1. [Predictive molecular pathological stratification of hematological neoplasms].
- Author
-
Andrulis M
- Subjects
- Antineoplastic Agents therapeutic use, Biomarkers, Tumor genetics, Chromosome Aberrations, DNA Mutational Analysis, Drug Delivery Systems, Female, Genetic Heterogeneity, Heat-Shock Proteins genetics, Hematologic Neoplasms classification, Hematologic Neoplasms drug therapy, Humans, Immunohistochemistry, Indoles therapeutic use, Leukemia, Hairy Cell classification, Leukemia, Hairy Cell drug therapy, Leukemia, Hairy Cell genetics, Leukemia, Hairy Cell pathology, Middle Aged, Multiple Myeloma classification, Multiple Myeloma drug therapy, Multiple Myeloma genetics, Multiple Myeloma pathology, Proto-Oncogene Proteins B-raf genetics, Sulfonamides therapeutic use, Vemurafenib, Hematologic Neoplasms genetics, Hematologic Neoplasms pathology
- Abstract
The comprehensive sequencing of the complete genome of various hematological neoplasms has allowed an in-depth insight into the genomic heterogeneity and led to the discovery of new genetic aberrations, which seem to be very promising as therapeutic target structures. The molecular target structures of new therapeutic agents are, however, nearly exclusively proteins and cannot be directly identified with nucleic acid-based investigation methods. There is a great potential in investigations at the protein level that reflect an expression of the target protein and/or alterations of the signal cascade in tumor cells. In this context immunohistochemistry is a procedure that can deliver the decisive information using mutation, phosphorylation and glycosylation-specific primary antibodies. This study was carried out to comprehensively investigate the diagnostic utilization of such antibodies for hematological neoplasms. The studies summarized in this article emphasize the significance of tissue-based diagnostic approaches at the protein level and are suitable for use in patient selection for targeted treatment. A particular success of these studies was to make an essential contribution to the predictive diagnostics of multiple myeloma.
- Published
- 2016
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