Cancer Histology Handout Oct 2024 PDF

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MMU

2024

Prof Roger Hunt

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cancer histology cancer types cancer grading pathology

Summary

This is a handout on cancer histology, covering various types of cancer, their characteristics, and grading systems. It includes details on benign and malignant tumors, different cancer types, and associated terminology, along with grading and staging systems.

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CANCER Prof Roger Hunt HISTOLOGY MMU OVERVIEW Tumour principles Benign v malignant Different forms of cancer Terminology Grading v Staging Datasets CHECK OUT …. WEBPATHOLOGY LEEDS VIRTUAL PATHOLOGY Pathology Outlines TUMOUR PRINCIPLES BENIGN...

CANCER Prof Roger Hunt HISTOLOGY MMU OVERVIEW Tumour principles Benign v malignant Different forms of cancer Terminology Grading v Staging Datasets CHECK OUT …. WEBPATHOLOGY LEEDS VIRTUAL PATHOLOGY Pathology Outlines TUMOUR PRINCIPLES BENIGN ANY TUMOUR CAN OCCUR ANYWHERE or PRIMARY MALIGANT SECONDARY aka METASTATIC CANCER The word ”CANCER” is a generic term for a wide variety of malignant disease processes The individual type of cancer varies from organ to organ The type of cancer is dependent on its cell of origin Some cancers are organ-specific, others can occur anywhere Benign fibroadenoma Malignant Breast carcinoma Benign v Malignant Underwood Benign v Malignant Underwood CANCER What cell types can be found in the body? CANCER – just a start Epithelial – squamous, glandular, urothelial Endothelial – vascular Neural – nerve/brain Adipose tissue – fat Muscle – smooth/skeletal/cardiac Mesothelial/Peritoneal Endocrine Renal - kidney Melanocytes Neuroendocrine cells Germ cell (testicular, ovarian) Blood cells – white, red, plasma cells, macrophages Bone Cartilage CANCER TERMINOLGY CELL TYPE BENIGN LESION MALIGNANT LESION GLANDULAR ADENOMA ADENOCARCINOMA SQUAMOUS PAPILLOMA SQUAMOUS CARCINOMA ADIPOSE LIPOMA LIPOSARCOMA ENDOTHELIAL ANGIOMA ANGIOSARCOMA LYMPHOID LYMPHOMA SMOOTH MUSCLE LEIOMYOMA LEIOMYOSARCOMA NEURAL SCHWANNOMA MALIG PERIPHERAL NERVE SHEATH TUMOUR MELANOCYTE NAEVUS MELANOMA MESOTHELIOMA BENIGN MESOTHELIOMA MALIGNANT MESOTHELIOMA BONE OSTEOMA OSTEOSARCOMA CARTILAGE CHONDROMA CHONDROSARCOMA GRADING versus STAGING GRADING What the malignancy looks like, usually in comparison to normal tissue Gives an indication of the likely biological behaviour/prognosis Described as its “differentiation” STAGING How far the tumour has spread throughout the body Usually expressed in the form of TNM Grading of malignancies Tumour site Best to worst prognosis GI/pancreatic/biliary G1 - Well G2 - Moderately G3 - Poorly adenocarcinoma differentiated differentiated differentiated Squamous cell Moderately Well differentiated Poorly differentiated carcinomas differentiated Breast G1 G2 G3 Prostatic Gleason score 4 (best) to score 10 (worst) – aggregate of 2 grades adenocarcinoma Renal cell carcinoma nuclear grade 1 (best) to grade 4 (worst) [ISUP formerly Fuhrman] WHO 2004 Low grade High grade Urothelial carcinoma WHO 1973 Grade 1 Grade 2 Grade 3 Sarcomas Grade 1 Grade 2 Grade 3 Follicular lymphomas Grade 1 Grade 2 Grade 3 Other lymphomas, Small cell carcinoma Basal cell carcinoma No grading scheme Melanoma, Seminoma, Teratoma Barrett’s metaplasia Dysplasia ADENOCARCINOMA Molecular genetics of adenoma-carcinoma sequence in colo-rectal carcinoma FAP Underwood’s ADENOMA to CARCINOMA Cacolon1a Colo-rectal adenocarcinoma File:Colon cancer.jpg WELL DIFFERENTIATED ADENOCARCINOMA LOW POWER to HIGH POWER ARCHITECTURE ASSESSMENT to CYTOLOGICAL ASSESSMENT Atypical glands or acini Cribriform pattern Composed of cells with pleomorphic nuclei Abnormal mitoses Luminal “dirty"necrosis ADENOCARCINOMA MUCINOUS ADENOCARCINOMA PROSTATE ADENOCARCINOMA PROSTATE NORMAL AND ADENOCARCINOMA PROSTATE NORMAL AND ADENOCARCINOMA RENAL CARCINOMA Dscn0850 RENAL RENAL CARCINOMA CARCINOMA International Society of Urologic Pathology ISUP GRADE 1 RENAL RENAL CARCINOMA CARCINOMA International Society of Urologic Pathology Grade 1: nucleoli inconspicuous or absent at 400x (objective magnification 40x) Grade 2: nucleoli prominent at 400x mag Grade 3: nucleoli prominent at 100x mag Grade 4: extreme nuclear pleomorphism, multinucleated giant cells, sarcomatoid or rhabdoid change UROTHELIAL CARCINOMA BREAST CARCINOMA Lumen Grade 1 Invasive ductal carcinoma Best prognosis P R O ↓ Tubules G ↑ Pleomorphism N ↑ Mitoses O S I Grade 3 S Invasive ductal carcinoma Worst prognosis Completely No vascular excised invasion Lymph node assessment Metastatic carcinoma >2mm RCPath dataset 2016 Isolated Tumour Cells (ITCs) 2mm Prognostic & Predictive information in the Histopath report Size Grade Tumour subtype Lymphovascular invasion Excision margins Lymph nodes Biomarkers (ER, PR, Ki67 and HER2) Gene expression studies (eg Oncotype DX) NORMAL SQUAMOUS EPITHELIUM CERVIX – HPV CHANGES KOILOCYTES CERVIX – HPV CHANGES to CIN HPV CIN 1 CIN 2 CIN 3 Aka elsewhere as severe dysplasia or carcinoma-in-situ Squamous cell carcinoma File:Oral cancer (1) squamous cell carcinoma histopathology.jpg SQUAMOUS CARCINOMA Irregular islands of atypical squamous cells Nuclear pleomorphism Abnormal mitoses Infiltrating margin Areas of keratinisation Intercellular bridges SQUAMOUS CARCINOMA SQUAMOUS CARCINOMA CGIN ADENOCARCINOMA SKIN – BASAL CELL CARCINOMA EXCELLENT PROGNOSIS IF COMPLETELY EXCISED MELANOCYTIC LESIONS MELANOCYTIC LESIONS OCCUR ON SKIN SURFACES OCCUR ON MUCOSAL SURFACES LESIONS ARE ASYMMETRICAL MELANOCYTIC LACK MATURATION GOING INTO DEEPER TISSUES NUCLEAR ATYPIA LESIONS ABNORMAL MITOSES PROGNOSIS DEPENDS ON DEPTH OF INVASION LUNG CARCINOMA SQUAMOUS ADENO CARCINOMA CARCINOMA SMALL CELL (NON-SMALL CELL CARCINOMA) CARCINOMA SMALL CELL CARCINOMA Sheets of closely packed cells Nuclear moulding Abnormal mitoses Nuclear debris “karyorrhexis” Nuclear smearing LYMPHOMAS HODGKIN FOLLICULAR LYMPHOMA LYMPHOMA DIFFUSE LARGE B-CELL BURKITT LYMPHOMA LYMPHOMA SARCOMA GRADING French Federation of Cancer Centers Sarcoma Group (FFCCSG): – Grade 1: total score of 2 - 3 points – Grade 2: total score of 4 - 5 points – Grade 3: total score of 6 - 8 points Tumour differentiation: – 1 point: resembles normal adult mesenchymal tissue, may be confused with a benign lesion, such as well differentiated liposarcoma – 2 points: histologic typing is certain, such as myxoid liposarcoma – 3 points: synovial sarcoma, osteosarcoma, Ewing’s sarcoma / PNET, sarcomas of doubtful tumour type, embryonal and undifferentiated sarcomas Mitotic count (count 10 successive high power fields [area of 0.17 mm squared] in most mitotically active areas): – 1 point: 0 - 9 mitoses – 2 points: 10 - 19 mitoses – 3 points: 20 or more mitoses Tumour necrosis: – 0 points: no necrosis on any slides – 1 point: less than 50% necrosis for all examined tumour surface – 2 points: tumour necrosis of 50% or more of examined tumour surface ANGIOMA/HAEMANGIOMA ANGIOSARCOMA VASOFORMATIVE LESIONS RCPath datasets RCPath datasets TUMOUR, NODES, METS - TNM The TNM Staging System is based on the extent of the tumour (T), the spread to the lymph nodes (N) and the presence of metastases (M). The T relates to the primary tumour TX - Primary tumour cannot be evaluated T0 - No evidence of primary tumour Tis - Carcinoma in situ (early cancer that has not spread) T1–T4 - Size and/or extent of the primary tumour The N category describes the lymph node status NX - Regional lymph nodes cannot be evaluated NO - No regional lymph node involvement N1 - N3 - Involvement of regional lymph nodes M = distant metastases MO - No distant metastasis M1 - Distant metastasis It’s useful to know some micro-anatomy for Staging cancers It’s useful to know some micro-anatomy for Staging cancers C:\Documents and Settings\ROGER HUNT\My Documents\My Pictures\NP GI Tutorial\BOWEL2.jpg T1 T2 T3 VASCULAR INVASION D2-40 LYMPH NODE ASSESSMENT Metastatic carcinoma >2mm RCPath dataset 2016 Isolated Tumour Cells (ITCs) 2mm Colonic carcinoma: TNM Stage pT4 N2 M1 Colonic carcinoma Extension through the bowel wall Poorly differentiated adenocarcinoma Peritoneal involvement 5 LNs involved by mets Apical node involved Known liver mets OVERVIEW Tumour principles Benign v malignant Different forms of cancer Terminology Grading v Staging Datasets

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