Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling
Joseph D Butner, Geoffrey V Martin, Zhihui Wang, Bruna Corradetti, Mauro Ferrari, Nestor Esnaola, Caroline Chung, David S Hong, James W Welsh, Naomi Hasegawa, Elizabeth A Mittendorf, Steven A Curley, Shu-Hsia Chen, Ping-Ying Pan, Steven K Libutti, Shridar Ganesan, Richard L Sidman, Renata Pasqualini, Wadih Arap, Eugene J Koay, Vittorio Cristini, Joseph D Butner, Geoffrey V Martin, Zhihui Wang, Bruna Corradetti, Mauro Ferrari, Nestor Esnaola, Caroline Chung, David S Hong, James W Welsh, Naomi Hasegawa, Elizabeth A Mittendorf, Steven A Curley, Shu-Hsia Chen, Ping-Ying Pan, Steven K Libutti, Shridar Ganesan, Richard L Sidman, Renata Pasqualini, Wadih Arap, Eugene J Koay, Vittorio Cristini
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 nonresponding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within 2 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.
Conclusions: 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 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC). BC 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). EK has also received support from the Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). MF 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. RP and WA 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 SHC (R01CA109322, R01CA127483, R01CA208703, and U54CA210181 CITO pilot grant) and to PYP (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.
Trial registration: ClinicalTrials.gov NCT02444741.
Keywords: biomarkers; human; immunotherapy; medicine; patient stratification; translational research.
Conflict of interest statement
JB, GM, ZW, BC, MF, NE, CC, NH, SC, SC, PP, SL, RS, RP, WA, EK, VC No competing interests declared, DH, JW, EM, SG See COI form submitted
© 2021, Butner et al.
Figures
References
- Anaya DA, Dogra P, Wang Z, Haider M, Ehab J, Jeong DK, Ghayouri M, Lauwers GY, Thomas K, Kim R, Butner JD, Nizzero S, Ramírez JR, Plodinec M, Sidman RL, Cavenee WK, Pasqualini R, Arap W, Fleming JB, Cristini V. A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases. Cancers. 2021;13:444. doi: 10.3390/cancers13030444.
- Antonia SJ, Bendell JC, Taylor MH, Calvo E, Jaeger D, De Braud FG, Ott PA, Pietanza MC, Horn L, Le DT, Morse M, Lopez-Martin JA, Ascierto PA, Christensen O, Grosso J, Simon JS, Lin CS, Eder JP. Phase I/II study of nivolumab with or without ipilimumab for treatment of recurrent small cell lung cancer (SCLC): CA209-032. Journal of Clinical Oncology. 2015;33:7503. doi: 10.1200/jco.2015.33.15_suppl.7503.
- Auslander N, Zhang G, Lee JS, Frederick DT, Miao B, Moll T, Tian T, Wei Z, Madan S, Sullivan RJ, Boland G, Flaherty K, Herlyn M, Ruppin E. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nature Medicine. 2018;24:1545–1549. doi: 10.1038/s41591-018-0157-9.
- Borghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, Chow LQ, Vokes EE, Felip E, Holgado E, Barlesi F, Kohlhäufl M, Arrieta O, Burgio MA, Fayette J, Lena H, Poddubskaya E, Gerber DE, Gettinger SN, Rudin CM, Rizvi N, Crinò L, Antonia SJ, Dorange C, Harbison CT, Graf Finckenstein F, Brahmer JR. Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. The New England Journal of Medicine. 2015;373:1627–1639. doi: 10.1056/NEJMoa1507643.
- Brahmer JR, Tykodi SS, Chow LQM, Hwu W-J, Topalian SL, Hwu P, Drake CG, Camacho LH, Kauh J, Odunsi K, Pitot HC, Hamid O, Bhatia S, Martins R, Eaton K, Chen S, Salay TM, Alaparthy S, Grosso JF, Korman AJ, Parker SM, Agrawal S, Goldberg SM, Pardoll DM, Gupta A, Wigginton JM. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. The New England Journal of Medicine. 2012;366:2455–2465. doi: 10.1056/NEJMoa1200694.
- Brocato TA, Coker EN, Durfee PN, Lin Y-S, Townson J, Wyckoff EF, Cristini V, Brinker CJ, Wang Z. Understanding the connection between nanoparticle uptake and cancer treatment efficacy using mathematical modeling. Scientific Reports. 2018;8:7538. doi: 10.1038/s41598-018-25878-8.
