Growth curves of four breast cancer organoid lines

Comparison of normalized AUC between the second and first drug screenings

Plasma ctDNA predicts the efficacy of neoadjuvant therapy and monitors drug resistance during treatment

Kaplan-Meier survival curves for disease-free survival (DFS) based on ctDNA clearance at T2, T3, and T4 time points

Dynamic monitoring of PIK3CA VAF and drug resistance following 14-Day Alpelisib treatment

Hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR + /HER2 - ) breast cancer, the most common subtype, shows a low pathological complete response (pCR) rate and limited benefit from immunotherapy, highlighting the need for more effective strategies. Although immunotherapy has become increasingly important in cancer treatment, its efficacy in this subtype remains modest. CDK4/6 inhibitors, first-line treatments for advanced HR + /HER2- breast cancer, not only suppress tumor proliferation but may also reshape the immune microenvironment, offering new opportunities for immunotherapy. In this study, multiplex immunohistochemistry, drug testing of HR + /HER2- breast cancer organoids, single-cell sequencing, and primary cell coculture showed that the CDK4/6 inhibitor palbociclib promotes fibroblast senescence, thereby increasing IGF1 and FGF7 levels. These factors drive macrophage polarization toward an M2-like phenotype through STAT3 Tyr705 phosphorylation and ARG1 upregulation, resulting in arginine depletion and reduced lymphocyte viability. To counteract this immunosuppressive microenvironment, we selected the CSF1R inhibitor pexidartinib. Pexidartinib inhibited macrophage activity, suppressed STAT3 phosphorylation, reduced ARG1 expression, and increased lymphocyte viability, thereby enhancing the antitumor efficacy of palbociclib in HR + /HER2- breast cancer. These findings reveal a previously unrecognized immunosuppressive mechanism induced by CDK4/6 inhibition and support CSF1R blockade as a promising combination strategy.

Clearance of cfDNA in the culture supernatant of organoids

ObjectiveThis study aims to investigate the feasibility of constructing machine learning models based on mammographic signs and clinical information to predict histological grading in ductal carcinoma in situ (DCIS) of the breast.MethodsA retrospective analysis was conducted on mammographic signs and clinical data from 243 patients diagnosed with breast DCIS, confirmed by pathology. The patients were divided into non-high-grade (n=110, including low- and intermediate-grade) and high-grade (n=133) groups based on histological results. Statistical analysis was performed on 10 clinical variables and key mammographic features (calcification presence, morphology, and distribution) based on the BI-RADS lexicon, and the features with significant differences were selected to develop three machine learning models: eXtreme Gradient Boosting (XGBoost), logistic regression (LR), and multinomial Naive Bayes (MNB). The models’ performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) as the primary metric, with pairwise AUC comparisons performed using the DeLong test.ResultsThe AUC values for the training sets of XGBoost, LR, and MNB were 0.788 (95% CI: 0.744–0.832), 0.796 (95% CI: 0.752–0.840), and 0.806 (95% CI: 0.761–0.851), respectively. The AUC values for the test sets were 0.763 (95% CI: 0.709–0.818), 0.756 (95% CI: 0.705–0.807), and 0.784 (95% CI: 0.735–0.833). The accuracy values were 0.761, 0.758, and 0.776; the sensitivity values were 0.726, 0.824, and 0.808; and the specificity values were 0.725, 0.692, and 0.744. Although MNB achieved the numerically highest performance, pairwise AUC comparisons showed no statistically significant differences among the three models (all p > 0.05), indicating comparable discriminative ability.ConclusionMachine learning-based models for predicting histological grading in DCIS show promising performance, with MNB demonstrating competitive predictive efficiency alongside the advantage of probabilistic interpretability. The findings highlight the potential utility of integrating mammographic features and clinical information for enhancing the accuracy of DCIS grading prediction. These results should be regarded as hypothesis-generating, pending external multi-center validation.

Clinical characteristics of 71 patients receiving neoadjuvant therapy

N-acetyltransferase 10 (NAT10) mediated N4-acetylcytidine (ac4C) modification has been implicated in tumor progression; however, the precise role and underlying mechanism of NAT10 in breast cancer progression remain largely undefined. The expression and prognostic significance of NAT10 in breast cancer were evaluated using clinical tissue samples and public databases. Functional assays were performed in vitro and in vivo to assess the effects of NAT10 on tumor growth and immune evasion. Mechanistic studies, including RNA immunoprecipitation (RIP), ac4C RNA immunoprecipitation (acRIP), and co-immunoprecipitation (Co-IP), were conducted to elucidate the interaction between NAT10 and histone deacetylase 4 (HDAC4) and their roles in regulating NF-κB signaling and programmed death-ligand 1 (PD-L1) expression. NAT10 expression was significantly upregulated in breast cancer and correlated with poor patient prognosis. NAT10 mediated ac4C modification enhanced the stability of HDAC4 mRNA, thereby promoting HDAC4 expression. Conversely, HDAC4 stabilized NAT10 protein through post-transcriptional deacetylation, forming a self-reinforcing regulatory loop. Elevated HDAC4 activated the NF-κB signaling pathway, resulting in increased PD-L1 transcription and enhanced immune evasion of breast cancer cells. Inhibition of the NAT10/HDAC4/NF-κB axis markedly reduced PD-L1 expression and restored antitumor immune responses. Our findings identify a self-reinforcing NAT10/HDAC4 signaling circuit that drives breast cancer progression and immune evasion. Targeting NAT10 represents a promising therapeutic strategy to overcome immunosuppression and improve patient outcomes in breast cancer.

Forty bright-field images (4× magnification) of breast cancer organoids

Normalized AUC values from the second round of drug screening for organoids with an increased ctDNA VAF after treatment with the initial sensitive drugs

Sequences of primers and MGB probes for detecting mutations using digital PCR

Clinical characteristics of 17 patients receiving neoadjuvant therapy

Clinical and Sequencing Information of 40 Patients with Breast Cancer Organoids

Dynamic changes in ctDNA variant allele frequency reflect real-time therapeutic efficacy. Timely treatment modification guided by ctDNA variant allele frequency may enable precision selection of optimized regimens to counteract evolving resistance in breast cancer. In this study, we developed a therapeutic approach integrating ctDNA dynamics with sequential organoid drug screening to optimize breast cancer treatment. Targeted deep sequencing on plasma samples from 71 patients with breast cancer revealed that ctDNA clearance correlates with improved disease-free survival. Additionally, analysis of 40 breast cancer organoid models demonstrated the potential of organoid culture supernatants for comprehensive mutation profiling and drug sensitivity testing. Together, these findings suggest that combining ctDNA monitoring with organoid-based drug screening can guide adjustments to treatment strategies, offering a promising solution to optimize patient outcomes.Significance: Monitoring circulating tumor DNA in combination with drug screening of organoids enables dynamic, personalized breast cancer therapy adjustments to overcome resistance and improve treatment efficacy.

Monitoring ctDNA to assess alpelisib-mediated reversal of pyrotinib resistance in her2-positive breast cancer

Breast Cancer 90-Gene Panel and Probe Sequences

ctDNA sequencing in 71 patients undergoing neoadjuvant therapy

Triple-negative breast cancer (TNBC) is an aggressive cancer with a poor prognosis.