BAG2 drives chemoresistance of breast cancer by exacerbating mutant p53 aggregate.

Rationale: Chemoresistance is a major challenge in the clinical management of patients with breast cancer. Mutant p53 proteins tend to form aggregates that promote tumorigenesis in cancers. We here aimed to explore the mechanism for the generation of mutant p53 aggregates in breast cancer and assess its role in inducing chemoresistance. Methods: Expression of BCL2-associated athanogene 2 (BAG2) was evaluated by qRT-PCR, western blotting, and immunohistochemistry in breast cancer patient specimens. The significance of BAG2 expression in prognosis was assessed by Kaplan-Meier survival analysis and the Cox regression model. The roles of BAG2 in facilitating the formation of mutant p53 aggregates were analyzed by co-immunoprecipitation, immunofluorescence, and semi-denaturing detergent-agarose gel electrophoresis assays. The effects of BAG2 on the chemoresistance of breast cancer were demonstrated by cell function assays and mice tumor models. Results: In the present study, we found that BAG2 was significantly upregulated in relapse breast cancer patient tissues and high BAG2 was associated with a worse prognosis. BAG2 localized in mutant p53 aggregates and interacted with misfolded p53 mutants. BAG2 exacerbated the formation of the aggregates and recruited HSP90 to promote the propagation and maintenance of the aggregates. Consequently, BAG2-mediated mutant p53 aggregation inhibited the mitochondrial apoptosis pathway, leading to chemoresistance in breast cancer. Importantly, silencing of BAG2 or pharmacological targeting of HSP90 substantially reduced the aggregates and increased the sensitivity of chemotherapy in breast cancer. Conclusion: These findings reveal a significant role of BAG2 in the chemoresistance of breast cancer via exacerbating mutant p53 aggregates and suggest that BAG2 may serve as a potential therapeutic target for breast cancer patients with drug resistance.

Androgen receptor expression and its prognostic value in T1N0 luminal/HER2- breast cancer.

Purpose: The authors aimed to evaluate the prognostic and predictive value of androgen receptor (AR) expression in patients with luminal/human EGFR2 negative (HER2-) T1N0 breast cancer. Methods: The cohort in this retrospective study comprised 471 patients with luminal/HER2- T1N0 breast cancer who had undergone surgery between 2013 and 2017 in the authors' center. Results: AR+ tumors were associated with favorable characteristics. AR+ patients had better 5-year recurrence-free survival rates and the risk of recurrence was greater for AR- than for AR+ patients. AR- status predicted the failure of adjuvant endocrine therapy with aromatase inhibitors and of adjuvant chemotherapy with docetaxel plus cyclophosphamide. Conclusion: AR+ expression is significantly related to a better prognosis. AR expression may be an additional biomarker for both endocrine and chemotherapy responsiveness.

Nomogram to Predict Tumor-Infiltrating Lymphocytes in Breast Cancer Patients.

Background: Tumor-infiltrating lymphocytes (TILs) play important roles in the prediction of prognosis and neoadjuvant therapy (NAT) efficacy in breast cancer (BRCA) patients, in this study, we identified clinicopathological factors related to BRCA TILs, then to construct and validate nomogram to predict high density of TILs. Methods: A total of 826 patients diagnosed with BRCA in Sun Yat-Sen University cancer center were enrolled in nomogram cohort. TILs were assessed using hematoxylin-eosin (H&E) staining by two pathologists. Complete clinical data were collected for analysis. Then the enrolled patients were split into a training set and validation set at a ratio of 8:2. and the backward multivariate binary logistic regression model was used to establish nomogram for predicting BRCA TILs, which were further evaluated and validated using the C-index, receiver operating characteristic (ROC) curves and calibration curves. Then another independent NAT cohort of 106 patients was established for verifying this nomogram in NAT efficacy prediction. Results: TILs were significantly correlated with body mass index (BMI), tumor differentiation, ER, PR, HER2 expression, Ki67, blood biochemical indicators including total bilirubin (TBIL), indirect bilirubin (IBIL), total protein (TP), Globulin (GLOB), inorganic phosphorus (IP), calcium (Ca). In which ER expression level [OR = 0.987, 95%CI (0.982-0.992), p < 0.001], IP [OR = 4.462, 95%CI (1.171∼17.289), p = 0.029], IBIL [OR = 0.906, 95%CI (0.845-0.966), p = 0.004] and TP [OR = 1.053, 95%CI (1.010-1.098, p = 0.016)] were independent predictors of TILs. Then nomogram was established, for which calibration curves (C-index = 0.759) and ROC curve (AUC = 0.759, 95%CI 0.717-0.801) in training sets, calibration curves (C-index = 0.708) and ROC curve (AUC = 0.708, 95%CI 0.617-0.800) in validation sets demonstrated great evaluation efficiency. Besides, independent NAT cohort verified this nomogram can distinguish patients with greater NAT efficacy (p = 0.041). Conclusion: The finds of clinicopathological factors associated with TILs could help clinicians to understand the tumor immunity of BRCA and improve treatment system for patients, and the established nomogram with high evaluation efficiency may be used as a complement tool for distinguishing patients with better NAT efficacy.

