Aritro Nath, Ph.D.

(877) 460-4673
Aritro Nath, Ph.D.
Academic Appointments
  • Assistant Professor, Division of Molecular Pharmacology, Department of Medical Oncology & Therapeutics Research

Dr. Nath is a cancer biologist with expertise in both experimental and computational biology. Through the integration of bioinformatics and artificial intelligence/machine learning (AI/ML) tools, his research aims to unravel the underlying mechanisms of tumor progression and establish cutting-edge biomarkers for improving the clinical outcomes of patients with cancer.

Dr. Nath's research employs both bulk and single-cell multi-omics approaches to identify the mechanisms behind tumor evolution and the subsequent development of drug resistance. Dr. Nath aspires to translate research findings into clinical impact, notably contributing to breast cancer treatment with novel prognostic AI/ML biomarkers. These biomarkers are now part of clinical trials for optimized treatment strategies in metastatic breast cancer patients.

Throughout his career, Dr. Nath has had the privilege to work in close collaboration with laboratory scientists, mathematicians, and medical oncologists. At City of Hope, Dr. Nath leads a collaborative research team that investigates the potential of AI/ML in enhancing cancer patient outcomes. Dr. Nath currently is a principal investigator (PI) of a National Cancer Institute/National Institute of Health (NCI/NIH) U01 grant, multiple NCI/NIH administrative supplements, a PHASE ONE foundation grant, a JKTG foundation grant, and co-investigator of a California Institute for Regenerative Medicine (CIRM) grant.

Location

Education & Experience

Degrees

  • 2015, Ph.D., Genetics Program, Michigan State University, East Lansing, MI
  • 2008, M.Sc., Biomedical Genetics, Graduate School VIT University, Vellore, India
  • 2006, B.Sc., Biotechnology,College or University Govt. Holkar Science College, Indore, India

Fellowship

  • 2017-2019, University of Minnesota, Minneapolis, MN
  • 2015–2017, University of Chicago, Chicago, IL

Professional Experience

  • 2023-present, Assistant Professor, Department of Medical Oncology & Therapeutics Research, City of Hope
  • 2020-2023, Assistant Research Professor, Department of Medical Oncology & Therapeutics Research, City of Hope
  • 2019-2020, Staff Scientist, Department of Medical Oncology & Therapeutics Research, City of Hope

Research

  1. Advancing precision oncology: Developing novel data-driven biomarkers and treatment strategies. In precision oncology, the goal is to personalize patient care by aligning each tumor with the most relevant treatment strategy based on its unique molecular composition. Advanced techniques capturing molecular profiles at an "omic" scale generate extensive data. Dr. Nath’s ongoing research explores innovative approaches to harness and tailor treatment strategies based on individual patient needs, utilizing data from both experimental systems and patient-derived samples.
    1. Incorporating the non-coding genome in drug response prediction models: Dr. Nath extended cancer drug response models to include lncRNAs, using large-scale screens with 800+ agents and 1000+ cell lines. New predictive models integrating genomic and transcriptomic data identified EGFR-AS1 and MIR205HG as potent anti-EGFR drug response biomarkers, surpassing clinical biomarkers (Nath et al. 2019, PNAS). Validation using resistant cell lines highlighted non-coding genes' predictive potential. To address transcriptomic data limitations, he developed ML tools for imputing miRNA and lncRNA levels with external datasets (Nath et al. 2020, Bioinformatics; Nath et al. 2020, Briefings in Bioinformatics).
    2. Developing novel prognostic biomarkers and treatment strategies for breast cancer Breast cancer, the leading cause of cancer-related deaths in women, presents a challenge with over 40,000 annual deaths in the US. Approximately 75% of cases are estrogen receptor-positive (ER+), initially treated with endocrine therapy. However, 30-40% experience relapse, necessitating alternative treatments. Dr. Nath addresses this gap by developing AI/ML-based prognostic models. Using transcriptomic data from 800+ ER+ breast cancer patients, the ENDORSE model identifies those likely to benefit from endocrine therapy (Nath et al. 2022, Molecular Systems Biology). Validation across four independent clinical trials, including advanced Stage IV cases, confirmed ENDORSE's reliability. Additionally, an ML biomarker for mTOR inhibitor response was developed, with both biomarkers protected by patents (U.S. Patent Application No. 18/031,855 and International Patent Application No. PCT/US2021/055285).
  2. Decoding tumor evolution: unveiling progression and drug resistance mechanisms through bulk and single-cell omics. Despite therapeutic advancements, drug resistance remains a challenge in tumors, impacting survival. Dr. Nath investigates tumor evolution and its role in drug resistance using bioinformatics and systems biology. The goal is to uncover mechanisms for designing improved therapeutic strategies against refractory tumors.
    1. Heterogeneity of resistance mechanisms in refractory breast cancers: In ongoing research, Dr. Nath is using scRNA-seq data from breast cancers across disease stages to investigate resistance mechanisms during multiple treatments. His group uses mathematical models to reveal signal amplification from resistance pathways as treatment progresses. ML models are being employed to predict drug response, providing detailed single-cell drug sensitivity profiles for refractory tumors. This research aims to gain insights into diverse resistance mechanisms, informing innovative therapeutic approaches in breast cancer.
    2. Evolution of resistance phenotypes in ovarian cancers: Dr. Nath investigated high-grade serous ovarian cancer (HGSOC), a lethal gynecological malignancy with low survival rates, focusing on platinum-based chemotherapy resistance. Utilizing single-cell RNA sequencing and DNA sequencing from longitudinal tumor samples, he applied machine learning to identify distinct transcriptional states (archetypes). Notably, the metabolism and proliferation archetype amplified after multiple therapies. In patient-derived cell lines, increased energy production occurred via oxidative phosphorylation and glycolysis, independent of specific genomic alterations, emphasizing the role of transcriptional plasticity in driving resistance (Nath et al. 2021, Nature Communications).

