A non-invasive artificial intelligence model for identifying axillary pathological complete response to neoadjuvant chemotherapy in breast cancer: a secondary analysis to multicenter clinical trial.

BACKGROUND: This study aims to develop a stacking model for accurately predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) using longitudinal MRI in breast cancer. METHODS: We included patients with node-positive breast cancer who received NAC following surgery from January 2012 to June 2022. We collected MRIs before and after NAC, and extracted radiomics features from the tumour, peritumour, and ALN regions. The Mann-Whitney U test, least absolute shrinkage and selection operator, and Boruta algorithm were used to select features. We utilised machine learning techniques to develop three single-modality models and a stacking model for predicting ALN response to NAC. RESULTS: This study consisted of a training cohort (n = 277), three external validation cohorts (n = 313, 164, and 318), and a prospective cohort (n = 81). Among the 1153 patients, 60.62% achieved ypN0. The stacking model achieved excellent AUCs of 0.926, 0.874, and 0.862 in the training, external validation, and prospective cohort, respectively. It also showed lower false-negative rates (FNRs) compared to radiologists, with rates of 14.40%, 20.85%, and 18.18% (radiologists: 40.80%, 50.49%, and 63.64%) in three cohorts. Additionally, there was a significant difference in disease-free survival between high-risk and low-risk groups (p < 0.05). CONCLUSIONS: The stacking model can accurately predict ALN status after NAC in breast cancer, showing a lower false-negative rate than radiologists. TRIAL REGISTRATION NUMBER: The clinical trial numbers were NCT03154749 and NCT04858529.

Organoid forming potential as complementary parameter for accurate evaluation of breast cancer neoadjuvant therapeutic efficacy.

BACKGROUND: 13-15% of breast cancer/BC patients diagnosed as pathological complete response/pCR after neoadjuvant systemic therapy/NST suffer from recurrence. This study aims to estimate the rationality of organoid forming potential/OFP for more accurate evaluation of NST efficacy. METHODS: OFPs of post-NST residual disease/RD were checked and compared with clinical approaches to estimate the recurrence risk. The phenotypes of organoids were classified via HE staining and ER, PR, HER2, Ki67 and CD133 immuno-labeling. The active growing organoids were subjected to drug sensitivity tests. RESULTS: Of 62 post-NST BC specimens, 24 were classified as OFP-I with long-term active organoid growth, 19 as OFP-II with stable organoid growth within 3 weeks, and 19 as OFP-III without organoid formation. Residual tumors were overall correlated with OFP grades (P < 0.001), while 3 of the 18 patients (16.67%) pathologically diagnosed as tumor-free (ypT0N0M0) showed tumor derived-organoid formation. The disease-free survival/DFS of OFP-I cases was worse than other two groups (Log-rank P < 0.05). Organoids of OFP-I/-II groups well maintained the biological features of their parental tumors and were resistant to the drugs used in NST. CONCLUSIONS: The OFP would be a complementary parameter to improve the evaluation accuracy of NST efficacy of breast cancers.

Noninvasive Artificial Intelligence System for Early Predicting Residual Cancer Burden during Neoadjuvant Chemotherapy in Breast Cancer.

OBJECTIVE: To develop an artificial intelligence (AI) system for the early prediction of residual cancer burden (RCB) scores during neoadjuvant chemotherapy (NAC) in breast cancer. SUMMARY BACKGROUND DATA: RCB III indicates drug resistance in breast cancer, and early detection methods are lacking. METHODS: This study enrolled 1048 patients with breast cancer from four institutions, who were all receiving NAC. Magnetic resonance images were collected at the pre- and mid-NAC stages, and radiomics and deep learning features were extracted. A multitask AI system was developed to classify patients into three groups (RCB 0-I, II, and III ) in the primary cohort (PC, n=335). Feature selection was conducted using the Mann-Whitney U- test, Spearman analysis, least absolute shrinkage and selection operator regression, and the Boruta algorithm. Single-modality models were developed followed by model integration. The AI system was validated in three external validation cohorts. (EVCs, n=713). RESULTS: Among the patients, 442 (42.18%) were RCB 0-I, 462 (44.08%) were RCB II and 144 (13.74%) were RCB III. Model-I achieved an area under the curve (AUC) of 0.975 in the PC and 0.923 in the EVCs for differentiating RCB III from RCB 0-II. Model-II distinguished RCB 0-I from RCB II-III, with an AUC of 0.976 in the PC and 0.910 in the EVCs. Subgroup analysis confirmed that the AI system was consistent across different clinical T stages and molecular subtypes. CONCLUSIONS: The multitask AI system offers a noninvasive tool for the early prediction of RCB scores in breast cancer, supporting clinical decision-making during NAC.

