Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.

Longitudinal time-to-event analysis is a statistical method to analyze data where covariates are measured repeatedly. In survival studies, the risk for an event is estimated using Cox-proportional hazard model or extended Cox-model for exogenous time-dependent covariates. However, these models are inappropriate for endogenous time-dependent covariates like longitudinally measured biomarkers, Carcinoembryonic Antigen (CEA). Joint models that can simultaneously model the longitudinal covariates and time-to-event data have been proposed as an alternative. The present study highlights the importance of choosing the baseline hazards to get more accurate risk estimation. The study used colon cancer patient data to illustrate and compare four different joint models which differs based on the choice of baseline hazards [piecewise-constant Gauss-Hermite (GH), piecewise-constant pseudo-adaptive GH, Weibull Accelerated Failure time model with GH & B-spline GH]. We conducted simulation study to assess the model consistency with varying sample size (N = 100, 250, 500) and censoring (20 %, 50 %, 70 %) proportions. In colon cancer patient data, based on Akaike information criteria (AIC) and Bayesian information criteria (BIC), piecewise-constant pseudo-adaptive GH was found to be the best fitted model. Despite differences in model fit, the hazards obtained from the four models were similar. The study identified composite stage as a prognostic factor for time-to-event and the longitudinal outcome, CEA as a dynamic predictor for overall survival in colon cancer patients. Based on the simulation study Piecewise-PH-aGH was found to be the best model with least AIC and BIC values, and highest coverage probability(CP). While the Bias, and RMSE for all the models showed a competitive performance. However, Piecewise-PH-aGH has shown least bias and RMSE in most of the combinations and has taken the shortest computation time, which shows its computational efficiency. This study is the first of its kind to discuss on the choice of baseline hazards.

Whole Exome Sequencing Identifies Cohesin Component STAG1 Mutation in de novo Acute Myeloid Leukemia (FAB M2): A Pilot Study with Cytogenetics, Clinical and Prognostic Implications.

The clinical implications of cohesin gene complex mutation in acute myeloid leukemia (AML) are not well characterized. In the present study, a cohort of 152 de novo unselected adult AML patients underwent conventional and molecular cytogenetic analysis for chromosomal aberrations. Further, we examined the frequency and clinical implications of mutations in cohesin gene complex STAG1, STAG2, RAD21, SMC1, and SMC3 using whole exome sequencing as a pilot study in 10 de novo patients with AML-FAB M2. Among the 10 cases, we identified a functionally heterozygous mutation in exon16 of STAG1 in one patient (10%), however no mutation was observed in STAG2, RAD21, SMC1, and SMC3. Sanger sequencing analysis for exon 16 of STAG1 in the remaining 142 AML cases did not reveal any further mutations, which underlined the observation that mutations took place throughout the cohesin gene complex without presence of a mutational hot spot region. The present study identified a positive correlation between serum bilirubin, LDH, and hematological parameters such as Hb, WBC, and platelet count with STAG1 mutation. Our data suggest that the cohesin complex may represent an attractive therapeutic target for future preclinical and clinical studies. However, more studies with a larger number of patients should be performed prospectively to determine the pathogenic involvement of STAG 1 mutation in AML patients.

Differential gene expression changes and their implication on the disease progression in patients with Chronic Myeloid Leukemia.

The molecular mechanisms responsible for disease progression of CML are not conclusive. The main functional changes associated with disease evolution in CML was high proliferation rate, decreased apoptosis, blockade of differentiation, and strong resistance to chemotherapeutic agents. The current study analyzed the relative expressional profiles of genes related with proliferation, apoptosis, differentiation, and resistance to chemotherapeutic agents such as c-MYC, BAD, BCL-2, C/EBPα/-ß and ABCB1 respectively in different clinical stages of CML by SYBR Green I quantitative real-time (qRT) PCR. We selected a total of 183 CML patients and 30 healthy control samples. The study populations were classified into four groups, including de novo CML-CP (50/183), CML-AP (32/183), CML-BC (51/183) and Imatinib Mesylate or IM resistant CML-CP (50/183) groups. qRT PCR analysis revealed that significant overexpression of c-MYC, ABCB1 and BCL-2 was observed in advanced phases and IM resistant CP of CML compared to healthy controls. Likewise, the mean expression level of BAD, C/EBPα/-ß genes were found to be significantly down regulated. Present study concluded that the complex interplay of several candidate genes like overexpression of c-MYC, ABCB1, BCL-2 and down regulation of BAD, C/EBPα/-ß played a significant role in the disease evolution and development of drug resistant in CML.