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Chromosomal alterations that occur frequently in cancers confer selective advantages for tumor progression by deleting tumor-suppressing genes or amplifying oncogenic drivers. However, the collateral effect of these, i.e., deletion of essential genes or upregulation of metabolic regulators, exposes cancers to biological pressures by suppressing essential metabolic pathways. Cancer cells rely on paralogous metabolic pathways to compensate for this loss of function, which in turn opens the door to exploit metabolic vulnerabilities arising as a direct consequence of chromosomal alterations. These pathways are collateral lethal (CL) targets and offer a unique precision medicine approach for metabolic therapeutics. We have developed an integrated machine learning and systems biology platform, collateral lethal gene identification via metabolic fluxes (CLIM) to identify metabolic targets across multiple cancers. Results: In ovarian cancers with 19p13.3 deletion, a loss of Complex III subunit-encoding gene UQCR11 occurs, leading to suppressed electron transport chain (ETC) activity. CLIM predicted that MTHFD2 acts noncanonically to oxidize NADH to NAD+ to compensate for reduced NAD+ recycling via the ETC. We demonstrate selective death of ETC deficient ovarian cancers by targeting MTHFD2 and provide mechanistic validation of oxidative MTHFD2 flux. We observed significant shrinkage of ovarian tumors with ETC deficiency upon MTHFD2 knockdown in mouse models. In triple negative breast cancers (TNBC), we discovered a chromosomal amplification leading to reprogramming of the serine biosynthesis pathway. CLIM predicts a target in the tryptophan metabolism. Methods: Patients are stratified using machine learning to identify tumors with distinct chromosomal alterations. Multiobjective metabolic flux analysis is employed to predict CL pathways in curated genome-scale metabolic models. Validation of metabolic rewiring predicted by CLIM were examined using 3-2H-glucose, 4-2H-glucose and 2,3,3-2H-serine to probe the one carbon metabolism. Oxidative MTHFD2 flux was quantified using innovative combinatorial tracer study with 2H-formate and 4-2H-glucose. We utilized gain- and loss-of-function assays in multiple cell-lines to show utility of the predicted CL targets in TNBCs with chromosomal amplifications. Conclusion: Our integrated platform successfully identifies CL gene pairs to decipher novel metabolic targets specific to chromosomal alterations across many cancer types. Importantly, targets identified by CLIM are based on quantitative flux analysis. This approach reveals functional connection between CL pairs, thereby providing a mechanistic understanding of emerging lethality. This is critical in developing therapeutic interventions that selectively target cancer cells, with minimal off-target or side effects. Citation Format: Abhinav Achreja, Jin Heon Jeon, Mark Slayton, Tao Yu, Olamide Animasahun, Minal Nenwani, Fulei Wuchu, Anjali Mittal, Xiongbin Lu, Sofia D. Merajver, Deepak Nagrath. Identification of collateral lethal targets in cancers using integrated machine learning and flux analysis platform for personalized metabolic therapy. [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 6037.

One of the most challenging aspects of PDAC is intense therapeutic resistance towards radiotherapy. Cellular signaling pathways are tightly regulated to protect and withstand constant DNA-damaging insults. Similarly, pancreatic cancer cells are known to activate important metabolic events in the advent of cellular stress. Fuel sources obtained via the high metabolic flexibility of PDAC cells confer on them enhanced survival and the ability to escape therapy-induced cell death. To this end, we aim to dissect the role of altered metabolic reprogramming in radioresistant PDAC cells. Investigation of systemic metabolic changes post-radiation of 8Gy in in vitro systems revealed reduced total NAD/NADH ratio and hinted at the increased dependency of PDAC cells on the one-carbon metabolic pathway. Using combinatorial stable-isotope tracing techniques, we confirmed the role of methylene tetrahydrofolate dehydrogenase (MTHFD1/2) enzymes as key regulators in maintaining total NAD/NADH ratio in irradiated cells. Moreover, inhibition of the one-carbon metabolic pathway hindered the ability of PDAC cells to resolve radiation-induced DNA damage. In summary, we propose that PDAC cells modulate the flux of MTHFD1/2 enzymes to reprogram their metabolic state and generate intermediates for DNA repair and cell survival, thus establishing MTHFD1/2 enzymes as potential therapeutic targets to tackle radioresistance. Citation Format: Minal Nenwani, Abhinav Achreja, Olamide Animasahun, Itisam Sarangi, Jyotirmoy Roy, Srinadh Choppara, Fulei Wuchu, Miya Paserba, Anjali Mittal, Sergio Quispe, Aradhana Mohan, Meredith Morgan, Theodore S. Lawrence, Deepak Nagrath. Targeting one-carbon metabolism radiosensitizes pancreatic ductal adenocarcinoma cells (PDAC) [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 1092.

