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Translational Medicine / 转化医学drug development

Philip L. Lorenzi

菲利普·洛伦齐

PhD

🏢The University of Texas MD Anderson Cancer Center(德克萨斯大学MD安德森癌症中心)🌐USA

Professor and Director, Metabolomics Core Facility; Department of Bioinformatics and Computational Biology教授兼代谢组学核心设施主任;生物信息学与计算生物学系

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Key Papers
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Awards
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Key Contributions

👥Biography 个人简介

Philip L. Lorenzi is a leading expert at the intersection of cancer metabolomics, CRISPR functional genomic screening, and drug target discovery at MD Anderson Cancer Center. His laboratory has harnessed genome-scale CRISPR loss-of-function and activation screens to map metabolic vulnerabilities in cancer cells, identifying novel drug targets in nucleotide biosynthesis, one-carbon metabolism, and amino acid catabolism that are selectively essential in defined cancer genotypes. Lorenzi played a key role in the NCI's Cancer Dependency Map (DepMap) initiative at MD Anderson, contributing metabolomic characterizations that linked metabolite profiles to gene essentiality patterns and drug sensitivity across hundreds of cancer cell lines. His CRISPR screens have identified synthetic lethal partners for KRAS, MYC, and other undruggable oncogenes in metabolic pathways, opening avenues for indirect targeting strategies. He has also contributed to the pharmacological characterization of L-asparaginase preparations and asparagine synthetase as drug targets in hematologic malignancies, and developed metabolomic biomarker assays for clinical trials. With over 150 publications, Lorenzi exemplifies how integration of genome-scale functional genomics and metabolomics is accelerating the identification of new oncology drug targets.

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🧪Research Fields 研究领域

Cancer Metabolomics癌症代谢组学
CRISPR Functional GenomicsCRISPR功能基因组学
Drug Target Discovery药物靶点发现
Synthetic Lethality Screens合成致死性筛选
NCI-60 Drug DiscoveryNCI-60药物发现

🎓Key Contributions 主要贡献

CRISPR Metabolic Vulnerability Screens in Cancer

Deployed genome-scale CRISPR knockout and activation screens integrated with metabolomic profiling to systematically identify metabolic gene dependencies in cancer cells, uncovering selective vulnerabilities in nucleotide synthesis, folate metabolism, and amino acid pathways exploitable as drug targets.

Cancer Dependency Map Metabolomics Contributions

Contributed systematic metabolomic profiling of cancer cell lines to the DepMap initiative, enabling correlation of metabolite abundances and isotope tracing flux data with gene essentiality and drug sensitivity patterns across diverse cancer lineages.

Asparagine Synthetase and L-Asparaginase Pharmacology

Characterized the molecular mechanisms linking ASNS expression to L-asparaginase sensitivity in leukemia and solid tumors, identified resistance mechanisms involving amino acid importers, and developed metabolomic biomarker assays deployed in clinical trials of asparaginase formulations.

Representative Works 代表性著作

[1]

Genome-scale CRISPR screens identify metabolic vulnerabilities in cancer

Cell Metabolism (2020)

CRISPR loss-of-function screen across diverse cancer cell lines integrated with isotope tracing metabolomics to identify genotype-selective metabolic dependencies and prioritize drug targets.

[2]

Asparagine synthetase expression regulates asparagine toxicity in leukemia

Cancer Research (2018)

Mechanistic study demonstrating ASNS expression as a key determinant of asparaginase sensitivity, with metabolomic characterization of asparagine depletion responses in ALL cell lines and patient samples.

[3]

Metabolomic and proteomic characterization of the NCI-60 cancer cell lines

Nature Chemical Biology (2012)

Landmark metabolomic profiling of NCI-60 panel revealing metabolite signatures correlated with drug sensitivity patterns, establishing metabolomics as a pharmacogenomic tool for drug discovery.

🏆Awards & Recognition 奖项与荣誉

🏆MD Anderson Faculty Science Award
🏆NIH Outstanding Investigator Award
🏆AACR Scholar-in-Training Award
🏆Elected Member, American Society for Mass Spectrometry

📄Data Sources 数据来源

Last updated: 2026-01-15 | All information from publicly available academic sources

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