Baseline Gene Expression Data Research Articles

Network-based response module comprised of gene expression biomarkers predicts response to infliximab at treatment initiation in ulcerative colitis

This cross-cohort study aimed to (1) determine a network-based molecular signature that predicts the likelihood of inadequate response to the tumor necrosis factor-ɑ inhibitor (TNFi) therapy, infliximab, in ulcerative colitis (UC) patients, and (2) address biomarker irreproducibility across different cohort studies. Whole-transcriptome microarray data were derived from biopsies of affected colon tissue from 2 cohorts of infliximab-treated UC patients (training N=24 and validation N=22). Response was defined as endoscopic and histologic healing at 4-6 weeks and 8 weeks, respectively. From the training cohort, genes with RNA expression that significantly correlated with clinical response outcomes were mapped onto the Human Interactome network map of protein-protein interactions to identify a largest connected component (LCC) of proteins indicative of infliximab response status in UC. Expression levels of transcripts within the LCC were fed into a probabilistic neural network model to generate a classifier that predicts inadequate response to infliximab. A classifier predictive of inadequate response to infliximab was generated and tested in a cross-cohort, blinded fashion; the AUC was 0.83 and inadequate response was predicted with a 100% positive predictive value and 64% sensitivity. Genes separately identified from the 2 cohorts that correlated with response to infliximab appeared distinct but mapped onto the same network region of the Human Interactome, reflecting a common underlying biology of response among UC patients. Cross-cohort validation of a classifier predictive of infliximab response status in UC patients indicates that a molecular signature of non-response to TNFi therapies is present in patients' baseline gene expression data. The goal is to develop a diagnostic test that predicts which patients will have an inadequate response to targeted therapies and define new targets and pathways for therapeutic development.

Abstract 1924: Genetic analysis of responses to eribulin versus vinorelbine and paclitaxel in 100 cancer cell lines from the cancer cell line Encyclopedia (CCLE)

Abstract Background: Eribulin mesylate, a synthetic analog of the marine sponge natural product halichondrin B, is a microtubule dynamics inhibitor that acts via both cytotoxic antimitotic mechanisms as well as non-cytotoxic effects on tumor vasculature, tumor phenotype and the tumor microenvironment. Eribulin is approved in the US for treatment of certain patients with advanced breast cancer or liposarcoma. Here we examined in vitro antiproliferative responses to eribulin versus vinorelbine and paclitaxel in 100 human cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE), followed by analysis of CCLE baseline gene expression data with the goal of assessing overlapping and non-overlapping pathways and genes associated with drug response and resistance. Methods: In vitro proliferation assays were conducted on 100 CCLE cell lines. Cancer types represented by ≥5 cell lines include breast, lung, blood, colorectum, ovary, pancreas, skin and kidney. Comparative systems-level analyses of genes, networks and pathways associated with drug response and/or resistance were performed to identify gene and pathway-level determinants of drug response that were either unique to eribulin or shared with vinorelbine and/or paclitaxel. Results: We evaluated genes and molecular pathways driving sensitivity to eribulin versus vinorelbine and paclitaxel in a panel of 100 CCLE cell lines and identified key molecular determinants of response and resistance unique to eribulin. Pathway expression analysis revealed key pathways significantly associated with response to eribulin, including those associated with oxidative stress-induced senescence, histone deacetylation, DNA methylation and Notch signaling. The oxidative stress-induced senescence pathway was significantly enriched for genes whose pretreatment/basal mRNA expression levels were positively associated with response to eribulin but not vinorelbine or paclitaxel. Expression levels of various tubulin isoforms were also identified as positively correlated with eribulin response. Higher and lower pretreatment expression of E-cadherin and N-cadherin, respectively, were also found in cell lines sensitive to eribulin treatment. Conclusion: Pretreatment gene expression analysis demonstrated key gene sets and molecular pathways that may drive response and resistance to eribulin and may differentiate this drug from vinorelbine and paclitaxel. Detailed analysis will be presented at the meeting. Citation Format: Pallavi Sachdev, Roy Ronen, Janusz Dutkowski, Bruce A. Littlefield. Genetic analysis of responses to eribulin versus vinorelbine and paclitaxel in 100 cancer cell lines from the cancer cell line Encyclopedia (CCLE) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1924.

A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging

BackgroundFibrous cap thickness (FCT), best measured by intravascular optical coherence tomography (OCT), is the most important determinant of plaque rupture in the coronary arteries. Statin treatment increases FCT and thus reduces the likelihood of acute coronary events. However, substantial statin-related FCT increase occurs in only a subset of patients. Currently, there are no methods to predict which patients will benefit. We use transcriptomic data from a clinical trial of rosuvastatin to predict if a patient's FCT will increase in response to statin therapy. MethodsFCT was measured using OCT in 69 patients at (1) baseline and (2) after 8–10 weeks of 40 mg rosuvastatin. Peripheral blood mononuclear cells were assayed via microarray. We constructed machine learning models with baseline gene expression data to predict change in FCT. Finally, we ascertained the biological functions of the most predictive transcriptomic markers. FindingsMachine learning models were able to predict FCT responders using baseline gene expression with high fidelity (Classification AUC = 0.969 and 0.972). The first model (elastic net) using 73 genes had an accuracy of 92.8%, sensitivity of 94.1%, and specificity of 91.4%. The second model (KTSP) using 18 genes has an accuracy of 95.7%, sensitivity of 94.3%, and specificity of 97.1%. We found 58 enriched gene ontology terms, including many involved with immune cell function and cholesterol biometabolism. InterpretationIn this pilot study, transcriptomic models could predict if FCT increased following 8–10 weeks of rosuvastatin. These findings may have significance for therapy selection and could supplement invasive imaging modalities.

