Color bars indicate Drug Responding Score (DRS). NIHMS1622926-product-8.xlsx (24K) GUID:?C6AB5A0C-5A45-4799-B7E0-DC7ACDCA5222 SUMMARY Liver cancers are highly heterogeneous with poor prognosis and Mirk-IN-1 drug response. A better understanding between genetic alterations and drug responses would facilitate precision treatment for liver cancers. To characterize the landscape of pharmacogenomic interactions in liver cancers, we developed a protocol to establish human liver malignancy cell models at a success rate around 50% and generated Liver Malignancy Model Repository (LIMORE) with 81 cell models. LIMORE represented genomic and transcriptomic heterogeneity of Mirk-IN-1 main cancers. Interrogation of the pharmacogenomic scenery of LIMORE discovered unexplored gene-drug associations, including synthetic lethalities to prevalent alterations in liver cancers. Moreover, predictive biomarker candidates were suggested for the selection of sorafenib-responding patients. LIMORE provides a rich resource facilitating drug discovery in liver cancers. models for various types of cancers (Boj et al., 2015; Broutier et al., 2017; Gao et al., 2014; Lee et al., 2018; Pauli et al., 2017; Sachs et al., 2018; van de Wetering et Mouse monoclonal to INHA al., 2015; Vlachogiannis et al., 2018), leading to international collaborations including Human Cancer Model Initiative (HCMI) and Malignancy Cell Line Manufacturing plant (CCLF). Most of these reports focused on generating cancer cell models as a first step, yet experienced analyzed limited pharmacogenomics (Boehm and Golub, 2015; Williams and McDermott, 2017). To bridge the precision medicine and malignancy heterogeneity, it is important to perform a full spectrum of pharmacogenomic characterization of patient-derived malignancy models Mirk-IN-1 at scale. For the liver cancer, there are only around 30 cell lines available to the community, which are insufficient to capture the genomic and transcriptomic diversity of this disease (Goodspeed et al., 2016). Moreover, available HCC cell lines underrepresent HBV-associated HCCs, which accounts for more than half of HCCs worldwide. On the top of that, it has been recently reported that many of the widely used HCC cell lines were actually contaminated by HeLa cells (Rebouissou et al., 2017). Therefore, to systematically analyze genetic heterogeneity and drug responses, it is imperative to develop a large panel of patient-derived liver cancer cell models and, accordingly, discover gene-drug associations. RESULTS Establishment of Liver Malignancy Model Repository (LIMORE) We built LIMORE by collecting 31 public liver malignancy cell lines and generating patient-derived models (Figures S1A and S1B). To generate liver malignancy cell models, we optimized the primary culture protocol by adding the ROCK inhibitor Y-27632 and the TGF- inhibitor A83-01, based on a previous study (Qiu et al., 2016). Y-27632 facilitates attachment of main cells whereas A83-01 inhibits mesenchymal cells and supports epithelia cell growth (Katsuda et al., 2017; Liu et al., 2012b). The addition of Y-27632 and A83-01 promoted the success rate of main culture to 46%, likely allowing long-term survival and proliferation of tumor epithelial cells (Figures S1C and S1D). These models were named as Chinese Liver Malignancy (CLC) cell models. In total, 50 models were generated from 49 Chinese HCCs (CLC19 and CLC20 were subclones from your same HCC) with detailed clinicopathological information (Table S1). Among them, 8 were from Edmondson Grade II HCCs and 40 from Edmondson Grade III. These models were enriched in HBV contamination (47/50) with other etiologies underrepresented. No significant correlation was found between clinicopathological parameters and the success of model establishment (Table S1). In line with previous findings (Qiu et al., 2016), comparison of Mirk-IN-1 cell models and primary cancers from 9 patients suggested that these generated models retained mutational and transcriptional landscapes of original main cancers (Figures S1ECS1G). LIMORE consisted of 81 authenticated liver cancer cell models, including 79 HCC models and 2 hepatoblastoma models (Table S1). Compared to CCLE and GDSC that.