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Abstract: PO1854

Multi-Omics Approach to Uncover Underlying Biology of Low-Risk Clear Cell Renal Cell Carcinoma Patients with Progressive Disease

Session Information

Category: Onco-Nephrology

  • 1500 Onco-Nephrology

Authors

  • Strauss, Philipp, Universitetet i Bergen, Bergen, Hordaland, Norway
  • Scherer, Andreas, spheromics, Helsinki, Finland
  • Eikrem, Oystein, Universitetet i Bergen, Bergen, Hordaland, Norway
  • Marti, Hans-Peter, Universitetet i Bergen, Bergen, Hordaland, Norway
Background

Renal Cell Carcinoma (RCC) constitutes approximately 3 % of all cancers and its incidence is rising worldwide, especially in Western countries. In the last two decades, enormous advances have been made in the development and implementation of medical therapies for metastatic ccRCC, however, surgery still represents the only curative option. One of the issues in developing a curative medical therapy lies in the high degree of inter,- and intra tumor heterogeneity. We believe that by applying multi-omics technology to highly specific subgroups and comparing them to closely matched controls we can mitigate the heterogeneity issue and deepen our understanding one step and subgroup at the time.

Methods

We assembled a cohort of ccRCC patients (n=443) and identified all “low-risk” patients which later developed progressing tumours (n=8). Subsequently we performed genome-wide expression profiling, miRNA profiling and proteomics profiling from formalin-fixed samples obtained at initial surgery from these “low-risk” patients and 16 matched patients not progressing to recurrence with metastasis. The patients were matched for Leibovich sore, creatinine, age, sex, tumor size and tumor stage.

Results

Pathway analysis yielded differences between progressive and non-progressive patients in categories such as Molecular Mechanisms of Cancer, B Cell Receptor Signaling in mRNA data and Acute Phase Response Signaling and FXR/RXR Activation in proteomics data. By integrating our three -omics analysis we revealed that acute Phase Response Signaling also plays a role on all three levels. Additionally, we developed a 14-component classifier, drawing from both mRNA, miRNA and protein-based data that reliably differentiated the different subgroups. We further examined the correlations between each of the components and uncovered a dense network of interactions.

Conclusion

Multi-omics methods represent an important tool in furthering our understanding renal cancer biology in the pursuit of medical therapies.