Real-world Clinical Effectiveness of Whole Genome and Transcriptome Analysis to Guide Advanced Cancer Care

NCT ID: NCT04141397

Last Updated: 2021-01-27

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 1200 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2014-07-01
• Study Completion Date: 2021-12-31

Brief Summary

This study aims to determine the clinical effectiveness of whole-genome and transcriptome analysis (WGTA) to guide advanced cancer care. The study setting is the British Columbia (BC) Personalized OncoGenomics (POG) program, a single group research study of WGTA guiding treatment planning for patients with advanced, incurable cancers (NCT02155621). To characterize clinical effectiveness, the survival impacts of POG's approach compared to usual care in matched controls will be estimated.

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.

Conditions

Metastatic Cancer; Advanced Cancer; Cancers That Cannot be Treated With Curative Intent

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

POG patients

Patients enrolled in POG who initiated WGTA between July 2014 and December 2017

Initiation of POG-related WGTA

Intervention Type: GENETIC

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

Intervention Type: GENETIC

Usual care, not involving the initiation of POG-related WGTA

Interventions

Initiation of POG-related WGTA

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.

Intervention Type: GENETIC

Usual care

Usual care, not involving the initiation of POG-related WGTA

Intervention Type: GENETIC

Eligibility Criteria

Inclusion Criteria

• 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

• 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

• BC Medical Services Plan personal health number missing or invalid
• Cancer case diagnosed at death
• Age at diagnosis ≤18

• Minimum Eligible Age: 19 Years
• Maximum Eligible Age: 85 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

BC Cancer Foundation

OTHER

Sponsor Role: collaborator

Genome British Columbia

INDUSTRY

Sponsor Role: collaborator

British Columbia Cancer Agency

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

References

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.

Reference Type: BACKGROUND

PMID: 27148575 (View on PubMed)

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.

Reference Type: BACKGROUND

Related Links

https://clinicaltrials.gov/ct2/show/NCT02155621

Protocol for BC Cancer Personalized OncoGenomics Program

https://www.personalizedoncogenomics.org/

Website for BC Cancer Personalized OncoGenomics Program

Other Identifiers

POG Clinical Effectiveness

Identifier Type: -

Identifier Source: org_study_id