Showing papers by "James E. Korkola published in 2015"

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

Abstract: 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.

Interrogation of a Context-Specific Transcription Factor Network Identifies Novel Regulators of Pluripotency

Abstract: The predominant view of pluripotency regulation proposes a stable ground state with coordinated expression of key transcription factors (TFs) that prohibit differentiation. Another perspective suggests a more complexly regulated state involving competition between multiple lineage-specifying TFs that define pluripotency. These contrasting views were developed from extensive analyses of TFs in pluripotent cells in vitro. An experimentally-validated, genome-wide repertoire of the regulatory interactions that control pluripotency within the in vivo cellular contexts is yet to be developed. To address this limitation, we assembled a TF interactome of adult human male germ cell tumors (GCTs) using the Algorithm for the Accurate Reconstruction of Cellular Pathways (ARACNe) to analyze gene expression profiles of 141 tumors comprising pluripotent and differentiated subsets. The network (GCTNet) comprised 1305 TFs, and its Ingenuity Pathway analysis identified pluripotency and embryonal development as the top functional pathways. We experimentally validated GCTNet by functional (silencing) and biochemical (ChIP-seq) analysis of the core pluripotency regulatory TFs POU5F1, NANOG, and SOX2 in relation to their targets predicted by ARACNe. To define the extent of the in vivo pluripotency network in this system, we ranked all TFs in the GCTNet according to sharing of ARACNe-predicted targets with those of POU5F1 and NANOG using an Odds-Ratio analysis method. To validate this network, we silenced the top 10 TFs in the network in H9 ES cells. Silencing of each led to downregulation of pluripotency and induction of lineage; 7 of the 10 TFs were identified as pluripotency regulators for the first time.

Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer

Abstract: 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.

Development and Validation of a Gene-Based Model for Outcome Prediction in Germ Cell Tumors Using a Combined Genomic and Expression Profiling Approach

Abstract: Germ Cell Tumors (GCT) have a high cure rate, but we currently lack the ability to accurately identify the small subset of patients who will die from their disease. We used a combined genomic and expression profiling approach to identify genomic regions and underlying genes that are predictive of outcome in GCT patients. We performed array-based comparative genomic hybridization (CGH) on 53 non-seminomatous GCTs (NSGCTs) treated with cisplatin based chemotherapy and defined altered genomic regions using Circular Binary Segmentation. We identified 14 regions associated with two year disease-free survival (2yDFS) and 16 regions associated with five year disease-specific survival (5yDSS). From corresponding expression data, we identified 101 probe sets that showed significant changes in expression. We built several models based on these differentially expressed genes, then tested them in an independent validation set of 54 NSGCTs. These predictive models correctly classified outcome in 64-79.6% of patients in the validation set, depending on the endpoint utilized. Survival analysis demonstrated a significant separation of patients with good versus poor predicted outcome when using a combined gene set model. Multivariate analysis using clinical risk classification with the combined gene model indicated that they were independent prognostic markers. This novel set of predictive genes from altered genomic regions is almost entirely independent of our previously identified set of predictive genes for patients with NSGCTs. These genes may aid in the identification of the small subset of patients who are at high risk of poor outcome.

Abstract S6-07: Detrimental effects of sequential compared to concurrent treatment of pertuzumab plus T-DM1 in HER2+ breast cancer cell lines

