Comparison of adverse effects of anti-tumor therapy for breast cancer shortly after COVID-19 diagnosis vs. the control period

BackgroundCOVID-19 is an acute infectious disease caused by SARS-CoV-2. The best time to restart antitumor therapy in breast cancer patients after SARS-CoV-2 infection is unknown. This study aimed to evaluate treatment-related adverse events in breast cancer patients who received antitumor therapies within a short time after SARS-CoV-2 infection (observation) as well as before (control) and to provide safety data.MethodsWe conducted a self-controlled cohort study using the data from the Breast Disease Center of Peking University First Hospital. We identified patients who received antitumor therapy within 28 days after COVID-19 infection between December 20, 2022, and January 20, 2023. The primary outcome was treatment-related adverse events. McNemar’s test was used to compare the incidence rate of adverse reactions between periods.ResultsWe identified 183 patients with breast cancer, of whom 109 were infected with SARS-CoV-2 within 28 days before antitumor treatment and were included. In total, 28 patients (25.7%) received neoadjuvant therapy, 60 (55.0%) received adjuvant therapy, and 21 (19.3%) received advanced rescue therapy. None of patients required hospitalization for severe or critical COVID-19, but 15 patients (13.8%) still had sequelae of COVID-19 while receiving antitumor treatment. The most common adverse events were peripheral neuropathy (n = 32 [29.4%]), pain (n = 29 [26.6%]), fatigue (n = 28 [25.7%]), nausea (n = 23 [21.1%]), and neutropenia (n = 19 [17.4%]). There was no increased risk of overall treatment-related adverse events (n = 87 [79.8%] vs. n = 91 [83.5%]; p = 0.42) or serious adverse events (n = 13 [11.9%] vs. n = 12 [11.0%]; p = 1.00) from receiving antitumor therapy shortly after the diagnosis of COVID-19. We also found no increased risk in subgroup analyses, and no patients discontinued antitumor therapy due to adverse events.ConclusionRestarting antitumor therapy 2-4 weeks after having mild or moderate COVID-19 is a relatively safe strategy for breast cancer patients that does not increase the risk of treatment-related adverse events

The clinicopathological factors associated with disease progression in Luminal a breast cancer and characteristics of metastasis: A retrospective study from a single center in China

Background/Aim: This study investigated the clinicopathological factors associated with outcomes in patients with Luminal A breast cancer. Patients and Methods: Retrospective analysis of the association of clinicopathological factors and breast cancer outcome in 421 patients with newly diagnosed Luminal-A breast cancer that were enrolled from January 2008 to December 2014. Clinicopathological data were analyzed to validate the relationship with disease free survival (DFS) and overall survival (OS). Kaplan-Meier curves and log-rank tests were used to analyze the value of clinicopathological factors (tumor size, node status and lymphovascular invasion), and subsequent Cox regression analysis revealed significant prognostic factors. Results: With a median of 61 months follow up, the 5-year DFS and 5-year OS rate were 98.3% and 99.3%. Cox multivariate regression analysis showed that clinical anatomic stage, tumor size, status of lymph nodes, lymphovascular invasion and systemic treatment are strong prognostic factors for clinical outcome in patients with Luminal-A breast cancer. Of all 413 patients with stage I-III breast cancer, 14 presented with metastasis (3.4%) during the follow up. Bone (6/14, 42.9%) was the most common site of metastasis followed by liver (5/14, 35.7%) and lung (4/14, 28.6%). The median survival time after metastasis was 20.4 months. Of all the sites of distant metastasis, liver metastasis was the only factor that affected survival time after metastasis (χ2=6.263, p=0.012). Conclusion: Patients with Luminal A breast cancer have excellent outcomes. Liver metastasis is an important factor compressing the survival time after distant metastasis presents

