Sentinel lymph node biopsy is the current standard approach in axillary lymph node (ALN) staging, which guides an oncologist’s decision for the implementation of ALN dissection (ALND), surgery, and subsequent treatment for patients with breast cancer. However, it is an invasive procedure and has been criticized because of its high false-negative rate. Moreover, it has low efficiency owing to the unavoidable extension of the surgery period because of the long wait time for the frozen section. It also has an uncertain diagnostic utility for neoadjuvant strategies.^1-3 Axillary lymph node metastasis (ALNM) strongly affects the prognosis of breast cancer recurrence. Therefore, accurately evaluating ALNM status could optimize the treatment strategy and reduce the risk of recurrence.

In current clinical practice, the standard evaluation for axillary lymph node (ALN) status in breast cancer has a low efficiency and is based on an invasive procedure that causes operative-associated complications in many patients. Therefore, we aimed to use machine learning techniques to develop an efficient preoperative magnetic resonance imaging (MRI) radiomics evaluation approach of ALN status and explore the association between radiomics and the tumor microenvironment in patients with early-stage invasive breast cancer. in this retrospective multicenter study, three independent cohorts of patients with breast cancer (n = 1,088) were used to develop and validate signatures predictive of ALN status. After applying the machine learning random forest algorithm to select the key preoperative MRI radiomic features, we used ALN and tumor radiomic features to develop the ALN-tumor radiomic signature for ALN status prediction by the support vector machine algorithm in 803 patients with breast cancer from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center (training cohort). By combining ALN and tumor radiomic features with corresponding clinicopathologic information, the multiomic signature was constructed in the training cohort. Next, the external validation cohort (n = 179) of patients from Shunde Hospital of Southern Medical University and Tungwah Hospital of Sun Yat-Sen University, and the prospective-retrospective validation cohort (n = 106) of patients treated with neoadjuvant chemotherapy in prospective phase 3 trials [NCT01503905], were included to evaluate the predictive value of the two signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC). This study was registered with ClinicalTrials.gov, number NCT04003558. the ALN-tumor radiomic signature for ALN status prediction comprising ALN and tumor radiomic features showed a high prediction quality with AUC of 0·88 in the training cohort, 0·87 in the external validation cohort, and 0·87 in the prospective-retrospective validation cohort. The multiomic signature incorporating tumor and lymph node MRI radiomics, clinical and pathologic characteristics, and molecular subtypes achieved better performance for ALN status prediction with AUCs of 0·90, 0·91, and 0·93 in the training cohort, the external validation cohort, and the prospective-retrospective validation cohort, respectively. Among patients who underwent neoadjuvant chemotherapy in the prospective-retrospective validation cohort, there were significant differences in the key radiomic features before and after neoadjuvant chemotherapy, especially in the gray-level dependence matrix features. Furthermore, there was an association between MRI radiomics and tumor microenvironment features including immune cells, long non-coding RNAs, and types of methylated sites. Interpretation this study presented a multiomic signature that could be preoperatively and conveniently used for identifying patients with ALN metastasis in early-stage invasive breast cancer. The multiomic signature exhibited powerful predictive ability and showed the prospect of extended application to tailor surgical management. Besides, significant changes in key radiomic features after neoadjuvant chemotherapy may be explained by changes in the tumor microenvironment, and the association between MRI radiomic features and tumor microenvironment features may reveal the potential biological underpinning of MRI radiomics. No funding.

Trastuzumab resistance emerges to be a major issue in anti-human epidermal growth factor receptor 2 (HER2) therapy for breast cancers. Here, we demonstrated that miR-21 expression was up-regulated and its function was elevated in HER2⁺ BT474, SKBR3, and MDA-MB-453 breast cancer cells that are induced to acquire trastuzumab resistance by long-term exposure to the antibody, whereas protein expression of the PTEN gene, a miR-21 target, was reduced. Blocking the action of miR-21 with antisense oligonucleotides re-sensitized the resistant cells to the therapeutic activities of trastuzumab by inducing growth arrest, proliferation inhibition, and G₁-S cell cycle checking in the presence of the antibody. Ectopic expression of miR-21 in HER2⁺ breast cancer cells confers resistance to trastuzumab. Rescuing PTEN expression with a p3XFLAG-PTEN-mut construct with deleted miR-21 targeting sequence at its 3′ UTR restored the growth inhibition of trastuzumab in the resistant cells by inducing PTEN activation and AKT inhibition. In vivo, administering miR-21 antisense oligonucleotides restored trastuzumab sensitivity in the resistant breast cancer xenografts by inducing PTEN expression, whereas injection of miR-21 mimics conferred trastuzumab resistant in the sensitive breast tumors via PTEN silence. Up-regulatin of miR-21 in tumor biopsies obtained from patients receiving pre-operative trastuzumab therapy was associated with poor trastuzumab response. Therefore, miR-21 overexpression contributes to trastuzumab resistance in HER2⁺ breast cancers and antagonizing miR-21 demonstrates therapeutic potential by sensitizing the malignancy to anti-HER2 treatment.

Advances in immuno-oncology are changing the standard of care for non–small cell lung cancer (NSCLC) through immunotherapies, including tumor vaccines, cellular immunotherapies, and immune checkpoint inhibitors (ICIs), that aim to establish or enhance effective immune responses toward a tumor.^1,2 However, immunotherapy has produced inconsistent results in previous randomized clinical trials (RCTs). In the KEYNOTE-024,³ CheckMate-057,⁴ and TIME⁵ trials and the study by Li et al,⁶ immunotherapies were found to significantly improve overall survival (OS) and progression-free survival (PFS) rates compared with chemotherapy in patients with advanced NSCLC, but inconsistent survival outcomes were shown in the CheckMate-026 trial⁷ and the studies by Takayama et al⁸ and Wu et al.⁹ Additionally, important questions remain regarding which immunotherapeutic strategy can be deployed to the best benefit.

Resistance development to one chemotherapeutic reagent leads frequently to acquired tolerance to other compounds, limiting the therapeutic options for cancer treatment. Herein, we find that overexpression of Rac1 is associated with multi-drug resistance to the neoadjuvant chemotherapy (NAC). Mechanistically, Rac1 activates aldolase A and ERK signaling which up-regulates glycolysis and especially the non-oxidative pentose phosphate pathway (PPP). This leads to increased nucleotides metabolism which protects breast cancer cells from chemotherapeutic-induced DNA damage. To translate this finding, we develop endosomal pH-responsive nanoparticles (NPs) which deliver Rac1-targeting siRNA together with cisplatin and effectively reverses NAC-chemoresistance in PDXs from NAC-resistant breast cancer patients. Altogether, our findings demonstrate that targeting Rac1 is a potential strategy to overcome acquired chemoresistance in breast cancer.

Cervical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This research aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology. The AICCS system was trained and validated using various datasets, including retrospective, prospective, and randomized observational trial data, involving a total of 16,056 participants. It utilized two artificial intelligence (AI) models: one for detecting cells at the patch-level and another for classifying whole-slide image (WSIs). The AICCS consistently showed high accuracy in predicting cytology grades across different datasets. In the prospective assessment, it achieved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher AUC, specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity. Thus, AICCS holds promise as an additional tool for accurate and efficient cervical cancer screening.