Elia Biganzoli,

Transcription

1 High dimensional data for supporting medical decision: are we meeting the challenge? Elia Biganzoli, Dept. of Medical Statistics and Biometry G.A.Maccacaro, Istituto t Nazionale Tumori Università ità degli Studi di Milano, Campus Cascina Rosa. Milano, Italy Challenges in design, analysis and reporting of prognostic and predictive marker research from single studies to an EBM based assessment Organisers: Willi Sauerbrei and Doug Altman Satellite Workshop to the Cochrane Colloquium, Freiburg, 8 October 2008

2 Three common types: Class Discovery Omic study objectives Golub et al Molecular Classification of Cancer: Class Discovery and Class Prediction by gene expression Monitoring Perou et al. Molecular portraits of human breast tumours. Nature Iwao K et al. Molecular classification of primary breast tumors possessing distinct prognostic properties. Hum Mol Genet Sørlie Molecular portraits of breast cancer: tumour subtypes as distinct disease entities. Eur J Cancer Class Comparison } Class Prediction Classes are predefined van t Veer et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature van de Vijver et al. A gene-expression expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002

3 Traditional prognostic criteria in early N- breast cancer When they should receive adjuvant therapy? Balance between undertreatment of few patients vs overtreatment of the majority Definition of low risk patient group (...)The panel agreed that t a population of patients t who have less then a 10% chance of relapse i.e. 90% disease free survival probability (DFS) within 10 years, would not be candidates for receiving routine adjuvant systemic therapy. (...) St.Gallen (Goldhirsch( et al. JCO, 2003; Annals of Oncology, 2007): ) ER>0 and/or PgR>0 (no medullary, apocrine) and pt 2 cm, grade I, age 35 years, no extensive peritumoral vascular invasion, HER2/neu not overexpressed/amplified NIH (Eifel l et al.jnci, 2001): pt 1 cm Low accuracy: large number of patients overtreated high risk group: 70-80% DFS without adjuvant therapy

4 (1) Genomic prognostic criteria in early N- breast cancer Need for improvement of prognostic accuracy Ideally, without adjuvant therapy: 100% DFS probability for the low risk group (a large group not needing to be treated) 0% DFS probability for the high risk group (a small group needing to be treated) cdna microarray techniques for the simultaneous evaluation of the expression of tens thousand genes

5 (2) Genomic prognostic criteria in early N- breast cancer Gene expression profiling for predicting clinical outcome (van t Veer et al. Nature, 2002) cdna microarrays for the expression of genes on 78 patients 70 marker genes for identify low/high risk groups Retrospective follow-up study on the low/high risk classificator on 70 genes (van de Vijver et al. NEJM, 2002) 90% 10 years DFS for the low risk group 45% 10 years DFS for the high risk group (Is it fully satisfactory the prognostic accuracy gain?)

6 Issues on genomic prognostic criteria Blind classification on 70 marker genes vs few established traditional prognostic factors. Another classifier on the same data of van t Veer et al.(2002) found 119 marker genes, showing similar performance but only 28 genes are shared (Lama N, Ambrogi F, Antolini L, Boracchi P and Biganzoli E, Some issues and perspectives in microarray data analysis in breast cancer: the need for an integrated research, 1st European Workshop on the Assessment of Diagnostic Performance, 7-9 July, Milano) Nicola Lama, Patrizia Boracchi, and Elia Mario Biganzoli, "Exploration of distributional models for a novel intensity-dependent normalization " (October 2006). COBRA Preprint Series. Article Another classifier on the same data but resorting to combined conventional biomarkers, showed similar performance of van t Veer et al.(2002) (Eden et al., Eur J Cancer, 2004) )

7 Class Prediction Issues Michiels Koscielny Hill: Lancet 2005 Reanalysis of 7 studies that attempted to predict prognosis: Instability of the set of genes included in the signature Five of the seven studies did not classify patients better than chance Studies with larger sample size are needed d before gene expression profiling can be used in clinic. It is unlikely that the oncologist would apply a decision criterion without clearly understanding its biological bases and its clinical value. This is the underlying risk of a black-box classification.

8 The instability of microarray data results is a major problem It is hard to think that it could be solved on a pure statistical basis. Microarray studies have a major exploratory role

9 Lama N., Boracchi P., Biganzoli E. Exploration of distributional models for a novel intensity-dependent normalization procedure in censored gene expression data, COBRA Preprint Series. Article p p Accepted on Computational Statistics and Data Analysis

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12 BIDIMENSIONAL ELECTROPHORESIS (2-DE) + - Low reproducible: analytical and biological variability Class comparison experiments

13 Proteomics: Multiplex Cytokine Array Assay Method multiplex sandwich ELISA for the quantitative measurement of 9 cytokines in serum samples. Each well of the microplate provided is pre-spotted with cytokines/angiogenetic factors-specific specific antibodies. The enzyme substrate reaction produces a luminescent signal. Study Design In plate arrays we detected 9 cytokines on 150 samples from patients with diagnosis of myocardial infarction. Furthermore, on all samples we measured Il-6, IL-10, IL-12 (using a Gold Standard method). We compared the results for these three cytokines evaluated by the two assays.

14 Proteomics: Multiplex Cytokine Array

15 Proteomics: Multiplex Cytokine Array Assay Method multiplex sandwich ELISA for the quantitative measurement of 9 cytokines in serum samples. Each well of the microplate provided is pre-spotted with cytokines/angiogenetic factors-specific specific antibodies. The enzyme substrate reaction produces a luminescent signal. Study Design In plate arrays we detected 9 cytokines on 150 samples from patients with diagnosis of myocardial infarction. Furthermore, on all samples we measured Il-6, IL-10, IL-12 12( (using a Gold ldst Standard d method). We compared dth the results for these three cytokines evaluated by the two assays. Results 1) Available plate array imaging evidenced differences between plates, performed at a time distance of six months, possibly due to matrix effects. 4) The comparison with the Gold standard methos showed relevant differences with even detectable/high values for those samples in which analytes resulted undetectable by the multiplex assay. Conclusion from our experience, the sensitivity of the system to matrix effect, the aspecific binding of the antibody, provided different results strictly associated to the plate array employed. This heavily affected the reproducibility of results.

16 A different perspective in the development of prognostic criteria i

17 A different perspective in the development of prognostic criteria i (Biganzoli & Boracchi, Editorial, Eur J of Cancer, 2004) Past research on single biomarkers with high prognostic power failed? (Cyclin-E still promising? Keyomarsi et al, NEJM, 2003) ) Combined use of clinical factors and biomarkers would improve prognostic accuracy Complex statistical ti ti models for exploring the biology of the disease (tumor bioprofiling and recurrence dynamics) to derive simple prognostic criteria for supporting medical decision (need to understand the clinical/biological reasons of therapy counselling)

18 A different perspective in the development of prognostic criteria i (Biganzoli & Boracchi, Editorial, Eur J of Cancer, 2004) Past research on single biomarkers with high prognostic power failed? (Cyclin Cyclin-E still promising? Keyomarsi et al, NEJM, 2003) Combined use of clinical factors and biomarkers would improve prognostic accuracy Complex statistical models for exploring the biology of the disease (tumor bioprofiling and recurrence dynamics) ) to derive simple prognostic criteria for supporting medical decision (need to understand the clinical/biological reasons of therapy counselling) The St.Gallen and NIH criteria may be too simple! (too low prognostic accuracy)

19 A changing perspective in the need of prognostic criteria: i steroid receptors (Goldhirsch et al. JCO, 2003) Gene expression profiling support different biologic profiles according to steroid receptors. Clinical studies provide evidence that receptor-absent disease is different from that with even low levels of receptor expression

20 A changing perspective in the need of prognostic criteria: i steroid receptors (Goldhirsch et al. JCO, 2003) Gene expression profiling support different biologic profiles according to steroid receptors Clinical studies provide evidence that receptor-absent disease is different from that with even low levels of receptor expression Change of laboratory reporting strategies (no longer arbitrary cutoff values!)

21 A changing perspective in the need of prognostic criteria: i steroid receptors (Goldhirsch et al. JCO, 2003) Gene expression profiling support different biologic profiles according to steroid receptors Clinical studies provide evidence that receptor-absent disease is different from that with even low levels of receptor expression Change of laboratory reporting strategies (no longer arbitrary cutoff values!) At the other extreme, patients with strong receptor expression may be effectively treated with endocrine therapy alone

22 A changing perspective in the need of prognostic criteria: i steroid receptors (Goldhirsch et al. JCO, 2003) Gene expression profiling support different biologic profiles according to steroid receptors Clinical studies provide evidence that receptor-absent disease is different from that with even low levels of receptor expression Change of laboratory reporting strategies (no longer arbitrary cutoff values!) At the other extreme, patients with strong receptor expression may be effectively treated with endocrine therapy alone A gradation of levels between the two extremes (Need of risk profiling and predicting the response to therapy)

23 Flexible Modelling in survival data analysis From knowledge Exploratory study of disease dynamics on the basis of censored survival data. Soft assumptions on covariate effects. Non-linear, non-additive and time dependent continuous covariate effects. to decision i Individual risk profiling from disease features for supporting clinical decision making

24 A two-phase perspective p All models are wrong, some, though are better than other but...for doing what? Knwoledge phase: exploratory modelling Check or generation of biological or clinical hypotheses Decision phase: predictive modelling Support medical decision

25 Knowledge vs Prediction Building a predictive model without previous exploratory studies is difficult Conducting both phases in the same study is difficult The assessment of prognostic factors in breast cancer is a typical example many markers studied (often resorting to arbitrary cutoff values) very few validated for clinical routine. high costs and increased risks in the genomic era

26 Knowledge from exploratory analysis Even for traditional clinical i l factors measured on a continuous scale is a recent acquisition. Example: age and breast cancer recurrence

27 Knowledge from exploratory analysis Veronesi et al.; J.Natl.Canc.Inst., 1995 Peaks in breast cancer recurrence dynamics and the tumour dormancy hypothesis

28 Flexible estimation of competing risk (RegressionSplines RegressionSplines)

29 degrees of freedom AIC (Nointeraction) AIC (Interaction) degrees of freedom AIC (No interaction) AIC (Interaction)

30 Knowledge from exploratory analysis Modelling covariate effects on failure dynamics

31 Knowledge from exploratory analysis Partial Logistic ANN (PLANN) modelling continuous interactions (time,covariates)

32 Women with primary invasive breast cancer (N=633), who underwent surgery between January 1983 to December (archieved at the Pathology dept. of the University of Ferrara) Tumour markers (by IHC) : ER, PgR, Ki-67/MIB1, c-erbb-2/neu and p53 Aim: clustering of tumours according biomarkers, for the search of underlying cancer subtypes and their clinical features. Study of conditional independecies among biomarkers

33 Conclusions Integration of exploratory studies (knowledge phase) with predictive ones to support clinical decision Use of biological knowledge in a clinical context (biological bases need to be understood for medical decision making)

34 Conclusions Integration of exploratory studies (knowledge phase) with predictive ones to support clinical decision Use of biological knowledge in a clinical context (biological bases need to be understood for medical decision making) According to Huang et al. (Lancet, 2003): Genomic data will not replace traditional clinical factors but will add substantial detail

35 Conclusions Integration of exploratory studies (knowledge phase) with prospective ones to support clinical decision Exploitation of biological knowledge in a clinical context (biological basis for the decision need to be understood) According to Huang et al. (Lancet, 2003): Genomic data will not replace traditional clinical factors but will add substantial detail Need for innovative prognostic studies to integrate traditional factors with bioprofiles, allowing for simple but more accurate decision rules.

36 Conclusions Integration of exploratory studies (knowledge phase) with prospective ones to support clinical decision Exploitation of biological knowledge in a clinical context (biological basis for the decision need to be understood) According to Huang et al. (Lancet, 2003): Genomic data will not replace traditional clinical factors but will add substantial detail Need for innovative prognostic studies on traditional factors to better use them, allowing for simple but more accurate decision rules. Need for the cooperation of clinicians, biologists, bioinformaticians and biostatisticians, looking for shared semantics.

37 ADDITIONAL REFERENCES Goldhirsch A, Wood WC, Gelber RD, Coates AS, Thurlimann B, Senn HJ. Meeting highlights: updated international expert consensus on the primary therapy of early breast cancer. J Clin Oncol 2003, 21, Eifel P, Axelson JA, Costa J, Crowley J, Curran Jr WJ, Deshler A, et al. National Institutes of Health Consensus Development Conference Statement: adjuvant therapy for breast cancer, November 1-3, J Natl Cancer Inst 2001, 93, van t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, et al. Gene expression pro.ling predicts clinical outcome of breast cancer. Nature 2002, 415, Eden P, Ritz C, Rose C, Ferno M, Peterson C. Good Old clinical markers have similar power in breast cancer prognosis as microarray gene expression pro.lers. Eur J Cancer 2004, 40, Keyomarsi K, Tucker SL, Buchholz TA, Callister M, Ding Y, Hortobagyi GN, et al. Cyclin E and survival in patients with breast cancer. New Engl J Med 2002, 347, Huang E, Cheng SH, Dressman H, Pittman J, Tsou MH, Horng CF, et al. Gene expression predictors of breast cancer outcomes.lancet 2003, 361,