- Brocato TA, Brown-Glaberman U, Wang Z, Selwyn RG, Wilson CM, Wyckoff EF, Lomo LC, Saline JL, Hooda-Nehra A, Pasqualini R, Arap W, Brinker CJ, Cristini V. Predicting breast cancer response to neoadjuvant chemotherapy based on tumor vascular features in needle biopsies. JCI Insight. 2019;4:e126518. doi: 10.1172/jci.insight.126518.
- Bunimovich-Mendrazitsky S, Shochat E, Stone L. Mathematical model of BCG immunotherapy in superficial bladder cancer. Bulletin of Mathematical Biology. 2007;69:1847–1870. doi: 10.1007/s11538-007-9195-z.
- Bunimovich-Mendrazitsky S, Halachmi S, Kronik N. Improving bacillus Calmette-Guérin (BCG) immunotherapy for bladder cancer by adding interleukin 2 (IL-2): A mathematical model. Mathematical Medicine and Biology. 2016;33:159–188. doi: 10.1093/imammb/dqv007.
- Butner JD, Elganainy D, Wang CX, Wang Z, Chen SH, Esnaola NF, Pasqualini R, Arap W, Hong DS, Welsh J, Koay EJ, Cristini V. Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy. Science Advances. 2020;6:eaay6298. doi: 10.1126/sciadv.aay6298.
- Butner JD, Wang Z, Elganainy D, Al Feghali KA, Plodinec M, Calin GA, Dogra P, Nizzero S, Ruiz-Ramirez J, Martin GV, Tawbi HA, Chung C, Koay EJ, Welsh JW, Hong DS, Cristini V. A mathematical model for the quantification of a patient’s sensitivity to checkpoint inhibitors and long-term tumour burden. Nature Biomedical Engineering. 2021;5:297–308. doi: 10.1038/s41551-020-00662-0.
- Carbognin L, Pilotto S, Milella M, Vaccaro V, Brunelli M, Caliò A, Cuppone F, Sperduti I, Giannarelli D, Chilosi M, Bronte V, Scarpa A, Bria E, Tortora G. Differential activity of nivolumab, pembrolizumab and MPDL3280A according to the tumor expression of programmed death-ligand-1 (PD-L1): Sensitivity analysis of trials in melanoma, lung and genitourinary cancers. PLOS ONE. 2015;10:e0130142. doi: 10.1371/journal.pone.0130142.
- Cormedi MCV, Van Allen EM, Colli LM. Predicting immunotherapy response through genomics. Current Opinion in Genetics & Development. 2021;66:1–9. doi: 10.1016/j.gde.2020.11.004.
- Cristini V, Koay EJ, Wang Z. In: An Introduction to Physical Oncology: How Mechanistic Mathematical Modeling Can Improve Cancer Therapy Outcomes. Cristini V, editor. CRC Press, Taylor & Francis Group; 2017. An Introduction to Physical Oncology.
- Das H, Wang Z, Niazi MKK, Aggarwal R, Lu J, Kanji S, Das M, Joseph M, Gurcan M, Cristini V. Impact of diffusion barriers to small cytotoxic molecules on the efficacy of immunotherapy in breast cancer. PLOS ONE. 2013;8:e61398. doi: 10.1371/journal.pone.0061398.
- de Pillis LG, Radunskaya AE, Wiseman CL. A validated mathematical model of cell-mediated immune response to tumor growth. Cancer Research. 2005;65:7950–7958. doi: 10.1158/0008-5472.CAN-05-0564.
- Dogra P, Adolphi NL, Wang Z, Lin Y-S, Butler KS, Durfee PN, Croissant JG, Noureddine A, Coker EN, Bearer EL, Cristini V, Brinker CJ. Establishing the effects of mesoporous silica nanoparticle properties on in vivo disposition using imaging-based pharmacokinetics. Nature Communications. 2018;9:4551. doi: 10.1038/s41467-018-06730-z.
- Dogra P, Butner JD, Chuang YL, Caserta S, Goel S, Brinker CJ, Cristini V, Wang Z. Mathematical modeling in cancer nanomedicine: a review. Biomedical Microdevices. 2019;21:e380-2. doi: 10.1007/s10544-019-0380-2.
