Viveksagar Krishnamurthy Radhakrishna

Abstract PISCES eases processing of large mRNA-seq experiments by encouraging capture of metadata using simple textual file formats, processing samples on either a single machine or in parallel on a high performance computing cluster (HPC), validating sample identity using genetic fingerprinting, and summarizing all outputs in analysis-ready data matrices. PISCES consists of two modules: 1) compute cluster-aware analysis of individual mRNA-seq libraries including species detection, SNP genotyping, library geometry detection, and quantitation using salmon, and 2) gene-level transcript aggregation, transcriptional and read-based QC, TMM normalization and differential expression analysis of multiple libraries to produce data ready for visualization and further analysis. PISCES is implemented as a python3 package and is bundled with all necessary dependencies to enable reproducible analysis and easy deployment. JSON configuration files are used to build and identify transcriptome indices, and CSV files are used to supply sample metadata and to define comparison groups for differential expression analysis using DEseq2. PISCES builds on many existing open-source tools, and releases of PISCES are available on GitHub or the python package index ( PyPI ).

Abstract Recent advances in next-generation sequencing have revealed the presence of genetic heterogeneity and clonal evolution within tumors. Intratumoral heterogeneity has been implicated in the disease progression, metastasis, therapeutic responses and development of drug resistance. Although cancer cell line xenograft models have been extensively used in cancer research to test drug efficacy, it is unknown how much clonal heterogeneity is maintained in the process of cell line xenograft establishment. In order to quantitatively assess the clonal complexity in xenograft models, here we applied a cellular barcoding technology using the HCC827 cell line. HCC827 is a non-small cell lung cancer cell line containing an exon 19 deletion which has been shown to be clinically relevant due to its initial response to EGFR inhibitors followed by resistance. This barcoding tool allowed us to label each individual cell with one unique DNA barcode via lentiviral infection and monitor the clonal heterogeneity within the implanted cell population by quantifying the number of unique barcodes. We were able to estimate the percentage of implanted clones that actually contributed to the formation of cell line xenografts. This study provides valuable insight on the clonal diversity present in xenograft models, which will further elucidate the heterogeneous nature of tumors. Citation Format: Justina X. Caushi, Hyo-eun C. Bhang, Jie Li, Iris Kao, Viveksagar Krishnamurthy Radhakrishna, Vesselina G. Cooke, Joshua M. Korn, David A. Ruddy, Shailaja Kasibhatla, Frank Stegmeier. Understanding clonal complexity of a tumor xenograft model via cellular barcoding technology. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3240. doi:10.1158/1538-7445.AM2015-3240

Abstract Approximately 10% of patients with colorectal cancer (CRC) harbor the BRAF V600E driver mutation. Unlike melanoma, the response rate of BRAF-mutant CRC to the combination of BRAF and MEK inhibitors is limited. In order to target the MAPK signaling pathway more effectively by blocking EGFR-mediated re-activation of the pathway, triple combination trials of BRAF, MEK and EGFR inhibitors are on-going, but the response is underwhelming. To find alternative combination strategies that could deepen therapeutic responses driven by a BRAFi and MEKi combination, we performed pooled shRNA screens under the treatment pressure of the dual combination of the BRAF inhibitor dabrafenib and MEK inhibitor trametinib. In some of the BRAF-mutant CRC models, we observed marked discrepancies in the therapeutic responses between in vitro and in vivo conditions. Therefore, shRNA screens were conducted in cancer cell lines grown both in vitro (i.e. 2D and 3D culture conditions) and in vivo in xenograft tumor models. The aim of the study was to identify novel targets to combine with BRAFi/MEKi, and to compare the results of the screens preformed in vitro and in vivo. The biggest technical challenge for an in vivo pooled screening approach is achieving adequate library representation after the bottleneck of cell implantation and engraftment in mice. Our in vivo screen had an additional bottleneck due to the dabrafenib/trametinib combination treatment. Therefore, by performing a pilot screen with the BRAF-mutant cell line model HT29 we aimed to address two questions: 1) whether the in vivo screen under treatment pressure would be technically feasible and 2) if novel combination partners to dabrafenib/trametinib would be identified to potentially improve efficacy beyond that observed with the triple combination with EGFR inhibitors. We were able to achieve comparable intra-group variability and repeatability between in vitro and in vivo conditions, whereby gene level analysis revealed several differential hits between the two conditions, which were both sensitizers and activators to the dabrafenib/trametinib combination treatment. We identified targets specific for the in vivo condition that had not been identified in vitro and vice versa. Thus, in vivo screening may identify powerful hits that would not be realized by in vitro investigations. With success of this pilot effort, the screen is currently being expanded into additional BRAF-mutant CRC models. Citation Format: Hyo-eun C. Bhang, Matthew T. DiMare, David P. Kodack, Lujian Tan, Grainne Kerr, Viveksagar Krishnamurthy Radhakrishna, Javad Golji, David A. Ruddy, Tina Yuan, Matthew J. Niederst, Joshua M. Korn, Diana Graus Porta, Peter S. Hammerman, Jeffrey A. Engelman, Tinya Abrams, Juliet Williams. In vivo shRNA screens under treatment pressure by BRAF and MEK inhibitors to identify novel combination treatment strategies for BRAF-mutant colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 394.