Construction and validation of a hypoxia-related gene signature to predict the prognosis of breast cancer.

BACKGROUND: Among the most common forms of cancer worldwide, breast cancer posed a serious threat to women. Recent research revealed a lack of oxygen, known as hypoxia, was crucial in forming breast cancer. This research aimed to create a robust signature with hypoxia-related genes to predict the prognosis of breast cancer patients. The function of hypoxia genes was further studied through cell line experiments. MATERIALS AND METHODS: In the bioinformatic part, transcriptome and clinical information of breast cancer were obtained from The Cancer Genome Atlas(TCGA). Hypoxia-related genes were downloaded from the Genecards Platform. Differentially expressed hypoxia-related genes (DEHRGs) were identified. The TCGA filtered data was evenly split, ensuring a 1:1 distribution between the training and testing sets. Prognostic-related DEHRGs were identified through Cox regression. The signature was established through the training set. Then, it was validated using the test set and external validation set GSE131769 from Gene Expression Omnibus (GEO). The nomogram was created by incorporating the signature and clinicopathological characteristics. The predictive value of the nomogram was evaluated by C-index and receiver operating characteristiccurve. Immune microenvironment and mutation burden were also examined. In the experiment part, the function of the two most significant hypoxia-related genes were further explored by cell-line experiments. RESULTS: In the bioinformatic part, 141 up-regulated and 157 down-regulated DEHRGs were screened out. A prognostic signature was constructed containing nine hypoxia genes (ALOX15B, CA9, CD24, CHEK1, FOXM1, HOTAIR, KCNJ11, NEDD9, PSME2) in the training set. Low-risk patients exhibited a much more favorable prognosis than higher-risk ones (P < 0.001). The signature was double-validated in the test set and GSE131769 (P = 0.006 and P = 0.001). The nomogram showed excellent predictive value with 1-year OS AUC: 0.788, 3-year OS AUC: 0.783, and 5-year OS AUC: 0.817. Patients in the high-risk group had a higher tumor mutation burden when compared to the low-risk group. In the experiment part, the down-regulation of PSME2 inhibited cell growth ability and clone formation capability of breast cancer cells, while the down-regulation of KCNJ11 did not have any functions. CONCLUSION: Based on 9 DEHRGs, a reliable signature was established through the bioinformatic method. It could accurately predict the prognosis of breast cancer patients. Cell line experiment indicated that PSME2 played a protective role. Summarily, we provided a new insight to predict the prognosis of breast cancer by hypoxia-related genes.

Survival outcomes after breast-conserving therapy compared with mastectomy for patients with early-stage metaplastic breast cancer: a population-based study of 2412 patients.

BACKGROUND: Previous studies revealed that patients with early-stage metaplastic breast cancer (MBC) underwent mastectomy more often than breast-conserving therapy (BCT) mainly due to the larger tumor size. This study was performed to compare the survival outcomes following BCT versus mastectomy for patients with early-stage MBC. METHODS: Surveillance, Epidemiology, and End Results (SEER) database was used to identify women diagnosed with early-stage MBC (T1-3N0-3M0) between 2001 and 2016, who were treated with either BCT or mastectomy. We assessed overall survival (OS) and breast cancer-specific survival (BCSS) using the Kaplan-Meier method and hazard ratios using Cox proportional hazards models. RESULTS: A total of 2412 MBC patients were identified, 881 (36.5%) of whom underwent BCT and 1531(63.5%) underwent mastectomy. The median follow-up time was 73 months. Most of patients had older age (≥50 years old), larger tumor size, higher American Joint Committee on Cancer (AJCC) stage and hormone receptor negativity. After adjustment for confounding variables, patients who underwent BCT had significantly improved OS (5-year OS: 84.3% vs 62.5%; 10-year OS: 73.0% vs 52.1%; adjusted HR = 0.76, 95%CI: 0.59-0.97, p = 0.028) and BCSS (5-year BCSS: 89.1% vs 70.8%; 10-year BCSS: 83.9% vs 67.5%; adjusted HR = 0.72, 95%CI: 0.53-0.96, p = 0.026) than those who underwent mastectomy, and this improvement remained significant for all T and N stages of MBC except for N2-3 stage. CONCLUSION: BCT conferred improved OS and BCSS compared with mastectomy for patients with early-stage MBC, and the improvement persisted in almost all of the subgroups of different T and N stages.

Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy.

OBJECTIVES: Breast cancers show different regression patterns after neoadjuvant chemotherapy. Certain regression patterns are associated with more reliable margins in breast-conserving surgery. Our study aims to establish a nomogram based on radiomic features and clinicopathological factors to predict regression patterns in breast cancer patients. METHODS: We retrospectively reviewed 144 breast cancer patients who received neoadjuvant chemotherapy and underwent definitive surgery in our center from January 2016 to December 2019. Tumor regression patterns were categorized as type 1 (concentric regression + pCR) and type 2 (multifocal residues + SD + PD) based on pathological results. We extracted 1158 multidimensional features from 2 sequences of MRI images. After feature selection, machine learning was applied to construct a radiomic signature. Clinical characteristics were selected by backward stepwise selection. The combined prediction model was built based on both the radiomic signature and clinical factors. The predictive performance of the combined prediction model was evaluated. RESULTS: Two radiomic features were selected for constructing the radiomic signature. Combined with two significant clinical characteristics, the combined prediction model showed excellent prediction performance, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval 0.8343-0.9701) in the primary cohort and 0.826 (95% confidence interval 0.6774-0.9753) in the validation cohort. CONCLUSIONS: Our study established a unique model combining a radiomic signature and clinicopathological factors to predict tumor regression patterns prior to the initiation of NAC. The early prediction of type 2 regression offers the opportunity to modify preoperative treatments or aids in determining surgical options.

Erratum: Analysis of Tau Protein Expression in Predicting Pathological Complete Response to Neoadjuvant Chemotherapy in Different Molecular Subtypes of Breast Cancer.

[This corrects the article on p. 47 in vol. 23, PMID: 32140269.].

Analysis of Tau Protein Expression in Predicting Pathological Complete Response to Neoadjuvant Chemotherapy in Different Molecular Subtypes of Breast Cancer.

PURPOSE: Tau is a microtubule-associated protein that can be found in both normal and abnormal breast cells. Whether the expression of Tau protein can predict the response to neoadjuvant chemotherapy (NACT) is still unclear. In this study, we assessed the role of Tau protein expression in predicting a pathological complete response (pCR) to NACT for different subtypes of breast cancer. METHODS: Four hundred and sixty-eight eligible patients were retrospectively recruited in our study. The relationship between clinicopathologic factors, including Tau protein expression, and pCR in different subtypes was evaluated using logistic regression analysis. Correlation between Tau and disease-free survival (DFS) and overall survival (OS) was performed using Kaplan-Meier analysis. RESULTS: The expression of Tau protein was negatively correlated with pCR, especially in triple-negative breast cancer (TNBC). No significant difference was observed in the luminal human epidermal growth factor receptor-2 (HER2)-negative subtype and HER2-positive subtype. Patients with pCR were associated with better DFS and OS (p < 0.05). However, Tau protein expression had no association with either DFS or OS (p > 0.05). CONCLUSION: Tau protein expression can predict pCR before NACT in TNBC, but there was no correlation between Tau expression and DFS or OS.

Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy.

