Gene expression correlates of clinical prostate cancer behavior

Transcription

1 Gene expression correlates of clinical prostate cancer behavior Cancer Cell : Singh D, Febbo P, Ross K, Jackson D, Manola J, Ladd C, Tamayo P, Renshaw A, D Amico A, Richie J, Lander E, Loda M, Kantoff P, Golub T, Sellers W. Topics in Bioinformatics Robert Kazmierski Oct 12, 2004

2 Primers Gleason score Signal to noise metric k-nn Leave one out CV (continued) Permutation Testing (Part 1)

3 Gleason Score From 1 Simple round glands, closely packed in rounded masses with well-defined edges. 2 Simple round glands, loosely packed in vague, rounded masses with loosely packed edges. 3A Medium-sized single glands of irregular shape and irregular spacing with ill-defined infiltrating edges. 3B Very similar to 3A, but small to very small glands which must not form significant chains or cords. 3C Papillary and cribriform epithelium in smooth, rounded cylinders and masses; no necrosis. 4A Small, medium, or large glands fused into cords, chains or ragged, infiltrating masses. 4B Very similar to 4A, but with many large clear cells, sometimes resembling "hypernephroma." 5A No glandular differentiation, solid sheets, cords, single cells, or solid nests of tumor with central necrosis. 5B Anaplastic adenocarcinoma in ragged sheets.

4 Gleason Score Pathologist examines two areas that make up the largest portion of the tissue Each area scored and resulting sum is Gleason score Answers how cancerous is the prostate tissue High = very cancerous Low = less cancerous

5 Signal to Noise Metric From Golub et al µ σ Class0 µ + σ Class1 Where µ is the mean and σ is the standard deviation In our case, Class 0 would be Cancer and Class 1 would be Normal Method for determining statistical significant difference S Class0 Class1 = 2 N

6 Signal to Noise Metric vs. T-Test S2N different from T-test t = nclass0 + n nclass0n Class1 Class1 ( n µ Class0 Class0 µ 1) σ n Class1 Class0 2 Class0 + + ( n n Class1 Class 1 1) σ 2 2 Class1 Here n is number of samples S2N does not take n into account

7 k-nearest Neighbor Given: d(x,x ) : metric to measure relative distances Euclidean distance used in this case L : training set f knn (x) = majority class among the k NN s of x in L Where x is a sample to be classified (cancer vs. normal) K specifies how many neighbors to consider Usually odd number

8 3-Nearest Neighbor 3 NN 2 Blue, 1 Red Green grouped as Blue

9 5-Nearest Neighbor 5 NN 2 Blue, 3 Red Green grouped as Red

10 k-nearest Neighbor Result influenced by Choice of k Training set Measure of distance Most basic classification tool

11 Leave one out CV (continued) Method for determining error in classifier Everything must be done inside of CV loops Outside adjustments lead to incorrect error estimates (usually too low)

12 Leave one out CV 102 Samples testing effectiveness of k-nn classifier (k is set to 3)(10 most significant genes used in k-nn) 1 sample set aside S2N of 101 samples determines 10 most significantly different genes in two classes 3-NN by Euclidean distance performed on single sample Sample classified as Class0 or Class1 Results recorded as correct classification or not Process repeated 101 more times (each sample set aside once) Results give accuracy of using 10 most significant genes with S2N metric and 3-NN classification method

13 Leave one out CV 102 Samples testing effectiveness of k-nn classifier, now allowing k to be 3, or 9 (10 most significant genes used in k-nn) 1 sample set aside 1 sample set aside, k set to 3 S2N of 100 samples determines 10 most significantly different genes in two classes 3-NN by Euclidean distance performed on single sample Sample classified as Class0 or Class1 Results recorded as correct classification or not Process repeated 100 more times (each sample set aside once) 1 sample set aside, k set to 9 S2N of 100 samples determines 10 most significantly different genes in two classes 9-NN by Euclidean distance performed on single sample Sample classified as Class0 or Class1 Results recorded as correct classification or not Process repeated 100 more times (each sample set aside once) Best -NN determined by least error in inner loop of CV S2N of 101 samples determines 10 most significantly different genes in two classes Best -NN by Euclidean distance performed on single sample Sample classified as Class0 or Class1 Results recorded as correct classification or not Process repeated 101 more times (each sample set aside once) Results give accuracy of using 10 most significant genes with S2N metric and 3,9-NN classification method

