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Rami Vanguri, PhD

Assistant Professor of Pathology, Computational Pathology & AI Center of Excellence (CPACE)

I am a member of the Computational Pathology and AI Center of Excellence (CPACE) and I co-lead the AIX Hub (AI × Experimental Pathology). My research develops multimodal frameworks that integrate quantitative features from digitized pathology with radiologic imaging, with the goal of uncovering disease mechanism and nominating new targets.

Archives of H&E slides, collected through ordinary clinical workflows, contain recurrent morphologic archetypes: reproducible tissue states that are present in the image but not explicitly described in the report. Identifying these archetypes at scale allows me to direct molecular profiling, including spatial transcriptomics and multiplexed imaging, at the states most likely to be informative rather than sampling tissue at random. Profiling tissue selected this way is intended to resolve the mechanisms driving each archetype, and in doing so to identify targets suitable for therapeutic development. I am applying this approach to transplantation, cardiac disease and cancer. Through the AIX Hub, I work with investigators in Experimental Pathology to connect clinical multimodal data with experimental systems, using each to generate and test hypotheses about the other. This includes building shared multimodal pipelines for use across Experimental Pathology and CPACE.

I earned my PhD in Physics at the University of Pennsylvania and completed postdoctoral fellowships in biomedical informatics and at the Data Science Institute, both at Columbia University. I then worked as a staff computational scientist at Memorial Sloan Kettering Cancer Center and at the Children's Hospital of Philadelphia. Before joining Pitt, I was an Assistant Professor in the Department of Medicine at NYU Grossman School of Medicine.

Representative Publications
  1. Foong YH, Singh A, Malik M, Zhou F, Liu C, Willett D, Golombeck D, Zuluaga T, Le H, Caplan AS, Barcia Durán JG, Fishman GI, Aizer A, Reyentovich A, Peyster E, Goldberg RI*, Vanguri R*. Spatial transcriptomic signatures of cardiac sarcoidosis. Journal of Cardiac Failure – Intersections. 2026;2(3):278–283. DOI
  2. Le H, Singh A, Donnino R, Stojanovska J, Liu C, Willett D, Fishman GI, Kushnir A, Goldberg RI*, Vanguri R*. Quantitative characterization of late gadolinium enhancement on cardiac magnetic resonance imaging to enhance diagnosis of cardiac sarcoidosis. Journal of Cardiac Failure – Intersections. 2026;2(2):162–165. DOI
  3. Foong YH, Jour G, Sinha G, Benhamida J, Aleynick N, Le H, Zellmer A, Li Y, Hollmann T, Gounder M, Sardana R, Saoud C, Vaynrub M, Boland P, Vanguri R*, Hameed M*. Molecular profiling of sacral chordomas through methylation, spatial transcriptomics and multiplexed immunofluorescence. ESMO Rare Cancers. 2026;5:100115. DOI
  4. Foong YH, Wang Q, Kearney M, Le H, Guo H, Chen D, Garlin Politis M, Connelly CF, Kalinsky K, Vanguri RS*, Connolly EP*. Spatial biomarkers of response to eribulin plus pembrolizumab in patients with metastatic triple negative breast cancer in the ENHANCE-1 trial. NPJ Breast Cancer. 2025;11(1):72. DOI
  5. Vanguri RS*, Smithy JW, Li Y, Zhuang M, Maher CA, Aleynick N, Peng X, Al-Ahmadie H, Funt SA, Rosenberg JE, Iyer G, Bajorin D, Mathews JC, Nadeem S, Panageas KS, Shen R, Callahan MK, Hollmann TJ. Integration of peripheral blood- and tissue-based biomarkers of response to immune checkpoint blockade in urothelial carcinoma. J Pathol. 2023;261(3):349–360. DOI
  6. Aleynick N, Li Y, Xie Y, Zhang M, Posner A, Roshal L, Pe'er D, Vanguri RS*, Hollmann TJ*. Cross-platform dataset of multiplex fluorescent cellular object image annotations. Sci Data. 2023;10(1):193. DOI
  7. Vanguri RS, Luo J, Aukerman AT, Egger JV, Fong CJ, Horvat N, Pagano A, Araujo-Filho JAB, Geneslaw L, Rizvi H, Sosa R, Boehm KM, Yang SR, Bodd FM, Ventura K, Hollmann TJ, Ginsberg MS, Gao J, MSK MIND Consortium, Hellmann MD, Sauter JL, Shah SP. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer. Nat Cancer. 2022;3(10):1151–1164. DOI
  8. Boehm KM, Aherne EA, Ellenson L, Nikolovski I, Alghamdi M, Vázquez-García I, Zamarin D, Long Roche K, Liu Y, Patel D, Aukerman A, Pasha A, Rose D, Selenica P, Causa Andrieu PI, Fong C, Capanu M, Reis-Filho JS, Vanguri R, Veeraraghavan H, Gangai N, Sosa R, Leung S, McPherson A, Gao J, MSK MIND Consortium, Lakhman Y, Shah SP. Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. Nat Cancer. 2022;3(6):723–733. DOI
  9. Boehm KM, Khosravi P, Vanguri R, Gao J, Shah SP. Harnessing multimodal data integration to advance precision oncology. Nat Rev Cancer. 2022;22(2):114–126. DOI
  10. Vanguri RS, Fenn KM, Kearney MR, Wang Q, Guo H, Marks DK, Chin C, Alcus CF, Thompson JB, Leu CS, Hibshoosh H, Kalinsky KM, Mathews JC, Nadeem S, Hollmann TJ, Connolly EP. Tumor immune microenvironment and response to neoadjuvant chemotherapy in hormone receptor/HER2+ early stage breast cancer. Clin Breast Cancer. 2022;22(6):538–546. DOI
  11. Moore MR, Friesner ID, Rizk EM, Fullerton BT, Mondal M, Trager MH, Mendelson K, Chikeka I, Kurc T, Gupta R, Rohr BR, Robinson EJ, Acs B, Chang R, Kluger H, Taback B, Geskin LJ, Horst B, Gardner K, Niedt G, Celebi JT, Gartrell-Corrado RD, Messina J, Ferringer T, Rimm DL, Saltz J, Wang J*, Vanguri R*, Saenger YM*. Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma. Sci Rep. 2021;11(1):2809. DOI

* Corresponding author