The landscape of cancer genes and mutational processes in breast cancer

Lessons from the cancer genome

Systematic studies of the cancer genome have exploded in recent years. These studies have revealed scores of new cancer genes, including many in processes not previously known to be causal targets in cancer. The genes affect cell signaling, chromatin, and epigenomic regulation; RNA splicing; protein homeostasis; metabolism; and lineage maturation. Still, cancer genomics is in its infancy. Much work remains to complete the mutational catalog in primary tumors and across the natural history of cancer, to connect recurrent genomic alterations to altered pathways and acquired cellular vulnerabilities, and to use this information to guide the development and application of therapies.

Assessment of computational methods for predicting the effects of missense mutations in human cancers

BACKGROUND

Recent advances in sequencing technologies have greatly increased the identification of mutations in cancer genomes. However, it remains a significant challenge to identify cancer-driving mutations, since most observed missense changes are neutral passenger mutations. Various computational methods have been developed to predict the effects of amino acid substitutions on protein function and classify mutations as deleterious or benign. These include approaches that rely on evolutionary conservation, structural constraints, or physicochemical attributes of amino acid substitutions. Here we review existing methods and further examine eight tools: SIFT, PolyPhen2, Condel, CHASM, mCluster, logRE, SNAP, and MutationAssessor, with respect to their coverage, accuracy, availability and dependence on other tools.

RESULTS

Single nucleotide polymorphisms with high minor allele frequencies were used as a negative (neutral) set for testing, and recurrent mutations from the COSMIC database as well as novel recurrent somatic mutations identified in very recent cancer studies were used as positive (non-neutral) sets. Conservation-based methods generally had moderately high accuracy in distinguishing neutral from deleterious mutations, whereas the performance of machine learning based predictors with comprehensive feature spaces varied between assessments using different positive sets. MutationAssessor consistently provided the highest accuracies. For certain combinations metapredictors slightly improved the performance of included individual methods, but did not outperform MutationAssessor as stand-alone tool.

CONCLUSIONS

Our independent assessment of existing tools reveals various performance disparities. Cancer-trained methods did not improve upon more general predictors. No method or combination of methods exceeds 81% accuracy, indicating there is still significant room for improvement for driver mutation prediction, and perhaps more sophisticated feature integration is needed to develop a more robust tool.

Simultaneous identification of multiple driver pathways in cancer

Distinguishing the somatic mutations responsible for cancer (driver mutations) from random, passenger mutations is a key challenge in cancer genomics. Driver mutations generally target cellular signaling and regulatory pathways consisting of multiple genes. This heterogeneity complicates the identification of driver mutations by their recurrence across samples, as different combinations of mutations in driver pathways are observed in different samples. We introduce the Multi-Dendrix algorithm for the simultaneous identification of multiple driver pathways de novo in somatic mutation data from a cohort of cancer samples. The algorithm relies on two combinatorial properties of mutations in a driver pathway: high coverage and mutual exclusivity. We derive an integer linear program that finds set of mutations exhibiting these properties. We apply Multi-Dendrix to somatic mutations from glioblastoma, breast cancer, and lung cancer samples. Multi-Dendrix identifies sets of mutations in genes that overlap with known pathways – including Rb, p53, PI(3)K, and cell cycle pathways – and also novel sets of mutually exclusive mutations, including mutations in several transcription factors or other genes involved in transcriptional regulation. These sets are discovered directly from mutation data with no prior knowledge of pathways or gene interactions. We show that Multi-Dendrix outperforms other algorithms for identifying combinations of mutations and is also orders of magnitude faster on genome-scale data. Software available at: http://compbio.cs.brown.edu/software.

Cancer is a disease driven largely by the accumulation of somatic mutations during the lifetime of an individual. The declining costs of genome sequencing now permit the measurement of somatic mutations in hundreds of cancer genomes. A key challenge is to distinguish driver mutations responsible for cancer from random passenger mutations. This challenge is compounded by the observation that different combinations of driver mutations are observed in different patients with the same cancer type. One reason for this heterogeneity is that driver mutations target signaling and regulatory pathways which have multiple points of failure. We introduce an algorithm, Multi-Dendrix, to find these pathways solely from patterns of mutual exclusivity between mutations across a cohort of patients. Unlike earlier approaches, we simultaneously find multiple pathways, an essential feature for analyzing cancer genomes where multiple pathways are typically perturbed. We apply our algorithm to mutation data from hundreds of glioblastoma, breast cancer, and lung adenocarcinoma patients. We identify sets of interacting genes that overlap known pathways, and gene sets containing subtype-specific mutations. These results show that multiple cancer pathways can be identified directly from patterns in mutation data, and provide an approach to analyze the ever-growing cancer mutation datasets.

