Combinatorial immunotherapy strategies: most gods throw dice, but fate plays chess
Authors: J. Ciccolini, D. Barbolosi, N. André, S. Benzekry, F. Barlesi
Journal: Annals of Oncology (2019)

Immuno-oncology drugs (IODs) are considered as game-changing treatments in oncology, because diseases with once dismal prognosis such as metastatic melanoma or lung cancer now show 3-year survival rates in the 20%–40% range. However, it has to be kept in mind that, to date, success stories with immunotherapy are limited to a small subset of lucky individuals in an even smaller subset of cancers. As a consequence, stretching the efficacy of IODs to a larger number of patients with a wider range of cancers is now the main challenge in clinical oncology. Developing combinatorial regimens (i.e. combining the most recent immune checkpoint inhibitors with canonical cytotoxics, targeted therapies, antiangiogenics or radiation therapy) is currently seen as the most promising strategy to turn once immunologically 'cold' tumors into 'hot' ones. Standard treatments are expected indeed to harness tumor immunity through a variety of mechanisms, ranging from promoting immunogenic cell death and neoantigen expression on cancer cells to increasing T lymphocytes infiltration or decreasing Tregs or myeloid-derived suppressive cells (MDSCs) expression in the tumor microenvironment [1.Chen G. Emens L.A. Chemoimmunotherapy: reengineering tumor immunity.Cancer Immunol Immunother. 2013; 62: 203-216Crossref PubMed Scopus (178) Google Scholar]. Countless combinational trials in a variety of tumor types have thus been initiated over the last few years, and clinical benefit has already been reported (for instance, in non-small-cell lung cancer when IODs are combined with cytotoxics) [2.Seliger B. Combinatorial approaches with checkpoint inhibitors to enhance anti-tumor.Front Immunol. 2019; 10: 999Crossref PubMed Scopus (38) Google Scholar]. In triple negative breast cancer (TNBC), the Tonic trial (NCT02499367) was the first clinical study to demonstrate how improved clinical outcome observed with nivolumab in patients pretreated with cytotoxics such as doxorubicin or cisplatin was actually associated with immunomodulating features, such as increases in CD8's and tumor-infiltrating lymphocytes or in up-regulation of a variety immune-related genes implicated in enhanced T-cell priming [3.Voorwerk L. Slagter M. Horlings H.M. et al.Immune induction strategies in metastatic triple-negative breast cancer to enhance the sensitivity to PD-1 blockade: the TONIC trial.Nat Med. 2019; 25: 920-928Crossref PubMed Scopus (428) Google Scholar]. However, out of the hundreds of trials investigating combinations of immune checkpoint inhibitors with other therapies, many—if not most—of them have failed to yield similar convincing results. For instance, attempts to combine IODs with radiation therapy, metronomic or maximal tolerated dose chemotherapy, antiangiogenics or oral targeted therapies have frequently led to disappointing results in several other settings [4.Joshi S. Durden D.L. Combinatorial approach to improve cancer immunotherapy: rational drug design strategy to simultaneously hit multiple targets to kill tumor cells and to activate the immune system.J Oncol. 2019; 2019: 1Crossref Scopus (67) Google Scholar, 5.Coosemans A. Vankerckhoven A. Baert T. et al.Combining conventional therapy with immunotherapy: a risky business?.Eur J Cancer. 2019; 113: 41-44Abstract Full Text Full Text PDF PubMed Scopus (20) Google Scholar]. Of note, the common theme in all of these trials relying on an expected synergism between IODs and other treatments is the apparently complete lack of a rationale when defining dosing, scheduling and sequencing of the combinations. For instance, concomitant dosing is frequent, as in the Modul trial exploring the combination of anti-programmed death-ligand 1 atezolizumab with standard cytotoxics (i.e. 5-FU, capecitabine) and a canonical antiangiogenesis agent (i.e. bevacizumab) in metastatic colorectal cancer [6.AGrothey, JTabernero, DArnoldet al Fluoropyrimidine and bevacizumab plus or minus atezolizumab as first-line treatment for BRAF wild type metastatic colorectal cancer: Findings from the MODUL trial of biomarker-driven maintenance. ESMO 2018 Congress 2018; Abstract LBA19.Google Scholar]. Bevacizumab is expected to help T lymphocytes to better infiltrate tumors by diapedesis and to reduce the anti-immunogenic effects of endothelial cells [7.Chen D.S. Hurwitz H. Combinations of bevacizumab with cancer immunotherapy.Cancer J. 2018; 24: 193-204Crossref PubMed Google Scholar], whereas fluoropyrimidine drugs should induce immunogenic cell death and could additionally decrease Tregs expression in the tumor microenvironment [1.Chen G. Emens L.A. Chemoimmunotherapy: reengineering tumor immunity.Cancer Immunol Immunother. 2013; 62: 203-216Crossref PubMed Scopus (178) Google Scholar]. However, all of these theoretical immunomodulating properties are more probably dose-dependent and time-dependent. For instance, acting on the 'brake' (i.e. decrease Tregs or MDSCs) or 'gas' (i.e. increase T lymphocytes infiltration or promote immunogenic cell death) pedals with cytotoxics could depend on the chosen dosing, as already suggested by experimental data [1.Chen G. Emens L.A. Chemoimmunotherapy: reengineering tumor immunity.Cancer Immunol Immunother. 