Proteogenomics is an emerging field that combines data from proteomics (the study of proteins) with genomics and transcriptomics (the study of genes and gene expression) to get a more complete picture of how cancer develops and behaves.
Mass spectrometry (MS) is the central technology in this field. It can precisely identify and measure proteins in biological samples, including rare or mutated versions of proteins that would otherwise be missed.
The goal is to bridge the gap between genetic mutations and the proteins those mutations produce, since it is ultimately proteins that carry out biological functions in cells and drive cancer progression.
Most cancers are defined by high mutation rates -- genetic changes that alter the proteins produced by cancer cells. These mutant proteins can drive tumor growth and are often unique to cancer cells, making them attractive targets for therapy.
While thousands of genetic mutations have been catalogued, the actual mutant proteins (proteoforms) they produce are much less studied. Standard proteomics mostly measures normal, consensus proteins rather than the altered versions found in tumors.
There are estimated to be more than 3 million non-synonymous single nucleotide polymorphisms (nsSNPs) in the human genome, meaning a huge number of single amino acid variants exist across individuals -- a largely unexplored layer of cancer biology.
Understanding which mutant proteins are present in a patient's tumor can reveal driver mutations, predict disease behavior, and guide the selection of targeted treatments.
Data-independent acquisition (DIA) is a high-throughput MS method that systematically measures all detectable proteins in a sample simultaneously, enabling comprehensive and reproducible protein identification.
Software tools such as DIA-NN, which uses neural networks, allow fast and reliable identification of proteins from complex samples. This has dramatically improved the speed and depth of proteomics studies.
Label-free ion intensity-based quantitative proteomics can measure the relative abundance of thousands of proteins across multiple samples without the need for chemical labeling, making large clinical studies more practical.
Specialized techniques like MALDI-TOF-MS (matrix-assisted laser desorption/ionization time-of-flight MS) can detect very large biomolecules, useful for identifying unusual protein variants such as the M-proteins found in blood cancers.
One study in this research collection used DIA-MS serum proteomic profiling to compare patients with prostate cancer (PCa) and benign prostatic hyperplasia (BPH) -- a non-cancerous enlargement of the prostate that can mimic cancer symptoms.
The current standard diagnostic marker, PSA (prostate-specific antigen), has well-known limitations in distinguishing aggressive cancer from non-aggressive disease. This work sought to find better biomarkers.
Combining PSA with two additional serum proteins -- osteopontin (SPP1) and ceruloplasmin (CP) -- significantly improved the ability to distinguish high Gleason score (aggressive) cancer from low-grade disease compared to PSA alone.
The Gleason score is a standard pathology grading system for prostate cancer. Higher scores indicate more aggressive disease and worse prognosis, making this kind of biomarker discrimination clinically valuable.
A lipidomics study using UHPLC-HRMS/MS found 85 significantly different lipid species in the blood of patients with colorectal adenoma (pre-cancerous polyps) versus colorectal carcinoma (full cancer). Seven lipid species were proposed as potential early detection biomarkers.
This finding points to dysregulated lipid metabolism -- particularly in fatty acids, phosphatidylcholines, and triacylglycerols -- as a key process during the transformation from pre-cancerous to cancerous tissue in the colon.
For lung cancer, a study of solid-predominant lung adenocarcinoma (SPA) -- a particularly aggressive subtype -- used laser-microdissection of tumor tissue followed by MS proteomics to identify co-expression protein networks specific to this subtype.
The proteomics data implicated several immune regulators, including the redox regulator NFE2L2 and the immune checkpoint molecule HLA-G, suggesting that SPA tumors may respond better to immunotherapy than to standard chemotherapy or targeted therapy.
A recurring theme across all studies highlighted in this editorial is the potential to move beyond single-marker diagnostics (like PSA alone) toward multi-protein or multi-omic panels that are more accurate and informative.
The ability to detect M-proteins in blood diseases like multiple myeloma and related conditions using MS -- without complex immune-enrichment steps -- could simplify and improve monitoring of patients during and after treatment.
Identifying the specific EGFR driver mutation subtype in lung cancer patients (such as Ex19del vs. L858R) using proteomic profiling could help oncologists personalize treatment selection, since different mutations may respond differently to targeted drugs.
Overall, MS-based proteogenomics offers a path toward more precise, personalized cancer care by revealing the functional protein landscape of each patient's tumor, going beyond what genomics alone can tell us.
This editorial summarizes a growing body of work demonstrating that MS-based proteogenomics is maturing into a clinically relevant discipline -- not just a research tool -- with tangible applications in biomarker discovery and treatment strategy development.
The integration of genomic mutation data with protein-level measurements enables researchers to identify functionally relevant cancer alterations that genomics alone would miss, since not all mutations produce detectable or significant changes at the protein level.
Future advances in both instrumentation and bioinformatics software will be critical. As data acquisition becomes faster and more comprehensive, the challenge shifts to accurately interpreting complex datasets -- a key ongoing area of development.
The field is also moving toward studying not just which proteins are present, but how they interact in protein-protein networks and how those networks change in cancer -- a systems-biology approach that could reveal entirely new therapeutic targets.