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Background and aims: Evaluation of the programmed cell death ligand‐1 (PD‐L1) combined positive score (CPS) is vital to predict the efficacy of the immunotherapy in triple‐negative breast cancer (TNBC), but pathologists show substantial variability in the consistency and accuracy of the interpretation. It is of great importance to establish an objective and effective method which is highly repeatable. Methods: We proposed a model in a deep learning‐based framework, which at the patch level incorporated cell analysis and tissue region analysis, followed by the whole‐slide level fusion of patch results. Three rounds of ring studies (RSs) were conducted. Twenty‐one pathologists of different levels from four institutions evaluated the PD‐L1 CPS in TNBC specimens as continuous scores by visual assessment and our artificial intelligence (AI)‐assisted method. Results: In the visual assessment, the interpretation results of PD‐L1 (Dako 22C3) CPS by different levels of pathologists have significant differences and showed weak consistency. Using AI‐assisted interpretation, there were no significant differences between all pathologists (P = 0.43), and the intraclass correlation coefficient (ICC) value was increased from 0.618 [95% confidence interval (CI) = 0.524–0.719] to 0.931 (95% CI = 0.902–0.955). The accuracy of interpretation result is further improved to 0.919 (95% CI = 0.886–0.947). Acceptance of AI results by junior pathologists was the highest among all levels, and 80% of the AI results were accepted overall. Conclusion: With the help of the AI‐assisted diagnostic method, different levels of pathologists achieved excellent consistency and repeatability in the interpretation of PD‐L1 (Dako 22C3) CPS. Our AI‐assisted diagnostic approach was proved to strengthen the consistency and repeatability in clinical practice. [ABSTRACT FROM AUTHOR]

Objectives Accurate evaluation of residual cancer burden remains challenging because of the lack of appropriate techniques for tumor bed sampling. This study evaluated the application of a white light imaging system to help pathologists differentiate the components and location of tumor bed in specimens. Methods The high dynamic range dual-mode white light imaging (HDR-DWI) system was developed to capture antiglare reflection and multiexposure HDR transmission images. It was tested in 60 specimens of modified radical mastectomy after neoadjuvant therapy. We observed the differential transmittance among tumor tissue, fibrosis tissue, and adipose tissue. Results The sensitivity and specificity of HDR-DWI were compared with x-ray or visual examination to determine whether HDR-DWI was superior in identifying tumor beds. We found that tumor tissue had lower transmittance (0.12 ± 0.03) than fibers (0.15 ± 0.04) and fats (0.27 ± 0.07) (P <.01). Conclusions HDR-DWI was more sensitive in identifying fiber and tumor tissues than cabinet x-ray and visual observation (P <.01). In addition, HDR-DWI could identify more fibrosis areas than the currently used whole slide imaging did in 12 samples (12/60). We have determined that HDR-DWI can provide more in-depth tumor bed information than x-ray and visual examination do, which will help prevent diagnostic errors in tumor bed sampling. [ABSTRACT FROM AUTHOR]

Neoadjuvant therapy (NAT) treats early‐stage breast cancers, especially triple‐negative breast cancers (TNBCs). NAT improves pathological complete response (pCR) rates for different breast cancer patients. Recently, immune checkpoint inhibitors that target programmed death 1 (PD‐1) or programmed death ligand 1 (PD‐L1) in combination with NAT have shown antitumor activity in patients with early breast cancer. However, the tumor immune microenvironment (TME) in different subtypes of breast cancers, like TNBC, hormone receptor‐positive (HR+), and human epidermal growth factor receptor 2 amplified (HER2+) and its changes by NAT remain to be fully characterized. We analyzed pre‐NAT tumor biopsies from TNBC (n = 27), HR+ (n = 24), and HER2+ (n = 30) breast cancer patients who received NAT, followed by surgery. The different immune makers (PD‐1, PD‐L1, CD3, and CD8) of tumor‐infiltrating lymphocytes (TILs) were identified with immunofluorescence‐based microenvironment analysis. TILs within cancer parenchyma (iTILs) and in cancer stroma (sTILs) were counted separately. We found that PD‐L1+ cells in tumor and stroma were significantly higher in TNBC patients than in others. PD‐L1+ sTILs were significantly higher in pCR than in non‐pCR patients of all the subtypes. The infiltration scores of B‐cell memory, T‐cell CD4+ memory activated, T‐cell follicular helper, and Macrophage M0 and M1 were relatively higher in TNBC patients, indicating immunoreactive TME in TNBC. Analysis of TCGA‐BRCA RNA‐seq indicated that PD‐L1 was highly expressed in TNBC patients compared with HR+ and HER2+ patients. Higher PD‐L1 expression in TNBC patients was associated with significantly longer overall survival (OS). Our results demonstrated that PD‐L1 expression level of iTILs and sTILs is highest in TNBC among breast cancers. TNBC patients had significantly different immunoreactive TME compared with HR+ and HER2+ patients, suggesting potentially favorable outcomes for immunotherapy in these patients. Also, PD‐L1+ could be a powerful predictor of pCR in TNBC patients after NAT. [ABSTRACT FROM AUTHOR]

