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

Advances in Breast Cancer - Looking Back over the Year

Treatment options as well as the characteristics for therapeutic decisions in patients with primary and advanced breast cancer are increasing in number and variety. New targeted therapies in combination with established chemotherapy schemes are broadening the spectrum, yet not every new, promising combination achieves a better result. New data from the field of pharmacogenomics point to prognostic and predictive factors that take not only the properties of the tumour but also the genetic disposition of the patient into consideration. Current therapeutic decision-making is thus based on a combination of classical clinical and modern molecular biomarkers. Health-economic concerns are also being taken into consideration more frequently, meaning political decisions may also become a factor. This review presents the trends over the past year.

Imaging mitochondrial redox potential and its possible link to tumor metastatic potential

Cellular redox states can regulate cell metabolism, growth, differentiation, motility, apoptosis, signaling pathways, and gene expressions etc. A growing body of literature suggest the importance of redox status for cancer progression. While most studies on redox state were done on cells and tissue lysates, it is important to understand the role of redox state in a tissue in vivo/ex vivo and image its heterogeneity. Redox scanning is a clinical-translatable method for imaging tissue mitochondrial redox potential with a submillimeter resolution. Redox scanning data in mouse models of human cancers demonstrate a correlation between mitochondrial redox state and tumor metastatic potential. I will discuss the significance of this correlation and possible directions for future research.

Mechanisms of acquired resistance to anti-EGF receptor treatment in colorectal cancer

SUMMARY The anti-EGFR monoclonal antibodies, cetuximab and panitumumab, are effective in a subset of colorectal cancer patients. However, acquisition of resistance to these drugs invariably develops. Elucidation of the molecular mechanisms underlying resistance is a crucial first step to develop therapeutic strategies to bypass secondary resistance. Three mechanisms of resistance have been characterized in patients so far: a mutation in the extracellular domain of EGFR preventing cetuximab-EGFR binding – interestingly, this mutation does not affect panitumumab effectiveness; activation of EGFR-related receptor HER2 by amplification or ligand overexpression; emergence of KRAS mutations or amplification. Importantly, already approved drugs can be used to bypass known mechanisms of resistance. Large-scale studies including biopsy at progression and prospective clinical trials are warranted.

Clonal evolution of high-grade serous ovarian carcinoma from primary to recurrent disease

High-grade serous carcinoma (HGSC) is the most common and fatal form of ovarian cancer. While most tumours are highly sensitive to cytoreductive surgery and platinum- and taxane-based chemotherapy, the majority of patients experience recurrence of treatment-resistant tumours. The clonal origin and mutational adaptations associated with recurrent disease are poorly understood. We performed whole exome sequencing on tumour cells harvested from ascites at three time points (primary, first recurrence, and second recurrence) for three HGSC patients receiving standard treatment. Somatic point mutations and small insertions and deletions were identified by comparison to constitutional DNA. The clonal structure and evolution of tumours were inferred from patterns of mutant allele frequencies. TP53 mutations were predominant in all patients at all time points, consistent with the known founder role of this gene. Tumours from all three patients also harboured mutations associated with cell cycle checkpoint function and Golgi vesicle trafficking. There was convergence of germline and somatic variants within the DNA repair, ECM, cell cycle control, and Golgi vesicle pathways. The vast majority of somatic variants found in recurrent tumours were present in primary tumours. Our findings highlight both known and novel pathways that are commonly mutated in HGSC. Moreover, they provide the first evidence at single nucleotide resolution that recurrent HGSC arises from multiple clones present in the primary tumour with negligible accumulation of new mutations during standard treatment.

iReckon: simultaneous isoform discovery and abundance estimation from RNA-seq data

High-throughput RNA sequencing (RNA-seq) promises to revolutionize our understanding of genes and their role in human disease by characterizing the RNA content of tissues and cells. The realization of this promise, however, is conditional on the development of effective computational methods for the identification and quantification of transcripts from incomplete and noisy data. In this article, we introduce iReckon, a method for simultaneous determination of the isoforms and estimation of their abundances. Our probabilistic approach incorporates multiple biological and technical phenomena, including novel isoforms, intron retention, unspliced pre-mRNA, PCR amplification biases, and multimapped reads. iReckon utilizes regularized expectation-maximization to accurately estimate the abundances of known and novel isoforms. Our results on simulated and real data demonstrate a superior ability to discover novel isoforms with a significantly reduced number of false-positive predictions, and our abundance accuracy prediction outmatches that of other state-of-the-art tools. Furthermore, we have applied iReckon to two cancer transcriptome data sets, a triple-negative breast cancer patient sample and the MCF7 breast cancer cell line, and show that iReckon is able to reconstruct the complex splicing changes that were not previously identified. QT-PCR validations of the isoforms detected in the MCF7 cell line confirmed all of iReckon's predictions and also showed strong agreement (r² = 0.94) with the predicted abundances.

