Germline mutations in BRCA1/2 predispose individuals to breast cancer (termed germline-mutated BRCA1/2 breast cancer, gBRCA-BC) by impairing homologous recombination (HR) and causing genomic ...instability. HR also repairs DNA lesions caused by platinum agents and PARP inhibitors. Triple-negative breast cancers (TNBCs) harbor subpopulations with BRCA1/2 mutations, hypothesized to be especially platinum-sensitive. Cancers in putative 'BRCAness' subgroups-tumors with BRCA1 methylation; low levels of BRCA1 mRNA (BRCA1 mRNA-low); or mutational signatures for HR deficiency and those with basal phenotypes-may also be sensitive to platinum. We assessed the efficacy of carboplatin and another mechanistically distinct therapy, docetaxel, in a phase 3 trial in subjects with unselected advanced TNBC. A prespecified protocol enabled biomarker-treatment interaction analyses in gBRCA-BC and BRCAness subgroups. The primary endpoint was objective response rate (ORR). In the unselected population (376 subjects; 188 carboplatin, 188 docetaxel), carboplatin was not more active than docetaxel (ORR, 31.4% versus 34.0%, respectively; P = 0.66). In contrast, in subjects with gBRCA-BC, carboplatin had double the ORR of docetaxel (68% versus 33%, respectively; biomarker, treatment interaction P = 0.01). Such benefit was not observed for subjects with BRCA1 methylation, BRCA1 mRNA-low tumors or a high score in a Myriad HRD assay. Significant interaction between treatment and the basal-like subtype was driven by high docetaxel response in the nonbasal subgroup. We conclude that patients with advanced TNBC benefit from characterization of BRCA1/2 mutations, but not BRCA1 methylation or Myriad HRD analyses, to inform choices on platinum-based chemotherapy. Additionally, gene expression analysis of basal-like cancers may also influence treatment selection.
Purpose To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of ...radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results Multiple linear regression analyses demonstrated significant associations (R
= 0.25-0.32, r = 0.5-0.56, P < .0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. Conclusion Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence.
RSNA, 2016 Online supplemental material is available for this article.
We sought to define whether there are intrinsic molecular subtypes of high-grade bladder cancer. Consensus clustering performed on gene expression data from a meta-dataset of high-grade, ...muscle-invasive bladder tumors identified two intrinsic, molecular subsets of high-grade bladder cancer, termed "luminal" and "basal-like," which have characteristics of different stages of urothelial differentiation, reflect the luminal and basal-like molecular subtypes of breast cancer, and have clinically meaningful differences in outcome. A gene set predictor, bladder cancer analysis of subtypes by gene expression (BASE47) was defined by prediction analysis of microarrays (PAM) and accurately classifies the subtypes. Our data demonstrate that there are at least two molecularly and clinically distinct subtypes of high-grade bladder cancer and validate the BASE47 as a subtype predictor. Future studies exploring the predictive value of the BASE47 subtypes for standard of care bladder cancer therapies, as well as novel subtype-specific therapies, will be of interest.
Research in several fields now requires the analysis of data sets in which multiple high-dimensional types of data are available for a common set of objects. In particular, The Cancer Genome Atlas ...(TCGA) includes data from several diverse genomic technologies on the same cancerous tumor samples. In this paper we introduce Joint and Individual Variation Explained (JIVE), a general decomposition of variation for the integrated analysis of such data sets. The decomposition consists of three terms: a low-rank approximation capturing joint variation across data types, low-rank approximations for structured variation individual to each data type, and residual noise. JIVE quantifies the amount of joint variation between data types, reduces the dimensionality of the data and provides new directions for the visual exploration of joint and individual structures. The proposed method represents an extension of Principal Component Analysis and has clear advantages over popular two-block methods such as Canonical Correlation Analysis and Partial Least Squares. A JIVE analysis of gene expression and miRNA data on Glioblastoma Multiforme tumor samples reveals gene—miRNA associations and provides better characterization of tumor types. Data and software are available at https://genome.unc.edu/jive/.
