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Description : Check out this medical presentation on Precision Medicine, which is titled "On the Road to Genomic Predictive Medicine", for an interim analysis on the developments in Genomic Predictive Medicine.

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Published on : Jul 20, 2021 | Views : 1326 | Downloads : 0

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On the Road to Genomic Predictive Medicine An Interim Analysis Richard Simon Chief, Biometric Research Branch National Cancer Institute
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How I got involved in genomics In the late 1990’s genomic data was for me the most exciting scientific data of our generation Analysis of that data shouldn’t be left to amateurs We had a great cadre of statisticians involved in clinical trials and we know how to do reliable clinical trials, but the drugs are often disappointing Statisticians should be involved in basic research, pre-clinical target discovery and policy 2
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Biomedical leaders were looking to computer scientists and physicists for help, not to statisticians Statisticians were viewed as useful for testing hypotheses and computing p values, not for discovery
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Many statisticians tend to see themselves as methods developers not as scientists focused on subject matter area 4
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Imatinib chronology 1960 - Philadelphia chromosome described (P Nowell) 1973 – Ph characterized as translocation of BCR on chromosome 9 with ABL on chromosome 22 (J Rowley) 1986 – BCR-ABL fusion gene characterized as constituatively activated kinase (D Baltimore)
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Imatinib chronology 1988 -1995 CIBA-GEIGY develops kinase inhibitors (A Matter, N Lydon, J Zimmermann, E Buchdunger) 1996 B Drucker (Dana Farber -> Oregon) screens in ex-vivo tumors and normal lymphocytes against compounds provided by Novartis and convinces company to sponsor clinical trials in CML in spite of only 5000 cases/yr in US
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Success depended on collaboration between industry and academia Delayed development resulted from reluctance of field to accept hypothesis that kinases can be selectively inhibited or that inhibiting a single gene could be very effective Industry involvement dependent on vision of a small leadership group in one company Clinical translation dependent on vision of one oncologist
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Success depends on serendiptiy Academic medicine (NIH) is a bottom-up system not optimized for risk taking or exploiting scientific leads for translating basic research to clinical products or for mounting large cooperative programs for overcoming bottlenecks in translation Academic medicine is very dependent on industry but industry has its own constraints
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Predictive Medicine Germline genetics GWAS 23andMe Tumor genomics Tumor Cell Genome Atlas
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Ioannidis et al.JNCI 102:846(2010) 56 GWAS 92 statistically significant associations between cancer phenotype and genetic variant Median OR = 1.22 IQR OR = 1.15 – 1.36
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Cancers of a given histologic diagnosis are genomically heterogeneous Cancers are mostly caused by somatic mutations not genetic polymorphisms Most of the information about the disease is in the tumor genome, not the germ-line genome
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Biomarkers for Early Detection Because of the long time between first mutation and clinical diagnosis of human solid tumors, there would seem to be great opportunity for early detection
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Phase II trials of early detection have used samples from patients at diagnosis Effective detection must have long lead time and high specificity for tumors which will evolve to be life threatening
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Biomarkers for Informing Treatment Selection Prognostic biomarkers Measured before treatment to indicate long-term outcome for patients untreated or receiving standard treatment To identify which patients have excellent prognosis on conservative treatment Predictive biomarkers Measured before treatment to identify who is likely or unlikely to benefit from a particular treatment
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Prognostic Markers Vast literature on prognostic markers Very few used in practice Most studies motivated by desire to learn about disease biology Broad selection of cases Little focus on intended use Little focus on analytical validation of assay
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Validation of Biomarkers Analytical validity Measures what it supposed to Reproducible Clinical validity Correlates with something clinically Clinical utility Is actionable Measuring marker leads to action that benefits patient Requires clarity on intended use
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If you don’t know where you are going, you might not get thereYogi Berra
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Prognostic Markers OncotypeDx: Which patients with node negative ER positive breast cancer who are receiving tamoxifin will have such good prognosis that they do not need cytotoxic chemotherapy? Analysis focused on whether marker identifies such a subset, not on statistical significance
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B-14 Results—Relapse-Free Survival 338 pts 149 pts 181 pts p<0.0001 Paik et al, SABCS 2003
