Genetics of gene expression (eQTLs or expression QTLs) has proved an indispensable tool for understanding biological pathways and pathomechanisms of trait-associated SNPs. However, power of most genome-wide eQTL studies is still limited. We performed a large eQTL study in peripheral blood mononuclear cells of 2112 individuals increasing the power to detect trans-effects genome-wide. Going beyond univariate SNP-transcript associations, we analyse relations of eQTLs to biological pathways, polygenetic effects of expression regulation, trans-clusters and enrichment of co-localized functional elements. We found eQTLs for about 85% of analysed genes, and 18% of genes were trans-regulated. Local eSNPs were enriched up to a distance of 5 Mb to the transcript challenging typically implemented ranges of cis-regulations. Pathway enrichment within regulated genes of GWAS-related eSNPs supported functional relevance of identified eQTLs. We demonstrate that nearest genes of GWAS-SNPs might frequently be misleading functional candidates. We identified novel trans-clusters of potential functional relevance for GWAS-SNPs of several phenotypes including obesity-related traits, HDL-cholesterol levels and haematological phenotypes. We used chromatin immunoprecipitation data for demonstrating biological effects. Yet, we show for strongly heritable transcripts that still little trans-chromosomal heritability is explained by all identified trans-eSNPs; however, our data suggest that most cis-heritability of these transcripts seems explained. Dissection of co-localized functional elements indicated a prominent role of SNPs in loci of pseudogenes and non-coding RNAs for the regulation of coding genes. In summary, our study substantially increases the catalogue of human eQTLs and improves our understanding of the complex genetic regulation of gene expression, pathways and disease-related processes.

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) is an invaluable tool for mapping chromatin-associated proteins. Current barcoding strategies aim to improve assay throughput and scalability but intense sample handling and lack of standardization over cell types, cell numbers and epitopes hinder wide-spread use in the field. Here, we present a barcoding method to enable high-throughput ChIP-seq using common molecular biology techniques. The method, called RELACS (restriction enzyme-based labeling of chromatin in situ) relies on standardized nuclei extraction from any source and employs chromatin cutting and barcoding within intact nuclei. Barcoded nuclei are pooled and processed within the same ChIP reaction, for maximal comparability and workload reduction. The innovative barcoding concept is particularly user-friendly and suitable for implementation to standardized large-scale clinical studies and scarce samples. Aiming to maximize universality and scalability, RELACS can generate ChIP-seq libraries for transcription factors and histone modifications from hundreds of samples within three days.

Testing novel anti-cancer agents across large panel of tumor models covering genetic diversity of cancers is increasingly considered as a cornerstone of preclinical development. For this purpose, Reaction Biology developed “ProLiFiler” a standard panel of 140 cell lines (CLs) covering most common cancer types to evaluate anti-proliferative activity of novel drugs. Partnering has been made with 4HF Biotec and their in-silico platform, named “Cancer Data Miner”, to investigate and to understand molecular basis of drug sensitivity. Here we report the use of our platforms to realize integrative pharmacogenomic studies for three recent small molecules targeting major altered pathways in cancers. It includes SOS1::KRAS interaction inhibitor BI-3406, MDM2 inhibitor Nutlin-3a, and PI3K inhibitor Taselisib. Main goal of the study is to provide meaningful information for these three drugs regarding their efficacy and potency, the validation of their mechanism of actions (MOA), the suitable clinical indications, possible drug combinations and the predictive biomarkers of sensitivity or resistance. The three compounds are tested for anti-proliferative activity in vitro in a 2D monolayer assay using the “ProLiFiler”CLs panel. For data analytics, the resulting in vitro data are loaded on the “Cancer Data Miner” platform and connected to CL annotations including whole exome mutations, chromosomal aberrations, gene expression profiles or drug sensitivity profiles. The drug response profiles will be reported for the three compounds individually and compare between them, showing respective efficacy, potency, and CL/cancer entity selectivity. Using the MOA Finder tool, we will correlate BI-3406, Nutlin-3a, and Taselisib individual IC50 profiles to those of more than 800 compounds with known MOA that are integrated on the platform. The analyses will show the drugs most closely related to the 3 compounds and that are expected to have similar MOA. With the biomarker discovery tools, we will run high throughput statistical analyses to reveal whole exome mutations, copy number variations and expression significantly associated with drug sensitivity/resistance. For interpretation, pathway and enrichment analysis will be presented. A focus will be made on key alterations like KRAS and MAPK related genes, TP53-MDM2 and PIK3CA-PTEN to evaluate their predictivity. The work will be also completed by functional analysis, for instance, by assessing the effect of BI-3406 on ERK-MEK activation, and the impact of MDM2 inhibition on apoptotic markers. The present work will show the whole panel of analyses proposed by 140 CL-ProLiFiler and Cancer Data Miner complementary platforms, allowing to acquire key information at an early stage of drug development and helping to setup next steps such as selection of models for in vivo testing. Citation Format: Vincent Vuaroqueaux, Daniel Feger, Hoor Al-Hasani, Oliver Siedentopf, Anne-Lise Peille, Sarah Ulrich, Sebastian Dempe, Heinz-Herbert Fiebig, Jan Erik Ehlert. ProLiFiler and Cancer Data Miner, combined platforms for preclinical investigation to scrutinize impact of inhibitors on the KRAS, PI3K and MDM2 signaling pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1027.

