Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling

  1. Joseph D Butner
  2. Geoffrey V Martin
  3. Zhihui Wang  Is a corresponding author
  4. Bruna Corradetti
  5. Mauro Ferrari
  6. Nestor Esnaola
  7. Caroline Chung
  8. David S Hong
  9. James W Welsh
  10. Naomi Hasegawa
  11. Elizabeth A Mittendorf
  12. Steven A Curley
  13. Shu-Hsia Chen
  14. Ping-Ying Pan
  15. Steven K Libutti
  16. Shridar Ganesan
  17. Richard L Sidman
  18. Renata Pasqualini
  19. Wadih Arap
  20. Eugene J Koay  Is a corresponding author
  21. Vittorio Cristini
  1. The Houston Methodist Research Institute, United States
  2. The University of Texas MD Anderson Cancer Center, United States
  3. University of Texas Health Science Center, United States
  4. Brigham and Women's Hospital, United States
  5. Baylor College of Medicine, United States
  6. Rutgers University, United States
  7. Harvard Medical School, United States
  8. Rutgers Cancer Institute of New Jersey, United States
  9. University of Texas MD Anderson Cancer Center, United States

Abstract

Background: Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. We have developed a mechanistic mathematical model for better understanding these factors and their relations in order to predict treatment outcome and optimize personal treatment strategies.

Methods: Here, we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (α), tumor immune infiltration (Λ), and immunotherapy-mediated amplification of anti-tumor response (µ). The model was calibrated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials.

Results: The derived parameters Λ and µ were both significantly different between responding versus non-responding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within two months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology.

Conclusion: These results have demonstrated reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis.

Funding: We gratefully acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center. We have also received Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey). This research has also been supported in part by grants from the National Science Foundation Grant DMS-1930583 (Z.W., V.C.), the National Institutes of Health (NIH) 1R01CA253865 (Z.W., V.C.), 1U01CA196403 (Z.W., V.C.), 1U01CA213759 (Z.W., V.C.), 1R01CA226537 (Z.W., R.P., W.A., V.C.), 1R01CA222007 (Z.W., V.C.), U54CA210181 (Z.W., V.C.), and the University of Texas System STARS Award (V.C.). B.C. acknowledges support through the SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie Skłodowska-Curie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF). E.K. has also received support from the Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). M.F. was supported through NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute. R.P. and W.A. received serial research awards from AngelWorks, the Gillson-Longenbaugh Foundation, and the Marcus Foundation. This work was also supported in part by grants from the National Cancer Institute to S.H.C. (R01CA109322, R01CA127483, R01CA208703, and U54CA210181 CITO pilot grant) and to P.Y.P. (R01CA140243, R01CA188610, and U54CA210181 CITO pilot grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data availability

No new clinical patient data was produced in this study. Data used that was obtained from literature is available in the original publications; we have been careful to cite each of these in the manuscript. Interested researchers should reach out directly to the primary authors of these studies. Data for the in-house clinical trial cohort are from the study reported in PMC7555111; interested researchers should contact the authors of this publication with any data requests.

Article and author information

Author details

  1. Joseph D Butner

    The Houston Methodist Research Institute, Houston, United States
    Competing interests
    No competing interests declared.
  2. Geoffrey V Martin

    The University of Texas MD Anderson Cancer Center, Houston, United States
    Competing interests
    No competing interests declared.
  3. Zhihui Wang

    The Houston Methodist Research Institute, Houston, United States
    For correspondence
    zwang@houstonmethodist.org
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6262-700X
  4. Bruna Corradetti

    The Houston Methodist Research Institute, Houston, United States
    Competing interests
    No competing interests declared.
  5. Mauro Ferrari

    The Houston Methodist Research Institute, Houston, United States
    Competing interests
    No competing interests declared.
  6. Nestor Esnaola

    The Houston Methodist Research Institute, Houston, United States
    Competing interests
    No competing interests declared.
  7. Caroline Chung

    The University of Texas MD Anderson Cancer Center, Houston, United States
    Competing interests
    No competing interests declared.
  8. David S Hong

    The University of Texas MD Anderson Cancer Center, Houston, United States
    Competing interests
    David S Hong, See COI form submitted.
  9. James W Welsh

    The Houston Methodist Research Institute, Houston, United States
    Competing interests
    James W Welsh, See COI form submitted.
  10. Naomi Hasegawa

    University of Texas Health Science Center, Houston, United States
    Competing interests
    No competing interests declared.
  11. Elizabeth A Mittendorf

    Brigham and Women's Hospital, Boston, United States
    Competing interests
    Elizabeth A Mittendorf, See COI form submitted.
  12. Steven A Curley

    Baylor College of Medicine, Houston, United States
    Competing interests
    No competing interests declared.
  13. Shu-Hsia Chen

    The Houston Methodist Research Institute, Houston, United States
    Competing interests
    No competing interests declared.
  14. Ping-Ying Pan

    The Houston Methodist Research Institute, Houston, United States
    Competing interests
    No competing interests declared.
  15. Steven K Libutti

    Rutgers University, New Brunswick, United States
    Competing interests
    No competing interests declared.
  16. Shridar Ganesan

    Rutgers University, New Brunswick, United States
    Competing interests
    Shridar Ganesan, See COI form submitted.
  17. Richard L Sidman

    Department of Neurology, Harvard Medical School, Boston, United States
    Competing interests
    No competing interests declared.
  18. Renata Pasqualini

    Radiation Oncology, Rutgers University, Newark, United States
    Competing interests
    No competing interests declared.
  19. Wadih Arap

    Hematology and Oncology, Rutgers Cancer Institute of New Jersey, Newark, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8686-4584
  20. Eugene J Koay

    University of Texas MD Anderson Cancer Center, Houston, United States
    For correspondence
    EKoay@mdanderson.org
    Competing interests
    No competing interests declared.
  21. Vittorio Cristini

    The Houston Methodist Research Institute, Houston, United States
    Competing interests
    No competing interests declared.

Funding

National Science Foundation (DMS-1930583)

  • Zhihui Wang
  • Vittorio Cristini

European Commission (SER Cymru II Programme)

  • Bruna Corradetti

National Institutes of Health (U01CA200468)

  • Eugene J Koay

National Institutes of Health (R01CA222959)

  • Mauro Ferrari

DOD Breast Cancer Research (Breakthrough Level IV Award W81XWH-17-1-0389)

  • Mauro Ferrari

AngelWorks

  • Renata Pasqualini
  • Wadih Arap

Gillson-Longenbaugh Foundation

  • Renata Pasqualini
  • Wadih Arap

Marcus Foundation

  • Renata Pasqualini
  • Wadih Arap

National Institutes of Health (R01CA109322)

  • Shu-Hsia Chen

National Institutes of Health (R01CA127483)

  • Shu-Hsia Chen

National Institutes of Health (R01CA208703)

  • Shu-Hsia Chen

National Institutes of Health (1R01CA253865)

  • Zhihui Wang
  • Vittorio Cristini

National Institutes of Health (R01CA140243)

  • Ping-Ying Pan

National Institutes of Health (R01CA188610)

  • Ping-Ying Pan

National Institutes of Health (1U01CA196403)

  • Zhihui Wang
  • Eugene J Koay
  • Vittorio Cristini

National Institutes of Health (1U01CA213759)

  • Zhihui Wang
  • Vittorio Cristini

National Institutes of Health (1R01CA226537)

  • Zhihui Wang
  • Renata Pasqualini
  • Wadih Arap
  • Vittorio Cristini

National Institutes of Health (1R01CA222007)

  • Zhihui Wang
  • Vittorio Cristini

National Institutes of Health (U54CA210181)

  • Zhihui Wang
  • Mauro Ferrari
  • Shu-Hsia Chen
  • Ping-Ying Pan
  • Eugene J Koay
  • Vittorio Cristini

National Institutes of Health (P30CA016672)

  • David S Hong
  • James W Welsh
  • Eugene J Koay

National Institutes of Health (P30CA072720)

  • Steven K Libutti
  • Shridar Ganesan
  • Renata Pasqualini
  • Wadih Arap

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: In-house patient cohort were obtained as de-identified data from a study conducted in accordance with the U.S. Common Rule and with Institutional Review Board Approval at MD Anderson (2014-1020), including waiver of informed consent. This work has been published in J Immunother Cancer. 2020; 8(2): e001001. PMC7555111. doi: 10.1136/jitc-2020-001001

Copyright

© 2021, Butner et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Joseph D Butner
  2. Geoffrey V Martin
  3. Zhihui Wang
  4. Bruna Corradetti
  5. Mauro Ferrari
  6. Nestor Esnaola
  7. Caroline Chung
  8. David S Hong
  9. James W Welsh
  10. Naomi Hasegawa
  11. Elizabeth A Mittendorf
  12. Steven A Curley
  13. Shu-Hsia Chen
  14. Ping-Ying Pan
  15. Steven K Libutti
  16. Shridar Ganesan
  17. Richard L Sidman
  18. Renata Pasqualini
  19. Wadih Arap
  20. Eugene J Koay
  21. Vittorio Cristini
(2021)
Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling
eLife 10:e70130.
https://doi.org/10.7554/eLife.70130

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https://doi.org/10.7554/eLife.70130

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