high-grade serous ovarian cancer and discuss the challenge that clonal evolution poses for efforts to achieve an optimal cancer control.

METHODS

A systemic search of peer-reviewed articles published between August 2007 and October 2016 was performed using PUBMED and Google Scholar database.

RESULTS AND CONCLUSIONS

Recent studies using next-generation sequencing have allowed us to look inside the evolutionary nature of high-grade serous ovarian cancer, which in the light of current evidence can explain the relapsing course of the disease frequently observed in the clinical practice. Since only minimal improvement in the survival of patients treated with standard therapy has been observed in the last decade, novel molecular targeted therapies are of great interest in high-grade serous ovarian cancer. However, both spatial and temporal intratumoral genetic heterogeneity is a major challenge for personalized medicine, and greater knowledge of the molecular rules that drive tumor evolution through space and time is required to achieve a long-term clinical benefit from personalized therapy.

Bone marrow evaluation for diagnosis and monitoring of acute myeloid leukemia

The diagnosis of acute myeloid leukemia (AML) can be made based on peripheral blood or bone marrow blasts. In this review, we will discuss the role of bone marrow evaluation and peripheral blood monitoring in the diagnosis, management, and follow up of AML patients. For patients with circulating blasts, it is reasonable to perform the necessary studies needed for diagnosis and risk stratification, including multiparametric flow cytometry, cytogenetics, and molecular analysis, on a peripheral blood specimen. The day 14 marrow is used to document hypocellularity in response to induction chemotherapy, but it is unclear if that assessment is necessary as it often does not affect immediate management. Currently, response assessments performed at count recovery for evaluation of remission and measurable residual disease rely on bone marrow sampling. For monitoring of relapse, peripheral blood evaluation may be adequate, but the sensitivity of bone marrow testing is in some cases superior. While bone marrow evaluation can certainly be avoided in particular situations, this cumbersome and uncomfortable procedure currently remains the de facto standard for response assessment.

Recurrent somatic mutations affecting B-cell receptor signaling pathway genes in follicular lymphoma

Follicular lymphoma (FL) is the most common form of indolent non-Hodgkin lymphoma, yet it remains only partially characterized at the genomic level. To improve our understanding of the genetic underpinnings of this incurable and clinically heterogeneous disease, whole-exome sequencing was performed on tumor/normal pairs from a discovery cohort of 24 patients with FL. Using these data and mutations identified in other B-cell malignancies, 1716 genes were sequenced in 113 FL tumor samples from 105 primarily treatment-naive individuals. We identified 39 genes that were mutated significantly above background mutation rates. CREBBP mutations were associated with inferior PFS. In contrast, mutations in previously unreported HVCN1, a voltage-gated proton channel-encoding gene and B-cell receptor signaling modulator, were associated with improved PFS. In total, 47 (44.8%) patients harbor mutations in the interconnected B-cell receptor (BCR) and CXCR4 signaling pathways. Histone gene mutations were more frequent than previously reported (identified in 43.8% of patients) and often co-occurred (17.1% of patients). A novel, recurrent hotspot was identified at a posttranslationally modified residue in the histone H2B family. This study expands the number of mutated genes described in several known signaling pathways and complexes involved in lymphoma pathogenesis (BCR, Notch, SWitch/sucrose nonfermentable (SWI/SNF), vacuolar ATPases) and identified novel recurrent mutations (EGR1/2, POU2AF1, BTK, ZNF608, HVCN1) that require further investigation in the context of FL biology, prognosis, and treatment.

Acquired Genetic and Epigenetic Variation in Human Pluripotent Stem Cells

Human pluripotent stem cells (hPSCs) can acquire non-random genomic variation during culture. Some of these changes are common in tumours and confer a selective growth advantage in culture. Additionally, there is evidence that reprogramming of human induced pluripotent stem cells (hiPSCs) introduces mutations. This poses a challenge to both the safety of clinical applications and the reliability of basic research using hPSCs carrying genomic variation. A number of methods are available for monitoring the genomic integrity of hPSCs, and a balance between practicality and sensitivity must be considered in choosing the appropriate methods for each use of hPSCs. Adjusting protocols by which hPSCs are derived and cultured is an evolving process that is important in minimising acquired genomic variation. Assessing genetic variation for its potential impact is becoming increasingly important as techniques to detect genome-wide variation improve.

The Subclonal Architecture of Metastatic Breast Cancer: Results from a Prospective Community-Based Rapid Autopsy Program "CASCADE"

BACKGROUND

Understanding the cancer genome is seen as a key step in improving outcomes for cancer patients. Genomic assays are emerging as a possible avenue to personalised medicine in breast cancer. However, evolution of the cancer genome during the natural history of breast cancer is largely unknown, as is the profile of disease at death. We sought to study in detail these aspects of advanced breast cancers that have resulted in lethal disease.

