Predicting Recurrence in Clear Cell Renal Cell Carcinoma Analysis of TCGA data using Outlier Analysis and GMLVQ Gargi Mukherjee … Rutgers University, New Jersey Kevin Raines … Stanford University, California Srikanth Sastry … JNC, Bengaluru, India Sebastian Doniach … Stanford University, California Gyan Bhanot … Rutgers University, New Jersey Michael Biehl … University of Groningen, The Netherlands

overview gene expression in tumor cells specific example: clear cell Renal Cell Carcinomas (ccRCC) clinical data: recurrence free intervals outlier analysis: identification of a panel of prognostic genes with respect to recurrence risk score: prediction of individual recurrence risk based on outlier status w.r.t. selected genes machine learning: analysis of extreme cases of low / high risk distance based classification and relevance learning (Generalized Matrix Relevance LVQ)

data clear cell Renal Cell Carcinoma (ccRCC) publicly available datasets: The Cancer Genome Atlas (TCGA) cancergenome.nih.gov also hosted at Broad Institute gdac.broadinstitute.org

469 tumor samples 65 normal samples data clear cell renal cell carcinoma TCGA data @ Broad Institute mRNA-Seq expression data X normalized, log-transformed: Y=log(1+X) 65 normal samples 65 matched tumor samples 469 tumor samples in total 469 tumor samples 65 normal samples 20532 genes number of recurrences recurrence data: days after diagnosis 65 + 65 matched

380 training samples 89 test samples outlier analysis 380 training samples 89 test samples randomized split

outlier analysis 380 training samples per gene: determine mean μ, standard deviation σ of Y 380 training samples for each gene: identify outlier samples Y > μ + σ “high outlier“ Y < μ - σ “low outlier“ restrict the following analysis to genes with ≥ 20 high outlier samples or ≥ 20 low outlier samples

outlier analysis Kaplan-Meier (KM) analysis per gene: test for significant association of outlier status of samples with recurrence 1546 „high-outlier genes“ with KM log rank p < 0.001 1628 „low-outlier genes“ with KM log rank p < 0.0005 1546 genes construct two binary outlier matrices „1“ for high-outlier samples „0“ else „1“ for low-outlier samples 380 samples  PCA 1628 genes 380 samples

A B C D outlier analysis high outlier genes PCA reveals four clusters of genes A 1475 B 71 genes in small clusters (B,D): outlier status associated with late recurrence low outlier genes genes in large clusters (A,C): outlier status associated with early recurrence C 1402 D 226

recurrence risk score top 20 genes (by KM p-value) from each cluster A,B,C,D reference set of 80 genes for each sample: - determine outlier status with respect to the 80 genes (Y >?< μ ± σ ) - add up contributions per gene - 1 if the sample is outlier w.r.t. to a gene in A or C (early rec.) 0 if the sample is not an outlier w.r.t. the gene + 1 if the sample is outlier w.r.t. to a gene in B or D (late rec.) recurrence risk score - 40 ≤ R ≤ + 40 observe: median = 2 over the 380 training samples crisp classification w.r.t. recurrence risk: high risk (early recurrence) if R < 2 low risk (late recurrence) if R ≥ 2

recurrence risk prediction KM plots with respect to high / low risk groups: training set (380 samples) test set (89 samples) log rank p < 1.e-16 log rank p < 1.e-4 risk score R is predictive of the actual recurrence risk the 80 selected genes can serve as a prognostic panel

extreme case analysis ≤ 2 years (early) > 5 years (undefined) number of recurrences: ≤ 2 years (early) > 5 years (undefined) (late or no recurrence) 2 classes: 109 samples class 2, high risk 107 samples class 1, low risk 80-dim. feature vectors (gene expression) representation by one prototype vector per class: adaptive distance measure for comparison of samples and prototypes: with relevance matrix distance-based classification, e.g. Nearest Prototype Classifier (NPC)

A B C D A B C D GMLVQ classifier Generalized Matrix Relevance Learning Vector Quantization (GMLVQ) training of prototypes and relevance matrix = minimization of an appropriate cost function with respect to performance on labeled training set components of diagonal elements of Λ A B C D A B C D low expression | high expression

GMLVQ classifier ROC of GMLVQ classifier (Leave-One-Out of the 216 extreme samples) log rank p < 1.e-7 KM plot w.r.t. all 469 samples ( L-1-O for 216 samples, plus 253 undefined )

extreme case analysis (107+109 samples) GMLVQ classifier Risk score classifier - AUC=0.84  R=2

diagnostics? the set of 80 genes is also diagnostic: GMLVQ separates normal from tumor cells (close to) perfectly PCA of corresponding gene expressions: gradient from normal to high risk: 65 normal samples 105 low risk samples (late recurrence) 109 high risk samples (early recurrence)

remarks and open questions prospective studies required with respect to use as an assay 80 genes do not necessarily reflect biological mechanisms compare, e.g., with known pathways / modules of genes GMLVQ suggests an even smaller panel of prognostic genes (12?) identify a minimum panel for diagnostics and prognostics can the performance be improved further ? study more sophisticated classifier systems include further clinical information (diet, life style, family history, … ) more direct, multivariate identification of relevant genes ? e.g. PCA+GMLVQ and back-transform easy-to-use GMLVQ-classifier: www.cs.rug.nl/~biehl/gmlvq