Although the FLCN gene was identified in 2002 by Nickerson et al., studies trying to define the prevalence of BHD and the risks of developing the symptoms of BHD have proven difficult to determine, as discussed in this blog post. It would be of particular use to be able to accurately calculate the risk of developing renal cancer, as this is the most potentially serious symptom of BHD syndrome, but is not life-threatening if monitored appropriately. This week’s blog discusses the issues and importance of determining the epidemiology of BHD syndrome, and how this may help with the management of the disease.
The main issue in determining the true prevalence of BHD syndrome is that because it is so rare, it is probably under-diagnosed (Menko et al., 2009). Furthermore, whilst most FLCN mutations are inherited from an affected parent, Menko et al. (2012) recently reported a patient with a de novo FLCN mutation who had all three BHD symptoms. Thus, although de novo FLCN mutations are likely to be a less common cause of BHD than inherited mutations, this case suggests that a diagnosis of BHD should be considered for patients with one or more symptoms of BHD, even in the absence of a family history of the disease. However, new sequencing technology has lead to the development of assays like the Illumina TruSight Cancer panel, allowing the fast and economical screening of all known cancer predisposition genes, including FLCN, in up to 96 individuals at once. An initiative at the Royal Marsden Hospital in the UK is putting this technology to use and aiming to make genetic testing available to all cancer patients at the Royal Marsden within three years. If this initiative was successful and reproduced nationally and internationally, this would improve diagnosis rates of rare diseases, like BHD, that genetically predispose individuals to cancer.
Ascertainment bias may also make it difficult to accurately calculate the risk of developing pneumothorax or renal cancer as cohorts may be enriched or depleted for those symptoms being studied, depending on how patients were recruited. By pooling genotype and matched clinical data from different centres, ascertainment biases of different cohorts may counter-balance one another whilst at the same time increasing the number of patients being analysed in a single study, thus reducing statistical error and allowing more accurate prevalence and risk calculations. This could reveal patterns between populations, or genotype-phenotype correlations that are not obvious in individual cohorts.
Predicting the risks associated with BHD is further confounded by the presence of genetic modifiers, genetic factors that modify the presentation of the disease. Klujit et al., (2009) report that one branch of a large Dutch family with BHD predominantly developed renal symptoms, whereas the other branches predominantly developed skin and lung manifestations. Genetic modifiers may be tractable by methods such as genome-wide association studies (GWAS). GWAS identify regions of the genome that confer a low risk of disease individually, but can interact with other regions of the genome to ameliorate or exacerbate disease symptoms. While typical GWAS cohorts are in the order of 1,000s, Turnbull et al., (2012) successfully used GWAS to identify risk alleles in a small cohort (~550 patients) of the rare paediatric kidney cancer, Wilms tumour. Determining what these genetic modifiers are and how they correspond to the different symptoms of BHD would be valuable information for risk prediction for patients and may shed light on the pathogenesis of BHD, thus suggesting a new therapy or intervention.
Better diagnosis rates coupled with improved risk predictions, could lead to the stratified management of BHD patients. This would allow patients at higher risk of developing renal cancer to be screened more often, and those at lower risk to be screened less frequently, thereby striking the optimal balance between timely diagnosis and unnecessary screening.
- Kluijt I, de Jong D, Teertstra HJ, Axwijk PH, Gille JJ, Bell K, van Rens A, van der Velden AW, Middelton L, & Horenblas S (2009). Early onset of renal cancer in a family with Birt-Hogg-Dubé syndrome. Clinical genetics, 75 (6), 537-43 PMID: 19320655
- Menko FH, van Steensel MA, Giraud S, Friis-Hansen L, Richard S, Ungari S, Nordenskjöld M, Hansen TV, Solly J, Maher ER, & European BHD Consortium (2009). Birt-Hogg-Dubé syndrome: diagnosis and management. The lancet oncology, 10 (12), 1199-206 PMID: 19959076
- Menko FH, Johannesma PC, van Moorselaar RJ, Reinhard R, van Waesberghe JH, Thunnissen E, Houweling AC, Leter EM, Waisfisz Q, van Doorn MB, Starink TM, Postmus PE, Coull BJ, van Steensel MA, & Gille JJ (2012). A de novo FLCN mutation in a patient with spontaneous pneumothorax and renal cancer; a clinical and molecular evaluation. Familial cancer PMID: 23264078
- Nickerson ML, Warren MB, Toro JR, Matrosova V, Glenn G, Turner ML, Duray P, Merino M, Choyke P, Pavlovich CP, Sharma N, Walther M, Munroe D, Hill R, Maher E, Greenberg C, Lerman MI, Linehan WM, Zbar B, & Schmidt LS (2002). Mutations in a novel gene lead to kidney tumors, lung wall defects, and benign tumors of the hair follicle in patients with the Birt-Hogg-Dubé syndrome. Cancer cell, 2 (2), 157-64 PMID: 12204536
- Turnbull C, Perdeaux ER, Pernet D, Naranjo A, Renwick A, Seal S, Munoz-Xicola RM, Hanks S, Slade I, Zachariou A, Warren-Perry M, Ruark E, Gerrard M, Hale J, Hewitt M, Kohler J, Lane S, Levitt G, Madi M, Morland B, Neefjes V, Nicholson J, Picton S, Pizer B, Ronghe M, Stevens M, Traunecker H, Stiller CA, Pritchard-Jones K, Dome J, Grundy P, & Rahman N (2012). A genome-wide association study identifies susceptibility loci for Wilms tumor. Nature genetics, 44 (6), 681-4 PMID: 22544364