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.
Reviewing Editor
- Caigang Liu, Shengjing Hospital of China Medical University, China
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
Version history
- Received: May 6, 2021
- Accepted: October 25, 2021
- Accepted Manuscript published: November 9, 2021 (version 1)
- Version of Record published: November 29, 2021 (version 2)
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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- Medicine
- Microbiology and Infectious Disease
Background:
End-stage renal disease (ESRD) patients experience immune compromise characterized by complex alterations of both innate and adaptive immunity, and results in higher susceptibility to infection and lower response to vaccination. This immune compromise, coupled with greater risk of exposure to infectious disease at hemodialysis (HD) centers, underscores the need for examination of the immune response to the COVID-19 mRNA-based vaccines.
Methods:
The immune response to the COVID-19 BNT162b2 mRNA vaccine was assessed in 20 HD patients and cohort-matched controls. RNA sequencing of peripheral blood mononuclear cells was performed longitudinally before and after each vaccination dose for a total of six time points per subject. Anti-spike antibody levels were quantified prior to the first vaccination dose (V1D0) and 7 d after the second dose (V2D7) using anti-spike IgG titers and antibody neutralization assays. Anti-spike IgG titers were additionally quantified 6 mo after initial vaccination. Clinical history and lab values in HD patients were obtained to identify predictors of vaccination response.
Results:
Transcriptomic analyses demonstrated differing time courses of immune responses, with prolonged myeloid cell activity in HD at 1 wk after the first vaccination dose. HD also demonstrated decreased metabolic activity and decreased antigen presentation compared to controls after the second vaccination dose. Anti-spike IgG titers and neutralizing function were substantially elevated in both controls and HD at V2D7, with a small but significant reduction in titers in HD groups (p<0.05). Anti-spike IgG remained elevated above baseline at 6 mo in both subject groups. Anti-spike IgG titers at V2D7 were highly predictive of 6-month titer levels. Transcriptomic biomarkers after the second vaccination dose and clinical biomarkers including ferritin levels were found to be predictive of antibody development.
Conclusions:
Overall, we demonstrate differing time courses of immune responses to the BTN162b2 mRNA COVID-19 vaccination in maintenance HD subjects comparable to healthy controls and identify transcriptomic and clinical predictors of anti-spike IgG titers in HD. Analyzing vaccination as an in vivo perturbation, our results warrant further characterization of the immune dysregulation of ESRD.
Funding:
F30HD102093, F30HL151182, T32HL144909, R01HL138628. This research has been funded by the University of Illinois at Chicago Center for Clinical and Translational Science (CCTS) award UL1TR002003.
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- Medicine
Background:
Among its extragonadal effects, follicle-stimulating hormone (FSH) has an impact on body composition and bone metabolism. Since androgen deprivation therapy (ADT) has a profound impact on circulating FSH concentrations, this hormone could potentially be implicated in the changes of fat body mass (FBM), lean body mass (LBM), and bone fragility induced by ADT. The objective of this study is to correlate FSH serum levels with body composition parameters, bone mineral density (BMD), and bone turnover markers at baseline conditions and after 12 months of ADT.
Methods:
Twenty-nine consecutive non-metastatic prostate cancer (PC) patients were enrolled from 2017 to 2019 in a phase IV study. All patients underwent administration of the luteinizing hormone-releasing hormone antagonist degarelix. FBM, LBM, and BMD were evaluated by dual-energy x-ray absorptiometry at baseline and after 12 months of ADT. FSH, alkaline phosphatase, and C-terminal telopeptide of type I collagen were assessed at baseline and after 6 and 12 months. For outcome measurements and statistical analysis, t-test or sign test and Pearson or Spearman tests for continuous variables were used when indicated.
Results:
At baseline conditions, a weak, non-significant, direct relationship was found between FSH serum levels and FBM at arms (r = 0.36) and legs (r = 0.33). Conversely, a stronger correlation was observed between FSH and total FBM (r = 0.52, p = 0.006), fat mass at arms (r = 0.54, p = 0.004), and fat mass at trunk (r = 0.45, p = 0.018) assessed after 12 months. On the other hand, an inverse relationship between serum FSH and appendicular lean mass index/FBM ratio was observed (r = −0.64, p = 0.001). This is an ancillary study of a prospective trial and this is the main limitation.
Conclusions:
FSH serum levels after ADT could have an impact on body composition, in particular on FBM. Therefore, FSH could be a promising marker to monitor the risk of sarcopenic obesity and to guide the clinicians in the tailored evaluation of body composition in PC patients undergoing ADT.
Funding:
This research was partially funded by Ferring Pharmaceuticals. The funder had no role in design and conduct of the study, collection, management, analysis, and interpretation of the data and in preparation, review, or approval of the manuscript.
Clinical trial number:
clinicalTrials.gov NCT03202381, EudraCT Number 2016-004210-10.
