Analysis of the relevance between molecular subtypes and efficacy of neoadjuvant chemotherapy in breast cancer as well as its prognostic factors

Locoregional recurrence and survival of breast-conserving surgery compared to mastectomy following neoadjuvant chemotherapy in operable breast cancer

Background

The risk of locoregional recurrence (LRR) and the long-term prognosis of breast-conserving surgery (BCS) after neoadjuvant chemotherapy (NAC) are still controversial. This study aimed to evaluate oncological outcomes for patients undergoing BCS after NAC and determine LRR and survival predictors.

Methods

This study was a retrospective cohort study of patients with locally advanced breast cancer (LABC) who received NAC and underwent BCS or mastectomy from June 2011 to November 2020. LRR, disease-free survival (DFS), and overall survival (OS) were compared in patients undergoing BCS or mastectomy. Univariate and multivariate analyses were performed to determine LRR, DFS, and OS predictors.

Results

A total of 585 patients were included, of whom 106 (18.1%) underwent BCS and 479 (81.9%) underwent a mastectomy. The LRR rate was 11.3% in the BCS group and 16.3% in the mastectomy group, revealing no significant difference(p = 0.200). In patients who underwent BCS, clinical lymph node status, histological grade and pathological complete response (pCR) were independent factors to predict LRR. There was no significant difference in DFS and OS between the BCS and the mastectomy groups. Multivariable analysis showed that lymph node status, histological grade, molecular subtypes, pCR and Miller&Payne (M&P) classification were independent predictors of DFS. Lymph node status, molecular subtypes and pCR were independent predictors of OS. BCS or mastectomy was not an independent predictor of DFS or OS.

Conclusion

Compared with mastectomy, BCS after NAC may not increase the risk of local recurrence or mortality, BCS can be performed in selected patients with small tumor size and good response to NAC.

Computed Tomography-Based Radiomics Analysis for Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer Patients

PURPOSE

Previous studies have pointed out that magnetic resonance- and fluorodeoxyglucose positron emission tomography-based radiomics had a high predictive value for the response of the neoadjuvant chemotherapy (NAC) in breast cancer by respectively characterizing tumor heterogeneity of the relaxation time and the glucose metabolism. However, it is unclear whether computed tomography (CT)-based radiomics based on density heterogeneity can predict the response of NAC. This study aimed to develop and validate a CT-based radiomics nomogram to predict the response of NAC in breast cancer.

METHODS

A total of 162 breast cancer patients (110 in the training cohort and 52 in the validation cohort) who underwent CT scans before receiving NAC and had pathological response results were retrospectively enrolled. Grades 4 to 5 cases were classified as response to NAC. According to the Miller-Payne grading system, grades 1 to 3 cases were classified as nonresponse to NAC. Radiomics features were extracted, and the optimal radiomics features were obtained to construct a radiomics signature. Multivariate logistic regression was used to develop the clinical prediction model and the radiomics nomogram that incorporated clinical characteristics and radiomics score. We assessed the performance of different models, including calibration and clinical usefulness.

RESULTS

Eight optimal radiomics features were obtained. Human epidermal growth factor receptor 2 status and molecular subtype showed statistical differences between the response group and the nonresponse group. The radiomics nomogram had more favorable predictive efficacy than the clinical prediction model (areas under the curve, 0.82 vs 0.70 in the training cohort; 0.79 vs 0.71 in the validation cohort). The Delong test showed that there are statistical differences between the clinical prediction model and the radiomics nomogram ( z = 2.811, P = 0.005 in the training cohort). The decision curve analysis showed that the radiomics nomogram had higher overall net benefit than the clinical prediction model.

CONCLUSION

The radiomics nomogram based on CT radiomics signature and clinical characteristics has favorable predictive efficacy for the response of NAC in breast cancer.

Tumor immune microenvironment components and the other markers can predict the efficacy of neoadjuvant chemotherapy for breast cancer

Breast cancer is an epithelial malignant tumor that occurs in the terminal ducts of the breast. Neoadjuvant chemotherapy (NACT) is an important part of breast cancer treatment. Its purpose is to use systemic treatment for some locally advanced breast cancer patients, to decrease the tumor size and clinical stage so that non-operable breast cancer patients can have a chance to access surgical treatment, or patients who are not suitable for breast-conserving surgery can get the opportunity of breast-conserving. However, some patients who do not respond to NACT will lead deterioration in their condition. Therefore, prediction of NACT efficacy in breast cancer is vital for precision therapy. The tumor microenvironment (TME) has a crucial role in the carcinogenesis and therapeutic response of breast cancer. In this review, we summarized the immune cells, immune checkpoints, and other biomarkers in the TME that can evaluate the efficacy of NACT in treating breast cancer. We believe that the detection and evaluation of the TME components in breast cancer are helpful to predict the efficacy of NACT, and the prediction methods are in the prospect. In addition, we also summarized other predictive factors of NACT, such as imaging examination, biochemical markers, and multigene/multiprotein profiling.

