KMWin – A Convenient Tool for Graphical Presentation of Results from Kaplan-Meier Survival Time Analysis

BACKGROUND: Analysis of clinical studies often necessitates multiple graphical representations of the results. Many professional software packages are available for this purpose. Most packages are either only commercially available or hard to use especially if one aims to generate or customize a huge number of similar graphical outputs. We developed a new, freely available software tool called KMWin (Kaplan-Meier for Windows) facilitating Kaplan-Meier survival time analysis. KMWin is based on the statistical software environment R and provides an easy to use graphical interface. Survival time data can be supplied as SPSS (sav), SAS export (xpt) or text file (dat), which is also a common export format of other applications such as Excel. Figures can directly be exported in any graphical file format supported by R. RESULTS: On the basis of a working example, we demonstrate how to use KMWin and present its main functions. We show how to control the interface, customize the graphical output, and analyse survival time data. A number of comparisons are performed between KMWin and SPSS regarding graphical output, statistical output, data management and development. Although the general functionality of SPSS is larger, KMWin comprises a number of features useful for survival time analysis in clinical trials and other applications. These are for example number of cases and number of cases under risk within the figure or provision of a queue system for repetitive analyses of updated data sets. Moreover, major adjustments of graphical settings can be performed easily on a single window. CONCLUSIONS: We conclude that our tool is well suited and convenient for repetitive analyses of survival time data. It can be used by non-statisticians and provides often used functions as well as functions which are not supplied by standard software packages. The software is routinely applied in several clinical study groups

VEGFR2 and VEGFA polymorphisms are not associated with an inferior prognosis in Caucasian patients with aggressive B-cell lymphoma

Purpose Previous published data showed an impact of single‐nucleotide polymorphisms in the VEGF A and VEGFR2 genes on the survival of patients with various malignancies, among others diffuse large B‐cell lymphoma (DLBCL). Patients and Methods We investigated the role of four VEGF‐A and two VEGFR‐2 gene polymorphisms on the outcome of 273 patients with diffuse large B‐cell lymphoma who were treated with R‐CHOP within a prospective, randomized trial of the German High‐Grade Non‐Hodgkin Lymphoma Study Group (DSHNHL). The genomic DNA samples were analyzed using commercial DNA Probes (Applied Biosystems, USA) to detect single‐nucleotide polymorphisms in the VEGF A rs699947, rs1570360, rs2010963, rs3025039 and rs1870377, and rs2305948 in the VEGFR2 receptor. Hundred healthy blood donors served as a control. Results There was no difference between the SNP allele frequencies in lymphoma patients compared to the control group for all investigated SNPs. None of the investigated SNPs was significantly associated with EFS or OS. After adjusting for the International Prognostic Index risk factors in a multivariate analysis, these results could be confirmed. Conclusion Single‐nucleotide polymorphisms of the VEGF and VEGFR2 were not associated with a worse outcome in Caucasian patients with DLBCL

IGHV mutational status of nodal marginal zone lymphoma by NGS reveals distinct pathogenic pathways with different prognostic implications

The precise B cell of origin and molecular pathogenesis of nodal marginal zone lymphoma (NMZL) remain poorly defined. To date, due to the rarity of NMZL, the vast majority of already-published studies have been conducted on a limited number of samples and the technical approach to analyze the immunoglobulin genes was of amplifying rearranged variable region genes with the classical direct sequencing of the PCR products followed by cloning. Here, we studied the B cell Ig heavy-chain repertoires by next-generation sequencing (NGS) in 30 NMZL cases. Most of the cases were mutated (20/28; 71.5%) with homologies to the respective germ line genes ranging from 85 to 97, 83%, whereas 8/28 (28.5%) were unmutated. In addition, our results show that NMZL cases have a biased usage of specific immunoglobulin heavy-chain variable (IGHV) region genes. Moreover, we documented intraclonal diversity in all (100%) of the mutated cases and ongoing somatic hypermutations (SHM) have been confirmed by hundreds of reads. We analyzed the mutational pattern to detect and quantify antigen selection pressure and we found a positive selection in 4 cases, whereas in the remaining cases there was an unspecific stimulation. Finally, the disease-specific survival and the progression-free survival were significantly different between cases with mutated and unmutated IGHV genes, pointing out mutational status as a possible new biomarker in NMZL