Investigating Mechanisms of Response or Resistance to Immune Checkpoint Inhibitors by Analyzing Cell-Cell Communications in Tumors Before and After Programmed Cell Death-1 (PD-1) Targeted Therapy: An Integrative Analysis Using Single-cell RNA and Bulk-RNA Sequencing Data.

Currently, a significant proportion of cancer patients do not benefit from programmed cell death-1 (PD-1)-targeted therapy. Overcoming drug resistance remains a challenge. In this study, single-cell RNA sequencing and bulk RNA sequencing data from samples collected before and after anti-PD-1 therapy were analyzed. Cell-cell interaction analyses were performed to determine the differences between pretreatment responders and nonresponders and the relative differences in changes from pretreatment to posttreatment status between responders and nonresponders to ultimately investigate the specific mechanisms underlying response and resistance to anti-PD-1 therapy. Bulk-RNA sequencing data were used to validate our results. Furthermore, we analyzed the evolutionary trajectory of ligands/receptors in specific cell types in responders and nonresponders. Based on pretreatment data from responders and nonresponders, we identified several different cell-cell interactions, like WNT5A-PTPRK, EGFR-AREG, AXL-GAS6 and ACKR3-CXCL12. Furthermore, relative differences in the changes from pretreatment to posttreatment status between responders and nonresponders existed in SELE-PSGL-1, CXCR3-CCL19, CCL4-SLC7A1, CXCL12-CXCR3, EGFR-AREG, THBS1-a3b1 complex, TNF-TNFRSF1A, TNF-FAS and TNFSF10-TNFRSF10D interactions. In trajectory analyses of tumor-specific exhausted CD8 T cells using ligand/receptor genes, we identified a cluster of T cells that presented a distinct pattern of ligand/receptor expression. They highly expressed suppressive genes like HAVCR2 and KLRC1, cytotoxic genes like GZMB and FASLG and the tissue-residence-related gene CCL5. These cells had increased expression of survival-related and tissue-residence-related genes, like heat shock protein genes and the interleukin-7 receptor (IL-7R), CACYBP and IFITM3 genes, after anti-PD-1 therapy. These results reveal the mechanisms underlying anti-PD-1 therapy response and offer abundant clues for potential strategies to improve immunotherapy.

Efficacy and predictive factors of immune checkpoint inhibitors in metastatic breast cancer: a systematic review and meta-analysis.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have shown encouraging treatment efficacy for metastatic breast cancer in several clinical trials. However, response only occurred in a small population. Evidence predicting response and survival of patients with metastatic breast cancer following ICI treatment with existing biomarkers has not been well summarized. This review aimed to summarize the efficacy and predictive factors of immune checkpoint therapy in metastatic breast cancer, which is critical for clinical practice. METHODS: PubMed, Embase, Cochrane Library, Web of Science, www.clinicaltrials.gov, and meeting abstracts were comprehensively searched to identify clinical trials. The outcomes were objective response rate (ORR), treatment-related adverse events (trAEs), immune-related adverse events (irAEs), progression-free survival (PFS), and overall survival (OS). RESULTS: In this review, 27 studies with 1746 patients were included for quantitative synthesis. The pooled ORR was 19% [95% confidence interval (CI) = 12-27%]. Programmed death-ligand 1 (PD-L1)-positive patients had a higher response rate [odds ratio (OR) = 1.44, p = 0.01]. First-line immunotherapy had a better ORR than second-line immunotherapy (OR = 2.00, p = 0.02). Tumor-infiltrating lymphocytes (TILs) ⩾5% (OR = 2.53, p = 0.002) and high infiltrated CD8+ T-cell level (OR = 4.33, p = 0.006) were ideal predictors of immune checkpoint therapy response. Liver metastasis indicated poor response (OR = 0.19, p = 0.009). However, the difference was non-significant in ORR based on age, performance status score, lymph node metastasis, and lactate dehydrogenase (LDH) level. In addition, the PD-L1-positive subgroup had a better 1-year PFS (OR = 1.55, p = 0.04) and 2-year OS (OR = 2.28, p = 0.02) following ICI treatment. The pooled incidence during ICI therapy of grade 3-4 trAEs was 25% (95% CI = 16-34%), whereas for grade 3-4 irAEs it was 15% (95% CI = 11-19%). CONCLUSIONS: Metastatic breast cancer had modest response to ICI therapy. PD-L1-positive, first-line immunotherapy, non-liver metastasis, and high TIL and CD8+ T-cell infiltrating levels could predict better response to ICI treatment. Patients with PD-L1-positive tumor could gain more survival benefits from immune checkpoint therapy.