Abstract 4758: Identifying metabolic dark matter (unknown) with therapeutic potential in tumor suppressor deficient colorectal cancer cells

Numerous unknown features than identified metabolites are detected in untargeted metabolomics experiments. Surprisingly, over 80% of the metabolic features are currently unknown, representing a ‘black hole (dark matter)9 of the metabolic output that is necessary to explore. Unfortunately, research specific to unknown identification and its role in tumor and therapeutics are largely understated. In recent studies, the chemical formula acquired from the high-resolution Mass Spectrometry (MS) detection and further targeted analysis by the Liquid Chromatography-Mass Spectrometry (LC-MS) have been proven useful in confirming the presence and functions of unknown metabolites or their isomers. Emerging evidence indicates that cancer is a metabolic disease and altered metabolism is a cancer hallmark. Genetic alterations including gene loss or gain of function mutations lead to altered metabolic regulation. Tumors frequently mutate genes in order for satisfy growth and survival. For example, tumor suppressor genes such as TP53 and APC are genes with the highest frequency of somatically acquired mutations in diverse cancers. In TCGA colorectal cancer (CRCs), p53 and APC genes show 58% and 81% loss of function mutations, respectively. Tumor suppressor genes with loss of function mutation involve in tumorigenicity of multiple cancers. Therefore, exploring unknown metabolites associated with tumor suppressors has excellent prospect for the discovery of cancer biomarker(s) and therapy. In our preliminary analysis, using LC-MS, we profiled the untargeted metabolomes between wild-type and isogenic clone of p53-KO or APC-KO HCT116 CRCs. Interestingly, we first identified a large number of significantly upregulated metabolic features in both p53-KO or APC-KO cells compared to the wild type cells. We further annotated these upregulated metabolic features using our in house metabolite library containing over 1,000 metabolite standards and identified less than 5% of known leaving over 90% unknown. To increase the metabolite annotation coverage, we then ran these unknown metabolites through open-source metabolite databases including KEGG, HMDB, METLIN, and LipidMaps and were able to annotate over 75% of unknown metabolic features. Strikingly, our data revealed 37 commonly upregulated significant metabolites in both clones and there are 48 and 127 unique metabolic features present in APC and p53 KO clones, respectively. Furthermore, by integrating tandem MS and in silico approaches, we identified N,N-Dihydroxy-L-Tyrosine as a novel metabolite which is substantially upregulated in both p53 and APC KO clones compared to wild-type. N,N-Dihydroxy-L-Tyrosine is involved in branched-chain amino acid and phenylalanine metabolism which may have a significant role in tumorigenesis. We are certain that identification of new unknowns will meaningfully contribute novel route in next generation cancer metabolism and drug development research. Citation Format: Iqbal Mahmud, Satya Narayan, Timothy J. Garrett. Identifying metabolic dark matter (unknown) with therapeutic potential in tumor suppressor deficient colorectal cancer cells [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4758.