Background: Tumor metabolism is the hallmark of cancer cells. Cancer cells utilize different nutrient sources to drive the metabolic pathways to sustain tumor growth. Glucose (Glu) and Glutamine (Gln) are the primary nutrient sources on which cancer cells thrive. Developing precision diet based on patient’s molecular characteristics can help treat the cancer with dietary modulations along with traditional approaches.
Methods: Computational Biology Model (CBM) captures the signaling and metabolic pathways to predict cancer phenotypes and biomarkers. Genomic aberrations (Mutations / Copy Number Variations (CNV)) from a patient’s tumor are input into the CBM to create the patient disease model. CBM is used for diet prediction based on the molecular characteristics of the patient’s disease.
CBM is validated using a data set of 54 cancer cell-lines across indications, by assessing nutrient dependency for Glu and Gln. Simulation based prediction of Glu and Gln dependency is based on the expression of transporters and rate limiting enzymes of cellular glucose (SLC2A1-4, HK2) and glutamine (SLC1A5, GLS) uptake. The enzymes regulating the de-novo synthesis of glucose (PCK1/2, FBP1) and glutamine (GLUL) are negative determinants.
In the CBM, an index is defined to measure Glu and Gln dependency.
Glu Dependency Index = (SLC2A1 + SLC2A2 + SLC2A3 + SLC2A4 + HK2) / (PCK1 + PCK2)
Gln Dependency Index = (SLC1A5 + GLS) / (GLUL)
Threshold values for Glu and Gln dependency was determined based on the simulation correlation with the cell-line data. The validated CBM was then used for diet prediction for patient genomics.
Results: Validation of genomics-based diet prediction by CBM using 54 cancer cell lines had an accuracy, positive predictive value and negative predictive value of 85%, 97% and 44% for Glu dependency and 82%, 94% and 50% for Gln dependency respectively.
Using this validated CBM, we present predictions of patient nutrient source dependency based on their tumor genomics:
Case Study 1: A Glioblastoma multiforme (GBM) patient case with PTEN EGFRVIII and ALK mutation and high copy number of HIF1A and MIR-145. CBM predicted the patient to be Glu dependent and Gln independent.
Case Study 2: A GBM patient case with CTNNB1 mutation and low copy number of PTEN, RB1 and NF1. This patient was predicted to be both Glu and Gln dependent.
Case Study 3: A Triple Negative Breast Cancer (TNBC) patient carrying mutations for MYC, BRD4, EP300 and CREBBP. CBM predicted this patient to be Gln dependent and Glu independent.
The rationales for the nutrient source dependency predictions based on disease pathway characteristics were determined.
Conclusion: Using CBM we could successfully use patient genomic data to predict nutrient dependency of patient’s tumor. This analysis enables creating options for precision diet for a patient to be used as an adjuvant alongside traditional approaches.
Citation Format: Shireen Vali, Taher Abbasi, Subrat Mohapatra, Vishwas Joseph, Ashish Kumar Agrawal, Anuj Tyagi, Neelesh Lunkad, Ashokraja Balla. Computational biology model (CBM) predicts nutrient dependency of cancer patients based on Tumor Genomics: Implication of precision diet in cancer therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1843.