Current Challenges for HER2 Testing in Diagnostic Pathology: State of the Art and Controversial Issues

HER2 overexpression and anti-HER2 agents represent probably the best story of success of individualized therapy in breast cancer. Due to the important therapeutic implications, the issue under the spotlight has been, since ever, the correct identification of true HER2 positivity on tissue specimens. Eligibility to anti-HER2 agents is strictly dependent on the demonstration of HER2 overexpression (by immunohistochemistry) or of HER2 gene amplification by in situ techniques (fluorescence in situ hybridization, FISH), however there are controversial issues involving cases with "equivocal" HER2 status based on conventional techniques (about 20% of specimens). In terms of HER2 expression a major debate is the presence of full-length and truncated forms of the protein and controversial clinical data have been reported on the therapeutic implications of these HER2 fragments. In terms of HER2 gene assessment, the occurrence of amplification of the chromosome 17 centromeric region (CEP17) has been proven responsible for misleading HER2 FISH results, precluding anti-HER2 based therapy to some patients. Finally HER2 activating mutations have been recently described as a biological mechanisms alternative to HER2 gene amplification. In this review we will focus on the controversies that pathologists and oncologists routinely face in the attempt to design the most tailored treatment for breast cancer patients. We will focus on the HER2 gene and on the protein, both at technical and interpretational levels.

Genomic Medicine Frontier in Human Solid Tumors: Prospects and Challenges

Recent discoveries of genomic alterations that underlie and promote the malignant phenotype, together with an expanded repertoire of targeted agents, have provided many opportunities to conduct hypothesis-driven clinical trials. The ability to profile each unique cancer for actionable aberrations by using high-throughput technologies in a cost-effective way provides unprecedented opportunities for using matched therapies in a selected patient population. The major challenges are to integrate and make biologic sense of the substantial genomic data derived from multiple platforms. We define two different approaches for the analysis, interpretation, and clinical applicability of genomic data: (1) the genomically stratified model originates from the “one test-one drug” paradigm and is currently being expanded with an upfront multicategorical approach following recent advances in multiplexed genotyping platforms; and (2) the comprehensive assessment model is based on whole-genome, -exome, and -transcriptome data and allows identification of novel drivers and subsequent therapies in the experimental setting. Tumor heterogeneity and evolution of the diverse populations of cancer cells during cancer progression, influenced by the effects of systemic treatments, will need to be addressed in the new scenario of early drug development. Logistical issues related to prescreening strategies and trial allocation, in addition to concerns in the economic and ethical domains, must be taken into consideration. Here we present a historical view of how increased understanding of cancer genomics has been translated to the clinic and discuss the prospects and challenges for further implementation of a personalized treatment strategy for human solid tumors.

Functional genomics lead to new therapies in follicular lymphoma

Recent technological advances allow analysis of genomic changes in cancer in unprecedented detail. The next challenge is to prioritize the multitude of genetic aberrations found and identify therapeutic opportunities. We recently completed a study that illustrates the use of unbiased genetic screens and murine cancer models to find therapeutic targets among complex genomic data. We genetically dissected the common deletion of chromosome 6q and identified the ephrin receptor A7 (EPHA7) as a tumor suppressor in lymphoma. Notably, EPHA7 encodes a soluble splice variant that acts as an extrinsic tumor suppressor. Accordingly, we developed an antibody-based strategy to specifically deliver EPHA7 back to tumors that have lost this gene. Recent sequencing studies have implicated EPHA7 in lung cancer and other tumors, suggesting a broader therapeutic potential for antibody-mediated delivery of this tumor suppressor for cancer therapy. Together, our comprehensive approach provides new insights into cancer biology and may directly lead to the development of new cancer therapies.

Oncogenic ERBB3 mutations in human cancers

The human epidermal growth factor receptor (HER) family of tyrosine kinases is deregulated in multiple cancers either through amplification, overexpression, or mutation. ERBB3/HER3, the only member with an impaired kinase domain, although amplified or overexpressed in some cancers, has not been reported to carry oncogenic mutations. Here, we report the identification of ERBB3 somatic mutations in ~11% of colon and gastric cancers. We found that the ERBB3 mutants transformed colonic and breast epithelial cells in a ligand-independent manner. However, the mutant ERBB3 oncogenic activity was dependent on kinase-active ERBB2. Furthermore, we found that anti-ERBB antibodies and small molecule inhibitors effectively blocked mutant ERBB3-mediated oncogenic signaling and disease progression in vivo.

Improving breast cancer survival analysis through competition-based multidimensional modeling

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.