Prediction of metastatic castrate-resistant prostate cancer response to abiraterone or enzalutamide by a baseline blood-based CTC gene expression signature.

e16529 Background: Prostate cancer is the most common cancer in men in the U.S., with 30% 5-year overall survival (OS) for patients (pts) with metastases. To take a precision medicine approach to the management of metastatic castrate-resistant prostate cancer (mCRPC), we developed a blood circulating tumor cell (CTC)-based test to identify mCRPC pts most likely to benefit from abiraterone (abi) or enzalutamide (enza). Methods: In this multi-institution prospective study, men with mCRPC were enrolled prior to starting abi (1,000 mg/d plus prednisone 10 mg/d) or enza (160 mg/d). At baseline (BL), 12 w, and progression, blood samples were collected for CellSearch-based CTC enumeration and qPCR-based gene expression analysis. Results: 69 pts (median age 68 y [50-82]) received abi (n = 25) or enza (n = 44) and had evaluable blood samples. Consistent with prior publications, among 43 pts with BL CTC > 0, clearance of detectable CTCs (BL CTCs > 0 and 12 w CTCs = 0), was achieved in 24 patients (55.8%), and was associated with greater median OS (31 mo vs. 18 mo, log-rank p = 0.03). The 43 pts with BL CTC > 0 were then randomly divided into training (n = 31) and validation (n = 12) sets. Baseline gene expression data for the training set was used to develop a model to predict CTC clearance, starting with a panel of 141 expressed genes/isoforms including those associated with prostate cancer. Of the models tested, random forest yielded the best performance, with respective training and validation set sensitivity of 0.7 and 1, specificity 0.75 and 0.71, AUC 0.88 and 0.91. Top genes identified include those previously associated with disease – HOXB13, ESRP2, KLK3, GRHL2, and KRT19, among others. Conclusions: A gene expression signature from a baseline blood sample with CellSearch-enriched CTCs can predict clearance of detectable CTCs in response to abi/enza with high AUC and may give insight into molecular mechanisms of response. A prospective study with a larger number of patients will be required to further validate our findings. Ultimately, this blood test has the potential to select the patients most likely to benefit from second-generation antiandrogen vs. non-hormonal systemic treatment.

Abstract B34: Antitumor activity and sensitivity evaluation of novel BET inhibitors in neuroblastoma

Abstract Children with neuroblastoma exhibit marked variability in outcome based on age at diagnosis, disease stage and tumor biologic characteristics. Genomic amplification of MYCN is a potent oncogenic driver and negative prognostic marker in neuroblastoma patients. Therefore, there is a pressing need to identify therapeutic agents that target MYCN gene expression. The bromodomain and extra-terminal (BET) family of proteins are epigenetic regulators known to control expression of genes involved in cell growth and oncogenesis. Previous reports demonstrate that BET inhibition (BETi) significantly attenuates cellular proliferation in numerous cancer models, perhaps via modulation of the MYC and/or MYCN oncogenes. Here, we show that potent BET inhibition induces anti-tumor effects in preclinical neuroblastoma models. Specifically, GSK726 and GSK762 (GlaxoSmithKline) were used for in vitro cytotoxicity and in vivo therapeutic studies, respectively. Neuroblastoma cell lines (n=22) were treated with GSK726 to calculate IC50 values in a luminescence-based cell viability assay and differential sensitivity was observed with an IC50 range of 27 nM to 9.5 µM. In sensitive cell lines, cell cycle distribution and induction of apoptosis were also measured. In these cell lines, GSK726 treatment resulted in MYCN depletion, G1 arrest within 24 hours, and apoptosis as measured by cleaved-PARP. To assess in vivo efficacy, GSK762 was subcutaneously administered in xenograft models and in genetically engineered neuroblastoma mouse models overexpressing MYCN and MYCN/ALK F1174L in the neural crest. In both models, GSK762 treatment resulted in tumor growth delay. Further assessment of results seen in vitro and in vivo indicated that MYCN amplification status did not fully predict sensitivity to GSK726 or GSK762. Thus, to determine additional biomarkers of sensitivity, we examined baseline gene expression data at the extremes of IC50 values by comparing sensitive (n=6; IC50<128 nM) and resistant (n=4; IC50>940 nM) neuroblastoma cell lines. Gene expression data from the neuroblastoma cell lines generated on HuGene1.0ST expression microarrays (Affymetrix) were utilized, and data were analyzed using the Limma package in R/Bioconductor. Univariate analysis, with a false discovery rate less than 0.25, revealed 6 genes (PTER, PCDHB14, RFTN1, JAK2, MYCBP, and DACH1) that were differentially expressed between sensitive and resistant cell lines in the MYCN amplified setting. While all 6 genes predicted BETi sensitivity in the MYCN-amplified subset of cell lines, only DACH1 expression predicted sensitivity to BET inhibition irrespective of MYCN amplification status (p=7.92x10-7). In addition, high DACH1 expression was correlated with poor patient outcome (p=1.74x10-5). As a result, high DACH1 expression serves as a candidate biomarker for future studies with respect to sensitivity to BETi. Ultimately, these studies will help to optimize the clinical utility of BETi in neuroblastoma and perhaps other MYC-driven malignancies. Citation Format: Jason R. Healy, Robert W. Schnepp, Lori S. Hart, Pichai Raman, Laura Danielson, Michael Russell, Priya Khurana, Maria Gagliardi, Ryan M. Kinsey, Anastasia Wyce, Olena Barbash, Peter J. Tummino, Louis Chesler, John M. Maris. Antitumor activity and sensitivity evaluation of novel BET inhibitors in neuroblastoma. [abstract]. In: Proceedings of the AACR Special Conference on Myc: From Biology to Therapy; Jan 7-10, 2015; La Jolla, CA. Philadelphia (PA): AACR; Mol Cancer Res 2015;13(10 Suppl):Abstract nr B34.