- Dogra P, Butner JD, Nizzero S, Ruiz Ramírez J, Noureddine A, Peláez MJ, Elganainy D, Yang Z, Le A-D, Goel S, Leong HS, Koay EJ, Brinker CJ, Cristini V, Wang Z. Image-guided mathematical modeling for pharmacological evaluation of nanomaterials and monoclonal antibodies. Wiley Interdisciplinary Reviews. Nanomedicine and Nanobiotechnology. 2020a;12:e1628. doi: 10.1002/wnan.1628.
- Dogra P, Butner JD, Ruiz Ramírez J, Chuang Y-L, Noureddine A, Jeffrey Brinker C, Cristini V, Wang Z. A mathematical model to predict nanomedicine pharmacokinetics and tumor delivery. Computational and Structural Biotechnology Journal. 2020b;18:518–531. doi: 10.1016/j.csbj.2020.02.014.
- Duffy MJ, Crown J. Biomarkers for Predicting Response to Immunotherapy with Immune Checkpoint Inhibitors in Cancer Patients. Clinical Chemistry. 2019;65:1228–1238. doi: 10.1373/clinchem.2019.303644.
- Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, Rubinstein L, Shankar L, Dodd L, Kaplan R, Lacombe D, Verweij J. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1) European Journal of Cancer. 2009;45:228–247. doi: 10.1016/j.ejca.2008.10.026.
- Erdag G, Schaefer JT, Smolkin ME, Deacon DH, Shea SM, Dengel LT, Patterson JW, Slingluff CL., Jr Immunotype and immunohistologic characteristics of tumor-infiltrating immune cells are associated with clinical outcome in metastatic melanoma. Cancer Research. 2012;72:1070–1080. doi: 10.1158/0008-5472.CAN-11-3218.
- Frieboes HB, Smith BR, Wang Z, Kotsuma M, Ito K, Day A, Cahill B, Flinders C, Mumenthaler SM, Mallick P, Simbawa E, AL-Fhaid AS, Mahmoud SR, Gambhir SS, Cristini V, Cinti C. Predictive modeling of drug response in non-Hodgkin’s lymphoma. PLOS ONE. 2015;10:e0129433. doi: 10.1371/journal.pone.0129433.
- Garon EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP, Patnaik A, Aggarwal C, Gubens M, Horn L, Carcereny E, Ahn M-J, Felip E, Lee J-S, Hellmann MD, Hamid O, Goldman JW, Soria J-C, Dolled-Filhart M, Rutledge RZ, Zhang J, Lunceford JK, Rangwala R, Lubiniecki GM, Roach C, Emancipator K, Gandhi L, KEYNOTE-001 Investigators Pembrolizumab for the treatment of non-small-cell lung cancer. The New England Journal of Medicine. 2015;372:2018–2028. doi: 10.1056/NEJMoa1501824.
- Goel S, Ferreira CA, Dogra P, Yu B, Kutyreff CJ, Siamof CM, Engle JW, Barnhart TE, Cristini V, Wang Z, Cai W. Size‐Optimized Ultrasmall Porous Silica Nanoparticles Depict Vasculature‐Based Differential Targeting in Triple Negative Breast Cancer. Small. 2019;15:1903747. doi: 10.1002/smll.201903747.
- Goel S, Zhang G, Dogra P, Nizzero S, Cristini V, Wang Z, Hu Z, Li Z, Liu X, Shen H, Ferrari M. Sequential deconstruction of composite drug transport in metastatic breast cancer. Science Advances. 2020;6:26. doi: 10.1126/sciadv.aba4498.
- Goodman AM, Kato S, Bazhenova L, Patel SP, Frampton GM, Miller V, Stephens PJ, Daniels GA, Kurzrock R. Tumor mutational burden as an independent predictor of response to immunotherapy in diverse cancers. Molecular Cancer Therapeutics. 2017;16:2598–2608. doi: 10.1158/1535-7163.MCT-17-0386.
