The Genomics of Septic Shock Hector R. Wong, MD Division of Critical Care Medicine Cincinnati Children’s Hospital Medical Center Cincinnati Children’s.

The Genomics of Septic Shock Hector R. Wong, MD Division of Critical Care Medicine Cincinnati Children’s Hospital Medical Center Cincinnati Children’s Hospital Research Foundation 1 st International Symposium on AKI in Children at the 7 th International Conference on Pediatric Continuous Renal Replacement Therapy September 2012

Disclosures The Cincinnati Children’s Hospital Research Foundation and the Speaker have submitted patent applications for biomarker-based stratification model presented in this lecture. The Speaker serves on the Scientific Advisory Board for DxTerity and is compensated with stock options.

Nine years of genome-level expression profiling in pediatric septic shock….. Discovery-oriented, exploratory genome-wide expression studies in children with septic shock FUNDAMENTAL OBSERVATIONS REGARDING THE GENOME- LEVEL BIOLOGY OF PEDIATRIC SEPTIC SHOCK DISCOVERY OF NOVEL CANDIDATE THERAPEUTIC TARGETS DISCOVERY OF GENE EXPRESSION- BASED CLASSES OF SEPTIC SHOCK WITH CLINICALLY RELEVANT PHENOTYPIC DIFFERENCES DISCOVERY OF NOVEL BIOMARKERS STRATIFICATION DIAGNOSIS

Discovery-oriented, exploratory genome-wide expression studies in children with septic shock FUNDAMENTAL OBSERVATIONS REGARDING THE GENOME- LEVEL BIOLOGY OF PEDIATRIC SEPTIC SHOCK DISCOVERY OF NOVEL CANDIDATE THERAPEUTIC TARGETS DISCOVERY OF GENE EXPRESSION- BASED CLASSES OF SEPTIC SHOCK WITH CLINICALLY RELEVANT PHENOTYPIC DIFFERENCES DISCOVERY OF NOVEL BIOMARKERS STRATIFICATION DIAGNOSIS Nine years of genome-level expression profiling in pediatric septic shock…..

Stratification Early assessment (i.e. within 24 hours of admission) of who is at risk for good or poor outcome.

Why Do We Care? Reliable outcome risk stratification is fundamental for effective clinical practice and clinical research. The oncology paradigm. Stratification for clinical trials. Informing individual patient decision making. Allocation of ICU resources. Quality metric. There is no reliable and validated outcome risk stratification tool for septic shock.

Discovery of candidate stratification biomarkers for septic shock Mining of genome-wide expression data to identify genes associated with 28-day mortality in children with septic shock. 117 genes with predictive capacity for mortality 12 gene products meeting the following criteria: Biological plausibility regarding sepsis biology. Gene product (i.e. protein) can be measured in serum/plasma.

Final list of candidate stratification biomarkers Gene SymbolDescription CCL3 C-C chemokine ligand 3; a.k.a. MIP-1  LCN2Lipocalin 2; a.k.a. NGAL MMP8Matrix metallopeptidase 8; a.k.a. neutrophil collagenase RETNResistin THBSThrombospondin 1 GZMBGranzyme B HSPA1BHeat shock protein 70kDa 1B CCL4 C-C chemokine ligand 4; a.k.a. MIP-1  IL8Interleukin-8 LTFLactotransferrin ELA2Neutrophil elastase 1 IL1A Interleukin 1 

PERSEVERE PEdiatRic SEpsis biomarkEr Risk modEl. Multi-biomarker-based risk model to predict outcome in septic shock.

Derivation of PERSEVERE 220 patients with septic shock. 10.5% mortality. Measured 12 candidate stratification biomarkers from serum. Serum samples represent the first 24 hours of admission to the PICU. “CART” analysis.

CART Analysis Classification and Regression Tree. Decision tree building technique. “Binary recursive partitioning”. Binary: splitting of patients into 2 groups. Recursive: can be done multiple times. Partitioning: entire dataset split into sections. Has the potential to reveal complex interactions between candidate predictor variables not evident using traditional approaches.