FDG PET/CT to detect bone marrow involvement in the initial staging of patients with aggressive non-Hodgkin lymphoma: results from the prospective, multicenter PETAL and OPTIMAL>60 trials

Purpose Fluorine-18 fluorodeoxyglucose positron emission tomography combined with computed tomography (FDG PET/CT) is the standard for staging aggressive non-Hodgkin lymphoma (NHL). Limited data from prospective studies is available to determine whether initial staging by FDG PET/CT provides treatment-relevant information of bone marrow (BM) involvement (BMI) and thus could spare BM biopsy (BMB). Methods Patients from PETAL (NCT00554164) and OPTIMAL>60 (NCT01478542) with aggressive B-cell NHL initially staged by FDG PET/CT and BMB were included in this pooled analysis. The reference standard to confirm BMI included a positive BMB and/or FDG PET/CT confirmed by targeted biopsy, complementary imaging (CT or magnetic resonance imaging), or concurrent disappearance of focal FDG-avid BM lesions with other lymphoma manifestations during immunochemotherapy. Results Among 930 patients, BMI was detected by BMB in 85 (prevalence 9%) and by FDG PET/CT in 185 (20%) cases, for a total of 221 cases (24%). All 185 PET-positive cases were true positive, and 709 of 745 PET-negative cases were true negative. For BMB and FDG PET/CT, sensitivity was 38% (95% confidence interval [CI]: 32–45%) and 84% (CI: 78–88%), specificity 100% (CI: 99–100%) and 100% (CI: 99–100%), positive predictive value 100% (CI: 96–100%) and 100% (CI: 98–100%), and negative predictive value 84% (CI: 81–86%) and 95% (CI: 93–97%), respectively. In all of the 36 PET-negative cases with confirmed BMI patients had other adverse factors according to IPI that precluded a change of standard treatment. Thus, the BMB would not have influenced the patient management. Conclusion In patients with aggressive B-cell NHL, routine BMB provides no critical staging information compared to FDG PET/CT and could therefore be omitted. Trial registration NCT00554164 and NCT0147854

Prognostic Significance of MYC Rearrangement and Translocation Partner in Diffuse Large B-Cell Lymphoma : A Study by the Lunenburg Lymphoma Biomarker Consortium

PURPOSE: MYC rearrangement (MYC-R) occurs in approximately 10% of diffuse large B-cell lymphomas (DLBCLs) and has been associated with poor prognosis in many studies. The impact of MYC-R on prognosis may be influenced by the MYC partner gene (immunoglobulin [IG] or a non-IG gene). We evaluated a large cohort of patients through the Lunenburg Lymphoma Biomarker Consortium to validate the prognostic significance of MYC-R (single-, double-, and triple-hit status) in DLBCL within the context of the MYC partner gene. METHODS: The study cohort included patients with histologically confirmed DLBCL morphology derived from large prospective trials and patient registries in Europe and North America who were uniformly treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone therapy or the like. Fluorescence in situ hybridization for the MYC, BCL2, BCL6, and IG heavy and light chain loci was used, and results were correlated with clinical outcomes. RESULTS: A total of 5,117 patients were identified of whom 2,383 (47%) had biopsy material available to assess for MYC-R. MYC-R was present in 264 (11%) of 2,383 patients and was associated with a significantly shorter progression-free and overall survival, with a strong time-dependent effect within the first 24 months after diagnosis. The adverse prognostic impact of MYC-R was only evident in patients with a concurrent rearrangement of BCL2 and/or BCL6 and an IG partner (hazard ratio, 2.4; 95% CI, 1.6 to 3.6; P < .001). CONCLUSION: The negative prognostic impact of MYC-R in DLBCL is largely observed in patients with MYC double hit/triple-hit disease in which MYC is translocated to an IG partner, and this effect is restricted to the first 2 years after diagnosis. Our results suggest that diagnostic strategies should be adopted to identify this high-risk cohort, and risk-adjusted therapeutic approaches should be refined further

