About

Olivier Elemento, Ph.D., is a Professor of Physiology and Biophysics, Walter B. Wriston Research Scholar, and Professor of Computational Genomics in Computational Biomedicine at Weill Cornell Medicine. He serves as the Associate Director of the Institute for Computational Biomedicine and the Director of the Englander Institute for Precision Medicine. Additionally, he is an Associate Director of the Institute for Computational Biomedicine. His research combines Big Data analytics with experimentation to develop new methods for cancer prevention, diagnosis, understanding, treatment, and cure. His lab utilizes ultrafast DNA sequencing, proteomics, high-performance computing, mathematical modeling, and artificial intelligence/machine learning techniques to investigate various aspects of cancer biology. His work focuses on the systems biology of regulatory networks in normal and malignant cells, with a particular emphasis on blood cancers such as lymphomas and leukemias. He employs techniques like ChIP-seq, RNA-seq, and computational modeling to study gene regulation in cancer cells and how it differs from normal cells. His research also involves cancer genomics and precision medicine, aiming to identify new cancer mutations and understand their occurrence, including the role of 3D chromatin architecture. He investigates the epigenomics of cancer, focusing on genes involved in DNA methylation and histone modifications, and how these are mutated in cancer. His lab studies tumor genome evolution and anticancer drug resistance through high-throughput sequencing, exploring how tumor genomes and epigenomes evolve over time, especially upon drug treatment. Furthermore, he applies machine learning approaches for early cancer detection and treatment guidance, with some algorithms licensed for clinical use. His development of computational tools like ChIPseeqer has advanced the analysis of high-throughput experiments. His recent publications reflect his broad engagement in cancer research, computational genomics, and AI-enabled cancer science, emphasizing his role in advancing precision medicine and innovative computational approaches in oncology.

Research topics

• Biology
• Medicine
• Genetics
• Internal medicine
• Virology
• Computer Science
• Computational biology
• Cell biology
• Cancer research
• Immunology
• Biochemistry
• Political Science
• Pathology
• Chemistry
• Oncology
• Data science
• Endocrinology
• Knowledge management
• Pharmacology
• Anatomy
• Demography
• Evolutionary biology
• Public relations

Selected publications

• Abstract 7371: High-definition spatial and single-cell multiomics reveal immunological remodeling and radiation resistance mechanisms in colorectal cancer

  Cancer Research · 2026-04-03

  article

  Abstract Radiation therapy remains central to colorectal cancer care, yet many patients experience incomplete response or recurrence. Understanding why tumors adapt instead of regress requires mapping how radiation reshapes both local and systemic immunity. The ImmunoRad ROBIN initiative addresses this need by integrating high definition spatial and single cell profiling to identify biological processes that sustain radiation resistance.Our cohort included six patients sampled before treatment, after radiation, and at surgery across tumor, adjacent mucosa, and lymph nodes. Using VisiumHD at 2 × 2 μm resolution, we generated more than one and a half million spatial spots, providing a near cellular view of therapy induced remodeling. A dominant pattern emerged at the tumor stroma boundary, where radiation triggered extracellular matrix activation, fibroblast signaling, and wound repair programs. These regions were enriched for M2 like macrophages expressing profibrotic and angiogenic genes. Their expansion suggests that a macrophage dependent repair niche forms at the invasive front and creates conditions that support tumor persistence.These stromal changes aligned with localized epithelial stress responses, showing that adaptation occurs through coordinated tissue level remodeling. Epithelial compartments displayed DNA damage repair, interferon signaling, and partial plasticity, especially near M2 rich zones. Adjacent mucosa showed weaker shifts in antigen presentation and barrier pathways. These patterns indicate that radiation injury extends across tumor and non-tumor tissue, generating new gradients that may influence treatment outcome.To assess systemic adaptation, we performed single cell RNA sequencing on matched blood. Early after radiation, circulating lymphocytes and dendritic cells showed transient interferon signatures. By surgery, these activated populations contracted and were replaced by suppressive myeloid subsets with reduced cytotoxic T cells, suggesting that initial immune activation shifts into a suppressive state. Lymph nodes exhibited disrupted follicular structure, reduced germinal center polarity, and expansion of mantle zones. Integrated analysis showed enrichment of regulatory T cells and exhausted CD8 T cells, consistent with impaired antigen driven immunity.Together, these findings support a model in which radiation triggers acute injury and immune activation that rapidly transitions into macrophage driven repair and systemic immune suppression. This coupled remodeling allows tumor cells to survive therapy and regain growth potential. The ImmunoRad ROBIN framework offers a scalable strategy for decoding treatment induced ecosystem changes and highlights stromal remodeling and lymph node dysfunction as central contributors to radiation resistance in colorectal cancer. Citation Format: Junbum Kim, Olivier Elemento, Christina Montagna, Nir Ben Chetrit, Silvia C. Formenti, Liron Yoffe. High-definition spatial and single-cell multiomics reveal immunological remodeling and radiation resistance mechanisms in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7371.

