SPECTRUM AND IMMUNOPHENOTYPIC PROFILE OF ACUTE LEUKEMIA: A TERTIARY CENTER FLOW CYTOMETRY EXPERIENCE

Main Article Content

Nishit Gupta
Ravikiran Pawar
Sambhunath Banerjee
Subhajit Brahma
Asish Rath
Sundar Shewale
Mayur Parihar
Manish Singh
Arun SR
Shekhar Krishnan
Arpita Bhattacharyya
Anirban Das
Jeevan Kumar
Saurabh Bhave
Vivek Radhakrishnan
Reena nair
Mammen Chandy
Neeraj Arora
Deepak Kumar Mishra

Keywords

AML; MPAL; B-ALL; T-ALL; FLT3/NPM1c; AUL

Abstract

Introduction: For diagnosis, sub-categorization and follow up of Acute Leukemia (AL), phenotypic analysis using flow cytometry is mandatory.


Material and methods: We retrospectively analyzed immunophenotypic data along with cytogenetics/molecular genetics data (wherever available) from 631 consecutive cases of AL diagnosed at our flow cytometry laboratory from January 2014 to August 2017.


Results: Of the total 631 cases, 52.9% (n=334) were acute lymphoblastic leukemia (ALL), 43.9% (n=277) acute myeloid leukemia (AML), 2.2% (n=14) mixed phenotypic acute leukemia (MPAL) and0.5% (n=3) each of acute undifferentiated leukemia (AUL) and chronic myeloid leukemia in blast crisis (CML-BC). ALL cases comprised of 81.7% (n=273/334) B-cell ALLs (95.2%, n=260/273 common B-ALLs and 4.8%, n=13/273 Pro B-ALLs). CD13 was the commonest cross lineage antigen expressed in B-ALL (25.6%), followed by CD33 (17.9%) and combined CD13/CD33 (11.3%) expression. T-ALLs constituted 18.3% (n=61/334) of total ALLs and included 27.9% (n=17) cortical T- ALLs. CD13 was commonest (32.7%) aberrantly expressed antigen in T-ALLs, followed by CD117 (16.0%). AML cases included 32.1% (n=89/277) AML with recurrent genetic abnormalities, 9.0% (n=25/277) with FLT3/NPM1c mutation and 58.9% (n=163/277) AML NOS including 14.7% (n=24/163) AML M4/M5, 1.8% (n=3/163) AML M6 and 3.7% (n=6/163) AML M7. In AMLs, CD19 aberrancy was the most common (16.3%) followed by CD7 (11.9%).


Conclusion: In this study we document the spectrum; correlate the immunophenotype with genetic data of all leukemias, especially with respect to T-ALL where the data from India is scarce.

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