Estimating causal effects of time-dependent exposures on a binary endpoint in a high-dimensional setting.

BACKGROUND: Recently, the intervention calculus when the DAG is absent (IDA) method was developed to estimate lower bounds of causal effects from observational high-dimensional data. Originally it was introduced to assess the effect of baseline biomarkers which do not vary over time. However, in many clinical settings, measurements of biomarkers are repeated at fixed time points during treatment and, therefore, this method needs to be extended. The purpose of this paper is to extend the first step of the IDA, the Peter Clarks (PC)-algorithm, to a time-dependent exposure in the context of a binary outcome. METHODS: We generalised the so-called "PC-algorithm" to take into account the chronological order of repeated measurements of the exposure and proposed to apply the IDA with our new version, the chronologically ordered PC-algorithm (COPC-algorithm). The extension includes Firth's correction. A simulation study has been performed before applying the method for estimating causal effects of time-dependent immunological biomarkers on toxicity, death and progression in patients with metastatic melanoma. RESULTS: The simulation study showed that the completed partially directed acyclic graphs (CPDAGs) obtained using COPC-algorithm were structurally closer to the true CPDAG than CPDAGs obtained using PC-algorithm. Also, causal effects were more accurate when they were estimated based on CPDAGs obtained using COPC-algorithm. Moreover, CPDAGs obtained by COPC-algorithm allowed removing non-chronological arrows with a variable measured at a time t pointing to a variable measured at a time t´ where t´ < t. Bidirected edges were less present in CPDAGs obtained with the COPC-algorithm, supporting the fact that there was less variability in causal effects estimated from these CPDAGs. In the example, a threshold of the per-comparison error rate of 0.5% led to the selection of an interpretable set of biomarkers. CONCLUSIONS: The COPC-algorithm provided CPDAGs that keep the chronological structure present in the data and thus allowed to estimate lower bounds of the causal effect of time-dependent immunological biomarkers on early toxicity, premature death and progression.

Integrating expert's knowledge constraint of time dependent exposures in structure learning for Bayesian networks.

Learning a Bayesian network is a difficult and well known task that has been largely investigated. To reduce the number of candidate graphs to test, some authors proposed to incorporate a priori expert knowledge. Most of the time, this a priori information between variables influences the learning but never contradicts the data. In addition, the development of Bayesian networks integrating time such as dynamic Bayesian networks allows identifying causal graphs in the context of longitudinal data. Moreover, in the context where the number of strongly correlated variables is large (i.e. oncology) and the number of patients low; if a biomarker has a mediated effect on another, the learning algorithm would associate them wrongly and vice versa. In this article we propose a method to use the a priori expert knowledge as hard constraints in a structure learning method for Bayesian networks with a time dependant exposure. Based on a simulation study and an application, where we compared our method to the state of the art PC-algorithm, the results showed a better recovery of the true graphs when integrating hard constraints a priori expert knowledge even for small level of information.

Systemic short chain fatty acids limit antitumor effect of CTLA-4 blockade in hosts with cancer.

Gut microbiota composition influences the clinical benefit of immune checkpoints in patients with advanced cancer but mechanisms underlying this relationship remain unclear. Molecular mechanism whereby gut microbiota influences immune responses is mainly assigned to gut microbial metabolites. Short-chain fatty acids (SCFA) are produced in large amounts in the colon through bacterial fermentation of dietary fiber. We evaluate in mice and in patients treated with anti-CTLA-4 blocking mAbs whether SCFA levels is related to clinical outcome. High blood butyrate and propionate levels are associated with resistance to CTLA-4 blockade and higher proportion of Treg cells. In mice, butyrate restrains anti-CTLA-4-induced up-regulation of CD80/CD86 on dendritic cells and ICOS on T cells, accumulation of tumor-specific T cells and memory T cells. In patients, high blood butyrate levels moderate ipilimumab-induced accumulation of memory and ICOS + CD4 + T cells and IL-2 impregnation. Altogether, these results suggest that SCFA limits anti-CTLA-4 activity.