Sarah Runswick

Olaparib for Metastatic Breast Cancer in Patients with a Germline BRCA Mutation

BACKGROUND Olaparib is an oral poly(adenosine diphosphate–ribose) polymerase inhibitor that has promising antitumor activity in patients with metastatic breast cancer and a germline BRCA mutation. METHODS We conducted a randomized, open‐label, phase 3 trial in which olaparib monotherapy was compared with standard therapy in patients with a germline BRCA mutation and human epidermal growth factor receptor type 2 (HER2)–negative metastatic breast cancer who had received no more than two previous chemotherapy regimens for metastatic disease. Patients were randomly assigned, in a 2:1 ratio, to receive olaparib tablets (300 mg twice daily) or standard therapy with single‐agent chemotherapy of the physicians choice (capecitabine, eribulin, or vinorelbine in 21‐day cycles). The primary end point was progression‐free survival, which was assessed by blinded independent central review and was analyzed on an intention‐to‐treat basis. RESULTS Of the 302 patients who underwent randomization, 205 were assigned to receive olaparib and 97 were assigned to receive standard therapy. Median progression‐free survival was significantly longer in the olaparib group than in the standard‐therapy group (7.0 months vs. 4.2 months; hazard ratio for disease progression or death, 0.58; 95% confidence interval, 0.43 to 0.80; P<0.001). The response rate was 59.9% in the olaparib group and 28.8% in the standard‐therapy group. The rate of grade 3 or higher adverse events was 36.6% in the olaparib group and 50.5% in the standard‐therapy group, and the rate of treatment discontinuation due to toxic effects was 4.9% and 7.7%, respectively. CONCLUSIONS Among patients with HER2‐negative metastatic breast cancer and a germline BRCA mutation, olaparib monotherapy provided a significant benefit over standard therapy; median progression‐free survival was 2.8 months longer and the risk of disease progression or death was 42% lower with olaparib monotherapy than with standard therapy. (Funded by AstraZeneca; OlympiAD ClinicalTrials.gov number, NCT02000622.)

Transcriptional Pathway Signatures Predict MEK Addiction and Response to Selumetinib (AZD6244)

Selumetinib (AZD6244, ARRY-142886) is a selective, non-ATP-competitive inhibitor of mitogen-activated protein/extracellular signal-regulated kinase kinase (MEK)-1/2. The range of antitumor activity seen preclinically and in patients highlights the importance of identifying determinants of response to this drug. In large tumor cell panels of diverse lineage, we show that MEK inhibitor response does not have an absolute correlation with mutational or phospho-protein markers of BRAF/MEK, RAS, or phosphoinositide 3-kinase (PI3K) activity. We aimed to enhance predictivity by measuring pathway output through coregulated gene networks displaying differential mRNA expression exclusive to resistant cell subsets and correlated to mutational or dynamic pathway activity. We discovered an 18-gene signature enabling measurement of MEK functional output independent of tumor genotype. Where the MEK pathway is activated but the cells remain resistant to selumetinib, we identified a 13-gene signature that implicates the existence of compensatory signaling from RAS effectors other than PI3K. The ability of these signatures to stratify samples according to functional activation of MEK and/or selumetinib sensitivity was shown in multiple independent melanoma, colon, breast, and lung tumor cell lines and in xenograft models. Furthermore, we were able to measure these signatures in fixed archival melanoma tumor samples using a single RT-qPCR-based test and found intergene correlations and associations with genetic markers of pathway activity to be preserved. These signatures offer useful tools for the study of MEK biology and clinical application of MEK inhibitors, and the novel approaches taken may benefit other targeted therapies.

