Early Triple-Negative Breast Cancers in a Singapore Cohort Exhibit High PIK3CA Mutation Rates Associated With Low PD-L1 Expression.

Mutations in the PI3K pathway, particularly PIK3CA, were reported to be intimately associated with triple-negative breast cancer (TNBC) progression and the development of treatment resistance. We profiled PIK3CA and other genes on 166 early-stage TNBC tumors from Singapore for comparison to publicly available TNBC cohorts. These tumors were profiled transcriptionally using a NanoString panel of immune genes and multiplex immunohistochemistry, then manually scored for PD-L1-positivity using 2 clinically relevant clones, SP142 and 22C3. We discovered a higher rate of PIK3CA mutations in our TNBC cohort than in non-Asian cohorts, along with TP53, BRCA1, PTPN11, and MAP3K1 alterations. PIK3CA mutations did not affect overall or recurrence-free survival, and when compared with PIK3CAWT tumors, there were no differences in immune infiltration. Using 2 clinically approved antibodies, PIK3CAmut tumors were associated with PD-L1 negativity. Analysis of comutation frequencies further revealed that PIK3CA mutations tended to be accompanied by MAP kinase pathway mutation. The mechanism and impact of PIK3CA alterations on the TNBC tumor immune microenvironment and PD-L1 positivity warrant further study.

SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes.

Spatial transcriptomics has been emerging as a powerful technique for resolving gene expression profiles while retaining tissue spatial information. These spatially resolved transcriptomics make it feasible to examine the complex multicellular systems of different microenvironments. To answer scientific questions with spatial transcriptomics and expand our understanding of how cell types and states are regulated by microenvironment, the first step is to identify cell clusters by integrating the available spatial information. Here, we introduce SC-MEB, an empirical Bayes approach for spatial clustering analysis using a hidden Markov random field. We have also derived an efficient expectation-maximization algorithm based on an iterative conditional mode for SC-MEB. In contrast to BayesSpace, a recently developed method, SC-MEB is not only computationally efficient and scalable to large sample sizes but is also capable of choosing the smoothness parameter and the number of clusters. We performed comprehensive simulation studies to demonstrate the superiority of SC-MEB over some existing methods. We applied SC-MEB to analyze the spatial transcriptome of human dorsolateral prefrontal cortex tissues and mouse hypothalamic preoptic region. Our analysis results showed that SC-MEB can achieve a similar or better clustering performance to BayesSpace, which uses the true number of clusters and a fixed smoothness parameter. Moreover, SC-MEB is scalable to large 'sample sizes'. We then employed SC-MEB to analyze a colon dataset from a patient with colorectal cancer (CRC) and COVID-19, and further performed differential expression analysis to identify signature genes related to the clustering results. The heatmap of identified signature genes showed that the clusters identified using SC-MEB were more separable than those obtained with BayesSpace. Using pathway analysis, we identified three immune-related clusters, and in a further comparison, found the mean expression of COVID-19 signature genes was greater in immune than non-immune regions of colon tissue. SC-MEB provides a valuable computational tool for investigating the structural organizations of tissues from spatial transcriptomic data.