Genomic landscape of ductal carcinoma in situ and association with progression.

PURPOSE: The detection rate of breast ductal carcinoma in situ (DCIS) has increased significantly, raising the concern that DCIS is overdiagnosed and overtreated. Therefore, there is an unmet clinical need to better predict the risk of progression among DCIS patients. Our hypothesis is that by combining molecular signatures with clinicopathologic features, we can elucidate the biology of breast cancer progression, and risk-stratify patients with DCIS. METHODS: Targeted exon sequencing with a custom panel of 223 genes/regions was performed for 125 DCIS cases. Among them, 60 were from cases having concurrent or subsequent invasive breast cancer (IBC) (DCIS + IBC group), and 65 from cases with no IBC development over a median follow-up of 13 years (DCIS-only group). Copy number alterations in chromosome 1q32, 8q24, and 11q13 were analyzed using fluorescence in situ hybridization (FISH). Multivariable logistic regression models were fit to the outcome of DCIS progression to IBC as functions of demographic and clinical features. RESULTS: We observed recurrent variants of known IBC-related mutations, and the most commonly mutated genes in DCIS were PIK3CA (34.4%) and TP53 (18.4%). There was an inverse association between PIK3CA kinase domain mutations and progression (Odds Ratio [OR] 10.2, p < 0.05). Copy number variations in 1q32 and 8q24 were associated with progression (OR 9.3 and 46, respectively; both p < 0.05). CONCLUSIONS: PIK3CA kinase domain mutations and the absence of copy number gains in DCIS are protective against progression to IBC. These results may guide efforts to distinguish low-risk from high-risk DCIS.

Differentiation-state plasticity is a targetable resistance mechanism in basal-like breast cancer.

Intratumoral heterogeneity in cancers arises from genomic instability and epigenomic plasticity and is associated with resistance to cytotoxic and targeted therapies. We show here that cell-state heterogeneity, defined by differentiation-state marker expression, is high in triple-negative and basal-like breast cancer subtypes, and that drug tolerant persister (DTP) cell populations with altered marker expression emerge during treatment with a wide range of pathway-targeted therapeutic compounds. We show that MEK and PI3K/mTOR inhibitor-driven DTP states arise through distinct cell-state transitions rather than by Darwinian selection of preexisting subpopulations, and that these transitions involve dynamic remodeling of open chromatin architecture. Increased activity of many chromatin modifier enzymes, including BRD4, is observed in DTP cells. Co-treatment with the PI3K/mTOR inhibitor BEZ235 and the BET inhibitor JQ1 prevents changes to the open chromatin architecture, inhibits the acquisition of a DTP state, and results in robust cell death in vitro and xenograft regression in vivo.

Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics.

Traditional means for scoring the effects of anti-cancer drugs on the growth and survival of cell lines is based on relative cell number in drug-treated and control samples and is seriously confounded by unequal division rates arising from natural biological variation and differences in culture conditions. This problem can be overcome by computing drug sensitivity on a per-division basis. The normalized growth rate inhibition (GR) approach yields per-division metrics for drug potency (GR50) and efficacy (GRmax) that are analogous to the more familiar IC50 and Emax values. In this work, we report GR-based, proliferation-corrected, drug sensitivity metrics for ~4,700 pairs of breast cancer cell lines and perturbagens. Such data are broadly useful in understanding the molecular basis of therapeutic response and resistance. Here, we use them to investigate the relationship between different measures of drug sensitivity and conclude that drug potency and efficacy exhibit high variation that is only weakly correlated. To facilitate further use of these data, computed GR curves and metrics can be browsed interactively at http://www.GRbrowser.org/.

Correction: Decoupling of the PI3K Pathway via Mutation Necessitates Combinatorial Treatment in HER2+ Breast Cancer.

[This corrects the article DOI: 10.1371/journal.pone.0133219.].

Integrative molecular network analysis identifies emergent enzalutamide resistance mechanisms in prostate cancer.

