Standardized Pathology Screening of Mature Tertiary Lymphoid Structures in Cancers.

Mature tertiary lymphoid structures (mTLSs) are organized lymphoid structures containing B lymphocytes admixed to CD23+ follicular dendritic cells. Their presence has been linked to improved survival and sensitivity to immune checkpoint inhibitors in several cancers, emerging as a promising pancancer biomarker. However, the requirements for any biomarker are clear methodology, proven feasibility, and reliability. In 357 patients' samples, we studied tertiary lymphoid structures (TLSs) parameters using multiplex immunofluorescence (mIF), hematoxylin-eosin-saffron (HES) staining, double CD20/CD23 staining, and single CD23 immunohistochemistry. The cohort included carcinomas (n = 211) and sarcomas (n = 146), gathering biopsies (n = 170), and surgical specimens (n = 187). mTLSs were defined as TLSs containing either a visible germinal center on HES staining or CD23+ follicular dendritic cells. Focusing on 40 TLSs assessed using mIF, double CD20/CD23 staining was less sensitive than mIF to assess maturity in 27.5% (n = 11/40) but was rescued by single CD23 staining in 90.9% (n = 10/11). In 97 patients, several samples (n = 240) were reviewed to characterize TLS distribution. The likelihood of finding TLSs in surgical material was 6.1 higher than in biopsy and 2.0 higher in primary samples than in metastasis after adjustment with a type of sample. Interrater agreement rates over 4 examiners were 0.65 (Fleiss kappa, 95% CI [0.46, 0.90]) for the presence of TLS and 0.90 for maturity (95% CI [0.83, 0.99]). In this study, we propose a standardized method to screen mTLSs in cancer samples using HES staining and immunohistochemistry that can be applied to all specimens.

Conditional copula models for correlated survival endpoints: Individual patient data meta-analysis of randomized controlled trials.

Correlations among survival endpoints are important for exploring surrogate endpoints of the true endpoint. With a valid surrogate endpoint tightly correlated with the true endpoint, the efficacy of a new drug/treatment can be measurable on it. However, the existing methods for measuring correlation between two endpoints impose an invalid assumption: correlation structure is constant across different treatment arms. In this article, we reconsider the definition of Kendall's concordance measure (tau) in the context of individual patient data meta-analyses of randomized controlled trials. According to our new definition of Kendall's tau, its value depends on the treatment arms. We then suggest extending the existing copula (and frailty) models so that their Kendall's tau can vary across treatment arms. Our newly proposed model, a joint frailty-conditional copula model, is the implementation of the new definition of Kendall's tau in meta-analyses. In order to facilitate our approach, we develop an original R function condCox.reg(.) and make it available in the R package joint.Cox (https://CRAN.R-project.org/package=joint.Cox). We apply the proposed method to a gastric cancer dataset (3288 patients in 14 randomized trials from the GASTRIC group). This data analysis concludes that Kendall's tau has different values between the surgical treatment arm and the adjuvant chemotherapy arm (p-value<0.001), whereas disease-free survival remains a valid surrogate at individual level for overall survival in these trials.

Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression.

Only a minority of patients derive long-term clinical benefit from anti-PD1/PD-L1 monoclonal antibodies. The presence of tertiary lymphoid structures (TLS) has been associated with improved survival in several tumor types. Here, using a large-scale retrospective analysis of three independent cohorts of cancer patients treated with anti-PD1/PD-L1 antibodies, we showed that the presence of mature TLS was associated with improved objective response rate, progression-free survival, and overall survival independently of PD-L1 expression status and CD8+ T-cell density. These results pave the way for using TLS detection to select patients who are more likely to benefit from immune checkpoint blockade.

A joint frailty-copula model for meta-analytic validation of failure time surrogate endpoints in clinical trials.

In a meta-analysis framework, the classical approach for the validation of time-to-event surrogate endpoint is based on a two-step analysis. This approach often raises estimation issues. Recently, we proposed a one-step validation approach based on a joint frailty model. This approach was quite time consuming, despite parallel computing, due to individual-level frailties used to take into account heterogeneity in the data at the individual level. We now propose an alternative one-step approach for evaluating surrogacy, using a joint frailty-copula model. The model includes two correlated random effects treatment-by-trial interaction and a shared random effect associated with the baseline risks. At the individual level, the joint survivor functions of time-to-event endpoints are linked using copula functions. We used splines for the baseline hazard functions. We estimated parameters and hazard function using a semiparametric penalized marginal likelihood method, considering various numerical integration methods. Both individual-level and trial-level surrogacy were evaluated using Kendall's tau and coefficient of determination. The performance of the estimators was evaluated using simulation studies. The model was applied to individual patient data meta-analyses in advanced ovarian cancer to assess progression-free survival as a surrogate for overall survival, as part of the evaluation of new therapy. The model showed good performance and was quite robust regarding the integration methods and data variation, regardless of the surrogacy evaluation criteria. Kendall's Tau was better estimated using the Clayton copula model compared to the joint frailty model. The proposed model reduces the convergence and model estimation issues encountered in the two-step approach.

One-step validation method for surrogate endpoints using data from multiple randomized cancer clinical trials with failure-time endpoints.

A surrogate endpoint can be used instead of the most relevant clinical endpoint to assess the efficiency of a new treatment. Before being used, a surrogate endpoint must be validated based on appropriate methods. Numerous validation approaches have been proposed with the most popular used in a context of meta-analysis, based on a two-step analysis strategy. For two failure-time endpoints, two association measurements are usually used, Kendall's τ at the individual level and the adjusted coefficient of determination ( Rtrial,adj2 ) at the trial level. However, Rtrial,adj2 is not always available due to model estimation constraints. We propose a one-step validation approach based on a joint frailty model, including both individual-level and trial-level random effects. Parameters have been estimated using a semiparametric penalized marginal log-likelihood method, and various numerical integration approaches were considered. Both individual- and trial-level surrogacy were evaluated using a new definition of Kendall's τ and the coefficient of determination. Estimators' performances were evaluated using simulation studies and satisfactory results were found. The model was applied to individual patient data meta-analyses in gastric cancer to assess disease-free survival as a surrogate for overall survival, as part of the evaluation of adjuvant therapy.