Technical Report PHD-2021-06

Title: Computational inference of cancer metabolic alterations for early diagnosis and treatment
Authors: Shoval Lagziel
Supervisors: Tomer Shlomi
PDFCurrently accessibly only within the Technion network
Abstract: Metabolic reprogramming is a hallmark of cancer, providing novel means to selectively target cancer cells, for precision medicine and early diagnosis. Understanding tumor-specific metabolic alterations facilitates the identification of induced dependency on specific enzymes whose inhibition selectively targets cancer cells. In addition, the altered metabolic activity of cancer cells, involving the consumption of metabolic nutrients and the secretion of byproducts from the tumor leaves metabolic traces that can be utilized for diagnostic purposes. Here, we tackled two main challenges based on the metabolic reprogramming of cancer: (1) construction of models suggesting potential metabolic mechanisms for cancer-specific dependence on metabolic genes, (2) early cancer diagnosis based on fast and sensitive metabolomics of blood samples.

Genome-wide RNAi and CRISPR screens are powerful tools for identifying genes essential for cancer proliferation and survival. Previous works integrated loss-of-function screens with cancer cell line molecular characterization to reveal the underlying mechanisms for cancer dependence on specific genes; however, explaining cancer dependence on metabolic genes was shown to be especially challenging. Considering that metabolic activity is highly dependent on nutrient availability, analyzing publicly available omics datasets, we have shown that utilizing different media types for culturing cancer cell lines has a major effect on intracellular metabolite levels and metabolic gene dependencies – calling for future analyses of published omics datasets such as that of the CCLE to account for this confounding effect. Considering culture media as well as accounting for molecular features of functionally related metabolic enzymes in a metabolic network enabled us to improve our understanding of cancer cell line-specific dependence on metabolic genes using machine learning models.

Early diagnosis of cancer greatly increases the chances for successful treatment of cancer. Major ongoing efforts are made to develop highly sensitive, cost-effective screening methods via a variety of molecular biomarkers. Mass spectrometry based metabolomics is a widely used approach in biomedical research. However, current methods coupling mass spectrometry with chromatography are time-consuming and not suitable for high-throughput analysis of thousands of samples. An alternative approach is flow-injection mass spectrometry (FI-MS) in which samples are directly injected into the ionization source. However, it was previously shown to provide a reduced sensitivity and reproducibility. We developed two rapid mass spectrometry based metabolomics methods, FI-MS based and LC-MS based, enabling a reproducible detection and quantitation of thousands of metabolites within less than one minute per sample. The developed approach facilitates high-throughput metabolomics for a variety of applications, including biomarker discovery and functional genomics screens. Applying the developed metabolomics method to hundreds of serum samples from cancer patients and healthy controls, utilizing machine learning techniques, we have demonstrated the potential and applicability of this approach for population-wide cancer screening.

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