Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy-Reference-Cited by-同舟云学术

Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy

Published:2020-05 Issue:18 Volume:6 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Butner Joseph D.¹^ORCID,Elganainy Dalia²^ORCID,Wang Charles X.³,Wang Zhihui¹⁴^ORCID,Chen Shu-Hsia⁵⁶,Esnaola Nestor F.⁶⁷,Pasqualini Renata⁸⁹,Arap Wadih⁸¹⁰,Hong David S.¹¹^ORCID,Welsh James²,Koay Eugene J.²^ORCID,Cristini Vittorio¹³^ORCID

Affiliation:

1. Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA.

2. Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

3. MD/PhD Program, Drexel University College of Medicine, Philadelphia, PA 19102, USA.

4. Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

5. Immunotherapy Research Center, Houston Methodist Research Institute, Houston, TX, USA.

6. Cancer Center, Houston Methodist Research Institute, Houston, TX, USA.

7. Department of Surgical Oncology, Fox Chase Cancer Center—Temple Health, Philadelphia, PA, USA.

8. Rutgers Cancer Institute of New Jersey at University Hospital, Newark, NJ, USA.

9. Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA.

10. Division of Hematology/Oncology, Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA.

11. Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Abstract

A mathematical model of checkpoint inhibitor immunotherapy yields patient-specific prognosis of tumor kill and patient survival.

Funder

National Institutes of Health

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference43 articles.

1. Fundamental Mechanisms of Immune Checkpoint Blockade Therapy

2. Combination Cancer Therapy with Immune Checkpoint Blockade: Mechanisms and Strategies

3. Immunologic Correlates of the Abscopal Effect in a Patient with Melanoma

4. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)

5. RECIST 1.1—Update and clarification: From the RECIST committee

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