Proteomic and bioinformatic analysis of mammalian SWI/SNF complexes identifies extensive roles in human malignancy

Subunits of mammalian SWI/SNF (mSWI/SNF or BAF) complexes have recently been implicated as tumor suppressors in human malignancies. To understand the full extent of their involvement, we conducted a proteomic analysis of endogenous mSWI/SNF complexes, which identified several new dedicated, stable subunits not found in yeast SWI/SNF complexes, including BCL7A, BCL7B and BCL7C, BCL11A and BCL11B, BRD9 and SS18. Incorporating these new members, we determined mSWI/SNF subunit mutation frequency in exome and whole-genome sequencing studies of primary human tumors. Notably, mSWI/SNF subunits are mutated in 19.6% of all human tumors reported in 44 studies. Our analysis suggests that specific subunits protect against cancer in specific tissues. In addition, mutations affecting more than one subunit, defined here as compound heterozygosity, are prevalent in certain cancers. Our studies demonstrate that mSWI/SNF is the most frequently mutated chromatin-regulatory complex (CRC) in human cancer, exhibiting a broad mutation pattern, similar to that of TP53. Thus, proper functioning of polymorphic BAF complexes may constitute a major mechanism of tumor suppression.

Targeting the PI3K/AKT/mTOR and Raf/MEK/ERK pathways in the treatment of breast cancer

Alterations of signal transduction pathways leading to uncontrolled cellular proliferation, survival, invasion, and metastases are hallmarks of the carcinogenic process. The phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) and the Raf/mitogen-activated and extracellular signal-regulated kinase kinase (MEK)/extracellular signal-regulated kinase (ERK) signaling pathways are critical for normal human physiology, and also commonly dysregulated in several human cancers, including breast cancer (BC). In vitro and in vivo data suggest that the PI3K/AKT/mTOR and Raf/MEK/ERK cascades are interconnected with multiple points of convergence, cross-talk, and feedback loops. Raf/MEK/ERK and PI3K/AKT/mTOR pathway mutations may co-exist. Inhibition of one pathway can still result in the maintenance of signaling via the other (reciprocal) pathway. The existence of such "escape" mechanisms implies that dual targeting of these pathways may lead to superior efficacy and better clinical outcome in selected patients. Several clinical trials targeting one or both pathways are already underway in BC patients. The toxicity profile of this novel approach of dual pathway inhibition needs to be closely monitored, given the important physiological role of PI3K/AKT/mTOR and Raf/MEK/ERK signaling. In this article, we present a review of the current relevant pre-clinical and clinical data and discuss the rationale for dual inhibition of these pathways in the treatment of BC patients.

Immunotherapeutic approaches in triple-negative breast cancer: latest research and clinical prospects

Triple-negative breast cancer (TNBC), as defined by the absence of estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 expression, is a challenging disease with the poorest prognosis of all breast cancer subtypes. Importantly, there are currently no known molecular targets for this subgroup of patients. Recent advances in genomics and gene expression profiling have shed new light on the molecule heterogeneity of TNBC. We present an overview of the scientific evidence suggesting that clinical outcome in TNBC is affected by tumor-infiltrating immune cells. We also describe tumor-associated antigens recently identified in TNBC. Finally, we review the current literature on promising immunotherapies for TNBC, including tumor vaccine approaches, immune-checkpoint inhibitors, antagonists of immunosuppressive molecules and adoptive cell therapies. It is our contention that selected patients with TNBC with lymphocytic tumor infiltrates at diagnosis may benefit from immune-based therapies and that these immunotherapies will be most beneficial in combination with cytotoxic drugs that potentiate adaptive anti-tumor immunity.

Genomic medicine: new frontiers and new challenges

BACKGROUND

The practice of personalized medicine has made large strides since the introduction of high-throughput technologies and the vast improvements in computational biotechnology. The personalized-medicine approach to cancer management holds promise for earlier disease detection, accurate prediction of prognosis, and better treatment options; however, the early experience with personalized medicine has revealed important concerns that need to be addressed before research findings can be translated to the bedside.

CONTENT

We discuss several emerging "practical" or "focused" applications of personalized medicine. Molecular testing can have an important positive impact on health and disease management in a number of ways, and the list of specific applications is evolving. This list includes improvements in risk assessment, disease prevention, identification of new disease-related mutations, accurate disease classification based on molecular signatures, selection of patients for enrollment in clinical trials, and development of new targeted therapies, especially for metastatic tumors that are refractory to treatment. Several challenges remain to be addressed before genomics information can be applied successfully in the routine clinical management of cancers. Further improvements and investigations are needed in data interpretation, extraction of actionable items, cost-effectiveness, how to account for patient heterogeneity and ethnic variation, and how to handle the risk of "incidental findings" in genetic testing.

SUMMARY

It is now clear that personalized medicine will not immediately provide a permanent solution for patient management and that further refinement in the applications of personalized medicine will be needed to address and focus on specific issues.

Association between SPARC mRNA expression, prognosis and response to neoadjuvant chemotherapy in early breast cancer: a pooled in-silico analysis

Introduction

SPARC is an important regulator of the extracellular matrix and has been suggested to improve delivery of albumin-bound cytotoxics. However, little is known regarding its role in breast cancer (BC).

