Reveiz Manuela, Bouhouita-Guermech Sarah, Blackmore Kristina M, Chiquette Jocelyne, Demers Éric, Dorval Michel, Lambert-Côté Laurence, Nabi Hermann, Pashayan Nora, Soucy Penny, Turgeon Annie, Walker Meghan J, Knoppers Bartha M, Chiarelli Anna M, Simard Jacques, Joly Yann
Centre of Genomics and Policy (CGP), McGill University, Montreal, QC, Canada.
Ontario Health (Cancer Care Ontario), Toronto, ON, Canada.
Front Genet. 2025 Mar 5;16:1481863. doi: 10.3389/fgene.2025.1481863. eCollection 2025.
The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) incorporates the effects of common genetic variants, from polygenic risk scores, pathogenic variants in major breast cancer (BC) susceptibility genes, lifestyle/hormonal risk factors, mammographic density, and cancer family history to predict risk levels of developing breast and ovarian cancer. While offering multifactorial risk assessment to the population could be a promising avenue for early detection of BC, obstacles to its implementation including fear of genetic discrimination (GD), could prevent individuals from undergoing screening.
The aim of our study was two-fold: determine the extent of legal protection in Canada available to protect information generated by risk prediction models such as the BOADICEA algorithm through a literature review, and then, assess individuals' knowledge of and concerns about GD in this context by collecting data through surveys.
Our legal analysis highlighted that while Canadian employment and privacy laws provide a good level of protection against GD, it remains uncertain whether the Genetic Non-Discrimination Act (GNDA) would provide protection for BC risk levels generated by a risk prediction model. The survey results of 3,055 participants who consented to risk assessment in the PERSPECTIVE I&I project showed divergent perspectives of how the law would protect BC risk level in the context of employment and that a high number of participants did not feel that their risk level was protected from access and use by life insurers. Indeed, 49,1% of participants reckon that the level of breast cancer risk could have an impact on a woman's ability to buy insurance and 58,9% of participants reckon that a woman's insurance might be cancelled if important health information (including level of breast cancer risk) is not given when buying or renewing life or health insurance.
The results indicate that much work needs to be done to improve and clarify the extent of protection against GD in Canada and to inform the population of how the legal framework applies to risk levels generated by risk prediction models.
乳腺癌和卵巢癌疾病发病率及携带者估计算法（BOADICEA）纳入了常见基因变异的影响，这些影响来自多基因风险评分、主要乳腺癌（BC）易感基因中的致病变异、生活方式/激素风险因素、乳腺X线密度以及癌症家族史，以预测患乳腺癌和卵巢癌的风险水平。虽然为人群提供多因素风险评估可能是早期发现乳腺癌的一个有前景的途径，但实施过程中的障碍，包括对基因歧视（GD）的担忧，可能会阻碍个体接受筛查。
我们研究的目的有两个：通过文献综述确定加拿大可用于保护由风险预测模型（如BOADICEA算法）生成的信息的法律保护程度，然后通过调查收集数据，评估个体在这种情况下对基因歧视的了解和担忧。
我们的法律分析强调，虽然加拿大的就业和隐私法为防范基因歧视提供了较好的保护水平，但《基因非歧视法》（GNDA）是否会为风险预测模型生成的乳腺癌风险水平提供保护仍不确定。在“PERSPECTIVE I&I项目”中同意进行风险评估的3055名参与者的调查结果显示，对于法律在就业背景下如何保护乳腺癌风险水平，人们的观点存在分歧，并且大量参与者认为他们的风险水平无法防止人寿保险公司获取和使用。事实上，49.1%的参与者认为乳腺癌风险水平可能会影响女性购买保险的能力，58.9%的参与者认为，如果在购买或续保人寿或健康保险时未提供重要健康信息（包括乳腺癌风险水平），女性的保险可能会被取消。
结果表明，在加拿大，需要做大量工作来改进和明确防范基因歧视的保护范围，并让民众了解法律框架如何适用于风险预测模型生成的风险水平。