Cancer stem cells, the epithelial to mesenchymal transition (EMT) and radioresistance: potential role of hypoxia

Numerous studies have demonstrated the presence of cancer stem cells (CSCs) within solid tumors. Although the precursor of these cells is not clearly established, recent studies suggest that the phenotype of CSCs may be quite plastic and associated with the epithelial-to-mesenchymal transition (EMT). In patients, the presence of EMT and CSCs has been implicated in increased resistance to radiotherapy. Hypoxia, a negative prognostic factor for treatment success, is a potent driver of a multitude of molecular signalling pathways that allow cells to survive and thrive in the hostile tumor microenvironment and can induce EMT. Hypoxia also provides tumor cells with cues for maintenance of a stem-like state and may help to drive the linkage between EMT and CSCs. Understanding the biology of CSCs, the EMT phenotype and their implications in therapeutic relapse may provide crucial new approaches in the development of improved therapeutic strategies.

Promise of personalized omics to precision medicine

The rapid development of high-throughput technologies and computational frameworks enables the examination of biological systems in unprecedented detail. The ability to study biological phenomena at omics levels in turn is expected to lead to significant advances in personalized and precision medicine. Patients can be treated according to their own molecular characteristics. Individual omes as well as the integrated profiles of multiple omes, such as the genome, the epigenome, the transcriptome, the proteome, the metabolome, the antibodyome, and other omics information are expected to be valuable for health monitoring, preventative measures, and precision medicine. Moreover, omics technologies have the potential to transform medicine from traditional symptom-oriented diagnosis and treatment of diseases toward disease prevention and early diagnostics. We discuss here the advances and challenges in systems biology-powered personalized medicine at its current stage, as well as a prospective view of future personalized health care at the end of this review.

Genomic sequencing in cancer

Genomic sequencing has provided critical insights into the etiology of both simple and complex diseases. The enormous reductions in cost for whole genome sequencing have allowed this technology to gain increasing use. Whole genome analysis has impacted research of complex diseases including cancer by allowing the systematic analysis of entire genomes in a single experiment, thereby facilitating the discovery of somatic and germline mutations, and identification of the insertions, deletions, and structural rearrangements, including translocations and inversions, in novel disease genes. Whole-genome sequencing can be used to provide the most comprehensive characterization of the cancer genome, the complexity of which we are only beginning to understand. Hence in this review, we focus on whole-genome sequencing in cancer.

Expression of epithelial to mesenchymal transition-related markers in lymph node metastases as a surrogate for primary tumor metastatic potential in breast cancer

Background

Breast cancers are phenotypically and genotypically heterogeneous tumors containing multiple cancer cell populations with various metastatic potential. Aggressive tumor cell subpopulations might more easily be captured in lymph nodes metastases (LNM) than in primary tumors (PT). We evaluated mRNA and protein levels of master EMT regulators: TWIST1, SNAIL and SLUG, protein levels of EMT-related markers: E-cadherin, vimentin, and expression of classical breast cancer receptors: HER2, ER and PgR in PT and corresponding LNM. The results were correlated with clinicopathological data and patients outcomes.

Methods

Formalin-fixed paraffin-embedded samples from PT and matched LNM from 42 stage II-III breast cancer patients were examined. Expression of TWIST1, SNAIL and SLUG was measured by reverse-transcription quantitative PCR. Protein expression was examined by immunohistochemistry on tissue microarrays. Kaplan-Meier curves for disease-free survival (DFS) and overall survival (OS) were compared using F-Cox test. Hazard ratios (HRs) with 95% confidence intervals (95% CI) were computed using Cox regression analysis.

Results

On average, mRNA expression of TWIST1, SNAIL and SLUG was significantly higher in LNM compared to PT (P < 0.00001 for all). Gene and protein levels of TWIST1, SNAIL and SLUG were highly discordant between PT and matched LNM. Increased mRNA expression of TWIST1 and SNAIL in LNM was associated with shorter OS (P = 0.04 and P = 0.02, respectively) and DFS (P = 0.02 and P = 0.01, respectively), whereas their expression in PT had no prognostic impact. Negative-to-positive switch of SNAIL protein correlated with decreased OS and DFS (HR = 4.6; 1.1-18.7; P = 0.03 and HR = 3.8; 1.0-48.7; P = 0.05, respectively).

Conclusions

LNM are enriched in cells with more aggressive phenotype, marked by elevated levels of EMT regulators. High expression of TWIST1 and SNAIL in LNM, as well as negative-to-positive conversion of SNAIL confer worse prognosis, confirming the correlation of EMT with aggressive disease behavior. Thus, molecular profiling of LNM may be used as surrogate marker for aggressiveness and metastatic potential of PT.

Genome and transcriptome sequencing in prospective metastatic triple-negative breast cancer uncovers therapeutic vulnerabilities