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Major problems with prognostic studies of gene expression signatures Inadequate focus on intended use Cases selected based on availability of specimens rather than for relevance to intended use Heterogeneous sample of patients with mixed stages and treatments. Attempt to disentangle effects using regression modeling Overemphasis on statistical significance and hazard ratios. Over-fitting data
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For p>n problems Fit of a model to the same data used to develop it is no evidence of prediction accuracy for independent data
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Simulation Training Validation 1 2 3 4 5 6 7 8 9 10 p= 7.0e - 05 p=0.70 p= 4.2e - 07 p=0.54 p= 2.4e - 13 p=0.60 p= 1.3e - 10 p=0.89 p= 1.8e - 13 p=0.36 p= 5.5e - 11 p=0.81 p= 3.2e - 09 p=0.46 p= 1.8e - 07 p=0.61 p= 1.1e - 07 p=0.49 p= 4.3e - 09 p=0.09
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Validation of Prognostic Model Completely independent validation dataset Splitting dataset into training and testing sets Cross-validation
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Partition data set D into K equal parts D1,D2,...,DK First training set T1=D-D1 Develop completely specified prognostic model M1 using only data T1 Compute prognostic score for cases in D1 Develop model M2 using only T2 and then score cases in D2
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Repeat for ... TK -> MK -> DK Group patients into risk groups (e.g. 2 or more) based on their cross-validated scores Calculate Kaplan-Meier survival curve for each risk-group
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Complete cross Validation Cross-validation simulates the process of separately developing a model on one set of data and predicting for a test set of data not used in developing the model All aspects of the model development process must be repeated for each loop of the cross-validation Feature selection Tuning parameter optimization
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Prediction on Simulated Null DataSimon et al. J Nat Cancer Inst 95:14, 2003 Generation of Gene Expression Profiles 20 specimens (Pi is the expression profile for specimen i) Log-ratio measurements on 6000 genes Pi ~ MVN(0, I6000) Can we distinguish between the first 10 specimens (Class 1) and the last 10 (Class 2)? Prediction Method Compound covariate predictor built from the log-ratios of the 10 most differentially expressed genes.
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Cross Validation The cross-validated estimate of misclassification error is an estimate of the prediction error for the model fit applying the specified algorithm to full dataset
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Statistical significance of the difference in survival among risk groups is usually not the point But to evaluate significance, the log-rank test cannot be used for cross-validated Kaplan-Meier curves because the survival times are not independent
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Statistical significance can be properly evaluated by approximating the null distribution of the cross-validated log-rank statistic Permute the survival times and repeat the entire cross-validation procedure to generate new cross-validated K-M curves for low risk and high risk groups Compute log-rank statistic for the curves Repeat for many sets of permutations
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Does measuring new biomarkers have medical utility over using a standard staging system? It is usually more useful to compute K-M curves for the high and low risk groups of the new model within each level of risk for the model based on standard staging variables. That representation will enable clinicians to determine whether the refinement in risk within groups of patients that are more homogeneous with regard to therapeutic decision-making is of practical significance
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Predictive Biomarkers Single gene or protein measurement ER protein expression HER2 amplification EGFR mutation KRAS mutation V600E mutation ALK translocation Index or classifier that summarizes expression levels of multiple genes
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The standard approach to designing phase III clinical trials is based on two assumptions Qualitative treatment by subset interactions are unlikely “Costs” of over-treatment are less than “costs” of under-treatment
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Cancers of a primary site often represent a heterogeneous group of diverse molecular diseases which vary fundamentally with regard to the oncogenic mutations that cause them their responsiveness to specific drugs Most new cancer drugs are very expensive the aspirin paradigm on which current clinical trial dogma is based can be a roadblock to progress
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Standard Clinical Trial Approaches Have led to widespread over-treatment of patients with drugs to which few benefit Are not scientifically well founded nor economically sustainable for future cancer therapeutics
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Neither current practices of subset analysis nor current practices of ignoring subset analysis are effective for evaluating treatments and informing physicians in heterogeneous diseases
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How can we develop new drugs in a manner more consistent with modern tumor biology and obtain reliable information about what regimens work for what kinds of patients?
