Visualizing Big Cancer Data: A Future of Cancer Care Andrew Stewart Chief, Oncology Data

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1 Visualizing Big Cancer Data: A Future of Cancer Care Andrew Stewart Chief, Oncology Data

2 The Promise of a Rapid Learning Health System a system in which science, informatics, incentives, and culture are aligned for continuous improvement and innovation with best practices seamlessly embedded in the delivery process and new knowledge captured as a byproduct of the delivery experience Best Care at Lower Cost: The Path to Continuously Learning Health Care in America September 6,

3 The Current State 1.7 MM people diagnosed with cancer in the US 97% of patient data locked away in unconnected files and servers 3

4 and everyday patients tend to be older less healthy and more diverse 25 % of clinical trial patients are vs 61 % of real-world patients are % of kidney cancer patients were not healthy enough to qualify for the trials that supported the approval of their treatments 2 90 % 23 % vs of patients in NCI trials are white 3 of the US POPULATION Is non-white 3 than clinical trial patients. 1. Lewis JH, et al. Participation of patients 65 years of age or older in cancer clinical trials. J Clin Oncol. 2003;21: Mitchell AP, et al. Clinical trial subjects compared to "real world" patients: generalizability of renal cell carcinoma trials. J Clin Oncol. 2014;32(suppl): Taking action to diversify clinical cancer research. National Cancer Institute Web site. Accessed July 23, 2014.

5 New Challenges Information is not the same as understanding ABL1,ACVR1B,AKT1,ALK,APC,AR,ARID1A,ARID1B,AR ID2,ASXL1,ATM,ATRX,AXIN1,B2M,BAP1,BCL2,BCOR, BRAF,BRCA1,BRCA2,CARD11,CASP8,CBL,CDC73,CDH 1,CDKN2A,CEBPA,CIC,CREBBP,CRLF2,CSF1R,CTNNB1,CYLD,DAXX,DNMT1,DNMT3A,EGFR,EP300,ERBB2,EZ H2,FAM123B,FBXW7,FGFR2,FGFR3,FLT3,FOXL2,FUBP1,GATA1,GATA2,GATA3,GNA11,GNAQ,GNAS,H3F3A,HI ST1H3B,HNF1A,HRAS,IDH1,IDH2,JAK1,JAK2,JAK3,KD M5C,KDM6A,KIT,KLF4,KRAS,MAP2K1,MAP3K1,MED12,MEN1,MET,MLH1,MLL2,MLL3,MPL,MSH2,MSH6,MYD 88,NCOR1,NF1,NF2,NFE2L2,NOTCH1,NOTCH2,NPM1,N RAS,PAX5,PBRM1,PDGFRA,PHF6,PIK3CA,PIK3R1,PPP2 R1A,PRDM1,PTCH1,PTEN,PTPN11,RB1,RET,RNF43,RUN X1,SETD2,SETBP1,SF3B1,SMAD2,SMAD4,SMARCA4,S MARCB1,SMO,SOCS1,SOX9,SPOP,SRSF2,STAG2,STK11, TET2,TNFAIP3,TRAF7,TP53,TSC1,TSHR,U2AF1,VHL,WT 1 Cancer Biomarkers, Vogelstein 2013 Hieroglyphs on stone tablet, circa 5000 BC

6 An Incredibly Straightforward Data Architecture Collaborative or specific private clouds

7 CLQ Practices and Data 87 Signed Practices 26 Practices on-boarded > 565K Cancer Patient Records 85% of the clinical events reported to CLQ fall into four categories: Clinical Observations Labs Medications, Ordered Medications, Administered An average CLQ patient has: 312 Clinical Observations 90 Lab Results 66 Drug Order Events 36 Drug Administration Events

8 CancerLinQ Clinical User Portal

9 Quality Performance Indicators

10 Prospective Quality Improvement

11 Patient Timeline

12 Patient Timeline

13 CLQi: An ad hoc Query Tool

14 CLQi: An ad hoc Query Tool

15 SEER Registries: Data collection challenges Cancer registries have traditionally used manual collection from hospital-based cancer registries. With the change in practice to outpatient services, these hospital registrars often lack access to critical information characterizing the patient and cancer including: Cases diagnosed and treated in community oncology practices Complex genomic tests captured in the oncology practice EMR Outpatient provided treatment and recurrence information

16 SEER CancerLinQ Collaboration Two components to the agreement CLQ use of SEER public use data on its platform Automated data exchange between SEER Registries and CLQ participating oncology practices

17 CLQ Use of SEER Data: Breast Cancers by Stage & Year 17

18 CLQ Use of SEER Data: Illustrating Outcomes

19 Automated data exchange The collaboration will involve data sharing between CancerLinQ and three SEER registries: Iowa, Georgia, and Utah. These practices represent significant proportions of cases in these registries SEER Registry Iowa Georgia Utah Oncology Practice participating in CancerLinQ Catholic Health Initiatives The Ghosh Center for Oncology and Hematology Medical Oncology and Hematology Associates of Iowa McFarland Clinic PC Augusta Oncology Associates Cancer Treatment Centers of American Global, Inc. The Longstreet Clinic, PC Intermountain Healthcare Utah Cancer Specialists Utah Hematology and Oncology

20 Automated data exchange: Proposed Schematic

21 A Win-Win Proposal CancerLinQ to Registries Registries to CancerLinQ Data from CLQ to SEER registries enhances data completeness for information on: Treatment Recurrence Comorbidity Cancer cases that are diagnosed and treated exclusively as outpatients Data from Registries back to Practices vis CLQ provides information critical to assessing quality of care and understanding their own practice patterns and outcomes Length of survival Cause of death Discrete, structured characterization of the cancer at the time of diagnosis Stage at diagnosis Surgery, Radiation Histopathology and biomarkers at diagnosis

22 A Win-Win Proposal This type of collaborative relationships is critical to both parties: Assure more complete, timely and accurate data reported to the registries Help oncologists meet legal reporting requirements Increase the capacity of oncologists to evaluate the quality of the care they provide to patients

23 When Fully Deployed CancerLinQ Will: Unlock, assemble, and analyze de-identified cancer patient medical records Uncover patterns that can improve patient care Allow doctors to compare their care against guidelines and the care of their peers Provide guidance by identifying the best evidence-based course of care

24 CancerLinQ Collaborators

25 CancerLinQ & SAP Committed to Improving Lives in the Fight Against Cancer 25