Welcome to CAP s Hot Topics in Pathology Webinar Series sponsored by the Personalized Health Care Committee

Transcription

1 Welcome to CAP s Hot Topics in Pathology Webinar Series sponsored by the Personalized Health Care Committee This webinar on Molecular Markers in Breast Cancer is presented by David G. Hicks, MD, FCAP. Your host is Jill Kaufman, PhD. For comments about this webinar or suggestions for upcoming webinars, please contact Jill Kaufman at jkaufma@cap.org THE WEBINAR WILL BEGIN MOMENTARILY. ENJOY!

2 David G. Hicks, MD, FCAP Professor of Pathology and Laboratory Medicine, University of Rochester School of Medicine Director of Surgical Pathology at the University of Rochester Medical Center Current research interests focus on the molecular genetic profiling of clinical samples from patients with cancer Authored or co-authored over 140 peer reviewed articles Member of the CAP s Breast Pathology Certificate Program Work Group and Breast Predictive Factors Testing Program Group 2013 College of American Pathologists. All rights reserved. 2

3 Hot Topics in Pathology Breast Cancer and Molecular David G. Hicks, MD, FCAP March 20, 2013 cap.org v. #

4 Disclaimer The College does not permit reproduction of any substantial portion of the material in this Webinar without its written authorization. The College hereby authorizes attendees of the CAP Webinar to use the pdf presentation solely for educational purposes within their own institutions. The College prohibits use of the material in the Webinar and any unauthorized use of the College s name or logo in connection with promotional efforts by marketers of laboratory equipment, reagents, materials, or services. Opinions expressed by the speaker are the speaker s own and do not necessarily reflect an endorsement by CAP of any organizations, equipment, reagents, materials or services used by participating laboratories College of American Pathologists. All rights reserved. 4

5 Disclosure Professor of Pathology Director of the Surgical Pathology Unit University of Rochester Medical Center Member of the Speakers Bureau Honorarium, Genentech Bio-Oncology 5

6 Changing Role of the Pathologist: Diagnosis and Therapeutic Decisions for Breast Cancer Patients Traditional role Establish morphologic diagnosis of malignancy Infiltrating carcinoma (ductal vs lobular) Tumor grade (SBR) Anatomic extent (size, lymph node status) Breast cancer markers (ER, PR, HER2) Challenges and Opportunities Ensure biologic quality of tissue sample Ensure accurate and reliable ER and HER2 results Guide assay selection per clinical setting Help interpret, and integrate breast cancer marker testing into clinical morphologic context for each patient Role: inform on therapeutic decision Surgical planning Adjuvant endocrine Rx Adjuvant chemotherapy Adjuvant XRT Neoadjuvant therapy Targeted therapies Increasingly, treatment based on an evaluation of tumor biology ER=estrogen receptor; PR=progesterone receptor; SBR=Scarff-Bloom-Richardson tumor grade; TK=tyrosine kinase; XRT=X-ray therapy. Hicks DG et al. Arch Pathol Lab Med. 2008;132: Hoff ER et al. Am J Clin Pathol. 2002;117:

7 Is Molecular/Biological Profiling the Future of Breast Cancer Diagnosis? The Answer is YES! Role of molecular in clinical practice still evolving o What is the most practical, relevant, cost-effective, & broadly applicable ancillary testing that can help determine prognosis? Can this approach be reliably used to guide the selection of beneficial treatment regimens? Can some patients be spared the cost and morbidity of treatments that will be ineffective? What is the level of evidence that the testing strategy can reliably answer these question? Does the test results correlate with the patients clinical profile?

8 Breast Cancer: Clinical, Morphologic and Molecular Heterogeneity Factors used to stratify patient s to understand diversity/predict behavior Age/menopausal status Tumor size/tumor burden Histologic features: type, grade, LVI, necrosis, margin status Lymph node status Immunophenotype: ER, PR, HER2 Proliferation Genomic gains and losses Gene expression profiling QRTPCR profiling Clinical Utility Classify patient s (subsets) Predict outcome Predict treatment response

9 Breast Cancer Management: Decisions on Adjuvant Therapy Based on Risk Assessment Weigh background level - risk of recurrence against benefits & burdens of adjuvant therapy Patient factors (clinically validated) o Age, menopausal status and co-morbidities Tumor-related factors (clinically validated) o Tumor size, grade, LN, LVI Factors are robust prognostic markers, weaker in predicting for Rx response

10 Tumor Size/Burden (Measure Greatest Diameter X3) Firm gritty mass with irregular infiltrating borders DFS vs Tumor Size (pt stage) T1 = up to 2.0cm T2 = cm T3 = >5cm T4 = chest wall invasion For node (-)patients: tumor size next most significant prognostic factor used for adjuvant treatment decisions Cancer 1989;63: , Br J Cancer 1984;49:

11 Lymph Node Status: Correlation with Recurrence & Survival Classic ILC, LN involvement DFS vs LN status (pn stage) Nodal stage Nodal involvement N0 Negative lymph nodes 82.8% N1 1 3 positive nodes 73% N2 4 9 positive nodes 45.7% N3 10 or more positive nodes 5 year survival 28.4% Cancer 1983;52: , JAMA 1996;276:

12 Histologic Grade (Elston & Ellis modification of SBR) Correlate with Prognosis in Breast Cancer msbr 1 msbr 2 msbr 3 Modified SBR Grade 3-5: Grade I - Well 6-7: Grade II - Moderate 8-9: Grade III Poor Significantly correlates with DFS & OS Histopathology. 1991;19(5): , J Clin Pathol Feb;55(2):88-92, J Clin Oncol. 2007;25(10):

13 GEP: Genomic Grade Index HG1 HG2 HG3 Challenge clinical relevance intermediate grade Low Risk Recurrence High Risk Recurrence Sotiriou, C. et al. J. Natl. Cancer Inst :

14 High Grade Breast Neoplasia Pathway: Aggressive Clinical Course +17q12 +11q13 +6p21-p25 Poor Prognosis Similar DNA changes GEP High Grade Pre-invasive/precursor lesion Invasive disease Normal Breast Similar DNA changes GEP +1q -16q Low Grade Breast Neoplasia Pathway: Indolent Clinical Course Good Prognosis Biomarkers useful for individual risk prediction? Low Grade

15 Guiding Treatment Decisions: Predicted Benefit from Systemic Adjuvant Therapy What criterion must be met to justify the use of endocrine therapy? Any ER staining by IHC (>1% ER+ invasive tumor) What criterion must be met to justify the use of anti-her2 therapy? ASCO/CAP HER2+ - IHC: >30% intense complete membrane staining, FISH: HER2/CEP17 >2.2 What criterion must be met to justify the use of chemotherapy? HER2+ disease = chemo + HER2-targeted therapy Triple negative = chemo ER+/HER2 negative variable indication, based on biology + risk Treatment thresholds are based on disease biology, tumor characteristics and estimated risk for recurrence

16 Chemo/Endocrine Therapy Decision Making in ER+/HER2- Breast Cancer (St. Gallen's) Pathologic Features ER & PR Relative indication for chemotherapy Low expression of ER & PR (or ER[+]/PR[-]) Factors not useful for decision Relative indication for endocrine therapy alone High expression of ER & PR Histologic grade Grade 3 Grade 2 Grade 1 Proliferation High (Ki-67 >20%) Borderline (10-20%) Low (Ki-67 <10%) Nodal status Positive (4 or more) Positive (1-3 nodes) Negative LVI Presence (especially extensive) +/- Absence Tumor size (pt) > 5 cm cm < 1-2 cm *Validated multigene assays may be an adjunct to high-quality pathologic phenotyping if doubt about the indications for chemotherapy persist after consideration of all pathologic factors Multigene assay* High risk score Intermediate Low risk score Goldhirsch Annals of Oncology, 20: (2009)

17 New Approaches to an Old Problem: Prognosis, Risk, Genomic Profiling & Multigene Assays State-of-the-art molecular technology can be used to analyze global genomic changes in clinical breast cancer samples Genomic activity in early breast cancer Refine breast cancer classification Assess prognosis Assess response to therapy Garber. Science. 2004;303:

18 Two Complementary RNA Analysis Methods: Study of Gene Expression in Clinical Samples DNA Arrays (Chips) RT-PCR Assay (e.g., Genomic Health) Unfixed, frozen tissue (high quality RNA) 1000s of genes Limited dynamic range and difficult to control Fixed paraffin or unfixed tissue 10s-100s of genes Wide dynamic range, high sensitivity, specificity, reproducibility Cronin et al. Am J Pathol. 2004;164:35-42, Karsten et al. Nucleic Acids Res. 2002; 30(2):E4.

