Abstract
The focus of pathology as a biomedical discipline is the identification of the pathomechanisms of diseases and the integration of this knowledge into routine diagnosis and classification. Standard tools are macroscopic and microscopic analysis complemented by immunohistochemistry and molecular pathology. So far, classification has been based on the paradigm of cellular pathology established by Rudolf Virchow and others more than 150 years ago, stating that diseases originate from diseased cells. This dogma is meanwhile challenged by the fact that cells can be fully reprogrammed. Many diseases are nowadays considered to originate from undifferentiated stem cells, induced into a diseased state by genetic or epigenetic alterations. In addition, the completion of the Human Genome Project, with the identification of more than 20.000 genes and a much higher number of gene variants and mutations, led to the concept that diseases are dominated by genetics/epigenetics rather than cells of origin. The axiom of cellular pathology, however, still holds true, as cells are the smallest animate units from which diseases originate. Medical doctors and researchers nowadays have to deal with a tremendous amount of data. The International Classification of Diseases will expand from 14.400 entities/codes in ICD-10 to more than 55.000 in ICD-11. In addition, large datasets generated by “genomics“, e.g., whole-genome sequencing, expression profiling or methylome analysis, are meanwhile not only applied in research but also introduced into clinical settings. It constitutes a major task to incorporate all the data into routine medical work. Pathway pathology may help solve this problem. It is based on the realization that diseases are characterized by three essential components: (i) cells of origin/cellular context and (ii) the alteration of cellular as well as (iii) molecular/signal transduction pathways. The concept is illustrated by elaborating on two key cellular pathways, i.e., the cellular senescence of normal cells and the immortality of cancer cells, and by contrasting single cell/single pathway diseases, such as mycoplasma and coughing pneumonia, with complex diseases such as cancer, with multiple cell types as well as multiple affected cellular and signaling pathways. Importantly, the concept of pathway pathology is not just intended to classify disease, but also to conceive new treatment modalities. This article is dedicated to Dr. Leonard Hayflick, who made basic discoveries in pathway pathology not only by identifying cells causing disease (Mycoplasma pneumoniae) and establishing cell strains for treating disease (WI-38 for viral vaccines), but also by first describing cellular senescence and immortality.
Highlights
Classical pathology analysis using immunohistochemistry together with molecular pathology has entered another clinical arena, i.e., the selection of patients for specific “individualized” therapeutic approaches constituting the field of predictive pathology
More than 5400 rare diseases coded in the French–European Orphanet were included in the International Classification of Diseases (ICD)-11 classification
The proponents of systems biology/pathology and Gene Ontology, as well as major computational companies such as Apple, Google and alike, argue that the acquisition and analysis of large-scale datasets by bioinformatical tools and apps are important for the understanding and treatment of disease
Summary
Pathology is a basic medical discipline that has been engaged in the visualization of diseased tissues and cells. Classical pathology analysis using immunohistochemistry together with molecular pathology has entered another clinical arena, i.e., the selection of patients for specific “individualized” therapeutic approaches constituting the field of predictive pathology In many instances, it is (still) sufficient to analyze a single or only a few parameters in order to decide on the existing treatment modalities [15]. Considering genetic variants, e.g., mutations, polymorphisms and splicing variants, in addition to non-coding RNAs, such as microRNAs, involved in gene regulation, the numbers can be multiplied These numbers are daunting and most humans will be appalled and may not even consider approaching the endeavor of knowing and understanding all genes and their relevance for diseases. The COBRA ontologies so far do not seem to incorporate molecular data of cancer specimens
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