Breast Cancer Risk Prediction with Heterogeneous Risk Profiles According to Breast Cancer Tumor Markers

American journal of epidemiology (Impact Factor: 5.23). 05/2013; 178(2). DOI: 10.1093/aje/kws457
Source: PubMed


Relationships between some risk factors and breast cancer incidence are known to vary by tumor subtype. However, breast tumors can be classified according to a number of markers, which may be correlated, making it difficult to identify heterogeneity of risk factors with specific tumor markers when using standard competing-risk survival analysis. In this paper, we propose a constrained competing-risk survival model that allows for assessment of heterogeneity of risk factor associations according to specific tumor markers while controlling for other markers. These methods are applied to Nurses' Health Study data from 1980-2006, during which 3,398 incident invasive breast cancers occurred over 1.4 million person-years of follow-up. Results suggested that when estrogen receptor (ER) and progesterone receptor (PR) status are mutually considered, some risk factors thought to be characteristic of "estrogen-positive tumors," such as high body mass index during postmenopause and increased height, are actually significantly associated with PR-positive tumors but not ER-positive tumors, while other risk factors thought to be characteristic of "estrogen-negative tumors," such as late age at first birth, are actually significantly associated with PR-negative rather than ER-negative breast cancer. This approach provides a strategy for evaluating heterogeneity of risk factor associations by tumor marker levels while controlling for additional tumor markers.

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    • "If they depend on the covariates we would speak of 'random effects models', e.g. [20] [5] [21] [22] [23]. If the distribution of frailty factors takes the form of discrete clusters (latent classes, [24]), we obtain the latent class models; see e.g. "
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    ABSTRACT: In heterogeneous cohorts and those where censoring by non-primary risks is informative many conventional survival analysis methods are not applicable; the proportional hazards assumption is usually violated at population level and the observed crude hazard rates are no longer estimators of what they would have been in the absence of other risks. In this paper, we develop a fully Bayesian survival analysis to determine the probabilistically optimal description of a heterogeneous cohort and we propose a novel means of recovering hazard rates and survival functions `decontaminated' of the effects of any competing risks. Most competing risks studies implicitly assume that risk correlations are induced by cohort or disease heterogeneity that is not captured by covariates. We additionally assume that proportional hazards hold at the level of individuals, for all risks, leading to a generic statistical description that allows us to decontaminate the effects of informative censoring, and from which Cox regression, frailty and random effects models, and latent class models can all be recovered as special cases. Synthetic data confirm that our approach can map a cohort's substructure, and remove heterogeneity-induced false protectivity and false aetiology effects. Application to survival data from the ULSAM cohort leads to plausible alternative explanations for previous counter-intuitive inferences to prostate cancer. The importance of managing cardiovascular disease as a comorbidity in women diagnosed with breast cancer is suggested on application to survival data from the AMORIS study.
    Full-text · Article · Nov 2015
    • "In this paper, we consider the estimation of regression models when the population under study is naturally divided into strata, or sub-groups, defined by a categorical variable Z such as gender, dosage or type of treatment, geographical area, etc, or a combination thereof (Rosner et al., 2013; Gertheiss and Tutz, 2012; Viallon et al., 2015). More formally, our aim is to study the relationship 30 between a response variable y ∈ IR and a set of p ≥ 1 predictors x ∈ IR p over these strata, and in particular to determine whether this relationship varies across these strata, that is whether the categorical covariate Z modifies the effect of x on y (Gertheiss and Tutz, 2012). "
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    ABSTRACT: We consider regression models to be estimated on $K>1$ pre-defined strata of a sample. Denote by $\beta^*_{k,j}$ the theoretical parameter associated to the $j$-th covariate in the $k$-th stratum. It is common practice to first arbitrarily chose a reference stratum, say $\ell$, and perform inference based on the decomposition $\beta^*_{k,j} = \beta^*_{\ell,j} + \gamma^*_{k,j}$. In particular, $\ell_1$-penalized regression models can be constructed to recover non-zero parameters among the $\beta^*_{\ell,j}$'s and the $ \gamma^*_{k,j}$'s. In this paper, we present a simple though efficient method that bypasses the arbitrary choice of the reference stratum at no cost. Its implementation can be done with available packages under a variety of models and, in the linear regression model, we show it is sparsistent under conditions similar to those ensuring sparsistency for an oracular version of the reference stratum strategy. Our empirical study further shows that our proposal performs at least as well as its competitors under the considered settings. As a final illustration, an analysis of road safety data is provided.
    No preview · Article · Aug 2015
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    • "It is generally recognized that the risk factors affect the ER, PR and HER2 expression: Post-menopause state, high body mass index and increased height, were associated with positive PR tumors and breastfeeding period equal to or longer than seven months was negatively associated with TN breast cancer [10,11]. The increased frequency of positive ER breast tumors in African American, Hispanic and white women, was associated with changing incidence of obesity and parity [12]. "
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    ABSTRACT: Background The frequencies of molecular breast cancer subtypes vary among different human populations. The Northeastern region of Brazil has a mixed population of African, Indigenous and European ancestry. This retrospective study investigated breast cancer subtypes and applied therapies in a public hospital of Northeastern Brazil. Methods Data of 633 patients with invasive breast cancer from 2005 to 2011 were obtained from medical records. Status of hormone receptor (HR), HER2 and Ki67 expression index of 269 out of 633 patients were used to define subtypes of Luminal A and B, HER2 and triple negative (TN) breast cancer. Expression index of Ki67 ≥ 14% was applied to distinguish Luminal A from Luminal B subtypes. Results Overall, 185 (68.77%) and 132 (49.07%) patients showed positive hormone receptor (HR+) and positive HER2 (HER2+) tumors. The mean age ranged from 53.33 to 58.25 years for patients with tumors of Luminal B and Luminal A subtypes, respectively (p = 0.0182). In general, 67.39% of patients with TN tumors aged over 50 and 19.57% aged between 31 and 40 years (p = 0.0046). The rate of small tumors (T1: ≤ 2.0 cm) varied from 22.73% to 52.46% for TN and Luminal A subtypes (p = 0.0088). The rate of high graded (G3) tumors was increased for HER2 and TN subtypes (35.29% and 34.28%) compared to Luminal A and Luminal B subtypes (3.92% and 12.62%), respectively (p < 0.0001). The five-year survival rate ranged from 92.86% to 75.00%, for Luminal A, HER2 and TN subtypes, respectively (HR: 0.260 to 1.015; 95% CI: 0.043 to 3.594; p = 0.2589). Patients with HER2 positive (HER2+) breast tumors did not receive immunotherapy and chemotherapy application varied from 54.84% to 86.49% for Luminal A and HER2 subtypes, respectively (p = 0.0131). Conclusions The results of this study revealed a high percentage of HER2+ breast tumors and an increased rate of patients with TN tumors aged over 50 years. This emphasizes the need for establishing immunotherapy as an additional therapeutic option to improve clinical outcomes for patients with HER2+ tumors and to investigate the risk factors of TN breast cancer.
    Full-text · Article · Sep 2014 · BMC Women's Health
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