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

Abstract

Breast cancer (BCa) is one of the most common malignancies among women worldwide. It is a complex disease that is characterized by morphological and molecular heterogeneity. In the early stages of the disease, most BCa cases are treatable, particularly hormone receptor-positive and HER2-positive tumors. Unfortunately, triple-negative BCa and metastases to distant organs are largely untreatable with current medical interventions. Recent advances in sequencing and proteomic technologies have improved our understanding of the molecular changes that occur during breast cancer initiation and progression. In this era of precision medicine, researchers and clinicians aim to identify subclass-specific BCa biomarkers and develop new targets and drugs to guide treatment. Although vast amounts of omics data including single cell sequencing data, can be accessed through public repositories, there is a lack of user-friendly platforms that integrate information from multiple studies. Thus, to meet the need for a simple yet effective and integrative BCa tool for multi-omics data analysis and visualization, 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 was developed to provide a unique resource for hypothesis generation and testing, as well as for the discovery of biomarkers and therapeutic targets. The platform also provides pre- and post-treatment data, which can help users identify treatment resistance markers and patient groups that may benefit from combination therapy.

Figure 1. Graphical Abstract.

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

Figure 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 expressionpage. The Heatmap shows the top over-expressed and under-expressed genes in the SCAN-B dataset, comparing non-TNBC and TNBC tumors.

Figure 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 PSAT1in 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.

Figure 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 ARID5Bacross various T cell clusters from Azizi et al. (2018) 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.

Figure 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.

Figure 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 STK11in MCF7 cell line is shown as an example here.