Browsing by Author "Lupichuk, Sasha"

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    Examining and predicting outcomes among early-onset breast cancer patients in Alberta using real-world and genomic data
    (2023-11-23) Basmadjian, Robert Barkev; Brenner, Darren; Cheung, Winson; Quan, May Lynn; Lupichuk, Sasha; Xu, Yuan
    Background: 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.
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    ItemOpen Access
    New method for determining breast cancer recurrence-free survival using routinely collected real-world health data
    (2022-03-16) Jung, Hyunmin; Lu, Mingshan; Quan, May L.; Cheung, Winson Y.; Kong, Shiying; Lupichuk, Sasha; Feng, Yuanchao; Xu, Yuan
    Abstract Background In cancer survival analyses using population-based data, researchers face the challenge of ascertaining the timing of recurrence. We previously developed algorithms to identify recurrence of breast cancer. This is a follow-up study to detect the timing of recurrence. Methods Health events that signified recurrence and timing were obtained from routinely collected administrative data. The timing of recurrence was estimated by finding the timing of key indicator events using three different algorithms, respectively. For validation, we compared algorithm-estimated timing of recurrence with that obtained from chart-reviewed data. We further compared the results of cox regressions models (modeling recurrence-free survival) based on the algorithms versus chart review. Results In total, 598 breast cancer patients were included. 121 (20.2%) had recurrence after a median follow-up of 4 years. Based on the high accuracy algorithm for identifying the presence of recurrence (with 94.2% sensitivity and 79.2% positive predictive value), the majority (64.5%) of the algorithm-estimated recurrence dates fell within 3 months of the corresponding chart review determined recurrence dates. The algorithm estimated and chart-reviewed data generated Kaplan–Meier (K-M) curves and Cox regression results for recurrence-free survival (hazard ratios and P-values) were very similar. Conclusion The proposed algorithms for identifying the timing of breast cancer recurrence achieved similar results to the chart review data and were potentially useful in survival analysis.