| Literature DB >> 30781715 |
Valentina A Zavala1, Silvia J Serrano-Gomez2, Julie Dutil3, Laura Fejerman4.
Abstract
The last 10 years witnessed an acceleration of our understanding of what genetic factors underpin the risk of breast cancer. Rare high- and moderate-penetrance variants such as those in the BRCA genes account for a small proportion of the familial risk of breast cancer. Low-penetrance alleles are expected to underlie the remaining heritability. By now, there are about 180 genetic polymorphisms that are associated with risk, most of them of modest effect. In combination, they can be used to identify women at the lowest or highest ends of the risk spectrum, which might lead to more efficient cancer prevention strategies. Most of these variants were discovered in populations of European descent. As a result, we might be failing to discover additional polymorphisms that could explain risk in other groups. This review highlights breast cancer genetic epidemiology studies conducted in Latin America, and summarizes the information that they provide, with special attention to similarities and differences with studies in other populations. It includes studies of common variants, as well as moderate- and high-penetrance variants. In addition, it addresses the gaps that need to be bridged in order to better understand breast cancer genetic risk in Latin America.Entities:
Keywords: Latin America; breast cancer; genetic epidemiology
Mesh:
Year: 2019 PMID: 30781715 PMCID: PMC6410045 DOI: 10.3390/genes10020153
Source DB: PubMed Journal: Genes (Basel) ISSN: 2073-4425 Impact factor: 4.096
Summary results from breast cancer candidate gene studies in Latin America showing a positive association with cancer risk (p ≤ 0.05).
| Country | Gene | Variant | Risk Genotype vs. Common Genotype | OR (95% CIs) | Model | Design | Reference |
|---|---|---|---|---|---|---|---|
|
| rs2232365A>G | AA vs. GG | 1.93 (1.01–3.66) | Controlled by age | 117 cases and 300 controls | Banin Hirata et al., 2017 [ | |
| Brazil |
| rs1801133C>T | TT vs. (CC + CT) | 2.53 (1.08–5.93) | Adjusted for age, alcohol and smoking consumption, and BMI | 100 cases and 144 controls | Zara-Lopes et al., 2016 [ |
|
| rs1048943A>G | (AG + GG) vs. AA | 1.50 (1.14–1.97) | Adjusted for age and ethnic origin | 742 cases and 742 controls | Oliveira et al., 2015 [ | |
|
| rs1946518C>A | CC vs AA | 2.78 (1.39–5.59) | Adjusted for age and BMI | 154 cases and 118 controls | Back et al., 2014 [ | |
| rs187238G>C | (GG + GC) vs. CC | 3.89 (1.43–10.64) | |||||
|
| rs366631C>T | −/− vs. (+/− and +/+) | 2.40 (1.1–5.6) | Not adjusted | 49 cases and 49 controls | Possuelo et al., 2013 [ | |
|
| rs11614913C>T | (TC+ TT) vs. CC | 1.50 (CI not provided) | Adjusted for age, race, menarche age, menopausal status, smoking habits, and first-degree breast cancer family history | 388 cases and 388 controls | Linhares et al., 2012 [ | |
| Chile | pre-mir-423 | rs6505162C>A | AA vs. CC | 1.70 (1.0–2.0) | In families with at least three BC and/or OC cases | 440 cases a and 807 controls | Morales at al., 2016 [ |
| pre-mir-618 | rs2682818C>A | CA vs. CC | 1.60 (1.0 − 2.4) | In families with a single case, diagnosed at ≤ 50 years of age | |||
| pre-mir-27a | rs895819A>G | GG vs. AA | 0.30 (0.1–0.8) | In families with 2 BC and/or OC cases | |||
|
| rs152451A>G | AG vs. AA | 2.0 (1.2–3.1) | In families with at least three BC and/or OC cases | 436 cases a and 809 controls | Leyton et al., 2015 [ | |
| rs45551636C>T | CT vs. CC | 3.0 (1.2–6.8) | |||||
|
| rs3803662C>T | TT vs. CC | 2.38 (1.44–3.90) | In families with at least 2 BC and/or OC cases | 344 cases a and 801 controls | Elematore et al., 2014 [ | |
| 2q35 | rs13387042 G>A | AA vs. GG | 1.99 (1.25–3.14) | ||||
|
| rs2981582C>T | CT vs. CC | 2.00 (1.26–3.23) | In families with a single case, diagnosed at ≤ 50 years of age | 351 cases a and 802 controls | Jara et al., 2013 [ | |
| rs2420946C>T | TT vs. CC | 2.05 (1.16–3.63) | |||||
| rs1219648A>G | GG vs. AA | 2.06 (1.16–3.66) | |||||
|
| rs889312A>C | CC vs. AA | 1.96 (1.13–3.37) | ||||
|
| rs28997576C>G | CG vs.CC | 3.4 (1.2–10.2) | In families with at least three BC and/or OC cases | 322 cases a and 570 controls | Gonzalez-Hormazabal et al., 2012 [ | |
|
| rs861539C>T | TT vs. CC | 3.2 (1.40–1.72) | In families with at least three BC and/or OC cases | |||
| TT vs. CC | 2.44 (1.34–4.43) | Not adjusted | 267 cases a and 500 controls | Jara et al., 2010 [ | |||
|
| rs1801516G>A | (GA + AA) vs. GG | 2.52 (1.33–4.77) | Not adjusted | 42 cases a and 200 controls | Tapia et al., 2008 [ | |
| rs1801516G>A | GA vs. GG | 1.74 (0.96–3.16) | Not adjusted | 126 cases a and 200 controls | Gonzalez-Hormazabal et al., 2008 [ | ||
| IVS24-9delT | T(-T) vs. TT | 1.74 (0.96–3.16) | |||||
