In triple-negative breast cancer (TNBC), the benefit of combining chemotherapy with checkpoint inhibitors is still not very clear. We utilize single-cell RNA- and ATAC-sequencing to examine the immune cell dynamics in 22 patients with advanced TNBC treated with paclitaxel or its combination with the anti-PD-L1 atezolizumab. We demonstrate that high levels of baseline CXCL13⁺ T cells are linked to the proinflammatory features of macrophages and can predict effective responses to the combination therapy. In responsive patients, lymphoid tissue inducer (LTi) cells, follicular B (Bfoc) cells, CXCL13⁺ T cells, and conventional type 1 dendritic cells (cDC1) concertedly increase following the combination therapy, but instead decrease after paclitaxel monotherapy. Our data highlight the importance of CXCL13⁺ T cells in effective responses to anti-PD-L1 therapies and suggest that their reduction by paclitaxel regimen may compromise the clinical outcomes of accompanying atezolizumab for TNBC treatment.

Combining immune checkpoint blockade (ICB) with chemotherapy shows promise for treating triple-negative breast cancer (TNBC), though the mechanisms remain incompletely understood. Here, we integrate published and new single-cell RNA sequencing (scRNA-seq) data to investigate the tumor immune microenvironment (TIME) in TNBC patients treated with paclitaxel (PTX), nab-paclitaxel (Nab-PTX), and their combinations with the anti-PD-L1 antibody atezolizumab (ATZ). Compared to ATZ plus PTX, ATZ plus Nab-PTX rewires TCF7⁺ stem-like effector memory CD8⁺ T cells (Tsem) and CD4⁺ T follicular helper (Tfh) cells. Nab-paclitaxel, unlike PTX, also reshapes the myeloid compartment, expanding mast cells and pro-inflammatory macrophages. Our analyses in human TNBC and murine models underscore the crucial role of mast cells in orchestrating anti-tumor immune responses, likely by promoting the recruitment and activation of T and B cells. In vivo experiments demonstrate that activating mast cells alongside PD-L1 blockade attenuates TNBC progression, suggesting mast cells as a promising adjunct for enhancing ICB therapy efficacy.

To analyze the impact of global population aging on cancer epidemiology, with a focus on the incidence and mortality rates among individuals aged 60 years and above. We utilized open-source data, retrieving population age estimates from the United Nations Population Division website. The GLOBOCAN 2020 database provided estimates for cancer cases and deaths in 2020 and 2040, while the Global Burden of Disease 2019 database supplied estimates of new cancer cases worldwide from 2000 to 2019. Inclusion criteria considered individuals aged 60 years and over, focusing on the top five deadliest cancers. The cohort-component method was employed for population prediction, with age-specific incidence and mortality rates estimated for 2020 used to forecast the cancer burden. In 2021, the global population aged over 60 years accounted for 13.7%, with Europe/North America and Australia/New Zealand having the highest proportions. The older population is predicted to reach 19.2% by 2040. In 2020, of the 19.3 million new cancer cases worldwide, 64% occurred in individuals aged 60 and above, contributing to 71.3% of cancer-related deaths. The five most common cancer sites were the lung, colorectum, prostate, breast, and stomach. Cancer incidence and deaths are projected to rise significantly among older individuals, reaching 20.7 million new cases and 12.7 million deaths by 2040. Older age, tobacco use, dietary factors, alcohol consumption, and high body mass index (BMI) were identified as major risk factors for various cancers in this demographic. This study reveals a significant rise in cancer incidence and mortality among the elderly due to global population aging. The urgency for targeted interventions in cancer prevention, screening, and treatment for older individuals is emphasized. Despite acknowledged limitations, these findings contribute valuable insights to inform strategies for managing cancer in the elderly amidst evolving demographic trends.

Background: Epithelial–mesenchymal transition (EMT) is implicated in the metastatic process and presents a challenge to epithelial cell adhesion molecule‐based detection of circulating tumor cells (CTCs), which have been demonstrated to be a prognostic indicator in metastatic breast cancer. Although evidence has indicated that heterogeneity of CTCs based on EMT markers is associated with disease progression, no standard recommendations have been established for clinical practice. This study aimed to evaluate the prognostic significance of dynamic CTC detection based on EMT for metastatic breast cancer patients. Methods: We enrolled 108 human epidermal growth factor receptor 2‐negative metastatic breast cancer patients from the prospective phase III CAMELLIA study and applied the CanPatrol CTC enrichment technique to identify CTC phenotypes (including epithelial CTCs, biphenotypic epithelial/mesenchymal CTCs, and mesenchymal CTCs) in peripheral blood samples. Receiver operating characteristic curve analyses of total CTC count and the proportion of mesenchymal CTCs for predicting the 1‐year progression‐free survival (PFS) rate were conducted to determine the optimal cut‐off values, and Kaplan–Meier analysis and Cox proportional hazards regression analysis were performed to investigate the prognostic value of the cut‐off values of both total CTC count and the proportion of mesenchymal CTCs in combination. Results: For predicting the 1‐year PFS rate, the optimal cut‐off value of total CTC count was 9.5 (Area under the curve [AUC] = 0.538, 95% confidence interval [CI] = 0.418–0.657), and that of the proportion of mesenchymal CTCs was 10.7% (AUC = 0.581, 95% CI = 0.463–0.699). We used the two cut‐off values in combination to forecast PFS in which the total CTC count was equaled to or exceeded 10/5 mL with the proportion of mesenchymal CTCs surpassed 10.7%. Patients who met the combined criteria had significantly shorter median PFS than did those who did not meet the criteria (6.2 vs. 9.9 months, P =0.010). A nomogram was constructed based on the criteria and significant clinicopathological characteristics with a C‐index of 0.613 ( P = 0.010). Conclusions: The criteria, which combine the total CTC count and the proportion of mesenchymal CTCs, may be used to monitor therapeutic resistance and predict prognosis in patients with metastatic breast cancer. Trial registration ClinicalTrials.gov . NCT01917279. Registered on 19 July 2013, https://clinicaltrials.gov/ct2/show/NCT01917279?term=NCT01917279&rank=1 .