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

A novel min-cost flow method for estimating transcript expression with RNA-Seq

Background

Through transcription and alternative splicing, a gene can be transcribed into different RNA sequences (isoforms), depending on the individual, on the tissue the cell is in, or in response to some stimuli. Recent RNA-Seq technology allows for new high-throughput ways for isoform identification and quantification based on short reads, and various methods have been put forward for this non-trivial problem.

Results

In this paper we propose a novel radically different method based on minimum-cost network flows. This has a two-fold advantage: on the one hand, it translates the problem as an established one in the field of network flows, which can be solved in polynomial time, with different existing solvers; on the other hand, it is general enough to encompass many of the previous proposals under the least sum of squares model. Our method works as follows: in order to find the transcripts which best explain, under a given fitness model, a splicing graph resulting from an RNA-Seq experiment, we find a min-cost flow in an offset flow network, under an equivalent cost model. Under very weak assumptions on the fitness model, the optimal flow can be computed in polynomial time. Parsimoniously splitting the flow back into few path transcripts can be done with any of the heuristics and approximations available from the theory of network flows. In the present implementation, we choose the simple strategy of repeatedly removing the heaviest path.

Conclusions

We proposed a new very general method based on network flows for a multiassembly problem arising from isoform identification and quantification with RNA-Seq. Experimental results on prediction accuracy show that our method is very competitive with popular tools such as Cufflinks and IsoLasso. Our tool, called Traph (Transcrips in gRAPHs), is available at: http://www.cs.helsinki.fi/gsa/traph/.

Genome evolution during progression to breast cancer

Cancer evolution involves cycles of genomic damage, epigenetic deregulation, and increased cellular proliferation that eventually culminate in the carcinoma phenotype. Early neoplasias, which are often found concurrently with carcinomas and are histologically distinguishable from normal breast tissue, are less advanced in phenotype than carcinomas and are thought to represent precursor stages. To elucidate their role in cancer evolution we performed comparative whole-genome sequencing of early neoplasias, matched normal tissue, and carcinomas from six patients, for a total of 31 samples. By using somatic mutations as lineage markers we built trees that relate the tissue samples within each patient. On the basis of these lineage trees we inferred the order, timing, and rates of genomic events. In four out of six cases, an early neoplasia and the carcinoma share a mutated common ancestor with recurring aneuploidies, and in all six cases evolution accelerated in the carcinoma lineage. Transition spectra of somatic mutations are stable and consistent across cases, suggesting that accumulation of somatic mutations is a result of increased ancestral cell division rather than specific mutational mechanisms. In contrast to highly advanced tumors that are the focus of much of the current cancer genome sequencing, neither the early neoplasia genomes nor the carcinomas are enriched with potentially functional somatic point mutations. Aneuploidies that occur in common ancestors of neoplastic and tumor cells are the earliest events that affect a large number of genes and may predispose breast tissue to eventual development of invasive carcinoma.

The complex genetic landscape of familial breast cancer

Familial breast cancer represents a minor percentage of all human breast cancers. Mutations in two high susceptibility genes BRCA1 and BRCA2 explain around 25 % of familial breast cancers, while other high, moderate and low susceptibility genes explain up to 20 % more of breast cancer families. Thus, it is important to decipher the genetic architecture of families that show no mutations to improve genetic counselling. The comprehensive description of familial breast cancer using different techniques and platforms has shown to be very valuable for better patient diagnosis, tumour surveillance, and ultimately patient treatment. This review focuses on the complex landscape of pathological, protein, genetic and genomic features associated with BRCA1-, BRCA2-, and non-BRCA1/BRCA2-related cancers described up to date. Special emphasis deserves the coexistence of distinct molecular breast cancer subtypes, the development of tumour classifiers to predict BRCA1/2 mutations, and the last insights from recent whole genome sequencing studies and miRNA profiling.

Identification of prognosis-relevant subgroups in patients with chemoresistant triple-negative breast cancer

PURPOSE

Patients with triple-negative breast cancer (TNBC) and residual disease after neoadjuvant chemotherapy generally have worse outcome; however, some patients with residual tumor after neoadjuvant chemotherapy do not relapse. We hypothesize that there are subgroups of patients with chemoresistant TNBC with different prognosis.

EXPERIMENTAL DESIGN

Forty-nine chemoresistant cases from 111 patients with TNBC treated with neoadjuvant chemotherapy (M.D. Anderson Cancer Center, Houston, TX) constituted the discovery cohort, and 25 chemoresistant samples from 47 neoadjuvant chemotherapy-treated TNBC (The Methodist Hospital, Houston, TX) were chosen for validation. Extended validation was carried out in 269 operable TNBC predicted to be chemoresistant by expression pattern from published datasets.

RESULTS

We established a seven-gene prognostic signature using dChip and gene set enrichment analyses. In the independent validation cohort, the classifier predicted correctly with positive predictive value of 75.0% and negative predictive value (i.e., relapse-free survival; RFS) of 76.9% at 3 years. Those predicted to relapse had a HR of 4.67 [95% confidence interval (CI): 1.27-17.15] for relapse in 3 years. In extended validation, patients predicted not to relapse exhibited 3-year RFS of 78.9%, whereas the 3-year RFS was 48.5% for patients predicted to relapse, with HR of 2.61 (95% CI: 1.52-4.49). The TNBC subgroup that predicted to have relatively favorable prognosis was characterized by high expression of "luminal-like" genes [androgen-receptor (AR) and GATA3], whereas the subgroup with worse prognosis was characterized by expression of cancer stem-cell markers.

CONCLUSION

We developed a clinically relevant signature for patients with chemoresistant TNBC. For these women, new therapeutic strategies like targeting AR activation or cancer stem cells may need to be developed.

