scholarly journals Molecular genetic tools shape a roadmap towards a more accurate prognostic prediction and personalized management of cancer

2007 ◽  
Vol 6 (3) ◽  
pp. 308-312 ◽  
Author(s):  
Dimitrios H. Roukos ◽  
Samuel Murray ◽  
Evangelos Briasoulis
2020 ◽  
pp. 397-422
Author(s):  
Noor-ul-Huda Ghori ◽  
Tahir Ghori ◽  
Sameen Ruqia Imadi ◽  
Alvina Gul

2020 ◽  
pp. 5181-5188
Author(s):  
Wendy N. Erber

The diagnosis of haematological malignancies requires an understanding of the diseases and the uses and limitations of the range of available investigations. The relative importance of different investigations varies by disease entity. The blood count is one of the most widely used tests in all of medicine and often the first indication of an underlying haematological malignancy. Some blood count features are ‘diagnostic’ and others may give an indication of a bone marrow defect. Morphological assessment of a stained blood film adds value to an abnormal blood count. It may identify abnormal morphology of red cells, leucocytes, or platelets which may be specific and diagnostic, or give clues suggesting a diagnosis. Bone marrow aspirate (liquid sample) gives cytological detail, and trephine biopsy provides information about marrow cellularity, architecture, cellular distribution, and extent of fibrosis. Immunophenotyping detects cellular antigens in clinical samples and is essential in the diagnosis and classification of haematological malignancies. It is also used for disease staging and monitoring, to detect surrogate markers of genetic aberrations, identify potential immunotherapeutic targets, and to aid prognostic prediction. Cytogenetics assesses the number and structure of whole chromosomes and chromosomal regions in neoplastic cells and is performed to diagnose and classify some haematological malignancies. Molecular genetic methods facilitate the detection of mutations, rearrangements, or translocations in genes. Applications in malignant haematology include confirming clonality, detecting disease-associated genotypes, determining prognosis, disease monitoring following therapy, predicting imminent clinical relapse, and identifying patients who are likely (or not) to respond to new targeted inhibitor therapies.


2016 ◽  
Vol 2016 (5) ◽  
pp. pdb.top087601 ◽  
Author(s):  
Johanne M. Murray ◽  
Adam T. Watson ◽  
Antony M. Carr

2009 ◽  
Vol 85 (5) ◽  
pp. 1251-1258 ◽  
Author(s):  
Reiner Finkeldey ◽  
Ludger Leinemann ◽  
Oliver Gailing

Yeast ◽  
2016 ◽  
Vol 33 (12) ◽  
pp. 633-646 ◽  
Author(s):  
Ahu Karademir Andersson ◽  
Stina Oredsson ◽  
Marita Cohn

2012 ◽  
Author(s):  
David R. Georgianna ◽  
Javier Gimpel ◽  
Michael J. Hannon ◽  
Stephen P. Mayfield

2018 ◽  
Vol 45 (6) ◽  
pp. 281-297 ◽  
Author(s):  
Chiranjibi Chhotaray ◽  
Yaoju Tan ◽  
Julius Mugweru ◽  
Md Mahmudul Islam ◽  
H.M. Adnan Hameed ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document