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Laura J. Van ‘t Veer Helen Diller Family Comprehensive Cancer Center University of California, San Francisco

BOP breast course Nov 2010. Biology of disease Who is at risk for what type of breast cancer and how does type affect outcome. Laura J. Van ‘t Veer Helen Diller Family Comprehensive Cancer Center University of California, San Francisco. Breast Cancer - Survival .

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Laura J. Van ‘t Veer Helen Diller Family Comprehensive Cancer Center University of California, San Francisco

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  1. BOP breast course Nov 2010 Biology of disease Who is at risk for what type of breast cancer and how does type affect outcome Laura J. Van ‘t Veer Helen Diller Family Comprehensive Cancer Center University of California, San Francisco

  2. Breast Cancer - Survival Kaplan-Meier Survival Curves Who gets what type of breast cancer? Which breast cancers return?

  3. Disease Biology • Genetic make-up of individual • Biology of screen-detected cancers and of interval • cancers • Biology informs need for systemic treatment • - who is at risk for what type of disease • - does type affect outcome • - how can type of detection inform management

  4. Who is at risk for what type of disease Opportunities for prevention Opportunities for management

  5. Breast cancer susceptibility loci Familial aggregation of breast cancer Multiple low-penetrance alleles (polygenic model) 5%(?) CHEK2 1100delC* 4.7% SNPs 25% BRCA1/2

  6. Recent breast cancer susceptibility loci - SNPs Easton et al; Cox et al; Stacey et al; Hunter et al

  7. Association of 10 susceptibility loci with tumor subtypes ER+PR+Her2+ ER+PR+Her2- Triple negative negative positive association (prevents) (increases) Broeks et al, BCAC, submitted

  8. Breast cancer outcome: Example rs3803662 in TNRC9 Second Breast Cancer Risk Adjusted HR (95% CI) 2.7 (1.7-4.3) in BOSOM breast cancer series rs3803662 in TNRC9: increase of contralateral breast cancer risk Ongoing: Validation in BCAC series (studies with follow-up data) Same analyses for other breast cancer risk-related SNPs Variant allele (homozygous carriers) N total = 1370

  9. Breast cancer outcome: MDM2 SNP309 *TP53 R72P in BCAC breast cancer series MDM2 SNP309 (G = variant allele) GG GT TT TP53 R72P ‘wildtype’ SNP-SNP interaction effect on survival: MDM2 SNP309 and TP53 R72P variants combined: 7% worse survival (p<0.05) also if adjusted for known prognostic factors TP53 R72P ‘variant allele’ N total =3739 Schmidt et al Cancer Res 2007

  10. Breast cancer outcome: CHEK2 1100delC in BOSOM breast cancer series CHEK2 1100del C carrier: worse breast cancer outcome Treatment interaction? Interaction with SNPs? Tumor characteristics? Ongoing data collection and analyses in BOSOM and pooled BCAC series Contralateral breast cancer risk HR (95%CI) 2.1 (1.0-4.3) Recurrence-free survival HR (95%CI) 1.7 (1.2-2.4) Breast cancer-specific survival HR (95%CI) 1.4 (1.0-2.1) Schmidt et al. JCO 2007

  11. Biology informs need of systemic treatment Opportunities to reduce over- and under-treatment Effect on morbidity

  12. Breast Cancer - Survival Kaplan-Meier Survival Curves Which breast cancers return?

  13. Of 100 women with breast cancer (stage 1/2)

  14. …………~25% will develop a recurrence

  15. ………..75% of all patients is treated with chemotherapy

  16. So, overall 50% of patients receive toxic chemotherapy of which they do not benefit, but may suffer the toxic side-effects Can we do better?

  17. Development of 70 gene MammaPrint Tumor samples of known clinical outcome Unbiased full genome gene expression analysis Prognosis reporter genes Distant metastases group No distant metastases group 70 prognosis genes b Tumor samples Metastases: white=+ Nature, 2002

  18. Multi Gene Expression Profilesin Clinical Practice

  19. Clinical Utility MammaPrint Prospective study implementing MammaPrint, 2003-2006 PIs Sabine Linn, Marc van de Vijver Sponsor: Dutch Health Insurance Council Bueno et al, Lancet Oncol, 2007, Knauer et al, SABCS 2008 #1084

  20. ~30 % discordant cases led in ~20% to adapted treatment advise Discordant cases MammaPrint signature versus Guidelines The Netherlands and Adjuvant-on-line Bueno et al, Lancet Oncol, 2007, Knauer et al, SABCS 2008 #1084

  21. Biology of screen detected cancers Method of detection may inform management

  22. US general population screening data from SEER 1973-2005 Age-adjusted incidence breast cancer by Stage at diagnosis -> Screening era Localized Regional In Situ Distant

  23. 70 significant prognosis genes Tumor samples van´t Veer et al., Nature 415, p. 530-536, 2002 70 Gene Prognosis Signature Supervised analysis on n=78 tumors, >96% adjuvantly untreated ultra-low threshold 2 threshold set with 0% false negatives Nature, (2002)

  24. Biology of Screen-detected Cancers Age 49-60 30% MammaPrint 12% P<0.001  Screen detected cancers show increase in ultra-low risk cancers Pre-screening n=143, sreen-detected n=73

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