Author: Tai, Wai Meng David ; Chow, Lisa Muei-Eng ; Lee, Joycelyn Jie Xin ; Chowbay, Balram ; Lim, Jhin Jieh ; Chow, Edward Kai-Hua ; Rashid, Masturah

e13614 Background: Functional screening methods are increasingly applied to guide patient treatment and could be beneficial in indications with poor survival and response rates, such as pancreatic cancer. However, challenges faced when implementing ex vivo testing include complexities in obtaining sufficient tumor cells for different cancer types and maximizing the number of treatments tested with limited sample material. The latter is especially significant in informing clinical decision making as combination therapies are common with cancer, exponentially increasing the number of regimens to select from. To address this hurdle, we developed Optim.AI, a combinatorial functional precision medicine platform, which utilizes efficiently designed experiments and small data AI to expand to over 530,000 phenotypic response data points and predictively rank all actionable treatments within a 12-drug panel. In this feasibility study, we explored the application of Optim.AI on patient-derived tumor cells in pancreatic cancer. Methods: We established a procedure to run Optim.AI with isolated tumor cells from pancreatic cancer patients. After dissociation, these cells were then incubated to allow for 3D organoid formation within the following week. Upon organoid formation, the cells were treated with an FDA-approved panel of chemotherapy and targeted drugs. Cell viability was quantified post-drug treatment for analysis with Optim.AI software to rank all possible single to 4-drug combinatorial therapies for report generation. Results: The viability of the cells post-processing was high (mean = 97.4% live cells), passing the quality check of having at least 60% live cells before proceeding with Optim.AI testing. The success rate of organoid formation was 100% within 1 week of sample processing. The turnaround time from the receipt of samples to dissemination of the reports to clinicians was approximately a week (mean = 7.67 days). Z’ factor, a measurement of statistical effect size for high throughput screening, was demonstrated to be more than 0.5 for the reports generated, indicative of very good assays. The top ranked drug combinations were sensitive, with their normalized cell viability reported to be under 0.2, while the prior line of treatment was shown to be resistant, 100% concordant with previously observed clinical outcomes. Notably, Gemcitabine with either a HDAC or PI3K inhibitor was ranked high amongst the reports generated. Conclusions: Optim.AI had a high success rate in generating reports within a very short timeframe for pancreatic cancer. Comparing the top ranked therapies against the prior lines of treatment allows the clinicians to better evaluate the alternative treatments. Based on this initial feasibility study, Optim.AI could potentially be viable in guiding clinicians in managing treatment options for pancreatic cancer patients.

15 Apr 2024·Cancer Research

Abstract P09: AN EX-VIVO COMBINATION DRUG SENSITIVITY PLATFORM FOR PREDICTING SENSITIVITY TO FLT3 INHIBITOR-BASED COMBINATION IN ACUTE MYELOID LEUKEMIA

Author: Ooi, Melissa ; Sahib, Noor Rashidha Bte Meera ; Jayalakshmi ; Rashid, Masturah ; Chow, Edward KH ; Mohamed, Jameelah Bte Sheik ; Chee, Yen Lin ; Ying, Jen Wei

