Morris, Joel

Abstract In response to diverse cellular cues, MAP3K1, a mitogen-activated protein kinase, participates in various cancer signaling networks including the NFκB, JNK, ERK, and p38 pathways. Functioning as a signaling kinase in these oncogenic pathways, MAP3K1 contributes to tumor growth and metastasis, thus making it an attractive therapeutic target for cancer. Structure-guided design using MAP3K1 AlphaFold and Schrödinger GLIDE led to the development of 51-106, a selective MAP3K1 small molecule inhibitor. Profiling using the KiNativTM platform in a cellular matrix revealed 51-106 as a selective ATP-competitive MAP3K1 inhibitor. 51-106 blocked TNFα-induced MAP3K1-IKKβ-mediated NFκB activity. Phosphoproteomics analysis following MAP3K1 inhibition by 51-106 showed a dose dependent decrease in NPM1 T199 phosphorylation indicating NPM1 as a novel substrate of MAP3K1. NPM1 plays a critical role in DNA damage repair; consistently we observed a dose dependent S-phase arrest upon MAP3K1 inhibition by 51-106, suggesting a dysfunctional DNA damage response. Higher MAP3K1 transcript level in pancreatic cancer patients is associated with poorer (50% vs. 15%) 5-year survival. Treatment of pancreatic cancer cell lines with the MAP3K1 inhibitor 51-106 inhibited cell growth and migration. In combination studies, 51-106 synergistically inhibited growth with gemcitabine in LSL-KrasG12D/+, LSL-Trp53R172H/+, Pdx1-Cre (KPC) cell lines in vitro and in KPC syngeneic orthotopic implantation mouse model of pancreatic cancer in vivo. Our study is the first to identify NPM1 as a potential substrate of MAP3K1, and these results warrant the investigation of MAP3K1 inhibition as a therapeutic option in cancer. Citation Format: Lidia Boghean, Sarbjit Singh, Kiran Mangalaparthi, Surendra K. Shukla, Smitha Kizhake, Amritha Kizhake, Donn Wishka, Jayapal Reddy Mallareddy, Joel Morris, Paul Grothaus, Pankaj K. Singh, Akhilesh Pandey, Amarnath Natarajan. Phosphoproteomic profiling following MAP3K1 kinase inhibition identifies NPM1 as a novel substrate [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2025.

Abstract High-throughput data is increasingly being integrated into the fields of biology, molecular biology, and pharmacology. However, a difficult problem has been the rapid and fluid access to and integration of the data, which tends to reside in huge unwieldy databases. One set of cell lines with substantial potential for benefit from this type of access and integration is the NCI-60 cancerous cell lines. We present here a set of tools within our CellMiner web-application designed to address this need for the areas of transcript expression, microRNA expression, gene DNA copy number, variant status, and drug activity. CellMiner allows the user to rapidly access data for relative levels of transcript expression for 26,065 genes, 360 microRNAs, and 20,602 compounds including 102 Food and Drug Administration (FDA)-approved drugs. These levels in turn create patterns across the NCI-60 that can be compared to one another using our “pattern match” tool. Together, these tools allow one to query the data for potential relationships between these parameters, in a manner specific to a users area of expertise and interest, in a rapid and flexible manner without the need for expertise in computer science or bioinformatics. Comparisons of transcript/drug will be demonstrated with SLFN11/topotecan; variant/drug with BRAF V600E/vemurafinib; colon tissue-of origin specific genes with TRIM15, RNF43, and VIL1; and DNA copy number change with CDKN2A (p16). The data are publicly available at http://discover.nci.nih.gov/cellminer. Citation Format: William C. Reinhold, Margot Sunshine, Sudir Varma, Hongfang Liu, Ogan Abaan, Paul Meltzer, Joel Morris, Kurt Kohn, James Doroshow, Yves Pommier. Web-based access using CellMiner for gene expression, DNA copy number, microRNA transcript levels, variant status, drug activity, and their pattern comparisons for the NCI-60. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3177. doi:10.1158/1538-7445.AM2013-3177

Abstract High-throughput data is increasingly being integrated into the fields of biology, molecular biology, and pharmacology. However, a difficult problem has been the rapid and fluid access to and integration of this data, which tends to reside in huge unwieldy databases. One set of cell lines with substantial potential for benefit from this type of access and integration is the NCI-60 cancerous cell lines. We present here a set of tools within our CellMiner web-application designed to address this need for the areas of transcript expression, microRNA expression, gene DNA copy number, and drug activity. CellMiner allows the user to rapidly access data for relative levels of transcript expression for 18,532 genes, 360 microRNAs, and 16,861 compounds including 91 Food and Drug Administration (FDA)-approved drugs. These levels in turn create patterns across the NCI-60 that can be compared to one another using our “pattern match” tool. Together, these tools allow one to query the data for potential relationships between these parameters, in a manner specific to a users area of expertise and interest, in a rapid and flexible manner without the need for expertise in computer science or bioinformatics. The output will be demonstrated with cancer interesting microRNAs 18a, 21, 29a, 30c-2, 30d, and 31, cancer interesting genes ABCB1, ATM, ATR, BRCA2, and TP53, and the clinically relevant drugs belinostat, crizotinib, pazopanib, selumetinib and tanespimycin. 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 2991. doi:1538-7445.AM2012-2991

Abstract High-throughput and high-content databases are increasingly important resources in molecular medicine, systems biology, and pharmacology. However, the information usually resides in unwieldy databases, limiting ready data analysis and integration. One resource that offers substantial potential for improvement in this regard is the NCI-60 cell line database compiled by the U.S. National Cancer Institute, which has been extensively characterized across numerous genomic and pharmacologic response platforms. In this report, we introduce a CellMiner (http://discover.nci.nih.gov/cellminer/) web application designed to improve the use of this extensive database. CellMiner tools allowed rapid data retrieval of transcripts for 22,379 genes and 360 microRNAs along with activity reports for 20,503 chemical compounds including 102 drugs approved by the U.S. Food and Drug Administration. Converting these differential levels into quantitative patterns across the NCI-60 clarified data organization and cross-comparisons using a novel pattern match tool. Data queries for potential relationships among parameters can be conducted in an iterative manner specific to user interests and expertise. Examples of the in silico discovery process afforded by CellMiner were provided for multidrug resistance analyses and doxorubicin activity; identification of colon-specific genes, microRNAs, and drugs; microRNAs related to the miR-17-92 cluster; and drug identification patterns matched to erlotinib, gefitinib, afatinib, and lapatinib. CellMiner greatly broadens applications of the extensive NCI-60 database for discovery by creating web-based processes that are rapid, flexible, and readily applied by users without bioinformatics expertise. Cancer Res; 72(14); 3499–511. ©2012 AACR.