Nuclear transport maintenance of USP22-AR by Importin-7 promotes breast cancer progression.

The translocation of biological macromolecules between cytoplasm and nucleus is of great significance to maintain various life processes in both normal and cancer cells. Disturbance of transport function likely leads to an unbalanced state between tumor suppressors and tumor-promoting factors. In this study, based on the unbiased analysis of protein expression differences with a mass spectrometer between human breast malignant tumors and benign hyperplastic tissues, we identified that Importin-7, a nuclear transport factor, is highly expressed in breast cancer (BC) and predicts poor outcomes. Further studies showed that Importin-7 promotes cell cycle progression and proliferation. Mechanistically, through co-immunoprecipitation, immunofluorescence, and nuclear-cytoplasmic protein separation experiments, we discovered that AR and USP22 can bind to Importin-7 as cargoes to promote BC progression. In addition, this study provides a rationale for a therapeutic strategy to restream the malignant progression of AR-positive BC by inhibiting the high expression state of Importin-7. Moreover, the knockdown of Importin-7 increased the responsiveness of BC cells to the AR signaling inhibitor, enzalutamide, suggesting that targeting Importin-7 may be a potential therapeutic strategy.

Multifactor artificial intelligence model assists axillary lymph node surgery in breast cancer after neoadjuvant chemotherapy: multicenter retrospective cohort study.

BACKGROUND: The high false negative rate (FNR) associated with sentinel lymph node biopsy often leads to unnecessary axillary lymph node dissection following neoadjuvant chemotherapy (NAC) in breast cancer. The authors aimed to develop a multifactor artificial intelligence (AI) model to aid in axillary lymph node surgery. MATERIALS AND METHODS: A total of 1038 patients were enrolled, comprising 234 patients in the primary cohort, 723 patients in three external validation cohorts, and 81 patients in the prospective cohort. For predicting axillary lymph node response to NAC, robust longitudinal radiomics features were extracted from pre-NAC and post-NAC magnetic resonance images. The U test, the least absolute shrinkage and selection operator, and the spearman analysis were used to select the most significant features. A machine learning stacking model was constructed to detect ALN metastasis after NAC. By integrating the significant predictors, we developed a multifactor AI-assisted surgery pipeline and compared its performance and false negative rate with that of sentinel lymph node biopsy alone. RESULTS: The machine learning stacking model achieved excellent performance in detecting ALN metastasis, with an area under the curve (AUC) of 0.958 in the primary cohort, 0.881 in the external validation cohorts, and 0.882 in the prospective cohort. Furthermore, the introduction of AI-assisted surgery reduced the FNRs from 14.88 (18/121) to 4.13% (5/121) in the primary cohort, from 16.55 (49/296) to 4.05% (12/296) in the external validation cohorts, and from 13.64 (3/22) to 4.55% (1/22) in the prospective cohort. Notably, when more than two SLNs were removed, the FNRs further decreased to 2.78% (2/72) in the primary cohort, 2.38% (4/168) in the external validation cohorts, and 0% (0/15) in the prospective cohort. CONCLUSION: Our study highlights the potential of AI-assisted surgery as a valuable tool for evaluating ALN response to NAC, leading to a reduction in unnecessary axillary lymph node dissection procedures.

Noninvasive discrimination of benign and malignant breast lesions using genome-wide nucleosome profiles of plasma cell-free DNA.