AbstractFMS-like tyrosine kinase-3 (FLT3) mutations are often linked with poor prognosis in Acute myeloid leukemia (AML). Despite increasing availability of targeted therapy, such as FLT3 inhibitors (FLT3i), remission is often short-term and followed by relapse and drug resistance. With varying response rates to standard treatments, the need to understand the molecular mechanisms driving drug resistance and accurately predict responses to treatment is crucial. Here, we present the ability of Quadratic phenotypic optimization platform (QPOP), in accurately identifying potential responders to possible combination therapies in AML.AML cell lines and AML patient samples were treated with a set panel of drugs including FLT3i. QPOP drug combination scores and ranking were derived from cell viability response following treatment. 40 patients were recruited for the study, of which 20 patients were evaluated for their clinical concordance. RNA sequencing and molecular analysis was further conducted to determine the accuracy of QPOP predicted response and to determine mechanism of resistance to FLT3i.QPOP predicted response was concordant to patient treatment outcome with 80% sensitivity and specificity. QPOP analysis on AML cell lines accurately identified responders to FLT3i including the differential response between a FLT3- ITD isogenic parental and FLT3i resistant cell line. Serial QPOP analysis in a FLT3 mutant patient sample indicated a decrease in FLT3i sensitivity concordant with an increasing FLT3 allelic burden and the development of resistance. The development of resistance to FLT3i based combinations was correlated to an increase in phosphorylated AKT expression. Activation of AKT is associated with the upregulation of Forkhead box M1 (FoxM1) in the resistant cell line. Co-treatment with an AKT inhibitor restored sensitivity to FLT3i treatment in the resistant cells. We demonstrate that QPOP analysis accurately predicts sensitivity to drug-combinations and can be used as a tool to identify sensitivity to FLT3i-based combination therapy in AML.Citation Format: Noor Rashidha Bte Meera Sahib, Jen Wei Ying, Melissa Ooi, Yen Lin Chee, Jayalakshmi, Jameelah Bte Sheik Mohamed, Masturah Rashid, Edward KH Chow. AN EX-VIVO COMBINATION DRUG SENSITIVITY PLATFORM FOR PREDICTING SENSITIVITY TO FLT3 INHIBITOR-BASED COMBINATION IN ACUTE MYELOID LEUKEMIA [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P09.

22 Mar 2024·Cancer Research

Abstract 6492: KRAS inhibitor-based combinations in pancreatic cancer identified through Optim.AI

Author: Chow, Edward Kai-Hua ; Lim, Jhin Jieh ; Rashid, Masturah

AbstractPURPOSE: KRAS is the most frequently mutated oncogene in almost all cancers, including pancreatic ductal carcinoma (PDAC) and colorectal cancer. Increased development efforts targeting KRAS mutations have yielded recent FDA approvals of two KRAS G12C inhibitors. However, this is beneficial for only a small proportion of patients especially with indications like PDAC, where G12C mutations are rare (1%) while G12D mutations account for approximately 45%. Hence, there is a need to develop methods to target a broader range of KRAS mutations. Beyond direct gene mutation, activation of oncogenic KRAS and downstream or parallel signaling through other genomic alterations may benefit from KRAS-specific therapeutic strategies, especially combination therapy. In this study, Optim.AI™, a hybrid computational-experimental platform is harnessed to identify mutation-specific optimal therapies and to stratify efficacious drug combinations containing KRAS inhibitors in pancreatic cancer.METHODS: Optim.AI™ utilizes small datasets to rationally converge upon optimal drug combinations within a defined drug search space. By mapping experimental data points to a second-order quadratic function, Optim.AI™ predicts cell killing efficacies for all possible combinations, independent of the background of the models utilized. In this study, pancreatic cancer cell lines, with varying KRAS mutations, were tested with an array of 155 different combinations consisting of 12 drugs at varying concentrations. The drugs include KRAS G12C inhibitors, Sotorasib and Adagrasib, KRAS inhibitors in development, and approved therapies used clinically for PDAC. Post-drug treatment viability was measured and used for Optim.AI™ analysis, to rank and compare the top therapies across the different cell lines.RESULTS: Through Optim.AI™ analysis, we observed a range of sensitivities towards KRAS inhibition, even for both Sotorasib and Adagrasib. Compared against HT-29 cell line, harboring wild type KRAS, we have identified distinct combinations of KRAS inhibitors with standard of care drugs on cell lines with G12D, G12V and G12C KRAS mutants. Subsequent validation of the top and bottom ranked combinations for the respective cell lines correspond to the predicted Optim.AI™ results.CONCLUSIONS: This study has demonstrated the differential sensitivities of the KRAS inhibitors across several pancreatic cancer cell lines, with different KRAS mutational profiles. Notably, we have identified KRAS inhibitor-based combinations that are distinctly different from wild type KRAS cell line. These preliminary findings could be used to drive precision medicine by identifying suitable biomarkers to potentially stratify pancreatic cancer patients and improve treatment outcomes. Future work includes evaluating the clinical significance of these combinations by validating them on patient-derived organoids.Citation Format: Jhin Jieh Lim, Edward Kai-Hua Chow, Masturah Rashid. KRAS inhibitor-based combinations in pancreatic cancer identified through Optim.AI [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6492.

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