METHODS AND FINDINGS

Three patients with oestrogen-receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer and one patient with triple negative breast cancer underwent rapid autopsy as part of an institutional prospective community-based rapid autopsy program (CASCADE). Cases represented a range of management problems in breast cancer, including late relapse after early stage disease, de novo metastatic disease, discordant disease response, and disease refractory to treatment. Between 5 and 12 metastatic sites were collected at autopsy together with available primary tumours and longitudinal metastatic biopsies taken during life. Samples underwent paired tumour-normal whole exome sequencing and single nucleotide polymorphism (SNP) arrays. Subclonal architectures were inferred by jointly analysing all samples from each patient. Mutations were validated using high depth amplicon sequencing. Between cases, there were significant differences in mutational burden, driver mutations, mutational processes, and copy number variation. Within each case, we found dramatic heterogeneity in subclonal structure from primary to metastatic disease and between metastatic sites, such that no single lesion captured the breadth of disease. Metastatic cross-seeding was found in each case, and treatment drove subclonal diversification. Subclones displayed parallel evolution of treatment resistance in some cases and apparent augmentation of key oncogenic drivers as an alternative resistance mechanism. We also observed the role of mutational processes in subclonal evolution. Limitations of this study include the potential for bias introduced by joint analysis of formalin-fixed archival specimens with fresh specimens and the difficulties in resolving subclones with whole exome sequencing. Other alterations that could define subclones such as structural variants or epigenetic modifications were not assessed.

CONCLUSIONS

This study highlights various mechanisms that shape the genome of metastatic breast cancer and the value of studying advanced disease in detail. Treatment drives significant genomic heterogeneity in breast cancers which has implications for disease monitoring and treatment selection in the personalised medicine paradigm.

Preleukemia: one name, many meanings

Definition of preleukemia has evolved. It was first used to describe the myelodysplastic syndrome (MDS) with a propensity to progress to acute myeloid leukemia (AML). Individuals with germline mutations of either RUNX1, CEBPA, or GATA2 can also be called as preleukemic because they have a markedly increased incidence of evolution into AML. Also, alkylating chemotherapy or radiation can cause MDS/preleukemia, which nearly always progress to AML. More recently, investigators noted that AML patients who achieved complete morphological remission after chemotherapy often have clonal hematopoiesis predominantly marked by either DNMT3A, TET2 or IDH1/2 mutations, which were also present at diagnosis of AML. This preleukemic clone represents involvement of an early hematopoietic stem cells, which is resistant to standard therapy. The same clonal hematopoietic mutations have been identified in older ‘normal' individuals who have a modest increased risk of developing frank AML. These individuals have occasionally been said, probably inappropriately, to have a preleukemia clone. Our evolving understanding of the term preleukemia has occurred by advancing technology including studies of X chromosome inactivation, cytogenetics and more recently deep nucleotide sequencing.

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 .

Representing genetic variation with synthetic DNA standards

The identification of genetic variation with next-generation sequencing is confounded by the complexity of the human genome sequence and by biases that arise during library preparation, sequencing and analysis. We have developed a set of synthetic DNA standards, termed 'sequins', that emulate human genetic features and constitute qualitative and quantitative spike-in controls for genome sequencing. Sequencing reads derived from sequins align exclusively to an artificial in silico reference chromosome, rather than the human reference genome, which allows them them to be partitioned for parallel analysis. Here we use this approach to represent common and clinically relevant genetic variation, ranging from single nucleotide variants to large structural rearrangements and copy-number variation. We validate the design and performance of sequin standards by comparison to examples in the NA12878 reference genome, and we demonstrate their utility during the detection and quantification of variants. We provide sequins as a standardized, quantitative resource against which human genetic variation can be measured and diagnostic performance assessed.

Comprehensive genomic analysis reveals FLT3 activation and a therapeutic strategy for a patient with relapsed adult B-lymphoblastic leukemia

The genomic events responsible for the pathogenesis of relapsed adult B-lymphoblastic leukemia (B-ALL) are not yet clear. We performed integrative analysis of whole-genome, whole-exome, custom capture, whole-transcriptome (RNA-seq), and locus-specific genomic assays across nine time points from a patient with primary de novo B-ALL. Comprehensive genome and transcriptome characterization revealed a dramatic tumor evolution during progression, yielding a tumor with complex clonal architecture at second relapse. We observed and validated point mutations in EP300 and NF1, a highly expressed EP300-ZNF384 gene fusion, a microdeletion in IKZF1, a focal deletion affecting SETD2, and large deletions affecting RB1, PAX5, NF1, and ETV6. Although the genome analysis revealed events of potential biological relevance, no clinically actionable treatment options were evident at the time of the second relapse. However, transcriptome analysis identified aberrant overexpression of the targetable protein kinase encoded by the FLT3 gene. Although the patient had refractory disease after salvage therapy for the second relapse, treatment with the FLT3 inhibitor sunitinib rapidly induced a near complete molecular response, permitting the patient to proceed to a matched-unrelated donor stem cell transplantation. The patient remains in complete remission more than 4 years later. Analysis of this patient's relapse genome revealed an unexpected, actionable therapeutic target that led to a specific therapy associated with a rapid clinical response. For some patients with relapsed or refractory cancers, this approach may indicate a novel therapeutic intervention that could alter outcome.