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) is an invaluable tool for mapping chromatin-associated proteins. However, sample preparation is still a largely individual and labor-intensive process that hinders assay throughput and comparability. Here, we present a novel method for ultra-parallelized high-throughput ChIP-seq that addresses the aforementioned

Pancreatic cancer (PC) is the 12th most common cancer worldwide. Despite a large panel of chemo- and targeted therapeutics options, patient prognosis remains poor with a 5-years overall survival below 10%. Thus, there is still a critical need to develop more efficient therapeutic alternatives. Antibody drug conjugates (ADC) and small molecule drug conjugates (SMDC) combine the oncolytic activity of highly potent chemotherapies with the target specificity of an antibody or a small molecule. Both ADC and SMDC are of increasing interest for cancer treatment, as they allow more specific delivery of chemotherapies to the tumor site. Facing the clinical needs for PC treatment, here we present an in-silico analysis to reveal specific targets for further ADC/SMDC development. 4HF Biotec has developed a proprietary platform connecting large clinical, OMICS and drug sensitivity data from various sources. It includes annotation for more than 1,800 preclinical models (cell lines, cell line-derived xenografts, and patient-derived xenografts), up to 11,000 patient tumors and 22,000 normal tissues (TCGA, GTEx and various GEO datasets). For tumor target discovery purposes, we designed and implemented the platform with specific analytics tools. To identify specific targets for PC, we first decided to analyze preclinical models, to focus on genes expressed by tumor cells and not by stroma cells. This aspect is particularly important in the context of PC which often have a high stroma content. Differential gene expression analysis of 113 PC preclinical models versus 1,737 tumor models from up to 30 tumor entities revealed 327 PC specific genes potentially targetable. Then, a similar analysis was performed by testing TCGA patient tumors (179 pancreatic tumors vs 9,521 patient tumors from other entities) and revealed 1,292 pancreatic specific genes. Finally, PC patient tumors were compared to 709 samples from various normal organs allowing to identify 1,156 tumor specific genes. At the intersection of these three analyses, we identified 56 PC-specific target candidates for ADC/SMDC development. Among the top candidates, MUCL3 (mucin like 3) was one of the most promising genes. Its mRNA expression is almost exclusively restricted to pancreatic and stomach samples in both preclinical models and TCGA patient tumors. It is overall not frequently expressed by normal tissues, and restricted to subsets of stomach, esophagus, and lung samples. The gene encodes for a transmembrane protein with a long weakly glycosylated extracellular part. A detailed analysis of the protein characteristics and expression modalities will be shown. The present work demonstrates that our in silico platform helps to identify promising targets for PC treatment using ADC/SMDC approaches. Our analyses revealed MUCL3 as one of the top candidates, further analyses will be needed to determine its druggability using small molecules or antibodies. Citation Format: Anne-Lise Peille, Alexandra Musch, Hoor Al-Hasani, Heinz-Herbert Fiebig, Vincent Vuaroqueaux. Identification of novel targets for the treatment of pancreatic cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1173.

