INITIAL SYSTEMIC TREATMENT IN STAGE IV NON-SMALL CELL LUNG CANCER

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1 INITIAL SYSTEMIC TREATMENT IN STAGE IV NON-SMALL CELL LUNG CANCER Fran Maguire, MPH, PhD candidate California Cancer Reporting and Epidemiologic Surveillance (CalCARES) Program NAACCR Annual Conference June 20, 2017

2 2 Objectives 1. Use California Cancer Registry data to describe the use of systemic treatments in stage IV non-small cell lung cancer (NSCLC) Emphasis on targeted therapy/immunotherapy 2. Identify disparities in treatment

3 BACKGROUND 3

4 4 LUNG CANCER Second most common cancer Leading cancer killer Non-small cell lung cancer (NSCLC) - 83% Approximately 50% diagnosed at stage IV 5 year survival rate of 4% No cure for stage IV NSCLC

5 Source: California Cancer Registry 5 NSCLC BY STAGE AT DIAGNOSIS, N=37,211 47% 47% 46% 22% 23% 24% 18% 17% 17% 8% 8% 8% 5% 4% 4% Stage I Stage II Stage III Stage IV Unknown

6 Survival Plot of NSCLC by Stage, Source: California Cancer Registry

7 Survival Plot of NSCLC by Stage, months Source: California Cancer Registry

8 Survival Plot of NSCLC by Stage, months Source: California Cancer Registry

9 9 Treatments Two main treatments: 1. Chemotherapy 2. Targeted therapy/immunotherapy Driver mutations Important molecules for cancer cell proliferation Immune checkpoints

10 10 Treatments Two main treatments: 1. Chemotherapy 2. Targeted therapy/immunotherapy Driver mutations Important molecules for cancer cell proliferation Immune checkpoints

11 11 Treatments Targeted Therapy Immunotherapy Small Molecules Monoclonal Antibodies Monoclonal Antibodies

12 12 Treatments Targeted Therapy Immunotherapy Small Molecules Monoclonal Antibodies Monoclonal Antibodies Erlotinib (Tarceva) Crizotinib (Xalkori) Gefitinib (Irresa) Atatinib (Gilotrif) Ceritinib (Zykadia) Osimertinib (Tagrisso) Alectinib (Alecensa) Brigatinib (Alungbrig) Bevacizumab (Avastin) Ramucirumab (Cyramza) Necitumumab (Portrazza) Nivolumab (Opdivo) Pembrolizumab (Keytruda) Atezolizumab (Tecentriq)

13 13 Treatments? Targeted Therapy Immunotherapy Small Molecules Monoclonal Antibodies Monoclonal Antibodies Erlotinib (Tarceva) Crizotinib (Xalkori) Gefitinib (Irresa) Atatinib (Gilotrif) Ceritinib (Zykadia) Osimertinib (Tagrisso) Alectinib (Alecensa) Brigatinib (Alungbrig) Bevacizumab (Avastin) Ramucirumab (Cyramza) Necitumumab (Portrazza) Nivolumab (Opdivo) Pembrolizumab (Keytruda) Atezolizumab (Tecentriq)

14 14 Treatment 45%-50% do not receive systemic treatment Elderly (>75) Differences by insurance type and race/ethnicity Many not tested for biomarkers/mutations

15 METHODS 15

16 16 Methods Identified stage IV NSCLC patients diagnosed in California Cancer Registry data Reviewed text fields from all reporting facilities, categorized systemic treatment into: Targeted therapy/immunotherapy Chemotherapy Used logistic regression to identify predictors of targeted therapy/immunotherapy

17 RESULTS 17

18 18 Results 17,314 people diagnosed with stage IV NSCLC ,873 records from all reporting facilities reviewed Focused on treatment text fields: Text_chemo Text_immuno Other text fields: Text_other_rx Text_lab Text_op_proc Text_path Text_phys_ex Text_rad Text_remarks Text_scopes Text_surg_1 (chemotherapy) (immunotherapy) (other medication) (laboratory) (operating procedures) (pathology) (physical exam) (radiation) (remarks) (scope procedures) (surgical procedures)

19 19 Systemic Treatment, n=17,314 Small molecules 11% Monoclonal antibody 8% Chemotherapy 38% Chemo NOS 5% Treatment Any 51% No Treatment 32% Unknown 17%

20 20 Systemic Treatment, n=17,314 Small molecules Monoclonal antibody 8% 11% Targeted Therapy/Immunotherapy 18% Chemotherapy 38% Chemo NOS 5% Treatment Any 51% No Treatment 32% Unknown 17%

21 Adjusted ORs Predicting Receipt of Targeted Therapy/Immunotherapy Variable OR 95% CI Race/ethnicity NH white (reference) 1.00 NH black Hispanic API Insurance Private/Military (reference) 1.00 Medicare Medicaid Charlson Comorbidity Score 0 (reference) >= Sex Male (reference) 1.00 Female Rural/Urban Residence Rural (reference) 1.00 Urban NCI Program No (reference) 1.00 Yes Age (5 year increments) NH: non-hispanic; API: Asian/Pacific Islander; NCI: National Cancer Institute 21

