Researchers Add 11 Molecular Subtypes to Online Cancer Database

Researchers in Alabama have conducted proteomic analysis on 2,002 primary tumors from 14 tissue-based cancer types and identified 11 new and distinct molecular subtypes. The findings provide systematic knowledge that greatly expands University of Alabama at Birmingham Cancer Data analysis portal (UALCAN), a searchable online database that has become a go-to platform for cancer data analysis by users worldwide.

UALCAN was developed and released for public use in 2017 as a user-friendly portal for pan-cancer omics data analysis, including transcriptomics, epigenetics and proteomics. UALCAN has had nearly 920,000 site visits from researchers in more than 100 countries, and it has been cited more than 2,750 times.

“UALCAN is an effort to distribute comprehensive cancer data to researchers and clinicians in a user-friendly format to make discoveries and find needles in the haystack,” says Sooryanarayana Varambally, Ph.D., professor in the University of Alabama at Birmingham (UAB) Department of Pathology Division of Molecular and Cellular Pathology and director of UAB’s Translational Oncologic Pathology Research program. “Cancer detection, diagnosis, treatment, cure and research need a global team effort, and making sense of the huge amount of data involved needs a way to analyze and interpret these data.”

The new study, published recently in Nature Communications, extends two early proteomics studies published in 2019 and 2021. Previously the team performed RNA transcripts analysis, providing the data to researchers through UALCAN, to determine which pathways the myriad forms of cancer use to aid growth, spread and aggressiveness. With this recent study, the team performed and incorporated large-scale proteomics analysis. The data and results provide new ideas for further research and possible therapeutic interventions.

The cancer types represented in the UALCAN proteomic dataset include breast, colorectal, gastric, glioblastoma, head and neck, liver, lung adenocarcinoma, lung squamous, ovarian, pancreatic, pediatric brain, prostate, renal, and uterine cancers. The number of tumors in each cancer type in the study ranged from 76 to 230, with an average of 143. Intriguingly, the pan-cancer, proteome-based subtypes the current study found cut across tumor lineages.

In general, the researchers found the protein expression of genes across tumors broadly correlated with corresponding mRNA levels or copy number alterations. However, there were some notable exceptions. They identified 11 distinct proteome-based pan-cancer subtypes—named s1 through s11—that can provide insights into the deregulated pathways and processes in tumors that make them cancerous. Each subtype spanned multiple tissue-based cancer types, though subtype s11 was specific to brain tumors, spanning glioblastomas and pediatric brain tumors.

Each subtype expressed specific gene categories, some seen before in a previous, less comprehensive proteomic study. Three subtypes showed new gene categories: subtype s7 with “axon guidance” and “frizzled binding” genes, subtype s10 with “DNA repair” and “chromatin organization” genes, and subtype s11 with “synapse,” “dendrite” and “axon” genes.

At the DNA level, the study detailed differences among the proteome-based subtypes in overall copy number alterations of genes, and somatic mutations in subtypes associated with higher pathway activity, as inferred by proteome or transcriptome data.

“Our study results provide a framework for understanding the molecular landscape of cancers at the proteome level to integrate and compare the data with other molecular correlates of cancers,” Varambally said. “The associated datasets and gene-level associations represent a resource for the research community, including helping to identify gene candidates for functional studies and further develop candidates as diagnostic markers or therapeutic targets for specific subset of cancers.”

The study also reinforces that cancers should be comprehensively surveyed at the protein level, though expression profiling on tumors has historically been mostly limited to the RNA transcript level, Varambally adds. “Many of the analyses in this ever-evolving cancer data analysis platform are based on user or expert requests, and the team is indebted to the support and encouragement from the researchers who use this platform to make discoveries that make a difference in cancer research.”