BACKGROUND: Breast malignancy is the most frequently diagnosed malignancy in women worldwide, and the diagnosis relies on invasive examinations. However, most clinical breast changes in women are benign, and invasive diagnostic approaches cause unnecessary suffering for the patients. Thus, a novel noninvasive approach for discriminating malignant breast lesions from benign lesions is needed. METHODS: We performed cell-free DNA (cfDNA) sequencing on plasma samples from 173 malignant breast lesion patients, 158 benign breast lesion patients, and 102 healthy women. We then analyzed the cfDNA-based nucleosome profiles, which reflect the various tissues of origin and transcription factor activities. Moreover, by using machine learning classifiers along with the cfDNA sequencing data, we built classifiers for discriminating benign from malignant breast lesions. Receiver operating characteristic curve analyses were used to evaluate the performance of the classifiers. RESULTS: cfDNA-based nucleosome profiles reflected the various tissues of origin and transcription factor activities in benign and malignant breast lesions. The cfDNA-based transcription factor activities and breast malignancy-specific transcription factor-binding site accessibility profiles could accurately distinguish benign and malignant breast lesions, with area under the curve values of 0.777 and 0.824, respectively. CONCLUSIONS: Our proof-of-principle study established a methodology for noninvasively discriminating benign from malignant breast lesions.

Association between the nucleosome footprint of plasma DNA and neoadjuvant chemotherapy response for breast cancer.

Gene expression signatures have been used to predict the outcome of chemotherapy for breast cancer. The nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of the original tissues and thus may be used to predict the response to chemotherapy. Here we carried out the nucleosome positioning on cfDNA from 85 breast cancer patients and 85 healthy individuals and two cancer cell lines T-47D and MDA-MB-231 using low-coverage whole-genome sequencing (LCWGS) method. The patients showed distinct nucleosome footprints at Transcription Start Sites (TSSs) compared with normal donors. In order to identify the footprints of cfDNA corresponding with the responses to neoadjuvant chemotherapy in patients, we mapped on nucleosome positions on cfDNA of patients with different responses: responders (pretreatment, n = 28; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 12) and nonresponders (pretreatment, n = 10; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 10). The coverage depth near TSSs in plasma cfDNA differed significantly between responders and nonresponders at pretreatment, and also after neoadjuvant chemotherapy treatment cycles. We identified 232 TSSs with differential footprints at pretreatment and 321 after treatment and found enrichment in Gene Ontology terms such as cell growth inhibition, tumor suppressor, necrotic cell death, acute inflammatory response, T cell receptor signaling pathway, and positive regulation of vascular endothelial growth factor production. These results suggest that cfDNA nucleosome footprints may be used to predict the efficacy of neoadjuvant chemotherapy for breast cancer patients and thus may provide help in decision making for individual patients.

A plasma-derived extracellular vesicle mRNA classifier for the detection of breast cancer.

BACKGROUND: According to the global cancer burden data released in 2020, breast cancer (BC) has become the most common cancer in the world. Similar to those of other cancers, the present methods used in clinic for diagnosing early BC are invasive, inaccurate, and insensitive. Hence, new non-invasive methods capable of early diagnosis are needed. METHODS: We applied next-generation sequencing and analyzed the messenger RNA (mRNA) profiles of plasma extracellular vesicles (EVs) derived from 14 BC patients and 6 patients with benign breast lesions. We used 3 regression models, namely support vector machine (SVM), linear discriminate analysis (LDA), and logistic regression (LR), to develop classifiers for use in making predictive BC diagnoses; and used 259 plasma samples, including those obtained from 144 patients with BC, 72 patients with benign breast lesions, and 43 healthy women, which were divided into training groups and validation groups to verify their performances as classifiers by quantitative reverse transcription polymerase chain reaction (RT-qPCR). The area under the curve (AUC) and accuracy, sensitivity, and specificity of the classifiers were cross-validated with the leave-1-out cross-validation (LOOCV) method. RESULTS: Among all combinations assessed with the 3 different regression models, an 8-mRNA combination, named EXOBmRNA, exhibited high performance [accuracy =71.9% and AUC =0.718, 95% confidence interval (CI): 0.652 to 0.784] in the training cohort after LOOCV was performed, showing the largest AUC in the SVM model. The mRNAs in EXOBmRNA were HLA-DRB1, HAVCR1, ENPEP, TIMP1, CD36, MARCKS, DAB2, and CXCL14. In the validation cohort, the AUC of EXOBmRNA was 0.737 (95% CI: 0.636 to 0.837). In addition, gene function and pathway analyses revealed that different levels of gene expression were associated with cancer. CONCLUSIONS: We developed a high-performing predictive classifiers including 8 mRNAs from plasma extracellular vesicles for diagnosing breast cancer.