Triple-negative breast cancer (TNBC) is characterized by the absence of expression of estrogen receptor, progesterone receptor, and HER-2. Thirty percent of patients recur after first-line treatment, and metastatic TNBC (mTNBC) has a poor prognosis with median survival of one year. Here, we present initial analyses of whole genome and transcriptome sequencing data from 14 prospective mTNBC. We have cataloged the collection of somatic genomic alterations in these advanced tumors, particularly those that may inform targeted therapies. Genes mutated in multiple tumors included TP53, LRP1B, HERC1, CDH5, RB1, and NF1. Notable genes involved in focal structural events were CTNNA1, PTEN, FBXW7, BRCA2, WT1, FGFR1, KRAS, HRAS, ARAF, BRAF, and PGCP. Homozygous deletion of CTNNA1 was detected in 2 of 6 African Americans. RNA sequencing revealed consistent overexpression of the FOXM1 gene when tumor gene expression was compared with nonmalignant breast samples. Using an outlier analysis of gene expression comparing one cancer with all the others, we detected expression patterns unique to each patient's tumor. Integrative DNA/RNA analysis provided evidence for deregulation of mutated genes, including the monoallelic expression of TP53 mutations. Finally, molecular alterations in several cancers supported targeted therapeutic intervention on clinical trials with known inhibitors, particularly for alterations in the RAS/RAF/MEK/ERK and PI3K/AKT/mTOR pathways. In conclusion, whole genome and transcriptome profiling of mTNBC have provided insights into somatic events occurring in this difficult to treat cancer. These genomic data have guided patients to investigational treatment trials and provide hypotheses for future trials in this irremediable cancer.

Cooperativity of Rb, Brca1, and p53 in malignant breast cancer evolution

Breast cancers that are "triple-negative" for the clinical markers ESR1, PGR, and HER2 typically belong to the Basal-like molecular subtype. Defective Rb, p53, and Brca1 pathways are each associated with triple-negative and Basal-like subtypes. Our mouse genetic studies demonstrate that the combined inactivation of Rb and p53 pathways is sufficient to suppress the physiological cell death of mammary involution. Furthermore, concomitant inactivation of all three pathways in mammary epithelium has an additive effect on tumor latency and predisposes highly penetrant, metastatic adenocarcinomas. The tumors are poorly differentiated and have histologic features that are common among human Brca1-mutated tumors, including heterogeneous morphology, metaplasia, and necrosis. Gene expression analyses demonstrate that the tumors share attributes of both Basal-like and Claudin-low signatures, two molecular subtypes encompassed by the broader, triple-negative class defined by clinical markers.

Translating genomics to the clinic: implications of cancer heterogeneity

BACKGROUND

Sequencing of cancer genomes has become a pivotal method for uncovering and understanding the deregulated cellular processes driving tumor initiation and progression. Whole-genome sequencing is evolving toward becoming less costly and more feasible on a large scale; consequently, thousands of tumors are being analyzed with these technologies. Interpreting these data in the context of tumor complexity poses a challenge for cancer genomics.

CONTENT

The sequencing of large numbers of tumors has revealed novel insights into oncogenic mechanisms. In particular, we highlight the remarkable insight into the pathogenesis of breast cancers that has been gained through comprehensive and integrated sequencing analysis. The analysis and interpretation of sequencing data, however, must be considered in the context of heterogeneity within and among tumor samples. Only by adequately accounting for the underlying complexity of cancer genomes will the potential of genome sequencing be understood and subsequently translated into improved management of patients.

SUMMARY

The paradigm of personalized medicine holds promise if patient tumors are thoroughly studied as unique and heterogeneous entities and clinical decisions are made accordingly. Associated challenges will be ameliorated by continued collaborative efforts among research centers that coordinate the sharing of mutation, intervention, and outcomes data to assist in the interpretation of genomic data and to support clinical decision-making.

Cancer heterogeneity and "the struggle for existence": diagnostic and analytical challenges

The notions of inter- and intra-tumour heterogeneity (ITH) have been recognised for many years but recent advances in sequencing technology are allowing the true extent of both forms of heterogeneity to be revealed in detail for the first time. In this review we examine the current evidence for ITH, the possibility of cancers following a branched rather than linear evolutionary path and the potential implications both of these may have for the mechanisms of drug resistance acquisition. We also note that although clearly present in many cases, heterogeneity and branched evolution are not universal, with cases of tumour homogeneity and linear evolution still detected relatively frequently. The complexity induced by cases of ITH presents a considerable challenge for bioinformatics analyses and we illustrate this by describing the specific case of point mutation detection and a number of approaches which have been taken to combat these issues. Equally, the sequencing procedures which generate these data are rendered much more difficult in the face of ITH and we present a discussion of these problems in addition to describing some of the alternate paradigms being considered to overcome them.

Annotating cancer variants and anti-cancer therapeutics in reactome

Reactome describes biological pathways as chemical reactions that closely mirror the actual physical interactions that occur in the cell. Recent extensions of our data model accommodate the annotation of cancer and other disease processes. First, we have extended our class of protein modifications to accommodate annotation of changes in amino acid sequence and the formation of fusion proteins to describe the proteins involved in disease processes. Second, we have added a disease attribute to reaction, pathway, and physical entity classes that uses disease ontology terms. To support the graphical representation of "cancer" pathways, we have adapted our Pathway Browser to display disease variants and events in a way that allows comparison with the wild type pathway, and shows connections between perturbations in cancer and other biological pathways. The curation of pathways associated with cancer, coupled with our efforts to create other disease-specific pathways, will interoperate with our existing pathway and network analysis tools. Using the Epidermal Growth Factor Receptor (EGFR) signaling pathway as an example, we show how Reactome annotates and presents the altered biological behavior of EGFR variants due to their altered kinase and ligand-binding properties, and the mode of action and specificity of anti-cancer therapeutics.