19 RNA Analysis Methods: Study of gene expression in clinical samples DNA Arrays (Chips) Unfixed, frozen tissue 1000s of genes Limited dynamic range and difficult to control Generates huge data sets,? Analysis? Cronin et al. Am J Pathol. 2004;164:35-42, Karsten et al. Nucleic Acids Res. 2002; 30(2):E4.

20 Gene Expression Profiling of Breast Cancer: Types of Analysis Unsupervised Cluster Analysis o Sorts tumors into related clusters based upon similarities in gene expression profiles o Dendrograms illustrate the degree of relatedness o Sorts tumors into groups of imputed biologic significance o? Clinical informative? Ramaswamy and Golub JCO 20:1932,2001 Class Discovery

21 Molecular Classification Gene Expression Profiling Basal, HER2 over-expression, Luminal A, Luminal B, ER+ ER- HER2 Lum B BLC erbb2 over-expressing Luminal B Type High proliferation index Basal Type CK5/6,17 & EGFR expression Lum A Luminal A Type Estrogen receptor positive Prognostic Significance Perou, Sorlie et al, 1999, 2000, 2001, 2003

22 Unsupervised Analysis of Breast Cancer Gene Expression Profiles (Intrinsic Molecular Subtypes) Identify reproducible, biologically distinct subgroups of breast cancers o Validated across multiple patient cohorts o Significantly different outcomes between subtypes o Differences in likelihood/patterns of recurrence o Added to our understanding of breast cancer biology/diversity Established drug targets (ER, HER2) and proliferation gene help define subgroups o Closely correlates with conventional histologic classification and IHC phenotypes of breast cancer Proc Natl Acad Sci USA 100: , 2003

23 Gene Expression Profiling of Breast Cancer: Types of Analysis Supervised Analysis o Initial step is to separate tumors into groups e.g., relapsed vs. not o Compare signatures of groups o Identify genes identifying tumor as belonging to one group or the other o Multiple tests leads to many false positives o Validation is necessary Ramaswamy and Golub JCO 20:1932,2001 Class Prediction

24 70-Gene Prognostic Signature Assay Developed Netherlands Cancer Institute Requires fresh/frozen tumor Matched BC cases with good & bad outcome - Node (-), tumors <5cm, <55 years age 70 prognosis classifier genes developed dichotomized to good or bad signature Independent predict outcome Classifier validated in 295 patients with breast cancer Nature. 2002;415: , N Eng J Med. 2002;347:

25 Probability Patients Would Remain Free of Distant Metastases & Probability of OS According to Whether They Had Good-Prognosis or Poor-Prognosis Signature Overall Good vs. Poor HR=5.1 All Pts All Pts Node - HR=5.5 Node - Node + Node + van de Vijver, M. J. et al. N Engl J Med 2002;347:

26 RNA Analysis Methods: Study of Gene Expression in Clinical Samples FFPE RT-PCR Assay (e.g., Genomic Health) Fixed paraffin or unfixed tissue 10s-100s of genes Wide dynamic range, high sensitivity, specificity, reproducibility Cronin et al. Am J Pathol. 2004;164:35-42, Karsten et al. Nucleic Acids Res. 2002; 30(2):E4.

27 Oncotype DX Technology: Candidate Gene Selection Approach From ~40,000 genes: Develop RTPCR FFPE Test candidates in 3 studies 250 cancer-related candidate genes 21 final gene set with algorithm Calculate Recurrence Score (RS) *Sources include: van't Veer et al. Nature. 2002;415: Sorlie et al. PNAS. 2001;98: Ramaswamy et al. Nat Genetics. 2003;33: Gruvberger et al. Cancer Res. 2001;61:

28 PROLIFERATION Ki-67 STK15 Survivin Cyclin B1 MYBL2 INVASION Stromolysin 3 Cathepsin L2 HER2 GRB7 HER2 Oncotype DX: 21 Gene Recurrence Score (RS) Assay 16 Cancer and 5 Reference Genes From 3 Studies ESTROGEN ER The RS recurrence = x score HER2 defined Group as: Score PR RS= x HER2 Group Score Bcl x ER Proliferation Group Score Group Score SCUBE x Proliferation Invasion Group Group Score Score x Invasion CD68 Group Score GSTM1 BAG x CD68 GSTM x GSTM1 BAG1 CD x BAG1 Scaled 0 to 100 REFERENCE Beta-actin GAPDH RPLPO GUS TFRC Coefficient x Expression Level Category Low risk Int risk High risk RS < 18 RS 18 and < 31 RS 31 Best RT-PCR performance and most robust predictors RS (0 100)