A tale of two approaches: complementary mechanisms of cytotoxic and targeted therapy resistance may inform next-generation cancer treatments

Chemotherapy and molecularly targeted approaches represent two very different modes of cancer treatment and each is associated with unique benefits and limitations. Both types of therapy share the overarching limitation of the emergence of drug resistance, which prevents these drugs from eliciting lasting clinical benefit. This review will provide an overview of the various mechanisms of resistance to each of these classes of drugs and examples of drug combinations that have been tested clinically. This analysis supports the contention that understanding modes of resistance to both chemotherapy and molecularly targeted therapies may be very useful in selecting those drugs of each class that will have complementing mechanisms of sensitivity and thereby represent reasonable combination therapies.

Navigating cancer network attractors for tumor-specific therapy

Cells employ highly dynamic signaling networks to drive biological decision processes. Perturbations to these signaling networks may attract cells to new malignant signaling and phenotypic states, termed cancer network attractors, that result in cancer development. As different cancer cells reach these malignant states by accumulating different molecular alterations, uncovering these mechanisms represents a grand challenge in cancer biology. Addressing this challenge will require new systems-based strategies that capture the intrinsic properties of cancer signaling networks and provide deeper understanding of the processes by which genetic lesions perturb these networks and lead to disease phenotypes. Network biology will help circumvent fundamental obstacles in cancer treatment, such as drug resistance and metastasis, empowering personalized and tumor-specific cancer therapies.

Analysis of circulating tumor DNA to monitor metastatic breast cancer

BACKGROUND

The management of metastatic breast cancer requires monitoring of the tumor burden to determine the response to treatment, and improved biomarkers are needed. Biomarkers such as cancer antigen 15-3 (CA 15-3) and circulating tumor cells have been widely studied. However, circulating cell-free DNA carrying tumor-specific alterations (circulating tumor DNA) has not been extensively investigated or compared with other circulating biomarkers in breast cancer.

METHODS

We compared the radiographic imaging of tumors with the assay of circulating tumor DNA, CA 15-3, and circulating tumor cells in 30 women with metastatic breast cancer who were receiving systemic therapy. We used targeted or whole-genome sequencing to identify somatic genomic alterations and designed personalized assays to quantify circulating tumor DNA in serially collected plasma specimens. CA 15-3 levels and numbers of circulating tumor cells were measured at identical time points.

RESULTS

Circulating tumor DNA was successfully detected in 29 of the 30 women (97%) in whom somatic genomic alterations were identified; CA 15-3 and circulating tumor cells were detected in 21 of 27 women (78%) and 26 of 30 women (87%), respectively. Circulating tumor DNA levels showed a greater dynamic range, and greater correlation with changes in tumor burden, than did CA 15-3 or circulating tumor cells. Among the measures tested, circulating tumor DNA provided the earliest measure of treatment response in 10 of 19 women (53%).

CONCLUSIONS

This proof-of-concept analysis showed that circulating tumor DNA is an informative, inherently specific, and highly sensitive biomarker of metastatic breast cancer. (Funded by Cancer Research UK and others.).

Functional analysis-make or break for cancer predictability

Copy number variations (CNVs) encompass a variety of genetic alterations including deletions and amplifications and cluster in regions of the human genome with intrinsic instability. Small-sized CNVs can act as initial genetic changes giving rise to larger CNVs such as acquired somatic copy number aberrations (CNAs) promoting cancer formation. Previous studies provided evidence for CNVs as an underlying cause of elevated breast cancer risk when targeting breast cancer susceptibility genes and of accelerated breast cancer progression when targeting oncogenes. With the development of novel techniques for genome-wide detection of CNVs at increasingly higher resolution, it became possible to qualitatively and quantitatively analyse manifestation of DNA damage resulting from defects in any of the large variety of DNA double-strand break (DSB) repair mechanisms. Breast carcinogenesis, particularly in familial cases, has been linked with a defect in the homologous recombination (HR) pathway, which in turn switches damage removal towards alternative, more error-prone DSB repair pathways such as microhomology-mediated non-homologous end joining (mmNHEJ). Indeed, increased error-prone DSB repair activities were detected in peripheral blood lymphocytes from individuals with familial breast cancer risk independently of specific gene mutations. Intriguingly, sequence analysis of breakpoint regions revealed that the majority of genome aberrations found in breast cancer specimens are formed by mmNHEJ. Detection of pathway-specific error-prone DSB repair activities by functional testing was proposed to serve as biomarker for hereditary breast cancer risk and responsiveness to therapies targeting HR dysfunction. Identification of specific error-prone DSB repair mechanisms underlying CNAs and ultimately mammary tumour formation highlights potential targets for future breast cancer prevention regimens.

Targeting the PyMT Oncogene to Diverse Mammary Cell Populations Enhances Tumor Heterogeneity and Generates Rare Breast Cancer Subtypes

Human breast cancer is a heterogeneous disease composed of different histologies and molecular subtypes, many of which are not replicated in animal models. Here, we report a mouse model of breast cancer that generates unique tumor histologies including tubular, adenosquamous, and lipid-rich carcinomas. Utilizing a nononcogenic variant of polyoma middle T oncogene (PyMT) that requires a spontaneous base-pair deletion to transform cells, in conjunction with lentiviral transduction and orthotopic transplantation of primary mammary epithelial cells, this model sporadically induces oncogene expression in both the luminal and myoepithelial cell lineages of the normal mouse mammary epithelium. Microarray and hierarchical analyses using an intrinsic subtype gene set revealed that lentiviral PyMT generates both luminal and basal-like tumors. Cumulatively, these results show that low-level expression of PyMT in a broad range of cell types significantly increases tumor heterogeneity and establishes a mouse model of several rare human breast cancer subtypes.