Next-generation sequencing in breast cancer: first take home messages

PURPOSE OF REVIEW

We are currently on the threshold of a revolution in breast cancer research, thanks to the emergence of novel technologies based on next-generation sequencing (NGS). In this review, we will describe the different sequencing technologies and platforms, and summarize the main findings from the latest sequencing articles in breast cancer.

RECENT FINDINGS

Firstly, the sequencing of a few hundreds of breast tumors has revealed new cancer genes. Although these were not frequently mutated, mutated genes from different patients could be grouped into the deregulation of similar pathways. Secondly, NGS allowed further exploration of intratumor heterogeneity and revealed that although subclonal mutations were present in all tumors, there was always a dominant clone, which comprised at least 50% of the tumor cells. Finally, tumor-specific DNA rearrangements could be detected in the patient's plasma, suggesting that NGS could be used to personalize the monitoring of the disease.

SUMMARY

The application of NGS to breast cancer has been associated with tremendous advances and promises for increasing the understanding of the disease. However, there still remain many unanswered questions, such as the role of structural changes of tumor genomes in cancer progression and treatment response/resistance.

Bioinformatics for cancer immunology and immunotherapy

Recent mechanistic insights obtained from preclinical studies and the approval of the first immunotherapies has motivated increasing number of academic investigators and pharmaceutical/biotech companies to further elucidate the role of immunity in tumor pathogenesis and to reconsider the role of immunotherapy. Additionally, technological advances (e.g., next-generation sequencing) are providing unprecedented opportunities to draw a comprehensive picture of the tumor genomics landscape and ultimately enable individualized treatment. However, the increasing complexity of the generated data and the plethora of bioinformatics methods and tools pose considerable challenges to both tumor immunologists and clinical oncologists. In this review, we describe current concepts and future challenges for the management and analysis of data for cancer immunology and immunotherapy. We first highlight publicly available databases with specific focus on cancer immunology including databases for somatic mutations and epitope databases. We then give an overview of the bioinformatics methods for the analysis of next-generation sequencing data (whole-genome and exome sequencing), epitope prediction tools as well as methods for integrative data analysis and network modeling. Mathematical models are powerful tools that can predict and explain important patterns in the genetic and clinical progression of cancer. Therefore, a survey of mathematical models for tumor evolution and tumor-immune cell interaction is included. Finally, we discuss future challenges for individualized immunotherapy and suggest how a combined computational/experimental approaches can lead to new insights into the molecular mechanisms of cancer, improved diagnosis, and prognosis of the disease and pinpoint novel therapeutic targets.

Evolution of the cancer genome

The advent of massively parallel sequencing technologies has allowed the characterization of cancer genomes at an unprecedented resolution. Investigation of the mutational landscape of tumours is providing new insights into cancer genome evolution, laying bare the interplay of somatic mutation, adaptation of clones to their environment and natural selection. These studies have demonstrated the extent of the heterogeneity of cancer genomes, have allowed inferences to be made about the forces that act on nascent cancer clones as they evolve and have shown insight into the mutational processes that generate genetic variation. Here we review our emerging understanding of the dynamic evolution of the cancer genome and of the implications for basic cancer biology and the development of antitumour therapy.

Who's driving anyway? Herculean efforts to identify the drivers of breast cancer

The continuing advancement of sequencing technologies has made the systematic identification of all driving somatic events in cancer a possibility. In the June 2012 issue of Nature, five papers show some significant headway in this endeavor, each a herculean effort of genome sequencing, and transcriptome and copy number analysis resulting in data on thousands of breast cancers. Integrating these massive datasets, the authors were able to further subdivide breast cancer and identify a number of novel driver genes. While the studies represent a leap forward in describing the genomics of breast cancer, and clearly highlight the tremendous diversity between tumors, the studies only scrape the surface of molecular changes in breast tumors, with more granularity to come from the study of epigenomics, single cell sequencing, and so on. The immediate importance of the data to clinical care is currently unknown, and will depend upon detailed identification and functional analysis of driver mutations.

Regulation of mRNA expression in breast cancer - a cis-tematic trans-action

Large research consortia such as the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas and International Cancer Genomics Consortium are systematically interrogating large sets of tumor samples through integrated analysis of genome-wide DNA copy number and promoter methylation, transcriptome-wide RNA expression, protein expression and exome-wide sequencing. A recent METABRIC study explored the effects of cis-acting and trans-acting factors of gene expression regulation in breast cancer. By making their data sets publicly available, these large consortia are inviting new types of analysis that have the potential to drive breast cancer research into previously unexplored avenues.

Keeping an open mind: highlights and controversies of the breast cancer stem cell theory

The discovery that breast cancers contain stem-like cells has fuelled exciting research in the last few years. These cells are referred to as breast cancer stem cells (BCSCs) and are thought to be involved in tumor initiation, progression, and metastasis. Being intrinsically resistant to chemo- and radiotherapy, they are also considered responsible for recurrence of the disease after treatment. BCSCs have been suggested to be at the basis of tumor complexity, as they have the ability to self-renew and give rise to highly proliferating and terminally differentiated cancer cells that comprise the heterogeneous bulk of the tumor. There has been much speculation on the BCSC model, and in this review we address some fundamental questions, such as the identity of BCSCs and their involvement in tumor intra- and interheterogeneity. As an alternative to the BCSC model, we discuss clonal evolution, as both theories show extensive evidence in support of their arguments. Finally, we discuss a unifying idea that reconciles both models, which is based on stem cell plasticity and epigenetic modifications induced by the tumor microenvironment. The implications of cancer stem cell plasticity for drug discovery and future therapeutic interventions are presented.