Methods

We conducted a pooled analysis of publically available datasets, in which BC patients who received no systemic therapy or received neoadjuvant chemotherapy were eligible. Patients were assigned to molecular subtypes using PAM-50. We computed a SPARC module (SPARC7), composed of genes with an absolute correlation with SPARC >0.7. In the systemically untreated cohort, we evaluated 1) expression of SPARC/SPARC7 according to breast cancer subtype, 2) association between SPARC/SPARC7 and biological processes related to proliferation, immune and stroma, and 3) association between SPARC/SPARC7 and relapse-free survival in a Cox model in all patients and in the different molecular subtypes adjusted for tumor size, nodal status, histological grade, and age. In the neoadjuvant cohort, we evaluated the association between SPARC and pCR in a logistic regression model, adjusted for the same clinicopathologic factors.

Results

948 (10 datasets), and 791 (8 datasets) patients were included in the systemically untreated and neoadjuvant cohorts, respectively. High SPARC expression was associated with small tumor size, low histological grade and luminal-A tumors (all p<0.0001). There was a positive correlation between SPARC and stroma-related modules but negative correlation with proliferation modules. High SPARC expression was associated with poor prognosis in patients with basal and HER2+ breast cancer even after adjusting for clinicopathologic parameters. In the neoadjuvant cohort, a subgroup analysis suggested that high SPARC is associated with low rates of pCR in the HER2 subtype. Same results were observed on replacing SPARC by SPARC7.

Conclusion

This analysis suggests a potential role of SPARC in determining prognosis and response to primary chemotherapy in early BC. This information could guide further development of albumin-bound cytotoxics in BC.

Activating mutations and senescence secretome: new insights into HER2 activation, drug sensitivity and metastatic progression

HER2 amplification and overexpression is observed in approximately 20% of breast cancers and is strongly associated with poor prognosis and therapeutic responsiveness to HER2 targeted agents. A recent study by Bose and colleagues suggests that another subset of breast cancer patients without HER2 amplification but with activating HER2 mutation might also benefit from existing HER2-targeted agents and the authors functionally characterize these somatic mutations in experimental models. In a second study on HER2-driven breast cancer, Angelini and colleagues investigate how the constitutively active, truncated carboxy-terminal fragment of HER2, p95HER2, promotes metastatic progression through non-cellautonomous secretion of factors from senescent cells. These new findings advance our understanding of HER2 biology in the context of HER2 activation as well as offer new insights into our understanding of drug sensitivity and metastatic progression.

Evolution and impact of subclonal mutations in chronic lymphocytic leukemia

Clonal evolution is a key feature of cancer progression and relapse. We studied intratumoral heterogeneity in 149 chronic lymphocytic leukemia (CLL) cases by integrating whole-exome sequence and copy number to measure the fraction of cancer cells harboring each somatic mutation. We identified driver mutations as predominantly clonal (e.g., MYD88, trisomy 12, and del(13q)) or subclonal (e.g., SF3B1 and TP53), corresponding to earlier and later events in CLL evolution. We sampled leukemia cells from 18 patients at two time points. Ten of twelve CLL cases treated with chemotherapy (but only one of six without treatment) underwent clonal evolution, predominantly involving subclones with driver mutations (e.g., SF3B1 and TP53) that expanded over time. Furthermore, presence of a subclonal driver mutation was an independent risk factor for rapid disease progression. Our study thus uncovers patterns of clonal evolution in CLL, providing insights into its stepwise transformation, and links the presence of subclones with adverse clinical outcomes.

Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer

Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models.

Genomics-driven oncology: framework for an emerging paradigm

A majority of cancers are driven by genomic alterations that dysregulate key oncogenic pathways influencing cell growth and survival. However, the ability to harness tumor genetic information for its full clinical potential has only recently become manifest. Over the past several years, the convergence of discovery, technology, and therapeutic development has created an unparalleled opportunity to test the hypothesis that systematic knowledge of genomic information from individual tumors can improve clinical outcomes for many patients with cancer. Rigorous evaluation of this genomics-driven cancer medicine hypothesis will require many logistic innovations that are guided by overarching conceptual advances in tumor genomic profiling, data interpretation, clinical trial design, and the ethical return of genetic results to oncologists and their patients. The results of these efforts and the rigor with which they are implemented will determine whether and how comprehensive tumor genomic information may become incorporated into the routine care of patients with cancer.

Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer

The optimal management of breast cancer (BC) presents challenges due to the heterogeneous molecular classification of the disease. We performed survival analysis on a cohort of 466 patients with primary invasive ductal carcinoma (IDC), the most frequent type of BC, by integrating mRNA, microRNA (miRNA), and DNA methylation next-generation sequencing data from The Cancer Genome Atlas (TCGA). Expression data from eight other BC cohorts were used for validation. The prognostic value of the resulting miRNA/mRNA signature was compared with that of other prognostic BC signatures. Thirty mRNAs and seven miRNAs were associated with overall survival across different clinical and molecular subclasses of a 466-patient IDC cohort from TCGA. The prognostic RNAs included PIK3CA, one of the two most frequently mutated genes in IDC, and miRNAs such as hsa-miR-328, hsa-miR-484, and hsa-miR-874. The area under the curve of the receiver-operator characteristic for the IDC risk predictor in the TCGA cohort was 0.74 at 60 mo of overall survival (P < 0.001). Most relevant for clinical application, the integrated signature had the highest prognostic value in early stage I and II tumors (receiver-operator characteristic area under the curve = 0.77, P value < 0.001). The genes in the RNA risk predictor had an independent prognostic value compared with the clinical covariates, as shown by multivariate analysis. The integrated RNA signature was successfully validated on eight BC cohorts, comprising a total of 2,399 patients, and it had superior performance for risk stratification with respect to other RNA predictors, including the mRNAs used in MammaPrint and Oncotype DX assays.

Cancer genome landscapes

Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of "mountains" (genes altered in a high percentage of tumors) and a much larger number of "hills" (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or "drive" tumorigenesis. A typical tumor contains two to eight of these "driver gene" mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.

Cancer genome landscapes

Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of "mountains" (genes altered in a high percentage of tumors) and a much larger number of "hills" (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or "drive" tumorigenesis. A typical tumor contains two to eight of these "driver gene" mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.

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.

Metasignatures identify two major subtypes of breast cancer

Genome-wide expression data from tumors and cell lines in breast cancer, together with drug response of cell lines, open prospects for integrative analyses that can lead to better personalized therapy. Drug responses and expression data collected from cell lines and tumors were used to generate tripartite networks connecting clusters of patients to cell lines and cell lines to drugs, to connect drugs to patients. Various approaches were applied to connect cell lines to tumor clusters: a standard method that uses a biomarker gene set, and new methods that compute metasignatures for transcription factors and histone modifications given upregulated genes in cell lines or tumors. The results from the metasignature analysis identify two major clusters of patients: one enriched for active histone marks and one for repressive marks. The tumors enriched for activation marks are correlated with poor prognosis. Overall, the analyses suggest new patient clustering, discover dysregulated pathways, and recommend individualized use of drugs to treat subsets of patients.

Real-time motion analysis reveals cell directionality as an indicator of breast cancer progression

Cancer cells alter their migratory properties during tumor progression to invade surrounding tissues and metastasize to distant sites. However, it remains unclear how migratory behaviors differ between tumor cells of different malignancy and whether these migratory behaviors can be utilized to assess the malignant potential of tumor cells. Here, we analyzed the migratory behaviors of cell lines representing different stages of breast cancer progression using conventional migration assays or time-lapse imaging and particle image velocimetry (PIV) to capture migration dynamics. We find that the number of migrating cells in transwell assays, and the distance and speed of migration in unconstrained 2D assays, show no correlation with malignant potential. However, the directionality of cell motion during 2D migration nicely distinguishes benign and tumorigenic cell lines, with tumorigenic cell lines harboring less directed, more random motion. Furthermore, the migratory behaviors of epithelial sheets observed under basal conditions and in response to stimulation with epidermal growth factor (EGF) or lysophosphatitic acid (LPA) are distinct for each cell line with regard to cell speed, directionality, and spatiotemporal motion patterns. Surprisingly, treatment with LPA promotes a more cohesive, directional sheet movement in lung colony forming MCF10CA1a cells compared to basal conditions or EGF stimulation, implying that the LPA signaling pathway may alter the invasive potential of MCF10CA1a cells. Together, our findings identify cell directionality as a promising indicator for assessing the tumorigenic potential of breast cancer cell lines and show that LPA induces more cohesive motility in a subset of metastatic breast cancer cells.

Heterogeneity of gene expression in murine squamous cell carcinoma development-the same tumor by different means

Transformation is a complex process, involving many changes in the cell. In this work, we investigated the transcriptional changes that arose during the development of squamous cell carcinoma (SCC) in mice. Using microarray analysis, we looked at gene expression during different stages in cancer progression in 31 mice. By analyzing tumor progression in each mouse separately, we were able to define the global changes that were common to all 31 mice, as well as significant changes that occurred in fewer individuals. We found that different genes can contribute to the tumorigenic process in different mice, and that there are many ways to acquire the malignant properties defined by Hanahan and Weinberg as "hallmarks of cancer". Eventually, however, all these changes lead to a very similar cancerous phenotype. The finding that gene expression is strongly heterogeneous in tumors that were induced by a standardized protocol in closely related mice underscores the need for molecular characterization of human tumors and personalized therapy.

