Breast Cancer Diagnostics, Therapy, and Outcomes in Sub-Saharan Africa: A Population-Based Registry Study

Background: Breast cancer (BC) is the most common cancer in sub-Saharan Africa (SSA). However, little is known about the actual therapy received by women with BC and their survival outcome at the population level in SSA. This study aims to describe the cancer-directed therapy received by patients with BC at the population level in SSA, compare these results with the NCCN Harmonized Guidelines for SSA (NCCN Harmonized Guidelines), and evaluate the impact on survival. Methods: Random samples of patients with BC (≥40 patients per registry), diagnosed from 2009 through 2015, were drawn from 11 urban population–based cancer registries from 10 countries (Benin, Congo, Cote d’Ivoire, Ethiopia, Kenya, Mali, Mozambique, Namibia, Uganda, and Zimbabwe). Active methods were used to update the therapy and outcome data of diagnosed patients (“traced patients”). Excess hazards of death by therapy use were modeled in a relative survival context. Results: A total of 809 patients were included. Additional information was traced for 517 patients (63.8%), and this proportion varied by registry. One in 5 traced patients met the minimum diagnostic criteria (cancer stage and hormone receptor status known) for use of the NCCN Harmonized Guidelines. The hormone receptor status was unknown for 72.5% of patients. Of the traced patients with stage I–III BC (n=320), 50.9% received inadequate or no cancer-directed therapy. Access to therapy differed by registry area. Initiation of adequate therapy and early-stage diagnosis were the most important determinants of survival. Conclusions: Downstaging BC and improving access to diagnostics and care are necessary steps to increase guideline adherence and improve survival for women in SSA. It will also be important to strengthen health systems and facilities for data management in SSA to facilitate patient follow-up and disease surveillance.

Clinical and Genetic Predictive Models for the Prediction of Pathological Complete Response to Optimize the Effectiveness for Trastuzumab Based Chemotherapy

BackgroundTrastuzumab shows excellent benefits for HER2+ breast cancer patients, although 20% treated remain unresponsive. We conducted a retrospective cohort study to optimize neoadjuvant chemotherapy and trastuzumab treatment in HER2+ breast cancer patients.MethodsSix hundred patients were analyzed to identify clinical characteristics of those not achieving a pathological complete response (pCR) to develop a clinical predictive model. Available RNA sequence data was also reviewed to develop a genetic model for pCR.ResultsThe pCR rate was 39.8% and pCR was associated with superior disease free survival and overall survival. ER negativity and PR negativity, higher HER2 IHC scores, higher Ki-67, and trastuzumab use were associated with improved pCR. Weekly paclitaxel and carboplatin had the highest pCR rate (46.70%) and the anthracycline+taxanes regimen had the lowest rate (11.11%). Four published GEO datasets were analyzed and a 10-gene model and immune signature for pCR were developed. Non-pCR patients were ER+PR+ and had a lower immune signature and gene model score. Hormone receptor status and immune signatures were independent predictive factors of pCR.ConclusionHormone receptor status and a 10-gene model could predict pCR independently and may be applied for patient selection and drug effectiveness optimization.

LncRNA BCAR4 expression and predicts the clinical response to neoadjuvant chemotherapy in patients with locally advanced breast cancer

BACKGROUND: Neoadjuvant chemotherapy (NAC) is an important treatment for locally advanced breast cancer (LABC). However, there are no effective biomarkers to predict the efficacy. Therefore, there is an urgent need for new biomarkers to predict the response of LABC to NAC. LncRNA BCAR4 has been detected in a variety of malignant tumor tissues and used as a new biomarker for diagnosis and prognosis. However, LncRNA BCAR4 predicts the response of LABC to NAC is unclear. OBJECTIVE: Explore the predictive effect of LncRNA BCAR4 on the efficacy of NAC for LABC in three different evaluation systems. METHODS: First, the TCGA database was used to analyze the expression of LncRNA BCAR4 in 33 kinds of malignant tumors, and further explore its expression in breast cancer and its impact on the survival and prognosis of breast cancer. Furthermore, quantitative methods were used to measure the expression level of LncRNA BCAR4 in cancer tissues of 48 LABC patients, and the correlation between LncRNA BCAR4 and clinicopathological status and response to NAC under the evaluation system of 3, RECIST1.1, Miller-Payne (MP) score and whether it reaches pCR,was analyzed. RESULTS: TCGA data analysis found that LncRNA is highly expressed in a variety of malignant tumor tissues, including breast cancer. And relatively low expression, the shorter the overall survival time of high expression patients. The high expression of LncRNA BCAR4 is related to the size of the tumor, and there are differences in expression between stage I and other stages, but there is no obvious correlation with the positive lymph node and hormone receptor status. Among the three evaluation systems, only in the RECIST 1.1 evaluation system LncRNA BCAR4 has a predictive effect on NAC for LABC. The expression of LncRNA BCAR4 has no significant correlation with clinical stage, Ki-67% and hormone receptor status, and has no significant correlation with whether patients with locally advanced breast cancer obtain pCR during neoadjuvant chemotherapy. CONCLUSION: LncRNA BCAR4 is highly expressed in LABC tissues and may be an effective marker for predicting the efficacy of NAC for LABC.

Multiparametric Integrated 18F-FDG PET/MRI-Based Radiomics for Breast Cancer Phenotyping and Tumor Decoding

Background: This study investigated the performance of simultaneous 18F-FDG PET/MRI of the breast as a platform for comprehensive radiomics analysis for breast cancer subtype analysis, hormone receptor status, proliferation rate and lymphonodular and distant metastatic spread. Methods: One hundred and twenty-four patients underwent simultaneous 18F-FDG PET/MRI. Breast tumors were segmented and radiomic features were extracted utilizing CERR software following the IBSI guidelines. LASSO regression was employed to select the most important radiomics features prior to model development. Five-fold cross validation was then utilized alongside support vector machines, resulting in predictive models for various combinations of imaging data series. Results: The highest AUC and accuracy for differentiation between luminal A and B was achieved by all MR sequences (AUC 0.98; accuracy 97.3). The best results in AUC for prediction of hormone receptor status and proliferation rate were found based on all MR and PET data (ER AUC 0.87, PR AUC 0.88, Ki-67 AUC 0.997). PET provided the best determination of grading (AUC 0.71), while all MR and PET analyses yielded the best results for lymphonodular and distant metastatic spread (0.81 and 0.99, respectively). Conclusion: 18F-FDG PET/MRI enables comprehensive high-quality radiomics analysis for breast cancer phenotyping and tumor decoding, utilizing the perks of simultaneously acquired morphologic, functional and metabolic data.