- Gopalakrishnan V, Spencer CN, Nezi L, Reuben A, Andrews MC, Karpinets TV, Prieto PA, Vicente D, Hoffman K, Wei SC, Cogdill AP, Zhao L, Hudgens CW, Hutchinson DS, Manzo T, Petaccia de Macedo M, Cotechini T, Kumar T, Chen WS, Reddy SM, Szczepaniak Sloane R, Galloway-Pena J, Jiang H, Chen PL, Shpall EJ, Rezvani K, Alousi AM, Chemaly RF, Shelburne S, Vence LM, Okhuysen PC, Jensen VB, Swennes AG, McAllister F, Marcelo Riquelme Sanchez E, Zhang Y, Le Chatelier E, Zitvogel L, Pons N, Austin-Breneman JL, Haydu LE, Burton EM, Gardner JM, Sirmans E, Hu J, Lazar AJ, Tsujikawa T, Diab A, Tawbi H, Glitza IC, Hwu WJ, Patel SP, Woodman SE, Amaria RN, Davies MA, Gershenwald JE, Hwu P, Lee JE, Zhang J, Coussens LM, Cooper ZA, Futreal PA, Daniel CR, Ajami NJ, Petrosino JF, Tetzlaff MT, Sharma P, Allison JP, Jenq RR, Wargo JA. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science. 2018;359:97–103. doi: 10.1126/science.aan4236.
- Gubin MM, Esaulova E, Ward JP, Malkova ON, Runci D, Wong P, Noguchi T, Arthur CD, Meng W, Alspach E, Medrano RFV, Fronick C, Fehlings M, Newell EW, Fulton RS, Sheehan KCF, Oh ST, Schreiber RD, Artyomov MN. High-dimensional analysis delineates myeloid and lymphoid compartment remodeling during successful immune-checkpoint cancer therapy. Cell. 2018;175:1443. doi: 10.1016/j.cell.2018.11.003.
- Herbst RS, Soria J-C, Kowanetz M, Fine GD, Hamid O, Gordon MS, Sosman JA, McDermott DF, Powderly JD, Gettinger SN, Kohrt HEK, Horn L, Lawrence DP, Rost S, Leabman M, Xiao Y, Mokatrin A, Koeppen H, Hegde PS, Mellman I, Chen DS, Hodi FS. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature. 2014;515:563–567. doi: 10.1038/nature14011.
- Hosoya H, Dobroff AS, Driessen WHP, Cristini V, Brinker LM, Staquicini FI, Cardó-Vila M, D’Angelo S, Ferrara F, Proneth B, Lin Y-S, Dunphy DR, Dogra P, Melancon MP, Stafford RJ, Miyazono K, Gelovani JG, Kataoka K, Brinker CJ, Sidman RL, Arap W, Pasqualini R. Integrated nanotechnology platform for tumor-targeted multimodal imaging and therapeutic cargo release. PNAS. 2016;113:1877–1882. doi: 10.1073/pnas.1525796113.
- Jenkins RW, Barbie DA, Flaherty KT. Mechanisms of resistance to immune checkpoint inhibitors. British Journal of Cancer. 2018;118:9–16. doi: 10.1038/bjc.2017.434.
- Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su M-J, Melms JC, Leeson R, Kanodia A, Mei S, Lin J-R, Wang S, Rabasha B, Liu D, Zhang G, Margolais C, Ashenberg O, Ott PA, Buchbinder EI, Haq R, Hodi FS, Boland GM, Sullivan RJ, Frederick DT, Miao B, Moll T, Flaherty KT, Herlyn M, Jenkins RW, Thummalapalli R, Kowalczyk MS, Cañadas I, Schilling B, Cartwright ANR, Luoma AM, Malu S, Hwu P, Bernatchez C, Forget M-A, Barbie DA, Shalek AK, Tirosh I, Sorger PK, Wucherpfennig K, Van Allen EM, Schadendorf D, Johnson BE, Rotem A, Rozenblatt-Rosen O, Garraway LA, Yoon CH, Izar B, Regev A. A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade. Cell. 2018;175:984–997. doi: 10.1016/j.cell.2018.09.006.
- Johannet P, Coudray N, Donnelly DM, Jour G, Illa-Bochaca I, Xia Y, Johnson DB, Wheless L, Patrinely JR, Nomikou S, Rimm DL, Pavlick AC, Weber JS, Zhong J, Tsirigos A, Osman I. Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma. Clinical Cancer Research. 2021;27:131–140. doi: 10.1158/1078-0432.CCR-20-2415.