Embryonic mammary signature subsets are activated in Brca1-/- and basal-like breast cancers

Introduction

Cancer is often suggested to result from development gone awry. Links between normal embryonic development and cancer biology have been postulated, but no defined genetic basis has been established. We recently published the first transcriptomic analysis of embryonic mammary cell populations. Embryonic mammary epithelial cells are an immature progenitor cell population, lacking differentiation markers, which is reflected in their very distinct genetic profiles when compared with those of their postnatal descendents.

Methods

We defined an embryonic mammary epithelial signature that incorporates the most highly expressed genes from embryonic mammary epithelium when compared with the postnatal mammary epithelial cells. We looked for activation of the embryonic mammary epithelial signature in mouse mammary tumors that formed in mice in which Brca1 had been conditionally deleted from the mammary epithelium and in human breast cancers to determine whether any genetic links exist between embryonic mammary cells and breast cancers.

Results

Small subsets of the embryonic mammary epithelial signature were consistently activated in mouse Brca1^-/- tumors and human basal-like breast cancers, which encoded predominantly transcriptional regulators, cell-cycle, and actin cytoskeleton components. Other embryonic gene subsets were found activated in non-basal-like tumor subtypes and repressed in basal-like tumors, including regulators of neuronal differentiation, transcription, and cell biosynthesis. Several embryonic genes showed significant upregulation in estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and/or grade 3 breast cancers. Among them, the transcription factor, SOX11, a progenitor cell and lineage regulator of nonmammary cell types, is found highly expressed in some Brca1^-/- mammary tumors. By using RNA interference to silence SOX11 expression in breast cancer cells, we found evidence that SOX11 regulates breast cancer cell proliferation and cell survival.

Conclusions

Specific subsets of embryonic mammary genes, rather than the entire embryonic development transcriptomic program, are activated in tumorigenesis. Genes involved in embryonic mammary development are consistently upregulated in some breast cancers and warrant further investigation, potentially in drug-discovery research endeavors.

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.

Interpretation, stratification and evidence for sequence variants affecting mRNA splicing in complete human genome sequences

Information theory-based methods have been shown to be sensitive and specific for predicting and quantifying the effects of non-coding mutations in Mendelian diseases. We present the Shannon pipeline software for genome-scale mutation analysis and provide evidence that the software predicts variants affecting mRNA splicing. Individual information contents (in bits) of reference and variant splice sites are compared and significant differences are annotated and prioritized. The software has been implemented for CLC-Bio Genomics platform. Annotation indicates the context of novel mutations as well as common and rare SNPs with splicing effects. Potential natural and cryptic mRNA splicing variants are identified, and null mutations are distinguished from leaky mutations. Mutations and rare SNPs were predicted in genomes of three cancer cell lines (U2OS, U251 and A431), which were supported by expression analyses. After filtering, tractable numbers of potentially deleterious variants are predicted by the software, suitable for further laboratory investigation. In these cell lines, novel functional variants comprised 6–17 inactivating mutations, 1–5 leaky mutations and 6–13 cryptic splicing mutations. Predicted effects were validated by RNA-seq analysis of the three aforementioned cancer cell lines, and expression microarray analysis of SNPs in HapMap cell lines.

8(th) Annual European Antibody Congress 2012: November 27-28, 2012, Geneva, Switzerland

The 8th European Antibody Congress (EAC), organized by Terrapin Ltd., was again held in Geneva, Switzerland, following on the tradition established with the 4th EAC. The new agenda format for 2012 included three parallel tracks on: (1) naked antibodies; (2) antibody drug conjugates (ADCs); and (3) bispecific antibodies and alternative scaffolds. The meeting started and closed with three plenary lectures to give common background and to share the final panel discussion and conclusions. The two day event included case studies and networking for nearly 250 delegates who learned of the latest advances and trends in the global development of antibody-based therapeutics.

The monoclonal antibody track was focused on understanding the structure-function relationships, optimization of antibody design and developability, and processes that allow better therapeutic candidates to move through the clinic. Discussions on novel target identification and validation were also included. The ADC track was dedicated to evaluation of the ongoing success of the established ADC formats alongside the rise of the next generation drug-conjugates. The bispecific and alternative scaffold track was focused on taking stock of the multitude of bispecific formats being investigated and gaining insight into recent innovations and advancements. Mechanistic understanding, progression into the clinic and the exploration of multispecifics, redirected T cell killing and alternative scaffolds were extensively discussed. In total, nearly 50 speakers provided updates of programs related to antibody research and development on-going in the academic, government and commercial sectors.

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.

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.

Mutation profiling of adenoid cystic carcinomas from multiple anatomical sites identifies mutations in the RAS pathway, but no KIT mutations

AIMS

The majority of adenoid cystic carcinomas (AdCCs), regardless of anatomical site, harbour the MYB-NFIB fusion gene. The aim of this study was to characterize the repertoire of somatic genetic events affecting known cancer genes in AdCCs.