  PublisherDOI

• 57 Translational evaluation of multimodal artificial intelligence for dermatology triage

  Journal of Clinical and Translational Science · 2026-04-01

  articleOpen access

  Objectives/Goals: To evaluate the translational reliability, reproducibility, diagnostic performance, and subgroup equity of multimodal artificial intelligence (AI) models for dermatology triage across multiple model platforms. Methods/Study Population: Limited access to dermatology expertise delays diagnosis and care, motivating development of multimodal AI systems that integrate clinical images with patient data for triage. We assembled 200 biopsy-confirmed PAD-UFES-20 lesions (melanoma, keratinocyte carcinoma, benign) with paired images and metadata, prioritizing demographic balance. Six multimodal AI models (GPT-5, GPT-5-mini; Gemini 2.5 Pro, Gemini 2.5 Flash; Claude Sonnet-4, Claude Opus-4) analyzed these lesions with identical prompts predicting diagnostic probabilities, triage (urgent vs routine), and rationale. Outcomes included sensitivity, specificity, AUROC, F1, and subgroup equity. Model rationales were reviewed for interpretability, and subset re-prompting tested reproducibility for translational robustness. Results/Anticipated Results: Across six models, sensitivity range was 0.89–1.00, specificity 0.21–0.65, AUROC 0.77–0.87, and F1 scores 0.72–0.81. GPT-5 achieved the most balanced performance (0.92 sensitivity, 0.65 specificity, AUROC 0.87, F1 0.81), while Gemini 2.5 Pro and Flash reached perfect sensitivity but low specificity (0.21–0.25). Claude Sonnet-4 showed near-perfect sensitivity (0.99) but over-called benign cases (0.24 specificity), while Opus-4 had the lowest sensitivity (0.89). Urgent triage aligned with dermatologist biopsy patterns (87–97%), and sensitivity was consistent across sex and skin type (p ≥ 0.29). Subset re-prompting produced similar results, supporting reproducibility. Model rationales reflected dermatologic reasoning, supporting interpretability, and translational readiness. Discussion/Significance of Impact: Multimodal AI models showed balanced diagnostic performance for dermatology triage, with platform-specific trade-offs between sensitivity and specificity. Subgroup equity, interpretable rationales, and subset reproducibility define key elements for reliable translation into dermatology workflows and prospective validation.

  PublisherOA PDFDOI

• Abstract 4948: Spatial architectures of colorectal cancer microenvironment underlying immune checkpoint inhibitor response