Predicting response to multidrug regimens in cancer patients using cell line experiments and regularised regression models.

BackgroundPatients suffering from cancer are often treated with a range of chemotherapeutic agents, but the treatment efficacy varies greatly between patients. Based on recent popularisation of regularised regression models the goal of this study was to establish workflows for pharmacogenomic predictors of response to standard multidrug regimens using baseline gene expression data and origin specific cell lines. The proposed workflows are tested on diffuse large B-cell lymphoma treated with R-CHOP first-line therapy.MethodsFirst, B-cell cancer cell lines were tested successively for resistance towards the chemotherapeutic components of R-CHOP: cyclophosphamide (C), doxorubicin (H), and vincristine (O). Second, baseline gene expression data were obtained for each cell line before treatment. Third, regularised multivariate regression models with cross-validated tuning parameters were used to generate classifier and predictor based resistance gene signatures (REGS) for the combination and individual chemotherapeutic drugs C, H, and O. Fourth, each developed REGS was used to assign resistance levels to individual patients in three clinical cohorts.ResultsBoth classifier and predictor based REGS, for the combination CHO, were of prognostic value. For patients classified as resistant towards CHO the risk of progression was 2.33 (95% CI: 1.6, 3.3) times greater than for those classified as sensitive. Similarly, an increase in the predicted CHO resistance index of 10 was related to a 22% (9%, 36%) increased risk of progression. Furthermore, the REGS classifier performed significantly better than the REGS predictor.ConclusionsThe regularised multivariate regression models provide a flexible workflow for drug resistance studies with promising potential. However, the gene expressions defining the REGSs should be functionally validated and correlated to known biomarkers to improve understanding of molecular mechanisms of drug resistance.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-015-1237-6) contains supplementary material, which is available to authorized users.

Identifying a Gene Expression (GEP)-Based Model Predicting for Progression from AMM to Cmm Requiring Therapy in S0120 Patients Treated at Mirt