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Integrative analysis identifies cancer cell-intrinsic RARRES1 as a predictor of prognosis and immune response in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with poor prognosis and limited treatment options. Although immune checkpoint inhibitors (ICIs) have been proven to improve outcomes in TNBC patients, the potential mechanisms and markers that determine the therapeutic response to ICIs remains uncertain. Revealing the relationship and interaction between cancer cells and tumor microenvironment (TME) could be helpful in predicting treatment efficacy and developing novel therapeutic agents. By analyzing single-cell RNA sequencing dataset, we comprehensively profiled cell types and subpopulations as well as identified their signatures in the TME of TNBC. We also proposed a method for quantitatively assessment of the TME immune profile and provided a framework for identifying cancer cell-intrinsic features associated with TME through integrated analysis. Using integrative analyses, RARRES1 was identified as a TME-associated gene, whose expression was positively correlated with prognosis and response to ICIs in TNBC. In conclusion, this study characterized the heterogeneity of cellular components in TME of TNBC patients, and brought new insights into the relationship between cancer cells and TME. In addition, RARRES1 was identified as a potential predictor of prognosis and response to ICIs in TNBC

Androgen Receptor: A New Marker to Predict Pathological Complete Response in HER2-Positive Breast Cancer Patients Treated with Trastuzumab Plus Pertuzumab Neoadjuvant Therapy

(1) Background: Neoadjuvant therapy is the main therapeutic strategy for human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients, and the combination of trastuzumab and pertuzumab (HP) has become a routine treatment. How to predict and screen patients who are less likely to respond to neoadjuvant therapy is the focus of research. The androgen receptor (AR) is a biomarker that is widely expressed in all breast cancer subtypes and is probably related to treatment response and prognosis. In this study, we investigated the relationship between AR expression and treatment response in HER2-positive breast cancer patients treated with HP neoadjuvant therapy. (2) Methods: We evaluated early breast cancer patients treated with HP neoadjuvant therapy from Jan. 2019 to Oct. 2020 at Peking University First Hospital Breast Cancer Center. The inclusion criteria were as follows: early HER2-positive breast cancer patients diagnosed by core needle biopsy who underwent both HP neoadjuvant therapy and surgery. We compared the clinical and pathological features between pathological complete response (pCR) and non-pCR patients. (3) Results: We included 44 patients. A total of 90.9% of patients received neoadjuvant therapy of taxanes, carboplatin, trastuzumab and pertuzumab (TCHP), and the total pCR rate was 50%. pCR was negatively related to estrogen receptor (ER) positivity (OR 0.075 [95% confidence interval (CI) 0.008–0.678], p = 0.021) and positively related to high expression levels of AR (OR 33.145 [95% CI 2.803–391.900], p = 0.005). We drew a receiver operating characteristic (ROC) curve to assess the predictive value of AR expression for pCR, and the area under the curve was 0.737 (95% CI 0.585–0.889, p = 0.007). The optimal cutoff of AR for predicting pCR was 85%. (4) Conclusion: AR is a potential marker for the prediction of pCR in HER2-positive breast cancer patients treated with HP neoadjuvant therapy

Correlation between Androgen Receptor Expression in Luminal B (HER–2 Negative) Breast Cancer and Disease Outcomes

(1) Background: Hormone receptor positive breast cancer is a subtype of breast cancer with relatively good prognosis, but luminal B (HER–2 negative) breast cancer has a higher risk of recurrence and metastasis. Patients with endocrine therapy resistance and chemotherapy insensitivity have poor prognosis. Androgen receptor (AR) is widely expressed in breast cancer, but there is no clear conclusion about its function and correlation with prognosis in luminal B breast cancer. Further research is needed to reveal the role of AR in luminal B (HER–2 negative) breast cancer. (2) Methods: Retrospectively analyzed patients with early–stage luminal B breast cancer. The correlation between AR and its associated indexes with long–term survival was determined. (3) Results: A total of 985 patients were included with 143 treated by neoadjuvant therapy. Of these, 83.5% of the patients had AR expression ≥65%. High AR expression was associated with good disease–free survival (DFS) and overall survival (OS). In the neoadjuvant population, AR/estrogen receptor (ER) > 1.06 and residual tumor Ki67 > 23% had significantly worse DFS. (4) Conclusion: Low AR ( 1.06 and residual tumor Ki67 > 23%

Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)