2013; 62: 203-216Crossref PubMed Scopus (178) Google Scholar]. Consequently, setting up such complex trials combining three different drugs would probably have required fine tuning to determine the optimal way to best combine treatments to achieve optimal immunomodulating properties and synergism with IODs. With respect to the high number of possible combinations between different dosing, scheduling and sequencing when combining immune checkpoint inhibitors with several other drugs, empirical designs with concomitant administration at standard dosing are doomed to yield disappointing, if not negative, results, as in the Modul trial. Rather surprisingly, adding atezolizumab to bevacizumab and carboplatin-paclitaxel doublet improved survival in lung cancer the IMpower150 study [8.Reck M. Mok T.S.K. Nishio M. et al.Atezolizumab plus bevacizumab and chemotherapy in non-small-cell lung cancer (IMpower150): key subgroup analyses of patients with EGFR mutations or baseline liver metastases in a randomised, open-label phase 3 trial.Lancet Respir Med. 2019; 7: 387-401Abstract Full Text Full Text PDF PubMed Scopus (550) Google Scholar], thus highlighting how combinatorial strategies could be drug-dependent as well. Interestingly, in the Tonic trial, rather than concomitant dosing, all the associated treatments or irradiation were delivered as induction therapy, i.e. 2 weeks before nivolumab started [3.Voorwerk L. Slagter M. Horlings H.M. et al.Immune induction strategies in metastatic triple-negative breast cancer to enhance the sensitivity to PD-1 blockade: the TONIC trial.Nat Med. 2019; 25: 920-928Crossref PubMed Scopus (428) Google Scholar]. Although not designed to be comparative, this study showed marked differences between treatments, because better clinical outcome was observed in TNBC patients pretreated with low-dose cisplatin and doxorubicin than in patients treated with low-dose cyclophosphamide or radiation therapy. This suggests that scheduling and/or dosing should probably be further customized depending on the drugs used, to enhance T-cell priming and to eventually achieve clinical benefit. Generating biological data certainly help to better delineate the immunomodulating properties of associated treatments and could help investigators to improve further the modalities of combination, in a trial-and-error fashion. Importantly, mathematical oncology and pharmacometrics could possibly help to narrow down the choices when setting up such combinations, i.e. by extensive in silico modeling and simulation strategies implemented before running the actual trials. Several mathematical modeling strategies can be used with varying degrees of complexity, from 'black-box' statistical models (e.g. machine learning) to very intricate multiscale models. Whereas machine learning analysis lacks the simulative power to explore the large number of scheduling possibilities, mechanistic models on the other hand have to be parsimoniously tailored to the dimensionality of available clinical data that often remain scarce [9.Barbolosi D. Ciccolini J. Lacarelle B. et al.Computational oncology–mathematical modelling of drug regimens for precision medicine.Nat Rev Clin Oncol. 2016; 13: 242-254Crossref PubMed Scopus (136) Google Scholar]. Indeed, models aiming at integrating all the biological complexity and multiple interplays when considering tumor immunity are prone to over-parameterization, which in turn may lead to overfitting and lack of predictive power. Parsimonious, semi-mechanistic approaches have already been successfully tested elsewhere to help investigators to design innovative, model-driven clinical trials with cytotoxics or targeted therapies [10.Zhang J. Cunningham J.J. Brown J.S. et al.Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer.Nature Com. 2017; 8: 1816Crossref PubMed Scopus (239) Google Scholar, 11.Barlesi F. Imbs D.C. Tomasini P. et al.Mathematical modeling for phase I cancer trials: a study of metronomic vinorelbine for advanced non-small cell lung cancer (NSCLC) and mesothelioma patients.Oncotarget. 2017; 8: 47161-47166Crossref PubMed Scopus (20) Google Scholar], including to define optimal combination modalities [12.Hénin E. Meille C. Barbolosi D. et al.Revisiting dosing regimen using PK/PD modeling: the MODEL1 phase I/II trial of docetaxel plus epirubicin in metastatic breast cancer patients.Breast Cancer Res Treat. 2016; 156: 331-341Crossref PubMed Scopus (20) Google Scholar]. Such strategies start to emerge as well in the field of combinatorial regimen with IODs. For instance, a simple model calculating an immunologically effective dose has been already proposed to compute radiation therapy fractionation schemes with immunogenicity as a readout [13.Serre R. Barlesi F. Muracciole X. et al.Immunologically effective dose: a practical model for immuno-radiotherapy.Oncotarget. 2018; 9: 31812-31819Crossref PubMed Scopus (19) Google Scholar]. Using this approach, optimal timing when administrating nivolumab and ipilimumab with respect to associated hypofractionated radiotherapy is now currently tested at the bedside as part of a phase I study in previously treated, advanced non-small-cell lung cancer (NCT03509584). This example shows how mathematical oncology could be used as a decision-making tool when setting up combinatorial studies, to shift from empirical designs to model-informed clinical trials. Time has probably come to stop throwing dice, especially when cancer plays chess. None declared.