The clinical utility of molecular genetic cancer profiling

INTRODUCTION

Next-Generation-Sequencing (NGS) has enabled gene mutation profiling - cataloguing sequence variants and modifications in clinical assays encompassing tens to thousands of genes in tumors and in germlines. The clinical benefit of applying multi-gene NGS to diverse applications in various malignancies remains to be demonstrated.

AREAS COVERED

Applications of gene mutation profiling in oncology include screening cancer-prone families, classification of malignancies, treatment selection, and monitoring the response to treatment of solid tumors (the 'liquid biopsy'). Google Scholar was used to search PubMed for the period 2011-2016 using combinations of the following search terms: 'clinical utility', NGS, 'molecular diagnostics'. Expert commentary: Clinical studies are in progress pairing mutation profiling with streamlined new trial designs to speed identification of promising drug-target combinations and to see if genotype-informed treatment selection will improve outcome across a spectrum of histologies. The analytical advantages and falling cost of NGS make focused gene panels likely to become the dominant modality in molecular diagnostic testing even if trials eventually discourage use of large panels to test all malignancies.

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.

Differential clonal evolution in oesophageal cancers in response to neo-adjuvant chemotherapy

How chemotherapy affects carcinoma genomes is largely unknown. Here we report whole-exome and deep sequencing of 30 paired oesophageal adenocarcinomas sampled before and after neo-adjuvant chemotherapy. Most, but not all, good responders pass through genetic bottlenecks, a feature associated with higher mutation burden pre-treatment. Some poor responders pass through bottlenecks, but re-grow by the time of surgical resection, suggesting a missed therapeutic opportunity. Cancers often show major changes in driver mutation presence or frequency after treatment, owing to outgrowth persistence or loss of sub-clones, copy number changes, polyclonality and/or spatial genetic heterogeneity. Post-therapy mutation spectrum shifts are also common, particularly C>A and TT>CT changes in good responders or bottleneckers. Post-treatment samples may also acquire mutations in known cancer driver genes (for example, SF3B1, TAF1 and CCND2) that are absent from the paired pre-treatment sample. Neo-adjuvant chemotherapy can rapidly and profoundly affect the oesophageal adenocarcinoma genome. Monitoring molecular changes during treatment may be clinically useful.

Evaluation of Nine Somatic Variant Callers for Detection of Somatic Mutations in Exome and Targeted Deep Sequencing Data

Next generation sequencing is extensively applied to catalogue somatic mutations in cancer, in research settings and increasingly in clinical settings for molecular diagnostics, guiding therapy decisions. Somatic variant callers perform paired comparisons of sequencing data from cancer tissue and matched normal tissue in order to detect somatic mutations. The advent of many new somatic variant callers creates a need for comparison and validation of the tools, as no de facto standard for detection of somatic mutations exists and only limited comparisons have been reported. We have performed a comprehensive evaluation using exome sequencing and targeted deep sequencing data of paired tumor-normal samples from five breast cancer patients to evaluate the performance of nine publicly available somatic variant callers: EBCall, Mutect, Seurat, Shimmer, Indelocator, Somatic Sniper, Strelka, VarScan 2 and Virmid for the detection of single nucleotide mutations and small deletions and insertions. We report a large variation in the number of calls from the nine somatic variant callers on the same sequencing data and highly variable agreement. Sequencing depth had markedly diverse impact on individual callers, as for some callers, increased sequencing depth highly improved sensitivity. For SNV calling, we report EBCall, Mutect, Virmid and Strelka to be the most reliable somatic variant callers for both exome sequencing and targeted deep sequencing. For indel calling, EBCall is superior due to high sensitivity and robustness to changes in sequencing depths.

Implications of Tumor Clonal Heterogeneity in the Era of Next-Generation Sequencing

Recent whole-genome sequencing (WGS) studies have demonstrated that tumors typically comprise a founding clone and multiple subclones (i.e., clonal heterogeneity is common). The possible combination of mutations in each tumor clone is enormous, making each tumor genetically unique. Clonal heterogeneity likely has a role in cancer progression, relapse, metastasis, and chemoresistance due to functional differences in genetically unique subclones. In current clinical practice, gene mutations are only classified as being present or absent, ignoring the clonal complexity of cancers. In this review, we address how tumor clonality is measured using next-generation sequencing (NGS) data, highlight that clonal heterogeneity is common across multiple tumor types, and discuss the potential clinical implications of tumor clonal heterogeneity.

Clonotyping for precision oncology

Advances in identifying subpopulations of cancer cells and reconstructing the clonal evolution of tumors greatly enhance our understanding of the molecular events within a patient and their context relative to one another. In the rapidly unfolding era of personalized medicine, the ability to monitor clonal dynamics throughout patient care has significant clinical implications for the appropriate development or application of targeted therapies as well as understanding the potential mechanisms driving resistance. In this review, we discuss advances in biotechnology and bioinformatics that improve precision treatment by dissecting clonal evolution, focusing first on the initial discoveries in lymphomas and leukemias followed by the more recent applications to advance our understanding of prostate cancer (PCa).