Pathomics biomarkers for stratification of clear cell kidney cancers

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Project Summary Pathologic attributes of cancers, such as histology and tumor growth patterns are not quantitatively assessed to date. In every cancer type these parameters effect patient outcomes and are included in risk models of tumor recurrence and overall survival. Algorithms using machine learning and convolutional neural networks allow us to quantify pathology and develop Pathomics biomarkers. Here, we propose to obtain pathomics biomarkers of cancer recurrence/progression that enumerate histology growth patterns (HGPs) in clear cell renal cell cancer (ccRCC). ccRCC is the most common subtype of kidney cancer. In its localized stage, it is treated by nephrectomy. However, about 30% of patients experience disease progression after surgery and may benefit from adjuvant treatment. Deciding whether or not treatment is warranted requires identifying patients who are at a high risk of recurrence. Here, we hypothesize that quantitative biomarkers will improve the risk assessment of patients with ccRCC and propose to develop computer-generated features of tumor growth patterns. We previously defined 13 HGPs and demonstrated their ability to predict overall survival in patients treated for ccRCC. Distinctive features for each HGP will be generated and validated using frameworks of convolutional neural networks that produce probabilities of expression across cancer regions. Further, the distribution of probabilities will be used to obtain biomarkers of expression of each HGP. Using parametric and non-parametric models, HGP-biomarkers will be examined for their association with tumor stage and local mechanisms of ccRCC progression, such as formation of tumor thrombi, regional lymph node metastases or invasion into perinephric adipose tissues. The performance of each algorithm in the project will be evaluated. Altogether, biomarkers developed in this project will provide a starting point to select patients with ccRCC for adjuvant treatment after surgery.