The development of molecular typing in canine mammary carcinomas

Mammary tumors are the most frequent neoplasia in old female dogs and present challenges in diagnosis and prognosis owing to heterogeneity. Along with the rapid development of biotechnology, the molecular subtyping of canine mammary carcinomas has been researched, and provides an important reference basis for diagnosis, treatment, prognosis, and even prediction of recurrence rate. Therefore, the molecular classification of canine mammary carcinomas has gained a broad clinical application prospect. However, the existing molecular markers of canine mammary carcinomas are still unable to meet the expanding clinical needs with poor clinical feasibility. Thus, it is urgent to develop more applicable biomarkers appropriate for personalized treatment modalities. At present, the molecular typing of canine mammary carcinomas is not fully understood, and it is first reviewed in this study.

Changes in Tumor Stem Cell Markers and Epithelial-Mesenchymal Transition Markers in Nonluminal Breast Cancer after Neoadjuvant Chemotherapy and Their Correlation with Contrast-Enhanced Ultrasound

Nonluminal breast cancer has high early metastasis and treatment resistance, and neoadjuvant chemotherapy (NAC) is needed. The presence of cancer stem cells (CSC) and epithelial-mesenchymal transition (EMT) leads to poor prognosis. This study investigated the changes in CSC markers and EMT markers after NAC in nonluminal breast cancer and their correlation with contrast-enhanced ultrasound (CEUS) features and chemotherapy efficacy. Before NAC, the range of nonluminal breast cancer on CEUS was larger than that of two-dimensional ultrasound, but after NAC, it was significantly smaller than that of two-dimensional ultrasound and closer to the postoperative pathological size. After NAC, the enlarged lesions and perfusion defects were significantly less than those before NAC. The time-intensity curve showed the characteristics of slow-in, low enhancement, and low perfusion. Nonluminal breast cancer downregulated the expression of CSC markers and EMT markers after NAC, but the epithelial phenotype of nonluminal breast cancer with good response to chemotherapy was upregulated. In nonluminal breast cancer with poor response to chemotherapy, markers of CSC and EMT were highly expressed before chemotherapy. In conclusion, CEUS is better than conventional ultrasound in estimating NAC efficacy in this mode. CEUS can also predict the prognosis of nonluminal breast cancer before NAC with the characteristics of enhanced enlargement and perfusion defects. The contrast-enhanced time-intensity curve of lesions with relatively poor blood supply may have more CSC and EMT characteristics.

Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer

Background: To investigate quantitative ultrasound (QUS) based higher-order texture derivatives in predicting the response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC).

Materials and Methods: 100 Patients with LABC were scanned before starting NAC. Five QUS parametric image-types were generated from radio-frequency data over the tumor volume. From each QUS parametric-image, 4 grey level co-occurrence matrix-based texture images were derived (20 QUS-Tex¹), which were further processed to create texture derivatives (80 QUS-Tex¹-Tex²). Patients were classified into responders and non-responders based on clinical/pathological responses to treatment. Three machine learning algorithms based on linear discriminant (FLD), k-nearest-neighbors (KNN), and support vector machine (SVM) were used for developing radiomic models of response prediction.

Results: A KNN-model provided the best results with sensitivity, specificity, accuracy, and area under curve (AUC) of 87%, 81%, 82%, and 0.86, respectively. The most helpful features in separating the two response groups were QUS-Tex¹-Tex² features. The 5-year recurrence-free survival (RFS) calculated for KNN predicted responders and non-responders using QUS-Tex¹-Tex² model were comparable to RFS for the actual response groups.

Conclusions: We report the first study demonstrating QUS texture-derivative methods in predicting NAC responses in LABC, which leads to better results compared to using texture features alone.

The POLR2E rs3787016 polymorphism is strongly associated with the risk of female breast and cervical cancer

The rs3787016 polymorphism, in polymerase II polypeptide E (POLR2E), was previously identified as being associated with the risk for prostate cancer, esophageal cancer, breast cancer, papillary thyroid carcinoma and liver cancer, suggesting that rs3787016 may server as a common genetic factor to affect individual susceptibility to cancer. To prove the hypothesis, we here performed a case-control study to explore the association between rs3787016 and cervical cancer risk, and to confirm the association between rs3787016 and breast cancer in a central Chinese population, which was followed by a meta-analysis to precisely estimate the association between rs3787016 and risk of female breast and cervical cancer. The genotyping was performed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and confirmed by sequencing. Our results indicated that rs3787016 was associated with the risk of both breast cancer and cervical cancer, and stratified analysis indicated that the association remained particularly for ≤60 years old females who smoke and drink. Moreover, after grouping breast cancer and cervical cancer together, our meta-analysis demonstrated that rs3787016 was associated with overall cancer risk and breast cancer risk. Collectively, the POLR2E rs3787016 polymorphism may be a valuable biomarker for female breast and cervical cancer predisposition.