- Kefford R, Ribas A, Hamid O, Robert C, Daud A, Wolchok JD, Joshua AM, Hodi FS, Gangadhar TC, Hersey P, Weber JS, Dronca RS, Patnaik A, Zarour HM, Dolled-Filhart M, Lunceford J, Emancipator K, Ebbinghaus S, Kang SP, Hwu WJ. Clinical efficacy and correlation with tumor PD-L1 expression in patients (pts) with melanoma (MEL) treated with the anti-PD-1 monoclonal antibody MK-3475. Journal of Clinical Oncology. 2014;32:3005. doi: 10.1200/jco.2014.32.15_suppl.3005.
- Koay EJ, Truty MJ, Cristini V, Thomas RM, Chen R, Chatterjee D, Kang Y, Bhosale PR, Tamm EP, Crane CH, Javle M, Katz MH, Gottumukkala VN, Rozner MA, Shen H, Lee JE, Wang H, Chen Y, Plunkett W, Abbruzzese JL, Wolff RA, Varadhachary GR, Ferrari M, Fleming JB. Transport properties of pancreatic cancer describe gemcitabine delivery and response. The Journal of Clinical Investigation. 2014;124:1525–1536. doi: 10.1172/JCI73455.
- Kronik N, Kogan Y, Elishmereni M, Halevi-Tobias K, Vuk-Pavlović S, Agur Z. Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models. PLOS ONE. 2010;5:12. doi: 10.1371/journal.pone.0015482.
- Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, Skora AD, Luber BS, Azad NS, Laheru D, Biedrzycki B, Donehower RC, Zaheer A, Fisher GA, Crocenzi TS, Lee JJ, Duffy SM, Goldberg RM, de la Chapelle A, Koshiji M, Bhaijee F, Huebner T, Hruban RH, Wood LD, Cuka N, Pardoll DM, Papadopoulos N, Kinzler KW, Zhou S, Cornish TC, Taube JM, Anders RA, Eshleman JR, Vogelstein B, Diaz LA., Jr PD-1 Blockade in tumors with mismatch-repair deficiency. The New England Journal of Medicine. 2015;372:2509–2520. doi: 10.1056/NEJMoa1500596.
- Le DT, Durham JN, Smith KN, Wang H, Bartlett BR, Aulakh LK, Lu S, Kemberling H, Wilt C, Luber BS, Wong F, Azad NS, Rucki AA, Laheru D, Donehower R, Zaheer A, Fisher GA, Crocenzi TS, Lee JJ, Greten TF, Duffy AG, Ciombor KK, Eyring AD, Lam BH, Joe A, Kang SP, Holdhoff M, Danilova L, Cope L, Meyer C, Zhou S, Goldberg RM, Armstrong DK, Bever KM, Fader AN, Taube J, Housseau F, Spetzler D, Xiao N, Pardoll DM, Papadopoulos N, Kinzler KW, Eshleman JR, Vogelstein B, Anders RA, Diaz LA., Jr Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science. 2017;357:409–413. doi: 10.1126/science.aan6733.
- Łuksza M, Riaz N, Makarov V, Balachandran VP, Hellmann MD, Solovyov A, Rizvi NA, Merghoub T, Levine AJ, Chan TA, Wolchok JD, Greenbaum BD. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature. 2017;551:517–520. doi: 10.1038/nature24473.
- Mempel TR, Bauer CA. Intravital imaging of CD8+ T cell function in cancer. Clinical & Experimental Metastasis. 2008;26:311–327. doi: 10.1007/s10585-008-9196-9.
- Moreira A, Gross S, Kirchberger MC, Erdmann M, Schuler G, Heinzerling L. Senescence markers: Predictive for response to checkpoint inhibitors. Ternational Journal of Cancer. 2019;144:1147–1150. doi: 10.1002/ijc.31763.
- Motzer RJ, Rini BI, McDermott DF, Redman BG, Kuzel TM, Harrison MR, Vaishampayan UN, Drabkin HA, George S, Logan TF, Margolin KA, Plimack ER, Lambert AM, Waxman IM, Hammers HJ. Nivolumab for metastatic renal cell carcinoma: results of a randomized phase II trial. Journal of Clinical Oncology. 2015;33:1430–1437. doi: 10.1200/JCO.2014.59.0703.
- Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nature Reviews. Cancer. 2012;12:252–264. doi: 10.1038/nrc3239.
- Park YJ, Kuen DS, Chung Y. Future prospects of immune checkpoint blockade in cancer: from response prediction to overcoming resistance. Experimental & Molecular Medicine. 2018;50:1–13. doi: 10.1038/s12276-018-0130-1.