MATH, a novel measure of intratumor genetic heterogeneity, is high in poor-outcome classes of head and neck squamous cell carcinoma

OBJECTIVES

Differences among cancer cells within a tumor are important in tumorigenesis and treatment resistance, yet no measure of intratumor heterogeneity is suitable for routine application. We developed a quantitative measure of intratumor genetic heterogeneity, based on differences among mutated loci in the mutant-allele fractions determined by next-generation sequencing (NGS) of tumor DNA. We then evaluated the application of this measure to head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

We analyzed published electronically available NGS results for 74 HNSCC. For each tumor we calculated mutant-allele tumor heterogeneity (MATH) as the ratio of the width to the center of its distribution of mutant-allele fractions among tumor-specific mutated loci.

RESULTS

Intratumor heterogeneity assessed by MATH was higher in three poor-outcome classes of HNSCC: tumors with disruptive mutations in the TP53 gene (versus wild-type TP53 or non-disruptive mutations), tumors negative versus positive for human papillomavirus (even when restricted to tumors having wild-type TP53), and HPV-negative tumors from smokers with more pack-years of cigarette exposure (with TP53 status taken into account).

CONCLUSION

The relation of this type of intratumor heterogeneity to HNSCC outcome classes supports its further evaluation as a prognostic biomarker. As NGS of tumor DNA becomes widespread in clinical research and practice, MATH should provide a simple, quantitative, and clinically practical biomarker to help evaluate relations of intratumor genetic heterogeneity to outcome in any type of cancer.

Comprehensive molecular portraits of human breast tumours

We analysed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at >10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the luminal A subtype. We identified two novel protein-expression-defined subgroups, possibly produced by stromal/microenvironmental elements, and integrated analyses identified specific signalling pathways dominant in each molecular subtype including a HER2/phosphorylated HER2/EGFR/phosphorylated EGFR signature within the HER2-enriched expression subtype. Comparison of basal-like breast tumours with high-grade serous ovarian tumours showed many molecular commonalities, indicating a related aetiology and similar therapeutic opportunities. The biological finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biological subtypes of breast cancer.

Intratumor heterogeneity: evolution through space and time

Recent technologic advances have permitted higher resolution and more rapid analysis of individual cancer genomes at the single-nucleotide level. Such advances have shown bewildering intertumor heterogeneity with limited somatic alterations shared between tumors of the same histopathologic subtype. Exacerbating such complexity, increasing evidence of intratumor genetic heterogeneity (ITH) is emerging, both within individual tumor biopsies and spatially separated between biopsies of the same tumor. Sequential analysis of tumors has also revealed evidence that ITH temporally evolves during the disease course. ITH has implications for predictive or prognostic biomarker strategies, where the tumor subclone that may ultimately influence therapeutic outcome may evade detection because of its absence or presence at low frequency at diagnosis or because of its regional separation from the tumor biopsy site. In this review, the implications of "trunk and branch" tumor evolution for drug discovery approaches and emerging evidence that low-frequency somatic events may drive tumor growth through paracrine signaling fostering a tumor ecologic niche are discussed. The concept of an "actionable mutation" is considered within a model of clonal dominance and heterogeneous tumor cell dependencies. Evidence that cancer therapeutics may augment ITH and the need to track the tumor subclonal architecture through treatment are defined as key research areas. Finally, if combination therapeutic approaches to limit the consequences of ITH prove challenging, identification of drivers or suppressors of ITH may provide attractive therapeutic targets to limit tumor evolutionary rates and adaptation.

The transcriptional landscape and mutational profile of lung adenocarcinoma

All cancers harbor molecular alterations in their genomes. The transcriptional consequences of these somatic mutations have not yet been comprehensively explored in lung cancer. Here we present the first large scale RNA sequencing study of lung adenocarcinoma, demonstrating its power to identify somatic point mutations as well as transcriptional variants such as gene fusions, alternative splicing events, and expression outliers. Our results reveal the genetic basis of 200 lung adenocarcinomas in Koreans including deep characterization of 87 surgical specimens by transcriptome sequencing. We identified driver somatic mutations in cancer genes including EGFR, KRAS, NRAS, BRAF, PIK3CA, MET, and CTNNB1. Candidates for novel driver mutations were also identified in genes newly implicated in lung adenocarcinoma such as LMTK2, ARID1A, NOTCH2, and SMARCA4. We found 45 fusion genes, eight of which were chimeric tyrosine kinases involving ALK, RET, ROS1, FGFR2, AXL, and PDGFRA. Among 17 recurrent alternative splicing events, we identified exon 14 skipping in the proto-oncogene MET as highly likely to be a cancer driver. The number of somatic mutations and expression outliers varied markedly between individual cancers and was strongly correlated with smoking history of patients. We identified genomic blocks within which gene expression levels were consistently increased or decreased that could be explained by copy number alterations in samples. We also found an association between lymph node metastasis and somatic mutations in TP53. These findings broaden our understanding of lung adenocarcinoma and may also lead to new diagnostic and therapeutic approaches.