14 Leave one out CV 102 Samples testing effectiveness of k-nn classifier, allowing k to be 3, or 9, and allowing 10, 20, 50 most significant genes to be used in k-nn Inner loop for selection of k as seen before Inner loop for number of genes to be used in k- NN classification Results give accuracy of using most accurate amount of genes and most accurate k in classification

15 Permutation Testing (part 1) Method for determining significance of correlations to classes i.e. how likely the gene is expressed more highly in cancer by chance

16 Permutation Testing Take data and randomly reassign class labels (cancer, normal) to gene expression levels Removes correlation between gene expression levels and class labels Repeat correlation method (S2N metric in this case) to see if correlation still exists between gene data and class labels Repeat reassignment of class labels (permutation) and test again for correlation If correlations persist through permutations, they are not significant If correlations are lost through permutations to a significant level, they can be considered significant

17 Singh et al. Objectives Identify genes in microarray expression analysis that might anticipate clinical behavior of prostate cancer Cancer vs. Normal Recurrent vs. Nonrecurrent

18 Motivation Prostate cancer very common among cancers Early diagnosis increases chances of survival Clinical tests (Gleason Score, serum PSA) not completely reliable Progress has already been made in linking differential gene expression to cancer (p53, myc, p27, PTEN) No gene found yet to have sufficient prognostic utility to warrant clinical implementation

19 Methods 12,600 genes in microarray analysis 52 tumor and 50 normal prostate specimens

20 Tumor vs normal classification Genes ranked according to their differential expression across the 2 classes using S2N metric Statistical significance of these rankings determined using a permutation test 1000 permutation of class labels determined 317 genes had significantly higher expression in tumor samples 139 genes had significantly higher expression in normal samples p = in this case means that the correlation existed at most once in the 1000 permutations for each of the significantly different genes

21 Top 50 genes with high expression in tumor along with top 50 genes with high expression in normal. (red = above mean of all samples, blue = below)

22 Tumor vs normal classification Built k-nn algorithm using significantly different genes Models using 4 or more genes classified samples with greater than 90% accuracy in leave one out CV (p < as measured by permutation testing) Methods of leave one out CV not included as well as value of k

23

24 Tumor vs normal classification 4 gene and 16 gene models were tested on independent data set 8 normal, 27 tumor 4 gene model accuracy 77% 16 gene model accuracy 86%

25 Prediction of pathological features Gene expression data within 52 tumor samples analyzed for correlations with clinical behavior Determined by comparing observed correlations with those in a randomly permutated dataset Correlation found only with Gleason score (GS)

26 Prediction of pathological features 15 genes had expression positively correlated with GS (Type I) 14 genes had expression negatively correlated with GS (Type II) Red = above mean Blue = below mean

27 Prediction of pathological features Same 29 genes were used to drive hierarchical clustering of independent data set Type I and Type II genes remained highly cosegregated suggesting this coexpression is reproducible

28 Prediction of clinical outcome 21 patients evaluated with respect to recurrence following surgery 8 relapsed 13 have remained cancer free for 4 years No single gene associated with recurrence k-nn classification with k=2 on a 5 gene model results in 90% classification accuracy during leave one out CV Again no information provided for CV methods No independent data available for testing

29 Prediction of clinical outcome As another test of significance, tested 1000 permutations of class labels and attempted to find multigene expression classifiers using same range of gene numbers 37 of 1000 permutations yielded accuracy of 90% or more p = 0.037

30 Discussion Clinical Use? Level of accuracy in classifying tumor vs. normal is 86-92% While high, still not sufficient to replace histological examination No association between serum PSA and gene expression Possible that more genes need to be included and/or more samples need to be evaluated

31 Discussion GS correlation GS was associated with patient outcome However, only 2 genes correlated with GS were used in outcome prediction model Genes most frequently used in model were not correlated with GS GS-independent markers and determinants of prostate cancer exist

32 Discussion recurrence predictor model 5 gene model correctly predicted 19 of 21 evaluable patients Authors concede that model may be result of overoptimization More datasets needed for model validation Some of genes used commonly in model are known to have correlation with prostate cancer

33 Conclusion This is a proof of concept paper used to suggest further research rather than suggest changes in clinical practice Authors use of leave one out CV not well explained and could be result of very good results from models Independent testing helped validate models