Abstract: Background. Pertuzumab and T-DM1 are two recently approved monoclonal antibody based therapies targeting HER2+ breast cancer. Pertuzumab interferes with dimerization of HER family members, while T-DM1 binds to HER2 and interferes with its oncogenic function while also specifically delivering a cytotoxic agent (emtansine). One arm of the I-SPY 2 clinical trial is to investigate the efficacy of a combination Pertuzumab plus T-DM1 in HER2+ breast cancer patients. Methods. We performed pre-clinical screening of response to each agent alone and in combination in a set of 21 HER2+ breast cancer cell lines, with an end goal of identifying markers of response to the therapies. There were five treatment regimens employed in the initial screen: i) pertuzumab alone for 72 h; ii) T-DM1 alone for 72h; iii) pertuzumab plus T-DM1 concurrently for 72h; iv) pertuzumab for 24h followed by addition of T-DM1 for 48h more; and iv) T-DM1 for 24h followed by addition of pertuzumab for 48h more. Response was assessed using the Cell Titer Glo assay as a measure of cell viability. To assess the effects of drug combinations, we used a stringent measure of synergy and antagonism employing the median effect method of Chou and Talalay that included 95% confidence intervals to determine significance. Results. Initial screens showed that concurrent treatment of cells with pertuzumab plus T-DM1 gave significant synergistic interactions in 15/21 cell lines as measured by the median effect method, with combination indices (CI) less than 0.5 (and 95% upper confidence levels less than 1.0) for at least one drug concentration. However, 24h pretreatment with pertuzumab followed by T-DM1 significantly diminished the response of cells to T-DM1, resulting in significant antagonism in 17/21 cell lines test (CI>1.5, lower confidence level greater than 1). Since this could be due to a shorter exposure time to T-DM1, and since patients are scheduled to be treated with pertuzumab first followed by T-DM1 one hour later, we repeated the experiment with one hour between pertuzumab and T-DM1 rather than 24h. While the inhibitory effect was diminished, this treatment regimen still resulted in significant antagonism when T-DM1 was given 1 hour after pertuzumab in 5/5 cell lines tested, in contrast to concurrent pertuzumab plus T-DM1 treatment, which showed synergy. Conclusions. Pertuzumab plus T-DM1 appears to be beneficial when given concurrently, but pretreatment with pertuzumab appears to blunt the efficacy of T-DM1. This has important potential ramifications for patient treatment, and may further elucidate mechanisms of action for both compounds. Further testing will be necessary to determine whether these timing effects are operational in vivo and whether immune effects mitigate the antagonism. Citation Format: James E Korkola, Moqing Liu, Tiera Liby, Laura Heiser, Heidi Feiler, Joe W Gray. Detrimental effects of sequential compared to concurrent treatment of pertuzumab plus T-DM1 in HER2+ breast cancer cell lines [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr S6-07.

Erratum to: Modeling precision treatment of breast cancer

Abstract: 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.

Heterogeneity in cancer dynamics: A convex formulation to dissect dynamic trajectories and infer LTV models of networked systems

Abstract: Breast cancer tumors have inherently heterogeneous cell types that respond differently to treatments. Although there is a wealth of studies describing canonical cell signaling networks, little is known about how these networks operate in different cancer cells and treatments. This paper proposes a method to split a set of responses gathered from experiments on different cancer cells up into common and specific components. The key to this retrieval is the derivation of a linear timevarying model of the shared dynamics among the different cell lines. A convex optimization problem is derived that retrieves both the model and the common and specific responses without a priori information. The method is tested on synthetic data, and verifies known facts when tested on a biological data set with protein expression data from breast cancer experiments. The technique can be used to analyze specific responses to understand what treatments can be combined to persistently treat a heterogeneous cancer tumor. The linear time-varying model sheds light on how proteins interact over time.

Disentangling multidimensional spatio-temporal data into their common and aberrant responses.

Abstract: With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight about the dynamic behavior of the system. For this potential to be realized, we need a suitable representation to understand the data. A general question is how to organize the observed data into meaningful structures and how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. Since the wide range of experiments and unknown complexity of the underlying system contribute to the heterogeneity of biological data, we develop a new method by proposing an extension of Robust Principal Component Analysis (RPCA), which models common variations across multiple experiments as the lowrank component and anomalies across these experiments as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses. Thus, the proposed method provides us a new representation of these data sets which has the potential to help users acquire new insight from data.

Compounds that bind dystroglycan and uses thereof

Abstract: Disclosed herein are methods and compositions involved in identifying cells that lack apico-basal polarity as well as methods and compositions involved in selectively delivering payload molecules to cells that lack apico-basal polarity, and methods of selecting test compounds that restore apico-basal polarity.