METHODS AND RESULTS

DNA was extracted from 13 microdissected breast AdCCs, and subjected to a mutation survey using the Sequenom OncoCarta Panel v1.0. Genes found to be mutated in any of the breast AdCCs and genes related to the same canonical molecular pathways, as well as KIT, a proto-oncogene whose protein product is expressed in AdCCs, were sequenced in an additional 68 AdCCs from various anatomical sites by Sanger sequencing. Using the Sequenom MassARRAY platform and Sanger sequencing, mutations in BRAF and HRAS were identified in three and one cases, respectively (breast, and head and neck). KIT, which has previously been reported to be mutated in AdCCs, was also investigated, but no mutations were identified.

CONCLUSIONS

Our results demonstrate that mutations in genes pertaining to the canonical RAS pathway are found in a minority of AdCCs, and that activating KIT mutations are either absent or remarkably rare in these cancers, and unlikely to constitute a driver and therapeutic target for patients with AdCC.

GATA3 mutations found in breast cancers may be associated with aberrant nuclear localization, reduced transactivation and cell invasiveness

Somatic and germline mutations in the dual zinc-finger transcription factor GATA3 are associated with breast cancers expressing the estrogen receptor (ER) and the autosomal dominant hypoparathyroidism-deafness-renal dysplasia syndrome, respectively. To elucidate the role of GATA3 in breast tumorigenesis, we investigated 40 breast cancers that expressed ER, for GATA3 mutations. Six different heterozygous GATA3 somatic mutations were identified in eight tumors, and these consisted of: a frameshifting deletion/insertion (944_945delGGinsAGC), an in-frame deletion of a key arginine residue (991_993delAGG), a seven-nucleotide frameshifting insertion (991_992insTGGAGGA), a frameshifting deletion (1196_1197delGA), and two frameshifting single nucleotide insertions (1224_1225insG found in three tumors and 1224_1225insA). Five of the eight mutations occurred in tumors that retained GATA3 immunostaining, indicating that absence of GATA3 immunostaining is an unreliable predictor of the presence of GATA3 mutations. Luciferase reporter assays, electrophoretic mobility shift assays, immunofluorescence, invasion and proliferation assays demonstrated that the GATA3 mutations resulted in loss (or reduction) of DNA binding, decrease in transactivational activity, and alterations in invasiveness but not proliferation. The 991_992insTGGAGGA (Arg330 frameshift) mutation led to a loss of nuclear localization, yet the 991_993delAGG (Arg330deletion) retained nuclear localization. Investigation of the putative nuclear localization signal (NLS) sites showed that the NLS of GATA3 does not conform to either a classical mono- or bi-partite signal, but contains multiple cooperative NLS elements residing around the N-terminal zinc-finger which comprises residues 264-288. Thus, approximately 20 % ER-positive breast cancers have somatic GATA3 mutations that lead to a loss of GATA3 transactivation activity and altered cell invasiveness.

APOBEC3B is an enzymatic source of mutation in breast cancer

Several mutations are required for cancer development, and genome sequencing has revealed that many cancers, including breast cancer, have somatic mutation spectra dominated by C-to-T transitions. Most of these mutations occur at hydrolytically disfavoured non-methylated cytosines throughout the genome, and are sometimes clustered. Here we show that the DNA cytosine deaminase APOBEC3B is a probable source of these mutations. APOBEC3B messenger RNA is upregulated in most primary breast tumours and breast cancer cell lines. Tumours that express high levels of APOBEC3B have twice as many mutations as those that express low levels and are more likely to have mutations in TP53. Endogenous APOBEC3B protein is predominantly nuclear and the only detectable source of DNA C-to-U editing activity in breast cancer cell-line extracts. Knockdown experiments show that endogenous APOBEC3B correlates with increased levels of genomic uracil, increased mutation frequencies, and C-to-T transitions. Furthermore, induced APOBEC3B overexpression causes cell cycle deviations, cell death, DNA fragmentation, γ-H2AX accumulation and C-to-T mutations. Our data suggest a model in which APOBEC3B-catalysed deamination provides a chronic source of DNA damage in breast cancers that could select TP53 inactivation and explain how some tumours evolve rapidly and manifest heterogeneity.

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.

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.