22 Adjusted ORs Predicting Receipt of Targeted Therapy/Immunotherapy Variable OR 95% CI Race/ethnicity NH white (reference) 1.00 NH black Hispanic API Insurance Private/Military (reference) 1.00 Medicare Medicaid Charlson Comorbidity Score 0 (reference) >= Sex Male (reference) 1.00 Female Rural/Urban Residence Rural (reference) 1.00 Urban NCI Program No (reference) 1.00 Yes Age (5 year increments) NH: non-hispanic; API: Asian/Pacific Islander; NCI: National Cancer Institute 22

23 Adjusted ORs Predicting Receipt of Targeted Therapy/Immunotherapy Variable OR 95% CI Race/ethnicity NH white (reference) 1.00 NH black Hispanic API Insurance Private/Military (reference) 1.00 Medicare Medicaid Charlson Comorbidity Score 0 (reference) >= Sex Male (reference) 1.00 Female Rural/Urban Residence Rural (reference) 1.00 Urban NCI Program No (reference) 1.00 Yes Age (5 year increments) NH: non-hispanic; API: Asian/Pacific Islander; NCI: National Cancer Institute 23

24 Adjusted ORs Predicting Receipt of Targeted Therapy/Immunotherapy Variable OR 95% CI Race/ethnicity NH white (reference) 1.00 NH black Hispanic API Insurance Private/Military (reference) 1.00 Medicare Medicaid Charlson Comorbidity Score 0 (reference) >= Sex Male (reference) 1.00 Female Rural/Urban Residence Rural (reference) 1.00 Urban NCI Program No (reference) 1.00 Yes Age (5 year increments) NH: non-hispanic; API: Asian/Pacific Islander; NCI: National Cancer Institute 24

25 Adjusted ORs Predicting Receipt of Targeted Therapy/Immunotherapy Variable OR 95% CI Race/ethnicity NH white (reference) 1.00 NH black Hispanic API Insurance Private/Military (reference) 1.00 Medicare Medicaid Charlson Comorbidity Score 0 (reference) >= Sex Male (reference) 1.00 Female Rural/Urban Residence Rural (reference) 1.00 Urban NCI Program No (reference) 1.00 Yes Age (5 year increments) NH: non-hispanic; API: Asian/Pacific Islander; NCI: National Cancer Institute 25

26 Adjusted ORs Predicting Receipt of Targeted Therapy/Immunotherapy Variable OR 95% CI Race/ethnicity NH white (reference) 1.00 NH black Hispanic API Insurance Private/Military (reference) 1.00 Medicare Medicaid Charlson Comorbidity Score 0 (reference) >= Sex Male (reference) 1.00 Female Rural/Urban Residence Rural (reference) 1.00 Urban NCI Program No (reference) 1.00 Yes Age (5 year increments) NH: non-hispanic; API: Asian/Pacific Islander; NCI: National Cancer Institute 26

27 Adjusted ORs Predicting Receipt of Targeted Therapy/Immunotherapy Variable OR 95% CI Race/ethnicity NH white (reference) 1.00 NH black Hispanic API Insurance Private/Military (reference) 1.00 Medicare Medicaid Charlson Comorbidity Score 0 (reference) >= Sex Male (reference) 1.00 Female Rural/Urban Residence Rural (reference) 1.00 Urban NCI Program No (reference) 1.00 Yes Age (5 year increments) NH: non-hispanic; API: Asian/Pacific Islander; NCI: National Cancer Institute 27

28 Adjusted ORs Predicting Receipt of Targeted Therapy/Immunotherapy Variable OR 95% CI Race/ethnicity NH white (reference) 1.00 NH black Hispanic API Insurance Private/Military (reference) 1.00 Medicare Medicaid Charlson Comorbidity Score 0 (reference) >= Sex Male (reference) 1.00 Female Rural/Urban Residence Rural (reference) 1.00 Urban NCI Program No (reference) 1.00 Yes Age (5 year increments) NH: non-hispanic; API: Asian/Pacific Islander; NCI: National Cancer Institute 28

29 29 Strengths/Limitations Strengths: Population based study Using existing registry data Limitations: Tedious High percentage of missing No information about dosing or treatment length

30 30 Conclusions Cancer Registry data can be used to further characterize treatment There are disparities in uptake of targeted therapy/immunotherapy and underutilization Next Steps: Compare manual text field review to an automated process Further study targeted treatments and patient outcomes

31 Acknowledgments Cyllene Morris University Of California Davis Health System, Institute for Population Health Improvement Arti Parikh-Patel University Of California Davis Health System, Institute for Population Health Improvement Ken Kizer University Of California Davis Health System, Institute for Population Health Improvement Theresa Keagan University Of California Davis Health System, Department of Hematology and Oncology Chin-Shang Li University Of California Davis, Division of Biostatistics Rosemary Cress Public Health Institute, Cancer Registry of Greater California Patrick Lin University Of California Davis Health System, Department of Hematology and Oncology 31

32 32 Contact Information Fran Maguire

33 Questions 33

34 34 CCR Summary Fields vs Text Fields 49% 38% 17% CCR summary CCR text fields 8% 3% 3% Immuno Sum Chemo Sum Unknown

35 35 Use of Targeted Therapy/Immunotherapy % 17.8% 18.0%

36 36 Receipt of Targeted Therapy/Immunotherapy by Histologic Type Adenocarcinoma 24% Squamous Cell Carcinoma 3% Non-Small Cell Carcinoma NOS 11% NOS = Not otherwise specified

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