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Breast Cancer Risk Assessment: How and Why
The document discusses the significance of cancer risk assessment for both affected and unaffected individuals, emphasizing the importance of identifying high-risk patients to reduce morbidity and mortality rates. It highlights the advancements in genetic testing, particularly for breast and ovarian cancer, and the growing complexity of cancer genetics. Additionally, it advocates for integrating artificial intelligence in clinical decision-making to enhance risk calculations and patient counseling.
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Breast Cancer Risk Assessment: How and Why
- 1. Cancer Risk Assessment TheWhy and How for Affected and Unaffected Patients Kevin S. Hughes, MD, FACS Founder: Hughes RiskApps.LLC Co-Director, Avon Comprehensive Breast Evaluation Center Massachusetts General Hospital Associate Professor of Surgery Harvard Medical School Medical Director Bermuda Cancer Genetics and Risk Assessment Clinic KsHughes@HughesRiskApps.Com
- 2. Why evaluate risk Notidentifying high risk individuals will lead to unnecessary morbidity and death
- 3. Cancer Risk Breast 50-87%50-87% Ovary 40-60% 10-20% Breast 6% F e m a l e M a l e BRCA1 BRCA2
- 4. Hereditary vs SporadicCancer Knudson’s2hithypothesis HEREDITARY CANCER SPORADIC CANCER
- 5. 18 years ofBRCA testing Female Carriers in the US ~560,000 to 720,000 Carriers found ~70,000, mostly affected 95% of unaffected carriers remain unaware and mismanaged
- 6. More genes totest for: Breast
- 7. • 1781 HBOCpatients • 25-Gene Panel (MyRisk) – 13.5% had a mutation • 9.3% in BRCA 1 or 2 • 4.2% in at least one other gene More genes to test for: Breast Tung et al ACMG2014
- 8. More genes totest for: Ovary
- 9. More genes totest for: Ovary • 360 women • Ovarian • Primary peritoneal • Fallopian tube cancer • 21 gene panel (12 found) • 24% had a mutation – 18% BRCA 1/2 – 6% another gene • 30% carriers had no family history • 35% of patients were > 60 y.o. University of Washington Walsh et al 2011
- 10. Significance and Managementby mutation
- 11. Prevalence and Penetrancefor breast cancer genes http://www.ambrygen.com/sites/default/files/pdfs/canc er%20forms/BreastNext_WhitePaper_100412.pdf
- 12. If you thinkyou can’t keep up with all this… Don’t worry! No one else can either!
- 13. The human brainis approaching its limit Yet we continue to practice memory based medicine Crane, Raymond, The Permanente Journal 7:62, 2003
- 14. Yoo et al.BMC Bioinformatics 2007 8(Suppl 9):S4 Knowledge is growing exponentially
- 15. Articles published onBreast Cancer Genetics 4335 articles in 2012
- 16. Disorders with genetictests available GeneTests 2014
- 17. Clinical Decision Support(CDS) •Apply Models/Guidelines to patient data •Identify best course of action •Results displayed as intuitive Visualizations BRCAPRO Mutation Risk 25% BRCAPRO Mutation Risk 25% Consider Genetic Testing
- 18. Opposing viewpoints definedin the 1950’s • Artificial Intelligence (AI) – The computer would replace humans • Intelligence amplification (IA) – Computers have strengths and weaknesses – Humans have strengths and weaknesses – Humans working with computers are: “Better...stronger...faster” “We have the technology”
- 19. Mechanical Amplification • Human3 miles/hour • Human plus bike 20 miles/hour • Human plus car 60 miles/hour
- 20. Intelligence Amplification • Human –Talk to patient, gather data – Draw pedigree and eyeball – Counsel • Human plus computer – Human Talk to patient, gather data – Computer Run risk models/guidelines – Computer Draw pedigree, visualizations – Human Synthesize, counsel patient
- 21. High Risk Program •Identify women at high risk • Breast imaging, Primary Care, Oncology, OB/GYN • High Risk Clinic/System • Manage women needing – Genetic testing – More intensive screening – Chemoprevention
- 22. • Risk ofmutation – BRCA testing • Risk of developing breast cancer – MRI – Chemoprevention – Personalized screening ID High Risk
- 23.
- 24. Hereditary Hormonal Pathologic Risk Factors Breast cancer Agediagnosed Ovarian Cancer Age diagnosed Male breast cancer Age diagnosed Degree relative Age all relatives Genetic testing Height BMI Parous vs nulliparous Age first live birth Age menarche Age menopause HRT years used HRT intended use years Combined vs estrogenNumber of biopsies Atypical hyperplasia LCIS Tumor markers
- 25.
- 26.
- 27. Age Vital Status Cancer status Agediagnosis Ethnicity/Religion Genetic testing Risk Factors Eyeball
- 28. Eyeball Multiple relatives affected Youngage at diagnosis Multiple primary cancers Unusual Cancer Male breast cancer
- 29. ID High Risk Eyeballthe pedigree Models Guidelines
- 30.
- 31. USPSTF for BRCAtesting 2013 USPTF Ann Intern Med 2013 Guidelines by Tools • If positive by any of the following tools – Ontario Family History Assessment Tool – Manchester Scoring System – Referral Screening Tool – Pedigree Assessment Tool – FHS-7
- 32.
- 33. 33 Women Most Highest risk Current Breast CancerScreening Guidelines: • American Cancer Society (ACS) • National Comprehensive Cancer Network (NCCN) >20% risk of breast cancer (Tyrer Cuzick, BRCAPRO, Claus) Guidelines by Models
- 34. ID High Risk Eyeballthe pedigree Models Guidelines
- 35.
- 36. Hereditary Hormonal Risk Mutation Myriad Genetic Testing Pathologic RiskMutation & Risk Breast Ca Risk Breast Ca Claus Chemoprevention MRI Personalized screening Gail Chemoprevention Personalized screening BRCAPRO Genetic Testing Chemoprevention MRI Personalized screening Tyrer Cuzick Genetic Testing Chemoprevention MRI Personalized screening
- 38. George Edward PelhamBox 1919 –2013) British mathematician/Professor of Statistics at the University of Wisconsin All models are wrong
- 39. George Edward PelhamBox 1919 –2013) British mathematician/Professor of Statistics at the University of Wisconsin All models are wrong, but some are useful
- 40. Hereditary • Breast cancer •Age diagnosed • Ovarian Cancer • Age diagnosed • Male breast cancer • Age diagnosed • Degree relative • Age all relatives • Genetic testing Hormonal • Height • BMI • Parous vs nulliparous • Age first live birth • Age menarche • Age menopause • HRT years used • HRT intended use years • Combined vs estrogen Pathologic • Number of biopsies • Atypical hyperplasia • LCIS • Tumor markersBRCAPRO
- 41.
- 42. Woman Vs. Machine 148pedigrees BRCAPRO Vs NPs and GCs
- 43.
- 44. You cannot havea highly trained NP or GC see every patient You can run risk models on every patient
- 45.
- 47.
- 48. Quick and easyrisk calculations Hughes RiskApps Express Thru HughesRiskApps.Com
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Editor's Notes
- #8 CHEK2 is the most common other gene to have mutations
- #10 18% had BRCA mutation
- #32 Similar guidelines have been published elsewhere, including in the United Kingdom by the National Institute for Clinical Excellence
- #33 Similar guidelines have been published elsewhere, including in the United Kingdom by the National Institute for Clinical Excellence