Subcellular localization of the APOBEC3 proteins during mitosis and implications for genomic DNA deamination

Humans have seven APOBEC3 DNA cytosine deaminases. The activity of these enzymes allows them to restrict a variety of retroviruses and retrotransposons, but may also cause pro-mutagenic genomic uracil lesions. During interphase the APOBEC3 proteins have different subcellular localizations: cell-wide, cytoplasmic or nuclear. This implies that only a subset of APOBEC3s have contact with nuclear DNA. However, during mitosis, the nuclear envelope breaks down and cytoplasmic proteins may enter what was formerly a privileged zone. To address the hypothesis that all APOBEC3 proteins have access to genomic DNA, we analyzed the localization of the APOBEC3 proteins during mitosis. We show that APOBEC3A, APOBEC3C and APOBEC3H are excluded from condensed chromosomes, but become cell-wide during telophase. However, APOBEC3B, APOBEC3D, APOBEC3F and APOBEC3G are excluded from chromatin throughout mitosis. After mitosis, APOBEC3B becomes nuclear, and APOBEC3D, APOBEC3F and APOBEC3G become cytoplasmic. Both structural motifs as well as size may be factors in regulating chromatin exclusion. Deaminase activity was not dependent on cell cycle phase. We also analyzed APOBEC3-induced cell cycle perturbations as a measure of each enzyme's capacity to inflict genomic DNA damage. AID, APOBEC3A and APOBEC3B altered the cell cycle profile, and, unexpectedly, APOBEC3D also caused changes. We conclude that several APOBEC3 family members have access to the nuclear compartment and can impede the cell cycle, most likely through DNA deamination and the ensuing DNA damage response. Such genomic damage may contribute to carcinogenesis, as demonstrated by AID in B cell cancers and, recently, APOBEC3B in breast cancers.

Mutational analysis of splicing machinery genes SF3B1, U2AF1 and SRSF2 in myelodysplasia and other common tumors

Recurrent somatic mutations in splicing machinery components, including SF3B1, U2AF1 and SRSF2 genes have recently been reported in myelodysplastic syndromes (MDS). Such a recurrent nature strongly suggests that these mutations play important roles in tumor development. To see whether SF3B1, U2AF1 and SRSF2 mutations occur in other human tumors besides MDS, we analyzed the hotspot mutation regions of these genes in 2,345 tumor tissues from various origins (61 MDS, other 616 hematologic tumors, 1,421 epithelial tumors and 247 non-epithelial stromal tumors) by single-strand conformation polymorphism analysis. We found SF3B1, U2AF1 and SRSF2 mutations in 5 (8.2%), 12 (19.7%) and 8 (13.1%) of 61 MDS, respectively. We also confirmed these mutations in other myeloid neoplasia, including de novo acute myelogenous leukemia (AML), chronic myelomonocytic leukemia and MDS/myeloproliferative disorder. In addition, we discovered that the SRSF2 gene was mutated in two childhood acute lymphoblastic leukemias (childhood ALL) (1.5%). In solid tumors, we found SF3B1 mutations in gastric and prostate cancers, and U2AF1 mutation in a borderline mucinous tumor of ovary, but the overall incidences of the hotspot mutation regions were very low (0.2%). Our data suggest that SF3B1, U2AF1 and SRSF2 mutations occur not only in myeloid lineage tumors but also in lymphoid lineage tumors. The data suggest that the splicing gene mutations play important roles in the pathogenesis of hematologic tumors, but rarely in solid tumors.

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.

Recruitment of NCOR1 to VDR target genes is enhanced in prostate cancer cells and associates with altered DNA methylation patterns

The current study investigated transcriptional distortion in prostate cancer cells using the vitamin D receptor (VDR) as a tool to examine how epigenetic events driven by corepressor binding and CpG methylation lead to aberrant gene expression. These relationships were investigated in the non-malignant RWPE-1 cells that were 1α,25(OH)(2)D(3) responsive (RWPE-1) and malignant cell lines that were 1α,25(OH)(2)D(3) partially responsive (RWPE-2) and resistant (PC-3). These studies revealed that selective attenuation and repression of VDR transcriptional responses in the cancer cell lines reflected their loss of antiproliferative sensitivity. This was evident in VDR target genes including VDR, CDKN1A (encodes p21( (waf1/cip1) )) and GADD45A; NCOR1 knockdown alleviated this malignant transrepression. ChIP assays in RWPE-1 and PC-3 cells revealed that transrepression of CDKN1A was associated with increased NCOR1 enrichment in response to 1α,25(OH)(2)D(3) treatment. These findings supported the concept that retained and increased NCOR1 binding, associated with loss of H3K9ac and increased H3K9me2, may act as a beacon for the initiation and recruitment of DNA methylation. Overexpressed histone methyltransferases (KMTs) were detectable in a wide panel of prostate cancer cell lines compared with RWPE-1 and suggested that generation of H3K9me2 states would be favored. Cotreatment of cells with the KMT inhibitor, chaetocin, increased 1α,25(OH)(2)D(3)-mediated induction of CDKN1A expression supporting a role for this event to disrupt CDKN1A regulation. Parallel surveys in PC-3 cells of CpG methylation around the VDR binding regions on CDKN1A revealed altered basal and VDR-regulated DNA methylation patterns that overlapped with VDR-induced recruitment of NCOR1 and gene transrepression. Taken together, these findings suggest that sustained corepressor interactions with nuclear-resident transcription factors may inappropriately transform transient-repressive histone states into more stable and repressive DNA methylation events.