Publications

Complete list of publications

  • Farmaki E, Nath A, Emond R, Karimi KL, Grolmusz VK, Cosgrove PA, Bild AH. ONC201/TIC10 enhances durability of mTOR inhibitor everolimus in metastatic ER+ breast cancer. Elife. 2023 Sep 29;12. doi: 10.7554/eLife.85898.
  • Emond R, Griffiths JI, Grolmuz VK, Nath A, Chen J, Medina EF, Sousa RS, Synold T, Adler FR, Bild AH. Ecological interactions in breast cancer: Cell facilitation promotes growth and survival under drug pressure. Nature Communications, 2023;14, 3851. https://doi.org/10.1038/s41467-023-39242-6
  • Nath A*, Cosgrove PA, Chang JT, Bild AH*. Predicting clinical response to everolimus in ER+ breast cancers using machine-learning. Frontiers in Molecular Biosciences, 2022; 9: 981962. doi: 10.3389/fmolb.2022.981962.
    *co-corresponding authors
  • Bishara I, Chen J, Griffiths JI, Bild AH, Nath A. A machine learning framework for scRNA-seq UMI threshold optimization and accurate classification of cell types. Frontiers in Genetics, 2022;13, 982019. doi: 10.3389/fgene.2022.982019.
  • Nath A. Unraveling phenotypic plasticity and evolution in small cell lung cancer. Cell Systems, 2022;13(9), 687-689. doi:10.1016/j.cels.2022.08.005.
  • Nath A, Cohen AL, Bild AH. ENDORSE: a prognostic model for endocrine therapy in estrogen‐receptor‐positive breast cancers. Molecular Systems Biology, 2022;18(6), e10558. doi:10.15252/msb.202110558.
  • Liu S, Song Y, Zhang IY, Zhang L, Gao H, Su Y, Yang Y, Yin S, Zheng Y, Ren L, Yin HH, Pillai R, Nath A, EF Medina, Cosgrove PA, Bild AH, Badie B. RAGE Inhibitors as Alternatives to Dexamethasone for Managing Cerebral Edema Following Brain Tumor Surgery. Neurotherapeutics, 2022; 1-14.
  • Nath A, Cosgrove PA, Mirsafian H, Christie EL, Copeland B, Pflieger L, et al. Evolution of core archetypal phenotypes in progressive high grade serous ovarian cancer. Nature Communications. 2021;12(1):3039. doi:10.1038/s41467-021-23171-3.
  • Nath A, Bild AH. Leveraging Single-Cell Approaches in Cancer Precision Medicine. Trends in Cancer. 2021; 7(4), 359-372.
  • Nath A, Oak A, Chen KY, Li I, Splichal RC, Portis J et al. Palmitate-Induced IRE1–XBP1–ZEB Signaling Represses Desmoplakin Expression and Promotes Cancer Cell Migration. Molecular Cancer Research. 2021; 19 (2), 240-248
  • Chi F, Liu J, Brady SW, Cosgrove PA, Nath A, McQuerry JA, Majumdar S, Moos PJ, Chang JT, Kahn M, Bild AH. A ‘one-two punch' therapy strategy to target chemoresistance in estrogen receptor positive breast cancer. Translational Oncology. 2021;14 (1), 100946.
  • the Translational Breast Cancer Research Consortium (TBCRC), O’Donnell PH, Trubetskoy V, Nurhussein-Patterson A, Hall JP, Nath A, et al. Clinical evaluation of germline polymorphisms associated with capecitabine toxicity in breast cancer: TBCRC-015. Breast Cancer Research & Treatment. 2020;181: 623–633. doi:10.1007/s10549-020-05603-8
  • Grolmusz VK, Chen J, Emond R, Cosgrove PA, Pflieger L, Nath A, et al. Exploiting collateral sensitivity controls growth of mixed culture of sensitive and resistant cells and decreases selection for resistant cells in a cell line model. Cancer Cell International. 2020;20: 253. doi:10.1186/s12935-020-01337-1
  • Nath A, Geeleher P, Huang RS. Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes. Briefings in Bioinformatics. 2020;21: 637–648. doi:10.1093/bib/bby129
  • Nath A, Huang RS. Emerging role of long non-coding RNAs in cancer precision medicine. Molecular & Cellular Oncology. 2020;7: 1684130. doi:10.1080/23723556.2019.1684130
  • Nath A, Chang J, Huang RS. iMIRAGE: an R package to impute microRNA expression using protein-coding genes. Bioinformatics. 2020;36: 2608–2610. doi:10.1093/bioinformatics/btz939
  • Pareek S, Nath A, Huang RS. MicroRNA targeting energy metabolism in ovarian cancer: a potent contender for future therapeutics. Annals of Translational Medicine. 2019;7: S299–S299. doi:10.21037/atm.2019.11.15
