Classification of cancer profiles. ABDBM Ron Shamir

Transcription

1 Classification of cancer profiles 1

2 Background: Cancer Classification Cancer classification is central to cancer treatment; Traditional cancer classification methods: location; morphology, cytogenesis; Limitations of morphology classification: tumors of similar histopathological appearance can have significantly different clinical courses and response to therapy; Traditionally cancer classification relied on specific biological insights challenges: finer classification of morphologically similar tumors at the molecular level; systematic and unbiased approaches; 2

3 Challenges Class prediction (classification) : Assign particular tumor samples to already-defined classes. Feature selection : Identify the most informative genes for prediction Class discovery : Define previously unrecognized tumor subtypes ( = clustering) Predict prognosis; suggest treatment! 3

4 Leukemia Golub et al., Science 286 (Oct 1999) Computational paper: Class Prediction and Discovery Using Gene Expression Data Donna K. Slonim, Pablo Tamayo, Jill P. Mesirov, Todd R. Golub, Eric S. Lander Proc. RECOMB 2000 Slides based on: Elashof-Horvath UCLA course, 4

5 Background: Leukemia Acute leukemia: variability in clinical outcome; subtle differences in nuclear morphology Subtypes: acute lymphoblastic leukemia (ALL) or acute myeloid leukemia (AML); ALL subcategories: T-lineage ALL and B-lineage ALL; 1999 status: A combination of different tests needed for diagnosis (morphology, histochemistry, immunophenotyping..) Although usually accurate, leukemia classification imperfect and errors do occur 5

6 Objective Develop a systematic approach to cancer classification based on gene expression data Use leukemia as test case 6

7 The Data Primary samples: 38 bone marrow samples (27 ALL, 11 AML) obtained from acute leukemia patients at the time of diagnosis; Independent samples: 34 leukemia samples (24 bone marrow and 10 peripheral blood samples); GE expression: Affymetrix arrays (6817 human genes) 1 st : training set. 2 nd : test set. Q: is there a class-specific signal in the data? 7

8 Metric for gene selection Want to find a set of predictive genes s.t. Typical exp patterns differ a lot between classes Low variance within each class c class vector (1,1,1,1,1,1,0,0,0,0,0,0,0) g expression vector of a gene µ i exp in class i, σ i - std in class i P(g,c) = (µ 1 - µ 0 )/(σ 1 + σ 0 ) Golub / S2N metric Pick k genes g with highest P(g,c) as predictor set. 8

9 Neighborhood Analysis: overview Define an "idealized expression pattern" c= (1,1,1,1,1,1,0,0,0,0,0,0,0) N(g)= no. of genes g s.t. P(g,c)> α Randomly permute c to π(c) R(g)= no. of genes g s.t. P(g,π(c))> α N(g) >> E(R(g)) would suggest classification is robust. 9

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11 Neighborhood analysis (contd) For each class c plot the no of genes g with P(g,c)>x as a function of x For the actual classes For randomly permuted classes 11

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13 Neighborhood Analysis: Results On the training set, ~1100 genes were more highly correlated with the AML-ALL class distinction than would be expected by chance. => ample data for informative class prediction 13

14 Predictor + Feature Selection Goal: create a classifier (predictor) Method: filtering. Choose k genes most correlated with the label on the training set 14

15 The Predictor size Pick k genes g with highest P(g,c) as predictor set,or, Pick k 1 genes highest P(g,c), k 2 with lowest Choosing k 1,k 2 : Roughly equal Few genes most statistically significant, best for clinical setting. Many genes more robust, many bio processes Too many genes unlikely to be meaningful and independent 15

16 Weighted voting S: set of features (genes) selected; x new sample Assign a weight w(g)=p(g,c) for each gene in S b g =(µ 1 - µ 2 )/2 half-way boundary for gene g Vote of gene g: V = w(g) (x g -b g ) V + = sum of positive votes; V - =sum of neg. votes The winning class: the one with larger abs value. prediction strength PS=(V winner -V loser )/ (V winner +V loser ) Assign x to the winning class if PS>0.3 Otherwise, x is undetermined. Many other voting schemes possible. 16