Cancer in light of experimental evolution

Cancer initiation, progression, and the emergence of therapeutic resistance are evolutionary phenomena of clonal somatic cell populations. Studies in microbial experimental evolution and the theoretical work inspired by such studies are yielding deep insights into the evolutionary dynamics of clonal populations, yet there has been little explicit consideration of the relevance of this rapidly growing field to cancer biology. Here, we examine how the understanding of mutation, selection, and spatial structure in clonal populations that is emerging from experimental evolution may be applicable to cancer. Along the way, we discuss some significant ways in which cancer differs from the model systems used in experimental evolution. Despite these differences, we argue that enhanced prediction and control of cancer may be possible using ideas developed in the context of experimental evolution, and we point out some prospects for future research at the interface between these traditionally separate areas.

What are we learning from the cancer genome?

Massively parallel approaches to nucleic acid sequencing have matured from proof-of-concept to commercial products during the past 5 years. These technologies are now widely accessible, increasingly affordable, and have already exerted a transformative influence on the study of human cancer. Here, we review new features of cancer genomes that are being revealed by large-scale applications of these technologies. We focus on those insights most likely to affect future clinical practice. Foremost among these lessons, we summarize the formidable genetic heterogeneity within given cancer types that is appreciable with higher resolution profiling and larger sample sets. We discuss the inherent challenges of defining driving genomic events in a given cancer genome amidst thousands of other somatic events. Finally, we explore the organizational, regulatory and societal challenges impeding precision cancer medicine based on genomic profiling from assuming its place as standard-of-care.

Molecular biology in breast cancer: should molecular classifiers be assessed by conventional tools or by gene expression arrays?

Breast cancer is a complex disease, with heterogeneous presentations and clinical courses. Standard clinico-pathological parameters, relying on single gene or protein characterization determined with sometimes poorly-reproducible technologies, have shown limitations in the classification of the disease and in the prediction of individual patient outcomes and responses to therapy. Gene-expression profiling has revealed great potential to accurately classify breast cancer and define patient prognosis and prediction to anti-cancer therapy. Nevertheless, the performance of molecular classifiers remains sub-optimal, and both technical and conceptual improvements are needed. It is likely that determining the ideal strategy for tailoring treatment of breast cancer will require a more systematic, structured and multi-dimensional approach than in the past. Besides implementing cutting-edge technologies to detect genetic and epigenetic cancer alterations, the future of breast cancer research will in all probability rely on the innovative and multilevel integration of molecular profiles with clinical parameters of the disease and patient-related factors.

Overcoming implementation challenges of personalized cancer therapy

Personalized cancer therapy is based on the precept that detailed molecular characterization of the patient's tumour and its microenvironment will enable tailored therapies to improve outcomes and decrease toxicity. The goal of personalized therapy is to target aberrations that drive tumour growth and survival, by administering the right drug combination for the right person. This is becoming increasingly achievable with advances in high-throughput technologies to characterize tumours and the expanding repertoire of molecularly targeted therapies. However, there are numerous challenges that need to be surpassed before delivering on the promise of personalized cancer therapy. These include tumour heterogeneity and molecular evolution, costs and potential morbidity of biopsies, lack of effective drugs against most genomic aberrations, technical limitations of molecular tests, and reimbursement and regulatory hurdles. Critically, the 'hype' surrounding personalized cancer therapy must be tempered with realistic expectations, which, today, encompass increased survival times for only a portion of patients.

Genomic Determinants of PI3K Pathway Inhibitor Response in Cancer

The phosphoinositide 3-kinase (PI3K) pathway is frequently activated in cancer as a result of genetic (e.g., amplifications, mutations, deletions) and epigenetic (e.g., methylation, regulation by non-coding RNAs) aberrations targeting its key components. Several lines of evidence demonstrate that tumors from different anatomical sites depend on the continued activation of this pathway for the maintenance of their malignant phenotype. The PI3K pathway therefore is an attractive candidate for therapeutic intervention, and inhibitors targeting different components of this pathway are in various stages of clinical development. Burgeoning data suggest that the genomic features of a given tumor determine its response to targeted small molecule inhibitors. Importantly, alterations of different components of the PI3K pathway may result in distinct types of dependencies and response to specific therapeutic agents. In this review, we will focus on the genomic determinants of response to PI3K, dual PI3K/mechanistic target of rapamycin (mTOR), mTOR, and AKT inhibitors in cancer identified in preclinical models and clinical trials to date, and the development of molecular tools for the stratification of cancer patients.

Functional analysis of receptor tyrosine kinase mutations in lung cancer identifies oncogenic extracellular domain mutations of ERBB2

We assessed somatic alleles of six receptor tyrosine kinase genes mutated in lung adenocarcinoma for oncogenic activity. Five of these genes failed to score in transformation assays; however, novel recurring extracellular domain mutations of the receptor tyrosine kinase gene ERBB2 were potently oncogenic. These ERBB2 extracellular domain mutants were activated by two distinct mechanisms, characterized by elevated C-terminal tail phosphorylation or by covalent dimerization mediated by intermolecular disulfide bond formation. These distinct mechanisms of receptor activation converged upon tyrosine phosphorylation of cellular proteins, impacting cell motility. Survival of Ba/F3 cells transformed to IL-3 independence by the ERBB2 extracellular domain mutants was abrogated by treatment with small-molecule inhibitors of ERBB2, raising the possibility that patients harboring such mutations could benefit from ERBB2-directed therapy.

Functional impact bias reveals cancer drivers

Identifying cancer driver genes and pathways among all somatic mutations detected in a cohort of tumors is a key challenge in cancer genomics. Traditionally, this is done by prioritizing genes according to the recurrence of alterations that they bear. However, this approach has some known limitations, such as the difficulty to correctly estimate the background mutation rate, and the fact that it cannot identify lowly recurrently mutated driver genes. Here we present a novel approach, Oncodrive-fm, to detect candidate cancer drivers which does not rely on recurrence. First, we hypothesized that any bias toward the accumulation of variants with high functional impact observed in a gene or group of genes may be an indication of positive selection and can thus be used to detect candidate driver genes or gene modules. Next, we developed a method to measure this bias (FM bias) and applied it to three datasets of tumor somatic variants. As a proof of concept of our hypothesis we show that most of the highly recurrent and well-known cancer genes exhibit a clear FM bias. Moreover, this novel approach avoids some known limitations of recurrence-based approaches, and can successfully identify lowly recurrent candidate cancer drivers.

Impact of genetic dynamics and single-cell heterogeneity on development of nonstandard personalized medicine strategies for cancer

Cancers are heterogeneous and genetically unstable. Current practice of personalized medicine tailors therapy to heterogeneity between cancers of the same organ type. However, it does not yet systematically address heterogeneity at the single-cell level within a single individual's cancer or the dynamic nature of cancer due to genetic and epigenetic change as well as transient functional changes. We have developed a mathematical model of personalized cancer therapy incorporating genetic evolutionary dynamics and single-cell heterogeneity, and have examined simulated clinical outcomes. Analyses of an illustrative case and a virtual clinical trial of over 3 million evaluable "patients" demonstrate that augmented (and sometimes counterintuitive) nonstandard personalized medicine strategies may lead to superior patient outcomes compared with the current personalized medicine approach. Current personalized medicine matches therapy to a tumor molecular profile at diagnosis and at tumor relapse or progression, generally focusing on the average, static, and current properties of the sample. Nonstandard strategies also consider minor subclones, dynamics, and predicted future tumor states. Our methods allow systematic study and evaluation of nonstandard personalized medicine strategies. These findings may, in turn, suggest global adjustments and enhancements to translational oncology research paradigms.

The rebel angel: mutant p53 as the driving oncogene in breast cancer

Breast cancer is the most frequent invasive tumor diagnosed in women, causing over 400 000 deaths yearly worldwide. Like other tumors, it is a disease with a complex, heterogeneous genetic and biochemical background. No single genomic or metabolic condition can be regarded as decisive for its formation and progression. However, a few key players can be pointed out and among them is the TP53 tumor suppressor gene, commonly mutated in breast cancer. In particular, TP53 mutations are exceptionally frequent and apparently among the key driving factors in triple negative breast cancer -the most aggressive breast cancer subgroup-whose management still represents a clinical challenge. The majority of TP53 mutations result in the substitution of single aminoacids in the central region of the p53 protein, generating a spectrum of variants ('mutant p53s', for short). These mutants lose the normal p53 oncosuppressive functions to various extents but can also acquire oncogenic properties by gain-of-function mechanisms. This review discusses the molecular processes translating gene mutations to the pathologic consequences of mutant p53 tumorigenic activity, reconciling cell and animal models with clinical outcomes in breast cancer. Existing and speculative therapeutic methods targeting mutant p53 are also discussed, taking into account the overlap of mutant and wild-type p53 regulatory mechanisms and the crosstalk between mutant p53 and other oncogenic pathways in breast cancer. The studies described here concern breast cancer models and patients-unless it is indicated otherwise and justified by the importance of data obtained in other models.

Use of mutation profiles to refine the classification of endometrial carcinomas

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

nFuse: discovery of complex genomic rearrangements in cancer using high-throughput sequencing

Complex genomic rearrangements (CGRs) are emerging as a new feature of cancer genomes. CGRs are characterized by multiple genomic breakpoints and thus have the potential to simultaneously affect multiple genes, fusing some genes and interrupting other genes. Analysis of high-throughput whole-genome shotgun sequencing (WGSS) is beginning to facilitate the discovery and characterization of CGRs, but further development of computational methods is required. We have developed an algorithmic method for identifying CGRs in WGSS data based on shortest alternating paths in breakpoint graphs. Aiming for a method with the highest possible sensitivity, we use breakpoint graphs built from all WGSS data, including sequences with ambiguous genomic origin. Since the majority of cell function is encoded by the transcriptome, we target our search to find CGRs that underlie fusion transcripts predicted from matched high-throughput cDNA sequencing (RNA-seq). We have applied our method, nFuse, to the discovery of CGRs in publicly available data from the well-studied breast cancer cell line HCC1954 and primary prostate tumor sample 963. We first establish the sensitivity and specificity of the nFuse breakpoint prediction and scoring method using breakpoints previously discovered in HCC1954. We then validate five out of six CGRs in HCC1954 and two out of two CGRs in 963. We show examples of gene fusions that would be difficult to discover using methods that do not account for the existence of CGRs, including one important event that was missed in a previous study of the HCC1954 genome. Finally, we illustrate how CGRs may be used to infer the gene expression history of a tumor.

