An integrative approach of digital image analysis and transcriptome profiling to explore potential predictive biomarkers for TGFß blockade therapy.

Increasing evidence suggests that the presence and spatial localization and distribution pattern of tumor infiltrating lymphocytes (TILs) is associate with response to immunotherapies. Recent studies have identified TGFß activity and signaling as a determinant of T cell exclusion in the tumor microenvironment and poor response to PD-1/PD-L1 blockade. Here we coupled the artificial intelligence (AI)-powered digital image analysis and gene expression profiling as an integrative approach to quantify distribution of TILs and characterize the associated TGFß pathway activity. Analysis of T cell spatial distribution in the solid tumor biopsies revealed substantial differences in the distribution patterns. The digital image analysis approach achieves 74% concordance with the pathologist assessment for tumor-immune phenotypes. The transcriptomic profiling suggests that the TIL score was negatively correlated with TGFß pathway activation, together with elevated TGFß signaling activity observed in excluded and desert tumor phenotypes. The present results demonstrate that the automated digital pathology algorithm for quantitative analysis of CD8 immunohistochemistry image can successfully assign the tumor into one of three infiltration phenotypes: immune desert, immune excluded or immune inflamed. The association between "cold" tumor-immune phenotypes and TGFß signature further demonstrates their potential as predictive biomarkers to identify appropriate patients that may benefit from TGFß blockade.

Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data.

Multiple methods have recently been developed to reconstruct full-length B-cell receptors (BCRs) from single-cell RNA sequencing (scRNA-seq) data. This need emerged from the expansion of scRNA-seq techniques, the increasing interest in antibody-based drug development and the importance of BCR repertoire changes in cancer and autoimmune disease progression. However, a comprehensive assessment of performance-influencing factors such as the sequencing depth, read length or number of somatic hypermutations (SHMs) as well as guidance regarding the choice of methodology is still lacking. In this work, we evaluated the ability of six available methods to reconstruct full-length BCRs using one simulated and three experimental SMART-seq datasets. In addition, we validated that the BCRs assembled in silico recognize their intended targets when expressed as monoclonal antibodies. We observed that methods such as BALDR, BASIC and BRACER showed the best overall performance across the tested datasets and conditions, whereas only BASIC demonstrated acceptable results on very short read libraries. Furthermore, the de novo assembly-based methods BRACER and BALDR were the most accurate in reconstructing BCRs harboring different degrees of SHMs in the variable domain, while TRUST4, MiXCR and BASIC were the fastest. Finally, we propose guidelines to select the best method based on the given data characteristics.