Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach

Survival Outcomes and Efficacy of Platinum in Early Breast Cancer Patients with Germline BRCA1 or BRCA2 Mutation: A Multicenter Retrospective Cohort Study

Purpose

The study aimed to compare the survival outcomes and efficacy of platinum in early breast cancer patients with BRCA1 and BRCA2 mutations.

Methods

Patients diagnosed with stage I-III breast cancer and carrying germline pathogenic/likely pathogenic BRCA mutations in three medical institutions in China from April 2016 to January 2021 were retrospectively analyzed. Data on clinical and pathological characteristics, treatment information, pathogenic variants of BRCA, and survival outcomes were collected for all eligible patients.

Outcomes

One hundred and sixty-nine patients with BRCA mutations were enrolled, including BRCA1 mutation (53.3%, n = 90) and BRCA2 mutation (46.7%, n = 79). The median age was 39 years, and most patients (68.1%, n = 115) were stage I-II. Patients with BRCA1 mutations were characterized by histological grade III (55.6%) and higher Ki-67 index (Ki-67 ≥ 30%, 78.9%) compared with patients with BRCA2 mutations (27.8%, 58.2%). BRCA1 mutation patients accounted for a significantly higher proportion of triple negative breast cancer than BRCA2 mutation patients (71.1% vs 19.0%, P < 0.0001). A total of 142 (84.0%) patients received neo/adjuvant chemotherapy, including anthracycline and/or taxane-based regimens (55.6%) or platinum-based regimens (27.2%). Median follow-up was 33.2 months. Three-year DFS (disease-free survival) and DRFS (distant recurrence-free survival) had no significant differences between patients with BRCA1 and BRCA2 mutations (82.0% vs 85.4%, P = 0.35; 94.3% vs 94.6%, P = 0.39). The 3-year DFS rate in BRCA1 mutation cohort of patients received platinum regimen was significantly higher than patients received non-platinum regimen (96.0% vs 75.2%, P = 0.01). No differences between DFS and DRFS were observed in patients with BRCA2 mutation received platinum regimen and non-platinum regimen.

Conclusion

Similar survival outcomes were observed in early breast cancer patients with BRCA1 and BRCA2 mutation, though they had different biological characteristics. Patients with BRCA1 mutations are more benefit from platinum-regimen. The value of platinum-regimen for early breast cancer patients with BRCA1 and BRCA2 needs to be verified further.

MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model

BACKGROUND

Accurately classifying complex diseases is crucial for diagnosis and personalized treatment. Integrating multi-omics data has been demonstrated to enhance the accuracy of analyzing and classifying complex diseases. This can be attributed to the highly correlated nature of the data with various diseases, as well as the comprehensive and complementary information it provides. However, integrating multi-omics data for complex diseases is challenged by data characteristics such as high imbalance, scale variation, heterogeneity, and noise interference. These challenges further emphasize the importance of developing effective methods for multi-omics data integration.

RESULTS

We proposed a novel multi-omics data learning model called MODILM, which integrates multiple omics data to improve the classification accuracy of complex diseases by obtaining more significant and complementary information from different single-omics data. Our approach includes four key steps: 1) constructing a similarity network for each omics data using the cosine similarity measure, 2) leveraging Graph Attention Networks to learn sample-specific and intra-association features from similarity networks for single-omics data, 3) using Multilayer Perceptron networks to map learned features to a new feature space, thereby strengthening and extracting high-level omics-specific features, and 4) fusing these high-level features using a View Correlation Discovery Network to learn cross-omics features in the label space, which results in unique class-level distinctiveness for complex diseases. To demonstrate the effectiveness of MODILM, we conducted experiments on six benchmark datasets consisting of miRNA expression, mRNA, and DNA methylation data. Our results show that MODILM outperforms state-of-the-art methods, effectively improving the accuracy of complex disease classification.

CONCLUSIONS

Our MODILM provides a more competitive way to extract and integrate important and complementary information from multiple omics data, providing a very promising tool for supporting decision-making for clinical diagnosis.

Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study

The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.

Detection of pan-cancer surface protein biomarkers via a network-based approach on transcriptomics data

Cell surface proteins have been used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. Many of these proteins lie at the top of signaling cascades regulating cell responses and gene expression, therefore acting as ‘signaling hubs’. It has been previously demonstrated that the integrated network analysis on transcriptomic data is able to infer cell surface protein activity in breast cancer. Such an approach has been implemented in a publicly available method called ‘SURFACER’. SURFACER implements a network-based analysis of transcriptomic data focusing on the overall activity of curated surface proteins, with the final aim to identify those proteins driving major phenotypic changes at a network level, named surface signaling hubs. Here, we show the ability of SURFACER to discover relevant knowledge within and across cancer datasets. We also show how different cancers can be stratified in surface-activity-specific groups. Our strategy may identify cancer-wide markers to design targeted therapies and biomarker-based diagnostic approaches.

A Signature of Three Apoptosis-Related Genes Predicts Overall Survival in Breast Cancer

Background

The commonest malignancy in women is known as breast cancer (BC). Numerous studies demonstrated that apoptosis appears to be critical to the management and clinical outcome of BC patients. The purpose of this study is to explore the potential connection between apoptosis and BC and establish the apoptosis-associated gene signature in BC.

Methods

The data of BC patient transcripts and related clinical information comes from the Cancer Genome Atlas Database (TCGA), and the genes related to apoptosis come from the Molecular Characterization Database (MSigDB). We identified the abnormally expressed apoptosis-related genes in BC samples. The optimal apoptosis-related genes screened by Cox regression analysis were designed to construct a prognostic model for predicting BC patients. Using the Nom Chart to Predict 1-Year, 3-Year, and 5-Year overall survival for BC patients. The gene signature-related functional pathways were explored by gene set enrichment analysis (GSEA).

Results

Three genes [alpha subunit of the interleukin 3 receptor (IL3RA), apoptosis-inducing factor mitochondrial-associated 1 (AIFM1), and phosphatidylinositol-3 kinase catalytic alpha (PIK3CA)] correlated with apoptosis were shown to be strongly linked to the overall survival of BC. Survival analysis shows that the risk score is directly proportional to the poor prognosis of BC patients. Risk assessment based on three genetic characteristics (age, pathological stage N, and pathological stage M) can independently predict the prognosis of patients with BC. The Nom chart is most suitable for assessing the long-term survival rate of BC patients. The results of GSEA demonstrated that numerous cell cycle-related pathways were abundant in the high-risk group.

Conclusion

We constructed an apoptosis-associated gene signature in BC, which had a potential clinical application prospect for BC patients.