Digging Deeper
into Cancer withProteogenomics
A growing collection of techniques for combining
genomic and proteomic data reveals ever more about
the molecular biology of cancer, and that knowledge
will lead to even more advanced treatments
by Mike MayD

ata drives many modern advances in understanding andtreating cancer. In particular, proteogenomics—the combinationof data from the genomes and proteomes—promises ways tobetter understand the pathways that underlie cancer and how
to treat them.1 “Integrated proteogenomics has shown unequivocally that proteomics adds value over a genome-only approachto understanding tumor biology,” says Amanda Paulovich, MD,PhD, professor and Aven
Foundation endowed chair at
the Fred Hutchinson Cancer
Center in Seattle.

Amanda Paulovich, MD, PhD,professor and Aven Foundation endowed
chair, Fred Hutchinson Cancer Center

More than the sequences of
DNA and RNA are required toexplore cancer more deeply.
“Genomic sequences andcopy-number analyses, aswell as RNASeq data, are notreliable indicators of protein
expression levels and protein
activities or post-translational
modifications,” Paulovich
explains. “Many post-transcriptional processes impact
the proteome.”

As Paulovich notes, an article by Henry Rodriquez, PhD—founding
director of the office of cancer clinical proteomics research at theU.S. National Cancer Institute—and his colleagues depicts thetransition in precision oncology from a genome-centric
approach to the use of proteogenomics.2
The activity of proteins, as well as their post-translational
modifications, really come into play in therapies. “Since mostmodern therapies target proteins, not nucleic acids, it is imperative that we be able to monitor proteins directly, rather thanmake unreliable inferences from nucleic-acid profiles,” Paulovichsays. “Fortunately, tremendous advances in mass spectrometrybased proteomics, both untargeted and targeted, over the past
decade have enabled analytically robust analysis of the humanproteome, in some cases in clinical settings.”3
Advances in technology also make it easier to study more
cancer patients when needed. “The ability to conduct large-scalestudies, both in terms of sample throughput and target content,
is enabling researchers to describe phenotypes or signatures of aparticular disease state in a much greater level of detail than waspreviously possible and in much larger populations or cohorts
for greater statistical power,” says Fiona Kaper, PhD, vice president, advanced science assay research at Illumina in San Diego.
“This is due to: the availability of large sample collections, suchas population-scale biobanks; new technologies, such as the ones
offered by Olink and SomaLogic, that combine high target plexity
proteomics with high-throughput readouts, such as next-generation sequencing or microarrays; and the favorable economics ofavailable technologies to fund studies at scale.” As Kaper adds,6 DECEMBER 2022 • INSIDE PRECISION MEDICINE • WWW.INSIDEPRECISIONMEDICINE.COM“Computer power and analytical tools and methods that canhandle very large data sets andintegrate different ’omic datatypes into interpretable results
are key.”
Overcoming poor outcomes
Most, maybe all, scientists
who apply proteogenomics tocancer research agree that newtechnology is crucial to digging
Fiona Kaper, PhD, vice president,advanced science assay research,
deeper into this disease. “MassIllumina
spectrometry-based proteomics
has gotten better so we now work with smaller amounts of materials,” says Janne Lehtiö, PhD, professor of medical proteomics at
the Karolinska Institute in Stockholm. “The clinical applicationof proteogenomics is facing a major leap forward because of thetechnology developments.”
Using a form of high-resolution liquid chromatography (LC)mass spectrometry (MS), Lehtiö and his colleagues analyzed
the proteomes of people withchronic lymphocytic leukemia(CLL).4 Then, the scientists
combined proteomic data withexisting genomic, transcriptomic, and drug-perturbation
data on these patients. From
this work, Lehtiö says thatthey “found a new chronic
leukemia subtype with a poor
prognosis” for patients, andthe researchers validated that
Janne Lehtiö, PhD,subtype with an additional
professor of medical proteomics,cohort. The new CLL subtype
Karolinska Instituteemerged from proteomic data.
“If you just looked at genomics, you wouldn’t be able to determine this subtype,” Lehtiö says.

This example of protein-correlation network analysis from a dataset
generated with high-resolution isoelectric focusing liquid chromatographymass spectrometry on a sample from a patient with chronic lymphocytic
leukemia (CLL) shows protein abundance, which is color-coded and varies
between patients with and without CLL. Credit: Janne Lehtiö

Beyond finding new molecular signatures of cancer, Lehtiösays, you can use a “smaller cohort for interesting results bymoving to proteogenomics, since by layering the data you canget sample-specific, genotype-phenotype analysis.” So, existing
proteogenomic tools allow scientists to work with both smaller
and larger cohorts.
This approach will work with a wide range of cancers. As anotherexample, Lehtiö and his colleagues applied proteogenomics
to non-small cell lung cancer (NSCLC).5 This work revealed six
proteome subtypes with interesting connections to oncogenicdrivers, outcomes, as well as aberrant proteins, so called cancer
neoantigens, that can be used for development of cancer
vaccines. “We determined the tumor mutation burden on theDNA level, but also used proteomics data to study more complex
aberrant proteins, caused by genomic aberrations the tumor
harbor,” Lehtiö explains. “To develop rational, targeted-therapy
combinations and connect immunotherapies to these, we need
proteogenomics as biomarker analysis to understand bothtargetable cancer-driving pathways as well as immune-evasion
mechanisms at once.”
Predicting treatment responses in ovarian cancer
Many recent research projects explore ways to predict the
impact of cancer treatments. As one example, Paulovich and hercolleagues studied resistance to platinum-based chemotherapy
in women with high grade serous ovarian cancers (HGSOCs).6,7
Using LC-MS/MS to quantify proteins and machine-learningalgorithms to analyze the data, Paulovich says that they “identified an ensemble prediction model of chemo-refractorinessbased on 64 proteins, and it detects a subset of chemotherapy
refractory tumors with very high specificity and is validatedin two independent patient cohorts.” In addition, the scientists
identified five novel subtypes of HGSOC based on protein-pathway expression, which might suggest “different mechanisms ofrefractoriness and implicate potential subtype-specific treatment
approaches, including immune therapies or metabolic inhibitors,” Paulovich says.
The scientists also took other approaches to supporting the ideathat protein-pathway expression in the five subtypes might
predict therapeutic vulnerabilities. For example, the team studied
patient-derived xenografts from patients who presented withchemo-refractory HGSOC. The researchers found that the effectof platinum-based chemotherapy could be improved with pharmacological inhibition or CRISPR knock out of a gene connected
to fatty-acid oxidation.
The results from these studies show just some of the powerof applying proteogenomics in oncology. “Despite over three
decades of research on platinum responses in cancer, no predictive
biomarker has been translated into clinical use,” Paulovich says.
“Predictors of refractory disease could spare these patients theunnecessary toxicity of a platinum-based regimen and provide
a means to triage these patients in clinical trials to identify
effective therapies for refractory disease.”

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points out that this platform can be used to “translate biologicalpathways to a list of candidate protein biomarkers, whichenables improved experiment planning and setup, post-run dataanalysis, and analytical tools.”
Olink’s technology is already being applied in many ways. As
one example, Lawley says that it “is enabling projects like theUK Biobank Pharma Proteomics Project where 13 pharma
partners have come together to drive proteomics on around60,000 UK Biobank samples.”Other companies also strive to expand datasets that can help
scientists understand and treat cancer and other diseases. In
many ways, that objective depends on enabling the readout of
non-DNA based modalities, such as proteomes, on high-throughput analytical platforms, such as NGS. As an example, Kapersays, “Illumina’s NGS technology roadmap has continually
increased the data quality and output per sequencing run,driving down the cost per data point, while at the same timeincreasing the speed of data generation.” Consequently, shesays, “NGS therefore provides the fastest, highest throughput,
most flexible, and most economical readout technology for
different multi-omic modalities, including genomes, methylomes,transcriptomes, and proteomes.”
Making the most of data analysis, however, often depends oncollections of technology platforms. For instance, “combining
Illumina’s NGS readout with large target–content panels, suchas SomaLogic’s SomaScan platform or Olink’s Explore platform,
will enable researchers to analyze thousands of protein targets
in hundreds of samples simultaneously,” Kaper notes. As aresult, scientists can explore protein abundance in more depthand at scale. Kaper says that such a capability “will drive wider
adoption and incorporation of proteomics in multi-omic studies,deriving ever increasing value and understanding from eachsample.”
Improving precision
The increase in proteogenomic-driven knowledge about cancer’sdevelopment should enhance treatment options—especiallycreating more therapies for specific patients. “Proteogenomics
is increasing our understanding of cancer biology, but the ultimate
goal is to use that knowledge to improve cancer outcomes,
especially through personalized/precision oncology,” Paulovichsays. “For personalized/precision oncology to succeed, we needpredictive biomarkers to match patients with efficacious therapies.” Single genes or proteins are not enough to understand thecomplexity of drug responses. Instead, collections of informationmust be applied to cancer. As Paulovich says: “An unansweredquestion is: Can multi-analyte proteogenomic predictive biomarker signatures be translated into clinical labs and change
clinical practice to improve outcomes while reducing healthcare
costs?”
Making the most of proteomics, though, depends on expertisein various fields. “One of the challenges of proteogenomics ingeneral is that you need a team of people—an expert inproteomics, an expert in genomics, and an expert in statistics—
to really be able to figure out how these puzzle pieces fit together,”Donovan explains. In some cases, a software solution could help.
For example, an expert in proteomics could use softwareto bring in genomics when analyzing data.
Such software also helps deal with the volumes of data produced
in proteogenomics. Just as an example, a genomics study on alarge cohort—say, 10,000 people—could produce a table of 10,000
rows (patients) and 20,000 columns (genes), which makes 200
million data cells. Add the estimated more than one million
proteoforms and that creates what Donovan describes as “a very
high dimensional problem.” Plus, that problem is destined toexpand. “There are other ‘omic' data types beyond genomics andproteomics,” she says. “Epigenomics tells us about the regulationof the genome and then post-translational modifications of proteins tells us more about a protein’s function.” As Donovan adds:“There can be a lot of growth from putting together all thesedifferent molecular phenotypes, because that’s systems biology.”
Tomorrow’s cancer studies will explore even more broadly
based versions of ’omics, but that must await advances intechnology. As Kaper says, “there are currently no technologies
available that can convert additional modalities, such as
metabolomics, into a format that is compatible with a DNA-based
readout, such as NGS.” As a result, she says, “these modalities
therefore cannot—yet—benefit from a high-throughput technologythat can interrogate many samples and analytes in parallel,
limiting their inclusion into large-scale, multi-omic studies.”
The speed of development in proteogenomic tools and techniques,as well as the ongoing advances in other areas of ’omics, promise
to soon make it possible to explore cancer in even more ways.
With that capability, scientists will learn much more about themolecular biology of cancer and find ways to treat it in more
precise and personalized ways. nReferences
1. Mani, D.R., Krug, K., Zhang, B., et al. Cancer proteogenomics: current impact and future
prospects. Nature Reviews Cancer 22(5):298–313 (2022).
2. Rodriguez, H., Zenklusen, J.C., Staudt, L.M., et al. The next horizon in precision oncology:
Proteogenomics to inform cancer diagnosis and treatment. Cell 184(7):1661–1670. (2021).
3. Zhang, B., Whiteaker, J.R., Hoofnaagle, A.N., et al. Clinical potential of mass spectrome
try-based proteogenomics. Nature Reviews Clinical Oncology 16(4):256–268. (2019).
4. Herbst, S.A., Verterlund, M., Helmbolt, A.J., et al. Proteogenomics refines themolecular classification of chronic lymphocytic leukemia. Nature Communications 		
13:6226 (2022).
5. Lehtiö, J., Arslan, T., Siavelis, I., et al. Proteogenomics of non-small cell lung cancer
reveals molecular subtypes associated with specific therapeutic targets andimmune-evasion mechanisms. Nature Cancer 2:1224–1242 (2021).
6. Huang, D., Savage, S.R., Calinawan, A.P., et al. A highly annotated database of genes
associated with platinum resistance in cancer. Oncogene 40(46):6395–6405 (2021).
7. Huang, D., Chowdhury, S., Wang, H.I., et al. Multiomic analysis identifies CPT1A as apotential therapeutic target in platinum-refractory, high-grade serous ovarian cancer.
Cell Reports Medicine 2(12):100471 (2021).
8. Donovan, M.K.R., Huang, Y., Blume, J.E., et al. Peptide-centric analyses of humanplasma enable increased resolution of biological insights into non-small
cell lung cancer relative to protein-centric analysis. BioRxiv (2022). https://doi.
org/10.1101/2022.01.07.475393.
Mike May, is a freelance writer and editor with more than 30 years of experience. Heearned an MS in biological engineering from the University of Connecticut and a PhD
in neurobiology and behavior from Cornell University. He worked as an associate editor at
American Scientist, and he is the author of more than 1,000 articles for clients that include
GEN, Nature, Science, Scientific American, and many others. In addition, he served as theeditorial director of many publications, including several Nature Outlooks and Scientific
American Worldview.

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