Clonal expansion of intra-epithelial T cells in breast cancer revealed by spatial transcriptomics.

The spatial distribution of tumor-infiltrating lymphocytes (TIL) predicts breast cancer outcome and response to systemic therapy, highlighting the importance of an intact tissue structure for characterizing tumors. Here, we present ST-FFPE, a spatial transcriptomics method for the analysis of formalin-fixed paraffin-embedded samples, which opens the possibility of interrogating archival tissue. The method involves extraction, exome capture and sequencing of RNA from different tumor compartments microdissected by laser-capture, and can be used to study the cellular composition of tumor microenvironment. Focusing on triple-negative breast cancer (TNBC), we characterized T cells, B cells, dendritic cells, fibroblasts and endothelial cells in both stromal and intra-epithelial compartments. We found a highly variable spatial distribution of immune cell subsets among tumors. This analysis revealed that the immune repertoires of intra-epithelial T and B cells were consistently less diverse and more clonal than those of stromal T and B cells. T-cell receptor (TCR) sequencing confirmed a reduced diversity and higher clonality of intra-epithelial T cells relative to the corresponding stromal T cells. Analysis of the top 10 dominant clonotypes in the two compartments showed a majority of shared but also some unique clonotypes both in stromal and intra-epithelial T cells. Hyperexpanded clonotypes were more abundant among intra-epithelial than stromal T cells. These findings validate the ST-FFPE method and suggest an accumulation of antigen-specific T cells within tumor core. Because ST-FFPE is applicable for analysis of previously collected tissue samples, it could be useful for rapid assessment of intratumoral cellular heterogeneity in multiple disease and treatment settings

Clinical factors associated with prolonged response and survival under olaparib as maintenance therapy in BRCA mutated ovarian cancers.

To investigate clinical factors associated with prolonged progression-free survival (PFS) and overall survival (OS) in relapsing epithelial ovarian cancer (EOC) patients with BRCA mutations and receiving olaparib as maintenance therapy in daily practice. Multicenter (8 hospitals) European retrospective study of relapsing EOC patients having germline or somatic mutations of BRCA1/BRCA2 genes and treated with olaparib as maintenance therapy after platinum-based chemotherapy. One hundred and fifteen patients were included. Median age was 54 years. There were 90 BRCA1 carriers, 24 BRCA2 carriers and one patient had germline mutation of BRCA1 and BRCA2. Six patients had somatic mutations (all BRCA1) and 109 had germline mutations. Ninety percent had serous carcinomas and were platinum-sensitive. Following ultimate platinum-based chemotherapy, 69% of the patients had normalization of CA-125 levels and 87% had RECIST objective responses, either partial (53%) or complete (34%). After a median follow-up of 21 months, median PFS was 12.7 months and median OS was 35.4 months. In multivariate analysis, factors associated with prolonged PFS under olaparib were: platinum-free interval (PFI) ≥ 12 months, RECIST complete response (CR) or partial response (PR) and normalization of CA-125 upon ultimate platinum-based chemotherapy. Factors associated with prolonged OS were PFI ≥ 12 months, CR and normalization of CA-125. Platinum-free interval ≥ 12 months, complete response and normalized CA-125 levels after ultimate platinum-based chemotherapy are associated with prolonged PFS and OS in relapsing BRCA1/BRCA2 mutated ovarian cancer patients who received olaparib as maintenance therapy

Identification of clinically relevant T cell receptors for personalized T cell therapy using combinatorial algorithms.

A central challenge in developing personalized cancer cell immunotherapy is the identification of tumor-reactive T cell receptors (TCRs). By exploiting the distinct transcriptomic profile of tumor-reactive T cells relative to bystander cells, we build and benchmark TRTpred, an antigen-agnostic in silico predictor of tumor-reactive TCRs. We integrate TRTpred with an avidity predictor to derive a combinatorial algorithm of clinically relevant TCRs for personalized T cell therapy and benchmark it in patient-derived xenografts