Browsing by Author "Brenner, Darren R."

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    Development and internal validation of a risk prediction model for high-risk adenomas among average risk colorectal cancer screening participants
    (2020-09-03) Sutherland, Robert Liam; Brenner, Darren R.; Hilsden, R. J.; Forbes, Nauzer
    Background: High-risk adenomas (HRAs) are precursors to colorectal cancer (CRC), and removing them during colonoscopy can halt progression to CRC. The aim of this study was to develop a risk prediction model for HRAs detected at screening colonoscopy based on readily available patient information. Subsequently, we aimed to understand if biomarkers of glucose metabolism were associated with HRAs, with hopes incorporating them into a baseline risk prediction model to enhance its clinical utility. Methods: The cohort consisted of 3,035 individuals aged 50 to 74 years with no prior history of cancer who underwent a primary screening colonoscopy at a centralized colon cancer screening centre between 2008 and 2016. A multivariable logistic regression model was created using CRC risk factors identified from prior research. Model covariates were collected from a baseline questionnaire and included patient demographics (age and sex), lifestyle parameters (body mass index, alcohol, smoking, and vitamin D supplement use) and medical history (family history of CRC and diabetes). Model calibration was assessed using the c-statistic. Glucose, insulin, glycated hemoglobin A1c, and c-peptide were all measured were assessed using a case control study design from from a subset of the CCSC biorepository.conditional logistic regression was used to understand their associations with HRAs. Results: Mean participant age was 58.8 years, and 54.7% were male. A total of 249 participants with HRAs were identified (8.2%). An optimism adjusted c-statistic of 0.67 was calculated, and a specificity and negative predictive value of 97.0% and 92.4% for the detection of HRAs respectively, were achieved using 20% predicted probability as a high-risk threshold. However, a sensitivity of only 10.8% was achieved. Our model has moderate predictive ability, with strengths in being able to rule those with an absence of HRAs on screening colonoscopy. Finally, after adjustment, no meaningful associations were found between these four biomarkers of glucose metabolism and HRAs. Conclusion: Although these glucose metabolism biomarkers were not found to be associated with HRAs, the production of a simple risk prediction model can still provide benefit to the current screening programs in Alberta. Maximizing screening efficiency through improved risk prediction can enhance resource allocation. Ultimately, this model has the potential to improve patient care by reducing unnecessary colonoscopies, limiting this invasive procedure to those most likely to have significant findings.
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    Influence of Inflammation, Insulin Resistance and Excess Body Size on Breast Cancer Risk: A Nested Case-Control Study
    (2020-02-06) Haig, Tiffany R.; Brenner, Darren R.; Friedenreich, Christine M.; Li, Haocheng; Robson, Paula
    Background: Breast cancer is the most common malignancy affecting women in Canada. In 2019, breast cancer represented 25% of all new cancers among Canadian women and 13% of all cancer deaths. Excess body size is associated with postmenopausal breast cancer risk. The mechanisms associating adiposity to breast cancer are unclear. Both inflammation and insulin resistance have been implicated in this association; however, literature to date has been inconsistent. Here, we aim to examine the associations between high-sensitivity C-reactive protein (hsCRP) and hemoglobin A1c (HbA1c), common measures of inflammation and insulin resistance, respectively, with breast cancer risk, while adjusting for measures of excess body size. Methods: We conducted a nested case-control study within the Alberta’s Tomorrow Project cohort (Alberta, Canada) including 197 invasive breast cancer cases and 394 matched controls. Serum concentrations of hsCRP and HbA1c were measured from blood samples collected prior to diagnosis, along with anthropometric measurements, general health, and lifestyle data. Conditional logistic regression was used to evaluate the associations between hsCRP, HbA1c, and breast cancer risk adjusted for body fat percentage and other risk factors for breast cancer. Results: Participants included in this study were a mean age of 65.1 years and mostly postmenopausal (147 cases and 293 controls). More than half were categorized as overweight/obese (60.5% for cases; 64.9% for controls), and median values of hsCRP (0.9; interquartile range (IQR) = 1.8) and HbA1c (5.6; IQR = 0.6) were similar between cases and controls. Higher concentrations of hsCRP were associated with elevated breast cancer risk (odds ratio [OR] = 1.27; 95% confidence interval [CI] = 1.03, 1.55). The observed associations were unchanged with adjustment for body fat percentage. Higher HbA1c concentrations were not significantly associated with an increased risk of incident breast cancer relative to controls (OR = 1.22; 95% CI = 0.17, 8.75). Conclusion: These data suggest that hsCRP, a marker of inflammation, may be associated with elevated breast cancer risk, independent of body fat percentage. However, elevated concentrations of HbA1c did not appear to increase breast cancer risk in this group of women in Alberta.
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    Mutational signatures among young-onset testicular cancers
    (2021-11-24) Mealey, Nicole E.; O’Sullivan, Dylan E.; Peters, Cheryl E.; Heng, Daniel Y. C.; Brenner, Darren R.
