The Pathology of Neoplasia Part II
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1 The Pathology of Neoplasia Part II February 2018 PAUL BOGNER, MD A S S O C I A T E P R O F E S S O R O F O N C O L O G Y P A T H O L O G Y A N D D E R M A T O L O G Y
2 Clinical goals of cancer pathology 1. Identify presence of cancer 2. Classify cancer 3. Predict biology of cancer and prognosis 4. Identify optimal therapy 5. Evaluate success of treatment
3 Clinical goals of cancer pathology 1. Identify presence of cancer Tissue biopsy, aspirate, smear Circulating tumor cells or nucleic acid: liquid biopsy 2. Classify cancer 3. Predict biology of cancer and prognosis 4. Identify optimal therapy 5. Evaluate success of treatment
4 Clinical goals of cancer pathology 1. Identify presence of cancer 2. Classify cancer Histology, immunohistochemistry FISH, cytogenetics, mutation profile 3. Predict biology of cancer and prognosis 4. Identify optimal therapy 5. Evaluate success of treatment
5 Clinical goals of cancer pathology 1. Identify presence of cancer 2. Classify cancer 3. Predict biology of cancer and prognosis Pathologic Grade and Stage Immunohistochemistry biomarkers Genetic evaluation 4. Identify optimal therapy 5. Evaluate success of treatment
6 Clinical goals of cancer pathology 1. Identify presence of cancer 2. Classify cancer 3. Predict biology of cancer and prognosis 4. Identify optimal therapy Immunohistochemical biomarkers Personalized medicine ( actionable targets ) Immune therapy 5. Evaluate success of treatment
7 Clinical goals of cancer pathology 1. Identify presence of cancer 2. Classify cancer 3. Predict biology of cancer and prognosis 4. Identify optimal therapy 5. Evaluate success of treatment Histology (residual tumor, tumor margins, etc) Molecular testing for residual disease
8 Clinical goals of cancer pathology 1. Identify presence of cancer 2. Classify cancer 3. Predict biology and prognosis of cancer 4. Identify optimal therapy 5. Evaluate success of treatment
9 GRADING AND STAGING MALIGNANCY
10 Grading Malignancy Grading is based on cytologic and/or architectural characteristics of the tumor Attempts to predict future behavior of a tumor High, Severe or Poor essentially means Bad behavior Generic systems for many tumors Well Moderate Poor Undifferentiated Severe Moderate Mild High Low
11 Grading Malignancy Some tumor types have specific, well validated grading systems: Prostate Gleason Grade Primarily based on tumor architecture Breast Nottingham Grade Tubule formation, Nuclear features, Mitotic rate Endometrium FIGO Grade Architecture with nuclear feature modifier
12 Gleason Grading of Prostate Carcinoma Figure Taken from Epstein J. et al. Am J Surg Pathol 29(9): 1228 (2005).
13 Cancer Staging Staging is a precise method for establishing the type and extent of tumor at a specific point in time during the course of the disease. Staging is critical to the modern management of cancer because it allows the clinical team to: 1. Plan treatment 2. Estimate prognosis 3. Group similar patients to evaluate results on treatment protocol studies 4. Facilitate information exchange between institutions 5. Otherwise contribute to the study of cancer
14 Staging Performed according to guidelines published by the American Joint Committee on Cancer (AJCC) and the International Union Against Cancer (UICC or Union Internationale Contre le Cancer) Stage is NOT the same thing as grade The staging system for each tumor type is different (see example for melanoma)
15 Staging Melanoma Breslow Depth (3.1 mm) Ulceration
16 Staging Melanoma (8 th AJCC edition) Tis T1 T2 T3 T4 Melanoma in-situ Melanoma measuring 1.0 mm or less a] no ulceration, depth <0.8 mm b] ulceration or depth mm Melanoma measuring mm a] no ulceration b] ulceration Melanoma measuring mm a] no ulceration b] ulceration Melanoma measuring >4.1 mm a] no ulceration b] ulceration
