Molecular and Immunohistochemical Profiling of Tumor From Patients With Parathyroid Tumors for Evaluation of Targeted Agents
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Parathyroid Gland Adenoma
- Sponsor
- M.D. Anderson Cancer Center
- Enrollment
- 310
- Locations
- 1
- Primary Endpoint
- Identify the potential genes that can be used in patients with parathyroid tumors for diagnostic purposes
- Status
- Recruiting
- Last Updated
- 5 months ago
Overview
Brief Summary
This trial studies molecular and immunohistochemical profiling of tumors in patients with parathyroid tumors. Studying molecular and immunohistochemical profiling of tumors may help doctors avoid inconsistencies in diagnosis, unnecessary or incomplete surgery, surgical morbidity, psychological stress, and inadequate follow up.
Detailed Description
PRIMARY OBJECTIVES: I. To determine the differences in clinical behavior and immunohistochemical (IHC) biomarkers between parathyroid carcinoma (PC), atypical neoplasm (AN) and parathyroid adenoma (PA). II. To determine which potential genes can be used in patients with parathyroid tumors for diagnostic purposes. OUTLINE: Previously collected tumor tissue and blood samples are analyzed via immunohistochemical profiling for identifying potential genes showing molecular aberrations as other types of cancer.
Investigators
Eligibility Criteria
Inclusion Criteria
- •The patient cohort for this study consists of all patients within the prospectively maintained parathyroid database within the Department of Surgical Oncology at the University of Texas MD Anderson Cancer Center (MDACC) or from collaborating sites, with tissue or blood available from 1968-2015, previously consented for the use of the tissue for research purposes (via Protocols LAB 08-0034, PA11-0695 and LAB03-0320 or the appropriate external mechanism for collaborating sites).
- •All patients with a known diagnoses of primary hyperparathyroidism (PHPT) operated/treated in the Department of Surgical Oncology of MDACC or at a collaborating site. Patients that have provided consent for the use of the tissue or blood for research purposes will then be considered for our study (via Protocol LAB03-0320 and LAB 08-0034 or the appropriate external mechanism for collaborating sites).
- •All consented patients with known diagnoses of PHPT with a histopathological diagnosis of: parathyroid carcinoma, atypical parathyroid neoplasm or parathyroid adenoma. There will be no restrictions on age, gender, or ethnicity.
- •Selected patients obtained through outside collaboration who meet the selection criteria for tissue availability and diagnostic suitability for inclusion in the study.
Exclusion Criteria
- •Patients without tissue available for analysis.
Outcomes
Primary Outcomes
Identify the potential genes that can be used in patients with parathyroid tumors for diagnostic purposes
Time Frame: Up to 4 years
Genomic analysis of tumor samples will be performed to identify molecular aberrations for which novel targeted therapies have been recently developed. Statistical analyses will be conducted by using 2-sample t-test. Other appropriate statistical methods may also be employed (e.g., non-parametric tests, analysis of variance \[ANOVA\], or statistical classifications) depending on the endpoint and research interest.
Biomarker levels
Time Frame: Up to 4 years
Will be reported as 0 (negative), 1 (low intensity), 2 (medium intensity), or 3 (high intensity). Sensitivity, specificity, and accuracy will be reported for each cutoff for each biomarker separately to categorize tumor types. Receiver operating characteristic (ROC) analysis will be employed to graphically summarize the tradeoffs between sensitivity and specificity for different cutoffs. Several different combinations of tumor types are of interest: parathyroid carcinoma (PC) versus (vs.) atypical neoplasm (AN), PC vs. parathyroid adenoma (PA), PN vs. PA, PC/AN vs. PA, and PC vs. AN/PA. Separately analyses will be performed for each. Following univariable analyses, a multivariable logistic regression model will be fit (again, separately for each combination of tumor types) to assess the ability of multiple markers to classify patients, and ROC analysis will also be used to summarize the performance of the resulting model.