Functional analysis of genes in regions commonly amplified in high-grade serous and endometrioid ovarian cancer

PURPOSE

Ovarian cancer has the highest mortality rate of all the gynecologic malignancies and is responsible for approximately 140,000 deaths annually worldwide. Copy number amplification is frequently associated with the activation of oncogenic drivers in this tumor type, but their cytogenetic complexity and heterogeneity has made it difficult to determine which gene(s) within an amplicon represent(s) the genuine oncogenic driver. We sought to identify amplicon targets by conducting a comprehensive functional analysis of genes located in the regions of amplification in high-grade serous and endometrioid ovarian tumors.

EXPERIMENTAL DESIGN

High-throughput siRNA screening technology was used to systematically assess all genes within regions commonly amplified in high-grade serous and endometrioid cancer. We describe the results from a boutique siRNA screen of 272 genes in a panel of 18 ovarian cell lines. Hits identified by the functional viability screen were further interrogated in primary tumor cohorts to determine the clinical outcomes associated with amplification and gene overexpression.

RESULTS

We identified a number of genes as critical for cellular viability when amplified, including URI1, PAK4, GAB2, and DYRK1B. Integration of primary tumor gene expression and outcome data provided further evidence for the therapeutic use of such genes, particularly URI1 and GAB2, which were significantly associated with survival in 2 independent tumor cohorts.

CONCLUSION

By taking this integrative approach to target discovery, we have streamlined the translation of high-resolution genomic data into preclinical in vitro studies, resulting in the identification of a number of genes that may be specifically targeted for the treatment of advanced ovarian tumors.

Gene rearrangements in hormone receptor negative breast cancers revealed by mate pair sequencing

Background

Chromosomal rearrangements in the form of deletions, insertions, inversions and translocations are frequently observed in breast cancer genomes, and a subset of these rearrangements may play a crucial role in tumorigenesis. To identify novel somatic chromosomal rearrangements, we determined the genome structures of 15 hormone-receptor negative breast tumors by long-insert mate pair massively parallel sequencing.

Results

We identified and validated 40 somatic structural alterations, including the recurring fusion between genes DDX10 and SKA3 and translocations involving the EPHA5 gene. Other rearrangements were found to affect genes in pathways involved in epigenetic regulation, mitosis and signal transduction, underscoring their potential role in breast tumorigenesis. RNA interference-mediated suppression of five candidate genes (DDX10, SKA3, EPHA5, CLTC and TNIK) led to inhibition of breast cancer cell growth. Moreover, downregulation of DDX10 in breast cancer cells lead to an increased frequency of apoptotic nuclear morphology.

Conclusions

Using whole genome mate pair sequencing and RNA interference assays, we have discovered a number of novel gene rearrangements in breast cancer genomes and identified DDX10, SKA3, EPHA5, CLTC and TNIK as potential cancer genes with impact on the growth and proliferation of breast cancer cells.

Fine mapping of breast cancer genome-wide association studies loci in women of African ancestry identifies novel susceptibility markers

Numerous single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility have been identified by genome-wide association studies (GWAS). However, these SNPs were primarily discovered and validated in women of European and Asian ancestry. Because linkage disequilibrium is ancestry-dependent and heterogeneous among racial/ethnic populations, we evaluated common genetic variants at 22 GWAS-identified breast cancer susceptibility loci in a pooled sample of 1502 breast cancer cases and 1378 controls of African ancestry. None of the 22 GWAS index SNPs could be validated, challenging the direct generalizability of breast cancer risk variants identified in Caucasians or Asians to other populations. Novel breast cancer risk variants for women of African ancestry were identified in regions including 5p12 (odds ratio [OR] = 1.40, 95% confidence interval [CI] = 1.11-1.76; P = 0.004), 5q11.2 (OR = 1.22, 95% CI = 1.09-1.36; P = 0.00053) and 10p15.1 (OR = 1.22, 95% CI = 1.08-1.38; P = 0.0015). We also found positive association signals in three regions (6q25.1, 10q26.13 and 16q12.1-q12.2) previously confirmed by fine mapping in women of African ancestry. In addition, polygenic model indicated that eight best markers in this study, compared with 22 GWAS-identified SNPs, could better predict breast cancer risk in women of African ancestry (per-allele OR = 1.21, 95% CI = 1.16-1.27; P = 9.7 × 10(-16)). Our results demonstrate that fine mapping is a powerful approach to better characterize the breast cancer risk alleles in diverse populations. Future studies and new GWAS in women of African ancestry hold promise to discover additional variants for breast cancer susceptibility with clinical implications throughout the African diaspora.

Semiautomated isolation and molecular characterisation of single or highly purified tumour cells from CellSearch enriched blood samples using dielectrophoretic cell sorting

BACKGROUND

Molecular characterisation of single circulating tumour cells (CTCs) holds considerable promise for predictive biomarker assessment and to explore CTC heterogeneity. We evaluate a new method, the DEPArray system, that allows the dielectrophoretic manipulation and isolation of single and 100% purified groups of CTCs from pre-enriched blood samples and explore the feasibility of their molecular characterisation.

METHODS

Samples containing known numbers of two cell populations were used to assess cell loss during sample loading. Cultured breast cancer cells were isolated from spiked blood samples using CellSearch CTC and Profile kits. Single tumour cells and groups of up to 10 tumour cells were recovered with the DEPArray system and subjected to transcriptional and mutation analysis.

RESULTS

On average, 40% cell loss was observed when loading samples to the DEPArray system. Expected mutations in clinically relevant markers could be obtained for 60% of single recovered tumour cells and all groups of tumour cells. Reliable gene expression profiles were obtained from single cells and groups of up to 10 cells for 2 out of 3 spiked breast cancer cell lines.

CONCLUSION

We describe a semiautomated workflow for the isolation of small groups of 1 to 10 tumour cells from whole blood samples and provide proof of principle for the feasibility of their comprehensive molecular characterisation.

An empirical Bayesian framework for somatic mutation detection from cancer genome sequencing data

Recent advances in high-throughput sequencing technologies have enabled a comprehensive dissection of the cancer genome clarifying a large number of somatic mutations in a wide variety of cancer types. A number of methods have been proposed for mutation calling based on a large amount of sequencing data, which is accomplished in most cases by statistically evaluating the difference in the observed allele frequencies of possible single nucleotide variants between tumours and paired normal samples. However, an accurate detection of mutations remains a challenge under low sequencing depths or tumour contents. To overcome this problem, we propose a novel method, Empirical Bayesian mutation Calling (https://github.com/friend1ws/EBCall), for detecting somatic mutations. Unlike previous methods, the proposed method discriminates somatic mutations from sequencing errors based on an empirical Bayesian framework, where the model parameters are estimated using sequencing data from multiple non-paired normal samples. Using 13 whole-exome sequencing data with 87.5–206.3 mean sequencing depths, we demonstrate that our method not only outperforms several existing methods in the calling of mutations with moderate allele frequencies but also enables accurate calling of mutations with low allele frequencies (≤10%) harboured within a minor tumour subpopulation, thus allowing for the deciphering of fine substructures within a tumour specimen.

Emerging targeted agents in metastatic breast cancer

Extensive preclinical experimentation has conceptually changed the way we perceive breast cancer, with the wide spectrum of genomic alterations governing its malignant progression now being recognized. Functional genomics has helped us identify important genetic defects that can be pharmaceutically targeted in the setting of metastatic disease. Rationally chosen combination regimens are now under clinical investigation. Recent data underline the functional importance of the tumour-associated stroma, with several candidate molecular targets now emerging. Data elucidating a cellular hierarchy within the breast cancer cellular compartment support the existence of a therapy-resistant subpopulation of breast cancer stem cells. Identification of the developmental pathways that dictate their malignant phenotype and use of high-throughput screening techniques are leading to new therapeutic avenues. In this Review, we present the biological rationale for the clinical development of more than 15 different classes of targeted agents in breast cancer, along with evidence supporting rational combinations. However, metastatic breast cancer resembles a Darwinian evolutionary system, with 'driver' mutations and epigenetic changes determining clonal selection according to branching trajectories. This evolution is reflected in the molecular heterogeneity of the disease and poses severe impediments to the successful clinical development of emerging targeted agents.

Needles in a haystack: finding recurrent genomic changes in breast cancer

Significant advances over the past decade have enabled scientists to obtain increasingly detailed molecular profiles of breast cancer. The recent analysis by The Cancer Genome Atlas published in the September 2012 issue of Nature is the most comprehensive description of breast cancer 'omics' to date. This study is impressive in its scope and scale, with the findings reconfirming the heterogeneity of breast cancer and highlighting the future challenges in translating these findings for clinical benefit.

High-resolution mutational profiling suggests the genetic validity of glioblastoma patient-derived pre-clinical models

Recent advances in the ability to efficiently characterize tumor genomes is enabling targeted drug development, which requires rigorous biomarker-based patient selection to increase effectiveness. Consequently, representative DNA biomarkers become equally important in pre-clinical studies. However, it is still unclear how well these markers are maintained between the primary tumor and the patient-derived tumor models. Here, we report the comprehensive identification of somatic coding mutations and copy number aberrations in four glioblastoma (GBM) primary tumors and their matched pre-clinical models: serum-free neurospheres, adherent cell cultures, and mouse xenografts. We developed innovative methods to improve the data quality and allow a strict comparison of matched tumor samples. Our analysis identifies known GBM mutations altering PTEN and TP53 genes, and new actionable mutations such as the loss of PIK3R1, and reveals clear patient-to-patient differences. In contrast, for each patient, we do not observe any significant remodeling of the mutational profile between primary to model tumors and the few discrepancies can be attributed to stochastic errors or differences in sample purity. Similarly, we observe ∼96% primary-to-model concordance in copy number calls in the high-cellularity samples. In contrast to previous reports based on gene expression profiles, we do not observe significant differences at the DNA level between in vitro compared to in vivo models. This study suggests, at a remarkable resolution, the genome-wide conservation of a patient’s tumor genetics in various pre-clinical models, and therefore supports their use for the development and testing of personalized targeted therapies.

FOXA1 mutations in hormone-dependent cancers

The forkhead protein, FOXA1, is a critical interacting partner of the nuclear hormone receptors, oestrogen receptor-α (ER) and androgen receptor (AR), which are major drivers of the two most common cancers, namely breast and prostate cancer. Over the past few years, progress has been made in our understanding of how FOXA1 influences nuclear receptor function, with both common and distinct roles in the regulation of ER or AR. Recently, another level of regulation has been described, with the discovery that FOXA1 is mutated in 1.8% of breast and 3–5% prostate cancers. In addition, a subset of both cancer types exhibit amplification of the genomic region encompassing the FOXA1 gene. Furthermore, there is evidence of somatic changes that influence the DNA sequence under FOXA1 binding regions, which may indirectly influence FOXA1-mediated regulation of ER and AR activity. These recent observations provide insight into the heterogeneity observed in ER and AR driven cancers.

Next generation sequencing in cancer research and clinical application

The wide application of next-generation sequencing (NGS), mainly through whole genome, exome and transcriptome sequencing, provides a high-resolution and global view of the cancer genome. Coupled with powerful bioinformatics tools, NGS promises to revolutionize cancer research, diagnosis and therapy. In this paper, we review the recent advances in NGS-based cancer genomic research as well as clinical application, summarize the current integrative oncogenomic projects, resources and computational algorithms, and discuss the challenge and future directions in the research and clinical application of cancer genomic sequencing.

Genetically engineered mouse models of PI3K signaling in breast cancer

Breast cancer is the most common type of cancer in women. A substantial fraction of breast cancers have acquired mutations that lead to activation of the phosphoinositide 3-kinase (PI3K) signaling pathway, which plays a central role in cellular processes that are essential in cancer, such as cell survival, growth, division and motility. Oncogenic mutations in the PI3K pathway generally involve either activating mutation of the gene encoding PI3K (PIK3CA) or AKT (AKT1), or loss or reduced expression of PTEN. Several kinases involved in PI3K signaling are being explored as a therapeutic targets for pharmacological inhibition. Despite the availability of a range of inhibitors, acquired resistance may limit the efficacy of single-agent therapy. In this review we discuss the role of PI3K pathway mutations in human breast cancer and relevant genetically engineered mouse models (GEMMs), with special attention to the role of PI3K signaling in oncogenesis, in therapeutic response, and in resistance to therapy. Several sophisticated GEMMs have revealed the cause-and-effect relationships between PI3K pathway mutations and mammary oncogenesis. These GEMMs enable us to study the biology of tumors induced by activated PI3K signaling, as well as preclinical response and resistance to PI3K pathway inhibitors.

A new genome-driven integrated classification of breast cancer and its implications

Breast cancer is a group of heterogeneous diseases that show substantial variation in their molecular and clinical characteristics. This heterogeneity poses significant challenges not only in breast cancer management, but also in studying the biology of the disease. Recently, rapid progress has been made in understanding the genomic diversity of breast cancer. These advances led to the characterisation of a new genome-driven integrated classification of breast cancer, which substantially refines the existing classification systems currently used. The novel classification integrates molecular information on the genomic and transcriptomic landscapes of breast cancer to define 10 integrative clusters, each associated with distinct clinical outcomes and providing new insights into the underlying biology and potential molecular drivers. These findings have profound implications both for the individualisation of treatment approaches, bringing us a step closer to the realisation of personalised cancer management in breast cancer, but also provide a new framework for studying the underlying biology of each novel subtype.

Activating HER2 mutations in HER2 gene amplification negative breast cancer

Data from 8 breast cancer genome-sequencing projects identified 25 patients with HER2 somatic mutations in cancers lacking HER2 gene amplification. To determine the phenotype of these mutations, we functionally characterized 13 HER2 mutations using in vitro kinase assays, protein structure analysis, cell culture, and xenograft experiments. Seven of these mutations are activating mutations, including G309A, D769H, D769Y, V777L, P780ins, V842I, and R896C. HER2 in-frame deletion 755-759, which is homologous to EGF receptor (EGFR) exon 19 in-frame deletions, had a neomorphic phenotype with increased phosphorylation of EGFR or HER3. L755S produced lapatinib resistance, but was not an activating mutation in our experimental systems. All of these mutations were sensitive to the irreversible kinase inhibitor, neratinib. These findings show that HER2 somatic mutation is an alternative mechanism to activate HER2 in breast cancer and they validate HER2 somatic mutations as drug targets for breast cancer treatment.

SIGNIFICANCE

We show that the majority of HER2 somatic mutations in breast cancer patients are activating mutations that likely drive tumorigenesis. Several patients had mutations that are resistant to the reversible HER2 inhibitor lapatinib, but are sensitive to the irreversible HER2 inhibitor, neratinib. Our results suggest that patients with HER2 mutation–positive breast cancers could benefit from existing HER2-targeted drugs.

An Integrative Genomics Approach for Associating GWAS Information with Triple-Negative Breast Cancer

Genome-wide association studies (GWAS) have identified genetic variants associated with an increased risk of developing breast cancer. However, the association of genetic variants and their associated genes with the most aggressive subset of breast cancer, the triple-negative breast cancer (TNBC), remains a central puzzle in molecular epidemiology. The objective of this study was to determine whether genes containing single nucleotide polymorphisms (SNPs) associated with an increased risk of developing breast cancer are connected to and could stratify different subtypes of TNBC. Additionally, we sought to identify molecular pathways and networks involved in TNBC. We performed integrative genomics analysis, combining information from GWAS studies involving over 400,000 cases and over 400,000 controls, with gene expression data derived from 124 breast cancer patients classified as TNBC (at the time of diagnosis) and 142 cancer-free controls. Analysis of GWAS reports produced 500 SNPs mapped to 188 genes. We identified a signature of 159 functionally related SNP-containing genes which were significantly (P <10⁻⁵) associated with and stratified TNBC. Additionally, we identified 97 genes which were functionally related to, and had similar patterns of expression profiles, SNP-containing genes. Network modeling and pathway prediction revealed multi-gene pathways including p53, NFkB, BRCA, apoptosis, DNA repair, DNA mismatch, and excision repair pathways enriched for SNPs mapped to genes significantly associated with TNBC. The results provide convincing evidence that integrating GWAS information with gene expression data provides a unified and powerful approach for biomarker discovery in TNBC.

Personal genomes, quantitative dynamic omics and personalized medicine

The rapid technological developments following the Human Genome Project have made possible the availability of personalized genomes. As the focus now shifts from characterizing genomes to making personalized disease associations, in combination with the availability of other omics technologies, the next big push will be not only to obtain a personalized genome, but to quantitatively follow other omics. This will include transcriptomes, proteomes, metabolomes, antibodyomes, and new emerging technologies, enabling the profiling of thousands of molecular components in individuals. Furthermore, omics profiling performed longitudinally can probe the temporal patterns associated with both molecular changes and associated physiological health and disease states. Such data necessitates the development of computational methodology to not only handle and descriptively assess such data, but also construct quantitative biological models. Here we describe the availability of personal genomes and developing omics technologies that can be brought together for personalized implementations and how these novel integrated approaches may effectively provide a precise personalized medicine that focuses on not only characterization and treatment but ultimately the prevention of disease.

