BackgroundCirculating tumor DNA (ctDNA) has demonstrated a strong predictive capacity for recurrence in early-stage breast cancer compared with imaging examinations. However, there remains a paucity of robust clinical evidence to guide the adjustment of adjuvant therapy based on minimal residual disease (MRD) status in early-stage breast cancer.Case presentationA 69-year-old female patient with early-stage triple-negative breast cancer (TNBC) with somatic BRCA2 mutations exhibited an exceptional response to adjuvant therapy with olaparib. Personalized ctDNA monitoring, utilizing a tumor-informed approach, was employed alongside imaging examinations and tumor biomarker testing to monitor tumor recurrence. MRD positivity was detected at four months and approximately one-month post-treatment discontinuation. Resumption of olaparib therapy resulted in a negative MRD status, while imaging examinations consistently demonstrated no evidence of recurrence in the patient.ConclusionsThis report underscores the potential benefit of olaparib for early-stage TNBC patients with somatic BRCA2 mutations and the utility of serial ctDNA monitoring for tailoring individualized treatment strategies.

As growing evidence from basic research and applied research has supported the use of music as a means of mental healing and rehabilitation, spanning its mechanisms, neural correlates, and its clinical application, the systematic use of music as a complementary and alternative intervention is gaining recognition in China within fields such as mental health, special education, rehabilitation, and oncology. These guidelines review the latest research on the application of music-based interventions in oncology, synthesizing current practices. With the aim of ensuring the accessibility and efficacy of these music-based interventions for cancer patients and their families, the paper places a particular focus on defining different intervention categories, providing recommendations for their application, outlining standard procedures, and establishing a hierarchical framework for music-based interventions in oncology.

This study aims to explore the effects of Tai Chi Chuan (TCC) on physical function, hematological metabolic biomarkers, sleep quality, and mental health in breast cancer patients. This was a prospective clinical trial that involved 37 breast cancer patients who had completed surgery treatment. Participants’ motor function, hematological examination, and self-rated questionnaire were assessed at the baseline and after the intervention. Through the 12-week TCC intervention, participants’ weights (p = 0.981), body mass index (BMI) (p = 0.913), and blood pressure (p = 0.374 for systolic BP, p = 0.299 for diastolic BP) remained stable; waist-hip ratio significantly decreased (p < 0.001); and post-intervention pulse rate (p = 0.011) and vital capacity (p = 0.062) slightly increased. In the aspect of motor function, the value of sit and reach (p < 0.001), grip strength of both hands (p < 0.001), and most active range of motion of neck and shoulder joint (p < 0.001 for anteflexion and both left and right lateroflexion of neck joint, both left and right adduction and rear protraction of shoulder joint) increased after intervention. Arm girth (p < 0.05 for arm girth in left styloid process of ulna, 5 cm below left cubital crease, left and right cubital crease, 10 cm above left and right cubital crease, and left axilla) showed a shrinking trend after intervention. According to the questionnaire of Pittsburgh Sleep Quality Index (PSQI) and the Hospital Anxiety and Depression Scale (HADS), participants’ sleep quality improved, and anxiety and depression remained stable after the trial (p = 0.021 for PSQI, p = 0.631 for anxiety, p = 0.182 for depression). Hematological examination of total protein (p = 0.016), albumin (p < 0.001), albumin-globulin ratio (A/G) (p < 0.001), high-density lipoprotein cholesterol (HDL-CH) (p = 0.002), and apolipoprotein A1 (APOA1) (p < 0.001) increased after intervention. Motor function and sleep quality improved significantly after TCC intervention for 3 months in breast cancer patients. At the same time, some hematological metabolic biomarkers also changed better. ChiCTR2200061422. Date of registration: June 24th, 2022.

IntroductionThe impact of distinct tumor estrogen receptor (ER) and progesterone receptor (PR) expression patterns on tumor behavior and treatment outcomes within HER2-positive breast cancer is not fully explored. This study aimed to comprehensively examine the clinical differences among patients with HER2-positive breast cancer harboring distinct ER and PR expression patterns in the neoadjuvant setting.MethodsThis retrospective analysis included 871 HER2-positive breast patients treated with neoadjuvant therapy at our hospital between 2011 and 2022. Comparisons were performed across the three hormone receptor (HR)-specific subtypes, namely the ER-negative/PR-negative/HER2-positive (ER-/PR-/HER2+), the single HR-positive (HR+)/HER2+, and the triple-positive breast cancer (TPBC) subtypes.ResultsOf 871 patients, 21.0% had ER-/PR-/HER2+ tumors, 33.6% had single HR+/HER2+ disease, and 45.4% had TPBC. Individuals with single HR+/HER2+ tumors and TPBC cases demonstrated significantly lower pathological complete response (pCR) rates compared to those with ER-/PR-/HER2+ tumors (36.9% vs. 24.3% vs 49.2%, p<0.001). Multivariate analysis confirmed TPBC as significantly associated with decreased pCR likelihood (OR=0.42, 95%CI 0.28-0.63, p<0.001). Survival outcomes, including disease-free survival (DFS) and overall survival (OS), showed no significant differences across HR-specific subtypes in the overall patient population. However, within patients without anti-HER2 therapy, TPBC was linked to improved DFS and a trend towards better OS.ConclusionsHER2-positive breast cancer exhibits three distinct HR-specific subtypes with varying clinical manifestations and treatment responses. These findings suggest personalized treatment strategies considering ER and PR expression patterns, emphasizing the need for further investigations to unravel molecular traits underlying HER2-positive breast cancer with distinct HR expression patterns.

<p>Supplementary Figure S4. miR-21 targets 3' UTR of smad7 and PTEN.</p>

These supplementary data contain supplementary methods

CCL18 is mostly produced by TAMs in PTs and can be used as a diagnostic marker to distinguish aggressive from benign PTs.

<p>Legend for Supplementary Figures S1-S6.</p>

<p>Supplementary Figure S1. Myofibroblast differentiation is associated with malignant progression of PTs</p>

CCL18 upregulates miR-21 expression and thus induces myoï¬broblast differentiation via activiting NF-κB.

<p>Supplementary Figure S4. miR-21 targets 3' UTR of smad7 and PTEN.</p>

Nuclear translocation of p65 is induced by CCL18 treatment.

Legend for Supplementary Figures S1-S6.

<p>Supplementary Figure S6. miR-21 induces myofibroblasts differentiation in breast PTs xenografts.</p>

Abstract Background Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms. Methods In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics. Results The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02–0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort. Conclusions This study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.

<p>Supplementary Table S1. Incidence and liver metastasis rate of Tumors from PTs cells in Nude Mice.</p>

<p>Description of additional methods and procedures used in the study.</p>

<p>Supplementary Figure S1. Myofibroblast differentiation is associated with malignant progression of PTs</p>