Quantitative Systems Pharmacology Modeling in Immuno-Oncology: Hypothesis Testing, Dose Optimization, and Efficacy Prediction.

Despite an increasing number of clinical trials, cancer is one of the leading causes of death worldwide in the past decade. Among all complex diseases, clinical trials in oncology have among the lowest success rates, in part due to the high intra- and inter-tumoral heterogeneity. There are more than a thousand cancer drugs and treatment combinations being investigated in ongoing clinical trials for various cancer subtypes, germline mutations, metastasis, etc.

Spatial Architecture of Single-Cell and Vasculature in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options, which warrants the identification of novel therapeutic targets. Deciphering nuances in the tumor microenvironment (TME) may unveil insightful links between antitumor immunity and clinical outcomes; however, such connections remain underexplored. Here, we employed a data set derived from imaging mass cytometry of 71 TNBC patient specimens at single-cell resolution and performed in-depth quantifications with a suite of multiscale computational algorithms.

Identifying biomarkers for treatment of uveal melanoma by T cell engager using a QSP model.

Uveal melanoma (UM), the primary intraocular tumor in adults, arises from eye melanocytes and poses a significant threat to vision and health. Despite its rarity, UM is concerning due to its high potential for liver metastasis, resulting in a median survival of about a year after detection. Unlike cutaneous melanoma, UM responds poorly to immune checkpoint inhibition (ICI) due to its low tumor mutational burden and PD-1/PD-L1 expression.