Integrating proteomics into diagnostic molecular pathology reports to support molecular tumor board decisions

Background and Aim

Patients suffering from particularly challenging tumor cases receive in-depth molecular diagnostics within the molecular tumor board (MTB). Based on the molecular findings the interdisciplinary MTB team collaboratively recommends the best therapeutic strategies for each patient. Current molecular diagnostics predominantly depend on genomic and transcriptomic methods. The continuous advancements in quantitative mass spectrometry (MS)-based proteomics enable robust and reliable proteome examination in diverse patient samples. Therefore, we set out to incorporate clinical proteomics into molecular diagnostics within the MTB, enhancing and broadening the current molecular diagnostic practices while offering additional biological insights.

Methods

We apply automated MS-based quantitative proteomic workflows to perform reproducible and robust in-depth proteomics of formalin-fixed paraffin-embedded (FFPE) tissue from patients in the MTB. Our proteomic workflow is seamlessly integrated into the molecular pathology routine. We present and discuss molecular diagnostic results, including proteomics, for each patient during biweekly interdisciplinary meetings.

Results

Our robust, reproducible, and automated sample preparation approach routinely identifies and quantifies more than 6000 proteins in patient-derived FFPE samples. We acquired protein profiles from over 300 MTB patients and in many cases, the proteomic analysis has contributed to the molecular pathology reports. In these cases, proteomics corroborated immunohistochemical staining results and yielded additional information on multiple antibody-drug conjugate targets and complex tumor markers such as the microsatellite stability status.
We have established a phosphoproteomic protocol for FFPE samples yielding functional insights into pathway activity in selected MTB cases. Notable findings include identifying phospho-ERK signaling in a patient with a BRAF-KIAA1549 fusion, providing evidence for the recommendation of MEK inhibition.
More recently, we developed a proteogenomic workflow that enables the integration of patient-matched genomics and proteomics data, paving the way for more comprehensive personalized medicine. Our proteogenomic analysis provided additional insights by detecting the KRAS G12D peptide, supporting previously uncertain genomic results due to low tumor cell content. The complete data analysis pipeline was integrated into a local Galaxy instance, maximizing robustness and reproducibility by storing the raw data, analysis tools (including software versions), and analysis steps in designated histories.

Conclusion

We present the additional insights generated by integrating proteomics in the molecular profiling of particularly challenging tumor cases, such as rare and recurrent/resistant malignancies. Integrating proteomics into the MTB paves the way for further clinical proteomic applications complementing current routine molecular diagnostics.