Real-world Clinical Effectiveness of Whole Genome and Transcriptome Analysis to Guide Advanced Cancer Care
Retrospective Observational Study Determining the Clinical Effectiveness of Whole Genome and Transcriptome Analysis to Guide Advanced Cancer Care
Study Overview
Status
Intervention / Treatment
Detailed Description
WGTA provides an opportunity to improve health outcomes for patients by tailoring treatments to each individual's genomic profile. The BC POG Program is a single arm research study integrating WGTA information into clinical decision-making for patients with advanced stage, incurable cancers.The clinical effectiveness of POG's approach is unknown. This retrospective quasi-experimental observational study will estimate the real-world effectiveness of WGTA for guiding advanced cancer care. To identify a counterfactual for POG's single-arm approach, matching methods combined with administrative healthcare data will be used. The survival impacts of POG's approach compared to usual care in matched controls will then be estimated.
Specific Aims and Hypotheses
This study aims to estimate the overall survival effects of POG's approach versus usual care for patients with advanced cancers.
Hypothesis (null): there is no difference in survival across POG and usual care patients
Hypothesis (alternative): POG patients who initiated WGTA live longer, on average, than usual care patients
Study Design
This study will apply a retrospective cohort design. Cohorts will include patients who consented to POG and underwent a biopsy for WGTA between July 2014 and December 2017 and matched usual care controls. POG patients who enrolled prior to July 2014 will be excluded from our study because during this feasibility period, referring clinicians employed a high level of case-by-case recruitment selection. Usual care patients will be matched to POG patients using supervised learning techniques. The study period will range from patient's time of metastatic cancer diagnosis to December 31 2018.
Data Sources
De-identified linked population-based administrative datasets will be obtained from BC Cancer for all adult patients (>18 years) diagnosed with cancers in BC prior to December 2017. POG patients will be identified from the BC Cancer Outcomes and Surveillance Integration System (OaSIS) POG Module Database. Eligible control patients will be identified from the BC Cancer Registry, a population-based provincial cancer registry. These data will be linked with data from the BC Cancer Pharmacy Database, Radiotherapy Database, and Cancer Agency Information System (CAIS) using agency-specific identifiers.
Statistical Approach
The investigators will match POG patients and usual care patients based on their date of metastatic disease diagnosis. They will apply 1:1 genetic algorithm-based matching (1:2 in sensitivity analysis) and match patients on propensity scores and baseline covariates, including patient demographics, clinical characteristics, treatment histories, and healthcare utilization prior to metastatic disease diagnosis. When necessary, matching analyses will be stratified to account for variation across cancer types.
To estimate overall survival in POG patients and matched controls, non-parametric and parametric survival analyses will be used. These analyses will be adjusted for censoring. The investigators will explore heterogeneity in clinical effectiveness across cancer subtypes through subgroup analysis and use scenario analysis to determine the impact of future changes in the application of WGTA on clinical effectiveness.
Study Type
Enrollment (Anticipated)
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
BC Cancer administrative data will be used to identify adult patients diagnosed with cancer and residing in BC during the study period who either:
Had advanced, incurable cancer and enrolled in the BC Cancer POG Program
OR
- Whose prior staging information, healthcare utilization and/or treatment history indicated they had advanced cancers and who were matched on a baseline covariates at their date of metastatic disease diagnosis.
Description
Inclusion Criteria (POG Patients):
- BC residency
- Metastatic disease considered incurable by their treating oncologist
- Life expectancy > 6 months
- Eastern Cooperative Oncology Group (ECOG) performance status of 0 or 1
- Consented to POG and undergone initial biopsy between July 2014 and December 2017
Inclusion Criteria (Usual Care Patients):
- Diagnosed with cancer prior to December 2017
- BC residents during study period
- Received care at BC Cancer during study period
- Alive July 1st 2014
Exclusion Criteria (All Patients):
- BC Medical Services Plan personal health number missing or invalid
- Cancer case diagnosed at death
- Age at diagnosis ≤18
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
|
POG patients
Patients enrolled in POG who initiated WGTA between July 2014 and December 2017
|
POG-related WGTA generally involves collecting biopsy samples, applying whole-genome and transcriptome sequencing, and using bioinformatics analysis to interpret sequence data and inform clinical decision-making.
|
|
Usual care controls
Matched controls who received usual care and were diagnosed with metastatic cancer prior to December 2017
|
Usual care, not involving the initiation of POG-related WGTA
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Overall Survival
Time Frame: From 1 year up to 4.5 years, adjusted for censoring
|
Identified from BC Cancer Registry data
|
From 1 year up to 4.5 years, adjusted for censoring
|
Collaborators and Investigators
Sponsor
Publications and helpful links
General Publications
- Laskin J, Jones S, Aparicio S, Chia S, Ch'ng C, Deyell R, Eirew P, Fok A, Gelmon K, Ho C, Huntsman D, Jones M, Kasaian K, Karsan A, Leelakumari S, Li Y, Lim H, Ma Y, Mar C, Martin M, Moore R, Mungall A, Mungall K, Pleasance E, Rassekh SR, Renouf D, Shen Y, Schein J, Schrader K, Sun S, Tinker A, Zhao E, Yip S, Marra MA. Lessons learned from the application of whole-genome analysis to the treatment of patients with advanced cancers. Cold Spring Harb Mol Case Stud. 2015 Oct;1(1):a000570. doi: 10.1101/mcs.a000570.
- Diamond A, Sekhon JS. Genetic matching for estimating causal effects: A general multivariate matching method for achieving balance in observational studies. Review of Economics and Statistics. 95(3):932-945, 2013.
Study record dates
Study Major Dates
Study Start (ACTUAL)
Primary Completion (ACTUAL)
Study Completion (ANTICIPATED)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (ACTUAL)
Study Record Updates
Last Update Posted (ACTUAL)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- POG Clinical Effectiveness
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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