NIH Featuring CPTAC Investigators: Marcin Cieslik

Featuring CPTAC Investigators: Marcin Cieslik

Original Article: Q&A with Marcin Cieslik, Ph.D. - NCI

The following is the ninth entry in a Q&A series highlighting selected Clinical Proteomic Tumor Analysis Consortium (CPTAC) researchers and their work. Join us as we discuss CPTAC, the importance of collaboration in science, and advice for the next generation of researchers with Marcin Cieslik, Ph.D., an Assistant Professor and Division Director at the University of Michigan in Ann Arbor, Michigan. Transcript is edited for clarity.

Q: Could you provide a brief overview of your academic and professional journey?

Marcin Cieslik, Ph.D. Marcin Cieslik, PhD: I'm an assistant professor at the University of Michigan in the Department of Pathology and the Department of Computational Medicine and Bioinformatics. I'm also the Director of bioinformatics in the pathology department's Division of Diagnostic Genetics and Genomics.

My journey has been pretty straightforward. I initially enrolled in the biotechnology undergraduate program at Jagiellonian University in Krakow. I graduated from Jagiellonian with a master's degree in biotechnology. However, my final year was actually carried out in the United States--I was recruited by a faculty member who organized a student exchange program. The program I enrolled in enabled students from European universities, mostly Polish universities at that time, to travel to the United States to gain research experience in the American academic setting. I studied at the University of Virgina and, after I returned to Poland to defend my masters, I immediately came back to the University of Virginia to begin my PhD in biophysics—that was the first time I got really acquainted with protein science.

Following my PhD, I moved to the University of Michigan to do a postdoc with Dr. Arul Chinnaiyan where I was exposed to precision oncology and learned how the skills of computational scientists, including bioinformaticians, can be used to help in practical, real-time medicine. After that postdoc in Dr. Chinnaiyan's lab, I stayed at the University of Michigan as an independent faculty member in the departments of Pathology and Bioinformatics, having my own group, being involved in CPTAC and, later, having a clinical role in the Division of Diagnostic Genetics and Genomics.

Q: Please describe your current work with University of Michigan and CPTAC.

MC: My role at the University of Michigan is actually quite complex. I wear three hats. My first hat is that I'm part of the university’s Proteogenomic Data Analysis Center (PGDAC). I'm responsible for making sure that our center can carry out all of the genomic analysis that is needed for the CPTAC working groups, and I also help guide many of the biological analyses as part of both the Proteome Characterization Center (PCC) and the Proteogenomic Translational Research Center (PTRC) working groups.

My second role, as I mentioned, is being the Director of Bioinformatics for the Division of Diagnostic Genetics and Genomics. My role here is more clinical--we are taking some of the tools and technologies that we have developed and trying to implement them in a real-world clinical setting. University of Michigan has a large hospital system that has big needs in terms of their diagnostics. So, as Director of Bioinformatics, I'm responsible for the implementation of those bioinformatics pipelines.

Third, I have my own research lab where we do technology development, algorithm development, and large-scale data analysis. As part of my independent research program, I work on a variety of projects from kidney cancer to the study of rare cancers, investigations of chromosomal instability, and a lot of collaborative work in the area of clinical genomics. Most of my students wear at least two of the hats I wear, participating in CPTAC but also having their independent projects and collaborative projects with other researchers.

Q: What is a project or projects that you are proud to have contributed to and why?

MC: I want to give a shoutout to all of the CPTAC projects our University of Michigan PGDAC has contributed to. Each of them has come with particular achievements and lessons learned. I'm very proud to have been involved in the three-kidney cancer related CPTAC projects and working groups. As part of those collaborative activities, three studies with novel findings were published, and the discoveries made as part of those studies enabled me to secure a Department of Defense award. I'm also proud of this project because it was our first proteogenomic project--As part of CPTAC, we learned the technical skills, adopted the common language used across disciplines, and built our ability to collaborate successfully. The collaborative structure of CPTAC has resulted in a tremendous number of publications, taking discoveries and implementing them clinically or exploring them further in dedicated studies.

