Shaoqian Du

Cancer-associated fibroblasts (CAFs) are abundant stromal cells in the tumor microenvironment that promote cancer progression and relapse. However, the heterogeneity and regulatory roles of CAFs underlying chemoresistance remain largely unclear. Here, we performed a single-cell analysis using high-dimensional flow cytometry analysis and identified a distinct senescence-like tetraspanin-8 (TSPAN8) ⁺ myofibroblastic CAF (myCAF) subset, which is correlated with therapeutic resistance and poor survival in multiple cohorts of patients with breast cancer (BC). TSPAN8 ⁺ myCAFs potentiate the stemness of the surrounding BC cells through secretion of senescence-associated secretory phenotype (SASP)–related factors IL-6 and IL-8 to counteract chemotherapy. NAD-dependent protein deacetylase sirtuin 6 (SIRT6) reduction was responsible for the senescence-like phenotype and tumor-promoting role of TSPAN8 ⁺ myCAFs. Mechanistically, TSPAN8 promoted the phosphorylation of ubiquitin E3 ligase retinoblastoma binding protein 6 (RBBP6) at Ser ⁷⁷² by recruiting MAPK11, thereby inducing SIRT6 protein destruction. In turn, SIRT6 down-regulation up-regulated GLS1 and PYCR1 , which caused TSPAN8 ⁺ myCAFs to secrete aspartate and proline, and therefore proved a nutritional niche to support BC outgrowth. By demonstrating that TSPAN8 ⁺ SIRT6 ^low myCAFs were tightly associated with unfavorable disease outcomes, we proposed that the combined regimen of anti-TSPAN8 antibody and SIRT6 activator MDL-800 is a promising approach to overcome chemoresistance. These findings highlight that senescence contributes to CAF heterogeneity and chemoresistance and suggest that targeting TSPAN8 ⁺ myCAFs is a promising approach to circumvent chemoresistance.

Significance: Breast cancer (BrCa) is the most common cancer worldwide, and high-performance metabolic analysis is emerging in diagnosis and prognosis of BrCa. Here, we used nanoparticle-enhanced laser desorption/ionization mass spectrometry to record serum metabolic fingerprints of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection. Our analytical method, combined with the aid of machine learning algorithms, was demonstrated to provide high diagnostic efficiency with accuracy of 88.8% and desirable prognostic prediction ( P < 0.005). Furthermore, seven metabolic biomarkers differentially enriched in BrCa serum and their related pathways were identified. Together, our findings provide a tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.

There is a lack of comprehensive understanding of breast cancer (BC) specific sEVs characteristics and composition on BC unique proteomic information from human samples. Here, we interrogated the proteomic landscape of sEVs in 167 serum samples from patients with BC, benign mammary disease (BD) and from healthy donors (HD). The analysis provides a comprehensive landscape of serum sEVs with totally 9,589 proteins identified, considerably expanding the panel of sEVs markers. Of note, serum BC-sEVs protein signatures were distinct from those of BD and HD, representing stage- and molecular subtype-specific patterns. We constructed specific sEVs protein identifiers that could serve as a liquid biopsy tool for diagnosis and classification of BC from benign mammary disease, molecular subtypes, as well as assessment of lymph node metastasis. We also identified 11 potential survival biomarkers for distant metastasis. This work may provide reference value for the accurate diagnosis and monitoring of BC progression using serum sEVs.