Derivation Cohort CART Analysis Results Overview Included 5 of the 12 candidate biomarkers. – CCL3: MIP-1α – Heat shock protein-70 – IL-8 – Elastase – NGAL 5 decision rules 10 daughter nodes

HSP70 ≤ 3310450 N = 181 OutcomeNumberRate Death60.033 Survived1750.967 ROOT N = 220 OutcomeNumberRate Death230.105 Survived1970.895 CCL3 ≤ 358 N = 195 OutcomeNumberRate Death120.062 Survived1830.938 CCL3 > 358 N = 25 OutcomeNumberRate Death110.439 Survived140.561 HSP70 > 3310450 N = 14 OutcomeNumberRate Death60.429 Survived80.571 Elastase ≤ 344596 N = 24 OutcomeNumberRate Death40.167 Survived200.833 IL8 ≤ 356 N = 133 OutcomeNumberRate Death20.015 Survived1310.985 IL8 > 356 N = 48 OutcomeNumberRate Death40.083 Survived440.917 Elastase > 344596 N = 24 OutcomeNumberRate Death00.000 Survived241.000 NGAL > 8712 N = 10 OutcomeNumberRate Death40.400 Survived60.600 NGAL ≤ 8712 N = 14 OutcomeNumberRate Death00.000 Survived141.000 Derivation Cohort Tree

HSP70 ≤ 3310450 N = 181 OutcomeNumberRate Death60.033 Survived1750.967 ROOT N = 220 OutcomeNumberRate Death230.105 Survived1970.895 CCL3 ≤ 358 N = 195 OutcomeNumberRate Death120.062 Survived1830.938 CCL3 > 358 N = 25 OutcomeNumberRate Death110.439 Survived140.561 HSP70 > 3310450 N = 14 OutcomeNumberRate Death60.429 Survived80.571 Elastase ≤ 344596 N = 24 OutcomeNumberRate Death40.167 Survived200.833 IL8 ≤ 356 N = 133 OutcomeNumberRate Death20.015 Survived1310.985 IL8 > 356 N = 48 OutcomeNumberRate Death40.083 Survived440.917 Elastase > 344596 N = 24 OutcomeNumberRate Death00.000 Survived241.000 NGAL > 8712 N = 10 OutcomeNumberRate Death40.400 Survived60.600 NGAL ≤ 8712 N = 14 OutcomeNumberRate Death00.000 Survived141.000 Low Risk Terminal Nodes N = 171

HSP70 ≤ 3310450 N = 181 OutcomeNumberRate Death60.033 Survived1750.967 ROOT N = 220 OutcomeNumberRate Death230.105 Survived1970.895 CCL3 ≤ 358 N = 195 OutcomeNumberRate Death120.062 Survived1830.938 CCL3 > 358 N = 25 OutcomeNumberRate Death110.439 Survived140.561 HSP70 > 3310450 N = 14 OutcomeNumberRate Death60.429 Survived80.571 Elastase ≤ 344596 N = 24 OutcomeNumberRate Death40.167 Survived200.833 IL8 ≤ 356 N = 133 OutcomeNumberRate Death20.015 Survived1310.985 IL8 > 356 N = 48 OutcomeNumberRate Death40.083 Survived440.917 Elastase > 344596 N = 24 OutcomeNumberRate Death00.000 Survived241.000 NGAL > 8712 N = 10 OutcomeNumberRate Death40.400 Survived60.600 NGAL ≤ 8712 N = 14 OutcomeNumberRate Death00.000 Survived141.000 High Risk Terminal Nodes N = 49

Non-survivorSurvivor Predicted Non-Survivor Predicted Survivor Test characteristics based on terminal nodes. All subjects in low risk nodes predicted as survivors. All subjects in high risk nodes predicted as non-survivors.

Non-survivorSurvivor Predicted Non-Survivor2128 Predicted Survivor2169 Sensitivity 91% CI 70 to 98% Specificity 86% CI 80 to 80% PPV 43% (CI 29 to 58%) +LR 6.4 (CI 4.5 to 9.3) NPV 99% (CI 95 to 100%) -LR 0.10 (CI 0.03 to 0.4) Test characteristics based on terminal nodes. All subjects in low risk nodes predicted as survivors. All subjects in high risk nodes predicted as non-survivors. AUC = 0.885

Testing PERSEVERE 135 different patients with septic shock. 13.3% mortality. Measured the same candidate biomarkers. “Dropped the patients through the tree”.