I would also like to highlight the CPTAC Ovarian Cancer PTRC project led by Dr. Amanda Paulovich. This project culminated in the publication of a very widely disseminated paper in Cell Exit Disclaimerand was remarkable to me because it was the first time in my career where I could really sense how a team can work together. This working group was highly focused, goal-oriented, and collaborative. Seeing all the team members come together and stay committed to achieving a single objective was truly remarkable.

The final project that I would like to highlight is the project that we are currently working on. This project holds special meaning for me. One of my students is leading the analysis, and it marks a deeper level of involvement for our team within the CPTAC effort. I'm very proud of this project. We have been able to bring it back to basics, back to fundamental discoveries, and make certain novel observations around acute myeloid leukemia. [These discoveries] link genetics with novel field phenotypes by exploiting post-translational modifications and metabolomic data. It's not just proteomics; it's metabolomics, lipidomics, and PTMs. All of that contributes to its value.

Q: Please comment the value of collaboration with researchers from other organizations and disciplines.

MC: I think I have realized that the primary value of collaboration is the fact that different people have very different expertise, and being able to tap into this expertise allows teams to simply do better science. So, I see collaboration as fundamental to the success of modern science, which has simply become too complex to be done by any individual person. I rely on my biologist colleagues, on their expertise, on their intuition. I rely on my clinical colleagues to tell me about real patient implications. I rely on my colleagues, who are statisticians, for their insights into the methodologies. The same for computer scientists. Once you are on the path to becoming an expert, you can get isolated from things that might be outside of your area of mastery. That’s where collaborations come in.

Q: What are some unmet needs or challenges you see in your field? How might they be addressed?

MC: In science, making room for reflection is essential. Taking a step back to critically assess whether a research question is truly meaningful can be just as important as pursuing the latest methods or technologies. Often, the most significant breakthroughs come not from simply doing something new, but from asking the right questions at the right time. In the same way, discovery-based and exploratory research—focused on observing, characterizing, and uncovering unexpected patterns—offers a powerful complement to hypothesis-driven work. These approaches can reveal phenomena we weren’t actively searching for, leading to entirely new directions in research. In today’s fast-paced and increasingly complex scientific landscape, progress depends on striking a balance between the structured rigor of hypothesis testing and the open-ended curiosity that fuels discovery.

The second problem I see is slow iteration loops in academia. Small companies, biotech companies--they operate in a loop where, for example, genomics and laboratory experimentation are essentially done by the same company. Within each iteration, the discovery from the lab fuels the analysis, and that analysis fuels the next experiment. In academia, labs instead tend to specialize, which results in very long iteration loops. They still happen, but they happen on a publication-by-publication timeframe, which is a challenge.

Another barrier is that, in academia, while there are many incentives to work together, being an independent investigator is kind of an achievement, right? I think it made sense back in the days when one individual could feasibly do all of the science, but for modern research this is not necessarily possible. Teams combining the expertise of multiple people towards one goal have a stronger ability to make breakthroughs. So, I think, the level of funding and incentives for team science is [not sufficient]. I believe all of those approaches were well intended, but as science gets more complex, it is not necessarily true that the approaches have adapted in turn. When we look at the paper in a tier one journal from 20 years ago, it looks not at all like a paper from the same journal 20 years later--it's much less complex. It is much more focused. It has much less data. Yet, over those 20 years, not much changed in terms of how teams of scientists are assembled or what the incentives are in academia by and large.

Q: What advice would you have for a student who aspires to be in your position?

MC: The first advice I would give them is that they need to become real experts. Being a scientist means having deep expertise. They should work on both their knowledge and their toolkit, get hands-off and hands-on experience, and acquire a deep understanding of a particular area. Be so good they cannot ignore you. I think this is the most surefire way to advance or to be successful.

The second thing I would emphasize is to learn to communicate. I always struggle to decide whether this is the most important or the second most important point, but I feel that much of my success in science comes from my ability to talk to people. So, knowing the language of clinicians, knowing the language of biologists, knowing the language of statisticians. It takes work to understand them, but it is possible. Once you understand them, you can advance because you will be the liaison between them.

The third thing is to understand that people around you are mostly rational beings, which means that it's relatively easy to coexist and derive benefit from collaborating with them. If you try to understand other people's motivations, you can take certain actions to avoid problems or alleviate concerns or make them happy. Put in the work to understand the motivations of people around you, and you will collaborate more effectively.