NEW YORK – A team led by researchers at the German Cancer Research Center (DKFZ) has developed a targeted mass spectrometry workflow that could improve the sensitivity of immunopeptidomic experiments.
Detailed in a paper published last month in Molecular & Cellular Proteomics, the approach, called optiPRM, could help researchers detect cancer neoantigens in patient tumor samples, aiding applications like the development of personalized cancer vaccines, said Angelika Riemer, head of the division of immunotherapy and immunoprevention at the DKFZ and senior author on the study.
Immunopeptidomics is the study of peptides presented by human leukocyte antigen (HLA) molecules. These molecules play a key role in immunity, displaying peptide antigens at the cell surface that generate immune cell responses to various infections or diseases. Identification and manipulation of these antigens is key to research in areas like cancer immunotherapy, where scientists are working to trigger patients' immune systems to fight their cancers by presenting cancer- and patient-specific HLA antigens.
One of the major challenges to developing effective cancer vaccines is determining which cancer-specific peptides are likely to bind to HLA molecules and thereby be presented at the cell surface. Currently, this is typically done using algorithms that use a combination of DNA, RNA, and immunopeptidomic data to predict peptides that are likely to be displayed.
In theory, immunopeptidomics could enable direct detection of cancer neoantigens presented by HLA molecules, allowing researchers and drug developers to identify neoantigens for inclusion in vaccines and confirm the presence of predicted neoantigens. In practice, however, the technical limitations of mass spec approaches, and a lack of sensitivity in particular, have limited such applications.
In their work, the DKFZ researchers aimed to boost the sensitivity of immunopeptidomic workflows by developing targeted mass spec assays carefully optimized for the specific cancer neoantigens they hoped to detect.
They started with DNA and RNA sequencing of the samples of interest, identifying sample-specific mutations. With that data, they generated a list of altered protein sequences produced by these genetic mutations and used the MHCcombine prediction tool to identify a list of the neoantigen candidates most likely to be presented by the tumor cells. They then produced synthetic stable isotope labeled (SIL) peptides to each of the candidate neoantigens.
The team then analyzed the SIL peptides using mass spec, running them on a Thermo Fisher Scientific Orbitrap Exploris 480 instrument. In these experiments they identified the optimal parameters for collision energies to be used for the analysis of each peptide.
This optimization, Riemer said, allowed the researchers to significantly improve the sensitivity of their assays. Using a dilution series of synthetic peptides spiked into a HeLa cell digest, they found that the optimized assay could identify peptides at roughly 12-fold lower concentration than the unoptimized assay. They also observed a boost in signal intensity for their target peptides using the optimized parameters. While the MCP study focused primarily on optimizing collision energy selection, Riemer noted that the same approach could be used to optimize a variety of mass spec parameters.
Applying the approach to tumor biopsies from five patients, the researchers generated targeted assays against a total of 274 predicted neoantigens and were able to identify five mutation-derived neoantigens across three of the five patients. In a sample from a patient with osteosarcoma and lung metastasis they detected two neoantigens, both produced by a single-nucleotide variant (SNV) in the gene ARHGAP35. In a sample from a patient with a small intestine carcinoma, they detected one neoantigen produced by an SNV in the gene RNF111. In a liposarcoma patient sample, they identified two neoantigens, one produced by a fusion involving the genes TSPAN8 and CPM and another by a fusion involving PAPOLA and MGAT4C.
The researchers were not able to detect any neoantigens in two of the five patient samples. Riemer said that there was nothing obviously unique about those samples or the number or characteristics of neoantigens predicted for them.
In their work with patient samples, the researchers were able to detect neoantigens in tumor biopsies consisting of as little as 35 mg of tissue, which Riemer said is an amount compatible with clinical workflows. Ultimately, she said, she hopes the method could help improve selection of tumor neoantigens to include in therapies like cancer vaccines or T-cell receptor T-cell therapy (TCR-T).
“What we are heading at is to make it clinically useful so that clinicians can rationally choose targets,” she said. “Our whole approach is to truly validate the targets. Then you may be able to include fewer peptides in a vaccine because you know they are truly there.”
Sensitivity still remains a challenge, but Riemer said she expects newer generations of mass spectrometers to help in this area. The Thermo Fisher Orbitrap Exploris 480 used in the MCP study was introduced five years ago, and since then, a number of higher-powered systems have come to market. In particular, Bruker has targeted the immunopeptidomics market with its TimsTOF Ultra and Ultra 2 instruments. Thermo Fisher's Orbitrap Astral has also shown promise for such work, and its new Stellar MS was designed specifically with targeted protein measurements in mind.
Riemer said she and her colleagues have had the opportunity to test their method on some of these newer instruments.
"My view is absolutely hopeful that with the new generation of instruments the sensitivity will be even better," she said.
Riemer said that with the MCP study as a proof of concept, she and her colleagues are now beginning to apply their approach more widely at DKFZ. Thus far, they have used it to retrospectively analyze around 30 patient samples and hope to begin using it prospectively.
"Obviously, it needs to be part of the clinical pipeline," she said, noting that sequencing and mutation calling is already done for all DKFZ patients. "With the patients already sequenced and the mutations known and the HLAs known, you can start this process."