29 Clinical Data Supporting the Oncotype DX: 21 Gene Recurrence Score Validation study (NSABP B14): ER(+)/node(-) breast cancer treated with tamoxifen (Tam = benefit) o Prospective, randomized clinical trial o Low RS (0-18): 6.8% 10-year recurrence risk (95% CI, 4-9.6%) o Intermediate RS (19-30): 14.3% 10-year recurrence risk (95% CI, %) o High RS (31-100): 30.5% 10-year recurrence risk (95% CI, %) 21 gene score validated as prognostic marker for ER(+)/Node (-) patient s treated with Tam

30 Clinical Data Supporting Oncotype DX: 21 Gene Recurrence Score Validation study ( NSABP B20): ER(+)/node(-) breast cancer randomized to Tam alone versus CMF + Tam (modest benefit from + chemotherapy) o Substantial benefit from + chemo for high RS HR=0.26; 95% CI, o No benefit from + chemo for low RS HR=1.31; 95% CI, o Uncertain benefit from chemo for intermediate RS Very few cases and small number of events 21 gene score validated as predictive marker for chemotherapy benefit (CMF) ER(+)/node(-)

31 Performance of individual genes and gene clusters in predicting recurrence HER2 Group

32 B-14 Results DRFS, Multivariate Analysis Analysis without Recurrence Score Variable P Value Hazard Ratio Analysis with Recurrence Score P Value Hazard Ratio Age at surgery Clinical tumor size Tumor grade Moderately differentiated Poorly differentiated < < HER2 amplification Estrogen-receptor protein fmol/mg fmol/mg > 200 fmol/mg Recurrence Score < Only RS and poor tumor grade are independently associated with recurrence Paik et al. N Engl J Med. 2004;351:

33 Histopathologic Variables Predict Oncotype DX Recurrence Score R = 0.59 P<0.01 R = 0.58 P<0.01 RS - significantly correlated with tubule formation, nuclear grade, mitotic count, ER IHC score, PR IHC score & HER2 (Flanagan MB, et al. Mod Pathol. 2008) Both morphology & gene expression appears to track biologic characteristics which impact clinical behavior and outcome in breast cancer Results should correlate with one another

34 Can GEP data be translated into IHC markers that are clinically useful and have therapeutic implications??? DNA errors (copy # changes) Altered gene expression Altered protein expression Altered morphology Clinical Behavior Therapeutic responsiveness Tumor Phenotype?

35 Lum A Lum B HER2-Like Basal-Like ER ER HER2 CK5 KI67 KI67 60% ER (-) Classic HER2 40% ER(+) Luminal HER2 High-Grade Ki-67-high Most Triple-Negative [ER(-), PR(-), HER2(-)] CK5/6 and/or EGFR(+) High-Grade Ki-67-high

36 Luminal A Subtype of Breast Cancer

37 Luminal A Breast Tumors: Characteristic Features Demonstrate higher level of expression of ER and ER related genes o Lower histologic grade o Lower levels of proliferation related genes o HER2 negative More indolent clinical course o May experience late recurrences Better prognosis compared with Lum-B and other subtypes

38 ER+ Breast Cancer (Luminal A) ER KI67

39 Luminal A Breast tumors Implications for therapy o Good prognosis o Respond well to endocrine therapy o May be adequately treated with hormonal therapy alone o Little if any benefit from adjuvant and neoadjuvant chemotherapy o Typically low recurrence score by 21 gene assay (Oncotype Dx)

40 Luminal B Subtype of Breast Cancer

41 Luminal-B Breast Tumors: Characteristic Features Lower levels of ER expression and ER-related genes o May be PR negative o May over-express GFR (HER2 & EGFR) Higher histologic grade More aggressive clinical course, worse prognosis o More likely lymph node positive Higher expression of proliferation related genes o KI-67 proliferation index may be useful in separating Lum B from Lum A tumors