Non-survivorSurvivor Predicted Non-Survivor Predicted Survivor Test characteristics in the test cohort

Non-survivorSurvivor Predicted Non-Survivor1642 Predicted Survivor275 Sensitivity 89% CI 64 to 98% Specificity 64% CI 55 to 73% PPV 28% (CI 17 to 41%) +LR 2.5 (CI 1.8 to 3.3) NPV 97% (CI 90 to 99%) -LR 0.18 (CI 0.05 to 0.69) Test characteristics in the test cohort AUC = 0.759

Updating PERSEVERE using the combined derivation and test cohorts (n = 355).

Updated Model Included 3 of the 5 candidate biomarkers from the initial model. – CCL3: MIP-1α – Heat shock protein-70 – IL-8 Eliminated 2 of the 5 candidate biomarkers from the original model. – Elastase – NGAL Added granzyme B, MMP-8, & age as decision rules. 7 decision rules. 14 daughter nodes.

HSPA1B ≤ 3.27E6 N = 207 OutcomeNumberRate Death80.039 Survived1990.961 ROOT N = 335 OutcomeNumberRate Death410.115 Survived3140.885 CCL3 ≤ 160 N = 234 OutcomeNumberRate Death140.060 Survived2200.940 CCL3 > 160 N = 121 OutcomeNumberRate Death270.223 Survived940.777 HSPA1B > 3.27E6 N = 27 OutcomeNumberRate Death60.222 Survived210.778 IL8 ≤ 507 N = 55 OutcomeNumberRate Death50.091 Survived500.909 IL8 ≤ 829 N = 174 OutcomeNumberRate Death20.011 Survived1720.989 IL8 > 829 N = 33 OutcomeNumberRate Death60.182 Survived270.818 IL8 > 507 N = 66 OutcomeNumberRate Death220.333 Survived440.667 GZMB > 55 N = 36 OutcomeNumberRate Death170.472 Survived190.528 GZMB ≤ 55 N = 30 OutcomeNumberRate Death50.167 Survived250.833 MMP8 > 47513 N = 15 OutcomeNumberRate Death40.267 Survived110.733 MMP8 ≤ 47513 N = 40 OutcomeNumberRate Death10.025 Survived390.975 Age ≤ 0.5 years N = 8 OutcomeNumberRate Death50.625 Survived30.375 Age > 0.5 years N = 22 OutcomeNumberRate Death00.000 Survived221.000

HSPA1B ≤ 3.27E6 N = 207 OutcomeNumberRate Death80.039 Survived1990.961 ROOT N = 335 OutcomeNumberRate Death410.115 Survived3140.885 CCL3 ≤ 160 N = 234 OutcomeNumberRate Death140.060 Survived2200.940 CCL3 > 160 N = 121 OutcomeNumberRate Death270.223 Survived940.777 HSPA1B > 3.27E6 N = 27 OutcomeNumberRate Death60.222 Survived210.778 IL8 ≤ 507 N = 55 OutcomeNumberRate Death50.091 Survived500.909 IL8 ≤ 829 N = 174 OutcomeNumberRate Death20.011 Survived1720.989 IL8 > 829 N = 33 OutcomeNumberRate Death60.182 Survived270.818 IL8 > 507 N = 66 OutcomeNumberRate Death220.333 Survived440.667 GZMB > 55 N = 36 OutcomeNumberRate Death170.472 Survived190.528 GZMB ≤ 55 N = 30 OutcomeNumberRate Death50.167 Survived250.833 MMP8 > 47513 N = 15 OutcomeNumberRate Death40.267 Survived110.733 MMP8 ≤ 47513 N = 40 OutcomeNumberRate Death10.025 Survived390.975 Age ≤ 0.5 years N = 8 OutcomeNumberRate Death50.625 Survived30.375 Age > 0.5 years N = 22 OutcomeNumberRate Death00.000 Survived221.000 High risk terminal nodes N = 119 Death risk: 18.2 to 62.5%

