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FRAMe: Familial Risk Assessment of Melanoma—a risk prediction tool to guide CDKN2A germline mutation testing in Australian familial melanoma
by
Blake, Kelly
, Schmid, Helen
, Drummond, Martin
, Mann, Graham J
, Cust, Anne E
, Palmer, Jane M
, Pritchard, Antonia L
, Hayward, Nicholas K
, Holland, Elizabeth A
, Lo Serigne
in
Genetic counseling
/ Invasiveness
/ Melanoma
/ Mutation
/ Nomograms
/ Pancreatic cancer
/ Prediction models
/ Risk assessment
/ Skin cancer
/ Ultraviolet radiation
2021
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FRAMe: Familial Risk Assessment of Melanoma—a risk prediction tool to guide CDKN2A germline mutation testing in Australian familial melanoma
by
Blake, Kelly
, Schmid, Helen
, Drummond, Martin
, Mann, Graham J
, Cust, Anne E
, Palmer, Jane M
, Pritchard, Antonia L
, Hayward, Nicholas K
, Holland, Elizabeth A
, Lo Serigne
in
Genetic counseling
/ Invasiveness
/ Melanoma
/ Mutation
/ Nomograms
/ Pancreatic cancer
/ Prediction models
/ Risk assessment
/ Skin cancer
/ Ultraviolet radiation
2021
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FRAMe: Familial Risk Assessment of Melanoma—a risk prediction tool to guide CDKN2A germline mutation testing in Australian familial melanoma
by
Blake, Kelly
, Schmid, Helen
, Drummond, Martin
, Mann, Graham J
, Cust, Anne E
, Palmer, Jane M
, Pritchard, Antonia L
, Hayward, Nicholas K
, Holland, Elizabeth A
, Lo Serigne
in
Genetic counseling
/ Invasiveness
/ Melanoma
/ Mutation
/ Nomograms
/ Pancreatic cancer
/ Prediction models
/ Risk assessment
/ Skin cancer
/ Ultraviolet radiation
2021
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FRAMe: Familial Risk Assessment of Melanoma—a risk prediction tool to guide CDKN2A germline mutation testing in Australian familial melanoma
Journal Article
FRAMe: Familial Risk Assessment of Melanoma—a risk prediction tool to guide CDKN2A germline mutation testing in Australian familial melanoma
2021
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Overview
Germline mutations in CDKN2A greatly increase risk of developing cutaneous melanoma. We have constructed a risk prediction model, Familial Risk Assessment of Melanoma (FRAMe), for estimating the likelihood of carrying a heritable CDKN2A mutation among Australian families, where the prevalence of these mutations is low. Using logistic regression, we analysed characteristics of 299 Australian families recruited through the Sydney site of GenoMEL (international melanoma genetics consortium) with at least three cases of cutaneous melanoma (in situ and invasive) among first-degree blood relatives, for predictors of the presence of a pathogenic CDKN2A mutation. The final multivariable prediction model was externally validated in an independent cohort of 61 melanoma kindreds recruited through GenoMEL Queensland. Family variables independently associated with the presence of a CDKN2A mutation in a multivariable model were number of individuals diagnosed with melanoma under 40 years of age, number of individuals diagnosed with more than one primary melanoma, and number of individuals blood related to a melanoma case in the first degree diagnosed with any cancer excluding melanoma and non-melanoma skin cancer. The number of individuals diagnosed with pancreatic cancer was not independently associated with mutation status. The risk prediction model had an area under the receiver operating characteristic curve (AUC) of 0.851 (95% CI 0.793, 0.909) in the training dataset, and 0.745 (95%CI 0.612, 0.877) in the validation dataset. This model is the first to be developed and validated using only Australian data, which is important given the higher rate of melanoma in the population. This model will help to effectively identify families suitable for genetic counselling and testing in areas of high ambient ultraviolet radiation. A user-friendly electronic nomogram is available at www.melanomarisk.org.au.
Publisher
Springer Nature B.V
Subject
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