Arul Chinnaiyan’s from University of Michigan and Michigan Medicine responses to interview questions from Tal Behar, Precision World Medicine Conference
- 1. Breaking the Gene Fusion Paradigm: Your discovery of the TMPRSS2-ERG gene fusion – now recognized as a defining molecular event in prostate cancer – broke new ground by revealing that gene fusions can drive common solid tumors. Could you share how this landmark finding has influenced prostate cancer care (for example, in developing new diagnostic tests or targeted therapies) and what it taught researchers about the importance of genomic subtyping in cancer?
A:The discovery of the TMPRSS2–ERG gene fusion fundamentally reshaped our understanding of prostate cancer by demonstrating that recurrent, oncogenic gene fusions are not limited to leukemias and sarcomas, but in fact can define common epithelial malignancies. In addition, to defining the molecular basis of a common tumor type (i.e. prostate cancer), this insight catalyzed the development of a new generation of molecular diagnostics- including clinical assays for ETS fusions and our urine-based test, MyProstateScore 2.0- that help distinguish indolent from aggressive disease and guide decisions around biopsy and surveillance. More broadly, the finding taught us that genomic subtyping is essential for unraveling cancer heterogeneity: once we began to stratify prostate tumors by their molecular drivers, entirely new therapeutic opportunities emerged, from targeted inhibition of ERG-associated pathways to rational drug combinations informed by fusion status. The fusion paradigm ultimately opened the door for researchers worldwide to uncover a wealth of previously unrecognized genomic rearrangements across solid tumors, establishing molecular taxonomy as a cornerstone of precision oncology.
- 2. Translating Genomics to Therapy Decisions: You have been a leader in precision oncology, integrating comprehensive DNA and RNA sequencing of patient tumors into treatment decisions since as early as 2011 through programs like Mi-ONCOSEQ Could you describe a specific case where this integrative approach uncovered an unexpected cancer driver (such as a gene fusion or mutation) and guided a patient’s therapy, and what this illustrates about the value of multi-dimensional genomic data in managing advanced cancers?
A: One memorable case from our MI-ONCOSEQ precision oncology program involved a young patient with widely metastatic cancer of unknown primary. Conventional pathology and imaging offered no clear diagnosis, but integrated DNA and RNA sequencing revealed an unexpected and highly actionable ETV6–NTRK3 fusion. This discovery immediately reframed the patient’s disease biology and allowed us to match them to TRK-inhibitor therapy, which resulted in a dramatic and durable clinical response. Cases like this illustrate the power of multi-dimensional genomic profiling: by examining DNA mutations, gene fusions, RNA expression, copy-number alterations, and pathway activation together, we can identify therapeutic vulnerabilities that would remain invisible using any single modality. This integrative approach has repeatedly allowed us to connect patients with targeted therapies, immunotherapies, or clinical trials uniquely suited to the molecular architecture of their cancer- transforming outcomes in situations where traditional approaches offered few options.
- 3 Beyond DNA – The Multi-Omics Frontier: The field is now moving beyond DNA variants into “multi-omics” approaches for monitoring and treating cancer. How do you envision integrating additional data layers – such as transcriptomics (RNA), epigenetic markers (e.g. DNA methylation), proteomics, or liquid biopsy readouts – into precision oncology? For example, what emerging multi-omic strategy do you find most promising for improving early detection or real-time monitoring of cancer (like minimal residual disease) and guiding therapy decisions in ways that single-modality genomic tests cannot?
A: The next era of precision oncology will be defined by our ability to harness AI and machine learning to integrate dynamic, multi-omic layers—transcriptomics, epigenetics, proteomics, and advanced liquid biopsy readouts- into real-time clinical decision making. While DNA alterations provide a critical foundation, RNA expression programs, DNA methylation signatures, chromatin state, and proteomic activity often reflect the true, actionable biology of a patient’s tumor. A particularly promising direction is multi-omic liquid biopsy profiling that combines circulating tumor DNA with cell-free RNA (including non-hematopoietic transcriptomes), methylation patterns, and proteomic markers to enable earlier detection and highly sensitive monitoring of minimal residual disease. Such approaches not only illuminate therapeutic vulnerabilities but also allow us to observe tumor evolution under therapy- often identifying resistance mechanisms months before radiographic progression. By integrating these complementary data layers, we can create a continuously updated, holistic portrait of each patient’s cancer, enabling earlier intervention, more tailored therapy selection, and a fundamentally more proactive model of cancer care.