HSPA1B ≤ 3.27E6 N = 207 OutcomeNumberRate Death80.039 Survived1990.961 ROOT N = 335 OutcomeNumberRate Death410.115 Survived3140.885 CCL3 ≤ 160 N = 234 OutcomeNumberRate Death140.060 Survived2200.940 CCL3 > 160 N = 121 OutcomeNumberRate Death270.223 Survived940.777 HSPA1B > 3.27E6 N = 27 OutcomeNumberRate Death60.222 Survived210.778 IL8 ≤ 507 N = 55 OutcomeNumberRate Death50.091 Survived500.909 IL8 ≤ 829 N = 174 OutcomeNumberRate Death20.011 Survived1720.989 IL8 > 829 N = 33 OutcomeNumberRate Death60.182 Survived270.818 IL8 > 507 N = 66 OutcomeNumberRate Death220.333 Survived440.667 GZMB > 55 N = 36 OutcomeNumberRate Death170.472 Survived190.528 GZMB ≤ 55 N = 30 OutcomeNumberRate Death50.167 Survived250.833 MMP8 > 47513 N = 15 OutcomeNumberRate Death40.267 Survived110.733 MMP8 ≤ 47513 N = 40 OutcomeNumberRate Death10.025 Survived390.975 Age ≤ 0.5 years N = 8 OutcomeNumberRate Death50.625 Survived30.375 Age > 0.5 years N = 22 OutcomeNumberRate Death00.000 Survived221.000 Low risk terminal nodes N = 236 Death risk: 0.0 to 2.5%

Non-survivorSurvivor Predicted Non-Survivor Predicted Survivor Test characteristics of updated model

Non-survivorSurvivor Predicted Non-Survivor3881 Predicted Survivor3233 Test characteristics of updated model Sensitivity 93% CI 79 to 98% Specificity 74% CI 69 to 79% PPV 32% (CI 24 to 41%) +LR 3.6 (CI 2.9 to 4.4) NPV 99% (CI 96 to 100%) -LR 0.1 (CI 0.0 to 0.3) AUC = 0.883

Non-survivorSurvivor Predicted Non-Survivor3881 Predicted Survivor3233 Biologically Plausible? False Positives True Negatives False positives should be “sicker” than true negatives.

Non-survivorSurvivor Predicted Non-Survivor3881 Predicted Survivor3233 Persistence of ≥2 organ failures at 7 days after ICU admission False Positives: 30% True Negatives: 9% P < 0.001

Non-survivorSurvivor Predicted Non-Survivor3881 Predicted Survivor3233 Median PICU Length of Stay False Positives: 11 days True Negatives: 7 days P = 0.003

Potential questions you may have... Manuscript in press: Crit Care. Derived an analogous model in adults. Outperforms PRISM. Have evaluated the performance of the updated tree in 54 new patients (13% mortality). – Correctly predicted 6 of 7 deaths (86% sensitivity). – 33 of 34 predicted survivors actually survived (97% NPV).

Potential applications of PERSEVERE Stratification for clinical trials. Inform individual patient decision making. Allocation of ICU resources. Quality improvement.

Acknowledgements: Contributing Centers Natalie Cvijanovich, MD: Children’s Hospital & Research Center Oakland, Oakland, CA. Thomas Shanley, MD: University of Michigan, C.S. Mott Children’s Hospital, Ann Arbor, MI. Geoffrey Allen, MD: Children’s Mercy Hospitals & Clinics, Kansas City, MO. Neal Thomas, MD: Penn State Hershey Children’s Hospital, Hershey, PA. Robert Freishtat, MD: Children’s National Medical Center, Washington, DC. Nick Anas, MD: Children’s Hospital of Orange County, Orange, CA. Keith Meyer, MD: Miami Children’s Hospital, Miami, FL. Paul Checchia, MD: Texas Children’s Hospital, Houston, TX. Richard Lin, MD: The Children’s Hospital of Philadelphia, Philadelphia, PA. Michael Bigham, MD: Akron Children’s Hospital, Akron, OH. Mark Hall, MD: Nationwide Children’s Hospital, Columbus, OH. Anita Sen, MD: New York-Presbyterian, Morgan Stanley Children’s Hospital, Columbia University Medical Center, New York, NY. Jeffery Nowak, MD: Children’s Hospital and Clinics of Minnesota, Minneapolis, MN. Michael Quasney, MD, PhD: Children’s Hospital of Wisconsin, Milwaukee, WI. Jared Henricksen, MD: Primary Children’s Medical Center, Salt Lake, UT. Arun Chopra, MD: St. Christopher’s Hospital for Children, Philadelphia, PA.

Funding Acknowledgement NIH R01GM064619 NIH RC1HL100474 NIH R01GM096994

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The Genomics of Septic Shock Hector R. Wong, MD Division of Critical Care Medicine Cincinnati Children’s Hospital Medical Center Cincinnati Children’s.

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