  Cancer Research · 2026-04-03

  articleSenior author

  Abstract Colorectal cancer (CRC) remains a leading cause of cancer mortality. Immune checkpoint inhibitors (ICI) are among the most effective systemic therapies, yet they benefit only a subset of MSI-H/MMRd patients. To investigate how spatial tumor-immune-stromal organization contributes to heterogeneous treatment responses, we profiled FFPE CRC tissues from PD-1 inhibitor pembrolizumab-treated (n=10) and treatment-naïve (n=14) patients using the CosMx Spatial Molecular Imager (SMI) with a 1,000-gene single-cell panel. After image processing and cell segmentation, cell states were annotated through unsupervised Leiden clustering, gene-module scoring, and supervised InsituType prediction using a public CRC single-cell atlas, followed by spatial analyses including niche identification, neighborhood enrichment, distance-based metrics, and ligand-receptor inference. Across 24 patients (balanced sex distribution; stages I-IV; median age 66.5), ICI-treated tumors exhibited markedly higher frequencies of all CD8+ T cell subsets, CXCL8+ cancer-associated fibroblasts (CAFs), myofibroblasts, and diverse macrophage and neutrophil populations compared with treatment-naïve tumors, alongside higher proportions of CMS1-like malignant cells. De novo nonnegative matrix factorization (NMF) revealed eight tumor-intrinsic programs, with Inflammatory/MHC-II, Type I IFN/Antigen Presentation, and Innate Inflammatory programs enriched in ICI-exposed tumors, whereas Invasion/Angiogenesis, Proliferation/Stress, and CEA-high programs characterized untreated tumors. Spatial mapping uncovered two recurrent architectures: immune-infiltrated tumors enriched for tertiary lymphoid structures (TLSs) marked by T-B lymphocyte aggregates and focal LTB and CXCL13 expression, as well as fibroblast-dominated tumors demonstrating stromal encapsulation, limited immune intermixing, and preliminary enrichment of CAF-immune suppressive ligand-receptor circuits. Together, these findings delineate inflamed versus fibrotic CRC microenvironments with distinct tumor-immune communication states. Integration of spatial features with clinical response will support refined stratification for ICI-based therapy and nominate TLS density, CAF patterning, and specific ligand-receptor modules as candidate spatial biomarkers for predicting or modulating treatment responsiveness. Citation Format: Chuyan Liu, Hang Yin, Joon Sang Lee, Julien Tessier, Junbum Kim, Donald Jackson, Angela Hadjipanayis, Olivier Elemento. Spatial architectures of colorectal cancer microenvironment underlying immune checkpoint inhibitor response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4948.

  PublisherDOI

• Abstract 4490: PRMT5 inhibition alters cellular chromatin landscape and drives vulnerability to BH3 mimetics in mantle cell lymphoma.

  Cancer Research · 2026-04-03

  article

  Abstract Background: Mantle cell lymphoma (MCL) is a rare and incurable blood cancer, comprising 10% of Non-Hodgkin lymphomas. Especially in relapse settings, there remains a crucial need for novel treatments. The enzyme protein arginine methyltransferase 5 (PRMT5) is an oncogenic driver in MCL which targets both histones and non-histone proteins for posttranslational symmetric arginine dimethylation (me2s). BCL2 family proteins govern apoptosis and their dysregulation is a hallmark of MCL, but treatment with pharmacological inhibitors (BH3 mimetics) has had limited success. We hypothesized that transcriptional and epigenomic perturbations via PRMT5 inhibitors (PRMT5i) will sensitize MCL cells to BH3 mimetics for synergistic drug action. Methods: Plate- and flow-cytometry-based BH3 profiling was used to read out the apoptotic priming state of n=5 MCL cell lines and n=2 primary patient samples. Effects of the small molecule PRMT5i PRT382 were assessed via BH3 profiling, transcriptomics (RNA sequencing) and genome-wide histone modification profiling (CUT&RUN sequencing for H3K4me3, H3K27me3, H3K27ac and two me2s marks H3R8me2s and H4R3me2s). Drug combination effects of PRT382 and BH3-mimetics (navitoclax, A852/A-1331852, PRT1419) were tested in MCL cell lines (n=5). Efficacy of navitoclax and PRT808, a related PRMT5i, was assessed alone or in combination in a murine patient-derived xenograft (PDX) model of aggressive, relapsed MCL. Results: Global analysis of histone modifications and RNA expression revealed strong association around transcription start sites (adjusted p<0.0001), including for PRMT5-mediated H4R3me2s (adjusted p<0.0001), but revealed no pronounced global loss of histone arginine methylation under PRT382. Epigenomic responses to PRT382 were highly distinct in MCL models Z-138 and CCMCL1, with the latter revealing lowered global H3K4me3 (p<0.0001) at loci specifically enriched for cell cycle pathways (adjusted p<0.0001). BH3 profiling revealed vulnerabilities to mitochondrial insults in cell lines, PDX cells, and patient samples, which was correlated with pro-survival BCL2 family RNA- and protein expression (p<0.01). Pronounced sensitization to depolarization with PRT382 in Z-138, but not CCMCL1, was reflective of transcriptomic differences in these models. We observed strong synergistic reduction in cell viability with n=16 distinct PRMT5i/BH3 mimetic combinations (p<0.01). In our PDX model, the PRT808/navitoclax combination outperformed single treatment cohorts in survival (median 93 vs. 65/75 days for PRT808/navitoclax alone, p<0.01) and circulating disease (p<0.01). Conclusions: Our study showed the broad synergistic potential of combining PRMT5i and BH3 mimetics both in vitro and in vivo. PRMT5i lead to epigenome-wide modulation of chromatin states with potential to create vulnerabilities to targeted agents. Citation Format: Christoph Weigel, Claire Hinterschied, Shirsha Koirala, Mackenzie Long, Shelby Sloan, Jessica Weist, Lynda Villagomez, Allesandro La Ferlita, Coinne Gao, Ian Hout, Sydney Leon, Fiona Brown-Burke, Betsy Pray, Maggie Harper, Neha Bhagwat, Kris Vaddi, Peggy A. Scherle, Cem Meydan, Selina Chen-Kiang, Maurizio DiLiberto, Olivier Elemento, Christopher E. Mason, Jihye Paik, Lapo Alinari, Rosa Lapalombella, Lalit Sehgal, Robert Baiocchi. PRMT5 inhibition alters cellular chromatin landscape and drives vulnerability to BH3 mimetics in mantle cell lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4490.

