A new study that analyzed protein levels in 2,002 primary tumors from 14 tissue cancer types identified 11 distinct molecular subtypes, providing systematic insights that greatly expand an online searchable database that has become a platform go-to form for cancer data analysis by users around the world.
The University of Alabama at Birmingham Cancer Data Analysis Portal, or UALCAN, was developed and made available to the public in 2017 as a user-friendly portal for pan-cancer data analysis , including transcriptomics, epigenetics and proteomics. UALCAN has received 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,” said Sooryanarayana Varambally, Ph.D., professor in the Department of Pathology from the UAB, Division of Molecular Biology. and Cellular Pathology and Director of the Translational Research Program in Oncological Pathology at UAB. “Detecting, diagnosing, treating, curing and researching cancer requires a global team effort, and making sense of the enormous amount of data involved requires a way to analyze and interpret that data. “
Cancer is a complex disease, and its initiation, progression and metastasis, spread to distant organs, involve dynamic molecular changes in each type of cancer. Individual cancer patients exhibit variations outside of some of the common genomic events.
In the new study, Varambaly worked with longtime collaborator Chad Creighton, Ph.D., Baylor College of Medicine, Houston, Texas. Creighton led the proteomics study, published in Nature Communication, “2002 Proteogenomic Characterization of Human Cancers Reveals Pancancer Molecular Subtypes and Associated Pathways.” This extends two first proteomics studies published in 2019 and 2021.
Previously, the team performed an analysis of RNA transcripts, providing the data to researchers via UALCAN, to determine the pathways used by the myriad forms of cancer to promote growth, spread and aggressiveness. With this recent study, the team performed and incorporated large-scale proteomic analysis. The data and findings provide new insights for further research and possible therapeutic interventions.
A proteome is the complement of proteins expressed in a cell or tissue, and these can be measured quantitatively thanks to recent technological advances in mass spectrometry. In cells, DNA makes mRNA and mRNA makes proteins, processes known as the central dogma of molecular biology. Proteins are major functional fractions of cells, essential for cell metabolism, structure, growth, signaling and movement.
Cancer types represented in the UALCAN proteomics dataset include breast, colorectal, gastric, glioblastoma, head and neck, liver, lung adenocarcinoma, squamous cell lung, ovarian , pancreas, pediatric brain, prostate, kidney and uterus. The number of tumors in each cancer type in the study ranged from 76 to 230, with an average of 143. Curiously, the proteome-based pancancer subtypes that the current study found cross tumor lineages.
The compendium proteomics dataset came from 17 individual studies. Corresponding multi-omics data were available for most of these tumors, including mRNA levels, small somatic DNA mutations and insertions/deletions, and somatic DNA copy number alterations.
In general, the researchers found that protein expression of genes in tumors was largely correlated with corresponding mRNA levels or copy number changes. However, there were a few notable exceptions.
They identified 11 distinct proteome-based pan-cancer subtypes – named s1 to s11 – that may provide insight into the dysregulated pathways and processes in tumors that make them cancerous. Each subtype covered several types of tissue cancers, although the s11 subtype was brain tumor specific, covering glioblastomas and pediatric brain tumors.
Each subtype expressed specific gene categories, some already seen in a previous less comprehensive proteomics study. Three subtypes showed new categories of genes: the s7 subtype with the “axon guidance” and “frizzled binding” genes, the s10 subtype with the “DNA repair” and “structure organization” genes. chromatin”, and the s11 subtype with the “synapse”, “dendrite” and “axon” genes.
At the DNA level, the study detailed differences between proteome-based subtypes in overall gene copy number alterations and somatic mutations in subtypes associated with higher pathway activity. , as inferred by proteome or transcriptome data.
“The results of our study provide a framework for understanding the molecular landscape of cancers at the proteome level in order to integrate and compare the data with other molecular correlates of cancers,” Varambaly said. “The associated datasets and gene-level associations represent a resource for the research community, including helping to identify candidate genes for functional studies and to further develop the candidates as diagnostic markers or therapeutic targets for a specific subset of cancers.
“Furthermore, this study reinforces the notion that cancers should be studied extensively at the protein level, although expression profiling on tumors has historically been primarily limited to the level of RNA transcription. Many analyzes of this ever-evolving cancer data analysis platform are based at the request of users or experts, and the team is indebted to the support and encouragement of researchers who use this platform to make discoveries that make a difference in cancer research.”
Some of the large datasets for the UAB site are generated by consortia such as The Cancer Genome Atlas, or TCGA, and the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium, or CPTAC.
Accurate targeting of cancer requires the identification of individual or subclass-specific genomic and molecular alterations. To help cancer researchers perform various data analyzes to better understand these large datasets, Darshan Shimoga Chandrashekar, Ph.D., led the development of the UALCAN portal under the mentorship of Varambally. Updates to this ever-evolving portal have recently been published in Neoplasia.
The UALCAN initiative and its continued development involves the contribution of a team of experts including bioinformaticians, computer scientists, statisticians, cancer biologists, pathologists and oncologists. “This is a team science approach to empower the global cancer research team to fight cancer,” Varambaly said.
Support came from National Institutes of Health grants CA125123 and CA118948 and United States Department of Defense grant W81XWH-19-1-0588.
The co-first authors of this study are Yiqun Zhang and Fengju Chen, Baylor College of Medicine, and Chandrashekar, UAB Department of Pathology Division of Molecular and Cellular Pathology.
Pathology is a department of the Marnix E. Heersink School of Medicine at UAB. Varambaly is a Principal Investigator at the O’Neal Comprehensive Cancer Center and the Institute of Informatics at UAB and Co-Lead of the Cancer Biology Theme of Graduate Biomedical Sciences at UAB. He holds an adjunct position at the Michigan Center for Translational Pathology, University of Michigan, Ann Arbor.
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