Polo-like kinase 1: a potential therapeutic option in combination with conventional chemotherapy for the management of patients with triple-negative breast cancer

Breast cancers are composed of molecularly distinct subtypes with different clinical outcomes and responses to therapy. To discover potential therapeutic targets for the poor prognosis-associated triple-negative breast cancer (TNBC), gene expression profiling was carried out on a cohort of 130 breast cancer samples. Polo-like kinase 1 (PLK1) was found to be significantly overexpressed in TNBC compared with the other breast cancer subtypes. High PLK1 expression was confirmed by reverse phase protein and tissue microarrays. In triple-negative cell lines, RNAi-mediated PLK1 depletion or inhibition of PLK1 activity with a small molecule (BI-2536) induced an increase in phosphorylated H2AX, G(2)-M arrest, and apoptosis. A soft-agar colony assay showed that PLK1 silencing impaired clonogenic potential of TNBC cell lines. When cells were grown in extracellular matrix gels (Matrigel), and exposed to BI-2536, apoptosis was observed specifically in TNBC cancerous cells, and not in a normal cell line. When administrated as a single agent, the PLK1 inhibitor significantly impaired tumor growth in vivo in two xenografts models established from biopsies of patients with TNBC. Most importantly, the administration of BI-2536, in combination with doxorubicin + cyclophosphamide chemotherapy, led to a faster complete response compared with the chemotherapy treatment alone and prevented relapse, which is the major risk associated with TNBC. Altogether, our observations suggest PLK1 inhibition as an attractive therapeutic approach, in association with conventional chemotherapy, for the management of patients with TNBC.

Targeting triple negative breast cancer: is p53 the answer?

Triple negative breast cancers, which are defined by lack of expression of estrogen, progesterone, or HER2 receptors, represent approximately 15% of all breast cancers, although they account for a much higher proportional of breast cancer mortality. This is due both to their innate aggressive biological characteristics, but also to lack of effective therapies. Conventional chemotherapy is currently the only treatment option, thus there is a critical need to find new and effective targeted therapies in this disease. While investigation of agents such as poly (ADP-ribose) polymerase (PARP) inhibitors and EGFR inhibitors continues, results from recent clinical trials indicate that these therapies are not as active in sporadic triple negative breast cancers as initially hoped. It is important therefore to consider other emerging therapeutic agents. Mutation in p53 is found in the vast majority of triple negative breast cancers, and as such is a target of particular interest. Within this review, several agents with potential activity against aberrant p53 signaling have been considered, as a novel approach to finding an effective targeted therapy for this aggressive breast cancer subtype.

An evolutionary perspective on anti-tumor immunity

The challenges associated with demonstrating a durable response using molecular-targeted therapies in cancer has sparked a renewed interest in viewing cancer from an evolutionary perspective. Evolutionary processes have three common traits: heterogeneity, dynamics, and a selective fitness landscape. Mutagens randomly alter the genome of host cells creating a population of cells that contain different somatic mutations. This genomic rearrangement perturbs cellular homeostasis through changing how cells interact with their tissue microenvironment. To counterbalance the ability of mutated cells to outcompete for limited resources, control structures are encoded within the cell and within the organ system, such as innate and adaptive immunity, to restore cellular homeostasis. These control structures shape the selective fitness landscape and determine whether a cell that harbors particular somatic mutations is retained or eliminated from a cell population. While next-generation sequencing has revealed the complexity and heterogeneity of oncogenic transformation, understanding the dynamics of oncogenesis and how cancer cells alter the selective fitness landscape remain unclear. In this technology review, we will summarize how recent advances in technology have impacted our understanding of these three attributes of cancer as an evolutionary process. In particular, we will focus on how advances in genome sequencing have enabled quantifying cellular heterogeneity, advances in computational power have enabled explicit testing of postulated intra- and intercellular control structures against the available data using simulation, and advances in proteomics have enabled identifying novel mechanisms of cellular cross-talk that cancer cells use to alter the fitness landscape.

Somatic mutations in GRM1 in cancer alter metabotropic glutamate receptor 1 intracellular localization and signaling

The activity of metabotropic glutamate receptors (mGluRs) is known to be altered as the consequence of neurodegenerative diseases such as Alzheimer, Parkinson, and Huntington disease. However, little attention has been paid to this receptor family's potential link with cancer. Recent reports indicate altered mGluR signaling in various tumor types, and several somatic mutations in mGluR1a in lung cancer were recently described. Group 1 mGluRs (mGluR1a and mGluR5) are coupled primarily to Gαq, leading to the activation of phospholipase C and to the formation of diacylglycerol and inositol 1,4,5-trisphosphate, leading to the release of Ca(2+) from intracellular stores and protein kinase C (PKC) activation. In the present study, we investigated the intracellular localization and G protein-dependent and -independent signaling of eight GRM1 (mGluR1a) somatic mutations. Two mutants found in close proximity to the glutamate binding domain and cysteine-rich region (R375G and G396V) show both decreased cell surface expression and basal inositol phosphate (IP) formation. However, R375G shows increased ERK1/2 activation in response to quisqualate stimulation. A mutant located directly in the glutamate binding site (A168V) shows increased quisqualate-induced IP formation and, similar to R375G, increased ERK1/2 activation. Additionally, a mutation in the G protein-coupled receptor kinase 2/PKC regulatory region (R696W) shows decreased ERK1/2 activation, whereas a mutation within the Homer binding region in the carboxyl-terminal tail (P1148L) does not alter the intracellular localization of the receptor, but it induces changes in cellular morphology and exhibits reduced ERK1/2 activation. Taken together, these results suggest that mGluR1a signaling in cancer is disrupted by somatic mutations with multiple downstream consequences.