Background: The definition of high risk smoldering multiple myeloma (HR-SMM) is in flux. There are several models using serologic, bone marrow and radiologic data that predict for time to progression (TTP) to clinical myeloma (CMM). Lenalidomide and Dexamethasone in HR-SMM is reported to delay onset of end organ damage and improve overall survival, stressing the clinical utility of early intervention. We previously reported a GEP70 based score cutoff (<-0.26), when applied to the S0120 population, that improved the predictive power (R2) of standard clinical variables by 11%. The combination of GEP70 score >-0.26, serum M spike ≥3g/dL, and involved SFLC >25 mg/dL identified a subset of patients with 67% risk of progression at 2 years. With longer follow up, we now examine whether unique gene probe sets can be identified at the AMM stage that portend an earlier time to therapy (TTT).Patients and Methods: We identified 105 patients with AMM who had baseline GEP data on our S0120 protocol, after IRB approval for retrospective data review, and evaluated each of 54,675 Affymetrix gene probes for their potential to predict TTT. Probes were ranked by their q-values; we found 40 probes with q-value < 0.05 and 7 probes with q-value < 0.01; the top probe had a q-value of 0.00066. Scores based on the number of significant probes at these cut-points were computed by subtracting the sum of the expressions of the up-regulated probes from the sum of the expressions of the down-regulated probes, then dividing by the total number of probes.Results: In the GEP40 model, an optimal cut-point for risk of progression was identified at 7.05. The 3-year TTT probability was 83% with scores >=7.05 and only 11% for patients with values under this threshold (Figure 1A; p<0.0001). TTT probabilities also differed markedly when examined by score quartiles, attesting to a gene dose effect (Figure 1B). For the Q1 subset of 26 patients, only 4% required therapy in 3 years. Univariate Cox analysis for TTT yielded age>65 (HR: 2.3), Albumin<3.5g/dl (HR: 3.7), M-protein>3g/dl (HR: 4.99), BM plasmacytosis>=10% (HR: 12.2), GEP70>-0.26 (HR: 3.4), GEP40>=7.05 (HR: 16.41), GEP proliferation index > -0.26 (HR: 2.8), GEP PR subgroup (HR: 9.4) and GEP PolyPC >11.6 (HR: 0.22) to be significant. In the multivariate model, GEP40>=7.05 was the most significant (HR: 13.7), followed by SFLC>10mg/dl and M-protein>3g/dl. GEP40 score positively correlated with proliferation index (R: 0.804), and showed no correlation with GEP polyPC score (R: -0.156). Next, we used recursive partitioning on data from 72 patients and identified 23 patients with GEP40 score >=7.05 of whom 22 suffered TTT by 3 years (87%). Among the remaining 49 patients with GEP40 <7.0325, a further cut-point of age >59 years identified 24 patients, of whom 11 suffered progression with a 3 year TTT estimate of 25%. In the 25 patients with GEP40 <7.0325 and age <59 years, no patient progressed to CMM, with a 3 year TTT estimate of 0%. The GEP7 and GEP1 models, at optimal cut-offs, yielded equivalent positive and negative predictive values compared to the GEP40 model, albeit with less power.Conclusion:Gene expression profiling can readily identify a subset of AMM patients who are high risk for progression to CMM. Further refinement of GEP based risk scoring can be achieved by combining clinical and correlative variables to select the super HR-SMM for intervention trials. [Display omitted] [Display omitted] DisclosuresZangari:Norvartis: Membership on an entity’s Board of Directors or advisory committees; Onyx: Research Funding; Millennium: Research Funding. Van Rhee:Janssen: Membership on an entity’s Board of Directors or advisory committees; Celgene: Membership on an entity’s Board of Directors or advisory committees; Millennium: Membership on an entity’s Board of Directors or advisory committees; Sanofi: Membership on an entity’s Board of Directors or advisory committees. Dhodapkar:Celgene: Research Funding. Morgan:Celgene Corp: Membership on an entity’s Board of Directors or advisory committees; Novartis: Membership on an entity’s Board of Directors or advisory committees; Janssen: Membership on an entity’s Board of Directors or advisory committees; Myeloma UK: Membership on an entity’s Board of Directors or advisory committees; International Myeloma Foundation: Membership on an entity’s Board of Directors or advisory committees; The Binding Site: Membership on an entity’s Board of Directors or advisory committees; MMRF: Membership on an entity’s Board of Directors or advisory committees.

Abstract B75: Defining the antitumor activity and sensitivity profiles of BET inhibitors in neuroblastoma

Abstract Background: MYCN amplification is the most frequent somatically acquired genomic alteration in neuroblastoma and is a potent oncogenic driver. Targeting MYCN therapeutically has been complicated by the challenges inherent in targeting transcription factors. Recently, several groups have shown that inhibiting bromodomain and extra-terminal (BET) proteins (BRD2, BRD3, BRD4, and BRDT) and their ability to bind to acetylated lysine residues within histone tails resulted in silencing of MYC family protein expression and demonstrated therapeutic efficacy in preclinical cancer models. We hypothesized that potent inhibition of bromodomain-chromatin interactions would show anti-tumor activity in preclinical models of neuroblastoma and that reliable biomarkers of activity could be discovered. Methods: GlaxoSmithKline compounds GSK726 and GSK762 potently inhibit BET proteins (BETi) and were used for in vitro cytotoxicity and in vivo therapeutic studies, respectively. We exposed a panel of 15 cell lines to a 4-log dose range of GSK726 to determine IC50s in a luminescence-based cell viability assay. We also measured cell cycle changes by flow cytometry and apoptosis by immunoblotting for cleaved PARP. We tested the in vivo efficacy of the GSK762 in subcutaneous xenograft models (3 sensitive and 2 resistant cell lines) and in genetically engineered neuroblastoma mouse models (overexpressing MYCN and MYCN/ALK F1174L in the neural crest). For biomarker discovery, gene expression data from the neuroblastoma cell lines generated on HuGene1.0ST expression microarrays (Affymetrix) were utilized, and data were analyzed using the Limma package in R/Bioconductor. Results: Neuroblastoma cell lines (N=15) were differentially sensitive to GSK726, with a median IC50 of 128nM and a range of 17nM to &amp;gt;10uM. In sensitive cell lines, GSK726 treatment resulted in MYCN depletion, G1 arrest within 24 hours, and apoptosis. In subcutaneous xenograft models and genetically engineered neuroblastoma mouse models, GSK762 treatment resulted in tumor growth delay. Importantly, MYCN amplification status did not fully predict sensitivity to GSK726 or GSK762. Thus, to determine additional biomarkers of sensitivity, we examined baseline gene expression data at the extremes of IC50s by comparing sensitive (N=6; IC50&amp;lt;128 nM) and resistant (N=4; IC50&amp;gt;940 nM) neuroblastoma cell lines. Univariate analysis, with an FDR &amp;lt; 0.25, revealed 6 genes (PTER, PCDHB14, RFTN1, JAK2, MYCBP, and DACH1) differentially expressed between sensitive and resistant cell lines in the MYCN amplified setting. While all 6 genes predicted BETi sensitivity in the MYCN-amplified subset of cell lines, only DACH1 expression predicted sensitivity to BET inhibition irrespective of MYCN amplification status (7.92x10-7). Moreover, DACH1 levels were heterogeneously expressed in a panel of 88 primary neuroblastoma tumors (Versteeg-88) and high expression was correlated with poor patient outcome (1.74x10-5). Conclusions: BET inhibitors demonstrated significant anti-tumor activity in a subset of neuroblastoma preclinical models. Although MYCN protein levels decreased in neuroblastoma cell lines that were sensitive to BETi, MYCN amplification status alone did not fully explain BETi sensitivity. DACH1 expression serves as a candidate biomarker for sensitivity to BET inhibition. These studies will help to optimize the clinical utility of BETi in neuroblastoma and perhaps other MYC-driven malignancies. Citation Format: Robert Schnepp, Lori Hart, Pichai Raman, Laura Danielson, Maria Gagliardi, Ryan Kinsey, Anastasia Wyce, Olena Barbash, Peter Tummino, Louis Chesler, John Maris. Defining the antitumor activity and sensitivity profiles of BET inhibitors in neuroblastoma. [abstract]. In: Proceedings of the AACR Special Conference on Pediatric Cancer at the Crossroads: Translating Discovery into Improved Outcomes; Nov 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2013;74(20 Suppl):Abstract nr B75.