Legislative Documents

Also, variously referred to as: Senate bills; Senate documents; Senate legislative documents; legislative documents; and General Court documents

Prognosemodelle für chemotherapieinduzierte hämatologische Nebenwirkungen bei Patienten mit aggressiven Non-Hodgkin-Lymphomen

Derzeit ist es gängige Praxis, die Chemotherapie entsprechend der Körperoberfläche des Patienten zu dosieren. Diese Praxis ist jedoch nicht ideal, da es Patienten gibt, die starke Nebenwirkungen haben und andere, die kaum Nebenwirkungen aufweisen. Damit intelligentere Dosierungsschemata entwickelt werden können und prophylaktische Maßnahmen zum Verhindern von Therapienebenwirkungen besser geplant werden können, ist die Kenntnis der Faktoren erforderlich, welche die Nebenwirkungen verursachen. Die hämatologischen Nebenwirkungen der Chemotherapie sind dabei am stärksten ausgeprägt und führen oft zu Dosiserosionen, Zeitverschiebungen zwischen den Chemotherapiezyklen oder sogar zu einem Abbruch der Therapie. Das hat wiederum negative Auswirkungen auf den Therapieerfolg. In dieser Arbeit wurden daher Prognosemodelle für chemotherapieinduzierte hämatologische Nebenwirkungen aufgebaut. Die Daten von 1399 Patienten mit aggressivem Non-Hodgkin-Lymphom und einem breiten Altersspektrum von 18-75 Jahren aus der NHL-B1/B2-Studie (Pfreundschuh et al. 2004a und b) gingen in die Analyse ein. Es wurden für die jüngeren (<= 60 Jahre) und die älteren Patienten (> 60 Jahre) multivariate Proportionale Odds Regressionsmodelle für die drei hämatopoetischen Linien der Leukozytopenie, Thrombozytopenie und Anämie gerechnet und an zwei unabhängigen Datensätzen, auch unter Rituximab-haltigen Chemotherapieschemata, validiert. Die hier entwickelten Modelle konnten ein breites Heterogenitätsspektrum für die hämatologischen Nebenwirkungen erklären. Bemerkenswert ist, dass einige der Faktoren für hämatologische Nebenwirkungen gleichzeitig auch Faktoren des Internationalen Prognostischen Index für das Therapieergebnis sind. Die im ersten Chemotherapiezyklus beobachtete Nebenwirkung war der stärkste prognostische Faktor. Mit einigen der Modelle konnte die kumulative Nebenwirkung über die Chemotherapiezyklen hinweg gezeigt werden. Die Demonstration des Zusammenhangs zwischen den für Leukozytopenie ermittelten Risikogruppen und den klinisch relevanten Größen Infektion, Antibiotikagabe, Hospitalisierungstage und therapieassoziierte Todesfälle ist ein sehr wichtiges Ergebnis der Arbeit. Es wurde eine Internetseite (www.toxcalculator.com) entwickelt, welche den Ärzten die Möglichkeit bietet, die bei dem Patienten vorliegenden Prognosefaktoren einzugeben und dann die Modellvorhersagen für die zu erwartenden hämatologischen Nebenwirkungen zu erhalten. Die Ergebnisse der Arbeit wurden in der hochrangigen Zeitschrift ‚Annals of Oncology‘ publiziert (Ziepert et al. 2008)

KMWin File Queue Window.

<p>Simplified representation of KMWin File Queue Window. Settings apart from default are black. Filled grey circles are labels for: 1-Input files table, 2-Button area, 3-Log text box.</p

KMWin Main Window.

<p>Simplified representation of KMWin Main Window. Settings apart from default are black. Filled grey circles are labels for: 1-Button area, 2-Variables table, 3-Factor levels table, 4-Axes settings, 5-Censored times setting, 6-Number under risk settings, 7-Legend settings, 8-Logrank test settings and 9-Logrank table.</p

KMWin Preferences Window (left) and Filter Window (right).

<p>Simplified representation of KMWin Preferences Window and Filter Window. Settings apart from default are black. Filled grey circles are labels for: 1-Item properties, 2-General line properties, 3-Button area, 4-Variables table, 5-Filter string edit box, 6-Input panel, 7-Button area.</p