Cholangiocarcinoma is the second most common primary liver cancer with extremely low survival rates. However, a combination of gemcitabine and cisplatin is the only treatment for advanced unresectable cholangiocarcinoma currently, and the cancer can become resistant to the treatment, contributing to the high mortality. CPI-613 is a lipoic acid analog, which can inhibit dehydrogenases and disrupt mitochondrial metabolism. It is regarded as a promising anti-cancer drug and has been tested in multiple clinical trials. Efficacy and working mechanisms of CPI-613 have not been established in cholangiocarcinoma. In this study, we aim to evaluate the performance of CPI-613 in combination with other therapeutic drugs and unravel the metabolic underpinnings. We treated cholangiocarcinoma cell lines with different genetic backgrounds with standard of care chemotherapy (gemcitabine and cisplatin) alone, CPI-613 alone, or the combination of the two. Results from viability assay showed that CPI-613 had synergistic (not simply additive) effects with standard of care chemotherapy. CPI-613 has a relatively short half-life, so longer infusion may be more beneficial even if the total dose remains the same. To mimic longer infusion, we treated the cells with multiple times of drug addition (fractional dosing), and compared the results to single addition. Oxygen consumption rate (an indicator of mitochondrial function) of cells under fractional dosing regimen was significantly lower in both short and long term, as measured by Agilent Seahorse and Resipher respectively. The results provided mechanistic support for longer infusion in clinical practice. We also collected plasma from patients before, during and after CPI-613 treatment in a clinical trial, and global metabolomics was performed. Global metabolomic changes induced by CPI-613 were identified and specific alternations in patients with complete response could be exploited as treatment targets or prediction markers. High frequency of isocitrate dehydrogenase 1 (IDH1) mutation is a characteristic of cholangiocarcinoma. Ivosidenib, an IDH1 inhibitor, has been shown to improve survival in chemotherapy-refractory cholangiocarcinoma patients in a phase 3 clinical trial. However, ivosidenib can increase alpha-ketoglutarate concentration, hence increasing the flux of TCA cycle and boosting mitochondrial fitness of the cancer cells. We hypothesized that the combination of CPI-613 and Ivosidenib could negate this effect and have better anti-cancer activities. Indeed, results from viability assay demonstrated synergy between CPI-613 and Ivosidenib. Metabolic flux analysis and global metabolomics were performed to illustrate the metabolic mechanisms of the combination of CPI-613 and Ivosidenib. Our study provides the mechanistic support for the use of CPI-613 in combination with other therapeutic agents and the results should be translated to clinical studies. Citation Format: Fulei Wuchu, Olamide Animasahun, Srinadh Choppara, Valerie Gunchick, Minal Nenwani, Jyotirmoy Roy, Miya Paserba, Abhinav Achreja, Vaibhav Sahai, Deepak Nagrath. The role of CPI-613 in cholangiocarcinoma treatment [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 2236.

Large-scale chromosomal alterations, particularly chromosomal deletions in cancer genomes confer functional advantages to cancer cells via the loss of tumor suppressor genes (TSGs). However, due to the nature of these focal and arm-level deletions, essential house-keeping genes in the neighborhood of TSGs are potentially lost. We explore the emergence of metabolic adaptations and vulnerabilities that arise due to the collateral loss of essential metabolic genes. In our previous work, we showed that genomic loss in the locus containing SMAD4 and ME2 in pancreatic ductal adenocarcinomas forces these cells to rely on ME3 to compensate for the collateral loss of ME2; thereby revealing a highly selective metabolic target in these cells. Cancer cells can not only exploit such genetic redundancies but also rely on redundancies built into their complex metabolic network to compensate for the loss of metabolic function. Importantly, there is an unexplored landscape of these genomic loss events beyond well-characterized TSGs. To address these challenges, we have developed a platform to identify patient-specific metabolic vulnerabilities emerging due to distinct patterns of genomic loss events across tumors. Our platform presents opportunities for precision-based therapeutic intervention by targeting metabolic vulnerabilities in cancer patients. It uses genomic and clinical data available in cancer patient databases to obtain candidate metabolic genes that are lost to genomic deletions in an unbiased manner. To delineate metabolic redundances and tackle the complexity of genome-scale metabolic models, we employ an innovative multi-objective metabolic flux analysis approach. The utility of this platform is demonstrated via the discovery of a novel metabolic target in a cohort of ovarian cancer patients. The predicted collateral lethal target is validated in vitro using RNA interference and small-molecule inhibitors. Furthermore, we verify the metabolic mechanism of vulnerability predicted by the algorithm using deuterium tracing experiments. The target is also validated in vivo with mice containing ovarian tumors derived from cancer cells with and without the genomic deletion. Surprisingly, the collateral lethal metabolic target was also found to exist in a subset of aggressive endometrial cancers. Finally, we developed a multi-layer machine learning model to predict occurrence of the particular genomic deletion in ovarian and endometrial cancer patients with minimal molecular information to remove the need for whole-genome sequencing data. The model was trained and tested using the publicly-available molecular data from TCGA and AACR GENIE datasets. Citation Format: Abhinav Achreja, Tao Yu, Anjali Mittal, Srinadh Choppara, Noah Meurs, Olamide Animasahun, Jin Heon Jeon, Aradhana Mohan, Anusha Jayaraman, Reva Kulkarni, Justin Reinhold, Michele Cusato, Analisa Difeo, Xiongbin Lu, Deepak Nagrath. Genomic loss in cancers enable discovery of metabolic targets for precision cancer therapy via multiobjective flux analysis and machine learning [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 236.