Successful development of targeted anti-cancer drugs is often coupled with predictive assays that enable selective treatment of patients more likely to benefit from therapy. Immunohistochemistry is often used to assess expression of drug targets, but it suffers from subjectivity and lack of quantitative measures. We developed a method for automated, quantitative analysis (AQUA) for assessing protein levels in situ. In response to PA-08-134, we propose to expand AQUA to simultaneously assess tumors and endothelial cells, and develop models to predict clinical benefit from adjuvant sorafenib and sunitinib for renal cell carcinoma (RCC), as well as models to predict prognosis in untreated patients. RCC has traditionally been a disease that is highly resistant to systemic therapy. However, multiple targeted therapies, including sorafenib and sunitinib, have recently revolutionized the approach to metastatic RCC. Both drugs are effective for subsets of RCC patients, and both are associated with some toxicity. Given their success in metastatic RCC, these drugs are being studied as adjuvant therapies in a large, randomized, double blinded, multi-center trial called E2805. Specimens are being collected on all patients, and they offer a unique opportunity to develop models to predict clinical benefit from these drugs and models to predict prognosis in untreated patients in a multi-center clinical trial setting on a very large cohort. Sorafenib and sunitinb have multiple targets, and our purpose is to identify the most important predictive marker/s. We will study angiogenic markers, members of the MAPK pathway and other known targets. In preliminary studies using AQUA, we showed that RCCs with high levels of vascular endothelial growth factor (VEGF) receptors in tumor cells tend to have lower microvessel density and poor survival. We hypothesize that patients with high VEGF receptor expression in TUMOR cells are more likely to benefit from therapy than those with high microvessel density. We will expand AQUA to enable concurrent assessment of targets in tumor and vessels, by masking the tumor and vessels with different fluorophores. We will establish staining conditions for all known targets of sorafenib and sunitinib and select mediators of angiogenesis in tumor, endothelium and adjacent normal tissue using historical cohorts of untreated RCC patients. We will then assess VHL mutations and expression of sorafenib and sunitinib targets in a training set (67%) of E2805 patients and generate predictive models for each of the drugs, to be validated in a testing set. Standard clinical co-variates and VHL mutational status will be incorporated into the model. We will also use these molecular markers and clinical co-variates to improve current prognostic models in the placebo-treated patients. These models can be used to select patients for the optimal adjuvant therapy for RCC (sorafenib, sunitinib or neither), and this approach can be studied in other clinical settings as well.

Public Health Relevance

In some cancers, targeted therapies (drugs that specifically inhibit certain key proteins in the cancer cell) have dramatically impacted management of the disease, and their success has usually been coupled with identification of the most important drug target/s and selective treatment of those patients whose tumors express the target. We propose to develop predictive models for sorafenib and sunitinib (new drugs that are being studied to decrease development of metastases for kidney cancer), which will enable us to selectively treat those patients that are more likely to derive benefit from these drugs, and spare the rest of the patients the toxicity and cost associated with this therapy. We will use a newly developed method of automated analysis of target levels from biopsy specimens, and in the future we will be able to apply this technology to other diseases and other targeted therapies.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Cancer Biomarkers Study Section (CBSS)
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Song, Min-Kyung H
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Yale University
Internal Medicine/Medicine
Schools of Medicine
New Haven
United States
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Jilaveanu, Lucia B; Puligandla, Maneka; Weiss, Sarah A et al. (2018) Tumor Microvessel Density as a Prognostic Marker in High-Risk Renal Cell Carcinoma Patients Treated on ECOG-ACRIN E2805. Clin Cancer Res 24:217-223
Ueno, Daiki; Xie, Zuoquan; Boeke, Marta et al. (2018) Genomic Heterogeneity and the Small Renal Mass. Clin Cancer Res 24:4137-4144
Kluger, Harriet M; Zito, Christopher R; Turcu, Gabriela et al. (2017) PD-L1 Studies Across Tumor Types, Its Differential Expression and Predictive Value in Patients Treated with Immune Checkpoint Inhibitors. Clin Cancer Res 23:4270-4279
Li, Huamin; Linderman, George C; Szlam, Arthur et al. (2017) Algorithm 971: An Implementation of a Randomized Algorithm for Principal Component Analysis. ACM Trans Math Softw 43:
Goldberg, Sarah B; Gettinger, Scott N; Mahajan, Amit et al. (2016) Pembrolizumab for patients with melanoma or non-small-cell lung cancer and untreated brain metastases: early analysis of a non-randomised, open-label, phase 2 trial. Lancet Oncol 17:976-983
Kluger, Harriet M; Zito, Christopher R; Barr, Meaghan L et al. (2015) Characterization of PD-L1 Expression and Associated T-cell Infiltrates in Metastatic Melanoma Samples from Variable Anatomic Sites. Clin Cancer Res 21:3052-60
Baine, Marina K; Turcu, Gabriela; Zito, Christopher R et al. (2015) Characterization of tumor infiltrating lymphocytes in paired primary and metastatic renal cell carcinoma specimens. Oncotarget 6:24990-5002
Barr, Meaghan L; Jilaveanu, Lucia B; Camp, Robert L et al. (2015) PAX-8 expression in renal tumours and distant sites: a useful marker of primary and metastatic renal cell carcinoma? J Clin Pathol 68:12-7
Shuch, Brian; Falbo, Ryan; Parisi, Fabio et al. (2015) MET Expression in Primary and Metastatic Clear Cell Renal Cell Carcinoma: Implications of Correlative Biomarker Assessment to MET Pathway Inhibitors. Biomed Res Int 2015:192406
Jilaveanu, L B; Shuch, B; Zito, C R et al. (2014) PD-L1 Expression in Clear Cell Renal Cell Carcinoma: An Analysis of Nephrectomy and Sites of Metastases. J Cancer 5:166-72

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