Dataset Information

Development and Validation of a Cytogenetic Prognostic Index Predicting Survival in Multiple Myeloma.

ABSTRACT: PURPOSE:The wide heterogeneity in multiple myeloma (MM) outcome is driven mainly by cytogenetic abnormalities. The current definition of high-risk profile is restrictive and oversimplified. To adapt MM treatment to risk, we need to better define a cytogenetic risk classification. To address this issue, we simultaneously examined the prognostic impact of del(17p); t(4;14); del(1p32); 1q21 gain; and trisomies 3, 5, and 21 in a cohort of newly diagnosed patients with MM. METHODS:Data were obtained from 1,635 patients enrolled in four trials implemented by the Intergroupe Francophone du Myélome. The oldest collection of data were used for model development and internal validation. For external validation, one of the two independent data sets was used to assess the performance of the model in patients treated with more current regimens. Six cytogenetic abnormalities were identified as clinically relevant, and a prognostic index (PI) that was based on the parameter estimates of the multivariable Cox model was computed for all patients. RESULTS:In all data sets, a higher PI was consistently associated with a poor survival outcome. Dependent on the validation cohorts used, hazard ratios for patients in the high-risk category for death were between six and 15 times higher than those of patients in the low-risk category. Among patients with t(4;14) or del(17p), we observed a worse survival in those classified in the high-risk category than in those in the intermediate-risk category. The PI showed good performance for discriminating between patients who died and those who survived (Harrell's concordance index greater than 70%). CONCLUSION:The cytogenetic PI improves the classification of newly diagnosed patients with MM in the high-risk group compared with current classifications. These findings may facilitate the development of risk-adapted treatment strategies.

SUBMITTER: Perrot A

PROVIDER: S-EPMC6804890 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and Validation of a Cytogenetic Prognostic Index Predicting Survival in Multiple Myeloma.

Perrot Aurore A Lauwers-Cances Valérie V Tournay Elodie E Hulin Cyrille C Chretien Marie-Lorraine ML Royer Bruno B Dib Mamoun M Decaux Olivier O Jaccard Arnaud A Belhadj Karim K Brechignac Sabine S Fontan Jean J Voillat Laurent L Demarquette Hélène H Collet Philippe P Rodon Philippe P Sohn Claudine C Lifermann François F Orsini-Piocelle Frédérique F Richez Valentine V Mohty Mohamad M Macro Margaret M Minvielle Stéphane S Moreau Philippe P Leleu Xavier X Facon Thierry T Attal Michel M Avet-Loiseau Hervé H Corre Jill J

Journal of clinical oncology : official journal of the American Society of Clinical Oncology 20190515 19

<h4>Purpose</h4>The wide heterogeneity in multiple myeloma (MM) outcome is driven mainly by cytogenetic abnormalities. The current definition of high-risk profile is restrictive and oversimplified. To adapt MM treatment to risk, we need to better define a cytogenetic risk classification. To address this issue, we simultaneously examined the prognostic impact of del(17p); t(4;14); del(1p32); 1q21 gain; and trisomies 3, 5, and 21 in a cohort of newly diagnosed patients with MM.<h4>Methods</h4>Data ...[more]

PMID: 31091136

Similar Datasets

Project description:BackgroundDysregulated long non-coding RNAs (lncRNAs) have been found to have oncogenic and/or tumor suppressive roles in the development and progression of cancer, implying their potentials as novel independent biomarkers for cancer diagnosis and prognosis. However, the prognostic significance of expression profile-based lncRNA signature for outcome prediction in patients with multiple myeloma (MM) has not yet been investigated.MethodsLncRNA expression profiles of a large cohort of patients with MM were obtained and analyzed by repurposing the publically available microarray data. An lncRNA-focus risk score model was developed from the training dataset, and then validated in the testing and another two independent external datasets. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic performance for survival prediction. The biological function of prognostic lncRNAs was predicted using bioinformatics analysis.ResultsFour lncRNAs were identified to be significantly associated with overall survival (OS) of patients with MM in the training dataset, and were combined to develop a four-lncRNA prognostic signature to stratify patients into high-risk and low-risk groups. Patients of training dataset in the high-risk group exhibited shorter OS than those in the low-risk group (HR = 2.718, 95 % CI = 1.937-3.815, p <0.001). The similar prognostic values of four-lncRNA signature were observed in the testing dataset, entire GSE24080 dataset and another two independent external datasets. Multivariate Cox regression and stratified analysis showed that the prognostic power of four-lncRNA signature was independent of clinical features, including serum beta 2-microglobulin (Sβ2M), serum albumin (ALB) and lactate dehydrogenase (LDH). ROC analysis also demonstrated the better performance for predicting 3-year OS. Functional enrichment analysis suggested that these four lncRNAs may be involved in known genetic and epigenetic events linked to MM.ConclusionsOur results demonstrated potential application of lncRNAs as novel independent biomarkers for diagnosis and prognosis in MM. These lncRNA biomarkers may contribute to the understanding of underlying molecular basis of MM.

Project description:BackgroundImmuno-oncology (IO) is rapidly evolving in early drug development. We aimed to develop and prospectively validate a prognostic index for patients treated in IO phase I trials to assist with patient selection.MethodsThe development cohort included 192 advanced solid tumor patients treated in 13 IO phase I trials, targeting immune checkpoint and/or co-stimulatory molecules. A prognostic scoring system was developed from multivariate survival analysis of 10 clinical factors, and subsequently validated in two independent validation cohorts (n = 152 and n = 80).ResultsIn the development cohort, median age was 57.5 years (range = 20.4-84.8 years). Median progression-free survival and overall survival (OS) were 13.4 and 73.6 weeks, respectively, 90-day mortality was 16%, and overall response rate was 20%. In multivariate analysis, Eastern Cooperative Oncology Group performance status greater than or equal to 1 (hazard ratio [HR] = 3.2, 95% confidence interval [CI] = 1.8 to 5.7; P < .001), number of metastatic sites greater than 2 (HR = 2.0, 95% CI = 1.3 to 3.1; P = .003), and albumin less than the lower limit of normal (HR = 1.8, 95% CI = 1.2 to 2.7; P = .007) were independent prognostic factors; comprising the Princess Margaret Immuno-oncology Prognostic Index (PM-IPI). Patients with a score of 2-3 compared with patients with a score of 0-1 had shorter OS (HR = 3.4, 95% CI = 1.9 to 6.1; P < .001), progression-free survival (HR = 2.3, 95% CI = 1.7 to 3.2; P < .001), higher 90-day mortality (odds ratio = 8.1, 95% CI = 3.0 to 35.4; P < .001), and lower overall response rate (odds ratio = 0.4, 95% CI = 0.2 to 0.8; P = .019). The PM-IPI retained prognostic ability in both validation cohorts and performed better than previously published phase I prognostic scores for predicting OS in all three cohorts.ConclusionsThe PM-IPI is a validated prognostic score for patients treated in phase I IO trials and may aid in improving patient selection.

Dataset Information

Development and Validation of a Cytogenetic Prognostic Index Predicting Survival in Multiple Myeloma.

Publications

Development and Validation of a Cytogenetic Prognostic Index Predicting Survival in Multiple Myeloma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets