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Proteogenomics, Precision Medicine and Oncology

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Major advances have occurred in the fields of proteomics and genomics over recent years. Now, scientists are utilizing the technologies from each approach collectively in a novel research area named proteogenomics. Proteogenomics looks set to revolutionize personalized medicine in the next few years. We spoke with Alexzander Asea, Professor and Director at Precision Therapeutics Proteogenomics Diagnostics Center, University of Toledo, to learn more about the applications of proteogenomics in precision medicine.

Molly Campbell (MC): Why is it important to study both the genome and the proteome for precision medicine? 

Alexzander Asea (AA):
The easiest way to understand why it is important to study the genome and the proteome for the best outcome in precision medicine is by looking at the caterpillar that eventually morphs into a butterfly. Both have the same identical genome but vastly different proteome. This allows the caterpillar to crawl from place to place and the butterfly, the ability to fly. 

Therefore, to fully deliver the benefits of precision medicine, which is an approach to patient care that allows doctors to select specific treatments that are most likely to help specific patients; we need study the genome, the proteome and the transcriptome, known as proteogenomics, and seamlessly integrate it into precision medicine and clinical care.

This is why the mission of our center is to seamlessly integrate proteogenomics into patient care by characterizing proteomic signals that correlate with response or resistance to target therapeutics (Precision Medicine), develop new diagnostics, design therapeutic IP and create new treatment IND directions.

MC: How can proteogenomics further advance personalized medicine?

The seamless integration of precision medicine into personalized medicine will improve prevention, early detection, diagnosis, and treatment of diseases and disorders. This is achieved by its ability to enhance the understanding of the molecular mechanisms and accelerating the translation of molecular findings into the clinic.

Proteogenomics is able to provide an overall systems perspective to a diseases or disorder. Specifically, genomic data (the sum total of all DNA sequences and expressed sequence tags), transcriptomic data (the sum total of all RNA sequences and ribosome profiles) and proteomic data (the sum total of protein synthesis and post-translational modifications) are simultaneously analysed. 

The potential of proteogenomics lies in its ability to enable us to elucidate disease mechanisms by discovering and validating novel prognostic and diagnostic biomarkers and responders or non-responders to specific targeted therapies. We can use this data to improve prevention, early detection, diagnosis, and treatment of diseases and disorders through the enhanced understanding of the molecular mechanisms and accelerating the translation of molecular findings into the clinic.

MC: What recent breakthroughs have occurred in the field of proteogenomics and precision medicine? 

AA: The development of high-throughput that allows the measurement of multiple proteins and post-translational mutations simultaneously has been a key breakthrough that has occurred in the field of proteogenomics and precision medicine. Additional breakthroughs in technology that allows high specificity, increased dynamic range and high throughput have been seminal.

Support from the National Cancer Institute’s Office of Cancer Clinical Proteomics Research (OCCPR) whose stated mission is to advance proteome and proteogenome science and technology development through community resources (data and reagent), and accelerate the translation of molecular findings into the clinic. As well as the contribution of consortia like the Clinical Proteomic Tumor Analysis Consortium (CPTAC), the International Cancer Proteogenome Consortium (ICPC) and the Human Proteome Project (HPP), are all significant breakthroughs that have greatly pushed the field of proteogenomics and precision medicine into the future. 

MC: What research techniques are commonly used in proteogenomics research for precision medicine? Are there any drawbacks or advantages to specific techniques? 

Common techniques used in proteogenomics research for precision medicine include RNA-sequencing and data analysis, LC-MS/MS and MALDI mass spectrometry. These techniques combined with database search engines that are able to bridge genomics and proteomics studies and facilitates cross-omics data integration are the most efficient. It is extremely important to also use and/or combine quantitation bioinformatics software including, Ingenuity Pathway analysis (IPA), Parallel Reaction Monitoring (PRM), Progenesis, Library of Integrated Network-Based Cellular Signatures (LINCS) and Skyline, DESeq, Limma, EdgeR, R and MStats, for the best, reproducible and most reliable data.

During the recent US Human Proteome Organization (HUPO) 2019 conference in Washington, DC, Professor Olga Vitek (Khoury College of Computer Sciences, Northeastern University) stressed the importance of combining multiple statistical methods in order to generate effective and reproducible research. Vitek suggests that an effective starting point for researchers to use is to start by translating scientific questions into statistics; “define the problem by translating your biological and clinical goals into statistical goals.” 

These techniques are able to integrate the genome, transcriptome and proteome data to creates three proteogenomics relationships based on; 1) Analysis of the correlation of mRNA and protein pairs across samples. 2) Analysis of mutations, PTM and signalling pathways. 3) Analysis of correlation of the regulatory effects on RNA and protein expression levels caused by genetic variants (eQTL), microRNAs (miRNAs) and copy number aberrations (CNA). 

MC: Are there any exciting research projects that you are able to provide an overview of and discuss? 

We are using a proteogenomics platform to understand why triple-negative breast cancer (TNBC) is such an aggressive disease. Although slightly responsive to chemotherapy, TNBC is more difficult to treat, generally insensitive to most available hormonal or targeted therapeutic agents and depending on its stage of diagnosis, TNBC can be extremely aggressive-recurring and metastasizing more often than other subtypes of breast cancer. Therefore, there is an overarching need to understand TNBC and to ultimately develop therapeutic interventions to treat this disease.

MC: What major advances in the proteogenomics precision medicine field do you anticipate to see over the next ten years?

I would like to see advances in single cell proteogenomics in terms of both mass spectrometry imaging of single cells with clear resolution of cytoplasmic and nuclear elements and the ability of LC-MS/MS to obtain accurate, reproducible data from a single cell. I would also like to see the cost of the assays come down significantly. This will make it more affordable to healthcare providers and patients.

Due to the huge amount of data that are generated during mass spectrometry-based experiments, Dr. Birgit Schilling from the Buck Institute for Aging, Novato, CA made it clear that improvements in bioinformatics algorithms is an essential strategy to the future of clinical proteomics. Dr. Schilling taught a course in quantitative proteomics at the US HUPO 2019 conference in Washington, DC, where she gave a hands-on tutorial on platform-independent and label free quantitation of proteomics data using MS1 extracted chromatograms in Skyline.

The US HUPO 2019 conference also featured a workshop that discussed how clinical practice has begun to reveal which biomarkers have clinical utility and that precision medicine has changed the rules for selecting therapies and has created new opportunities for biomarkers.

The panellist included scientists such as Tina Gatlin (NHGRI, NIH), Udayan Guha (NCI, NIH), Jacob Kegan (NCI, NIH), Aleksandra Nita-Lazar (NIAID, NIH), Pamela Marino (NIGMS, NIH) and Amanda Paulovich (Fred Hutchinson) to name a few. They further discussed what constitutes a good biomarker and the challenges in translating biomarkers to the clinic. The panellists concluded that major advances in biomarker discovery and their effective translation into the clinic, will be instrumental in driving the future of precision medicine.

Alexzander Asea was speaking to Molly Campbell, Science Writer, Technology Networks.