Cancer Cell Line Encyclopedia Data Research Articles

In Vitro Characterization and Clinical Correlation of BRCAness as a Personalized Biomarker for Radiosensitization With Homologous Recombination Directed Therapies

BRCA1/2-mutant tumors demonstrate sensitivity to cisplatin, a cross-linking agent, but only mild sensitivity to ionizing radiation (IR). Cisplatin with concurrent IR is one of the most common methods to improve outcomes with radiotherapy. However the mechanism of synergy and the population of patients who benefit is poorly understood. Recently, BRCAness (homologous recombination deficiency; HRD) has been described in the absence of germline BRCA1/2 mutations. We hypothesize that BRCAness tumors exhibit sensitivity to cisplatin + IR (c-IR), and that HRD is a significant driver of the observed clinical benefit of combination therapy. Sensitivity to c-IR was examined in vitro in isogenic cell line pairs and sporadic breast cancer cell lines. HR function was assessed in H1299 cells with BRCA1 inactivation and DLD1 cells with BRCA2 knockout using 3 assays: radiation-induced Rad51 foci, a flow-based DR-GFP DNA repair reporter assay and a clonogenic survival (CSA) with cisplatin +/- IR. Interaction ratios (INTR) were calculated to identify super-additivity as follows: (cisplatin survival x IR survival)/c-IR survival. Four sporadic, non-BRCA1/2 mutated breast cancer cell lines were identified using the Cancer Cell Line Encyclopedia (CCLE) and evaluated for HRD with these 3 assays. Since higher rates of HRD are observed in triple-negative breast cancer (TNBC), we also examined 10 TNBC patients treated with neoadjuvant c-IR on a clinical trial and assessed HRD by ex-vivo radiation-induced formation of Rad51 foci. The BRCA1/2 isogenic pairs demonstrated functional HRD based on the Rad51 and DRGFP assays. CSA results demonstrate no super-additivity of c-IR in HR proficient lines (INTR 0.5), but super-additivity in HRD cell lines (INTR 17.3). A genomic scar score, LST (large scale transition), was generated for all breast cancer cell lines based on CCLE data, with higher LST scores suggesting HRD. Of the 4 selected cell lines, the 2 with low LST demonstrated functional HR and additive c-IR sensitivity (INTR 1.4, 1.7), while the 2 with high LST demonstrated HRD and super-additive sensitivity to c-IR (INTR 3.4, 4.9). Of 6 evaluable TNBC patients treated with neoadjuvant c-IR, three each demonstrated complete pathological response and partial response. Multiple in vitro assays suggest that BRCA1/2-mutated tumors are much more sensitive to the widely used combination of c-IR as compared with HR proficient tumors. BRCAness cell lines demonstrate a similar but milder sensitivity to c-IR. Therefore, reliable methods for the determination of “BRCAness” should identify a subpopulation of non-germline BRCA1/2-mutated breast cancer patients who may benefit from therapy with c-IR. The value of BRCAness testing is likely to extend beyond breast and ovarian cancer as additional cancers with HRD are identified.

Abstract 832: In vitro characterization and clinical correlation of BRCAness as a personalized biomarker for radiosensitization with homologous recombination-directed therapies

Abstract Introduction: BRCA1 and BRCA2 are involved in double-strand break (DSBs) repair via homologous recombination (HR). Clinically, BRCA1/2 mutant tumors show sensitivity to cisplatin, a cross-linking agent, but only mild sensitivity to ionizing radiation (IR). The addition of cisplatin to IR has been one of the largest advances in improving patient outcomes with radiotherapy in the past 50 years. Recently, BRCAness has been described wherein sporadic deficits in HR are observed in the absence of germline BRCA1/2 mutations. We hypothesize that BRCAness tumors also exhibit sensitivity to the combination of cisplatin + IR, and are a significant driver of the observed clinical benefit of combination therapy. Methods: Sensitivity to cisplatin + IR was examined in vitro and correlated with clinical data. Functional HR deficiency (HRD) was assessed in isogenic BRCA1 (H1299; lung) and BRCA2 (DLD1; colon) knockout pairs using 3 assays: the Rad51 foci assay, a flow-based DRGFP assay (DSB reporter assay) and clonogenic survival assays (CSA) with cisplatin +/- IR. Cancer Cell Line Encyclopedia (CCLE) data was used to identify breast cancer cell lines with sporadic, non-BRCA1/2 mutations. Functional HRD in 4 breast cancer cell lines was assessed with the same 3 assays used for the BRCA1/2 isogenic pairs. We also examined 14 triple-negative breast cancer (TNBC) patients treated with neoadjuvant cisplatin + IR. Nine of the 14 had available tissue, which was stained for Rad51 foci. Results: Functional HRD is observed in the BRCA1/2 isogenic pairs, as demonstrated by the Rad51 foci and DRGFP assay results. Increased sensitivity to cisplatin + IR is observed in the BRCA1/2 knockout lines in CSA results compared to isogenic controls. A genomic scar score, LST (large scale transition), was generated for each line based on CCLE data, with higher LST scores suggesting increased genomic instability and therefore HRD. Four cell lines were selected for further study. Similar functional HRD and cisplatin + IR sensitivity is observed in 2 breast cancer cell lines with high LST scores, as compared to 2 with low LST scores. Epistasis was observed upon siBRCA1 knockdown in the 2 cell lines with high LST scores. Half of the 14 TNBC patients demonstrated pathological complete response to neoadjuvant cisplatin + IR. Nine of the 14 patients had available tissue, of which 4 demonstrated functional HRD by the Rad51 foci assay. Conclusions: Multiple in vitro assays suggest that HRD tumors are much more sensitive to the widely used combination of cisplatin + IR as compared with HR proficient tumors. Data from our small cohort of triple-negative breast cancer patients appear concordant with these findings. Reliable determination of “BRCAness” tumors should identify a subpopulation of non-germline BRCA1/2-mutated breast cancer patients who may benefit from targeted therapy with cisplatin + IR. Citation Format: Jennifer Ma, Andrew C. Bell, Jeremy Setton, Justin Haseltine, Marcher Thompson, Rachna Shah, Benjamin H. Lok, Robert Delsite, Rebecca Aft, Nadeem Riaz, Simon Powell. In vitro characterization and clinical correlation of BRCAness as a personalized biomarker for radiosensitization with homologous recombination-directed therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 832. doi:10.1158/1538-7445.AM2017-832