Recent work demonstrates that castration-resistant prostate cancer (CRPC) tumors harbor countless genomic aberrations that control many hallmarks of cancer. While some specific mutations in CRPC may be actionable, many others are not. We hypothesized that genomic aberrations in cancer may operate in concert to promote drug resistance and tumor progression, and that organization of these genomic aberrations into therapeutically targetable pathways may improve our ability to treat CRPC. To identify the molecular underpinnings of enzalutamide-resistant CRPC, we performed transcriptional and copy number profiling studies using paired enzalutamide-sensitive and resistant LNCaP prostate cancer cell lines. Gene networks associated with enzalutamide resistance were revealed by performing an integrative genomic analysis with the PAthway Representation and Analysis by Direct Reference on Graphical Models (PARADIGM) tool. Amongst the pathways enriched in the enzalutamide-resistant cells were those associated with MEK, EGFR, RAS, and NFKB. Functional validation studies of 64 genes identified 10 candidate genes whose suppression led to greater effects on cell viability in enzalutamide-resistant cells as compared to sensitive parental cells. Examination of a patient cohort demonstrated that several of our functionally-validated gene hits are deregulated in metastatic CRPC tumor samples, suggesting that they may be clinically relevant therapeutic targets for patients with enzalutamide-resistant CRPC. Altogether, our approach demonstrates the potential of integrative genomic analyses to clarify determinants of drug resistance and rational co-targeting strategies to overcome resistance.

Genome co-amplification upregulates a mitotic gene network activity that predicts outcome and response to mitotic protein inhibitors in breast cancer.

BACKGROUND: High mitotic activity is associated with the genesis and progression of many cancers. Small molecule inhibitors of mitotic apparatus proteins are now being developed and evaluated clinically as anticancer agents. With clinical trials of several of these experimental compounds underway, it is important to understand the molecular mechanisms that determine high mitotic activity, identify tumor subtypes that carry molecular aberrations that confer high mitotic activity, and to develop molecular markers that distinguish which tumors will be most responsive to mitotic apparatus inhibitors. METHODS: We identified a coordinately regulated mitotic apparatus network by analyzing gene expression profiles for 53 malignant and non-malignant human breast cancer cell lines and two separate primary breast tumor datasets. We defined the mitotic network activity index (MNAI) as the sum of the transcriptional levels of the 54 coordinately regulated mitotic apparatus genes. The effect of those genes on cell growth was evaluated by small interfering RNA (siRNA). RESULTS: High MNAI was enriched in basal-like breast tumors and was associated with reduced survival duration and preferential sensitivity to inhibitors of the mitotic apparatus proteins, polo-like kinase, centromere associated protein E and aurora kinase designated GSK462364, GSK923295 and GSK1070916, respectively. Co-amplification of regions of chromosomes 8q24, 10p15-p12, 12p13, and 17q24-q25 was associated with the transcriptional upregulation of this network of 54 mitotic apparatus genes, and we identify transcription factors that localize to these regions and putatively regulate mitotic activity. Knockdown of the mitotic network by siRNA identified 22 genes that might be considered as additional therapeutic targets for this clinically relevant patient subgroup. CONCLUSIONS: We define a molecular signature which may guide therapeutic approaches for tumors with high mitotic network activity.

Cellular androgen content influences enzalutamide agonism of F877L mutant androgen receptor.

Prostate cancer is the most commonly diagnosed and second-most lethal cancer among men in the United States. The vast majority of prostate cancer deaths are due to castration-resistant prostate cancer (CRPC) - the lethal form of the disease that has progressed despite therapies that interfere with activation of androgen receptor (AR) signaling. One emergent resistance mechanism to medical castration is synthesis of intratumoral androgens that activate the AR. This insight led to the development of the AR antagonist enzalutamide. However, resistance to enzalutamide invariably develops, and disease progression is nearly universal. One mechanism of resistance to enzalutamide is an F877L mutation in the AR ligand-binding domain that can convert enzalutamide to an agonist of AR activity. However, mechanisms that contribute to the agonist switch had not been fully clarified, and there were no therapies to block AR F877L. Using cell line models of castration-resistant prostate cancer (CRPC), we determined that cellular androgen content influences enzalutamide agonism of mutant F877L AR. Further, enzalutamide treatment of AR F877L-expressing cell lines recapitulated the effects of androgen activation of F877L AR or wild-type AR. Because the BET bromodomain inhibitor JQ-1 was previously shown to block androgen activation of wild-type AR, we tested JQ-1 in AR F877L-expressing CRPC models. We determined that JQ-1 suppressed androgen or enzalutamide activation of mutant F877L AR and suppressed growth of mutant F877L AR CRPC tumors in vivo, demonstrating a new strategy to treat tumors harboring this mutation.

Patient-specific factors influence somatic variation patterns in von Hippel-Lindau disease renal tumours.