Pan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use information from multiple ...sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict clinical outcomes. We address the issue of prediction across multiple high-dimensional sources of data and sample sets by using molecular patterns identified by BIDIFAC+, a method for integrative dimension reduction of bidimensionally-linked matrices, in a Bayesian hierarchical model. Our model performs variable selection through spike-and-slab priors that borrow information across clustered data. We use this model to predict overall patient survival from the Cancer Genome Atlas with data from 29 cancer types and 4 omics sources and use simulations to characterize the performance of the hierarchical spike-and-slab prior. We found that molecular patterns shared across all or most cancers were largely not predictive of survival. However, our model selected patterns unique to subsets of cancers that differentiate clinical tumor subtypes with markedly different survival outcomes. Some of these subtypes were previously established, such as subtypes of uterine corpus endometrial carcinoma, while others may be novel, such as subtypes within a set of kidney carcinomas. Through simulations, we found that the hierarchical spike-and-slab prior performs best in terms of variable selection accuracy and predictive power when borrowing information is advantageous, but also offers competitive performance when it is not. We address the issue of prediction across multiple sources of data by using results from BIDIFAC+ in a Bayesian hierarchical model for overall patient survival. By incorporating spike-and-slab priors that borrow information across cancers, we identified molecular patterns that distinguish clinical tumor subtypes within a single cancer and within a group of cancers. We also corroborate the flexibility and performance of using spike-and-slab priors as a Bayesian variable selection approach.
Epidermal growth factor receptor (EGFR) is a targetable receptor frequently overexpressed in basal-like breast cancer, which comprises most triple-negative breast cancers (TNBCs), the only subtype ...without established targeted therapy.
In this randomized phase II trial, patients with metastatic TNBC received anti-EGFR antibody cetuximab (400 mg/m(2) load then 250 mg/m(2) per week intravenously IV) alone, with carboplatin (area under the curve of 2, once per week IV) added after progression or as concomitant therapy from the beginning. Response rate (RR) was the primary end point; others included time to progression (TTP), overall survival (OS), and toxicity. Embedded correlative studies included molecular subtyping on archival tissue. Fresh tumor tissue before and after 7 to 14 days of therapy was used for microarray analyses exploring EGFR pathway activity and inhibition.
In 102 patients with TNBC, RRs were 6% (two of 31) to cetuximab and 16% (four of 25) to cetuximab plus carboplatin after progression. RR to those treated from the beginning with cetuximab plus carboplatin was 17% (12 of 71); 31% of patients responded or had prolonged disease stabilization. The cetuximab plus carboplatin regimen was well tolerated, but both TTP and OS were short at 2.1 months (95% CI, 1.8 to 5.5 months) and 10.4 months (95% CI, 7.7 to 13.1 months), respectively. Of 73 patients with archival tissue for analysis, 74% had basal-like molecular subtype. Sixteen patients had tumor biopsies before and 1 week after therapy; genomic patterns of the EGFR pathway showed activation in 13 and inhibition by therapy in five.
Despite strong preclinical data, combination cetuximab plus carboplatin in metastatic TNBC produced responses in fewer than 20% of patients. EGFR pathway analysis showed that most TNBCs involved activation. However, cetuximab blocked expression of the EGFR pathway in only a minority, suggesting that most had alternate mechanisms for pathway activation.
The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM). We describe a robust gene expression-based molecular classification of GBM into ...Proneural, Neural, Classical, and Mesenchymal subtypes and integrate multidimensional genomic data to establish patterns of somatic mutations and DNA copy number. Aberrations and gene expression of
EGFR,
NF1, and
PDGFRA/IDH1 each define the Classical, Mesenchymal, and Proneural subtypes, respectively. Gene signatures of normal brain cell types show a strong relationship between subtypes and different neural lineages. Additionally, response to aggressive therapy differs by subtype, with the greatest benefit in the Classical subtype and no benefit in the Proneural subtype. We provide a framework that unifies transcriptomic and genomic dimensions for GBM molecular stratification with important implications for future studies.
