Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling
- The Houston Methodist Research Institute, United States
- The University of Texas MD Anderson Cancer Center, United States
- University of Texas Health Science Center, United States
- Brigham and Women's Hospital, United States
- Baylor College of Medicine, United States
- Rutgers University, United States
- Harvard Medical School, United States
- Rutgers Cancer Institute of New Jersey, United States
- 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
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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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
Further reading
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Background:
Post-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) is a severe and deadly adverse event following ERCP. The ideal method for predicting PEP risk before ERCP has yet to be identified. We aimed to establish a simple PEP risk score model (SuPER model: Support for PEP Reduction) that can be applied before ERCP.
Methods:
This multicenter study enrolled 2074 patients who underwent ERCP. Among them, 1037 patients each were randomly assigned to the development and validation cohorts. In the development cohort, the risk score model for predicting PEP was established via logistic regression analysis. In the validation cohort, the performance of the model was assessed.
Results:
In the development cohort, five PEP risk factors that could be identified before ERCP were extracted and assigned weights according to their respective regression coefficients: –2 points for pancreatic calcification, 1 point for female sex, and 2 points for intraductal papillary mucinous neoplasm, a native papilla of Vater, or the pancreatic duct procedures (treated as ‘planned pancreatic duct procedures’ for calculating the score before ERCP). The PEP occurrence rate was 0% among low-risk patients (≤0 points), 5.5% among moderate-risk patients (1–3 points), and 20.2% among high-risk patients (4–7 points). In the validation cohort, the C statistic of the risk score model was 0.71 (95% CI 0.64–0.78), which was considered acceptable. The PEP risk classification (low, moderate, and high) was a significant predictive factor for PEP that was independent of intraprocedural PEP risk factors (precut sphincterotomy and inadvertent pancreatic duct cannulation) (OR 4.2, 95% CI 2.8–6.3; p<0.01).
Conclusions:
The PEP risk score allows an estimation of the risk of PEP prior to ERCP, regardless of whether the patient has undergone pancreatic duct procedures. This simple risk model, consisting of only five items, may aid in predicting and explaining the risk of PEP before ERCP and in preventing PEP by allowing selection of the appropriate expert endoscopist and useful PEP prophylaxes.
Funding:
No external funding was received for this work.
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