M
Mohammad Shahrokh Esfahani
Researcher at Stanford University
Publications - 65
Citations - 5496
Mohammad Shahrokh Esfahani is an academic researcher from Stanford University. The author has contributed to research in topics: Prior probability & Diffuse large B-cell lymphoma. The author has an hindex of 18, co-authored 56 publications receiving 2906 citations. Previous affiliations of Mohammad Shahrokh Esfahani include University of Washington & Texas A&M University.
Papers
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Journal ArticleDOI
Determining cell type abundance and expression from bulk tissues with digital cytometry.
Aaron M. Newman,Chloé B. Steen,Chloé B. Steen,Chih Long Liu,Andrew J. Gentles,Aadel A. Chaudhuri,Florian Scherer,Michael S. Khodadoust,Mohammad Shahrokh Esfahani,Bogdan A. Luca,David F. Steiner,Maximilian Diehn,Ash A. Alizadeh +12 more
TL;DR: The utility of CIBERSORTx is evaluated in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade.
Journal ArticleDOI
Early Detection of Molecular Residual Disease in Localized Lung Cancer by Circulating Tumor DNA Profiling
Aadel A. Chaudhuri,Jacob J. Chabon,Alexander F. Lovejoy,Aaron M. Newman,Henning Stehr,Tej D. Azad,Michael S. Khodadoust,Mohammad Shahrokh Esfahani,Chih Long Liu,Li Zhou,Florian Scherer,David M. Kurtz,Carmen Say,Justin N. Carter,D.J. Merriott,Jonathan C. Dudley,Michael S. Binkley,Leslie A. Modlin,Sukhmani K. Padda,Michael F. Gensheimer,Robert B. West,Joseph B. Shrager,Joel W. Neal,Heather A. Wakelee,Billy W. Loo,Ash A. Alizadeh,Maximilian Diehn +26 more
TL;DR: This study shows that ctDNA analysis can robustly identify posttreatment MRD in patients with localized lung cancer, identifying residual/recurrent disease earlier than standard-of-care radiologic imaging, and thus could facilitate personalized adjuvant treatment at early time points when disease burden is lowest.
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Circulating tumour DNA profiling reveals heterogeneity of EGFR inhibitor resistance mechanisms in lung cancer patients
Jacob J. Chabon,Andrew Simmons,Alexander F. Lovejoy,Mohammad Shahrokh Esfahani,Aaron M. Newman,Henry J. Haringsma,David M. Kurtz,Henning Stehr,Florian Scherer,Chris Karlovich,Thomas Harding,Kathleen A. Durkin,Gregory A. Otterson,W. Thomas Purcell,D. Ross Camidge,Jonathan W. Goldman,Lecia V. Sequist,Zofia Piotrowska,Heather A. Wakelee,Joel W. Neal,Ash A. Alizadeh,Maximilian Diehn +21 more
TL;DR: In this article, the authors employ CAPP-Seq ctDNA analysis to study resistance mechanisms in 43 non-small cell lung cancer (NSCLC) patients treated with the third-generation epidermal growth factor receptor (EGFR) inhibitor rociletinib.
Journal ArticleDOI
Distinct biological subtypes and patterns of genome evolution in lymphoma revealed by circulating tumor DNA.
Florian Scherer,David M. Kurtz,Aaron M. Newman,Henning Stehr,Alexander F.M. Craig,Mohammad Shahrokh Esfahani,Alexander F. Lovejoy,Jacob J. Chabon,Daniel M. Klass,Chih Long Liu,Li Zhou,Cynthia Glover,Brendan C. Visser,George A. Poultsides,Ranjana H. Advani,Lauren S. Maeda,Neel K. Gupta,Ronald Levy,Robert S. Ohgami,Christian A. Kunder,Maximilian Diehn,Ash A. Alizadeh +21 more
TL;DR: The authors demonstrated that circulating tumor DNA in the patients’ blood is suitable for this analysis, allowing for periodic monitoring of each patient without repeated invasive biopsies and facilitating individualized therapy.
Journal ArticleDOI
Integrating genomic features for non-invasive early lung cancer detection
Jacob J. Chabon,Emily G. Hamilton,David M. Kurtz,Mohammad Shahrokh Esfahani,Everett J. Moding,Henning Stehr,Joseph G Schroers-Martin,Barzin Y. Nabet,Binbin Chen,Aadel A. Chaudhuri,Chih Long Liu,Angela B. Hui,Michael C. Jin,Tej D. Azad,Diego Almanza,Young-Jun Jeon,Monica Nesselbush,Lyron Co Ting Keh,Rene F. Bonilla,Christopher H. Yoo,Ryan B. Ko,Emily Chen,D.J. Merriott,Pierre P. Massion,Pierre P. Massion,Aaron S. Mansfield,Jin Jen,Hong Z. Ren,Steven H. Lin,Christina L. Costantino,Risa Burr,Risa Burr,Robert Tibshirani,Sanjiv S. Gambhir,Gerald J. Berry,Kristin C. Jensen,Kristin C. Jensen,Robert B. West,Joel W. Neal,Heather A. Wakelee,Billy W. Loo,Christian A. Kunder,Ann N. Leung,Natalie S. Lui,Mark F. Berry,Joseph B. Shrager,Joseph B. Shrager,Viswam S. Nair,Viswam S. Nair,Viswam S. Nair,Daniel A. Haber,Daniel A. Haber,Lecia V. Sequist,Ash A. Alizadeh,Maximilian Diehn +54 more
TL;DR: It is shown that, although levels are very low in early-stage lung cancers, ctDNA is present prior to treatment in most patients and its presence is strongly prognostic, and a machine-learning method termed ‘lung cancer likelihood in plasma’ (Lung-CLiP) is developed, which can robustly discriminate early-Stage lung cancer patients from risk-matched controls.