Supervised classification by filter methods and recursive feature elimination predicts risk of radiotherapy-related fatigue in patients with prostate cancer.

BACKGROUNDFatigue is a common side effect of cancer (CA) treatment. We used a novel analytical method to identify and validate a specific gene cluster that is predictive of fatigue risk in prostate cancer patients (PCP) treated with radiotherapy (RT).METHODSA total of 44 PCP were categorized into high-fatigue (HF) and low-fatigue (LF) cohorts based on fatigue score change from baseline to RT completion. Fold-change differential and Fisher’s linear discriminant analyses (LDA) from 27 subjects with gene expression data at baseline and RT completion generated a reduced base of most discriminatory genes (learning phase). A nearest-neighbor risk (k-NN) prediction model was developed based on small-scale prognostic signatures. The predictive model validity was tested in another 17 subjects using baseline gene expression data (validation phase).RESULTThe model generated in the learning phase predicted HF classification at RT completion in the validation phase with 76.5% accuracy.CONCLUSIONThe results suggest that a novel analytical algorithm that incorporates fold-change differential analysis, LDA, and a k-NN may have applicability in predicting regimen-related toxicity in cancer patients with high reliability, if we take into account these results and the limited amount of data that we had at disposal. It is expected that the accuracy will be improved by increasing data sampling in the learning phase.

Abstract A45: Investigation of efficacy of the NAE inhibitor MLN4924 across large-scale cell line panels reveals potentially sensitive cancer indications and candidate predictive biomarkers.

Abstract MLN4924 is an investigational inhibitor of NAE (Nedd8 Activating Enzyme). NAE mediates NEDD8 conjugation and thus regulates the cullin-RING ligase (CRL) proteasomal degradation pathway. MLN4924 is currently being tested in Phase 1 clinical trials of patients with hematological or solid tumors. To support future clinical development and identify potential biomarkers of tumor sensitivity or resistance, two large cancer cell line panels (Panel 1, N=653; Panel 2, N=240) were treated with MLN4924 and cell viability data (IC50, EC50, and POC - Percentage of Control) were generated. These data were used to correlate sensitivity/resistance with the cell line's tumor histology and with underlying genetic data for the specific cell lines. Overlapping cell lines from the two panels (114) showed consistent growth inhibition effects (Spearman Correlation coefficient = 0.72). Using Fisher's exact test and POC cutoffs at 25% and 50% quartiles on viability data, we identified bladder cancer and head and neck cancer lines as associated with MLN4924 sensitivity in both panels. Using median percentage of control values as cutoff, we applied Fisher's exact test to evaluate associations of individual mutations in the cell lines to MLN4924 sensitivity or resistance. Examples of mutations linked to sensitivity include KDM6A (p-value = 0.02, Panel 1; p-value = 0.06, Panel 2). APC mutation is associated with resistance to MLN4924 (p-value = 5.75 × 10−5, Panel 1; p-value = 0.04, Panel 2). Combining cell viability and baseline gene expression data from cell line Panel 2, a PLSR predictive model was developed, identifying a gene signature (102 genes) for MLN4924 sensitivity and resistance. A median cutoff of PLSR scores was applied to separate predicted sensitive/resistant cell lines. Consistent with previous association of cell viability with histology, PLSR analysis of gene expression predicted that bladder cancer cell lines are particularly sensitive to MLN4924. Two pathway analysis approaches were applied to identify MLN4924 sensitivity related pathways using either whole genome information (GSEA) or the PLSR gene signature list. The two approaches consistently indicated an over-representation of signaling pathways including cell cycle, development hedgehog signaling and ErbB signaling. The over-represented cell cycle pathway is in good agreement with previous MLN4924 mechanism of action studies. In particular, this finding was independently validated via a whole genome siRNA screen that also identified the cell cycle pathway as associated with tumor sensitivity. The consistent observations from two large cell line panels and a siRNA screen support the utility of these approaches for clarifying sensitive and resistant tumor cell types and also potentially identifying candidate biomarkers. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr A45.

