Multiplex computational pathology for treatment response predication

Ming Y Lu¹, Houssein A Sater², Faisal Mahmood³

Affiliations

¹ Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA 02115, USA.
² Cleveland Clinic Martin Health Center for Clinical Research, Stuart, FL 34994, USA.
³ Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA 02115, USA. Electronic address: faisalmahmood@bwh.harvard.edu.

PMID: 34375608
DOI: 10.1016/j.ccell.2021.07.014

Free article

Comment

Multiplex computational pathology for treatment response predication

Ming Y Lu et al. Cancer Cell. 2021.

Free article

. 2021 Aug 9;39(8):1053-1055.

doi: 10.1016/j.ccell.2021.07.014.

Authors

Ming Y Lu¹, Houssein A Sater², Faisal Mahmood³

Affiliations

¹ Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA 02115, USA.
² Cleveland Clinic Martin Health Center for Clinical Research, Stuart, FL 34994, USA.
³ Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA 02115, USA. Electronic address: faisalmahmood@bwh.harvard.edu.

PMID: 34375608
DOI: 10.1016/j.ccell.2021.07.014

Abstract

Recently published in Science, AstroPath outlines a standardized workflow for multiplex immunofluorescence (mIF) panel development, imaging, and analysis; showcases its potential in biomarker discovery for predicting response to anti-PD-1 treatment; and paves the way for large-scale computational pathology studies on high-quality mIF datasets using data-driven machine-learning techniques.

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Conflict of interest statement

Declaration of interests The authors declare no competing interests.

Comment on

Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade.
Berry S, Giraldo NA, Green BF, Cottrell TR, Stein JE, Engle EL, Xu H, Ogurtsova A, Roberts C, Wang D, Nguyen P, Zhu Q, Soto-Diaz S, Loyola J, Sander IB, Wong PF, Jessel S, Doyle J, Signer D, Wilton R, Roskes JS, Eminizer M, Park S, Sunshine JC, Jaffee EM, Baras A, De Marzo AM, Topalian SL, Kluger H, Cope L, Lipson EJ, Danilova L, Anders RA, Rimm DL, Pardoll DM, Szalay AS, Taube JM. Berry S, et al. Science. 2021 Jun 11;372(6547):eaba2609. doi: 10.1126/science.aba2609. Science. 2021. PMID: 34112666 Free PMC article.

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Multiplex computational pathology for treatment response predication

Affiliations

Multiplex computational pathology for treatment response predication

Authors

Affiliations

Abstract

Conflict of interest statement

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