Four gene expression subtypes of GBM: Proneural, Neural, Classical, and Mesenchymal ► NF1 mutation and loss define Mesenchymal GBM ► Focal EGFR events define Classical GBM ► PGFRA\IDH1 events define Proneural GBM
Kinase inhibitors have limited success in cancer treatment because tumors circumvent their action. Using a quantitative proteomics approach, we assessed kinome activity in response to MEK inhibition ...in triple-negative breast cancer (TNBC) cells and genetically engineered mice (GEMMs). MEK inhibition caused acute ERK activity loss, resulting in rapid c-Myc degradation that induced expression and activation of several receptor tyrosine kinases (RTKs). RNAi knockdown of ERK or c-Myc mimicked RTK induction by MEK inhibitors, and prevention of proteasomal c-Myc degradation blocked kinome reprogramming. MEK inhibitor-induced RTK stimulation overcame MEK2 inhibition, but not MEK1 inhibition, reactivating ERK and producing drug resistance. The C3Tag GEMM for TNBC similarly induced RTKs in response to MEK inhibition. The inhibitor-induced RTK profile suggested a kinase inhibitor combination therapy that produced GEMM tumor apoptosis and regression where single agents were ineffective. This approach defines mechanisms of drug resistance, allowing rational design of combination therapies for cancer.
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► Inhibition of the MEK-ERK pathway rapidly reprograms kinome activity in tumors ► c-Myc degradation induces expression and activation of receptor tyrosine kinases ► Receptor tyrosine kinase activation overcomes MEK inhibition, causing resistance ► Kinome inhibitor response profiling rationally predicts combination therapies
Chemical proteomics reveals the dynamic rewiring of kinase networks in response to treatment with MEK inhibitors, uncovering how resistance to these inhibitors emerges in tumors and highlighting combination therapy approaches for bypassing drug resistance.
The transcription factor FOXM1 contributes to cell cycle progression and is significantly upregulated in basal-like breast cancer (BLBC). Despite its importance in normal and cancer cell cycles, we ...lack a complete understanding of mechanisms that regulate FOXM1. We identified USP21 in an RNAi-based screen for deubiquitinases that control FOXM1 abundance. USP21 increases the stability of FOXM1, and USP21 binds and deubiquitinates FOXM1 in vivo and in vitro, indicating a direct enzyme-substrate relationship. Depleting USP21 downregulates the FOXM1 transcriptional network and causes a significant delay in cell cycle progression. Significantly, USP21 depletion sensitized BLBC cell lines and mouse xenograft tumors to paclitaxel, an anti-mitotic, frontline therapy in BLBC treatment. USP21 is the most frequently amplified deubiquitinase in BLBC patient tumors, and its amplification co-occurs with the upregulation of FOXM1 protein. Altogether, these data suggest a role for USP21 in the proliferation and potentially treatment of FOXM1-high, USP21-high BLBC.
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•Cell cycle transcription factor FOXM1 is activated in basal-like breast cancer•USP21 deubiquitinates FOXM1 via a direct enzyme-substrate relationship•USP21 promotes cell cycle and paclitaxel resistance in basal-like breast cancer•USP21 is recurrently overexpressed and represents a potential therapeutic target
The cell cycle transcription factor FOXM1 is activated in basal-like breast cancer (BLBC) and associated with therapeutic resistance and poor patient outcomes. Arceci et al. show USP21 antagonizes FOXM1 degradation, thereby promoting proliferation and paclitaxel resistance. USP21 is catalytically active and recurrently overexpressed in BLBC, representing a potential therapeutic target.
Breast cancer metastasis remains a clinical challenge, even within a single patient across multiple sites of the disease. Genome-wide comparisons of both the DNA and gene expression of primary tumors ...and metastases in multiple patients could help elucidate the underlying mechanisms that cause breast cancer metastasis. To address this issue, we performed DNA exome and RNA sequencing of matched primary tumors and multiple metastases from 16 patients, totaling 83 distinct specimens. We identified tumor-specific drivers by integrating known protein-protein network information with RNA expression and somatic DNA alterations and found that genetic drivers were predominantly established in the primary tumor and maintained through metastatic spreading. In addition, our analyses revealed that most genetic drivers were DNA copy number changes, the TP53 mutation was a recurrent founding mutation regardless of subtype, and that multiclonal seeding of metastases was frequent and occurred in multiple subtypes. Genetic drivers unique to metastasis were identified as somatic mutations in the estrogen and androgen receptor genes. These results highlight the complexity of metastatic spreading, be it monoclonal or multiclonal, and suggest that most metastatic drivers are established in the primary tumor, despite the substantial heterogeneity seen in the metastases.
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