42 ER+ Breast Cancer (Luminal-B) ER KI67

43 ER+/HER2+ Breast Cancer (Luminal-B/Luminal-HER2) ER HER2 KI67

44 Luminal-B/Luminal HER2 Breast Tumors Implication for therapy o Aggressive clinical course o May be less likely to respond to tamoxifen o More likely to benefit from chemotherapy added to endocrine treatment o Typically - high recurrence score by Oncotype Dx o Similar benefit from Herceptin+chemo compared with HER2+/ER- tumors in adjuvant clinical trials

45 IHC Classification and Prognosis Univariate survival by breast cancer subtype among 943 patients with lymph node-negative, hormone receptor-positive breast cancer who received no adjuvant systemic therapy Luminal A = ER+, and/or PR+, HER2-, Ki-67<14% Luminal B = ER+, and/or PR+, HER2-, Ki-67>14% Luminal HER2 = ER+, and/or PR+, HER2+ Cheang, J. Natl. Cancer Inst :

46 IHC Classification and Prediction Univariate survival by breast cancer subtype among 976 patients with hormone receptorpositive breast cancer who received tamoxifen as their sole adjuvant systemic therapy Luminal A = ER+, and/or PR+, HER2-, Ki-67<14% Luminal B = ER+, and/or PR+, HER2-, Ki-67>14% Luminal HER2 = ER+, and/or PR+, HER2+ Cheang, J. Natl. Cancer Inst :

47 HER2-Enriched Subtype of Breast Cancer

48 HER2+ Breast Tumors: Characteristic Features HER2+ breast tumors by GEP are ER negative o Over-expression of other genes in HER2-amplicon (GRB7, TOP2A) o High proliferative index o More likely to harbor p53 mutations o Higher histologic grade o Younger age at presentation Aggressive clinical course, poor prognosis

49 HER2+ Breast Tumors Implications for therapy o Good response to trastuzumab therapy (adjuvant and metastatic) in combination with chemotherapy o More likely to show pcr to neo-adjuvant chemotherapy + trastuzumab compared with other subtypes o More likely to respond to anthracyclines May be explain by co-amplification of TOP2A

50 Basal Subtype of Breast Cancer ( Basal-like Carcinoma)

51 Basal Subtype Breast Tumors GEP reminiscent of normal myoepithelial cells o Lack of expression of ER and related genes o Low expression of HER2, o High expression of basal cytokeratins (CK5,6,14,17) o High expression of proliferation-related genes, GFR (EGFR) Aggressive clinical course, poor prognosis o Increase likelihood of early systemic recurrence o Visceral recurrence & brain mets more likely than other subtypes Hereditary breast cancer o BRCA1 mutation generally develop basal-like breast tumors

52 Basal-Like Carcinoma: Morphologic Features & Immunophenotype (TNBC) EGFR CK 5/6

53 Basal Subtype Breast Tumors Implications for therapy o No role for endocrine therapy or trastuzumab o Aggressive clinical course = combination cytotoxic chemotherapy Absence or impaired BRCA1 function- o BRCA1 regulated repair of DNA damage (homologous recombination) o Cell lacking BRCA1 function prone to replication errors and genomic instability initiating oncogenic events o Cells dependent on less reliable mechanisms for DNA repair o Hypersensitivity to DNA damaging agents (loss of DNA repair)

54 Therapeutic Strategies in Investigation for Basal subtype breast cancer Targeting aberrant DNA repair o Platinum agents o PARP inhibitors (AZD2281; BSI-201) o Trabectedin (DNA transcription inhibitor) Antiangiogenesis o Bevacizumab o Sunitinib EGFR targeting o Cetuximab o Erlotinib Epigenetic modifications o Trichostatin A o Butyrate o Vorinostat Src inhibitor o Dasatinib Eur J Cancer Dec;44(18):

55 Translation of Gene Expression Data into 5 Reagent IHC Test for ER+/N0 Breast Cancer Patients to Determine Prognosis: Mammostrat (Applied Genomics/Clairent) Patient Samples (RNA) Breast Cancer Genes Patient Samples (paraffin blocks) Raise Poly/Monoclonal Antibodies Help predict likelihood of recurrence & likelihood of benefit from adjuvant chemo J Clin Oncol 2006 ; 24 : , Clin Cancer Res. 2008;20:

56 Five Monoclonal Immunohistochemical Assay P53 - NDRG1 - SLC7A5 - CEACAM5 - HTF9C Cox Model selects 5 antibody reagents: SLC7A5 * P53 *1.12 Low < 0 + NDRG1 *1.06 Moderate > 0 + HTF9C *0.72 High > CEACAM * = Risk Index SLC7A5 amino acid transport P53 cell cycle control NDRG1 stress/hypoxia response protein HTF9C methyl transferase gene family homology CEACAM5 embryonic expressed protein Potential to help predict likelihood of recurrence & likelihood of benefit from adjuvant chemotherapy J Clin Oncol 2006 ; 24 : , Clin Cancer Res. 2008;20:

57 NSABP B14 & B20 ER+, node negative tamoxifen-treatment arms combined NSABP/AGI B14 & B20 TAM-Treated Patients Team trial: cumulative risk of distant recurrence by Mammostrat risk index All patients Proportion of Free from Recurrence GMB=0 GMB=1 GMB=2 Log-rank test: p=0.001 Tam followed by exemestane Time in Years Clin Cancer Res. 2008;20: Low risk = 7.6% Distant recurrence at 10yrs exemestane alone High risk = 20.9% Distant recurrence at 10yrs p=0.003 Bartlett J M et al. JCO 2012;30: Bartlett et al, BCR 2010

58 IHC Approach for Profiling Breast Cancer Advantages o Wide availability o Cost/TAT o Morphologic confirmation of tissue Disadvantages o Susceptibility to preanalytic variables o Qualitative evaluation and readout Needs: o Establish optimal antibody reagents/panels o Correlation of results with outcome/response o Standardized tissue handling/fixation protocols o Standarized assay procedures, controls, criterion for interpretation o Quantitative results (image analysis)

59 Take Home Message: Intrinsic Properties of Breast Cancer & Clinical Course: Proliferation is Critically Important! Intrinsic Biologic Properties of Tumor Patient Tumor Burden Wound signature Genomic Grade Index Tumor Size & Nodal Status 70 gene signature 76 gene signature High Tumor Proliferation Aggressive Clinical Course Poor Prognosis 21 gene recurrence score Intrinsic Molecular Classification Proliferation genes common driving force in prognostic signatures Tumor burden independently associated with prognosis Sotiriou and Paccart, 2007

60 Risk Assessment and Acceptance of Tumor Markers: Balance of Carrots and Sticks Intrinsic Molecular Classification Technical validation Genomic Grade Index Rapid Clinical Acceptance Patient and clinician desire Financial and academic benefits 70 gene signature 76 gene signature 21 gene recurrence score IHC antibody panels Validated Clinical Utility LOE I studies Financial burden Low Payoff Clinical validation

61 Take Home Message: Integration of Profiling/Molecular Testing into Treatment Planning Clinical Presentation Proliferation and Differentiation (Grade) Tumor Burden Molecular results must correlate with clinical & pathologic features Does this all Make sense? Discordance with clinical or pathologic findings must be reconciled prior to treatment decisions Pathologist s are perfectly position to do integrate these results 61

62 Next in the Hot Topics in Pathology Webinar Series Transforming the Diagnostic Evaluation of Inherited Disorders with Next-Generation Sequencing Tuesday, April 23, 10:00-11:00 AM Central o Karl V Voelkerding, MD, FCAP The commercial introduction of next-generation sequencing (NGS) in 2005 ushered in a new era in biomedical research. Now eight years later, NGS is being increasingly applied as a molecular diagnostic tool in fields as diverse as oncology, infectious diseases and inherited disorders. This presentation will highlight how NGS is transforming the diagnostic evaluation of inherited disorders. Emphasized will be the growing adoption of NGS as a methodological approach for performing multi-gene panel and exome sequencing. Multi-gene panels are being employed in a variety of inherited disorders wherein mutations in any one of several genes can result in phenotypic overlap. Exome sequencing is being leveraged to identify causal and candidate genes in patients with undiagnosed disorders. For both of these use case scenarios, the presentation will address technical, bioinformatics and reporting considerations and challenges. As a powerful new diagnostic modality, NGS offers unique practice opportunities for pathologists in the emerging era of genomic medicine. Register by going to College of American Pathologists. All rights reserved. 62