- Pascal J, Ashley CE, Wang Z, Brocato TA, Butner JD, Carnes EC, Koay EJ, Brinker CJ, Cristini V. Mechanistic modeling identifies drug-uptake history as predictor of tumor drug resistance and nano-carrier-mediated response. ACS Nano. 2013a;7:11174–11182. doi: 10.1021/nn4048974.
- Pascal J, Bearer EL, Wang Z, Koay EJ, Curley SA, Cristini V. Mechanistic patient-specific predictive correlation of tumor drug response with microenvironment and perfusion measurements. PNAS. 2013b;110:14266–14271. doi: 10.1073/pnas.1300619110.
- Pilard C, Ancion M, Delvenne P, Jerusalem G, Hubert P, Herfs M. Cancer immunotherapy: it’s time to better predict patients’ response. British Journal of Cancer. 2021;125:927–938. doi: 10.1038/s41416-021-01413-x.
- Postow MA, Callahan MK, Wolchok JD. Immune checkpoint blockade in cancer therapy. Journal of Clinical Oncology. 2015;33:1974–1982. doi: 10.1200/JCO.2014.59.4358.
- Powles T, Eder JP, Fine GD, Braiteh FS, Loriot Y, Cruz C, Bellmunt J, Burris HA, Petrylak DP, Teng S, Shen X, Boyd Z, Hegde PS, Chen DS, Vogelzang NJ. MPDL3280A (anti-PD-L1) treatment leads to clinical activity in metastatic bladder cancer. Nature. 2014;515:558–562. doi: 10.1038/nature13904.
- Riaz N, Havel JJ, Makarov V, Desrichard A, Urba WJ, Sims JS, Hodi FS, Martín-Algarra S, Mandal R, Sharfman WH, Bhatia S, Hwu W-J, Gajewski TF, Slingluff CL, Jr, Chowell D, Kendall SM, Chang H, Shah R, Kuo F, Morris LGT, Sidhom J-W, Schneck JP, Horak CE, Weinhold N, Chan TA. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell. 2017;171:934–949. doi: 10.1016/j.cell.2017.09.028.
- Ribas A, Shin DS, Zaretsky J, Frederiksen J, Cornish A, Avramis E, Seja E, Kivork C, Siebert J, Kaplan-Lefko P, Wang X, Chmielowski B, Glaspy JA, Tumeh PC, Chodon T, Pe’er D, Comin-Anduix B. PD-1 blockade expands intratumoral memory T cells. Cancer Immunology Research. 2016;4:194–203. doi: 10.1158/2326-6066.CIR-15-0210.
- Robert C, Long GV, Brady B, Dutriaux C, Maio M, Mortier L, Hassel JC, Rutkowski P, McNeil C, Kalinka-Warzocha E, Savage KJ, Hernberg MM, Lebbé C, Charles J, Mihalcioiu C, Chiarion-Sileni V, Mauch C, Cognetti F, Arance A, Schmidt H, Schadendorf D, Gogas H, Lundgren-Eriksson L, Horak C, Sharkey B, Waxman IM, Atkinson V, Ascierto PA. Nivolumab in previously untreated melanoma without BRAF mutation. The New England Journal of Medicine. 2015a;372:320–330. doi: 10.1056/NEJMoa1412082.
- Robert C, Schachter J, Long GV, Arance A, Grob JJ, Mortier L, Daud A, Carlino MS, McNeil C, Lotem M, Larkin J, Lorigan P, Neyns B, Blank CU, Hamid O, Mateus C, Shapira-Frommer R, Kosh M, Zhou H, Ibrahim N, Ebbinghaus S, Ribas A, KEYNOTE-006 investigators Pembrolizumab versus ipilimumab in advanced melanoma. The New England Journal of Medicine. 2015b;372:2521–2532. doi: 10.1056/NEJMoa1503093.
- Rohatgi A. WebPlotDigitalizer: HTML5 based online tool to extractnumerical data from plot images. 2010. [April 11, 2019].
- Serre R, Benzekry S, Padovani L, Meille C, André N, Ciccolini J, Barlesi F, Muracciole X, Barbolosi D. Mathematical modeling of cancer immunotherapy and its synergy with radiotherapy. Cancer Research. 2016;76:4931–4940. doi: 10.1158/0008-5472.CAN-15-3567.
- Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell. 2017;168:707–723. doi: 10.1016/j.cell.2017.01.017.
