Weinstein, John N.

Abstract Background: We recently have reported that co-mutations within KRAS-mutated lung adenocarcinoma (LUAD) define a distinct biological phenotype and may offer insights into therapeutic targeting using this classification system. The analysis revealed striking differences in checkpoint ligand expression and T cell infiltration between TP53 and STK11 (LKB1) co-mutated KRAS LUAD. We, therefore, hypothesized that loss of LKB1promotes an immune-suppressed phenotype in KRAS LUAD. To test this, we analyzed 100 immune-related genes from The Cancer Genome Atlas datasets, as well as three other sets of resected tumors from NSLC patients from MD Anderson Cancer Center. Results: Our results revealed that tumors lacking LKB1 (LKB1-loss), as defined by mutation or gene-expression signature, resulted in a down regulation of: tumor associated immunosuppressive ligands, PD-L1, PD-L2, B7-H3, B7-H4, HVEM; T cell associated immunosuppressive ligands PD-1, CTLA-4, BTLA; co-stimulation ligands such as CD80, -86, -40L, and -40. We further examined if this loss of immunosuppression and co-stimulation resulted in changes in markers of T cell phenotypes. Indeed, tumors lacking LKB1-loss tumors demonstrated a distinct lack absence of several T cell markers including CD3, -4, & -8, but also RORC, T-bet, GATA-3, and FoxP3. To determine if myeloid cell populations, known mediators of immune suppression, were also diminished in LKB1-loss tumors, we analyzed expression of CD11c, -103, -11b, -68, and CD33. Consistent with T cell makers and suppressive ligand markers, myeloid markers were down regulated in LKB1-loss tumors. Because common immune suppression markers were lost, we then examined the expression of antigen presentation (HLA-I, HLA-II) and amino acid transporters as possible alternative methods for immune suppression. In LKB1-loss tumors, both HLA-I and HLA-II markers were down regulated, compared to tumors with intact LKB1 expression. Furthermore, genes associated with the antigen presentation complex, LMP2, TAP1&2, and B2M were also down regulated. In contrast to the immune markers described above, cationic transporters (SLC7A2), anionic transporters (SLC7A11), and neutral transporters (SLC3A2) were universally upregulated in LKB1-loss tumors. Conclusions: LKB1 loss is associated with an inert immune phenotype. Our data suggests that reduced antigen presentation and amino acid depletion, but not checkpoint factors or MDSCs, are likely mediator of this phenotype. Citation Format: Warren L. Denning, Ferdinandos Skoulidis, Li Shen, Vassiliki Papadimitrakopoulou, Lixia Diao, Yanyan Lou, Lauren A. Byers, Jing Wang, Jaime R. Canales, Ignacio I. Wistuba, John Weinstein, Don L. Gibbons, John V. Heymach. Loss of LKB1 mediates an immune inert phenotype in human lung adenocarcinoma. [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 4137.

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