Examining and predicting outcomes among early-onset breast cancer patients in Alberta using real-world and genomic data

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dc.contributor.advisorBrenner, Darren
dc.contributor.advisorCheung, Winson
dc.contributor.authorBasmadjian, Robert Barkev
dc.contributor.committeememberQuan, May Lynn
dc.contributor.committeememberLupichuk, Sasha
dc.contributor.committeememberXu, Yuan
dc.date.accessioned2023-11-24T17:10:25Z
dc.date.available2023-11-24T17:10:25Z
dc.date.issued2023-11-23
dc.description.abstractBackground: It is well accepted patients with early-onset breast cancer (EoBC), defined by a diagnosis <40 years of age, are at greater risks of recurrence and mortality compared to later-onset cases (≥40 years). However, robust evidence of tailored treatment approaches in EoBC is lacking. This thesis intersected causal inference methodology, outcomes prediction research, and bioinformatics to better understand the effectiveness of real-world treatments and decision support tools in EoBC, as well as discover biological drivers of poor prognosis. Methods: Three manuscripts were produced using population-based data of adult breast cancer diagnoses <40 years in Alberta from 2004 to 2020 and whole-exome sequence data from 100 tumour samples in this population. In Manuscript One, we described treatment patterns of ovarian function suppression (OFS) and applied the target trial emulation framework to estimate two treatments effects: 1) 2-year per-protocol effect of tamoxifen alone (TAM) vs. TAM + OFS (T-OFS) vs. aromatase inhibitor + OFS (AI-OFS); and 2) the effect of remaining on hormone therapy + OFS (H-OFS) for ≥2 years vs. <2 years on recurrence-free survival (RFS). In Manuscript Two, we assessed the performance of PREDICT v2.1 for predicting 10-year all-cause mortality in EoBC and developed 10-year mortality prediction models using machine learning. In Manuscript Three, we characterize somatic mutational signatures in 100 EoBC tumour samples and examine their association with clinicopathological variables and survival outcomes. Results: In a target trial that included 2647 premenopausal hormone receptor-positive breast cancer patients, RFS tended to be better in the AI-OFS group (HR=0.76; 95% CI: 0.41-1.37) and T-OFS group (HR=0.87; 95% CI: 0.50-1.45) compared to TAM. Patients on H-OFS for ≥ 2 years had significantly better RFS compared to those on H-OFS for <2 years (HR=0.69; 95% CI:0.54-0.90). In data from 1414 EoBC patients, PREDICT showed good discrimination (AUC=0.76) but tended to overestimate 10-year mortality in patients with high predicted risk. Building a 10-year mortality prediction model on EoBC patient data using penalized multivariable Cox regression showed better discrimination and calibration statistics versus using random survival forests. Among 100 EoBC tumour samples, we extracted five single-base substitution (SBS) and two insertion-deletion signatures. The SBS13-like signature was more common in the HER2 subtype. Higher than median expression of the SBS13-like signature may be associated with better RFS (HR=0.29; 95% CI: 0.08-1.06). Conclusions: These investigations contribute knowledge of tailored approaches in the clinical management of EoBC in Alberta. Our findings provide clearer understandings of the effectiveness of real world treatments and the performance of routinely used prediction models in EoBC. We also provide insights on how additional routinely collected variables and novel mutational variables may improve outcome prediction.
dc.identifier.citationBasmadjian, R. B. (2023). Examining and predicting outcomes among early-onset breast cancer patients in Alberta using real-world and genomic data (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/117587
dc.language.isoen
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectEarly onset breast cancer
dc.subjectReal world evidence
dc.subjectPrecision medicine
dc.subjectClinical outcomes
dc.subject.classificationEpidemiology
dc.subject.classificationOncology
dc.titleExamining and predicting outcomes among early-onset breast cancer patients in Alberta using real-world and genomic data
dc.typedoctoral thesis
thesis.degree.disciplineMedicine – Community Health Sciences
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudentI do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.
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