63 Access Archived Webinars-View Recording & Download Presentation PDF go to Archived Webinars on Getting Started and Taking Next Steps in Molecular Pathology The Why, What, and How of Identifying Patients at Risk for Hereditary Cancer Syndromes in Surgical Pathology Practice (Alexis Carter, MD, FCAP) Molecular Tests and Pathology Practice: What Every Community Pathologist Should Know/Clinical Requests for Molecular Tests (Alexis B Carter, MD, FCAP) How to Build and Fund a Viable Molecular Lab (Frederick Kiechle, MD, PhD, FCAP) Cancer: The Critical Role of Pathology in Personalized Health Care (Eric Walk, MD, FCAP) Archived Webinars on Genomic Analysis, Large Molecular Panels, Exome, Genome Whole Genome Analysis as a Universal Diagnostic: A Pathologist s Perspective (Mark Boguski MD, PhD, FCAP) Clinical Use of Whole Genomic and Whole Exome Today(Paula North, MD, FCAP and David Bick MD) Next-Generation Sequencing for the Clinical Laboratory (Karl V. Voelkerding, MD, FCAP) Next-Generation Sequencing: Just Another Lab Test (John Pfeifer, MD, FCAP) 2011 College of American Pathologists. All rights reserved. 63

64 Access Archived Webinars-View Recording & Download Presentation PDF go to Archived Webinars on Organ Based Pathology Molecular Diagnosis for Colorectal Cancer Patients (Antonia R. Sepulveda MD, PhD, FCAP) Molecular Testing Guidelines for Selection of EGFR and ALK Tynsine Karnase Inhibitors in Non-Small Cell Lung Cancer (Neal I Lindeman, MD, FCAP and Marc Ladanyi, MD, FCAP) Molecular Diagnostics of Lung Cancer (John Iafrate, MD, PhD) Molecular Testing and Hematopathologic Conditions (David Czuchlewski, MD, and Mohammad Vasef, MD, FCAP) Molecular Genetics of Pancreatic Neoplasms (Ralph Hruban, MD) 2012 College of American Pathologists. All rights reserved. 64

65 CAP Learning Molecular Markers in Breast Cancer Course Archives Applied - Metaplastic Breast Carcinoma and Mimickers (SAM eligible) CME/SAM 1.0 Archives Applied - Molecular Pathology of Breast Cancer (SAM eligible) CME/SAM 1.0 Learning Objectives -Identify the key morphologic features for diagnosing metaplastic carcinomas with low-grade spindle cell morphology. -Recognize the most useful pathologic and immunohistochemical features in the differential diagnosis of metaplastic carcinoma. -Identify key diagnostic features in core biopsy samples for the diagnosis of metaplastic carcinoma. -Recognize the four groups of spindle cell lesions to consider when evaluating atypical spindle cell proliferation in the breast. Identify challenges in the diagnosis of metaplastic carcinomas. -Recognize the challenges of making an accurate morphologic classification of breast carcinoma. -Recognize the advantages of brightfield in situ hybridization methods over fluorescence in situ hybridization (FISH) detection methods. -Differentiate the most commonly recognized molecular subtypes of breast carcinoma. -Identify the advantages and disadvantages of array-based comparative genomic hybridization (acgh) in classifying breast neoplasms. -Identify the advantages and disadvantages of matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry for investigating breast neoplasms College of American Pathologists. All rights reserved. 65

66 CAP Learning Molecular Markers in Breast Cancer Course BPF Testing Self Study (SAM eligible) CME/SAM 2.5 ER IHC Test Interpretation Accuracy (SAM eligible) CME/SAM 2.0 HER2 IHC Test Interpretation Accuracy (SAM eligible) CME/SAM 2.0 Use of Immunohistochemistry in the Diagnosis of Breast Lesions (SAM eligible) CME/SAM 1.5 Learning Objectives -Explain the ASCO-CAP ER/PR Testing Guidelines and their implications for lab procedures, test results and patient care. -Explain the ASCO-CAP HER2 Testing Guidelines and their implications for lab procedures, test results and patient care. -Determine if the assay and tissue sample are appropriately matched per the ASCO/CAP Guidelines. -Explain the biology of fixation interactions with assay performance. -Explain the potential use of molecular analysis in patient care decisions. -Plan and perform a proper ER IHC test validation. -Accurately perform and interpret ER IHC tests, including the proper evaluation of appropriate controls and test tissues. -Evaluate and integrate ER staining patterns with clinical and morphologic findings. -Identify the relationship and impact of ER IHC test results on patient treatment. -Plan and perform a proper HER2 IHC test validation in accordance with ASCO- CAP guidelines for HER2 testing. -Accurately perform and interpret HER2 IHC tests, including the proper evaluation of appropriate controls and test tissues. -Evaluate and integrate HER2 staining patterns with clinical and morphologic findings to help improve concordance with HER2 FISH results. -Identify the relationship and impact of HER2 IHC test results on patient treatment. -Interpret commonly used immunohistochemical studies. -Recognize potential interpretative pitfalls. -Utilize correlative morphologic features to avoid misdiagnosis College of American Pathologists. All rights reserved. 66

