Breast cancer patient-derived scaffolds enhance the understanding of PD-L1 regulation and T cell cytotoxicity

Recent advances as well as obstacles for immune-based cancer treatment strategies, highlight the notable impact of patient cancer microenvironments on the immune cells and immune targets. Here, we use patient-derived scaffolds (PDS) generated from 110 primary breast cancers to monitor the impact of the cancer microenvironment on immune regulators. Pronounced variation in PD-L1 expression is observed in cancer cells adapted to different patient scaffolds. This variation is further linked to clinical observations and correlated with specific proteins detected in the cell-free PDSs using mass spectrometry. When adding T cells to the PDS-based cancer cultures, the killing efficiency of activated T cells vary between the cultures, whereas non-activated T cells modulate the cancer cell PD-L1 expression to treatment-predictive values, matching killing capacities of activated T cells. Surviving cancer cells show enrichment in cancer stem cell and epithelial-to-mesenchymal transition (EMT) features, suggesting that T cells may not efficiently target cells with metastatic potential. We conclude that clinically relevant insights in how to optimally target and guide immune-based cancer therapies can be obtained by including patient-derived scaffolds and cues from the cancer microenvironment in cancer patient handling and drug development.

Evaluation of tumor immune contexture among intrinsic molecular subtypes helps to predict outcome in early breast cancer

Background

The prognosis of early breast cancer is linked to clinic-pathological stage and the molecular characteristics of intrinsic tumor cells. In some patients, the amount and quality of tumor-infiltrating immune cells appear to affect long term outcome. We aimed to propose a new tool to estimate immune infiltrate, and link these factors to patient prognosis according to breast cancer molecular subtypes.

Methods

We performed in silico analyses in more than 2800 early breast cancer transcriptomes with corresponding clinical annotations. We first developed a new gene expression deconvolution algorithm that accurately estimates the quantity of immune cell populations (tumor immune contexture, TIC) in tumors. Then, we studied associations between these immune profiles and relapse-free and overall survival among the different intrinsic molecular subtypes of breast cancer defined by PAM50 classification.

Results

TIC estimates the abundance of 15 immune cell subsets. Both myeloid and lymphoid subpopulations show different spread among intrinsic molecular breast cancer subtypes. A high abundance of myeloid cells was associated with poor outcome, while lymphoid cells were associated with favorable prognosis. Unsupervised clustering describing the 15 immune cell subsets revealed four subgroups of breast tumors associated with distinct patient survival, but independent from PAM50. Adding this information to clinical stage and PAM50 strongly improves the prediction of relapse or death.

Conclusions

Our findings make it possible to refine the survival stratification of early patients with breast cancer by incorporating TIC in addition to PAM50 and clinical tumor burden in a prognostic model validated in training and validation cohorts.