Cancer development is presumed to be an evolutionary process that is influenced by genetic background and environment. In laboratory animals, genetics and environment are variables that can largely be held constant. In humans, it is possible to compare independent tumours that have developed in the same patient, effectively constraining genetic and environmental variation and leaving only stochastic processes. Patients affected with von Hippel-Lindau disease are at risk of developing multiple independent clear cell renal carcinomas. Here we perform whole-genome sequencing on 40 tumours from six von Hippel-Lindau patients. We confirm that the tumours are clonally independent, having distinct somatic single-nucleotide variants. Although tumours from the same patient show many differences, within-patient patterns are discernible. Single-nucleotide substitution type rates are significantly different between patients and show biases in trinucleotide mutation context. We also observe biases in chromosome copy number aberrations. These results show that genetic background and/or environment can influence the types of mutations that occur.

ARTIK-52 induces replication-dependent DNA damage and p53 activation exclusively in cells of prostate and breast cancer origin.

The realization, that the androgen receptor (AR) is essential for prostate cancer (PC) even after relapse following androgen deprivation therapy motivated the search for novel types of AR inhibitors. We proposed that targeting AR expression versus its function would work in cells having either wild type or mutant AR as well as be independent of androgen synthesis pathways. Previously, using a phenotypic screen in androgen-independent PC cells we identified a small molecule inhibitor of AR, ARTIK-52. Treatment with ARTIK-52 caused the loss of AR protein and death of AR-positive, but not AR-negative, PC cells. Here we present data that ARTIK-52 induces degradation of AR mRNA through a mechanism that we were unable to establish. However, we found that ARTIK-52 is toxic to breast cancer (BC) cells expressing AR, although they were not sensitive to AR knockdown, suggesting an AR-independent mechanism of toxicity. Using different approaches we detected that ARTIK-52 induces replication-dependent double strand DNA breaks exclusively in cancer cells of prostate and breast origin, while not causing DNA damage, or any toxicity, in normal cells, as well as in non-PC and non-BC tumor cells, independent of their proliferation status. This amazing specificity, combined with such a basic mechanism of toxicity, makes ARTIK-52 a potentially useful tool to discover novel attractive targets for the treatment of BC and PC. Thus, phenotypic screening allowed us to identify a compound, whose properties cannot be predicted based on existing knowledge and moreover, uncover a barely known link between AR and DNA damage response in PC and BC epithelial cells.

Exome sequencing of desmoplastic melanoma identifies recurrent NFKBIE promoter mutations and diverse activating mutations in the MAPK pathway.

Desmoplastic melanoma is an uncommon variant of melanoma with sarcomatous histology, distinct clinical behavior and unknown pathogenesis. We performed low-coverage genome and high-coverage exome sequencing of 20 desmoplastic melanomas, followed by targeted sequencing of 293 genes in a validation cohort of 42 cases. A high mutation burden (median of 62 mutations/Mb) ranked desmoplastic melanoma among the most highly mutated cancers. Mutation patterns strongly implicate ultraviolet radiation as the dominant mutagen, indicating a superficially located cell of origin. Newly identified alterations included recurrent promoter mutations of NFKBIE, encoding NF-κB inhibitor ɛ (IκBɛ), in 14.5% of samples. Common oncogenic mutations in melanomas, in particular in BRAF (encoding p.Val600Glu) and NRAS (encoding p.Gln61Lys or p.Gln61Arg), were absent. Instead, other genetic alterations known to activate the MAPK and PI3K signaling cascades were identified in 73% of samples, affecting NF1, CBL, ERBB2, MAP2K1, MAP3K1, BRAF, EGFR, PTPN11, MET, RAC1, SOS2, NRAS and PIK3CA, some of which are candidates for targeted therapies.

Exome Sequencing of Cell-Free DNA from Metastatic Cancer Patients Identifies Clinically Actionable Mutations Distinct from Primary Disease.

The identification of the molecular drivers of cancer by sequencing is the backbone of precision medicine and the basis of personalized therapy; however, biopsies of primary tumors provide only a snapshot of the evolution of the disease and may miss potential therapeutic targets, especially in the metastatic setting. A liquid biopsy, in the form of cell-free DNA (cfDNA) sequencing, has the potential to capture the inter- and intra-tumoral heterogeneity present in metastatic disease, and, through serial blood draws, track the evolution of the tumor genome. In order to determine the clinical utility of cfDNA sequencing we performed whole-exome sequencing on cfDNA and tumor DNA from two patients with metastatic disease; only minor modifications to our sequencing and analysis pipelines were required for sequencing and mutation calling of cfDNA. The first patient had metastatic sarcoma and 47 of 48 mutations present in the primary tumor were also found in the cell-free DNA. The second patient had metastatic breast cancer and sequencing identified an ESR1 mutation in the cfDNA and metastatic site, but not in the primary tumor. This likely explains tumor progression on Anastrozole. Significant heterogeneity between the primary and metastatic tumors, with cfDNA reflecting the metastases, suggested separation from the primary lesion early in tumor evolution. This is best illustrated by an activating PIK3CA mutation (H1047R) which was clonal in the primary tumor, but completely absent from either the metastasis or cfDNA. Here we show that cfDNA sequencing supplies clinically actionable information with minimal risks compared to metastatic biopsies. This study demonstrates the utility of whole-exome sequencing of cell-free DNA from patients with metastatic disease. cfDNA sequencing identified an ESR1 mutation, potentially explaining a patient's resistance to aromatase inhibition, and gave insight into how metastatic lesions differ from the primary tumor.