67 CAP Learning Molecular Markers in Breast Cancer Course Validation of ER and PgR IHC Assays (SAM eligible) CME/SAM 1.5 HER2 FISH Test Interpretation Accuracy (SAM eligible) CME/SAM 1.5 BPFT Reporting (SAM eligible) CME/SAM 1.5 Learning Objectives -Distinguish between validation and verification of an ER/PgR assay and when each is required. -Describe an appropriate sample set for an ER/PgR assay validation or verification study. -Identify appropriate test conditions and requirements for an ER/PgR assay validation or verification study. -Accurately interpret HER2 FISH tests. -Correct for HER2 FISH interpretative errors. -Recognize the relationship between HER2 FISH test results and patient treatment. -Apply the ASCO-CAP ER/PR and HER2 Guideline criteria to all reports in a standardized manner. -Create consistent, standardized and integrated reports. -Remediate inconsistent data and provide a resolution in an integrated report. -Create patient friendly reports. -Use formatting techniques to create clear and understandable reports College of American Pathologists. All rights reserved. 67

68 CAP Learning Portal CAP Learning Portal The CAP Learning Portal includes content and tools designed to support the learning needs of pathologists. A user must login to cap.org in order to access the portal. In the portal, you will find: o o o o o Learning Options search/catalog Competency Model for Pathologists Personal Progress Check My Learning Plan Help Center (Guides, Video, FAQs) Benefits Increase effectiveness to plan and manage learning Increase efficiency to target learning needs and identify premium learning solutions Increase satisfaction with learning solutions that meet specific learner needs Increase capability to maintain professional certifications 2013 College of American Pathologists. All rights reserved. 68

69 To learn more For more details and to register for/access educational offerings: 1. Log in to the cap.org website 2. Click on the Learning Portal tab. 3. Click on the Browse Our Learning Catalog tab 4. Type your desired topic in the Search box or make a selection from the list provided. A list of available learning options displays 2013 College of American Pathologists. All rights reserved. 69

70 A New CAP Tool- Short Presentations On Emerging Concepts (SPECs) Pathology SPECs are: o o short PowerPoint presentations, created for pathologists, focused on selected diseases where molecular tests play a key role in patient management. valuable resource for your discussions with Tumor Boards or other physician colleagues. Now Available: Emerging Concepts in the Workup of Colorectal Cancer Emerging Concepts in Therapeutic Guidance for Metastatic Melanoma Emerging Concepts in the Diagnosis and Workup of Thyroid Cancer Emerging Concepts in Colorectal Cancer Hereditary Non-Polyposis Cancer (Lynch Syndrome) Emerging Concepts in the Workup of Polycythemia and Thrombocythemia: JAK2 To register, go to the CAP Member tab on cap.org. You do not need to be a member to utilize this free tool College of American Pathologists. All rights reserved. 70

71 A New CAP Tool-Pathology Resource Guides The CAP has created the Pathology Resource Guides, a new tool to assist pathologists in understanding key emering technologies. These Resource Guides are a new CAP member benefit available at no charge. Molecular Diagnostic (small panel, single gene) Genomic Analysis (large molecular panels, exome, genome) Digital Pathology In Vivo Microscopy Register through the CAP member tab. You will receive periodic updates for two years. Questions? Contact capguides@cap.org College of American Pathologists. All rights reserved. 71

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75 THANK YOU! Thank you for attending our webinar Molecular Markers in Breast Cancer by David G Hicks, MD, FCAP. For comments about this webinar or suggestions for upcoming webinars, please contact Jill Kaufman, PhD, Director of Personalized Health Care at jkaufma@cap.org NOTE: There is no CME/CE credit available for today s free webinar. 75