The evolving landscape of protein kinases in breast cancer: clinical implications

Dysfunction of protein kinases has been associated with the development of the various molecular subtypes of breast cancer. The best example is the known role of HER2 in the tumorigenesis of a proportion of breast tumors. In this article, we review the state of the art knowledge on protein kinases involved in breast cancer. Special attention is given to those that are potentially druggable and those for which targeted agents are currently under clinical evaluation. Options for targeted drug combinations will be discussed, as well as the optimal way to integrate new kinase inhibitors in the clinical armamentarium to fight breast cancer. We will review recent results from clinical studies with agents targeting different kinases involved in the pathophysiology of breast cancer. In addition, we will evaluate the clinical development of kinase inhibitors to identify areas of knowledge that could be explored in future preclinical and clinical studies.

Whole-genome sequencing of liver cancers identifies etiological influences on mutation patterns and recurrent mutations in chromatin regulators

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. We sequenced and analyzed the whole genomes of 27 HCCs, 25 of which were associated with hepatitis B or C virus infections, including two sets of multicentric tumors. Although no common somatic mutations were identified in the multicentric tumor pairs, their whole-genome substitution patterns were similar, suggesting that these tumors developed from independent mutations, although their shared etiological backgrounds may have strongly influenced their somatic mutation patterns. Statistical and functional analyses yielded a list of recurrently mutated genes. Multiple chromatin regulators, including ARID1A, ARID1B, ARID2, MLL and MLL3, were mutated in ∼50% of the tumors. Hepatitis B virus genome integration in the TERT locus was frequently observed in a high clonal proportion. Our whole-genome sequencing analysis of HCCs identified the influence of etiological background on somatic mutation patterns and subsequent carcinogenesis, as well as recurrent mutations in chromatin regulators in HCCs.

Whole-genome sequencing of liver cancers identifies etiological influences on mutation patterns and recurrent mutations in chromatin regulators

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. We sequenced and analyzed the whole genomes of 27 HCCs, 25 of which were associated with hepatitis B or C virus infections, including two sets of multicentric tumors. Although no common somatic mutations were identified in the multicentric tumor pairs, their whole-genome substitution patterns were similar, suggesting that these tumors developed from independent mutations, although their shared etiological backgrounds may have strongly influenced their somatic mutation patterns. Statistical and functional analyses yielded a list of recurrently mutated genes. Multiple chromatin regulators, including ARID1A, ARID1B, ARID2, MLL and MLL3, were mutated in ∼50% of the tumors. Hepatitis B virus genome integration in the TERT locus was frequently observed in a high clonal proportion. Our whole-genome sequencing analysis of HCCs identified the influence of etiological background on somatic mutation patterns and subsequent carcinogenesis, as well as recurrent mutations in chromatin regulators in HCCs.

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

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

CallSim: Evaluation of Base Calls Using Sequencing Simulation

Accurate base calls generated from sequencing data are required for downstream biological interpretation, particularly in the case of rare variants. CallSim is a software application that provides evidence for the validity of base calls believed to be sequencing errors and it is applicable to Ion Torrent and 454 data. The algorithm processes a single read using a Monte Carlo approach to sequencing simulation, not dependent upon information from any other read in the data set. Three examples from general read correction, as well as from error-or-variant classification, demonstrate its effectiveness for a robust low-volume read processing base corrector. Specifically, correction of errors in Ion Torrent reads from a study involving mutations in multidrug resistant Staphylococcus aureus illustrates an ability to classify an erroneous homopolymer call. In addition, support for a rare variant in 454 data for a mixed viral population demonstrates “base rescue” capabilities. CallSim provides evidence regarding the validity of base calls in sequences produced by 454 or Ion Torrent systems and is intended for hands-on downstream processing analysis. These downstream efforts, although time consuming, are necessary steps for accurate identification of rare variants.

Isolation and characterization of Pseudomonas aeruginosa PAO mutant that produces altered elastase

Triple-negative breast cancer (TNBC) is cancer that tested as negative for estrogen receptors (ER), progesterone receptors (PR), and excess human epidermal growth factor receptor 2 (HER2) protein which accounts for 15%–20% of all breast cancer cases. TNBC is considered to be a poorer prognosis than other types of breast cancer, mainly because it involves more aggressive phenotypes that are similar to stem cell–like cancer cells (cancer stem cell, CSC). Thus, targeted treatment of TNBC remains a major challenge in clinical practice. This review article surveys the latest evidence concerning the role of genomic alteration in current TNBC treatment responses, current clinical trials and potential targeting sites, CSC and drug resistance, and potential strategies targeting CSCs in TNBC. Furthermore, the role of insulin-like growth factor 1 receptor (IGF-1R) and nicotinic acetylcholine receptors (nAChR) in stemness expression, chemoresistance, and metastasis in TNBC and their relevance to potential treatments are also discussed and highlighted.