Hierarchy in somatic mutations arising during genomic evolution and progression of follicular lymphoma

Follicular lymphoma (FL) is currently incurable using conventional chemotherapy or immunotherapy regimes, compelling new strategies. Advances in high-throughput sequencing technologies that can reveal oncogenic pathways have stimulated interest in tailoring therapies toward actionable somatic mutations. However, for mutation-directed therapies to be most effective, the mutations must be uniformly present in evolved tumor cells as well as in the self-renewing tumor-cell precursors. Here, we show striking intratumoral clonal diversity within FL tumors in the representation of mutations in the majority of genes as revealed by whole exome sequencing of subpopulations. This diversity captures a clonal hierarchy, resolved using immunoglobulin somatic mutations and IGH-BCL2 translocations as a frame of reference and by comparing diagnosis and relapse tumor pairs, allowing us to distinguish early versus late genetic eventsduring lymphomagenesis. We provide evidence that IGH-BCL2 translocations and CREBBP mutations are early events, whereas MLL2 and TNFRSF14 mutations probably represent late events during disease evolution. These observations provide insight into which of the genetic lesions represent suitable candidates for targeted therapies.

Alterations of EGFR, p53 and PTEN that mimic changes found in basal-like breast cancer promote transformation of human mammary epithelial cells

Breast cancer can be classified into different molecular subtypes with varying clinical and pathological characteristics. The basal-like breast cancer subtype represents one of the most aggressive and lethal types of breast cancer, and due to poor mechanistic understanding, it lacks targeted therapy. Many basal-like breast cancer patient samples display alterations of established drivers of cancer development, including elevated expression of EGFR, p53 inactivating mutations and loss of expression of the tumor suppressor PTEN; however, their contribution to human basal-like breast cancer pathogenesis remains ill-defined. Using non-transformed human mammary epithelial cells, we set out to determine whether altering EGFR, p53 and PTEN in different combinations could contribute to basal-like breast cancer progression through transformation of cells. Altering PTEN in combination with either p53 or EGFR in contrast to any of the single alterations caused increased growth of transformed colonies in soft agar. Concomitantly modifying all three genes led to the highest rate of cellular proliferation and the greatest degree of anchorage-independent colony formation. Results from our effort to engineer a model of BBC expressing alterations of EGFR, p53 and PTEN suggest that these changes are cooperative and likely play a causal role in basal-like breast cancer pathogenesis. Consideration should be given to targeting EGFR and restoring p53 and PTEN signaling simultaneously as a strategy for treatment of this subtype of breast cancer.

Recurrent SETBP1 mutations in atypical chronic myeloid leukemia

Atypical chronic myeloid leukemia (aCML) shares clinical and laboratory features with CML, but it lacks the BCR-ABL1 fusion. We performed exome sequencing of eight aCMLs and identified somatic alterations of SETBP1 (encoding a p.Gly870Ser alteration) in two cases. Targeted resequencing of 70 aCMLs, 574 diverse hematological malignancies and 344 cancer cell lines identified SETBP1 mutations in 24 cases, including 17 of 70 aCMLs (24.3%; 95% confidence interval (CI) = 16-35%). Most mutations (92%) were located between codons 858 and 871 and were identical to changes seen in individuals with Schinzel-Giedion syndrome. Individuals with mutations had higher white blood cell counts (P = 0.008) and worse prognosis (P = 0.01). The p.Gly870Ser alteration abrogated a site for ubiquitination, and cells exogenously expressing this mutant exhibited higher amounts of SETBP1 and SET protein, lower PP2A activity and higher proliferation rates relative to those expressing the wild-type protein. In summary, mutated SETBP1 represents a newly discovered oncogene present in aCML and closely related diseases.

Emerging treatments for metastatic breast cancer: update from 2012

SUMMARY Metastatic breast cancer is still incurable and, thus, the goals of treatment focus on optimal palliation and extending the quality of survival through effective anticancer therapies that have acceptable toxicities. Cancer progression and development of drug resistance are dynamic processes of clonal adaptation both to evolving microenvironments with acquired tumor mutations and to continuous drug exposure. Preclinical research is constantly improving our understanding of breast cancer biology at a molecular level and, as such, current clinical research is focusing on designing trials with new targeted drugs and their appropriate combinations with endocrine and chemotherapy agents. In this review, we provide an overview of the most recent advances in the metastatic breast cancer setting by clinical subtype, giving emphasis to recently concluded or ongoing trials that have the potential to challenge and change current clinical practice.

The genomic landscape of breast cancer as a therapeutic roadmap

The application of high-throughput techniques to profile DNA, RNA, and protein in breast cancer samples from hundreds of patients has profoundly increased our knowledge of the disease. The etiologic events that drive breast cancer are finally coming into focus and should be used to set priorities for clinical trials. In this Prospective, we summarize some of the headline conclusions from 6 recent breast cancer "omics profiling" articles in Nature, with an emphasis on the implications for systemic therapy.

The mutational landscape of chromatin regulatory factors across 4,623 tumor samples

BACKGROUND

Chromatin regulatory factors are emerging as important genes in cancer development and are regarded as interesting candidates for novel targets for cancer treatment. However, we lack a comprehensive understanding of the role of this group of genes in different cancer types.

RESULTS

We have analyzed 4,623 tumor samples from thirteen anatomical sites to determine which chromatin regulatory factors are candidate drivers in these different sites. We identify 34 chromatin regulatory factors that are likely drivers in tumors from at least one site, all with relatively low mutational frequency. We also analyze the relative importance of mutations in this group of genes for the development of tumorigenesis in each site, and indifferent tumor types from the same site.

CONCLUSIONS

We find that, although tumors from all thirteen sites show mutations in likely driver chromatin regulatory factors, these are more prevalent in tumors arising from certain tissues. With the exception of hematopoietic, liver and kidney tumors, as a median, the mutated factors are less than one fifth of all mutated drivers across all sites analyzed. We also show that mutations in two of these genes, MLL and EP300, correlate with broad expression changes across cancer cell lines, thus presenting at least one mechanism through which these mutations could contribute to tumorigenesis in cells of the corresponding tissues.

