Clinical trial NCT04141397: Real-world Clinical Effectiveness of Whole Genome and Transcriptome Analysis to Guide Advanced Cancer Care
Official title: Retrospective Observational Study Determining the Clinical Effectiveness of Whole Genome and Transcriptome Analysis to Guide Advanced Cancer Care
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Metastatic Cancer
Advanced Cancer
Cancers That Cannot be Treated With Curative Intent
Keywords: precision medicine
genomic sequencing
administrative data
quasi-experimental methods
matching
clinical effectiveness
Lead sponsor: British Columbia Cancer Agency
Sponsor class: OTHER
Collaborators: name: BC Cancer Foundation
name: Genome British Columbia
First posted: 2019-10-28
Enrollment: 1200
Sex: ALL
Minimum age (years): 19
Maximum age (years): 85
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
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:

1. Had advanced, incurable cancer and enrolled in the BC Cancer POG Program

   OR
2. 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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Overall Survival
timeFrame: From 1 year up to 4.5 years, adjusted for censoring
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
References:
citation: 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.
citation: 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.