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

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.

Study Overview

• Status: Completed
• Conditions: Breast Cancer
• Intervention / Treatment: Diagnostic test: Breast Cancer Risk Prediction Software

Detailed Description

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.

• Study Type: Observational
• Enrollment (Actual): 322

Contacts and Locations

Study Locations

• Indonesia
  • Jakarta Raya
    • Jakarta, Jakarta Raya, Indonesia, 12930
      • MRCC Siloam Hospitals Semanggi

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 35 years to 75 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

Controls are taken from clients who visited Breast Cancer Care Alliance (BCCA) We will not impose any selection to age at diagnosis for both cases and controls

Description

Inclusion Criteria:

• For case group

  1. Had been diagnosed with primary breast cancer or tested positive for high penetrance genes (e.g. BRCA 1/2)
  2. Menarche age >12 years old
  3. Premenopausal

• For control group

  1. Premenopausal
  2. Menarche age >12 years old
  3. Asymptomatic
  4. Consented for the study and follow up

Exclusion Criteria:

• For case group:

First degree family history of breast or ovarian cancer

• For control group:

  1. Family history of breast or ovarian cancer
  2. First-degree relationship with the cases

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Cases; 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; 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.
Cohort; 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; 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.

What is the study measuring?

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

Collaborators and Investigators

• Sponsor: Nalagenetics Pte Ltd
• Collaborators: SJH Initiatives; MRCCC Siloam Hospitals Semanggi
• Investigators: Principal Investigator: Samuel Haryono, MD, PhD, SJH Initiatives

