Do immune checkpoint inhibitors need new studies methodology?

Abstract

Immune checkpoint inhibitors (ICI) have widely reshaped the treatment paradigm of advanced cancer patients. Although multiple studies are currently evaluating these drugs as monotherapies or in combination, the choice of the most accurate statistical methods, endpoints and clinical trial designs to estimate the benefit of ICI remains an unsolved methodological issue. Considering the unconventional patterns of response or progression [i.e., pseudoprogression, hyperprogression (HPD)] observed with ICI, the application in clinical trials of novel response assessment tools (i.e., iRECIST) able to capture delayed benefit of immunotherapies and/or to quantify tumor dynamics and kinetics over time is an unmet clinical need. In addition, the proportional hazard model and the conventional measures of survival [i.e., median overall or progression free survival (PFS) and hazard ratios (HR)] might usually result inadequate in the estimation of the long-term benefit observed with ICI. For this reason, innovative methodologies such as milestone analysis, restricted mean survival time (RMST), parametric models (i.e., Weibull distribution, weighted log rank test), should be systematically investigated in clinical trials in order to adequately quantify the fraction of patients who are "cured", represented by the tails of the survival curves. Regarding predictive biomarkers, in particular PD-L1 expression, the integration and harmonization of the existing assays are urgently needed to provide clinicians with reliable diagnostic tests and to improve patient selection for immunotherapy. Finally, developing original and high-quality study designs, such as adaptive or basket biomarker enriched clinical trials, included in large collaborative platforms with multiple active sites and cross-sector collaboration, represents the successful strategy to optimally assess the benefit of ICI in the next future.

Immunotherapy for Early Stage Colorectal Cancer: A Glance into the Future

“…The hazard ratio is largely used to quantify the treatment effect for time-to-event end points, but its use requires that there be proportional hazards in the treatment arms. However, non-proportional hazards have been frequently reported in ICI trials due to the long-term survival and delayed clinical effect [57,58]. Complementary methods to evaluate treatment effects such as the ratio of restricted mean survival time should be anticipated in pre-planned statistical analyses [59,60].…”

Hyperprogression Under Immune Checkpoint-Based Immunotherapy—Current Understanding, The Role of PD-1/PD-L1 Tumour-Intrinsic Signalling, Future Directions and a Potential Large Animal Model

“…The real incidence may be higher, considering that patients who deteriorated fastest could not be fully evaluated. HP was observed in the following types of cancer: non-small-cell lung carcinoma (8%-37%), melanoma (6%-34%), gastrointestinal (15%-21%), head and neck (9-18% and 29% in case of Squamous Cell Carcinoma), gynecological (16%), other lung (10-15%), cutaneous squamous cell carcinoma (9%), renal (5%-7%), colorectal (6%), urothelial (6%) [4,11,[32][33][34][35][36][37][38]. All the discussed studies pertained to PD-1/PD-L1-based immune checkpoint blockade.…”

“…While not all patients respond to this line of therapy, a substantial subset experiences rapid disease progression-a recently described phenomenon called hyperprogression (HP) or Hyperprogressive Disease (HPD). While the clinical data and some biological explanations have been comprehensively described before [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17], this review aims to discuss several unexplored questions and mechanisms that may contribute to HP, with a particular focus on tumour-intrinsic PD-1/PD-L1 signalling. Importantly, we point out the limitations of the studies in the murine model and discuss the spontaneously occurring canine cancer as a better alternative for preclinical trials.…”

Current challenges for assessing the long-term clinical benefit of cancer immunotherapy: a multi-stakeholder perspective

“…The standard method of survival extrapolation is parametric regression modeling, which is more suited to traditional chemotherapeutic regimens. 46 The mechanism of action for IOs exacerbates multiple methodological challenges: assessing non-proportional hazards, the plateau effect, loss of statistical power in the tail of the survival curves due to censoring, and unobserved heterogeneity in the patient population. More flexible and complex approaches, such as piecewise models, cubic splines, response-based models, cure fraction modeling, and mixture cure modeling, may be needed to capture the characteristic IO pattern of delayed treatment effects and, for a subset of patients, the plateau of long-term survival.…”