Metastasis-Associated in Colon Cancer 1 (MACC1) is a strong prognostic marker for human colorectal cancers (CRC). The gene is a key regulator of the HGF/c-Met pathway and its overexpression is associated with cancer cell proliferation, migration, invasiveness and metastasis. The contexture of MACC1 overexpression in cancers is still poorly understood. Here we used the data released by The Cancer Genome Atlas (TCGA) and in silico approaches for a deep and integrative analysis of MACC1 expression modalities in CRCs. First, we curated COAD-READ TCGA datasets and obtained full clinical, genomic, and transcriptomic annotation for a total of 259 CRC samples. In tumors, we showed high heterogeneity of MACC1 mRNA expression levels with data varying from 5.58 to 13.07 U (RNAseq log transformed data - RSEM normalized). By correlating MACC1 expression to clinical data, we showed its levels were associated with anatomic regions of the disease (p=6.35.10-5). The tumors from descending colon and rectum having higher levels than those of the ascending colon and caecum (p=7.44-06). High MACC1 was also associated with higher tumor stages (p=0.01) and nodal invasion (p=0.03). At genomic level, MACC1 mutation was not frequent in CRCs (<2%). The gene is however frequently subjected to gene copy number variation (CNV) due to chromosomal instability and polyploidy (MACC1 CNV values > normal in 155/259: 60%). Correlation analysis indicated strong positive association between MACC1 copy number variations and mRNA expression levels (p<10-16). In an independent dataset, we validate that gain of MACC1 gene copy number and associated higher expression levels correlated with metastasis dissemination and worse outcome of the patients. By analyzing MACC1 with respect of CRC subtypes, we confirmed higher MACC1 levels in MSS tumors with high chromosomal instability (CIN) compared to MSI or genomic stable tumors (GS) (p=1.16-14). Regarding association with CMS1-4 transcriptomic subtypes, the highest expression of MACC1 were observed in CMS2-(epithelial), and CMS4 (mesenchymal) as compared with CMS3 (epithelial with metabolic dysregulation) or CMS1 (MMR deficient). The subset of tumors with very low MACC1 expression were mainly those altered for MLH1 (MSI-H- MMR tumors), notably most of them have normal ploidy. Deconvolution analysis of immune contexture revealed negative correlation between MACC1 expression and immune activation. Stratification using MACC1 as prognostic marker within these tumor types will be evaluated and the gene will also be presented with respect of alterations of key cancer genes in these tumors. Overall, the present study gives insight into the contexture of MACC1 expression in CRC. We showed MACC1 overexpression is largely driven by chromosomal instability and DNA copy number gains. Its expression was dependent on the tumor molecular subtypes. Citation Format: Vincent Vuaroqueaux, Hoor Al-Hasani, Dennis Kobelt, Thomas Risch, Susen Burock, Marie-Laure Yaspo, Heinz-Herbert Fiebig, Ulrike Stein. MACC1 overexpression in colorectal cancer is driven by chromosomal instability and is associated with molecular subtype [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2879.

MI-773 is a recently developed small-molecule inhibitor of the mouse double minute 2 (MDM2) proto-oncogene. Preclinical data on the anti-tumour activity of MI-773 are limited and indicate that tumour cell lines (CLs) with mutated TP53 are more resistant to MI-773 than wild type TP53. Here, we explored the compound’s therapeutic potential in vitro using a panel of 274 annotated CLs derived from a diversity of tumours. MI-773 exhibited a pronounced selectivity and moderate potency, with anti-tumour activity in the sub-micromolar range in about 15% of the CLs. The most sensitive tumour types were melanoma, sarcoma, renal and gastric cancers, leukaemia, and lymphoma. A COMPARE analysis showed that the profile of MI-773 was similar to that of Nutlin-3a, the first potent inhibitor of p53–MDM2 interactions, and, in addition, had a superior potency. In contrast, it poorly correlates with profiles of compounds targeting the p53 pathway with another mechanism of action. OMICS analyses confirmed that MI-773 was primarily active in CLs with wild type TP53. In silico biomarker investigations revealed that the TP53 mutation status plus the aggregated expression levels of 11 genes involved in the p53 signalling pathway predicted sensitivity or resistance of CLs to inhibitors of p53–MDM2 interactions reliably. The results obtained for MI-773 could help to refine the selection of cancer patients for therapy.