17 Staging Melanoma NX N1 N2 N3 Lymph nodes not assessed One lymph node positive OR In-transit/satellite metastasis with no positive nodes Two or three nodes positive OR In-transit/Satellite metastasis and one positive node Four or more nodes positive OR In-transit/Satellite metastasis AND 2+ nodes positive M0 M1 No distant metastasis Distant metastasis
18 AJCC Stage Groups: Melanoma Stage 0 Tis N0 M0 Stage IA T1a,T1b N0 M0 IB T2a N0 M0 Stage IIA T2b N0 M0 T3a N0 M0 IIB T3b N0 M0 T4a N0 M0 IIC T4b N0 M0 Stage IIIA-D any T any N+ M0 Stage IV any T any N+ M1
19 Figure taken from Melanoma Molecular Map Project ( Melanoma Survival by AJCC Stage
20 ESOPHAGUS BREAST National Cancer Institute SEER Data
21 Grade and Stage are Different Grade Stage How does it look? Predict tumor behavior Similar to degree of differentiation Can vary over time, within a tumor, or between tumor masses and metastases How much and where? Predict prognosis and plan treatment Measure of tumor extent at one time Overall stage includes all tumor burden and sites
22 Grade and Stage are Different BETTER 1. LOW GRADE EARLY STAGE 2. HIGH GRADE EARLY STAGE 3. LOW GRADE LATE STAGE 4. HIGH GRADE LATE STAGE WORSE
23 ADVANCES in pathology and pathology in research
24 Clinical goals of cancer pathology 1. Identify presence of cancer 2. Classify cancer 3. Predict biology and prognosis of cancer 4. Identify optimal therapy 5. Evaluate success of treatment
25 The Changing World of Pathology Traditional histopathology merges with molecular and genetic evaluation: Molecular or genetic classification of cancer. Stratify tumor risk, predict behavior Identify primary origin of metastasis ( unknown primary ) Compare tumor genetic profile to profiles from known primaries Personalized therapy Use biopsy tissue to plan best treatment OmniSeq multigene platform Immune therapy
26 Classifying Myeloid Leukemia 70 s style French-American-British (FAB) system M0 Undifferentiated acute myeloblastic leukemia M1 AML with minimal maturation M2 AML with maturation M3 Acute promyelocytic leukemia (APL) M4 Acute myelomonocytic leukemia M5 Acute monocytic leukemia M6 Acute erythroid leukemia M7 Acute megakaryoblastic leukemia
27 2016 WHO Classification: myeloid neoplasms and acute leukemia.
28 Lung Adenocarcinoma: Driver Mutations 26% k-ras EGFR 41% ALK Her2 AKT1 b-raf FGFR PIK3CA VEGFR 10% cmet PDGFR Unknown 1% 5% 1% 4% 3% 1% 2% 1% 5% Based on data from: Ding et al., Nature 455:1069, 2008 And modified from a figure developed by: Dr. F. Hirsch, University of Colorado
29 Molecular Classification Should we group tumors by genotype or driving mutation/target rather than conventional histology or site of origin? BRAF V600E mutated tumors: Melanoma Colorectal adenocarcinoma Lung adenocarcinoma Papillary thyroid carcinoma
30 The facts of life: Finding cutoffs DISEASE HOW PATHOLOGISTS SEE DISEASE HOW SURGEONS WISH WE SAW DISEASE
31 Melanocytic Tumor of Uncertain Malignant Potential Melanocytic lesions in which traditional histological criteria may be insufficient to fully predict biology Example: Spitz Nevus Atypical Spitz Nevus Atypical Spitz Tumor MELTUMP Spitzoid melanoma
32 Melanoma
33 Spitz Nevus (photo from pathologyoutlines.com)
34 Spitz Nevus Spitz S. Am J Pathol. 24:591, 1948.
35 Spitz Nevus Spitz S. Am J Pathol. 24:591, 1948.
36 Spitzoid Melanocytic Lesions NEVUS MELTUMP MELANOMA
37 How can we improve our diagnosis? Immunohistochemistry sometimes helpful Comparative genomic hybridization Fluorescence in-situ hybridization Proprietary molecular tests controversial
38 CGH and Sequencing Conventional melanoma Genomic instability Most have multiple chromosome copy number gains or losses Telomerase reverse transcriptase (TERT) promoter mutations Spitz nevi Very rare to have multiple abnormalities Some single abnormalities (gain 11p, gain 7q) BAP1 mutation, HRAS mutation, kinase fusions