Abstract C70: Integrative analysis of pharmacologic, genomic, and transcriptomic assays in cancer

Abstract We describe an integrative analysis approach leveraging the publicly available Cancer Cell Line Encyclopedia to uncover regulatory pathways in cancer. Six different lines of evidence are considered in identifying candidate factors associated with disease: 1) gene mutation status, 2) copy number variations, 3) RNAi growth inhibitions, 4) pharmacological growth inhibitions, 5) baseline gene expression data, and 6) physical protein-protein interactions. As each of these evidence sources measure a different aspect of cellular regulation, they may be considered in combination to uncover regulatory mechanisms associated with tumorigenesis. For example, gene mutation status or copy number variation data may be combined with baseline gene expressions to identify genes that are differentially expressed in cell lines with aberrant or deactivated versions of specific tumor suppressors and oncogenes. To assess our approach, we applied it to identification of genes involved in the well-known RAS-RAF regulatory signaling pathway in both tissuespecific and nonspecific contexts. The RAS-RAF signaling pathway plays a key role in cellular differentiation and proliferation and is frequently associated with tumorigenesis. Specifically, we identified differentially expressed genes in BRAF mutant vs. wild-type cell lines, genes associated with high and low RAF copy number alteration cell lines, genes correlating in expression to growth inhibition by RAF-targeting compounds, and genes that either rescue or sensitize cell lines to BRAF knockdown by RNA interference. We further considered these genes within the overall context of physical interactions between their protein products. Highlighting the significant, research potential of our approach, our RAF-focused analysis identified both well-known and novel genes, supported by different lines of evidence, that mediate RAS-RAF signaling across multiple tissues. Positive control genes include members of the classical pathway, such as MAPK1, MAP2K1, and PI3K. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the Second AACR International Conference on Frontiers in Basic Cancer Research; 2011 Sep 14-18; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2011;71(18 Suppl):Abstract nr C70.

Gene expression profiling of breast tumor cell lines to predict for therapeutic response to microtubule-stabilizing agents

Microtubule-targeting agents, including taxanes (Tax) and ixabepilone (Ixa), are important components of modern breast cancer chemotherapy regimens, but no molecular parameter is currently available that can predict for their efficiency. We sought to develop pharmacogenomic predictors of Tax- and Ixa-response from a large panel of human breast tumor cell lines (BTCL), then to evaluate their performance in clinical samples. Thirty-two BTCL, representative of the molecular diversity of breast cancers (BC), were treated in vitro with Tax (paclitaxel (Pac), docetaxel (Doc)), and ixabepilone (Ixa), then classified as drug-sensitive or resistant according to their 50% inhibitory concentrations (IC50s). Baseline gene expression data were obtained using Affymetrix U133 Plus 2.0 human oligonucleotide microarrays. Gene expression set (GES) predictors of response to taxanes were derived, then tested for validation internally and in publicly available gene expression datasets. In vitro IC50s of Pac and Doc were almost identical, whereas some Tax-resistant BTCL retained sensitivity to Ixa. GES predictors for Tax-sensitivity (333 genes) and Ixa-sensitivity (79 genes) were defined. They displayed a limited number of overlapping genes. Both were validated by leave-n-out cross-validation (n = 4; for overall accuracy (OA), P = 0.028 for Tax, and P = 0.0005 for Ixa). The GES predictor of Tax-sensitivity was tested on publicly available external datasets and significantly predicted Pac-sensitivity in 16 BTCL (P = 0.04 for OA), and pathological complete response to Pac-based neoadjuvant chemotherapy in BC patients (P = 0.0045 for OA). Applying Tax and Ixa-GES to a dataset of clinically annotated early BC patients identified subsets of tumors with potentially distinct phenotypes of drug sensitivity: predicted Ixa-sensitive/Tax-resistant BC were significantly (P < 0.05, Fischer's exact test) more frequently ER/PR-positive, Ki67-negative, and luminal subtype than predicted Ixa-resistant/Tax-sensitive BC. Genomic predictors for Tax- and Ixa-sensitivity can be derived from BTCL and may be helpful for better selecting cytotoxic treatment in BC patients.

Abstract 25: PD 0332991, a selective CDK 4/6 inhibitor, preferentially inhibits growth of ovarian cancer cells with high Rb and low p16 (CDKN2A) expression

Abstract Background: Cell cycle dysregulation is a common molecular finding in ovarian cancer and the cyclin-D kinases (CDK) represent an attractive target in this pathway. PD 0332991 (Pfizer Inc.) is an orally active potent and highly selective inhibitor of CDK 4 and CDK6 kinases with the ability to block pRb phosphorylation at serine 780 and 795 in the low nanomolar range. To identify predictors of response to PD 0332991, we determined the in vitro sensitivity to PD 0332991 in a large panel of molecularly characterized ovarian cancer cell lines, and then used baseline expression profiles and Western blot analyses to identify biomarkers associated with response to PD 0332991. These biomarkers were then validated in a large cohort of 324 ovarian cancer patients by immunohistochemistry. Methods: 40 human ovarian cancer cell lines were treated with PD 0332991 using two-fold dilutions over 12 concentrations. Dose response curves were generated using a cell count assay to calculate the IC50 across the panel. These data were then analyzed against baseline gene expression data to identify genes associated with sensitivity to PD 0332991. Western blot analysis was performed to validate these markers and to study the effects of PD 0332991 on pRb phosphorylation. Cell cycle analysis was performed using flow-cytometry. Combination studies were performed to analyze the interaction between PD 0332991 and paclitaxel or carboplatin chemotherapy. Tissue microarrays were constructed from 324 ovarian cancer patients and IHC was performed for p16 and Rb expression. Long-term clinical follow up was available for all patients. Results: Concentration-dependent anti-proliferative effects of PD 0332991were seen in all ovarian cancer cell lines tested, but varied significantly between individual cell lines with up to a 3 log-fold difference in the IC50s (IC50 range: 0.02 to &amp;gt;20 μmol/L). Cell lines with high Rb expression and low p16 expression by gene expression array and Western blot analyses were most sensitive to inhibition by PD 0332991. Cell lines treated with PD 0332991 showed clear G0/G1 arrest and a decrease in S-phase fraction in sensitive but not in resistant cell lines. In addition, Western blot showed that pRb phosphorylation is blocked in a concentration and time dependent manner. IHC for Rb and p16 allows the identification of ovarian cancer patients most likely to benefit from CDK4/6 inhibition. Outcome analyses are ongoing. Conclusion: PD 0332991 shows promising biologic activity in ovarian cancer cells. Assessment of Rb and p16 expression may help select patients who benefit from therapeutic strategies targeting CDK4/6 in ovarian cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 25.

PD 0332991, a selective cyclin D kinase 4/6 inhibitor, preferentially inhibits proliferation of luminal estrogen receptor-positive human breast cancer cell lines in vitro

IntroductionAlterations in cell cycle regulators have been implicated in human malignancies including breast cancer. PD 0332991 is an orally active, highly selective inhibitor of the cyclin D kinases (CDK)4 and CDK6 with ability to block retinoblastoma (Rb) phosphorylation in the low nanomolar range. To identify predictors of response, we determined the in vitro sensitivity to PD 0332991 across a panel of molecularly characterized human breast cancer cell lines.MethodsForty-seven human breast cancer and immortalized cell lines representing the known molecular subgroups of breast cancer were treated with PD 0332991 to determine IC50 values. These data were analyzed against baseline gene expression data to identify genes associated with PD 0332991 response.ResultsCell lines representing luminal estrogen receptor-positive (ER+) subtype (including those that are HER2 amplified) were most sensitive to growth inhibition by PD 0332991 while nonluminal/basal subtypes were most resistant. Analysis of variance identified 450 differentially expressed genes between sensitive and resistant cells. pRb and cyclin D1 were elevated and CDKN2A (p16) was decreased in the most sensitive lines. Cell cycle analysis showed G0/G1 arrest in sensitive cell lines and Western blot analysis demonstrated that Rb phosphorylation is blocked in sensitive lines but not resistant lines. PD 0332991 was synergistic with tamoxifen and trastuzumab in ER+ and HER2-amplified cell lines, respectively. PD 0332991 enhanced sensitivity to tamoxifen in cell lines with conditioned resistance to ER blockade.ConclusionsThese studies suggest a role for CDK4/6 inhibition in some breast cancers and identify criteria for patient selection in clinical studies of PD 0332991.

Clinical evaluation of chemotherapy response predictors developed from breast cancer cell lines.

The goal of this study was to develop pharmacogenomic predictors in response to standard chemotherapy drugs in breast cancer cell lines and test their predictive value in patients who received treatment with the same drugs. Nineteen human breast cancer cell lines were tested for sensitivity to paclitaxel (T), 5-fluorouracil (F), doxorubicin (A) and cyclophosphamide (C) in vitro. Baseline gene expression data were obtained for each cell line with Affymetrix U133A gene chips, and multigene predictors of sensitivity were derived for each drug separately. These predictors were applied individually and in combination to human gene expression data generated with the same Affymetrix platform from fine needle aspiration specimens of 133 stage I-III breast cancers. Tumor samples were obtained at baseline, and each patient received 6 months of preoperative TFAC chemotherapy followed by surgery. Cell line-derived prediction results were correlated with the observed pathologic response to chemotherapy. Statistically robust differentially expressed genes between sensitive and resistant cells could only be found for paclitaxel. False discovery rates associated with the informative genes were high for all other drugs. For each drug, the top 100 differentially expressed genes were combined into a drug-specific response predictor. When these cell line-based predictors were applied to patient data, there was no significant correlation between observed response and predicted response either for individual drug predictors or combined predictions. Cell line-derived predictors of response to four commonly used chemotherapy drugs did not predict response accurately in patients.

Panobinostat (LBH589), a pan-DAC inhibitor, induces cell death in ER+ and HER2 amplified cell lines in vitro and is synergistic in vivo with trastuzumab.

