Searching for susceptibility alleles: Emphasis on bilateral breast cancer

Imyanitov, Evgeny N.; Cornelisse, Cees J.; Devilee, Peter

doi:10.1002/ijc.22785

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…5,7–9 This “comparison of extremes” approach affects study power by increasing the detectable “signal.” Specifically, if, as before, the relative risk is assumed to be independent of background risk, the odds ratio from the “extremes” design will be the square of the odds ratio for a conventional design. That is, if the odds ratio comparing patients with double primaries to those with single primaries is the same as the odds ratio comparing patients with single primaries to population controls, then the odds ratio from a comparison of double primaries with population controls will be the square of this baseline odds ratio.…”

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confidence: 99%

Evaluating Cancer Epidemiologic Risk Factors Using Multiple Primary Malignancies

Kuligina

Reiner²,

Imyanitov³

et al. 2010

Epidemiology

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Background Several authors have proposed the use of patients with double primary malignancies affecting the same or contralateral organ as a genetically enriched resource of cases for epidemiologic case-control studies in cancer. Such an approach is based on the assumption that the factors that increase the risk of a second primary are the same ones that influence the risk of a first primary. The advantages for statistical power are premised on the assumption that relative risks in survivors of a first primary cancer are similar to relative risks in the unaffected population. We explore these assumptions theoretically and empirically using published data from breast cancer studies involving bilateral breast cancer. Methods We conducted a literature review to identify case-control studies of variants in 4 genes known to affect breast cancer risk: CHEK2*1100delC; multiple variants in BRCA1 and BRCA2; and FGFR2 rs2981582. Summary odds ratios were obtained for each of three study designs: a conventional case-control design, the design comparing bilateral cases with unilateral controls, and the design comparing bilateral cases with population controls. Results The data show strong patterns of steadily increasing prevalence of risk factors from healthy controls to primary cases to bilateral cases, as expected. Relative risks in survivors of unilateral breast cancer are either the same as in the general population, or modestly attenuated. Conclusions Patients with double primary malignancies are a very important underused resource for cancer epidemiologic investigation. Such patients are especially useful for broad genome-wide discovery studies, and for studies of rare, strong risk factors such as high penetrance genes.

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confidence: 99%

Evaluating Cancer Epidemiologic Risk Factors Using Multiple Primary Malignancies

Kuligina

Reiner²,

Imyanitov³

et al. 2010

Epidemiology

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“…However, two recent developments have encouraged us to pursue the goal of estimating the influence of individual rare variants on disease risk from case‐control data. First, it has been recognized that risk factors can be evaluated in novel case‐control designs whereby the “cases” are individuals who experience two independent primaries, and the “controls” are individuals with a single primary [Begg and Berwick, 1997; Fletcher et al, 2009; Imyanitov et al, 2007; Kuligina et al, 2010]. The participants in these designs are greatly enriched for risk variants, and this enrichment is especially valuable for rare variants with substantial relative risks.…”

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confidence: 99%

Assessment of rare BRCA1 and BRCA2 variants of unknown significance using hierarchical modeling

et al. 2011

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Current evidence suggests that the genetic risk of breast cancer may be caused primarily by rare variants. However, while classification of protein-truncating mutations as deleterious is relatively straightforward, distinguishing as deleterious or neutral the large number of rare missense variants is a difficult on-going task. In this article we present one approach to this problem, hierarchical statistical modeling of data observed in a case-control study of contralateral breast cancer in which all participants were genotyped for variants in BRCA1 and BRCA2. Hierarchical modeling permits leverage of information from observed correlations of characteristics of groups of variants with case-control status to infer with greater precision the risks of individual rare variants. A total of 181 distinct rare missense variants were identified among the 705 cases with contralateral breast cancer and the 1398 controls with unilateral breast cancer. The model identified 3 bioinformatic hierarchical covariates, align-GV, align-GD and SIFT scores, each of which was modestly associated with risk. Collectively, the 11 variants that were classified as adverse on the basis of all three bioinformatic predictors demonstrated a stronger risk signal. This group included 5 of 6 missense variants that were classified as deleterious at the outset by conventional criteria. The remaining 6 variants can be considered as plausibly deleterious, and deserving of further investigation (BRCA1 R866C; BRCA2 G1529R, D2665G, W2626C, E2663V and R3052W). Hierarchical modeling is a strategy that has promise for interpreting the evidence from future association studies that involve sequencing of known or suspected cancer genes.

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A strategy for distinguishing optimal cancer subtypes

Begg

2010

Intl Journal of Cancer

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Much attention is directed currently to identifying subtypes of cancers that are genetically and clinically distinct. The expectation is that subtyping on the basis of somatic genomic characteristics will supplant traditional pathological subtypes with respect to relevance for targeted therapies and clinical course. Less attention has been paid to the goal of validating subtypes on the basis of the distinctiveness of their etiologies. In this article it is shown that studies of individuals with double primary malignancies provide uniquely valuable information for establishing the etiologic distinctiveness of candidate tumor subtypes. Studies of double primaries have the potential to definitively rank candidate taxonomic systems with respect to their etiological relevance by determining which subtypes are most highly correlated in the double primaries. The concept is illustrated with data from studies of the concordance of estrogen and progestin status in bilateral breast cancers, where it is shown that double primaries are much more likely to be concordant with respect to estrogen receptor (ER) status than for PR status. The high concordance of ER status is consistent with a growing literature demonstrating the etiologic distinctiveness of ER1 and ER2 tumors.Much attention is currently being directed at the goal of classifying cancers into distinct molecular subtypes, using extensive genomic data that distinguishes the somatic characteristics of these subtypes.1,2 It is anticipated that sub-classifications based on molecular characteristics will lead to a better understanding of cancer biology, and better avenues for determining appropriate targeted therapies. 3,4 Efforts to validate the relevance of candidate subtypes have usually focused on establishing clinical distinctiveness on the basis of, say, different survival for patients in the subtypes, or by merely showing that the genomically determined subtypes differ systematically with respect to conventional pathologic criteria. It is reasonable to speculate, however, that subtypes that are genuinely biologically distinct are likely also to possess distinct etiologies. Conventionally, epidemiologists have examined the potential etiologic heterogeneity of cancer subtypes by comparing the candidate subtypes with respect to the presence of known risk factors for the cancer in question. 5 Because it is widely believed that many risk factors for cancer, especially genetic factors, have not yet been identified, this strategy is necessarily limited in scope. The thesis of this article is that optimal subtyping of cancer from an etiologic perspective can be accomplished without the need to know any risk factors, merely by studying the co-occurrence of the subtypes in series of patients with double primary malignancies.The ageing of the population, allied to a gradual increase in survival following cancer has led to a rapid increase in the occurrence of second primary cancers. In fact, over 16% of all cancers reported to the Surveillance Epidemiology and End Results (S...

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Searching for susceptibility alleles: Emphasis on bilateral breast cancer

Cited by 9 publications

References 29 publications

Evaluating Cancer Epidemiologic Risk Factors Using Multiple Primary Malignancies

Evaluating Cancer Epidemiologic Risk Factors Using Multiple Primary Malignancies

Assessment of rare BRCA1 and BRCA2 variants of unknown significance using hierarchical modeling

A strategy for distinguishing optimal cancer subtypes

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