  PublisherDOI

• Whole genome sequencing approach to assess homologous recombination deficiency in a pan-cancer cohort

  Communications Medicine · 2026-01-12 · 2 citations

  articleOpen access

  Homologous recombination deficiency (HRD) impacts cancer treatment strategies, particularly effective utilization of PARP inhibitors. However, the variability of different HRD assays has hampered the selection of oncology patients who may benefit from these therapies. Our study aims to use the whole genome landscape to better define HRD in a pan-cancer cohort. We employed a whole genome sequencing HRD classifier that includes genome-wide signatures associated with HRD to analyze 580 tumor/normal paired samples. The HRD phenotype was correlated with genomic variants in BRCA1/2 and other homologous recombination repair genes. In this paper we show that the HRD phenotype is identified in various cancers including breast (21%), pancreaticobiliary (20%), gynecological (17%), prostate (9%), upper gastrointestinal (GI) (2%), and other cancers (1%). HRD cases are not confined to BRCA1/2 mutations; 24% of HRD cases are BRCA1/2 wild-type. A diverse range of gene alterations involved in HRD are elucidated, including biallelic mutations in FANCF, XRCC2, and FANCC, and deleterious structural variants. In a subset of cases, the whole genome sequencing-based classifier offers more insights and a better correlation to treatment response when compared to other assays. Although HRD is a biomarker used to determine which cancer patients would benefit from PARP inhibitors, a lack of harmonization of tests to determine HRD status makes it challenging to interpret their results. Our study highlights the use of comprehensive whole genome sequencing analysis to better predict HRD and elucidates genomic mechanisms associated with this phenotype. Homologous recombination deficiency is a condition in which a cancer cell cannot repair certain types of DNA damage. It causes genetic instability and is often due to changes in parts of the DNA called genes, such as BRCA1 and BRCA2. Cancers with this deficiency can be more readily killed by certain drugs that prevent DNA repair. Some of these drugs are approved for the treatment of several types of cancer, including ovarian, breast, pancreatic, and prostate cancers. To better identify tumors with this deficiency, we characterize the whole genome of cancer samples. We find that a comprehensive analysis of the entire genome improves the detection of homologous recombination deficiency. This type of analysis may provide a more accurate way to guide treatment decisions for people with cancer. Assaad, Hadi and Levine et al. develop a whole-genome sequencing classifier to improve the detection of homologous recombination deficiency (HRD) across a pan cancer cohort. The classifier detects HRD beyond BRCA1/2 mutations, reveals HRD-related genomic events, and correlates with treatment response in a subset of patients.