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18 Testing the predictors Used a 50-gene predictor LOOCV : Assigned 36 / 38 samples as either AML or ALL, 2 as uncertain (PS < 0.3). All predictions were correct Independent test: Assigned 29 / 34 samples, at 100% accuracy Median PS = 0.77 in cross-validation, 0.73 in independent test (Fig. 3A). 18

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20 Testing the predictors (contd) The average PS was lower for samples from one laboratory, which used a very different protocol for sample preparation; Should standardize sample preparation in clinical implementation. 20

21 How many features? Choosing k=50 was a bit arbitrary Results were insensitive to k: Predictors based on genes were all 100% accurate strong correlation of many genes with the AML- ALL distinction. 21

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23 Class Discovery If the AML-ALL distinction was not already known, could we discover it simply on the basis of gene expression? Strategy: Cluster the samples Assign a new sample to class with closest centroid. Test by cross-validation Compare to results on random classes 25

24 Class Discovery: Results A 2-cluster SOM was applied to cluster the 38 initial leukemia samples using exp patterns of all 6817 genes. The clusters were first evaluated by comparing them to the known AML-ALL classes (Fig. 4A). Class A1: mostly ALL (24 out of 25) Class A2: mostly AML (10 of 13 samples). 26

25 Testing discovered classes (a) Construct predictors for "type A1" or "type A2." (b) Cross-validation: Predictors with wide range of different numbers of informative genes performed well; (c) Independent test: median PS: % of samples were above threshold. High prediction strengths indicate that the structure seen in the initial data set is also seen in the independent data set. 27

26 Testing discovered classes (2) (d) Random clusters yielded significantly poorer results in CV and the independent data set (Fig. 4B). => A1-A2 distinction is meaningful, not a statistical artifact of the initial data set. => the AML-ALL distinction could have been automatically discovered and confirmed without previous biological knowledge. 28

27 Multiple cluster analysis 4-cluster SOM divides the samples into clusters, which largely corresponded to AML, T-ALL, B-ALL x 2 (Fig. 4C). Evaluated these classes by constructing class predictors. The four classes could be distinguished from one another, with the exception of B3 versus B4 (Fig. 4D). 29

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29 Multiple Clusters (2) The prediction tests confirmed the distinctions corresponding to AML, B-ALL, and T-ALL, Suggested to merge classes B3 and B4, composed primarily of B-lineage ALL. 31

30 Todd Golub, Donna Slonim 32

31 Breast Cancer Van t Veer et al, Nature

32 The Challenge Out of young women who have breast cancer, only 15-20% will develop metastases. These women must be treated aggressively (chemotherapy) - but not the rest Can expression data help to identify this group? Understand disease process? 34

33 Van t Veer s data Goal: predict clinical outcome from expression 98 primary breast cancers: Sporadic: 34 with metastases within 5 years (poor prognosis group, mean time to metastasis 2.5 yrs) 44 without (good prognosis group, mean follow-up 8.7 yrs) All <55 yrs old, lymph node negative Carriers: 18 BRCA1, 2 BRCA2 mutation carriers Measured expression levels of ~25K genes (reference: mixture of sporadic) Selected 5K genes that showed significant change. 35

34 Hierarchical clustering (unsupervised) gives two main clusters: Most carriers fall into one cluster ER & lymphocytic infiltrate different clsuters 36

35 Supervised clustering: On sporadics, selected genes significantly correlated w metastasis 231 genes with CC >.3 ranked by CC Added 5 at a time and checked classification using leave-one-out Optimal accuracy with 70 genes: 83% ( ) Raised threshold so as to miss less poor prognosis patients (- - -) Independent validation: On 19 other cases, 2 misclassifications OR for metastasis for women with poor prognosis signature: 15 Prev methods:

36 How many of the women would current medical guidelines subject to chemotherapy? 39

37 van de Vijver et al. NEJM consecutive patients w breast cancer 151 lymph node negative 144 lymph node positive disease Applied the 70 gene poor prognosis signature to each: 180 poor, 115 good Ave 10 year survival rate: 55% vs 95% Odds to be free of metastasis at 10 years: 50% vs 85% (Hazard ratio: 5.1) 40

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40 Conclusions The gene-expression profile we studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria. 44

41 Laura van t Veer 45

42 46 Act 2

43 A first breast cancer diagnostic chip Phoenix, AZ April 21, The Molecular Profiling Institute, Inc. (MPI) announced today that it is now providing the MammaPrint breast cancer test to breast cancer patients in the United States. This is the first commercially available microarray cancer diagnostic that analyzes patients' breast tumors for their individual DNA expression profile. "MammaPrint more accurately distinguishes between lymph node-negative breast cancer patients who would benefit from additional therapy from those who would not, helping oncologists offer more effective therapy to their patients. The 70 genes in a woman's tumor analyzed by 47 MammaPrint predict the 10-year survival of the

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45 Caveats Mammaprint Act 3 54

46 Ein-Dor et al. Bioinformatics 05 Reanalyzed the 96 sporadics samples of vant Veer Is the 70-gene signature selected unique? Training set: the same 77 patients of vv Ranked all genes by correlation to survival Features for classifier: (vv) genes 1-70; (1) ; (2) , (7) Applied each classifier to all 96 samples 55

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48 Effect of Training set Vant Veer Rmas wamy 03 Selected 10 times a random set of 77 training samples out of the 96. For each, ranked the top 70 genes by correlation Compared to rank in 1st training set 57

49 Conclusions Many genes can be used to predict survival No gene correlates very strongly A gene s rank may fluctuate strongly Identities of the top 70 genes are not robust Much larger number of patients needed to identify those genes indicative of gene s importance to cancer pathology But: For prognosis, can produce fairly reliable signatures, using large enough gene set. 58

50 The dilemma If the results from adjuvant trials confirm the strong benefit for HER2-positive patients using adjuvant chemotherapy plus trastuzumab, would there be clinicians prepared to withhold adjuvant chemotherapy in a young patient with a node-negative, HER2-positive breast cancer and a good prognosis signature? Brenton et al., Journal of Clinical Oncology 23 (29) pp7350 (2005) 59

51 A prospective study Mammaprint Act 4 60

52 10 year prospective study of 6,693 patients from 112 institutions, 9 countries C classic clinical risk, MP genomic risk 61

53 The study design Of all patients with high clinical risk, treating based on MP would have saved chemo for 46% of the patients 62

54 Results With Chemo: 1.5% higher 63

55 Multi-Class Cancer Classification Ramaswamy et al (Golub s group), PNAS

56 Data 218 profiles of tumors of 14 types Affy chips, 16K genes, 11K after variation filtering Training set: 144 samples; test: 54 samples Additional set: PD. 20 poorly differentiated carcinomas. Difficult to classify with traditional methods as they lack characteristic morphological hallmarks of the organ from which they arise. 66

57 Class Discovery Applied hierarchical clustering (Eisen), SOM Mixed success 67

58 Classification One vs. All (OVA) approach Use a 2-way classifier alg A Label the members of a class 1, rest 0; train A; classify all samples and get confidence values to assignments Repeat for each class. Get 14 Assign each sample to the class on which it was accepted with highest confidence. 68

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60 Weighted voting, KNN and SVM had significant prediction accuracy SVM was consistent ly best Genes: best S2N metric in OVA for each class 70

61 Classification results 71

62 Recursive feature elimination OVA SVM classifier outputs a hyperplane w. 2 class = sign (Σw k x ik + b) Recursively remove the 10% with least w k values and retrain Stop when accuracy decreases (0r use to study gene number effects) 1 min w 2 st.. y( w x + b) 1, x i i i 72

63 Accuracy vs. gene number OVA: One vs All AP: all pairs WV: weighted voting 73