Subtypes of primary colorectal tumors correlate with response to targeted treatment in colorectal cell lines

BackgroundColorectal cancer (CRC) is a heterogeneous and biologically poorly understood disease. To tailor CRC treatment, it is essential to first model this heterogeneity by defining subtypes of patients with homogeneous biological and clinical characteristics and second match these subtypes to cell lines for which extensive pharmacological data is available, thus linking targeted therapies to patients most likely to respond to treatment.MethodsWe applied a new unsupervised, iterative approach to stratify CRC tumor samples into subtypes based on genome-wide mRNA expression data. By applying this stratification to several CRC cell line panels and integrating pharmacological response data, we generated hypotheses regarding the targeted treatment of different subtypes.ResultsIn agreement with earlier studies, the two dominant CRC subtypes are highly correlated with a gene expression signature of epithelial-mesenchymal-transition (EMT). Notably, further dividing these two subtypes using iNMF (iterative Non-negative Matrix Factorization) revealed five subtypes that exhibit activation of specific signaling pathways, and show significant differences in clinical and molecular characteristics. Importantly, we were able to validate the stratification on independent, published datasets comprising over 1600 samples. Application of this stratification to four CRC cell line panels comprising 74 different cell lines, showed that the tumor subtypes are well represented in available CRC cell line panels. Pharmacological response data for targeted inhibitors of SRC, WNT, GSK3b, aurora kinase, PI3 kinase, and mTOR, showed significant differences in sensitivity across cell lines assigned to different subtypes. Importantly, some of these differences in sensitivity were in concordance with high expression of the targets or activation of the corresponding pathways in primary tumor samples of the same subtype.ConclusionsThe stratification presented here is robust, captures important features of CRC, and offers valuable insight into functional differences between CRC subtypes. By matching the identified subtypes to cell line panels that have been pharmacologically characterized, it opens up new possibilities for the development and application of targeted therapies for defined CRC patient sub-populations.

Enhanced Apoptosis and Tumor Growth Suppression Elicited by Combination of MEK (Selumetinib) and mTOR Kinase Inhibitors (AZD8055)

The mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase/AKT signaling pathways interact at multiple nodes in cancer, including at mTOR complexes, suggesting an increased likelihood of redundancy and innate resistance to any therapeutic effects of single pathway inhibition. In this study, we investigated the therapeutic effects of combining the MAPK extracellular signal-regulated kinase (MEK)1/2 inhibitor selumetinib (AZD6244) with the dual mTORC1 and mTORC2 inhibitor (AZD8055). Concurrent dosing in nude mouse xenograft models of human lung adenocarcinoma (non-small cell lung cancers) and colorectal carcinoma was well tolerated and produced increased antitumor efficacy relative to the respective monotherapies. Pharmacodynamic analysis documented reciprocal pathway inhibition associated with increased apoptosis and Bim expression in tumor tissue from the combination group, where key genes such as DUSP6 that are under MEK functional control were also modulated. Our work offers a strong rationale to combine selumetinib and AZD8055 in clinical trials as an attractive therapeutic strategy.

Long-term responders on olaparib maintenance in high-grade serous ovarian cancer: Clinical and molecular characterization

Purpose: Maintenance therapy with olaparib has improved progression-free survival in women with high-grade serous ovarian cancer (HGSOC), particularly those harboring BRCA1/2 mutations. The objective of this study was to characterize long-term (LT) versus short-term (ST) responders to olaparib. Experimental Design: A comparative molecular analysis of Study 19 (NCT00753545), a randomized phase II trial assessing olaparib maintenance after response to platinum-based chemotherapy in HGSOC, was conducted. LT response was defined as response to olaparib/placebo >2 years, ST as <3 months. Molecular analyses included germline BRCA1/2 status, three-biomarker homologous recombination deficiency (HRD) score, BRCA1 methylation, and mutational profiling. Another olaparib maintenance study (Study 41; NCT01081951) was used as an additional cohort. Results: Thirty-seven LT (32 olaparib) and 61 ST (21 olaparib) patients were identified. Treatment was significantly associated with outcome (P < 0.0001), with more LT patients on olaparib (60.4%) than placebo (11.1%). LT sensitivity to olaparib correlated with complete response to chemotherapy (P < 0.05). In the olaparib LT group, 244 genetic alterations were detected, with TP53, BRCA1, and BRCA2 mutations being most common (90%, 25%, and 35%, respectively). BRCA2 mutations were enriched among the LT responders. BRCA methylation was not associated with response duration. High myriad HRD score (>42) and/or BRCA1/2 mutation was associated with LT response to olaparib. Study 41 confirmed the correlation of LT response with olaparib and BRCA1/2 mutation. Conclusions: Findings show that LT response to olaparib may be multifactorial and related to homologous recombination repair deficiency, particularly BRCA1/2 defects. The type of BRCA1/2 mutation warrants further investigation. Clin Cancer Res; 23(15); 4086–94. ©2017 AACR.

RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor Cediranib

Pre-clinical models of tumour biology often rely on propagating human tumour cells in a mouse. In order to gain insight into the alignment of these models to human disease segments or investigate the effects of different therapeutics, approaches such as PCR or array based expression profiling are often employed despite suffering from biased transcript coverage, and a requirement for specialist experimental protocols to separate tumour and host signals. Here, we describe a computational strategy to profile transcript expression in both the tumour and host compartments of pre-clinical xenograft models from the same RNA sample using RNA-Seq. Key to this strategy is a species-specific mapping approach that removes the need for manipulation of the RNA population, customised sequencing protocols, or prior knowledge of the species component ratio. The method demonstrates comparable performance to species-specific RT-qPCR and a standard microarray platform, and allowed us to quantify gene expression changes in both the tumour and host tissue following treatment with cediranib, a potent vascular endothelial growth factor receptor tyrosine kinase inhibitor, including the reduction of multiple murine transcripts associated with endothelium or vessels, and an increase in genes associated with the inflammatory response in response to cediranib. In the human compartment, we observed a robust induction of hypoxia genes and a reduction in cell cycle associated transcripts. In conclusion, the study establishes that RNA-Seq can be applied to pre-clinical models to gain deeper understanding of model characteristics and compound mechanism of action, and to identify both tumour and host biomarkers.

Abstract 5365: Molecular and pharmacological (EGFRi, MEKi) characterisation of a colorectal cancer (CRC) cell line panel to evaluate cellular phenotype and efficacy of targeted therapies in CRC

A broad range of targeted agents are in early development for treatment of solid tumours. It is important that patients receive treatments which are tailored to work optimally based on their individual tumour biology. Retrospective analysis of clinical data for the EGFR tyrosine kinase inhibitor, Iressa, in lung cancer demonstrated cell line panels can provide a platform to direct targeted therapies towards specific patient subpopulations. In order to evaluate targeted agents in colorectal cancer we have characterized a panel of 49 colorectal tumour cell lines derived from Dukes stage A-D of CRC for commonly occurring mutations (KRas, BRAF, PI3Ka, PTEN), microsatellite instability, gene copy number alterations (Agilent 244K ArrayCGH), mRNA expression (Affymetrics HG_U133_plus_2) and miRNA expression (TLDA – 177 miRNAs). These data have been used to characterize the differentiation status of the cell lines and to link to compound activity. We have probed the anti-proliferative activity of compounds from several growth factor pathways, EGFR, RAS/MEK and PI3K to evaluate pathway dependence and linkage to molecular data. The greatest activity of the EGFR TKI inhibitor was in Ras, Raf, PTEN, PI3K wild type (quadruple negative (QN)) CRC lines, in agreement with clinical data for EGFR antibodies. However, of the 9 QN lines profiled, only 4 were hypersensitive (GI50 Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5365. doi:10.1158/1538-7445.AM2011-5365

Use of colorectal cancer subtypes identified through iterative clustering to predict response to therapy.

