| 1. |
- Kanai, M, et al.
(author)
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- 2023
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swepub:Mat__t
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| 2. |
- Ivshina, Anna V., et al.
(author)
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Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer
- 2006
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In: Cancer Research. - 0008-5472 .- 1538-7445. ; 66:21, s. 10292-10301
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Journal article (peer-reviewed)abstract
- Histologic grading of breast cancer defines morphologic subtypes informative of metastatic potential, although not without considerable interobserver disagreement and clinical heterogeneity particularly among the moderately differentiated grade 2 (G2) tumors. We posited that a gene expression signature capable of discerning tumors of grade 1 (G1) and grade 3 (W) histology might provide a more objective measure of grade with prognostic benefit for patients with G2 disease. To this end, we studied the expression profiles of 347 primary invasive breast tumors analyzed on Affymetrix microarrays. Using class prediction algorithms, we identified 264 robust grade-associated markers, six of which could accurately classify G1 and G3 tumors, and separate G2 tumors into two highly discriminant classes (termed G2a and G2b genetic grades) with patient survival outcomes highly similar to those with G1 and G3 histology, respectively. Statistical analysis of conventional clinical variables further distinguished G2a and G2b subtypes from each other, but also from histologic G1 and G3 tumors. In multivariate analyses, genetic grade was consistently found to be an independent prognostic indicator of disease recurrence comparable with that of lymph node status and tumor size. When incorporated into the Nottingham prognostic index, genetic grade enhanced detection of patients with less harmful tumors, likely to benefit little from adjuvant therapy. Our findings show that a genetic grade signature can improve prognosis and therapeutic planning for breast cancer patients, and support the view that low- and high-grade disease, as defined genetically, reflect independent pathobiological entities rather than a continuum of cancer progression.
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| 3. |
- Joshy, G, et al.
(author)
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Predicting 7-year mortality for use with evidence-based guidelines for Prostate-Specific Antigen (PSA) testing: findings from a large prospective study of 123 697 Australian men
- 2018
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In: BMJ open. - : BMJ. - 2044-6055. ; 8:12, s. e022613-
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Journal article (peer-reviewed)abstract
- To develop and validate a prediction model for short-term mortality in Australian men aged ≥45years, using age and self-reported health variables, for use when implementing the Australian Clinical Practice Guidelines for Prostate-Specific Antigen (PSA) Testing and Early Management of Test-Detected Prostate Cancer. Implementation of one of the Guideline recommendations requires an estimate of 7-year mortality.DesignProspective cohort study using questionnaire data linked to mortality data.SettingMen aged ≥45years randomly sampled from the general population of New South Wales, Australia, participating in the 45 and Up Study.Participants123 697 men who completed the baseline postal questionnaire (distributed from 1 January 2006 to 31 December 2008) and gave informed consent for follow-up through linkage of their data to population health databases.Primary outcome measuresThe primary outcome was all-cause mortality.Results12 160 died during follow-up (median=5.9 years). Following age-adjustment, self-reported health was the strongest predictor of all-cause mortality (C-index: 0.827; 95% CI 0.824 to 0.831). Three prediction models for all-cause mortality were validated, with predictors: Model-1: age group and self-rated health; Model-2: variables common to the 45 and Up Study and the Australian Health Survey and subselected using stepwise regression and Model-3: all variables selected using stepwise regression. Final predictions calibrated well with observed all-cause mortality rates. The 90th percentile for the 7-year mortality risks ranged from 1.92% to 83.94% for ages 45–85 years.ConclusionsWe developed prediction scores for short-term mortality using age and self-reported health measures and validated the scores against national mortality rates. Along with age, simple measures such as self-rated health, which can be easily obtained without physical examination, were strong predictors of all-cause mortality in the 45 and Up Study. Seven-year mortality risk estimates from Model-3 suggest that the impact of the mortality risk prediction tool on men’s decision making would be small in the recommended age (50–69 years) for PSA testing, but it may discourage testing at older ages.
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