| IVS38-8T>C | TC vs. TT | 3.09 (1.11–8.59) | |||||
|
| rs1801320G>C | (GC + CC) vs. GG | 2.17 (1.11–4.24) | In patients <50 years at diagnosis | 143 cases a and 247 controls | Jara et al., 2007 [ | |
| Ecuador |
| rs3803304C>G | GG vs. CC | 5.20 (1.3–20.9) | Not adjusted | 91 cases 185 controls | Lopez-Cortes et al., 2018 [ |
|
| rs1801133C>T | TT vs. CC | 2.9 (1.2–7.2) | Not adjusted | 114 cases and 195 controls | Lopez-Cortes et al., 2015 [ | |
| Mexico |
| rs1862513C>G | (CG + GG) vs. CC | 1.62 (1.025–2.557) | Not adjusted | 100 cases and 308 controls | Muñoz-Palomeque et al., 2018 [ |
|
| rs35749351G>A | (GA + AA) vs. GG | 2.19 (1.075–4.475) | Not adjusted | |||
|
| rs1045642C>T | CC vs. TT | 2.91 (1.48–5.74) | Not adjusted | 243 cases and 118 controls | Jaramillo-Rangel et al., 2018 [ | |
| CT vs. CC | 2.27 (1.11–4.67) | In premenopausal women | 248 cases and 180 controls | Gutierrez-Rubio et al., 2015 [ | |||
|
| rs1056827G>T | TT vs. GG | 1.21 (0.85–1.72) | Adjusted for age, years of education, first-degree relative with breast cancer, age at first full-term pregnancy, breastfeeding at first birth, consumption of alcohol and tobacco, and genetic ancestry (Native American, European, and African). | 952 cases and 998 controls * | Garcia-Martinez et al., 2017 [ | |
|
| rs366631C>T | TT vs. (CC + CT) | 1.30 (1.02–1.8) | Not adjusted | 558 cases and 276 controls | Soto-Quintana et al., 2015 [ | |
| TT vs. (CC + CT) | 2.19 (1.50–3.21) | Not adjusted | 243 cases and 118 controls | Jaramillo-Rangel et al., 2015 [ | |||
|
| rs1801133C>T | TT vs. CC | 2.50 (1.6–3.8) | Not adjusted | 497 cases and 339 controls | Ramos-Silva et al., 2015 [ | |
|
| 844ins68 | (−/+) vs. (−/−) | 2.2 (1.5–3.3) | Not adjusted | 323 cases and 371 controls | Gallegos-Arreola et al., 2014 [ | |
|
| IVS24-9delT | T(-T) vs. TT | 3.02 (1.24–7.30) | Not adjusted | 94 cases and 97 controls | Calderón-Zúñiga et al., 2014 [ | |
|
| rs1695A>G | GG vs. AA | 3.28 (1.44–7.50) | In premenopausal women | 150 cases and 382 controls | Martinez-Ramirez et al., 2013 [ | |
|
| 4a/b polymorphism | ab vs. bb | 2.0 (1.3–3.1) | Not adjusted | 429 cases and 281 controls | Ramírez-Patiño et al., 2013 [ | |
|
| rs1048943A>G | GG vs. AA | 2.77 (1.30–5.89) | In premenopausal women | 150 cases and 382 controls | Martinez-Ramirez et al., 2013 [ | |
| rs4646903T>C | CC vs. TT | 3.38 (1.05–10.87) | In postmenopausal women | 91 cases and 94 controls | Moreno-Galvan et al., 2010 [ | ||
|
| rs2981582C>T | TT vs. CC | 1.69 (1.25–2.27) | Adjusted by design for place of residence, health service institution membership, and 5-year age interval | 687 cases and 907 controls * | Murillo-Zamora et al., 2013 [ | |
| Puerto Rico |
| rs1805329C>T | TT vs. (CT + CC) | 3.14 (1.65–5.97) | Adjusted for age, civil status, education level, and contraceptive use | 228 cases and 418 controls | Perez-Mayoral et al., 2013 [ |
CI: confidence interval; BC: breast cancer; BMI: body mass index; OC: ovarian cancer; OR: odds ratio. a Patients were tested for BRCA1/2 mutations. * Population-based controls.
Breast cancer genetic epidemiology in women of Latin American origin: Current limitations and possible solutions.
| Current Limitations | Possible Solutions |
|---|---|
| Diverse genetic backgrounds and mutational frequencies among Latin American populations. |
Foster collaborations between Latin American countries. Account and stratify for ancestry proportions. |
| Small sample size in comparison with studies including European and European American individuals. |
Promote access and exchange of information among researches to establish research partnerships within and across countries to generate large consortiums for joint data analysis. Homogenize the design and data processing of studies from different countries to facilitate data sharing and sample pooling. Extension of National Cancer Registries and quality improvement, including biospecimen collection. |
| Limited access to high-cost technologies for variant discovery in multiple Latin American countries. |
Promote collaborative relationships with specialized multicenter initiatives to reduce costs and assure quality control. Promote collaborations that allow Latin American countries to access technologies in the United States and other countries (e.g., European countries, Australia, Japan, etc.) with appropriate data sharing protocols that allow analyses to be conducted by scientists in Latin America. |
| Inequality in access to healthcare by genetic ancestry might limit the representation of highly Indigenous American women in genetic studies. |
Foster active role of public hospitals in patient accrual. Decentralization of institutions in charge of patient/individual accrual. |