Background: Tertiary lymphoid structures (TLSs) have been proven to be predictive biomarkers of favorable clinical outcomes and response to immunotherapies in several solid malignancies. Nevertheless, the effect of TLSs in patients with breast cancer (BC) remains controversial. The objective of the current study is to investigate the clinicopathological and prognostic significance of TLSs in BC. Given the unique difficulties for detecting and quantifying TLSs, a TLS-associated gene signature based on The Cancer Genome Atlas (TCGA) BC cohort was used to validate and supplement our results. Methods: Electronic platforms (PubMed, Web of Science, EMBASE, the Cochrane Library, CNKI, and Wanfang) were searched systematically to identify relevant studies as of January 11, 2022. We calculated combined odds ratios (ORs) with 95% confidence intervals (CIs) to determine the relationship between clinicopathological parameters and TLSs. The pooled hazard ratios (HRs) and 95% CIs were also calculated to evaluate the prognostic significance of TLSs. The TLS signature based on the TCGA BC cohort was applied to validate and supplement our results. Results: Fifteen studies with 3,898 patients were eligible for enrollment in our study. The combined analysis indicated that the presence of TLSs was related to improved disease-free survival (DFS) (HR = 0.61, 95% CI: 0.41–0.90, p < 0.05) and overall survival (OS) (HR = 1.66, 95% CI: 1.26–2.20, p < 0.001). Additionally, the presence of TLSs was positively correlated with early tumor TNM stage and high tumor-infiltrating lymphocytes. TLS presence was positively related to human epidermal growth factor receptor 2 (HER-2) and Ki-67 but inversely correlated with the status of estrogen and progesterone receptor. Simultaneously, our study found that tumor immune microenvironment was more favorable in the high-TLS signature group than in the low-TLS signature group. Consistently, BC patients in the high-TLS signature group exhibited better survival outcomes compared to those in the low-TLS signature group, suggesting that TLSs might be favorable prognostic biomarkers. Conclusions: TLS presence provides new insight into the clinicopathological features and prognosis of patients with BC, whereas the factors discussed limited the evidence quality of this study. We look forward to consistent methods to define and characterize TLSs, and more high-quality prospective clinical trials designed to validate the value of TLSs alone or in combination with other markers. [ABSTRACT FROM AUTHOR]

Purpose: Pathologic complete response (pCR) after neoadjuvant therapy is an important indicator of long-term prognosis and the primary endpoint of many neoadjuvant studies. For breast cancer patients who do not achieve pCR, prognostic indicators related to prognosis are particularly important. This study is constructing a prediction model with more accurate and reliable prediction results by combining multiple clinicopathological factors, so as to provide a more accurate decision-making basis for subsequent clinical treatment. Patients and Methods: In this study, 1,009 cases of invasive breast cancer and surgically resected after neoadjuvant therapy from 2010 to 2017. All indicators in this trial were interpreted in a double-blind manner by two pathologists with at least 10 years of experience, including histological grading, Tils, ER, PR, HER2, and Ki67. The prediction model used R language to calculate the calibration degree and ROC curve of the prediction model in the training set and validation set. Results: Through univariate survival analysis, the results showed histological grade (P=0.037), clinical stage (P<0.001), HER2 (P=0.044), RCB class (P<0.001), Tils (P<0.001), lymph node status (P =0.049), MP grade (P=0.013) are related to OS in non-PCR patients after neoadjuvant. Data were analyzed by substituting in a multivariate analysis, and the results were that clinical stage, HER2, RCB grading, and Tils grading were correlated with OS in non-PCR patients after neoadjuvant therapy for breast cancer. Among all cases in the training set, the prediction model predicted that the 3-year survival AUC value was 0.95 and 5-year survival AUC value was 0.79, and the RCB classification of 3-year survival and 5-year survival were 0.70 and 0.67, respectively, which proved that the prediction model could predict the OS of non-PCR patients after neoadjuvant therapy for breast cancer more accurately than the RCB classification, and showed the same results in HR, HER2+, and TN classifications. It also showed the same results in validation set. Conclusion: These data indicate that the predicted values of the prediction model developed in this study match the actual survival rates without underestimating the mortality risk and have a relatively accurate prediction effect. [ABSTRACT FROM AUTHOR]

• An assay called BFML-qMC was developed for multiplex methylation detection. • The BFML-qMC assay combines blocking FRET probes with machine learning. • The assay blocks unmethylated amplification and produces melting curves with probes. • Validation with 80 clinical samples showed 83.33% sensitivity and 84.62% specificity. Breast cancer (BC) is the second most prevalent form of cancer, and poses a significant threat to public health. DNA methylation is an ideal marker for the early detection of BC. Fluorescence quantitative polymerase chain reaction (PCR)-based DNA methylation detection is simpler and faster but is constrained by its multiplexing capability and specificity. To address this, we developed a multiplex quantitative methylation PCR assay for the simultaneous analysis of methylation status at multiple sites specific to BC (cg11754974, cg13828440, cg18637238, and cg16652347). The machine learning model was trained using 1200 cases of multipeak data to enhance the melting curve resolution. Performance testing demonstrated the method's ability to selectively amplify methylated genes at a DNA concentration of 1 × 105 copies μL−1, with high replicability (coefficient of variation <5 %) and mutation detection capabilities as low as 10 %. When applied to 80 clinical BC samples, the assay effectively distinguished patients with early-stage BC from normal controls, achieving an area under the curve of 0.8938, sensitivity of 83.33 %, and specificity of 84.62 %. Our essay exhibits superior clinical performance when compared to the quantitative methylation-specific PCR assays for noninvasive detection of early-stage BC, which is poised to become a favorable clinical diagnostic method for early-stage BC owing to its simplicity, speed, and capacity to improve diagnostic accuracy. [ABSTRACT FROM AUTHOR]