Supercomputing. Medical big data

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3 Big data analysis and Simulation for medicine Prediction of physical state, therapeutic efficiency, side effects, and outcomes. Identification of markers for early discovery of conditions. Optimization of treatment, medical decision support. Medical big data Pattern extraction from data Machine learning of complex patterns Prediction of events from patterns Supercomputing AI More data than parseable by humans Complex data correlations not resolvable by simple statistics.

4 Data driven EBM More effective, safer care Answering unmet medical needs Real world data analysis Target population for therapy evaluation with restricted therapy conditions Differences in distribution Real patients Clinical trial Standard of care Clinical practice Drug development Approval Pharmacovigilence Rare cases (hundreds, not thousands) Mainly adults; children, pregnant mothers excluded Simultaneous drug/condition constraints reduce target population; potentially biased results Long term treatment effect unclear Insufficient comparative treatment evaluation Evaluation by specialists of a disease, objective? (Post marketing tracking)

5 Oncology prediction problem: Prognosis prediction: tracking patient outcomes is critical due to possible metastasis and drug resistance. Side effects prediction: monitoring patient symptom is critical because of active therapy with anti cancer drugs. Plots of rate of neutrophils for KUHP Oncology patient Data for analysis Kyoto U. Hospital chemotherapy recipients 5285 patients 2863 recorded deaths Survival span after initial exam / start of therapy max days, average days Patient number Examination date

6 Time course data of KUHP patient neutrophil to lymphocyte (NLR) ratio NLR is reported to be a predictor of prognosis (low NLR: good outcome) Patient number 5285 patients 2863 recorded deaths Survival span data max days average Survival rate Examination date Survival following first exam (days) Computation using groups split at threshold of NLR = 4

7 Time course data of KUHP patient neutrophil to lymphocyte (NLR) ratio NLR is reported to be a predictor of prognosis (low NLR: good outcome) Patient number 5285 patients 2863 recorded deaths Survival span data max days average Patient number 2863 deaths Examination date Reverse calculation: number of days prior to death Selection of patients with recorded death and alignment using date of death

8 Time course data of KUHP patient neutrophil to lymphocyte (NLR) ratio NLR is reported to be a predictor of prognosis (low NLR: good outcome) NLR changes dramatically rise 1 year prior to death 2863 deaths NLR average Patient number Reverse: #days prior to death Reverse: #days prior to death NLR likely better predictor of remaining life than prognosis

9 Time course data of KUHP patient neutrophil to lymphocyte (NLR) ratio NLR is reported to be a predictor of prognosis (low NLR: good outcome) NLR changes dramatically rise 1 year prior to death 2863 deaths NLR average Patient number Cases of long lifespan despite high CRP value Cannot feed group statistics back to individuals Reverse: What I really #days want prior to to know deathis my lifespan, the average Reverse: is #days a helpless prior number. to death NLR likely better predictor of remaining life than prognosis

10 Oncology prediction problem: Prognosis prediction: tracking patient outcomes is critical due to possible metastasis and drug resistance. Side effects prediction: monitoring patient symptom is critical because of active therapy with anti cancer drugs. Plots of rate of neutrophils for KUHP Oncology patient Data for analysis Kyoto U. Hospital chemotherapy recipients 5285 patients 2863 recorded deaths Survival span after initial exam / start of therapy max days, average days Patient number Examination date

11 Example of small error Example of small error Distribution of prediction error Example of large error

12 Impulse response method: Estimate cause effect between variables by observing change in each variable after perturbation of a specific single variable Effect Cause Monocytes strongly impact neutrophils. RBC, ALB, CL, ALP are also effectors.

13 Personal Genome Data Clinical sequence KUHP Cancer Center Personalized Drug Therapy Determination of treatments and side-effects from individual genotypes. Genotype-driven therapy selection from existing guidelines and research literature. In oncology, genome dynamical change due to drug resistance via mutation Mechanisms for resistance acquisition not fully understood. Mutation in silico

14 Crizotinib Alectinib Ceritinib T1151 L1196 Crizotinib G1202 S1206 G1269 Target Protein: ALK

15 Wild type vs I1171T mutant Wild type vs V1180L mutant αc helix I/T1171 CH E1167 V/L1180 CH

16 wild I1171T F1174I F1174V V1180L V1185L L1196M L1198F G1202R G1269A wt Ceritinib Calculated G (kcal/mol) Experimental pic50 Crizotinib 8 wt F1174V Calculated G (kcal/mol) Alectinib 8.2 I1171T 8 wt V1180L Calculated G (kcal/mol) Experimental pic50 Experimental pic50

17 Binding free energies using the MP-CAFEE method G coulomb vdw wild-ch (green) I1171T-CH (cyan) V1180L-CH (magenta) (kcal/mol) αc helix I/T1171 E1167 H.B. broken αc helix E1167 Alectinib L1196 V/L1180 Alectinib

18 Personal Genome Data Clinical sequence KUHP Cancer Center Personalized Drug Therapy Determination of treatments and side-effects from individual genotypes. Genotype-driven therapy selection from existing guidelines and research literature. In oncology, genome dynamical change due to drug resistance via mutation Mechanisms for resistance acquisition not fully understood. Predicting response to drug therapy and side effects using simulation Uncovering mechanisms explaining side effects and drug resistance Rational design of new molecules that avoid drug resistance

19 Post-K, Priority issues program CREST Big Data Applications Mitsui Knowledge Industry Co. Ltd. Chugai Pharmaceutical Co. Ltd. COE program from Kobe and Hyogo The KBDD Consortium RIKEN Adv. Inst. Comput. Sci. Res. Org. for Info. Sci. Tech (RIST) Foundation Biomed. Res. Innov. Osaka Univ. Cybermedia Center Biogrid Center Kansai Kyoto University Grad. Sch. Medicine Profs. Manabu Muto, Assoc. Prof. Shigemi Matsumoto Assoc. Prof. Masashi Kanai All the members of Okuno Labo. All the members of Priority Issue 1 All the members of KBDD Consortium Japn. Foudation for Cancer Res. Dr. Ryohei Katayama RIKEN / AICS Dr. Makoto Taiji Dr. Mitsugu Araki Kei Taneishi Foundation Biomed. Res. Innov. Dr. Tasuku Honjyo Dr. Yoichi Nabeshima Dr. Hiroaki Iwata Dr. Atsuto Fujita