RESUMO
BACKGROUND: Myelofibrosis is reported in around 40% of newly diagnosed chronic myeloid leukemia (CML) patients and have an important role in the pathobiology and prognosis of CML. This retrospective study aimed to evaluate the effects of bone marrow (BM) fibrosis on disease prognosis and the effects of specific tyrosine-kinase inhibitors (TKIs) on BM fibrosis in CML patients. METHODS: The study included 96 patients (>18 years) diagnosed with chronic phase (CP) CML. The clinical and demographic information were collected from the medical files. Post-treatment BM aspirate and core biopsy samples were analyzed for the presence of fibrosis and dysplasia. RESULTS: The mean age of the study patients was 52.69 years; 47.9% of the patients were female. At the onset, 53 (63.1%) patients had BM fibrosis. The difference in the overall survival of the patients with respect to BM fibrosis grades was significant (p = .001). Within the BM fibrosis grade groups, there were significant differences between grade 0 vs. grade 2, grade 0 vs. grade 3, and grade 1 vs. grade 3 (p = .005, p = .002, and p = .003 respectively) There was no significant association between the presence of BM fibrosis at the onset and not responding to first-line therapy (p = .724). Moreover, no significant association was found between the presence of BM fibrosis at the onset and molecular (p = .623) or cytogenetic response (p = .535) to first-line therapy. Additionally, the association between the type of second-line and third-line therapy and molecular response (p = .773 and p = .424, respectively) or cytogenetic response (p = .298 and p = .641) was not significant. CONCLUSION: Although BM fibrosis seems to be a crucial complication of CML with a poor prognosis, it can be reversed via TKI treatment which may result in improved survival. It might be considered to check the BM for this complication on a regular basis during therapies to test its prognostic influence in CML patients in prospective controlled trials. Further studies focused on this issue are required to utilize BM fibrosis as a candidate prognostic factor.
Assuntos
Leucemia Mielogênica Crônica BCR-ABL Positiva , Leucemia Mieloide de Fase Crônica , Mielofibrose Primária , Humanos , Feminino , Pessoa de Meia-Idade , Masculino , Mielofibrose Primária/diagnóstico , Mielofibrose Primária/tratamento farmacológico , Mielofibrose Primária/etiologia , Prognóstico , Estudos Retrospectivos , Estudos Prospectivos , Leucemia Mielogênica Crônica BCR-ABL Positiva/patologia , Leucemia Mieloide de Fase Crônica/tratamento farmacológico , Fibrose , Inibidores de Proteínas Quinases/efeitos adversosRESUMO
Abstract Introduction The availability of a clinical decision algorithm for diagnosis of chronic lymphocytic leukemia (CLL) may greatly contribute to the diagnosis of CLL, particularly in cases with ambiguous immunophenotypes. Herein we propose a novel differential diagnosis algorithm for the CLL diagnosis using immunophenotyping with flow cytometry. Methods The hierarchical logistic regression model (Backward LR) was used to build a predictive algorithm for the diagnosis of CLL, differentiated from other lymphoproliferative disorders (LPDs). Results A total of 302 patients, of whom 220 (72.8%) had CLL and 82 (27.2%), B-cell lymphoproliferative disorders other than CLL, were included in the study. The Backward LR model comprised the variables CD5, CD43, CD81, ROR1, CD23, CD79b, FMC7, sIg and CD200 in the model development process. The weak expression of CD81 and increased intensity of expression in markers CD5, CD23 and CD200 increased the probability of CLL diagnosis, (p < 0.05). The odd ratio for CD5, C23, CD200 and CD81 was 1.088 (1.050 - 1.126), 1.044 (1.012 - 1.077), 1.039 (1.007 - 1.072) and 0.946 (0.921 - 0.970) [95% C.I.], respectively. Our model provided a novel diagnostic algorithm with 95.27% of sensitivity and 91.46% of specificity. The model prediction for 97.3% (214) of 220 patients diagnosed with CLL, was CLL and for 91.5% (75) of 82 patients diagnosed with an LPD other than CLL, was others. The cases were correctly classified as CLL and others with a 95.7% correctness rate. Conclusions Our model highlighting 4 markers (CD81, CD5, CD23 and CD200) provided high sensitivity and specificity in the CLL diagnosis and in distinguishing of CLL among other LPDs.