Breast cancer genetics in perspective: Results from population-based studies
Authors: John Hopper
Journal: Cancer Research (2008)

AACR Annual Meeting-- Apr 12-16, 2008; San Diego, CA

SY36-02 

Population-based studies are essential for putting the impact of breast cancer genes into perspective. For example, genes with mutations associated with a high personal risk of breast cancer have generally been found from studying so-called 'high-risk' multiple-case families ascertained through non-systematic means, such as from cancer family genetics clinics. Population-based studies have shown, however, that these sorts of families do not represent the typical carrier family, most of which involve little or no breast cancer.    When it comes to understanding the impact of these gene mutations, especially in terms of the cancer risks associated with being a carrier, clinic-based (non-population-based) resources pose two major problems. First, valid statistical inference needs to take into account the fact that these families were selected because they contained multiple affected individuals. One needs to condition on the affected status of family members, because that is what brought the family into consideration (ascertainment), and this in effect means that the information on risk comes only from the un affected (known or likely) carriers. For population-based families the conditioning to adjust for ascertainment is much simpler, being based on the proband alone, and therefore there is much more information per carrier family.    Second, the risks associated with being a carrier from such families may not necessarily be the same as for carriers with different family histories; the mutations found to be segregating in multiple-case families may inherently be those which confer a greater risk, and/or there may be other familial factors operating within those families which increase risk of breast cancer for mutation carriers.    With respect to the first issue, for colorectal cancer and the DNA mismatch repair genes, failure to correctly adjust for ascertainment has meant that all but a few papers have published incorrect over-estimates of risk for carriers. Fortunately this has not been the case for published studies to date on breast cancer susceptibility genes. Although there appear to be in general lower estimates of risk from population-based studies the differences in absolute risk are not substantial. There has also been a lack of precision in the clinic-based estimates as a consequence of the need for strong conditioning to adjust for ascertainment referred to above.    There has been much discussion about the second issue, though little formal effort at quantifying just how much difference there is between population-based and clinic-based estimates of penetrance let alone whether the differences are greater than would be explained by chance, given the typical poor precision of estimates (see above). Nevertheless, there does appear to be direct and indirect evidence that there is heterogeneity in risk. For example, a recent population-based study or risk to relatives in carriers identified from unilateral and bilateral cases found indirect evidence for unmeasured factors influencing risks. Direct evidence related to specific gene variants is also emerging.    As more genes possibly implicated in breast cancer risk have been identified, either as candidates or from genome wide linkage and association studies, the role of population-based studies has come more to the fore. For example, the risks associated with rare mutations are best elucidated by population-based family studies (case-control designs and clinic-based families have weak power). This was nicely illustrated by a population-based case-control-family study of the 7271T>G missense mutation in ATM .    Studies of rare mutations using familial (clinic-based) families and controls are problematic in that valid estimates of risk do not come from a direct comparison of the relative frequency for familial cases with that for controls. If a risk factor clusters in families it will be more common in familial cases through two effects; its association with risk per se and its association with familial disease in particular. Estimating risks therefore must rely on model fitting to adjust for not having studied population-based cases unselected for family history. Confusion has arisen. Some authors have applied a multiplicative polygenic model to such familial case-control data on mutations in ATM , BRIP1 , PALB2 , and CHEK2 . They have, in effect, estimated the increased risk for the average woman without realising that their model explicitly assumes that absolute risk depends also on the polygenic risk status. Family history is a good surrogate for polygenic risk, so the absolute for carriers with a strong family history may be quite high, even comparable to that for mutations in BRCA1 and BRCA2 . This issue is best resolved by population-based family studies, as illustrated by analysis of data from Finland on a founder mutation in PALB2 .    Population-based family designs will continue to become more important for understanding the multi-dimensional landscape of breast cancer genetics, complementing the role of opportunistically sampled multiple-case families whose contributions are best in finding susceptibility genes, but not in informing what these genes mean.