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Optimization of personalized therapies for anticancer treatment

Optimization of personalized therapies for anticancer treatment. Alexei Vazquez The Cancer Institute of New Jersey. Human cancers are heterogeneous. Meric-Bernstam, F. & Mills, G. B. ( 2012) Nat. Rev. Clin. Oncol. doi:10.1038/nrclinonc.2012.127. Human cancers are heterogeneous.

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Optimization of personalized therapies for anticancer treatment

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  1. Optimization of personalized therapies for anticancer treatment Alexei Vazquez The Cancer Institute of New Jersey

  2. Human cancers are heterogeneous Meric-Bernstam, F. & Mills, G. B.(2012)Nat. Rev. Clin. Oncol. doi:10.1038/nrclinonc.2012.127

  3. Human cancers are heterogeneous DNA-sequencing of aggressive prostate cancers Beltran H et al (2012) Cancer Res

  4. Personalized cancer therapy Personalized Therapy Meric-Bernstam F & Mills GB(2012) Nat Rev Clin Oncol

  5. Targeted therapies Aggarwal S (2010) Nat Rev Drug Discov

  6. Drug combinations are needed Overall response rate (%) Number of drugs

  7. Personalized cancer therapy: Input information Samples/markers Drugs/markers X1 Y1 X2 Y2 X3 Y3 X4 Y4 X5 Xi sample barcode Yi drug barcode (supported by some empirical evidence, not necessarily optimal, e.g. Viagra)

  8. Drug-to-sample protocol Samples/markers Drugs/markers fj(Xi,Yj) X1 Y1 X2 Y2 X3 Y3 X4 Y4 X5 fj(Xi,Yj)drug-to-sample protocol e.g., suggest if the sample and the drug have a common marker

  9. Sample protocol Samples/markers Drugs/markers g fj(Xi,Yj) X1 Y1 X2 Y2 X3 Y3 X4 Y4 X5 g sample protocol e.g., Treat with the suggested drug with highest expected response

  10. Optimization Samples/markers Drugs/markers g fj(Xi,Yj) X1 Y1 X2 Y2 Overall response rate (O) X3 Y3 X4 Y4 X5 Find the drug marker assignments Yj, the drug-to-sample protocols fj and sample protocol g that maximize the overall response rate O.

  11. Drug-to-sample protocol fj Boolean function with Kj=|Yj| inputs Kj number of markers used to inform treatment with dug j

  12. Sample protocol From clinical trials we can determine q0jk the probability that a patient responds to treatment with drug j given that the cancer does not harbor the marker k q1jk the probability that a patient responds to treatment with drug j given that the cancer harbors the marker k Estimate the probability that a cancer i responds to a drug j as the mean of qljk over the markers assigned to drug j, taking into account the status of those markers in cancer i

  13. Sample protocol: one possible choice Specify a maximum drug combination size c For each sample, choose the c suggested drugs with the highest expected response (personalized drug combination) More precisely, given a sample i, a list of di suggested drugs, and the expected probabilities of respose p*ij Sort the suggested drugs in decreasing order of p*ij Select the first Ci=max(di,c) drugs

  14. Overall response rate non-interacting drugs approximation In the absence of drug-interactions, the probability that a sample responds to its personalized drug combination is given by the probability that the sample responds to at least one drug in the combination Overall response rate

  15. Optimization Add/remove marker Change function (Kj,fj) (Kj,f’j)

  16. Case study • S=714 cancer cell lines • M*=921 markers (cancer type, mutations, deletions, amplifications). • M=181 markers present in at least 10 samples • D=138 drugs • IC50ij, drug concentration of drug j that is needed to inhibit the growth of cell line i 50% relative to untreated controls • Data from the Sanger Institute: Genomics of Drug Sensitivity in Cancer

  17. Case study: empirical probability of response:pij Drug concentration to achieve response (IC50ij) Treatment drug concentration (fixed for each drug)  models drug metabolism variations in the human population Probability density  Drug concentration reaching the cancer cells pij probability that the concentration of drug j reaching the cancer cells of type i is below the drug concentration required for response

  18. Case study: response-by-marker approximation By-marker response probability: Sample response probability, response-by-marker approx.

  19. Case study: overall response rate Response-by-marker approximation (for optimization) Empirical (for validation)

  20. Case study: Optimization with simulated annealing • Kj=0,1,2 • Metropolis-Hastings step • Select a rule from (add marker, remove marker, change function) • Select a drug consistent with that rule • Update its Boolean function • Accept the change with probability • Annealing • Start with =00=0 • Perform N Metropolis-Hastings steps N=D • +, exit when =max =0.01, max=100

  21. Case study: convergence

  22. Case study: ORR vs combination size

  23. Case study: number of drugs vs combination size

  24. Outlook • Efficient algorithm, bounds • Drug interactions and toxicity • Constraints • Cost • Insurance coverage • Bayesian formulation

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