Plasma-Derived Extracellular Vesicles Circular RNAs Serve as Biomarkers for Breast Cancer Diagnosis.

Breast cancer is the second cause of cancer-associated death among women and seriously endangers women's health. Therefore, early identification of breast cancer would be beneficial to women's health. At present, circular RNA (circRNA) not only exists in the extracellular vesicles (EVs) in plasma, but also presents distinct patterns under different physiological and pathological conditions. Therefore, we assume that circRNA could be used for early diagnosis of breast cancer. Here, we developed classifiers for breast cancer diagnosis that relied on 259 samples, including 144 breast cancer patients and 115 controls. In the discovery stage, we compared the genome-wide long RNA profiles of EVs in patients with breast cancer (n=14) and benign breast (n=6). To further verify its potential in early diagnosis of breast cancer, we prospectively collected plasma samples from 259 individuals before treatment, including 144 breast cancer patients and 115 controls. Finally, we developed and verified the predictive classifies based on their circRNA expression profiles of plasma EVs by using multiple machine learning models. By comparing their circRNA profiles, we found 439 circRNAs with significantly different levels between cancer patients and controls. Considering the cost and practicability of the test, we selected 20 candidate circRNAs with elevated levels and detected their levels by quantitative real-time polymerase chain reaction. In the training cohort, we found that BCExoC, a nine-circRNA combined classifier with SVM model, achieved the largest AUC of 0.83 [95% CI 0.77-0.88]. In the validation cohort, the predictive efficacy of the classifier achieved 0.80 [0.71-0.89]. Our work reveals the application prospect of circRNAs in plasma EVs as non-invasive liquid biopsies in the diagnosis and management of breast cancer.

T3/T4 thoracic sympathictomy and compensatory sweating in treatment of palmar hyperhidrosis.

BACKGROUND: Compensatory sweating (CS) is one of the most common postoperative complications after thoracic sympathectomy, sympathicotomy or endoscopic sympathetic block (ESB) for palmar hyperhidrosis. This study was conducted to examine the relevance between CS and the sympathetic segment being transected in the surgical treatment of palmar hyperhidrosis, and thus to detect the potential mechanism of the occurrence of CS. METHODS: Between October 2004 and June 2006, 163 patients with primary hyperhidrosis were randomly divided into two groups, T(3) sympathicotomy (78 patients) and T(4) sympathicotomy (85), who were operated upon under general anesthesia via single lumen intubation and intercostal video-mediastinoscopy (VM). RESULTS: No morbidity or mortality occurred. Palmar hyperhidrosis was cured in all patients. Follow-up (mean (13.8 +/- 6.2) months) showed no recurrence of palmar hyperhidrosis. The difference of rates of mild CS in groups T(3) and T(4) was of no statistical significance. The rate of moderate CS was significantly lower in group T(4) than in group T(3). No severe CS occurred. CONCLUSION: The rates of occurrence and severity of CS are lowered with the lower sympathetic chain being transected.

[Intercostal video-mediastinoscopy: a report of 701 cases].

OBJECTIVE: To summarize the experience of intercostal video-mediastinoscopy (VMS) in treatment for mediastinal masses, malignant pleural effusion and palmar hyperhidrosis. METHODS: The clinical data of 701 patients received intercostal VMS from November 2001 to June 2007 were summarized retrospectively. Forty-eight patients with mediastinal masses and 46 patients with suspected malignant pleural effusion underwent intercostal VMS pleural biopsy (39 cases with talc pleurodesis) and 607 patients with palmar hyperhidrosis underwent bilateral intercostals VMS thoracic sympathectomy. RESULTS: No mortality and morbidity were reported in this group. Definitive pathologic diagnosis had been made through VMS mediastinal masses biopsy in mediastinal masses and pleural biopsy in pleura effusion. The efficiency of talc pleurodesis was 100% for 39 cases. The symptoms of sweating of hands in 607 patients with palmar hyperhidrosis disappeared completely, all patients' hands became dry with a 1.5 degrees C to 3.0 degrees C increase of the skin temperature immediately after operation. No recurrence occurred during the follow-up. CONCLUSION: VMS is a simple, convenient and alternative procedure for the treatment of mediastinal masses, malignant pleural effusion and palmar hyperhidrosis.