This is the most widely used system in clinical oncology.
🧱 Epithelial Origin — Carcinomas Account for ~90% of human cancers
Derived from epithelial cells (lining tissues)
Subtypes:
Adenocarcinoma – arises from glandular tissue (e.g., lung, breast, colon, prostate)
Squamous cell carcinoma – from squamous epithelium (e.g., skin, esophagus, cervix)
💪 Mesenchymal Origin — Sarcomas Arise from connective tissue (bone, muscle, fat, cartilage)
Examples:
Osteosarcoma
Liposarcoma
Leiomyosarcoma
🩸 Hematologic Origin — Leukemias and Lymphomas Originating from blood-forming tissues or lymphatic system
Subtypes:
Leukemia – e.g., acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL)
Lymphoma – e.g., Hodgkin lymphoma, non-Hodgkin lymphoma
Myeloma – plasma cell malignancy (e.g., multiple myeloma)
🧠Neuroectodermal Origin Tumors from the nervous system or melanocytes
Examples:
Gliomas (e.g., glioblastoma, astrocytoma)
Medulloblastoma
Melanoma
Defined by the organ or body system where the cancer originated:
Lung cancer
Breast cancer
Brain cancer
Colorectal cancer
Prostate cancer
Pancreatic cancer
Ovarian cancer
Benign: Non-invasive, non-metastatic
Malignant: Invasive, with potential to metastasize
Increasingly used in precision oncology:
HER2-positive breast cancer
EGFR-mutated non-small cell lung cancer (NSCLC)
IDH1-mutant glioma
MSI-high colorectal cancer
Although cancer classification has improved, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge 1).
Leukemia and prostate cancer are the most common systemic cancers associated with subdural hematoma SDH, and gliomas may predispose to SDH more often than previously recognized. Coagulopathy is common and associated with the worst outcome, but many patients experience good functional outcome and survival 2).
Circulating microRNAs (MicroRNAs) hold great promise as novel clinically blood-based biomarkers for cancer diagnosis and prognosis.