MammOnc-DB, an integrative breast cancer data analysis platform for target discovery

Abstract

Breast cancer (BCa), a leading malignancy among women, is characterized by morphological and molecular heterogeneity. While early-stage, hormone receptor, and HER2-positive BCa are treatable, triple-negative BCa and metastatic BCa remains largely untreatable. Advances in sequencing and proteomic technologies have improved our understanding of the molecular alterations that occur during BCa initiation and progression and enabled identification of subclass-specific biomarkers and therapeutic targets. Despite the availability of abundant omics data in public repositories, user-friendly tools for multi-omics data analysis and integration are scarce. To address this, we developed a comprehensive BCa data analysis platform called MammOnc-DB ( http://resource.path.uab.edu/MammOnc-Home.html ), comprising data from more than 20,000 BCa samples. MammOnc-DB facilitates hypothesis generation and testing, biomarker discovery, and therapeutic targets identification. The platform also includes pre- and post-treatment data, which can help users identify treatment resistance markers and support combination therapy strategies, offering researchers and clinicians a comprehensive tool for BCa data analysis and visualization.

Fig. 1. Graphical abstract.

MammOnc-DB, a web-based proteo-genomics platform for analysis and visualization of multi-omics breast cancer data.

Fig. 2. An overview of gene expression analysis.

A Users can switch between RNA-seq and scRNA-seq data. In Panel 1, users can access a compilation of studies, along with relevant clinical characteristics, allowing for the examination of over-expressed and under-expressed genes. Panel 2 allows users to assess the expression of genes of interest across various studies. B Heatmap generated from Panel 1 of the gene expression page. The Heatmap shows the top over-expressed and under-expressed genes in the SCAN-B dataset, comparing non-TNBC and TNBC tumors.

Fig. 3. Overview of gene exploration across various studies.

A Users can explore genes of interest by entering their names into the text box and selecting from available studies. Upon submission, users are redirected to an intermediate page listing links to analyze expression and survival associations. B Box-whisker and jitter plots illustrating PSAT1 expression in subgroups of the METABRIC study, including ER Status, PR Status, and PAM50 and Claudin subtypes, and lists additional available classifications. C Bar plot depicting the gene effect score of PSAT1 in multiple breast cancer cells using data from DepMap. D Kaplan–Meier plots showing the association between PSAT1 expression and patient survival in the METABRIC dataset.

Fig. 4. Illustration of the single cell RNA-seq data analysis functionalities.

A Users can input a gene of interest and select from available studies. B Expression of ARID5B across various T cell clusters from Azizi et al. study. The expression is visualized using UMAP, violin plot, and ridge plot, providing insights into the gene’s expression patterns in distinct T cell clusters.

Fig. 5. Protein expression analysis in MammOnc-DB.

A Users can input a gene of interest and perform various analysis from the available studies. B Expression pattern of TK1 total and phospho-protein are shown as an example in various clinical features available in CPTAC.

Fig. 6. Gene regulation analysis functionalities.

A Option for selecting gene of interest to investigate its regulation from studies available in MammOnc-DB. B IGV plot showing ER ligand binding in the region of STK11 in MCF7 cell line is shown as an example here.