Panobinostat sensitizes cyclin E high, homologous recombination-proficient ovarian cancer to olaparib

ObjectiveHomologous recombination (HR) proficient ovarian cancers, including CCNE1 (cyclin E)-amplified tumors, are resistant to poly (ADP-ribose) polymerase inhibitors (PARPi). Histone deacetylase inhibitors (HDACi) are effective in overcoming tumor resistance to DNA damaging drugs. Our goal was to determine whether panobinostat, a newly FDA-approved HDACi, can sensitize cyclin E, HR-proficient ovarian cancer cells to the PARPi olaparib. MethodsExpression levels of CCNE1 (cyclin E), BRCA1, RAD51 and E2F1 in ovarian tumors and cell lines were extracted from The Cancer Genome Atlas (TCGA) and Broad-Novartis Cancer Cell Line Encyclopedia (CCLE). In HR-proficient ovarian cancer cell line models (OVCAR-3, OVCAR-4, SKOV-3, and UWB1.289+BRCA1 wild-type), cell growth and viability were assessed by sulforhodamine B and xenograft assays. DNA damage and repair (pH2AX and RAD51 co-localization and DRGFP reporter activity) and apoptosis (cleaved PARP and cleaved caspase-3) were assessed by immunofluorescence and Western blot assays. ResultsTCGA and CCLE data revealed positive correlations (Spearman) between cyclin E E2F1, and E2F1 gene targets related to DNA repair (BRCA1 and RAD51). Panobinostat downregulated cyclin E and HR repair pathway genes, and reduced HR efficiency in cyclin E-amplified OVCAR-3 cells. Further, panobinostat synergized with olaparib in reducing cell growth and viability in HR-proficient cells. Similar co-operative effects were observed in xenografts, and on pharmacodynamic markers of HR repair, DNA damage and apoptosis. ConclusionsThese results provide preclinical rationale for using HDACi to reduce HR in cyclin E-overexpressing and other types of HR-proficient ovarian cancer as a means of enhancing PARPi activity.

Abstract 1520: Identifying differential dependency networks accounting for response to NEDD8-inhibitor in large-scale cancer cell line data

Abstract The Cancer Cell Line Encyclopedia (CCLE) houses molecular profiles of ∼800 human long term cell lines spanning several different histological types of cancer. In addition, Cancer Therapeutics Response Portal (CTRP) provides drug response measurements for 481 small molecules. Integration of these data enables investigation of the molecular correlates of drug response (sensitivity and resistance). In this current effort, we studied the NEDDylation small molecule inhibitor, MLN4924, in the context of genomic data to uncover novel mechanistic correlates of drug response across the panel of cell lines. We recently reported (Jung and Kim 2016 NAR) development of a robust computational method that shows promise to identify novel insights when applied to multi-dimensional data sets as outlined above. The Evaluation of Differential Dependency (EDDY) employs Bayesian networks to represent statistically distinct differences in relationships between genes within a specific biological pathway as queried between two conditions, in this instance, cell lines that are sensitive and those that are non-sensitive to MLN4924. While EDDY has been successfully employed in the analysis of specific diseases such as TCGA adrenocortical carcinoma, its statistical rigor incurred a prohibitive computational load to assess conditional differences across larger datasets. Recent computational enhancements to EDDY enable processing of larger datasets in reasonable time while maintaining sensitivity. The capability of analyzing broader pan-cancer datasets such as CCLE has enabled EDDY to become more capable in identifying general trends across disease subtypes. Specifically, we demonstrate the enhanced EDDY in analysis of MLN4924 response across the CCLE data set combined with CTRP data set. Initial outcomes from EDDY point to both anticipated and unanticipated biological determinants of response. For example, it is noted that specific oncogenic pathways, such as those centered on PIK3CA, appear to show differential dependencies in the sensitive and non-sensitive cell lines. We also observe genes and candidate pathways related to apoptotic mechanisms that may reveal mechanistic insights to predicting drug response. Specifically, genes and pathways associated with certain apoptotic mechanisms around mitochondrial proteins and glutathione peroxidase may serve as unique determinants of drug response. Multidimensional data analyzed by EDDY uncovers candidate mechanisms of vulnerability to specific small molecule inhibitors, which may guide development of predictive models for treatment planning when using agents with highly context-dependent efficacies. Supported by NIH U01CA168397 Citation Format: Gil Speyer, Harshil Dhruv, Jeff Kiefer, Stuart Schreiber, Paul Clemons, Michael E. Berens, Seungchan Kim. Identifying differential dependency networks accounting for response to NEDD8-inhibitor in large-scale cancer cell line data. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1520.