PURPOSE: To evaluate the value of multiparametric magnetic resonance imaging (MRI) in pretreatment prediction of breast cancers insensitive to neoadjuvant chemotherapy (NAC). METHODS: A total of 125 breast cancer patients (63 in the primary cohort and 62 in the validation cohort) who underwent MRI before receiving NAC were enrolled. All patients received surgical resection, and Miller-Payne grading system was applied to assess the response to NAC. Grade 1-2 cases were classified as insensitive to NAC. We extracted 1941 features in the primary cohort. After feature selection, the optimal feature set was used to construct a radiomic signature using machine learning. We built a combined prediction model incorporating the radiomic signature and independent clinical risk factors selected by multivariable logistic regression. The performance of the combined model was assessed with the results of independent validation. RESULTS: Four features were selected for the construction of the radiomic signature based on the primary cohort. Combining with independent clinical factors, the combined prediction model for identifying the Grade 1-2 group reached a better discrimination power than the radiomic signature, with an area under the receiver operating characteristic curve of 0.935 (95% confidence interval 0.848-1) in the validation cohort, and its clinical utility was confirmed by the decision curve analysis. CONCLUSION: The combined model based on radiomics and clinical variables has potential in predicting drug-insensitive breast cancers.

Comparison of Overall Survival Between Invasive Lobular Breast Carcinoma and Invasive Ductal Breast Carcinoma: A Propensity Score Matching Study Based on SEER Database.

OBJECTIVE: Invasive lobular carcinoma (ILC) and invasive ductal carcinoma (IDC) account for most breast cancers. However, the overall survival (OS) differences between ILC and IDC remain controversial. This study aimed to compare nonmetastatic ILC to IDC in terms of survival and prognostic factors for ILC. METHODS: This retrospective cohort study used data from the Surveillance, Epidemiology and End Results (SEER) Cancer Database (www.seer.cancer.gov). Women diagnosed with nonmetastatic ILC and IDC between 2006 and 2016 were included. A propensity score matching (PSM) method was used in our analysis to reduce baseline differences in clinicopathological characteristics and survival outcomes. Kaplan-Meier curves and log-rank test were used for survival analysis. RESULTS: Compared to IDC patients, ILC patients were diagnosed later in life with poorly differentiated and larger lesions, as well as increased expression of estrogen receptors (ERs) and/or progesterone receptors (PRs). A lower rate of radiation therapy and chemotherapy was observed in ILC. After PSM, ILC, and IDC patients exhibited similar OS (HR=1.017, p=0.409, 95% CI: 0.967-1.069). In subgroup analysis of HR-negative, AJCC stage III, N2/N3 stage patients, or those who received radiotherapy, ILC patients exhibited worse OS compared to IDC patients. Furthermore, multivariate analysis revealed a 47% survival benefit for IDC compared to ILC in HR-negative patients who received chemotherapy (HR=1.47, p=0.01, 95% CI: 1.09-1.97). CONCLUSIONS: Our results demonstrated that ILC and IDC patients had similar OS after PSM. However, ILC patients with high risk indicators had worse OS compared to IDC patients by subgroup analysis.

Mammography-based radiomic analysis for predicting benign BI-RADS category 4 calcifications.

PURPOSE: We developed and validated a radiomic model based on mammography and assessed its value for predicting the pathological diagnosis of Breast Imaging Reporting and Data System (BI-RADS) category 4 calcifications. MATERIALS AND METHODS: Patients with a total of 212 eligible calcifications were recruited (159 cases in the primary cohort and 53 cases in the validation cohort). In total, 8286 radiomic features were extracted from the craniocaudal (CC) and mediolateral oblique (MLO) images. Machine learning was used to select features and build a radiomic signature. The clinical risk factors were selected from the independent clinical factors through logistic regression analyses. The radiomic nomogram incorporated the radiomic signature and an independent clinical risk factor. The diagnostic performance of the radiomic model and the radiologists' empirical prediction model was evaluated by the area under the receiver operating characteristic curve (AUC). The differences between the various AUCs were compared with DeLong's test. RESULTS: Six radiomic features and the menopausal state were included in the radiomic nomogram, which discriminated benign calcifications from malignant calcifications with an AUC of 0.80 in the validation cohort. The difference between the classification results of the radiomic nomogram and that of radiologists was significant (p < 0.05). Particularly for patients with calcifications that are negative on ultrasounds but can be detected by mammography (MG+/US- calcifications), the identification ability of the radiomic nomogram was very strong. CONCLUSIONS: The mammography-based radiomic nomogram is a potential tool to distinguish benign calcifications from malignant calcifications.