482 Background: Colorectal cancer (CRC) is generally stratified based on genetic and epigenetic features, such as KRAS mutation and microsatellite instability status. In order to facilitate the development of new targeted drugs and treatment regimens, it is important to redefine CRC at the molecular level by identifying subtypes that are relevant for response to targeted therapy. METHODS We applied a new unsupervised approach for iteratively stratifying tumor samples using genome-wide mRNA expression data. The resulting gene expression signatures were used to subtype CRC cell line panels and publicly available CRC tumor datasets. We employed pharmacological data on the cell line panels to link the subtypes to therapy response. RESULTS Starting from a gene expression dataset of 63 CRC tumor samples, we employed non-negative matrix factorization (NMF) and identified two dominant CRC subtypes. In agreement with previously published results, one of the types showed a mesenchymal and the other an epithelial-like gene expression pattern. In a second step, we applied NMF on these two dominant subtypes and further stratified them into two and three subtypes, respectively. The resulting five CRC subtypes show many differences, most notably activation of specific signaling pathways. Importantly, we recovered these five subtypes in several independent, publicly available CRC datasets. This strongly suggests that the signatures capture disease-relevant features of CRC. Furthermore, we found that the different subtypes corresponded to different cell lines in a panel of CRC cell lines. The clustered CRC cell lines displayed differential responses to a number of targeted compounds, indicating that the new CRC clusters may represent disease subtypes that of differential drug sensitivity. CONCLUSIONS The CRC subtypes discovered using our new method offer new insights into the functional and molecular processes driving CRC. Furthermore, the evidence suggests that these subtypes may differ in activated pathway status and the response to some targeted inhibitors, indicating that targeting pathways conserved in these subtypes may provide new drug discovery opportunities.

Abstract 2995: Identifying subtypes of colorectal cancer tumors and cell lines with treatment relevance using iterative clustering (iNMF)

Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL Genetic and epigenetic features such as KRAS mutation status have been used to define colorectal cancer (CRC) subtypes and to take treatment decisions. In order to develop new targeted drugs, however, it is necessary to gain a better understanding of the molecular differences of CRC subtypes. We developed a new unsupervised approach for stratifying tumor samples using genome-wide mRNA expression data. Our method is based on the iterative application of non-negative matrix factorization (iNMF) on randomly selected sets of genes. In a sample set consisting of 63 CRC tumors, we identified two dominant subtypes. These subtypes were highly concordant with the epithelial-mesenchymal-transition (EMT) gene expression signatures consistent with recent publications. However further stratification of the tumor samples revealed five subtypes. These subtypes exhibit distinct differences, most notably differential activation of specific signaling pathways. Importantly, assessment of this method and derived subtype gene signatures stratified several independent, published datasets, suggesting that the signatures capture disease-relevant intrinsic features of CRC. Furthermore, application of the gene signatures to expression data obtained from three independent colon cell line panels revealed that the tumor subtypes were represented in all these different panels. Additional integration of pharmacological response data allowed us to identify several targeted compounds showing differential response across the subtypes. The CRC stratification obtained with our new method, iNMF, offers valuable insight into the differences between CRC subtypes at a functional level. Most importantly, it captures features of the disease that are highly relevant for the development of new targeted drugs in defined CRC patient sub-populations. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2995. doi:1538-7445.AM2012-2995

Abstract A13: Characterization of CRC primary explant models in comparison to standard human tumor cell-line-derived CRC xenografts.

Primary tumor xenograft models (PTX) are established in immunodeficient mice by implantation of material directly from patient tumors. These models are propagated in vivo so that they are not subjected to additional selection pressures within tissue culture. The models reputedly reflect a greater genetic diversity of disease than can be recapitulated within tumor cell line derived xenografts and may have morphological differences. Consequently, such models may be of greater relevance for the evaluation of drug efficacy. To verify differences versus cell line derived xenograft models, we have examined samples of 42 CRC PTX models, derived from Dukes stage A-D tumor samples, which were obtained from European contract research organisations) Characterisation included analysis of common genetic mutations (KRas, BRAF, PI3Ka, PTEN, P53 and APC), mRNA expression via microarray platforms (Affymetrics HG_U133_plus_2), high-throughput RT-PCR of specific probes (mouse and human) to examine stromal genes, and the activation of particular proteins using reverse phase antibody arrays and immunohistochemistry. Comparisons were made with 6 commonly used CRC cell line derived xenografts. The PTX models overall represent a broader range of molecular pathology - for example the inclusion of wild-type Kras tumors. Histopathological characterisation also confirmed that the PTX models have a higher stromal content that is routinely observed in cell line derived xenografts, and a more complex morphology with higher tumor cell differentiation. PTX models may potentially complement drug discovery activities by providing a wider platform in which to test preclinical hypotheses - based upon a defined biological mechanism and/or a genetic determinant of sensitivity. 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 A13.