High-dimensional genomic data bias correction and data integration using MANCIE.

High-dimensional genomic data analysis is challenging due to noises and biases in high-throughput experiments. We present a computational method matrix analysis and normalization by concordant information enhancement (MANCIE) for bias correction and data integration of distinct genomic profiles on the same samples. MANCIE uses a Bayesian-supported principal component analysis-based approach to adjust the data so as to achieve better consistency between sample-wise distances in the different profiles. MANCIE can improve tissue-specific clustering in ENCODE data, prognostic prediction in Molecular Taxonomy of Breast Cancer International Consortium and The Cancer Genome Atlas data, copy number and expression agreement in Cancer Cell Line Encyclopedia data, and has broad applications in cross-platform, high-dimensional data integration.

A systematic study on drug-response associated genes using baseline gene expressions of the Cancer Cell Line Encyclopedia.

We have studied drug-response associated (DRA) gene expressions by applying a systems biology framework to the Cancer Cell Line Encyclopedia data. More than 4,000 genes are inferred to be DRA for at least one drug, while the number of DRA genes for each drug varies dramatically from almost 0 to 1,226. Functional enrichment analysis shows that the DRA genes are significantly enriched in genes associated with cell cycle and plasma membrane. Moreover, there might be two patterns of DRA genes between genders. There are significantly shared DRA genes between male and female for most drugs, while very little DRA genes tend to be shared between the two genders for a few drugs targeting sex-specific cancers (e.g., PD-0332991 for breast cancer and ovarian cancer). Our analyses also show substantial difference for DRA genes between young and old samples, suggesting the necessity of considering the age effects for personalized medicine in cancers. Lastly, differential module and key driver analyses confirm cell cycle related modules as top differential ones for drug sensitivity. The analyses also reveal the role of TSPO, TP53, and many other immune or cell cycle related genes as important key drivers for DRA network modules. These key drivers provide new drug targets to improve the sensitivity of cancer therapy.

Abstract 4174: An integrative genomic analysis for clinically relevant cancer genetic aberration and targeted therapeutic prediction

Abstract While the genomic and genetic basis of many cancers has been partly elucidated, less is known about the identity of specific cancer genes that are associated with clinical parameters such as clinical stage, metastasis, and survival. To address this question, we compiled multiple genomic data types (mutations, copy number alterations, gene expression and methylation status) as well as clinical meta-data for twelve different cancers in the Cancer Genome Atlas Project. We used an elastic-net regularized regression method on the combined genomic data to identify genetic aberrations and their associated cancer genes that are indicators of clinical parameters in each cancer type. To facilitate the identification of clinically actionable genetic aberrations from this cancer genomic data, we utilized genomic and drug response data from the Cancer Cell Line Encyclopedia (CCLE). These expansive data sets of hundreds of cancer cell lines include three genomic data types (mutations, copy number alterations, gene expression) and drug response data. To demonstrate the performance and validity of this approach, we demonstrated that our bioinformatics pipeline successfully identified i) synthetic genes that have their CNVs perfectly associated with stages from a simulated data ii) mutations of TGFBR2, methylation of MLH1 and other driver genes that have genetic changes associated with colorectal cancer (CRC) that demonstrate microsatellite instability CRC from TCGA data, and iii) IDH1 that obtained mutations associated with survival according to glioblastoma (GBM) data. We identify the top ranked genes that delineate key clinical features such as survival analysis or clinical stages for 13 cancers. A fit of the elastic-net regularized regression to more than 200 samples/cancer type and integrative analysis of integrative genomic platforms identified the set of top gene predictors of clinical parameters (TCGA data) and drug response (CCLE data). There are several genes ranked top in multiple cancer types while most of them are unique to a cancer type. These results provide us with new insights into the underlying genetic similarities and differences among 13 cancers; namely, common genetic characteristics that have implications for clinical phenotype. Finally, we are identifying the core set of gene aberrations that are both clinically relevant and drug responsible. Citation Format: HoJoon Lee, Hanlee P. Ji. An integrative genomic analysis for clinically relevant cancer genetic aberration and targeted therapeutic prediction. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4174. doi:10.1158/1538-7445.AM2014-4174