About

Robert Babak Faryabi, Ph.D., is an Associate Professor of Pathology and Laboratory Medicine at the University of Pennsylvania's Perelman School of Medicine. He is also an Associate Investigator at the Abramson Family Cancer Research Institute, a member of the Abramson Cancer Center, and a Senior Fellow at the Institute for Biomedical Informatics. Dr. Faryabi serves as a Core Faculty Member at the Penn Epigenetics Institute and is involved in multiple research initiatives related to cancer genomics and epigenetics. His research focuses on advancing the mechanistic understanding of human cancer genome architecture and regulation. His lab employs multidisciplinary approaches to explore chromatin organization, transcriptional regulation, and gene expression in cancer, with current projects centered on lymphoma and breast cancer. These projects investigate how epigenetic control of gene expression is disrupted, how transcriptional dependencies develop, and how heterogeneity and plasticity in transcriptional regulation contribute to drug resistance. Dr. Faryabi's work aims to elucidate the cause-and-effect relationships between chromatin organization and cancer progression, contributing to the development of targeted therapies and diagnostic tools.

Research topics

• Biology
• Cancer research
• Cell biology
• Computational biology
• Computer science

Selected publications

• Figure S6 from The Novel Immunocompetent Eµ-SOX11CCND1 Mouse Model Phenotypically and Molecularly Resembles Human Mantle Cell Lymphoma

  2025-11-03

  articleOpen access

  <p>The adoptive transfer model mimics pathways that are constitutively activated in human MCL.</p>

  PublisherDOI

• Supplemental table 6 from The Novel Immunocompetent Eµ-SOX11CCND1 Mouse Model Phenotypically and Molecularly Resembles Human Mantle Cell Lymphoma

  2025-11-03

  articleOpen access

  <p>Disease characteristics of the mice that developed lymphoma</p>

  PublisherOA PDFDOI

• Supplementary Table4 from The Novel Immunocompetent Eµ-SOX11CCND1 Mouse Model Phenotypically and Molecularly Resembles Human Mantle Cell Lymphoma

  2025-11-03

  articleOpen access

  <p>Reagents used for histology and immunohistochemistry </p>

  PublisherOA PDFDOI

• Lineage-determining transcription factors constrain cohesin to drive multi-enhancer oncogene regulation

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-12

  preprintOpen accessSenior authorCorresponding

  Multiple enhancers, often located across vast genomic distances, regulate key genes. However, how chromatin topology organization at individual alleles enables cell-type-restricted multi-enhancer gene regulation remains unclear. Using acute protein degradation and time-course population-average chromatin conformation capture in lymphoma, we found that the B-cell- lineage-determining transcription factor EBF1 preferentially positions multiple enhancers at loci containing sparsely distributed genes essential for B-cell identity and oncogenesis. Our time-resolved sub-diffraction optical chromatin architecture tracing of >100,000 alleles in individual lymphoma cells further revealed diverse topological conformations facilitating multi-enhancer interactions. Mechanistically, we found that positioning of enhancers at allelic topological centers is required for their interactions with target promoters, with EBF1 serving as a barrier to the loop-extruding cohesin on enhancers. These findings, which we demonstrate their generalizability to the T-cell-lineage-determining transcription factor TCF1 in T-cell leukemia, suggest that lineage-determining transcription factors radially position enhancers and promoters to enable multi-enhancer regulation of key oncogenes.

  PublisherOA PDFDOI

• Supplementary attachment 1 from The Novel Immunocompetent Eµ-SOX11CCND1 Mouse Model Phenotypically and Molecularly Resembles Human Mantle Cell Lymphoma

  2025-11-03

  articleOpen access

  <p>ion torrent mouse chip design</p>

  PublisherOA PDFDOI

• Figure S2 from The Novel Immunocompetent Eµ-SOX11CCND1 Mouse Model Phenotypically and Molecularly Resembles Human Mantle Cell Lymphoma

  2025-11-03

  articleOpen access

  <p>Heterogenous morphology of MCL-like developed by Eµ-SOX11CCND1 animals</p>

  PublisherDOI

• Azurify integrates cancer genomics with machine learning to classify the clinical significance of somatic variants

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-04-23

  preprintOpen accessSenior authorCorresponding

  SUMMARY Accurate classification of somatic variations from high-throughput sequencing data has become integral to diagnostics and prognostics across various cancers. However, the classification of these variations remains highly manual, inherently variable, and largely inaccessible outside specialized laboratories. Here, we introduce Azurify - a computational tool that integrates machine learning, public resources recommended by professional societies, and clinically annotated data to classify the pathogenicity of variations in precision cancer medicine. Trained on over 15,000 clinically classified variants from 8,202 patients across 138 cancer phenotypes, Azurify achieves 99.1% classification accuracy for concordant pathogenic variants in data from two external clinical laboratories. Additionally, Azurify reliably performs precise molecular profiling in leukemia cases. Azurify’s unified, scalable, and modular framework can be easily deployed within bioinformatics pipelines and retrained as new data emerges. In addition to supporting clinical workflows, Azurify offers a high-throughput screening solution for research, enabling genomic studies to identify meaningful variant-disease associations with greater efficiency and consistency.

  PublisherOA PDFDOI

• Figure S5 from The Novel Immunocompetent Eµ-SOX11CCND1 Mouse Model Phenotypically and Molecularly Resembles Human Mantle Cell Lymphoma

  2025-11-03

  articleOpen access

  <p>Additional immune profiling of adoptive transfer and transgenic mice.</p>

  PublisherDOI

• Supplementary Table2 from The Novel Immunocompetent Eµ-SOX11CCND1 Mouse Model Phenotypically and Molecularly Resembles Human Mantle Cell Lymphoma

  2025-11-03

  articleOpen access

  <p>Antibodies and markers used for spectral flowcytometry.</p>

  PublisherOA PDFDOI

• The Novel Immunocompetent Eµ-SOX11CCND1 Mouse Model Phenotypically and Molecularly Resembles Human Mantle Cell Lymphoma

  Clinical Cancer Research · 2025-08-26

  articleOpen access

  PURPOSE: Mantle cell lymphoma (MCL) remains incurable despite therapeutic advances, highlighting the need for improved preclinical models. Existing transgenic MCL mouse models have significant limitations, restricting their translational value. EXPERIMENTAL DESIGN: We generated an immunocompetent MCL model by overexpressing the key oncogenic drivers SRY-box transcription factor 11 (SOX11) and Cyclin D1 (CCND1) under the Eµ enhancer in C57BL/6 mice, aiming to replicate human MCL's biological and pathologic features. RESULTS: Eµ-SOX11CCND1 mice developed lymphoma marked by clonal B1a cell expansion in lymphatic and extranodal tissues. Morphologic, immunophenotypic, and transcriptional profiling revealed strong similarity to human MCL, with pathway analysis confirming significant molecular overlap. Importantly, lymphoma cells could be adoptively transferred into wild-type recipients, enabling therapeutic testing within an intact immune system. CONCLUSIONS: The Eµ-SOX11CCND1 mouse represents a robust and biologically relevant model that faithfully recapitulates human MCL. Its immunocompetent nature and adoptive transfer capability make it a valuable model for studying disease mechanisms and evaluating novel therapeutic approaches for patients with MCL.

  PublisherOA PDFDOI

Recent grants

• Human Pancreas Analysis Program for Type 2 Diabetes (HPAP-T2D)

  NIH · $25.0M · 2019–2029

• Epigenetic Mechanisms Underpinning Mantle Cell Lymphoma Sensitivity and Resistance to Notch Inhibitors

  NIH · $2.1M · 2022–2027

• Notch-driven Epigenetic Program of MYC and CCND1 in Triple-Negative Breast Cancer

  NIH · $2.1M · 2019–2026

Frequent coauthors

• Gregory W. Schwartz

  University Health Network

  74 shared

• Yeqiao Zhou

  California University of Pennsylvania

  59 shared

• Warren S. Pear

  58 shared

• Jelena Petrovic

  55 shared

• Golnaz Vahedi

  University of Pennsylvania

  51 shared

• Maria Fasolino

  California University of Pennsylvania

  31 shared

• Naomi Goldman

  California University of Pennsylvania

  27 shared

• Lanwei Xu

  University of Pennsylvania

  27 shared

Labs

• Faryabi LabPI

Education

• Postdoctoral training

  National Cancer Institute

  2015