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Using phase II data, develop predictor of response to new drug Develop Predictor of Response to New Drug Patient Predicted Responsive New Drug Control Patient Predicted Non-Responsive Off Study Targeted (Enrichment) Design
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Evaluating the Efficiency of Targeted Design Simon R and Maitnourim A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clinical Cancer Research 10:6759-63, 2004; Correction and supplement 12:3229, 2006 Maitnourim A and Simon R. On the efficiency of targeted clinical trials. Statistics in Medicine 24:329-339, 2005. http://brb.nci.nih.gov
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Relative efficiency of targeted design depends on proportion of patients test positive effectiveness of new drug (compared to control) for test negative patients When less than half of patients are test positive and the drug has little or no benefit for test negative patients, the targeted design requires dramatically fewer randomized patients than the standard design in which the marker is not used
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Companion diagnostic test with intended use of identifying patients who have disease subtype for which the drug is proven effective
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Stratification Design for New Drug Development with Companion Diagnostic
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Fallback Analysis Plan Compare the new drug to the control overall for all patients ignoring the classifier. If poverall ≤ 0.01 claim effectiveness for the eligible population as a whole Otherwise perform a single subset analysis evaluating the new drug in the classifier + patients If psubset ≤ 0.04 claim effectiveness for the classifier + patients. The test in the subset is not dependent on finding an overall significant finding or a significant interaction The trial is sized for powering both tests The validity of the analysis does not depend on stratifying the randomization by the test
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Strong confidence in test: Small r2 and large r1 Weak confidence in test: Small r2 and small r1 p00 selected to control type I error rates
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The Objectives of a Phase III Clinical Trial Test the strong null hypothesis that the new treatment E is uniformly ineffective relative to a control C while preserving the type I error of the study If the null hypothesis is rejected, develop an internally validated labeling indication for informing physicians in their decisions about which patients they treat with the drug. Not a hypothesis testing problem
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The keys to developing effective drugs The target of the drug must be central to the progression of the disease Drug should be selective for the target so that it can be administered at a concentration that totally shuts down the de-regulated pathway Need a test that identifies the patients who have disease driven by de-regulation of the target
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Tumors can contain large numbers of genetic alterations Passenger mutations Occur at rate of non-synonymous mutations Driver mutations Occur more frequently than non-synonymous mutations and presumably have a functional role in oncogenesis and pathogenesis of the tumor Determined from sequencing of many tumors of a histological type Founder Mutations Originating mutation
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Extend Previous Methods to Allow Background mutation rate to vary among tumors Background mutation rate to depend on sequence context of mutation
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Founder mutations Mathematical modeling studies indicate that 2-4 rate-limiting events occurring at normal mammalian mutation rates can account for age-incidence statistics for many types of human solid tumors These rate limiting events may correspond to the founder mutations of a tumor They may be rate-limiting because they occur when the tumor is restricted to small anatomic compartments prior to the occurrence of genome destabilizing mutations They may permit the tumor to grow to a size in which acquisition of subsequent mutations is not rate-limiting Additional driver mutations occur that aid tumor invasion and metastatic dissemination
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Founder mutations may be of special importance They exist in all sub-clones of the tumor and so all tumor cells may be susceptible to founder mutation targeted treatment Subsequent mutations develop in the context of the founder mutations and be viable only in that context rendering the tumors “addicted” to the early mutations
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Closing Germ-line genomics has so far had a limited impact on individual risk prediction in oncology and in understanding the nature of oncogenesis Tumor genomics is revolutionizing our understanding of cancer and is providing important opportunities to identify key molecular targets and for improving therapeutic decision making
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Closing Treatment of broad populations with regimens that do not benefit most patients is increasingly less necessary nor economically sustainable The established molecular heterogeneity of cancer requires the use new approaches to clinical trial design and analysis Developments in high dimensional assays and NGS have stimulated many areas of biostatistics and placed greater emphasis on discovery and prediction
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To Meet the Challenges and Opportunities Available to Impact on Human Disease Biostatistics Should Continue to broaden identity beyond probabilistic inference and methods development Reward statistical scientists, not just statistical mathematicians Embrace information technology Include the informatics end of bioinformatics Transcend hypothesis testing to include discovery, prediction and decision making
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Joi Ito – Director, MIT Media Lab In the old days, the world didn’t change very much, so once you became a plumber, you didn’t really need to learn that much more about plumbing. Today you have to keep learning and learning is somewhat of a childlike behavior. We want the Media Lab to me more like kindergarten and less like a lumber mill. 92
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“Prediction is difficult; particularly the future.” Neils Bohr
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Acknowledgements Boris Freidlin Wenyu Jiang Stella Karuri Aboubakar Maitournam Michael Radmacher Jyothi Subramanian Ahrim Youn Yingdong Zhao