- Spira AI, Park K, Mazières J, Vansteenkiste JF, Rittmeyer A, Ballinger M, Waterkamp D, Kowanetz M, Mokatrin A, Fehrenbacher L. Efficacy, safety and predictive biomarker results from a randomized phase II study comparing MPDL3280A vs docetaxel in 2L/3L NSCLC (POPLAR) Journal of Clinical Oncology. 2015;33:8010. doi: 10.1200/jco.2015.33.15_suppl.8010.
- Stein WD, Figg WD, Dahut W, Stein AD, Hoshen MB, Price D, Bates SE, Fojo T. Tumor growth rates derived from data for patients in a clinical trial correlate strongly with patient survival: a novel strategy for evaluation of clinical trial data. The Oncologist. 2008;13:1046–1054. doi: 10.1634/theoncologist.2008-0075.
- Taube JM, Klein A, Brahmer JR, Xu H, Pan X, Kim JH, Chen L, Pardoll DM, Topalian SL, Anders RA. Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy. Clinical Cancer Research. 2014;20:5064–5074. doi: 10.1158/1078-0432.CCR-13-3271.
- Teng MWL, Ngiow SF, Ribas A, Smyth MJ. Classifying cancers based on T-cell infiltration and PD-L1. Cancer Research. 2015;75:2139–2145. doi: 10.1158/0008-5472.CAN-15-0255.
- Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, Powderly JD, Carvajal RD, Sosman JA, Atkins MB, Leming PD, Spigel DR, Antonia SJ, Horn L, Drake CG, Pardoll DM, Chen L, Sharfman WH, Anders RA, Taube JM, McMiller TL, Xu H, Korman AJ, Jure-Kunkel M, Agrawal S, McDonald D, Kollia GD, Gupta A, Wigginton JM, Sznol M. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. The New England Journal of Medicine. 2012;366:2443–2454. doi: 10.1056/NEJMoa1200690.
- Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJM, Robert L, Chmielowski B, Spasic M, Henry G, Ciobanu V, West AN, Carmona M, Kivork C, Seja E, Cherry G, Gutierrez AJ, Grogan TR, Mateus C, Tomasic G, Glaspy JA, Emerson RO, Robins H, Pierce RH, Elashoff DA, Robert C, Ribas A. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515:568–571. doi: 10.1038/nature13954.
- Vallejo AN. CD28 extinction in human T cells: altered functions and the program of T-cell senescence. Immunological Reviews. 2005;205:158–169. doi: 10.1111/j.0105-2896.2005.00256.x.
- Wang Z, Butner JD, Kerketta R, Cristini V, Deisboeck TS. Simulating cancer growth with multiscale agent-based modeling. Seminars in Cancer Biology. 2015;30:70–78. doi: 10.1016/j.semcancer.2014.04.001.
- Wang Z, Kerketta R, Chuang Y-L, Dogra P, Butner JD, Brocato TA, Day A, Xu R, Shen H, Simbawa E, AL-Fhaid AS, Mahmoud SR, Curley SA, Ferrari M, Koay EJ, Cristini V, Di Bernardo D. Theory and experimental validation of a spatio-temporal model of chemotherapy transport to enhance tumor cell kill. PLOS Computational Biology. 2016;12:e1004969. doi: 10.1371/journal.pcbi.1004969.
- Weber JS, D’Angelo SP, Minor D, Hodi FS, Gutzmer R, Neyns B, Hoeller C, Khushalani NI, Miller WH, Jr, Lao CD, Linette GP, Thomas L, Lorigan P, Grossmann KF, Hassel JC, Maio M, Sznol M, Ascierto PA, Mohr P, Chmielowski B, Bryce A, Svane IM, Grob J-J, Krackhardt AM, Horak C, Lambert A, Yang AS, Larkin J. Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): A randomised, controlled, open-label, phase 3 trial. The Lancet. Oncology. 2015;16:375–384. doi: 10.1016/S1470-2045(15)70076-8.
- Wilkie KP, Hahnfeldt P. Tumor-immune dynamics regulated in the microenvironment inform the transient nature of immune-induced tumor dormancy. Cancer Research. 2013;73:3534–3544. doi: 10.1158/0008-5472.CAN-12-4590.
- Wolfram Research I Illinois. 11.2MathematicaChampaign. 2017
Source: PubMed