  • Nath A, Lau EYT, Lee AM, Geeleher P, Cho WCS, Huang RS. Discovering long noncoding RNA predictors of anticancer drug sensitivity beyond protein-coding genes. Proceedings of the National Academy of Sciences. 2019; 201909998. doi:10.1073/pnas.1909998116
    • Highlighted in: Smallegan MJ, Rinn JL. Linking long noncoding RNA to drug resistance. Proceedings of the National Academy of Sciences. 2019;116: 21963–21965. doi:10.1073/pnas.1915690116
  • AstraZeneca-Sanger Drug Combination DREAM Consortium, Menden MP, Wang D, Mason MJ, Szalai B, Bulusu KC, et al. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature Communications. 2019;10: 2674. doi:10.1038/s41467-019-09799-2
    * Nath A, Huang RS
  • Geeleher P, Nath A, Wang F, Zhang Z, Barbeira AN, Fessler J, et al. Cancer expression quantitative trait loci (eQTLs) can be determined from heterogeneous tumor gene expression data by modeling variation in tumor purity. Genome Biology. 2018;19: 130. doi:10.1186/s13059-018-1507-0
  • Wang F, Chang JT-H, Zhang Z, Morrison G, Nath A, Bhutra S, et al. Discovering drugs to overcome chemoresistance in ovarian cancers based on the cancer genome atlas tumor transcriptome profile. Oncotarget. 2017;8: 115102–115113. doi:10.18632/oncotarget.22870
  • Nath A, Wang J, Stephanie Huang R. Pharmacogenetics and Pharmacogenomics of Targeted Therapeutics in Chronic Myeloid Leukemia. Molecular Diagnosis & Therapy. 2017;21: 621–631. doi:10.1007/s40291-017-0292-x
  • Geeleher P, Zhang Z, Wang F, Gruener RF, Nath A, Morrison G, et al. Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies. Genome Research. 2017;27: 1743–1751. doi:10.1101/gr.221077.117
  • Geeleher P, Nath A, Huang RS. Institutional Profile: Pharmacogenomic research in R Stephanie Huang Laboratory. Pharmacogenomics. 2017;18: 519–522. doi:10.2217/pgs-2017-0031
  • Bilgin B, Nath A, Chan C, Walton SP. Characterization of transcription factor response kinetics in parallel. BMC Biotechnology. 2016;16: 62. doi:10.1186/s12896-016-0293-6
  • Nath A, Chan C. Genetic alterations in fatty acid transport and metabolism genes are associated with metastatic progression and poor prognosis of human cancers. Scientific Reports. 2016;6: 18669. doi:10.1038/srep18669
  • Kowalsky CA, Faber MS, Nath A, Dann HE, Kelly VW, Liu L, et al. Rapid Fine Conformational Epitope Mapping Using Comprehensive Mutagenesis and Deep Sequencing. Journal of Biological Chemistry. 2015;290: 26457–26470. doi:10.1074/jbc.M115.676635
  • Nath A, Li I, Roberts LR, Chan C. Elevated free fatty acid uptake via CD36 promotes epithelial-mesenchymal transition in hepatocellular carcinoma. Scientific Reports. 2015;5: 14752. doi:10.1038/srep14752
  • Cho H, Wu M, Zhang L, Thompson R, Nath A, Chan C. Signaling dynamics of palmitate-induced ER stress responses mediated by ATF4 in HepG2 cells. BMC Systems Biology. 2013;7: 9. doi:10.1186/1752-0509-7-9
  • Hijazi H, Wu M, Nath A, Chan C. Ensemble Classification of Cancer Types and Biomarker Identification: Ensemble Classification of Cancer. Drug Development Research. 2012;73: 414–419. doi:10.1002/ddr.21032
  • Nath A, Chan C. Relevance of Network Hierarchy in Cancer Drug-Target Selection. In: Systems Biology in Cancer Research and Drug Discovery. Dordrecht: Springer Netherlands; 2012. pp. 339–362. doi:10.1007/978-94-007-4819-4_15
  • Wang X*, Nath A*, Yang X, Portis A, Walton SP, Chan C. Synergy Analysis Reveals Association between Insulin Signaling and Desmoplakin Expression in Palmitate Treated HepG2 Cells. Isalan M, editor. PLoS ONE. 2011;6: e28138. doi:10.1371/journal.pone.0028138
    * Co-first authors
  • Yang X, Nath A, Opperman MJ, Chan C. The Double-stranded RNA–dependent Protein Kinase Differentially Regulates Insulin Receptor Substrates 1 and 2 in HepG2 Cells. Parent C, editor. Molecular Biology of the Cell. 2010;21: 3449–3458. doi:10.1091/mbc.e10-06-0481