Abstract Abstract #4047 Background: Histone modifications have been investigated as causing altered gene expression resulting in activation of oncogenic pathways. Panobinostat (LBH589) is a potent pan-deacetylase (DAC) inhibitor in clinical development. Given the potentially broad biological effects of this class of agent, we assessed the in vitro effects of panobinostat on a large panel of molecularly profiled human breast cancer cell lines to better define the differential effects of panobinostat across subtypes and to help guide clinical development. In addition, preclinical studies evaluated the effects of panobinostat and the HER2-targeted antibody, trastuzumab.&amp;#x2028; Methods: 43 human cell lines representing known molecular subgroups of breast cancer, and 3 immortalized breast lines were treated in duplicate with panobinostat using two-fold dilutions over 12 concentrations. Dose response curves were generated using a cell count assay to calculate IC50 and LD50 of panobinostat. Data were analyzed against baseline gene expression data (Agilent microarray) to identify genes associated with sensitivity (S) and resistance (R) to panobinostat. 12 cell lines (6 S; 6 R) were then treated with clinically relevant concentrations of panobinostat; RNA was isolated after 48 hrs. Agilent expression profiles compared pre-and post-treatment cell lines to identify differentially expressed genes (DEGs) that change in S and R cell lines. In vivo, nude mice bearing HER2+ and pathway-dependent BT-474 xenografts were treated with panobinostat alone and in combination with trastuzumab. Tumor growth inhibition and delay were measured.&amp;#x2028; Results: In vitro panobinostat has potent anti-proliferative effects across all breast cancer subtypes with most IC50 values &amp;lt;100nM. When analyzed by effects on cell death, luminal ER+ and HER2 amplified cell lines were more sensitive to lethality than non-luminal subtypes using a cut-off of LD50 50nM (Chi-square&amp;lt;0.05 for subtype). Of interest, the non-malignant, immortalized lines were very resistant (LD50 &amp;gt;1000nM), which suggests some differential activity in malignant cell lines versus those that are more “normal”. When gene expression profiles were analyzed pre-and post-treatment, a remarkable overlap was seen in number and specific genes changing after panobinostat exposure in both S and R lines. However, the degree of gene changes was greater in S lines than R lines. In vivo, panobinostat acted synergistically with trastuzumab to inhibit growth of the HER2 amplified breast cancer cell line, BT-474.&amp;#x2028; Conclusion: These studies suggest that ER+ and HER2 amplified breast cancers may be more likely to respond to the pan-DAC inhibitor panobinostat. Profiling studies identified 218 DEGs that were significantly changed between S and R lines. These data support the ongoing development of panobinostat in breast cancer and provide rationale for patient selection. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 4047.

PD 0332991, a selective CDK 4/6 inhibitor, preferentially inhibits proliferation of luminal ER+ breast cancer cells and acts synergistically with tamoxifen and trastuzumab in vitro.

Abstract Abstract #5064 Background: Cell cycle dysregulation is a common molecular finding in malignancy and the cyclin-D kinases (CDK) represent an attractive target in this pathway. PD 0332991 (Pfizer Inc.) is an orally active potent and highly selective inhibitor of CDK 4 and CDK6 kinases with the ability to block pRb phosphorylation at serine 780 and 795 in the low nanomolar range. To identify predictors of response to PD 0332991, we determined the in vitro sensitivity to PD 0332991 of a large panel of molecularly characterized breast cancer cell lines, and then used baseline expression profiles to determine genes associated with response. Methods: 44 human breast cancer cell lines representing the known molecular subgroups of breast cancer (i.e. luminal, HER2, basal, etc) and 3 immortalized breast lines were treated in duplicate with PD 0332991 using two-fold dilutions over 12 concentrations. Dose response curves were generated using a cell count assay to calculate the IC50 across the panel. These data were then analyzed against baseline gene expression data (Agilent microarray) to identify genes associated with sensitivity and resistance to PD 0332991. ANOVA analysis identified genes associated with response and/or resistance to PD 0332991. Western blot analysis was performed analyzing the effects of PD 0332991 on pRb phosphorylation. Cell cycle analysis was performed using NIM-DAPI staining and flow-cytometry. In addition, combination studies were performed to analyze the interaction between PD 0332991 and tamoxifen and trastuzumab. These data were analyzed using Calcusyn software to generate a combination index (CI) to define the interaction as additive (CI=1), synergisitc (CI&amp;lt;1), or antagonistic (CI&amp;gt;1). Results: Cell lines representing the luminal ER+ subtype (including HER2 amplified) were most sensitive to inhibition by PD 0332991 and non-luminal/basal (triple-negative) were most resistant. ANOVA analysis identified 563 differentially expressed genes between the sensitive and resistant groups. While, several of these genes are associated with breast cancer subtype, pRb was elevated and CDKN2A (p16) was decreased in the most sensitive lines. Cell lines treated with PD 0332991 showed clear G0/G1 arrest and a decrease in S-phase fraction in sensitive but not in resistant cell lines. In addition, Western blot showed that pRb phosphorylation is blocked in a time dependent manner. Finally, the combination of tamoxifen and PD 0332991 was strongly synergistic in three ER+ cell lines evaluated and appeared synergistic with trastuzumab as well. Conclusion: These studies suggest that a subgroup of breast cancers may be more likely to benefit from treatment with the PD 0332991 CDK4/6 inhibitor than others. This group represents ER+ luminal breast cancer and is associated with elevated pRb and low p16. The combination of PD 0332991 and tamoxifen and trastuzumab shows promising biologic activity in ER+ and HER2 amplified breast cancer cell lines, respectively. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 5064.