39 Between benign and malignant...
40 Spitz Tumor assessment by FISH HIGH RISK Progression beyond SLN 9p21 homozygous deletion (CDKN2A gene/p16) INTERMEDIATE RISK 6p25 gain (RREB1 gene) 11q13 gain (CCND1 gene) LOW 6q23 deletion (MYB gene) No abnormalities by FISH
41 Clinical goals of cancer pathology 1. Identify presence of cancer 2. Classify cancer 3. Predict biology and prognosis of cancer 4. Identify optimal therapy 5. Evaluate success of treatment
42 The Future of Pathology Traditional histopathology merges with molecular and genetic evaluation: Molecular or genetic classification of cancer. Stratify tumor risk, predict behavior Identify primary origin of metastasis Personalized therapy Use biopsy tissue to plan best treatment OmniSeq multigene platform Immune therapy
43 Lung Adenocarcinoma: Molecular targets 26% k-ras EGFR 41% ALK Her2 AKT1 b-raf FGFR PIK3CA VEGFR 10% cmet PDGFR Unknown 1% 5% 1% 4% 3% 1% 2% 1% 5% Based on data from: Ding et al., Nature 455:1069, 2008 And modified from a figure developed by: Dr. F. Hirsch, University of Colorado
44 the EGFR Pathway Erlotinib, gefitinib From: Ladanyi M, Pao, W. Mod Pathol 21 Supp2, S16-21, 2008.
45 Tumor Biopsy Small Cell Non-small cell Basic Algorithm for Handling Lung Cancer Specimens Advanced Disease Stains to further classify Surgical Candidate Option to further classify Squamous Non-squamous Surgery Biomarkers? EGFR K-ras EML4/ALK Definitive Classification Prognostic Assessment Molecular? Morphologic? Treatment Personalized Treatment
46 Personalized Medicine
47 Personalized Medicine OmniSeq Comprehensive TM Next gen molecular sequencing of DNA and RNA 144 cancer associated actionable genes such as ALK, EGFR, PTEN, KRAS, NRAS, BRAF, etc Detects mutations, translocations/fusions, copy number changes, etc.
48 OmniSeq Panel
49 Immune Therapy: PD-L1 Blockade
50 PD-L1 Staining
51 Immune Therapy: PD-L1 Blockade Pembrolizumab and Nivolumab FDA approved for certain tumors to block PD-1 and PD-L1 interaction Facilitate immune mediated tumor killing Melanoma Non-small cell lung cancer Renal cell carcinoma Proof of PD-L1 staining needed prior to drug in some treatment situations (eg: Pembro and NSCLC)
52 Pathology and Research Initiate both basic and translational research Tissue procurement and specimen archiving Bridge between clinic and research bench Analyze human/animal experimental histology Build and evaluate tissue based experiments example: tissue microarray Provide clinical perspective to scientific groups
53 Pathology Resource Network (PRN)
54 LIMS: Biospecimens A tally of primary lung adenocarcinomas with genomic DNA banked at RPCI.
55 Tumor Procurement Procurement events from one random Friday s surgical schedule Tissue may be distributed FRESH to labs or flash frozen for long term banking. Procurement group provides QA evaluation of samples.
56 Pathology and Research Initiate both basic and translational research Tissue procurement and specimen archiving Bridge between clinic and research bench Analyze human/animal experimental histology Build and evaluate tissue based experiments one example: tissue microarray Provide clinical perspective to scientific groups
57 Tissue Microarray
58 Pathology: Bringing it all together and describing a new tumor entity
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63 Outline What is Pathology? What is a Pathology Department? The pathologist s tools Pathology Definitions and Concepts How do we approach a pathology specimen? A General Classification of Neoplasia. The ugly histologic face of cancer (recognizing malignancy). Grading and Staging Malignancy. The Changing World of Pathology Pathology and Research
64 The End feel free to contact me with questions or comments:
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