1. 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

2. 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)

3. 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

4. 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
matched

5. 380 training samples 89 test samples
outlier analysis
380
training
samples
89 test samples
randomized split

6. 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

7. 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 <
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

8. 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

9. 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
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
if the sample is outlier w.r.t. to a gene in B or D (late rec.)
recurrence risk score ≤ 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

10. 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

11. 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)

12. 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

13. 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 )

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

15. 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)

16. 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: