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Canadian Bioinformatics Workshops

Canadian Bioinformatics Workshops. www.bioinformatics.ca. Module #: Title of Module. 2. Module 9 Clinical Data Integration. Anna Lapuk, PhD Bioinformatics for Cancer Genomics May 25-29, 2015. Module 3 overview. Part I Clinical Data and Biomarkers Part II Statistical aspects Lab:

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Canadian Bioinformatics Workshops

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  1. Canadian Bioinformatics Workshops www.bioinformatics.ca

  2. Module #: Title of Module 2

  3. Module 9 Clinical Data Integration Anna Lapuk, PhD Bioinformatics for Cancer Genomics May 25-29, 2015

  4. Module 3 overview • Part I • Clinical Data and Biomarkers • Part II • Statistical aspects • Lab: • Survival analysis

  5. Learning Objectives • To understand how clinical information is used • To understand the process of genomic data integration with clinical information • To review the current advances on the biomarker/clinical applications development • To learn how to evaluate the biomarker and perform survival analysis • Be able to analyze tumour cohorts with regard to association of molecular subgroups with outcome.

  6. What is the clinical data

  7. Usage of clinical data alone: prediction Adjuvant! Online (breast cancer) Nomograms (prostate cancer)

  8. Cancer ‘omics data SEQUENCING ARRAYS Transcriptome Genome Methylome Histone marks Protein binding sites Rearrangementss Mutations Gene expression Copy number ChIP-Seq Alternative splicing CpG methylation Fusion genes 100s-1000s of aberrations

  9. Goal: use ‘omics data to aid clinical decisions Example: SNV Biomarker Clinical use Van Allen, JCO 2013

  10. Biomarkers & therapeutic targets (NCI) • Biomarker is a biological molecule (or a set thereof) found in blood, other body fluids, or tissues that is a sign of a normal or abnormal process, or of a condition or disease. Also called molecular marker and signature molecule. • Therapeutic target is a biological molecule, an enzyme, receptor or other protein that can be modified by an external stimulus (drug). The implication is that a molecule is "hit" by a signal and its behavior is thereby changed.

  11. Biomarker features • Biomarker comes from alterations: • Germline/somatic mutation • Genomic amplification/deletion • Transcriptional change • Post-transcriptional modification • Biomarker types: • Proteins • Nucleic acids (mRNA, miRNA, non-coding RNA) • Cells (Circulating Tumour Cells) • Peptides • Individual molecules vs Sets (signatures): • Gene expression (n genes) • Proteomic (n proteins) • Metabolomic (n metabolites)

  12. Biomarker features (cont’d) • Biomarker screening: • Circulation (blood, serum, plasma) • Excretions/secretions (stool, urine, etc.) • Tissues (biopsy + imaging) • Biomarkers may be also therapeutic targets, but not always: • HER2 (breast cancer): biomarker and therapeutic target • PSA (prostate cancer): biomarker. AR – target. • KRAS mutations (colorectal cancer): biomarker. EGFR – target.

  13. Clinical use of biomarkers • To diagnose or subclassify the disease state => diagnostic • BCR-ABL fusion leukemia (Philadelphia chromosome) • To make prognosis about a clinical outcome (survival or recurrence) => prognostic • OncotypeDx gene expression (estimates the risk of breast cancer recurrence) • To predict the activity of a therapy => predictive • HER2 and herceptin (predicts response in breast cancer) • To identify a subgroup of patients for whom therapy has shown benefit => companion diagnostic markers • BRAF V600E mutation and BRAF inhibitor (confers sensitivity in melanoma)

  14. Examples of use

  15. Biomarkers used in clinic Prostate Colon NSC Lung Glioma Breast Glioma NCCN

  16. Fusion gene biomarker: ALK in NSCLC ALK-3’ • Activating ALK fusions in 2-7% of NSCLC/adenocarcinoma/non-smokers (EML4-ALK) • Crizotinib – ALK inhibitor • FDA-approved FISH test for ALK-fusion as a companion test for crizotinib treatment ALK-5’ Response to crizotinib (tumour burden) Kwak, NEJM 2010

  17. Oncotype DX: gene chip for breast cancer • Breast cancer patients treated with hormone therapy alone (tamoxifen) recur only in 15% within 10 years. =>85% may not need additional chemotherapy. • Start: 250 candidate genes from 3 independent studies (447 patients) • End: 21-gene RT-PCR assay in FFPE samples => recurrence score • Test for recurrence in node-neg, ER-pos breast tumours treated with Tamoxifen 21-gene set Difference in outcome for predicted risk groups (P<0.001) Paik, NEJM 2004

  18. Methylation biomarker: CpG in colon cancer Cohort 2 Cohort 1 CIMP1 high • Colorectal cancer (CRC) – 40% lethal outcome • CpG island methylator (CIMP) phenotype – subclasses. • CIMP-high (15-20%) • CIMP-low (20%-45%) • CIMP-high CRCs – unclear association with outcome • Other factors: microsatellite instability (MSI – clinical marker of better prognosis); BRAF mutations CIMP low CIMP2 high • CIMP-high • with MSS • worse • outcome • (HR>3) Methylation profile Dahlin, Clin Can Res 2010

  19. CTC biomarker: breast and NE cancers • In metastatic breast cancer CTCs count in blood sample (>5 or <5) is associated with outcome • Dynamic of CTC count is important • In metastatic NET (neuroendocrine tumours) CTCs count in blood sample (>1 or <1) is predictive of outcome, HR>6 (compared with NET marker CgA) Hayes, ImagDiagProg 2006 Khan, JCO 2013

  20. Biomarker development • Identification. Discovery approach to identify biomarkers that are different between cohorts of tumours using variety of technologies • Microarrays/ sequencing/ mass spectrometry. • Important: careful study design to avoid bias in biomarker discovery (matched cases and controls)! • Validation. • Analytical validity. • Biomarker assay: reproducibility, sensitivity, specificity. • Clinical validity • How reliably the biomarker divides the populations into 2 groups of different outcomes. Important: validation should be done on independent cohorts of tumours! • Clinical utility • Does the biomarker able to improve the clinical decision-making. Depends on the strength of association of biomarker with outcome, the size of the effect, particular disease and overall benefits, risks and economics. Example: marker identifies 2 subgroups of tumours with very different survival. However, no treatment options are available => no clinical utility.

  21. Established clinical utility: KRAS mutations in colorectal cancer EGFR • Frequent up-regulation of EGFR in human tumours. • EGFR – targeted therapy • Resistance mechanisms: • EGFR mutations • Alternative pathways • Activation of downstream effectors (PI3-K, KRAS,BRAF) Dempke, Antican Res, 2010

  22. Established clinical utility: KRAS mutations in colorectal cancer(cont’d) • KRAS-mutin 40% of CRC, associated with poor survival • Screening of KRAS mutations in patients treated with anti-EGFR: • responders – KRAS-wt • non-responders – high frequency KRAS-mut • In vitro studies confirm the role of KRAS-mut in resistance • 4 prospective clinical trials investigating the effect of KRAS-mut on anti-EGFR therapy gave consistent results • NCCN, ASCO recommended test for KRAS mutations in metastatic CRC in conjunction with EGFR-treatment. Anti- EGFR-treatment KRAS-wt KRAS-mut Lievre, Can Res, 2006; Benvenuti, Can Res 2007

  23. Allegra, JCO, 2009

  24. No clinical utility: nomogramsvs genomic markers in prostate cancer Nomograms perform well Genomic markers – no/little benefit Nomogram alone + gene expression • Gene expression c-index – 0.75 • Nomogram c-index – 0.84 • Combined model concordance index - 0.89 Note: c-index is a generalisation of the area under the ROC curve (AUC); c <0.5 – no classification; >0.5 – successful classification; c=1 – perfect. Iremashvili, Onc 2013 Stephenson, Can 2005

  25. Decipher: from transcriptomics to prostate cancer test with clinical utility (GenomeDx) Discovery (Mayo Clinic cohort/mixed adjTx) (2013) Validation (independent Mayo Clinic cohort/mixed adjTx) (2013) Validation (independent Thomas Jef cohort/adj RT) (2014) Prognostic/predictive value in RT setting (Thomas Jeff + Mayo Clinic cohorts) (2015) Clinical utility (2015)

  26. Discovery (Mayo Clinic cohort/mixed adjTx) Decipher: Discovery • Mayo clinic cohort, nested design(Nakagawa, 2008): • ~10,000 RP Pts • ~2,000 with rising PSA • 213 SYS Pts • 213 No evidence of disease • 213 BCR • Selected Pts randomly assigned to training and validation sets (n=359; 186) • Platform: Affymetrix Human Exon array • Feature filtering • Random forest classifier, 22 features • Validation, performance evaluation, comparison with other classifiers • Strong prognostic value for Mets and early CS death • Good performance (AUC =0.75) • Better than clinicopathologic features • Better than other expression signatures • - GC developed and tested on the same platform, • and same institution cohort. • - Nakagawa 2008 markers on the same cohort • ( based on ~500 genes pool). No gene overlap. • Markers are platform specific -> important to • apply in the same platform setting.

  27. Decipher: Discovery cohort biases Cohort • Bias of adverse pathology: cases have higher incidence of SM+, SVI, ECE and LN+ • Mixed Adjuvant therapy -> effect survival times • Strong GS bias: controls are enriched with low GS; • cases enriched with high GS • Markers associated more with aggressiveness in general including metastatic potential, clinical/pathological confounding factors limit the ability to capture specifically metastaticbiology

  28. Decipher: Clinical validation -1/prognostic value Validation (independent Mayo Clinic cohort/mixed adjTx, n=256) Cohort is similarly distributed by clinicopathologic features • Better performance than clinicopathologic features. Strong association with Mets. • !No thorough performance evaluation on other mentioned independent cohorts

  29. Decipher: Clinical validation -2/predictive value BF DMets Low score High score CAPRA-S GC Validation (independent Thomas Jef cohort/adj RT) n=139 Prognostic/predictive value in RT setting (Thomas Jeff + Mayo cohorts) n=137+51 • GC performs better than clinicopathologic-based scores • Informs on the need and timing of post-RP RT • High GC score Pts benefit from Adj RT

  30. Decipher: Clinical utility GC changes decision in ~43 and 50% of cases in Adjuvant and Salvage Radiotherapy Treatment change – Radiotherapy. Clinical utility DECIDE study 24 Pts 21 Urologist from 18 institutions

  31. Rigorous clinical validation: early stage lung cancer Hazard ratio • 442 lung cancers, 6 collection sites • 4 institutions profiled gene expression using the same platform • Uniform sample selection, processing and data pre-processing • 8 distinct biomarkers developed on a training cohorts by 4 institutions; blinded validation on two independent cohorts • Conclusion: combination of biomarker A (multi-gene) with clinical info had best performance. Kaplan-Meier survival Validation set 1 ROC curves Validation set 2 Shedden, Nat Med 2008

  32. Biased biomarker: prostate cancer • Signature serum peptides for discrimination of cancers vs healthy controls • Prostate cancer cohort: 32 patients (age =66) • Control cohort: 33 healthy individuals (age 34, mostly females) • Biomarkers are related to age/sex, not prostate cancer Vellanueva, J Clin Invest 2006

  33. Statistical aspects of biomarker development

  34. Identification of biomarkers Supervised analysis KRAS mutations in responders vs non-responders Unsupervised analysis Biomarker=classifier

  35. Example Novel subgroups classifier Testing, validation Curtis, Nature 2012

  36. Classifier (biomarker) purpose Classification methods Feature selection discrimination Classification rule classifier Prediction

  37. Classifier development strategy resubstitution error rate Learning/(training) set V-fold CV Performance assessment V subset All but V subset classifier classifier CV average test set error rate Test set error rate Independent test set Note: Learning and Test sets have to be identically distributed

  38. Classifier performance assessment • How accurate is classifier (confusion matrix, accuracy) • How well classifier worked on learning set (resubstitution error rate) • How well classifier worked on test set (test set error rate) • Cross validation • How do different classifiers compare (ROC curves)

  39. Confusion matrix Accuracy ACC = (TP + TN) / (P + N) or ACC=Sensitivity*Fractionpos + specificity*Fractionneg

  40. Example 2010 best cut-off values of CA125 for preoperative selection of intermediate- to high-risk, and high-risk diseases

  41. ROC curves Definition: receiver operating characteristic (ROC), is a graphical plot of the sensitivity, or true positive rate, vs. false positive rate (1 − specificity), for a binary classifier system as its discrimination threshold is varied. Purpose: - to find the best threshold for discrimination (value of expression of a gene-classifier) - compare performance of different classifiers Summary: - AUC (area under the curve, c-index) (c <0.5 – no classification; c>0.5 successful classification and closer to 1 is best) Best method no discrimination line True Pos Rate False Pos Rate

  42. Survival data – special case Survival times – time to a given end point Survival analysis

  43. Survival analysis

  44. Survival data • Survival time – is the time from a fixed point to an end point • Almost never observe the event of interest in all subjects (censoring of data) • Need for a special analytical techniques

  45. Censored observations • Arise whenever the dependent variable of interest represents the time to a terminal event, and the duration of the study is limited in time. • Incomplete observation - the event of interest did not occur at the time of the analysis. • Type I and II censoring (time fixed/proportion of subjects fixed) • Right and left censoring

  46. Kaplan-Meier Curve 1 Patient Group 1 Survival probability Patient Group 2 0.5 Censored observations 0 0 1 2 3 4 5 6 7 Time (months) r – still at risk f – failure (reached the end point)

  47. Kaplan-Meier Curve 1 Survival probability What is the probability of a patient to survive 2.5 months? 0.5 Censored observations 0 0 1 2 3 4 5 6 7 Time (months) P-value?

  48. Logranktest: compare survival experience of two different groups of individuals k- groups of patients to compare O – observed proportion (summed over time points) E – expected proportion (summed over time points) V – variance of (O-E) (summed over time points) Then compare with the χ2 distribution with (k-1) degrees of freedom and get the p-value Log-rank (Doesn’t tell how different)

  49. Hazard ratio Hazard ratio compares two groups differing in treatments or prognostic variables etc. Measures relative survival in two groups based on the complete period studied. R=0.43 – relative risk (hazard) of poor outcome under the condition of group 1 is 43% of that of group 2. R= 2.0 then the rate of failure in group 1 is twice the rate in the group 2. (tells how different)

  50. Cox-proportional hazard model Used to investigate the effect of several variables on survival experience. Multivariable proportional hazards regression model described by D.R. Cox for modeling survival times. It is also called proportional hazards model because it estimates the ratio of the risks (hazard ratio or relative hazard). There are multiple predictor variables (such as prognostic markers whose individual contribution to the outcome is being assessed in the presence of the others) and the outcome variable .

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