  PublisherOA PDFDOI

• Machine learning analysis of population-wide plasma proteins identifies hormonal biomarkers of Parkinson’s disease

  Frontiers in Aging Neuroscience · 2026-03-10

  articleOpen access

  With the number of Parkinson's patients expected to rise due to an aging population, there is an increasing need to identify new diagnostic markers. These markers should be affordable and suitable for routine use to monitor the population, help stratify patients for treatment pathways, and provide new avenues for therapy. Genetic predisposition and familial forms account for approximately 10% of Parkinson's disease (PD) cases, leaving a large fraction of the population with minimal effective markers for identifying high-risk individuals. The establishment of population-wide omics and longitudinal health monitoring studies provides an opportunity to apply machine learning approaches to these unbiased cohorts to identify novel PD markers. In this study, we present the application of three machine learning models to identify protein plasma biomarkers of PD using plasma proteomic measurements from 43,408 UK Biobank subjects as the training and test set and an additional 103 samples from the Parkinson's Progression Markers Initiative (PPMI) as external validation. We identified a group of highly predictive protein plasma markers, including known markers Dopa decarboxylase (DDC) and Calbindin 2 (CALB2) as well as new markers involved in the JAK-STAT and PI3K-AKT pathways and hormonal signaling. We further demonstrated that these features are well correlated with UPDRS severity scores and stratified these into protective and risk-associated features that potentially contribute to the pathogenesis of PD.

  PublisherOA PDFDOI

• Abstract A058: Integrating DNA-RNA sequencing analysis to identify low-variant allele frequency fusions in glioblastoma

  Cancer Research · 2026-03-23

  article

  Abstract The goal of this study was to test whether integrating somatic structural variant (SV) calls from whole-genome sequencing (WGS) with RNA-sequencing (RNA-seq) fusion calls and differential expression (DE) can recover clinically relevant low variant-allele-frequency (VAF) rearrangements in glioblastoma (GBM).We analyzed 10 GBM samples with paired tumor-normal short read WGS and tumor bulk RNA-seq, providing orthogonal profiling of somatic SVs and transcription in GBM tissue. WGS SVs were called with SvABA and annotated using GENCODE. RNA-seq reads were aligned via STAR; fusions were called with Arriba and STAR-Fusion. For each fusion, we performed gene-aware, windowed breakpoint matching to identify DNA breakends within ±100 kb of the fusion breakpoint on the same chromosome pair and stratified support into confidence tiers based on coordinate concordance and gene proximity. We defined RNA-consensus fusions as Arriba∩STAR-Fusion calls and DNA-supported fusions as RNA fusions with an orthogonal SvABA breakend match within ±100 kb. Transcriptomic impact was assessed by DESeq2 (incorporating publicly-available transcriptome profiles garnered from GTEx brain controls), testing enrichment of DNA-supported fusion partners among DE genes (FDR<0.05), and Hallmark gene set enrichment analysis (GSEA). VAF for fusion-linked SVs was estimated through SVTyper.Across the cohort, we observed 1,561 unique strand-aware fusion events, of which 138 (8.8%) had DNA support within ±100 kb (49; 3.1% at ±10 kb) and 30/138 (22%) DNA-supported events called by both Arriba and STAR-Fusion. RNA-consensus fusions were enriched for orthogonal DNA support compared with single-caller fusions (63.6% vs 7.3%, RR=8.7) while DNA-supported events were more likely to show a transcriptional consequence than fusions without DNA support (≥1 DE gene 93.9% vs 66.7%; Fisher’s exact test, OR 7.67, p=0.0014). Overall, 101/138 (74.2%) of DNA-supported fusions had ≥1 DE gene (FDR<0.05). Hallmark GSEA identified 21 significantly enriched pathways (FDR<0.05), dominated by cell-cycle regulators (E2F targets; G2M checkpoint), with additional interferon-γ and epithelial–mesenchymal transition signals, of which 6 (28.6%) included at least one low-VAF fusion-associated gene (VAF<0.2). SV VAF was predominantly low among concordant DNA-RNA fusion-linked events: 73.3% of unique events (n=30) had VAF<0.2, suggesting that many of these rearrangements occur at frequencies consistent with heterogeneous subclonal populations.We highlight concordant DNA-RNA fusion-linked SVs with expression impact, including genes such as CDC25C, COL4A2, and EGFR as candidates for follow-up. Our integrated DNA-RNA framework identifies genomically supported fusion events with transcriptomic impact, enabling the interpretation of biologically relevant low-VAF rearrangements that may reflect subclonality while highlighting candidates of GBM tumorigenesis. Together, these results suggest that DNA-RNA integration can improve sensitivity and support the recovery of low-VAF events compared with single-modality approaches. Citation Format: Shray Parimoo, Eeshaan Rehani, Suraj Rajendran, David C. Wilkes, Michael Sigouros, Eda Nur Kozan, Juan Miguel Mosquera, Andrea Sboner, Olivier Elemento, Iman Hajirasouliha. Integrating DNA-RNA sequencing analysis to identify low-variant allele frequency fusions in glioblastoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Brain Cancer; 2026 Mar 23-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(6_Suppl):Abstract nr A058.

