Genetic and Non-Genetic Breast Cancer Risk Prediction Evaluation in Indonesian Samples

Study Overview
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Brief Summary
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Breast cancer is the most common cancer and cause of cancer- related deaths among women, accounting for 1.67 million (25.2%) new cases and 521,907 (14.7%) deaths worldwide. The prevalence and survival rates of breast cancer differ per country. In Indonesia, majority of patients (70.9%) go to the clinic with advanced stages of breast cancer. Five-year survival rate is 51.07%. One of the most important determinants of survival is education level and stage of breast cancer. Current screening methods include mammography and radiology assessments, both of which have disadvantages specifically in Asian population. Mammography is less useful in Asian population because the population has denser breast, resulting to failure to diagnose cases of breast cancer in this population in 37-70% of cases. Moreover, screening methods provide binary answers, and therefore does not inform risk profile of the patients. The investigators aim to implement PRS into the breast cancer screening process while observing the differences of genetic and non-genetic risk factor in patients with breast cancer and patients without any medical/family history of breast cancer in Indonesian population.

Detailed Description
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Breast cancer is the most common cancer and cause of cancer- related deaths among women, accounting for 1.67 million (25.2%) new cases and 521,907 (14.7%) deaths worldwide. The prevalence and survival rates of breast cancer differ per country. In Indonesia, majority of patients (70.9%) go to the clinic with advanced stages of breast cancer. Five-year survival rate is 51.07%. One of the most important determinants of survival is education level and stage of breast cancer. Current screening methods include mammography and radiology assessments, both of which have disadvantages specifically in Asian population. Mammography is less useful in Asian population because the population has denser breast, resulting to failure to diagnose cases of breast cancer in this population in 37-70% of cases. Moreover, screening methods provide binary answers, and therefore does not inform risk profile of the patients. Traditionally, risk prediction algorithms such as the GAIL model, BODACIEA, and Tyler-Cuzick use medical history and clinical factors of patients. However recently, genetics have grown in importance due to the heritability nature of cancer and availability of testing services and guidelines. About 10-30% of all cases are attributed to familial breast cancers, and of these, only 5%-10% correlate with hereditary factors linked with high penetrance. The most common genetic test to screen today is BRCA 1 and 2, and then other 22 genes curated by expert opinions on NCCN and other guidelines. The prevalences estimated for carriers of mutations in BRCA1/2 are, respectively, 0.11% and 0.12% in the general population, and between 12.8%-16% in high risk families with three or more cases of breast or ovarian cancer. Approximately 10-15% of ovarian cancer cases are believed to be due to a BRCA1/2 mutation, however  50% of individuals with a pathogenic BRCA mutation may not report a strong family history of cancer. NCCN, ASCO, St Gallen and has established guidelines to screen patients, but the low awareness in patients to go screening in the first place is hard. Genetic testing using polygenic risk scores (PRS) combines the effects of low penetrance genes that together creates predictive value as strong as high-penetrance genes, but is much more common than high-penetrance gene testing. A PRS is most commonly calculated as a weighted sum of the number of risk alleles carried by an individual, where the risk alleles and their weights are defined by the loci and their measured effects as detected by genome wide association studies. For some common adult-onset diseases, the polygenic risk conveyed to a substantial segment (10-20%) of the population whose genomes are enriched in risk alleles is comparable to the risk conveyed by commonly used clinical risk factors. A recent large-scale comprehensive GWAS for breast cancer found that 45% of familial relative risk of breast cancer can be explained by genetic variants captured by genotyping and imputation. As genotyping technologies advance, and consortia build algorithms on more samples, the predictive values of PRS algorithms are maturing. After analysis of 120,000 patients and optimizing for highest predictability, a PRS score combining 313 SNPs and clinical factors have a predictive value of 68%, compared to only 58% using clinical risk factors. A study conducted in the Breast Cancer Association Consortium showed that PRS combined with environmental risk factors can be used to distinguish women at different levels of breast cancer risk in the general population. This score gives providers the opportunity to stratify the patients may result in some people with higher risk profile to start risk-reducing therapy earlier, start screening at a younger age, and modify their lifestyles with the aim of reducing their risk. For example, those who are at the top 1.5% of polygenic risk score have an odds ratio of 3 or more compared to the general population. Polygenic risk scores have been applied in leading institutions in the world as clinical trials and in the commercial settings. However, there has been little application in developing countries to use polygenic risk score to increase awareness of risk-reducing strategies of breast cancer in patients. One of the main concerns about the clinical implementation of population-based genetic screening is experts' availability post-test. A study in the UK for physicians' attitude towards risk stratification of ovarian cancer showed that 70% oncologists and 50% of GPs would be willing to offer genetic testing to their patients. About 60% believe that the test would give patients a sense of control, and over 80% of providers are willing to personalize recommendations based on risk stratification. The investigators aim to implement PRS into the breast cancer screening process while observing the differences of genetic and non-genetic risk factor in patients with breast cancer and patients without any medical/family history of breast cancer in Indonesian population.

Official Title
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Genetic and Non-Genetic Breast Cancer Risk Prediction Evaluation in Indonesian Samples

Conditions
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Breast Cancer

Intervention / Treatment
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* Diagnostic Test: Breast Cancer Risk Prediction Software


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: For case group Had been diagnosed with primary breast cancer or tested positive for high penetrance genes (e.g. BRCA 1/2) Menarche age >12 years old Premenopausal For control group Premenopausal Menarche age >12 years old Asymptomatic Consented for the study and follow up Exclusion Criteria: For case group: First degree family history of breast or ovarian cancer For control group: Family history of breast or ovarian cancer First-degree relationship with the cases

Ages Eligible for Study
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Minimum Age: 35 Years
Maximum Age: 75 Years

Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Cases<br>Cases are taken by recruiting women who: have no first degree family history of breast or ovarian cancer are or had been diagnosed with primary breast cancer or tested positive for high penetrance genes (e.g. BRCA 1/2) menarche age >12 years old premenopausal | Diagnostic Test: Breast Cancer Risk Prediction Software<br>* Genotyping of known breast cancer-related markers (313 variants) will be conducted using a microarray genotyping chip (Genetic Risk). Survey answers will determine Gail Model scores and thus Clinical Risk Score.<br>|
| Cohort<br>Controls are taken from clients who visited Breast Cancer Care Alliance (BCCA) and: have no family history of breast or ovarian cancer premenopausal menarche age >12 years old asymptomatic do not have any first-degree relationship with the cases consented for the study and follow up | Diagnostic Test: Breast Cancer Risk Prediction Software<br>* Genotyping of known breast cancer-related markers (313 variants) will be conducted using a microarray genotyping chip (Genetic Risk). Survey answers will determine Gail Model scores and thus Clinical Risk Score.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Absolute risk difference between breast cancer patients and non-breast cancer patients in terms of their non-genetic risk | Absolute non-genetic risk is calculated using the MDCalc Gail Model | First quarter of 2023 | 
| Absolute risk difference between breast cancer patients and non-breast cancer patients in terms of their genetic risk | Genetic risk is derived from polygenic risk score acquired from running a microarray sample result through an algorithm (see Mavaddat et al 2019) | First quarter of 2023 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Breast Cancer, Polygenic Risk Score