    Abstract Background Incidence of testicular cancer is highest among young adults and has been increasing dramatically for men born since 1945. This study aimed to elucidate the factors driving this trend by investigating differences in mutational signatures by age of onset. Methods We retrieved somatic variant and clinical data pertaining to 135 testicular tumors from The Cancer Genome Atlas. We compared mutational load, prevalence of specific mutated genes, mutation types, and mutational signatures between age of onset groups (< 30 years, 30–39 years, ≥ 40 years) after adjusting for subtype. A recursively partitioned mixture model was utilized to characterize combinations of signatures among the young-onset cases. Results Mutational load was significantly higher among older-onset tumors (p < 0.05). There were no highly prevalent driver mutations among young-onset tumors. Mutated genes and types of nucleotide mutations were not significantly different by age group (p > 0.05). Signatures 1, 8 and 29 were more common among young-onset tumors, while signatures 11 and 16 had higher prevalence among older-onset tumors (p < 0.05). Among young-onset tumors, clustering of signatures resulted in four distinct tumor classes. Conclusions Signature contributions differ by age with signatures 1, 8 and 29 were more common among younger-onset tumors. While these signatures are connected with endogenous deamination of 5-methylcytosine, late replication errors and chewing tobacco, respectively, additional research is needed to further elucidate the etiology of young-onset testicular cancer. Large studies of mutational signatures among young-onset patients are required to understand epidemiologic trends as well as inform targeted prevention and treatment strategies.
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    Predicting Early Discontinuation of Adjuvant Chemotherapy and its Impact on Survival among Individuals with Stage III Colon Cancer
    (2020-08-05) Boyne, Devon J; Brenner, Darren R.; Friedenreich, Christine M.; Cheung, Winson Y.; Hilsden, Robert J.; Sajobi, Tolulope T.
    Background: Approximately one in three patients with stage III colon cancer fail to complete the entirety of their adjuvant chemotherapy prescription. Two questions arise from this observation: 1) Can we predict which patients will discontinue adjuvant chemotherapy? and 2) Does a shortened duration of adjuvant chemotherapy impact overall survival? Evidence pertaining to the first question is limited. While question two was recently addressed within a large randomized trial, results from this trial have been controversial. Methods: To address question one, we conducted a systematic review and survey of medical oncologists to identify factors that predict non-completion of adjuvant chemotherapy. Building upon the results of this investigation, we developed an online calculator to predict the risk of discontinuation at the individual-level. For question two, a systematic review and meta-analysis was performed. In addition, we emulated a target trial that examined the effect of a shortened duration of adjuvant chemotherapy on overall survival using real-world data.Results: According to a systematic review of 18 studies and survey of 14 medical oncologists, there was evidence that increased comorbidity, worse performance status, higher T stage, and adjuvant CAPOX chemotherapy or poor oxaliplatin candidacy were associated with an increased risk of discontinuation. Using information from 1,378 patients, an online risk calculator was developed. Internal validation suggested that this calculator accurately predicted and classified patients with respect to their risk of discontinuation (optimism-adjusted C-statistic=0.80; 95% CI:0.79-0.82; calibration plots were within acceptable limits). A meta-analysis of 22 studies suggested that a shortened duration of adjuvant chemotherapy was harmful among patients prescribed a monotherapy (HR: 0.59; 95% CI: 0.52-0.68) but not among among those prescribed FOLFOX or CAPOX (HR: 0.80; 95% CI: 0.58-1.09). In a target trial analysis of 485 colon cancer patients, both the overall and subgroup-specific hazard ratios were consistent with those from a randomized trial. Conclusions: Results from this investigation can help assess and communicate the risk of early discontinuation within this study population. Results from our meta-analysis and target trial emulation suggest that a shortened duration of adjuvant chemotherapy may be appropriate for some patients which supports findings from a recent randomized trial.
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    Text analysis framework for identifying mutations among non-small cell lung cancer patients from laboratory data
    (2024-03-11) Yusuf, Amman; Boyne, Devon J.; O’Sullivan, Dylan E.; Brenner, Darren R.; Cheung, Winson Y.; Mirza, Imran; Jarada, Tamer N.
    Abstract Background Laboratory data can provide great value to support research aimed at reducing the incidence, prolonging survival and enhancing outcomes of cancer. Data is characterized by the information it carries and the format it holds. Data captured in Alberta’s biomarker laboratory repository is free text, cluttered and rouge. Such data format limits its utility and prohibits broader adoption and research development. Text analysis for information extraction of unstructured data can change this and lead to more complete analyses. Previous work on extracting relevant information from free text, unstructured data employed Natural Language Processing (NLP), Machine Learning (ML), rule-based Information Extraction (IE) methods, or a hybrid combination between them. Methods In our study, text analysis was performed on Alberta Precision Laboratories data which consisted of 95,854 entries from the Southern Alberta Dataset (SAD) and 6944 entries from the Northern Alberta Dataset (NAD). The data covers all of Alberta and is completely population-based. Our proposed framework is built around rule-based IE methods. It incorporates topics such as Syntax and Lexical analyses to achieve deterministic extraction of data from biomarker laboratory data (i.e., Epidermal Growth Factor Receptor (EGFR) test results). Lexical analysis compromises of data cleaning and pre-processing, Rich Text Format text conversion into readable plain text format, and normalization and tokenization of text. The framework then passes the text into the Syntax analysis stage which includes the rule-based method of extracting relevant data. Rule-based patterns of the test result are identified, and a Context Free Grammar then generates the rules of information extraction. Finally, the results are linked with the Alberta Cancer Registry to support real-world cancer research studies. Results Of the original 5512 entries in the SAD dataset and 5017 entries in the NAD dataset which were filtered for EGFR, the framework yielded 5129 and 3388 extracted EGFR test results from the SAD and NAD datasets, respectively. An accuracy of 97.5% was achieved on a random sample of 362 tests. Conclusions We presented a text analysis framework to extract specific information from unstructured clinical data. Our proposed framework has shown that it can successfully extract relevant information from EGFR test results.