  PublisherDOI

• Training the Next Generation of AI-Enabled Cancer Scientists

  Cancer Discovery · 2026-04-13

  article1st authorCorresponding

  Despite unprecedented opportunities at the convergence of artificial intelligence (AI) and cancer research, few scientists possess fluency in both domains. We propose a six-principle framework for training "AI-oncology bilingual" scientists who can bridge this gap and translate AI-driven discoveries into improved patient outcomes.

  PublisherDOI

• 19q13 amplification with AKT2 and ERCC2 gains in sarcomatoid carcinoma of the urinary bladder

  Cancer Genetics · 2026-03-11

  article
  PublisherDOI

• Multi-Hospital Electronic Health Record Foundation Models Without Data Sharing: A Comparison of Federated Learning and Inference-Time Ensembling

  medRxiv · 2026-04-27

  articleOpen access1st authorCorresponding

  Background: Foundation models for electronic health records (EHRs) perform strongly on clinical prediction, but every published model has been trained within a single health system. No multi-institutional EHR foundation model currently exists, largely because privacy regulations and governance barriers block data pooling across hospitals. Two strategies could build such models without pooling: federated learning (exchanges model weights) and inference-time ensembling (exchanges only predictions at query time). Whether either is viable for autoregressive EHR foundation models, and whether individual hospitals benefit from participating, is not established. Methods: We trained a generative pretrained transformer (GPT) style EHR foundation model on 100,163 Medical Information Mart for Intensive Care (MIMIC-IV) patients, partitioned into five heterogeneously distributed (non-IID) sites by Dirichlet allocation over International Classification of Diseases (ICD) chapters. We compared centralized training, federated averaging, and inference-time ensembling, and each hospital's solo model against the ensemble including it. Models were evaluated on 15,012 held-out patients using per-condition area under the receiver operating characteristic curve (AUROC) for five acute conditions and micro-averaged area under the precision-recall curve (AUPRC) across 2,590 diagnoses. Results: Centralized training achieved per-condition AUROC 0.75-0.85 and overall AUPRC 0.376. Federated averaging recovered 85% of centralized AUPRC (0.321) and 98-100% of per-condition AUROC. Inference-time ensembling, requiring no training-time exchange, recovered 77% of AUPRC (0.291) and 97-99% of per-condition AUROC. An estimated 87% of participating hospitals received a better model from the ensemble than from training alone; only hospitals with ~40% of the network's patients matched the ensemble on their own. FedProx collapsed to the marginal baseline. Conclusions: Multi-institutional EHR foundation models can be built without pooling patient data. Inference-time ensembling benefits most participating hospitals and imposes the lightest governance burden; federated learning recovers more performance but requires weight sharing. These findings offer a practical path toward collaborative clinical AI.

  PublisherOA PDFDOI

Recent grants

• The joint WCM-NYGC Center for Functional and Clinical Interpretation of Tumor Profiles

  NIH · $1.9M · 2016–2022

• Core A: Administrative Core

  NIH · $17.8M · 2018–2025

• Project 2: Targeting N-Myc and EZH2-driven Castrate Resistant Prostate Cancer

  NIH · $21.8M · 2017–2023

• COHESIN REGULATORY PROTEINS AND CHROMOSOMAL ARCHITECTURE IN NORMAL AND MALIGNANT B-CELLS

  NIH · $541k · 2018–2019

• The identification and validation of mechanisms and biomarkers for relapse in diffuse large B-cell lymphoma

  NIH · $2.6M · 2015–2022

Frequent coauthors

• Andrea Sboner

  Weill Cornell Medicine

  1023 shared

• Juan Miguel Mosquera

  Weill Cornell Medicine

  950 shared

• Himisha Beltran

  680 shared

• Mark A. Rubin

  University of Bern

  524 shared

• Rohan Bareja

  Lander Institute

  476 shared

• Michael Sigouros

  441 shared

• Bhavneet Bhinder

  398 shared

• Akanksha Verma

  397 shared

Awards & honors

• Walter B. Wriston Research Scholar