Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting

Using Call Data and Stigmergic Similarity to Assess the Integration of Syrian Refugees in Turkey

By absorbing more than 3.4 millions Syrians, Turkey has shown a remarkable resilience. But the host community hostility toward these newcomers is rising. Thus, the formulation of effective integration policies is needed. However, assessing the effectiveness of such policies demands tools able to measure the integration of refugees despite the complexity of such phenomena. In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrians refugees, by analyzing the CDR dataset of the challenge. Specifically, we aim at assessing the integration of refugees, by exploiting the similarity between refugees and locals in terms of calling behavior and mobility, considering different spatial and temporal features. Together with the already known methods for data analysis, in this work we use a novel computational approach to analyze users' mobility: computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational stigmergy associates each sample to a virtual pheromone deposit (mark) defined in a multidimensional space and characterized by evaporation over time. Marks in spatiotemporal proximity are aggregated into functional structures called trail. The stigmergic trail summarizes the spatiotemporal dynamics in data and allows to compute the stigmergic similarity between them