Optimizing the tumor shrinkage threshold for evaluating immunotherapy efficacy for advanced non-small-cell lung cancer.

PURPOSE: The rise of immune checkpoint inhibitors (ICIs) in recent years has coincided with unusual clinical response patterns. Modification of the sum of longest diameters (SLD)-based threshold that reflecting dynamic change of the tumor burden and predicting response to ICIs, may markedly improve current treatment regimens. METHODS: The baseline and post-treatment SLD of target lesion was recorded and the maximum percent change of the SLD from baseline was designated as SLD-change score. The optimal cut-off value was obtained using the X-tile program. The relationship between SLD-change score and survival outcome (PFS, OS) was evaluated. RESULTS: 10% cut-off value of SLD-change score was found to be most distinctive for PFS. Responders defined according to this cut-off value showed a significant improvement for PFS and OS. Bone metastasis and brain metastasis were also two independent prognostic factors of PFS and OS, respectively. CONCLUSIONS: 10% SLD change score could discriminate for ICIs treatment response, which holds great promise in promoting the development of precise immunotherapeutic strategy.

GMDR: Versatile Software for Detecting Gene-Gene and Gene-Environ- ment Interactions Underlying Complex Traits.

Identification of multifactor gene-gene (G×G) and gene-environment (G×E) interactions underlying complex traits poses one of the great challenges to today's genetic study. Development of the generalized multifactor dimensionality reduction (GMDR) method provides a practicable solution to problems in detection of interactions. To exploit the opportunities brought by the availability of diverse data, it is in high demand to develop the corresponding GMDR software that can handle a breadth of phenotypes, such as continuous, count, dichotomous, polytomous nominal, ordinal, survival and multivariate, and various kinds of study designs, such as unrelated case-control, family-based and pooled unrelated and family samples, and also allows adjustment for covariates. We developed a versatile GMDR package to implement this serial of GMDR analyses for various scenarios (e.g., unified analysis of unrelated and family samples) and large-scale (e.g., genome-wide) data. This package includes other desirable features such as data management and preprocessing. Permutation testing strategies are also built in to evaluate the threshold or empirical p values. In addition, its performance is scalable to the computational resources. The software is available at http://www.soph.uab.edu/ssg/software or http://ibi.zju.edu.cn/software.