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Current state of breast cancer classification

Current state of breast cancer classification . Marcella Mottolese UOC Anatomia Patologica . screening programs and adjuvant therapy changed the management of breast cancer.

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Current state of breast cancer classification

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  1. Current state of breast cancer classification Marcella Mottolese UOC Anatomia Patologica

  2. screening programs and adjuvant therapy changed the management of breast cancer impact in outcomewith a decrease in mortality in mostof the western world despiteincreasingbreastcancerincidence

  3. OPEN QUESTIONS? Howtoidentifypatientswithnode negative disease at very low riskofrelapseforwhom the risk/benefit ratiomightbe in favourofchemotherapy? Howtopredict the responsetocurrentlyavailablecytotoxicchemotherapy ? Howtoidentifytumortargetsfordirectedtherapies?

  4. Responseto the adjuvantchemotherapy , mainly in early stage of the disease, are affectedby a complex interplay offactors

  5. Prognosticfactors in breastcancer

  6. BiomarkersCategories 1.Thosethatpredictrelapse or progressionindependentof future treatment effects designate as PROGNOSTIC FACTORS 2.Thosethatpredictresponse or resistanceto a specifictherapy designate asPREDICTIVE FACTORS

  7. Routinely used clinico-pathological parameters Age tumorSize lymphnodeslympho-vascularinvasion Clinico-pathological (tumorburden) Biological (intrinsicchacteristics) ER, PR; HER2; Ki67 Tumorgrade

  8. Are theseroutinelyusedparameterssufficientforindividualizedtherapy NO, becauseBrestCanceris HETEROGENEOUS Clinically Biologically

  9. This clinical heterogeneity is driven by the genetic variability of patients and tumors A continuum ofabnormal gene expressionpredicts: • the tumorigenic phenotype • the sensitivity of tumors to treatment

  10. Clinical tools

  11. St Gallen 2009 : news and progress Genetic predisposition Whole-genome studies Circulating tumor cells Angiogenesis Stem cells Pharmacogenetics microRNAs Networks in cellular system Resistance to treatment by crosstalk Multigene assays INTEGRATING MOLECULAR AND OTHER PATHOLOGICAL FEATURES

  12. St. Gallen 2009

  13. St Gallen 2011 • Focus on the identification of “tumor subtypes” to plan therapy more accurately • Tumor Subtypes defined starting from molecular classification • Immunohistochemical characterization as molecular classification surrogate • Morphological evaluation of special histological types

  14. Biology-driventrials Andre F et al 2011 JCO

  15. How can moleculartools help standard pathology? • Molecular sub-grouping • Prognosis/Stratification • Predictivemarkers • Functional pathway read-outs Standardization, reproducibility & qualification

  16. Molecular sub-grouping

  17. ….verydistinct gene expressionpatternsirrespectiveof stage (size, nodal, status) !

  18. Sotiriou C, Pusztai L. N Engl J Med 2009;360:790-800

  19. St Gallen 2011

  20. Ki67 (IHC) British Journal of Cancer (2007)

  21. Ki-67 prognostic cut-off point for all breast cancer subtypes (multivariate analysis) The best cut-off point with the lowest p-value and highest HR was found at the Ki-67 index of 20%

  22. Claudin-low breast cancer are characterized by: • low to absent expression of luminal differentiation markers, • high enrichment for epithelial-to-mesenchymal transition markers, immune response genes and cancer stem cell likefeatures. Clinically, the majority of claudin-low tumors : • are triple negative invasive ductal carcinomas with a high frequency of metaplastic and medullary differentiation. • have a response rate to standard preoperative chemotherapy intermediate between that of basal-like and luminal tumors. • resembles the mammary epithelial stem cell.

  23. Prognosis/stratification

  24. Distinct Clinicaloutcome BreastcancershouldbeconsideredaccordingtoHormonalReceptor, HER2, Ki-67

  25. Disease free survivalaccordingtomolecularsubtype in 1015 breastcancerpatientstreated at IRE between 2000-2006 P-value (log-rank test) <0,0001

  26. Estrogen receptors α and β show different associations to clinicopathological parameters and their co-expression might predict a better response to endocrine treatment in breast cancer S Borgquist J Clin Pathol 2008;61:197-203

  27. 934 prospective breast cancer patients

  28. To replace or to complement the traditional biological parameters? • This may be highly misleading, because the molecular classes are heterogeneous and encompass different tumor types with different risk profiles and different responsiveness to the therapy

  29. Heterogeneity may be evident in breast cancer with basal-like profile since: Not all the tumors with the basal-like profile are high risk tumors.This molecular class also includes: low-grade metaplastic carcinomas adenoid-cystic carcinomas, medullary carcinomas low grade apocrine carcinomas whichhave a veryfavorableprognosis

  30. Predictive markers

  31. ER and HER2 BreastCancer HER-2 neu ER Negative predictivevalue Positive predictivevalue HIGH 95% 30-50 % (<5% changetorespondtoanti-estrogens or trastuzumab)

  32. a b 30μ 30μ d c 10μ a 10μ b b

  33. Whatabout gene Expressionsignatures ?

  34. The Role of Single Gene Analyses and Multigene Assays in Addressing the Controversy on Chemotherapy or Not when Stage/Biology are Discordant 21 Gene recurrence Score (RS) 70 Gene High Risk Relative Endocrine “Resistance” Relative Chemo “Sensitivity”

  35. Three Strategies for the Development of a Gene-Expression PrognosticSignature 1. “top-down” approach: gene- expression data from patients with known clinical outcomes are compared to identify genes that are associated with prognosis without any a priori biologic assumption. • 2. “bottom-up” approach: gene-expression patterns associated with a specific biologic phenotype or a deregulated molecular pathway are first identified and then correlated with the clinical outcome. • 3. candidate-gene approach: selected genes of interest on the basis of existing biologic knowledge are combined into a multivariate predictive model.

  36. Summaryofmulti-parametrictestsforbreastcancer

  37. Ongoing RCT’s ofpredictionofchemotherapybymulti-parameterassays

  38. 21 gene Recurrence Score Assay: Strongly Predictive in NSABP B-20 (ER+No)

  39. Mammaprint TM adds prognostic information in pT1 tumors N=965 patients wit T1a,b,c tumors; adjuvant systemic treatment in 41%; median 7 years Distant Metastasis-Free Survival PT1ab PT1c

  40. T1A,BNoMo: ONCOTYPE DXand MAMMAPRINTTM prolifiles

  41. 1 st generation signatures add prognostic information to current “best”clinical tools

  42. Breast Cancer Gene Expression Signatures and Cell proliferation

  43. Learning about adverse biological features of T1a,bNoMo breast cancers

  44. Stromal Gene ExpressionIdentifiesDistinctTumorSubclasses StromalClustersReflectDifferentMicroenvironments Epithelia Stroma StromalSubtypes are AssociatedwithOutcome

  45. The tumormicroenviromentplays a key role in tumorprogression Betterunderstandingofintra-tumordiversity and epithelial-stromalcellinteractionswillleadto more efficaciouscancer treatment

  46. FunctionalPathway

  47. PI3K pathway gene signatures Best way to develop these signatures

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