Distinguishing somatic and germline copy number events in cancer patient DNA hybridized to whole-genome SNP genotyping arrays

Chromosomal aneuploidy and segmental copy number changes are common genomic aberrations in -cancer. Copy number alterations (CNAs) arise from deletions, insertions, or duplications resulting in -chromosomal aberrations and aneuploidy. Genomes of normal cells also exhibit variable copy number called germline copy number variants (CNVs). CNVs in the general population tend to confound interpretation of predictions when attempting to extract relevant driver somatic events in cancer. In large studies of CNAs in cancer patients, it becomes necessary to accurately identify and separate CNAs and CNVs so as to prioritize candidate tumor suppressors and oncogenes. We have developed a probabilistic approach, HMM-Dosage, for segmenting and distinguishing CNAs and CNVs as separate, discrete events in cancer SNP genotyping array data. We outline the steps and computer code for the analysis of whole-genome cancer DNA hybridized to SNP genotyping arrays, focusing on distinguishing somatic CNA and germline CNVs, and describe the combined approach of HMM-Dosage for probabilistic inference and classification of somatic and germline copy number changes.

Understanding genomic alterations in cancer genomes using an integrative network approach

In recent years, cancer genome sequencing and other high-throughput studies of cancer genomes have generated many notable discoveries. In this review, novel genomic alteration mechanisms, such as chromothripsis (chromosomal crisis) and kataegis (mutation storms), and their implications for cancer are discussed. Genomic alterations spur cancer genome evolution. Thus, the relationship between cancer clonal evolution and cancer stems cells is commented. The key question in cancer biology concerns how these genomic alterations support cancer development and metastasis in the context of biological functioning. Thus far, efforts such as pathway analysis have improved the understanding of the functional contributions of genetic mutations and DNA copy number variations to cancer development, progression and metastasis. However, the known pathways correspond to a small fraction, plausibly 5-10%, of somatic mutations and genes with an altered copy number. To develop a comprehensive understanding of the function of these genomic alterations in cancer, an integrative network framework is proposed and discussed. Finally, the challenges and the directions of studying cancer omic data using an integrative network approach are commented.

DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer

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

Active kinase profiling, genetic and pharmacological data define mTOR as an important common target in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer. Despite response to chemotherapy, relapses are frequent and resistance to available treatments is often seen in the metastatic setting. Therefore, identification of new therapeutic targets is required. With this aim, we have profiled the activation status of 44 receptor tyrosine kinases (RTKs) and their major signaling pathways in patient-derived TNBC tumors. Frequent co-activation of several RTKs as well as the extracellular signal-regulated protein kinases 1 and 2 (Erk1/2) and mammalian target of rapamycin (mTOR) routes was found. Pharmacologic targeting of the activated kinases indicated that agents that attack the mTOR route are more potent and efficient antitumoral treatments than agents targeting RTKs. mTOR signals through two multiprotein complexes, mTORC1 and mTORC2. We used a genetic approach to explore the contribution of each of the two mTOR branches to the regulation of cell number of TNBC cells. RNA interference experiments indicated that mTORC1 predominated over mTORC2 in the control of TNBC cell proliferation. Moreover, RNA interference of mTOR had a superior antiproliferative action than separately acting on mTORC1 or mTORC2. To analyze the relevance of mTOR targeting in vivo, we used mice with TNBC. Treatment of these mice with BEZ235, a drug that targets mTOR, slowed tumor growth. Mechanistically, BEZ235 delayed cell cycle progression without affecting cell viability. Our results show that TNBCs are particularly sensitive to inhibition of the mTOR pathway, and indicate that mTOR targeting may be a more efficient anti-TNBC therapy than exclusively acting on the mTORC1 branch of the pathway. This is relevant as most mTOR inhibitors used in the clinic act on mTORC1. Collectively with the fact that BEZ235 synergized with drugs commonly used in the treatment of TNBC, our data support the clinical development of agents that act on mTOR as a therapy for this disease.

A phase II study of UCN-01 in combination with irinotecan in patients with metastatic triple negative breast cancer

Mutations in TP53 lead to a defective G1 checkpoint and the dependence on checkpoint kinase 1 (Chk1) for G2 or S phase arrest in response to DNA damage. In preclinical studies, Chk1 inhibition resulted in enhanced cytotoxicity of several chemotherapeutic agents. The high frequency of TP53 mutations in triple negative breast cancer (TNBC: negative for estrogen receptor, progesterone receptor, and HER2) make Chk1 an attractive therapeutic target. UCN-01, a non-selective Chk1 inhibitor, combined with irinotecan demonstrated activity in advanced TNBC in our Phase I study. The goal of this trial was to further evaluate this treatment in women with TNBC. Patients with metastatic TNBC previously treated with anthracyclines and taxanes received irinotecan (100–125 mg/m² IV days 1, 8, 15, 22) and UCN-01 (70 mg/m² IV day 2, 35 mg/m² day 23 and subsequent doses) every 42-day cycle. Peripheral blood mononuclear cells (PBMC) and tumor specimens were collected. Twenty five patients were enrolled. The overall response (complete response (CR) + partial response (PR)) rate was 4 %. The clinical benefit rate (CR + PR + stable disease ≥6 months) was 12 %. Since UCN-01 inhibits PDK1, phosphorylated ribosomal protein S6 (pS6) in PBMC was assessed. Although reduced 24 h post UCN-01, pS6 levels rose to baseline by day 8, indicating loss of UCN-01 bioavailability. Immunostains of γH2AX and pChk1^S296 on serial tumor biopsies from four patients demonstrated an induction of DNA damage and Chk1 activation following irinotecan. However, Chk1 inhibition by UCN-01 was not observed in all tumors. Most tumors were basal-like (69 %), and carried mutations in TP53 (53 %). Median overall survival in patients with TP53 mutant tumors was poor compared to wild type (5.5 vs. 20.3 months, p = 0.004). This regimen had limited activity in TNBC. Inconsistent Chk1 inhibition was likely due to the pharmacokinetics of UCN-01. TP53 mutations were associated with a poor prognosis in metastatic TNBC.