Publications and helpful links

General Publications

• Costa M, Saldanha P. Risk Reduction Strategies in Breast Cancer Prevention. Eur J Breast Health. 2017 Jul 1;13(3):103-112. doi: 10.5152/ejbh.2017.3583. eCollection 2017 Jul.
• Mavaddat N, Michailidou K, Dennis J, Lush M, Fachal L, Lee A, Tyrer JP, Chen TH, Wang Q, Bolla MK, Yang X, Adank MA, Ahearn T, Aittomaki K, Allen J, Andrulis IL, Anton-Culver H, Antonenkova NN, Arndt V, Aronson KJ, Auer PL, Auvinen P, Barrdahl M, Beane Freeman LE, Beckmann MW, Behrens S, Benitez J, Bermisheva M, Bernstein L, Blomqvist C, Bogdanova NV, Bojesen SE, Bonanni B, Borresen-Dale AL, Brauch H, Bremer M, Brenner H, Brentnall A, Brock IW, Brooks-Wilson A, Brucker SY, Bruning T, Burwinkel B, Campa D, Carter BD, Castelao JE, Chanock SJ, Chlebowski R, Christiansen H, Clarke CL, Collee JM, Cordina-Duverger E, Cornelissen S, Couch FJ, Cox A, Cross SS, Czene K, Daly MB, Devilee P, Dork T, Dos-Santos-Silva I, Dumont M, Durcan L, Dwek M, Eccles DM, Ekici AB, Eliassen AH, Ellberg C, Engel C, Eriksson M, Evans DG, Fasching PA, Figueroa J, Fletcher O, Flyger H, Forsti A, Fritschi L, Gabrielson M, Gago-Dominguez M, Gapstur SM, Garcia-Saenz JA, Gaudet MM, Georgoulias V, Giles GG, Gilyazova IR, Glendon G, Goldberg MS, Goldgar DE, Gonzalez-Neira A, Grenaker Alnaes GI, Grip M, Gronwald J, Grundy A, Guenel P, Haeberle L, Hahnen E, Haiman CA, Hakansson N, Hamann U, Hankinson SE, Harkness EF, Hart SN, He W, Hein A, Heyworth J, Hillemanns P, Hollestelle A, Hooning MJ, Hoover RN, Hopper JL, Howell A, Huang G, Humphreys K, Hunter DJ, Jakimovska M, Jakubowska A, Janni W, John EM, Johnson N, Jones ME, Jukkola-Vuorinen A, Jung A, Kaaks R, Kaczmarek K, Kataja V, Keeman R, Kerin MJ, Khusnutdinova E, Kiiski JI, Knight JA, Ko YD, Kosma VM, Koutros S, Kristensen VN, Kruger U, Kuhl T, Lambrechts D, Le Marchand L, Lee E, Lejbkowicz F, Lilyquist J, Lindblom A, Lindstrom S, Lissowska J, Lo WY, Loibl S, Long J, Lubinski J, Lux MP, MacInnis RJ, Maishman T, Makalic E, Maleva Kostovska I, Mannermaa A, Manoukian S, Margolin S, Martens JWM, Martinez ME, Mavroudis D, McLean C, Meindl A, Menon U, Middha P, Miller N, Moreno F, Mulligan AM, Mulot C, Munoz-Garzon VM, Neuhausen SL, Nevanlinna H, Neven P, Newman WG, Nielsen SF, Nordestgaard BG, Norman A, Offit K, Olson JE, Olsson H, Orr N, Pankratz VS, Park-Simon TW, Perez JIA, Perez-Barrios C, Peterlongo P, Peto J, Pinchev M, Plaseska-Karanfilska D, Polley EC, Prentice R, Presneau N, Prokofyeva D, Purrington K, Pylkas K, Rack B, Radice P, Rau-Murthy R, Rennert G, Rennert HS, Rhenius V, Robson M, Romero A, Ruddy KJ, Ruebner M, Saloustros E, Sandler DP, Sawyer EJ, Schmidt DF, Schmutzler RK, Schneeweiss A, Schoemaker MJ, Schumacher F, Schurmann P, Schwentner L, Scott C, Scott RJ, Seynaeve C, Shah M, Sherman ME, Shrubsole MJ, Shu XO, Slager S, Smeets A, Sohn C, Soucy P, Southey MC, Spinelli JJ, Stegmaier C, Stone J, Swerdlow AJ, Tamimi RM, Tapper WJ, Taylor JA, Terry MB, Thone K, Tollenaar RAEM, Tomlinson I, Truong T, Tzardi M, Ulmer HU, Untch M, Vachon CM, van Veen EM, Vijai J, Weinberg CR, Wendt C, Whittemore AS, Wildiers H, Willett W, Winqvist R, Wolk A, Yang XR, Yannoukakos D, Zhang Y, Zheng W, Ziogas A; ABCTB Investigators; kConFab/AOCS Investigators; NBCS Collaborators; Dunning AM, Thompson DJ, Chenevix-Trench G, Chang-Claude J, Schmidt MK, Hall P, Milne RL, Pharoah PDP, Antoniou AC, Chatterjee N, Kraft P, Garcia-Closas M, Simard J, Easton DF. Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes. Am J Hum Genet. 2019 Jan 3;104(1):21-34. doi: 10.1016/j.ajhg.2018.11.002. Epub 2018 Dec 13.
• Sinaga ES, Ahmad RA, Shivalli S, Hutajulu SH. Age at diagnosis predicted survival outcome of female patients with breast cancer at a tertiary hospital in Yogyakarta, Indonesia. Pan Afr Med J. 2018 Nov 7;31:163. doi: 10.11604/pamj.2018.31.163.17284. eCollection 2018.

Study record dates

Study Major Dates

• Study Start (Actual): October 13, 2020
• Primary Completion (Actual): January 10, 2023
• Study Completion (Actual): May 25, 2023

Study Registration Dates

• First Submitted: October 4, 2022
• First Submitted That Met QC Criteria: October 4, 2022
• First Posted (Actual): October 6, 2022

Study Record Updates

• Last Update Posted (Actual): September 13, 2023
• Last Update Submitted That Met QC Criteria: September 11, 2023
• Last Verified: September 1, 2023

More Information

Terms related to this study

• Keywords: Breast Cancer; Polygenic Risk Score
• Additional Relevant MeSH Terms: Skin Diseases; Neoplasms; Neoplasms by Site; Breast Diseases; Breast Neoplasms
• Other Study ID Numbers: ID-RPSBC-01-20201012

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No