A geno-clinical decision model for the diagnosis of myelodysplastic syndromes.

The differential diagnosis of myeloid malignancies is challenging and subject to interobserver variability. We used clinical and next-generation sequencing (NGS) data to develop a machine learning model for the diagnosis of myeloid malignancies independent of bone marrow biopsy data based on a 3-institution, international cohort of patients. The model achieves high performance, with model interpretations indicating that it relies on factors similar to those used by clinicians. In addition, we describe associations between NGS findings and clinically important phenotypes and introduce the use of machine learning algorithms to elucidate clinicogenomic relationships.

Consensus criteria for diagnosis, staging, and treatment response assessment of T-cell prolymphocytic leukemia.

T-cell prolymphocytic leukemia (T-PLL) is a rare, mature T-cell neoplasm with a heterogeneous clinical course. With the advent of novel treatment options that will potentially change the management of patients with T-PLL, it has become necessary to produce consensus guidelines for the design and conduct of clinical trials. The T-PLL International Study group (TPLL-ISG) set out to define standardized criteria for diagnosis, treatment indication, and evaluation of response. These criteria will facilitate comparison of results from clinical trials in T-PLL, and will thus support clinical decision making, as well as the approval of new therapeutics by healthcare authorities.

Integrating clinical features and genetic lesions in the risk assessment of patients with chronic myelomonocytic leukemia.

Chronic myelomonocytic leukemia (CMML) is a myelodysplastic/myeloproliferative neoplasm with variable clinical course. To predict the clinical outcome, we previously developed a CMML-specific prognostic scoring system (CPSS) based on clinical parameters and cytogenetics. In this work, we tested the hypothesis that accounting for gene mutations would further improve risk stratification of CMML patients. We therefore sequenced 38 genes to explore the role of somatic mutations in disease phenotype and clinical outcome. Overall, 199 of 214 (93%) CMML patients carried at least 1 somatic mutation. Stepwise linear regression models showed that these mutations accounted for 15% to 24% of variability of clinical phenotype. Based on multivariable Cox regression analyses, cytogenetic abnormalities and mutations in RUNX1, NRAS, SETBP1, and ASXL1 were independently associated with overall survival (OS). Using these parameters, we defined a genetic score that identified 4 categories with significantly different OS and cumulative incidence of leukemic evolution. In multivariable analyses, genetic score, red blood cell transfusion dependency, white blood cell count, and marrow blasts retained independent prognostic value. These parameters were included into a clinical/molecular CPSS (CPSS-Mol) model that identified 4 risk groups with markedly different median OS (from >144 to 18 months, hazard ratio [HR] = 2.69) and cumulative incidence of leukemic evolution (from 0% to 48% at 4 years, HR = 3.84) (P < .001). The CPSS-Mol fully retained its ability to risk stratify in an independent validation cohort of 260 CMML patients. In conclusion, integrating conventional parameters and gene mutations significantly improves risk stratification of CMML patients, providing a robust basis for clinical decision-making and a reliable tool for clinical trials.

Serial assessment of suspected myelodysplastic syndromes: significance of flow cytometric findings validated by cytomorphology, cytogenetics, and molecular genetics.

The significance of flow cytometry indicating myelodysplasia without proof of myelodysplasia by cytomorphology remains to be clarified. We evaluated follow-up analyses in 142 patients analyzed in parallel by flow cytometry, cytomorphology and cytogenetics for suspected myelodysplasia without proof of myelodysplasia by cytomorphology. At initial assessment, flow cytometry indicated myelodysplasia in 64 of 142 (45.1%) patients. In 9 of 142 (6.3%) patients, cytogenetics revealed aberrant karyotypes at first evaluation that were found in 5 of 64 (7.8%) patients rated with myelodysplasia by flow cytometry. The remaining 133 patients without proof of myelodysplasia by cytomorphology and with normal karyotype underwent follow-up analyses that confirmed myelodysplasia by cytomorphology, cytogenetics or molecular genetics in 47 (35.3%) after a median interval of nine months (range 1-53 months). As far as initial flow cytometry results are concerned, this applied to 30 of 59 (50.1%) with myelodysplasia, 10 of 42 (23.8%) with "possible myelodysplasia" (minor antigen aberrancies only) and 7 of 32 (21.9%) without myelodysplasia (P=0.004). Notably, in these latter 7 patients, flow cytometry results changed at follow up to "possible myelodysplasia" (n=4) and "myelodysplasia" (n=2). These data argue in favor of including flow cytometry along with cytomorphology, cytogenetics and molecular genetics to diagnose myelodysplasia, and suggest a closer monitoring of patients with myelodysplasia-typical aberrant antigen expression found by flow cytometry.

Evaluation of flow cytometric assessment of myeloid nuclear differentiation antigen expression as a diagnostic marker for myelodysplastic syndromes in a series of 269 patients.

BACKGROUND: Myeloid nuclear differentiation antigen (MNDA) is expressed in myelomonocytic cells with highest levels in mature granulocytes and monocytes. It is suggested to be expressed more weakly in patients with myelodysplastic syndromes (MDS). The analysis of MNDA therefore may improve diagnostic capabilities of multiparameter flow cytometry (MFC) in MDS. METHODS: We used MFC for detection of MNDA expression in 269 patients with suspected or known MDS, acute myeloid leukemia (AML) or chronic myelomonocytic leukemia (CMML), cytopenia of unknown cause or without malignancy (negative controls). Results were compared with the diagnoses revealed by cytomorphology (CM) and cytogenetics (CG). RESULTS: Percentages of granulocytes and monocytes with diminished MNDA expression (dimG and dimM) were higher in patients with MDS (mean ± SD, 20% ± 20%, P < 0.001 and 31% ± 24%, P < 0.001) and AML (27% ± 27%, P = 0.007 and 45% ± 31%, P = 0.001) diagnosed by CM, vs. patients without MDS (8% ± 10% and 16% ± 11%), respectively. Significant differences were also found for mean fluorescence intensity (MFI) of MNDA in monocytes which was lower in MDS (mean ± SD, 71 ± 36, P = 0.004) and AML (55 ± 39, P < 0.001) vs. no MDS samples (85 ± 28), respectively. Within patients with MDS, cases with cytogenetic aberrations showed a trend to higher %dimG (24% ± 18%, P = 0.083) compared with those without (16% ± 21%). Cut-off values for %dimG (12%) and %dimM (22%) as well as for MFI in monocytes (72) were defined capable of discriminating between MDS and non-MDS. CONCLUSION: MNDA expression in bone marrow cells can be assessed reliably by MFC and may facilitate evaluation of dyspoiesis when added to a standard MDS MFC panel.

Integrated analysis of copy number alterations and gene expression: a bivariate assessment of equally directed abnormalities.

MOTIVATION: The analysis of a number of different genetic features like copy number (CN) variation, gene expression (GE) or loss of heterocygosity has considerably increased in recent years, as well as the number of available datasets. This is particularly due to the success of microarray technology. Thus, to understand mechanisms of disease pathogenesis on a molecular basis, e.g. in cancer research, the challenge of analyzing such different data types in an integrated way has become increasingly important. In order to tackle this problem, we propose a new procedure for an integrated analysis of two different data types that searches for genes and genetic regions which for both inputs display strong equally directed deviations from the reference median. We employ this approach, based on a modified correlation coefficient and an explorative Wilcoxon test, to find DNA regions of such abnormalities in GE and CN (e.g. underexpressed genes accompanied by a loss of DNA material). RESULTS: In an application to acute myeloid leukemia, our procedure is able to identify various regions on different chromosomes with characteristic abnormalities in GE and CN data and shows a higher sensitivity to differences in abnormalities than standard approaches. While the results support various findings of previous studies, some new interesting DNA regions can be identified. In a simulation study, our procedure also shows more reliable results than standard approaches. AVAILABILITY: Code and data available as R packages edira and ediraAMLdata from http://www.statistik.tu-dortmund.de/~schaefer/ CONTACT: martin.schaefer@udo.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.