Growth Pattern Analysis in Low Grade Clear Cell Renal Cell Carcinomas: Prognostic Value and Biologic Significance
Creators: name: Diaz-Cano, Salvador J.
name: R Sutherland
name: J Moorhead
name: A Blanes
name: R Dobson
Publisher: name: figshare
Year: 2016
Subjects: subject: 60602 Animal Physiology - Cell
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: 60102 Bioinformatics
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: Cancer
subject: Cell Biology
subject: 60106 Cellular Interactions (incl. Adhesion, Matrix, Cell Wall)
subject: Computational Biology
subject: 111201 Cancer Cell Biology
subject: FOS: Clinical medicine
subject: FOS: Clinical medicine
subject: 111202 Cancer Diagnosis
subject: 111203 Cancer Genetics

description: Background: The Pan-Cancer Analysis Project aimed to identify the genomic changes in cancer types from the Cancer Genome Atlas (TCGA). The meaning of architectural features in clear cell renal cell carcinomas (ccRCC) by Fuhrman grade (FG) has not been investigated at clinic-pathologic or genetic levels in this set.<br>Design: A systematic evaluation of 401 ccRCC included: microscopic satellites, heterogeneous clones, FG (1-4), con uent necrosis, spindle cell presence, primary and secondary growth pattern (tubular-nested-thick trabecular-solid), tumor in ltrating lymphocytes, stromal reaction (none/myxoid/desmoplastic), and edges (pushing/ in ltrative). A combined nuclear-architectural grade (CNAG) de ned 3 subgroups: low nuclear(N)-architectural(A) grade, low N-high A grade, and high N-A grade; low- intermediate-high CNAG, respectively. Clinical data were also collected (gender, age, and stage). Whole-exome sequencing was performed on tumor and normal tissues from ccRCC available at the TCGA. We used a Random Forest machine learning approach comparing low FG (1-2, 174 cases) vs. high FG (3-4, 139 cases) grade.<br>Model Analysis<br>► Data was retrieved from the Pan-Cancer Analysis repository<br>► Functional somatic mutations unique to tumors were identi ed and represented as samples x genes mutation matrix (mutated=1, non-mutated=0).<br>► Pairwise Random Forest models were built for the low-intermediate-high CNAG subgroups<br>► Variable selection using Fisher’s Exact test was conducted to reduce the number of predictors with 50 fold cross-validation design.<br>Random Forest models were based on the training set using the caret package in R, and predictive accuracy measured in an independent test set.<br>Results: Complete histological-molecular data were available in 313 cases (120 low CNAG, 54 intermediate CNAG, 139 high CNAG). Intermediate CNAG were signi cantly different from low CNAG regarding microscopic satellites (48/54 vs. 105/120, P=0.007), and morphologically heterogeneous clones (43/54 vs. 112/120, P&lt;0.001). The age, gender, proteins and variants model performed with an AUC of 0.803 and the predictive features for intermediate-high CNAG were: TP53 (OR = 8.940, P &lt; 2.0E-16), PIK3CA (OR = 0.239, P = 4.75E-05), PIK3R1 (OR = 0.235, P = 1.72E-03). Conclusions: Intermediate CNAG clear cell RCCs are distinctive neoplasms, morphologically heterogeneous and angioinvasive malignancies progressing through TP53 and PIK3 pathways, which offers options for targeted therapy.