Phylogenetic analyses of melanoma reveal complex patterns of metastatic dissemination.

Melanoma is difficult to treat once it becomes metastatic. However, the precise ancestral relationship between primary tumors and their metastases is not well understood. We performed whole-exome sequencing of primary melanomas and multiple matched metastases from eight patients to elucidate their phylogenetic relationships. In six of eight patients, we found that genetically distinct cell populations in the primary tumor metastasized in parallel to different anatomic sites, rather than sequentially from one site to the next. In five of these six patients, the metastasizing cells had themselves arisen from a common parental subpopulation in the primary, indicating that the ability to establish metastases is a late-evolving trait. Interestingly, we discovered that individual metastases were sometimes founded by multiple cell populations of the primary that were genetically distinct. Such establishment of metastases by multiple tumor subpopulations could help explain why identical resistance variants are identified in different sites after initial response to systemic therapy. One primary tumor harbored two subclones with different oncogenic mutations in CTNNB1, which were both propagated to the same metastasis, raising the possibility that activation of wingless-type mouse mammary tumor virus integration site (WNT) signaling may be involved, as has been suggested by experimental models.

Decoupling of the PI3K Pathway via Mutation Necessitates Combinatorial Treatment in HER2+ Breast Cancer.

We report here on experimental and theoretical efforts to determine how best to combine drugs that inhibit HER2 and AKT in HER2(+) breast cancers. We accomplished this by measuring cellular and molecular responses to lapatinib and the AKT inhibitors (AKTi) GSK690693 and GSK2141795 in a panel of 22 HER2(+) breast cancer cell lines carrying wild type or mutant PIK3CA. We observed that combinations of lapatinib plus AKTi were synergistic in HER2(+)/PIK3CA(mut) cell lines but not in HER2(+)/PIK3CA(wt) cell lines. We measured changes in phospho-protein levels in 15 cell lines after treatment with lapatinib, AKTi or lapatinib + AKTi to shed light on the underlying signaling dynamics. This revealed that p-S6RP levels were less well attenuated by lapatinib in HER2(+)/PIK3CA(mut) cells compared to HER2(+)/PIK3CAwt cells and that lapatinib + AKTi reduced p-S6RP levels to those achieved in HER2(+)/PIK3CA(wt) cells with lapatinib alone. We also found that that compensatory up-regulation of p-HER3 and p-HER2 is blunted in PIK3CA(mut) cells following lapatinib + AKTi treatment. Responses of HER2(+) SKBR3 cells transfected with lentiviruses carrying control or PIK3CA(mut )sequences were similar to those observed in HER2(+)/PIK3CA(mut) cell lines but not in HER2(+)/PIK3CA(wt) cell lines. We used a nonlinear ordinary differential equation model to support the idea that PIK3CA mutations act as downstream activators of AKT that blunt lapatinib inhibition of downstream AKT signaling and that the effects of PIK3CA mutations can be countered by combining lapatinib with an AKTi. This combination does not confer substantial benefit beyond lapatinib in HER2+/PIK3CA(wt) cells.

Upregulation of ER Signaling as an Adaptive Mechanism of Cell Survival in HER2-Positive Breast Tumors Treated with Anti-HER2 Therapy.

PURPOSE: To investigate the direct effect and therapeutic consequences of epidermal growth factor receptor 2 (HER2)-targeting therapy on expression of estrogen receptor (ER) and Bcl2 in preclinical models and clinical tumor samples. EXPERIMENTAL DESIGN: Archived xenograft tumors from two preclinical models (UACC812 and MCF7/HER2-18) treated with ER and HER2-targeting therapies and also HER2+ clinical breast cancer specimens collected in a lapatinib neoadjuvant trial (baseline and week 2 posttreatment) were used. Expression levels of ER and Bcl2 were evaluated by immunohistochemistry and Western blot analysis. The effects of Bcl2 and ER inhibition, by ABT-737 and fulvestrant, respectively, were tested in parental versus lapatinib-resistant UACC812 cells in vitro. RESULTS: Expression of ER and Bcl2 was significantly increased in xenograft tumors with acquired resistance to anti-HER2 therapy compared with untreated tumors in both preclinical models (UACC812: ER P = 0.0014; Bcl2 P < 0.001 and MCF7/HER2-18: ER P = 0.0007; Bcl2 P = 0.0306). In the neoadjuvant clinical study, lapatinib treatment for 2 weeks was associated with parallel upregulation of ER and Bcl2 (Spearman coefficient: 0.70; P = 0.0002). Importantly, 18% of tumors originally ER-negative (ER(-)) converted to ER(+) upon anti-HER2 therapy. In ER(-)/HER2(+) MCF7/HER2-18 xenografts, ER reexpression was primarily observed in tumors responding to potent combination of anti-HER2 drugs. Estrogen deprivation added to this anti-HER2 regimen significantly delayed tumor progression (P = 0.018). In the UACC812 cells, fulvestrant, but not ABT-737, was able to completely inhibit anti-HER2-resistant growth (P < 0.0001). CONCLUSIONS: HER2 inhibition can enhance or restore ER expression with parallel Bcl2 upregulation, representing an ER-dependent survival mechanism potentially leading to anti-HER2 resistance.

Erratum to: Modeling precision treatment of breast cancer.

During the type-setting of the final version of the article [1] some of the additional files were swapped. The correct files are republished in this Erratum.

Transcription restores DNA repair to heterochromatin, determining regional mutation rates in cancer genomes.

Somatic mutations in cancer are more frequent in heterochromatic and late-replicating regions of the genome. We report that regional disparities in mutation density are virtually abolished within transcriptionally silent genomic regions of cutaneous squamous cell carcinomas (cSCCs) arising in an XPC(-/-) background. XPC(-/-) cells lack global genome nucleotide excision repair (GG-NER), thus establishing differential access of DNA repair machinery within chromatin-rich regions of the genome as the primary cause for the regional disparity. Strikingly, we find that increasing levels of transcription reduce mutation prevalence on both strands of gene bodies embedded within H3K9me3-dense regions, and only to those levels observed in H3K9me3-sparse regions, also in an XPC-dependent manner. Therefore, transcription appears to reduce mutation prevalence specifically by relieving the constraints imposed by chromatin structure on DNA repair. We model this relationship among transcription, chromatin state, and DNA repair, revealing a new, personalized determinant of cancer risk.

A community effort to assess and improve drug sensitivity prediction algorithms.

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

Modeling precision treatment of breast cancer.

BACKGROUND: First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. RESULTS: We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples. CONCLUSIONS: These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.

Xenografts faithfully recapitulate breast cancer-specific gene expression patterns of parent primary breast tumors.

Though xenografts are used extensively for drug development in breast cancer, how well xenografts reflect the breadth of primary breast tumor subtypes has not been well characterized. Moreover, few studies have compared the gene expression of xenograft tumors to the primary tumors from which they were derived. Here we investigate whether the ability of human breast tumors (n = 20) to create xenografts in immune-deficient mice is associated with breast cancer immunohistochemical (IHC) and intrinsic subtype. We also characterize how precisely the gene expression of xenografts reprises that of parent breast tumors, using hierarchical clustering and other correlation-based techniques applied to Agilent 44K gene expression data from 16 samples including four matched primary tumor-xenograft pairs. Of the breast tumors studied, 25 % (5/20) generated xenografts. Receptor and intrinsic subtype were significant predictors of xenograft success, with all (4/4) triple-negative (TN) tumors and no (0/12) HR+Her2- tumors forming xenografts (P = 0.0005). Tumor cell expression of ALDH1, a stem cell marker, trended toward successful engraftment (P = 0.14), though CDK5/6, a basal marker, did not. Though hierarchical clustering across the 500 most variable genes segregated human breast tumors from xenograft tumors, when clustering was performed over the PAM50 gene set the primary tumor-xenograft pairs clustered together, with all IHC subtypes clustered in distinct groups. Greater similarity between primary tumor-xenograft pairs relative to random pairings was confirmed by calculation of the within-pair between-pair scatter ratio (WPBPSR) distribution (P = 0.0269), though there was a shift in the xenografts toward more aggressive features including higher proliferation scores relative to the primary. Triple-negative breast tumors demonstrate superior ability to create xenografts compared to HR+ tumors, which may reflect higher proliferation or relatively stroma-independent growth of this subtype. Xenograft tumors' gene expression faithfully resembles that of their parent tumors, yet also demonstrates a shift toward more aggressive molecular features.