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An international data set for CMML validates prognostic scoring systems and demonstrates a need for novel prognostication strategies.

Padron E, Garcia-Manero G, Patnaik MM, Itzykson R, Lasho T, Nazha A, Rampal RK, Sanchez ME, Jabbour E, Al Ali NH, Thompson Z, Colla S, Fenaux P, Kantarjian HM, Killick S, Sekeres MA, List AF, Onida F, Komrokji RS, Tefferi A, Solary E - Blood Cancer J (2015)

Bottom Line: Of note, we found that the majority of CMML patients were classified as World Health Organization CMML-1 and that a 7.5% bone marrow blast cut-point may discriminate prognosis with higher resolution in comparison with the existing 10%.Using random forest survival analysis for variable discovery, we demonstrated that the prognostic power of clinical variables alone is limited.Last, we confirmed the independent prognostic relevance of ASXL1 gene mutations and identified the novel adverse prognostic impact imparted by CBL mutations.

View Article: PubMed Central - PubMed

Affiliation: Department of Malignant Hematology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.

ABSTRACT
Since its reclassification as a distinct disease entity, clinical research efforts have attempted to establish baseline characteristics and prognostic scoring systems for chronic myelomonocytic leukemia (CMML). Although existing data for baseline characteristics and CMML prognostication have been robustly developed and externally validated, these results have been limited by the small size of single-institution cohorts. We developed an international CMML data set that included 1832 cases across eight centers to establish the frequency of key clinical characteristics. Of note, we found that the majority of CMML patients were classified as World Health Organization CMML-1 and that a 7.5% bone marrow blast cut-point may discriminate prognosis with higher resolution in comparison with the existing 10%. We additionally interrogated existing CMML prognostic models and found that they are all valid and have comparable performance but are vulnerable to upstaging. Using random forest survival analysis for variable discovery, we demonstrated that the prognostic power of clinical variables alone is limited. Last, we confirmed the independent prognostic relevance of ASXL1 gene mutations and identified the novel adverse prognostic impact imparted by CBL mutations. Our data suggest that combinations of clinical and molecular information may be required to improve the accuracy of current CMML prognostication.

No MeSH data available.


Related in: MedlinePlus

Relative prognostic power of existing CMML models using the entire cohort. ROC curves of all clinical models tested in 1011 evaluable cases in shown for OS (a) and LFS (b) at 36 months. A comparison between the AUC of the ROC curves and the Harrell's C-index is shown in (c). *P<0.05 when comparing AUC of R-IPSS to IPSS.
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fig2: Relative prognostic power of existing CMML models using the entire cohort. ROC curves of all clinical models tested in 1011 evaluable cases in shown for OS (a) and LFS (b) at 36 months. A comparison between the AUC of the ROC curves and the Harrell's C-index is shown in (c). *P<0.05 when comparing AUC of R-IPSS to IPSS.

Mentions: To confirm that existing CMML prognostic models were valid in our merged database, we calculated the prognostic score for the IPSS (n=1599), R-IPSS (n=1618), MD Anderson Scoring System (n=1297), MD Anderson Prognostic Score (n=1584), Dusseldorf Score (n=1234), Mayo (n=1653) and CPSS (n=1281) for each evaluable case. All tested prognostic models were valid and able to predict OS by the KM method and the log-rank test (P<0.0001) (Figure 1). Next, we compared the relative model performance using 1013 complete cases with sufficient data to calculate all risk models using ROC curves and their AUC. ROC curves were calculated for OS at 36 months. The C-index, which evaluates prognostic power across time points, was also used to orthogonally validate the relative prognostic power of each model. The R-IPSS model had the highest AUC (0.694), whereas the Dusseldorf Score model had the lowest (0.635). The difference in AUC between the R-IPSS, IPSS and Dusseldorf Score models was statistically significant (P=0.003), whereas there was no significant difference between any other models tested, suggesting that the majority of models were comparable (Figure 2). Because there was a significant survival difference between MDS-CMML and MPN-CMML, suggesting discordant disease behavior, we parsed our cases by French–American–British category to determine whether a specific model would be superior when considering only these subgroups. However, calculating the AUC of the ROC and the C-index again could not identify a statistically superior model (Figure 3).


An international data set for CMML validates prognostic scoring systems and demonstrates a need for novel prognostication strategies.

Padron E, Garcia-Manero G, Patnaik MM, Itzykson R, Lasho T, Nazha A, Rampal RK, Sanchez ME, Jabbour E, Al Ali NH, Thompson Z, Colla S, Fenaux P, Kantarjian HM, Killick S, Sekeres MA, List AF, Onida F, Komrokji RS, Tefferi A, Solary E - Blood Cancer J (2015)

Relative prognostic power of existing CMML models using the entire cohort. ROC curves of all clinical models tested in 1011 evaluable cases in shown for OS (a) and LFS (b) at 36 months. A comparison between the AUC of the ROC curves and the Harrell's C-index is shown in (c). *P<0.05 when comparing AUC of R-IPSS to IPSS.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4526779&req=5

fig2: Relative prognostic power of existing CMML models using the entire cohort. ROC curves of all clinical models tested in 1011 evaluable cases in shown for OS (a) and LFS (b) at 36 months. A comparison between the AUC of the ROC curves and the Harrell's C-index is shown in (c). *P<0.05 when comparing AUC of R-IPSS to IPSS.
Mentions: To confirm that existing CMML prognostic models were valid in our merged database, we calculated the prognostic score for the IPSS (n=1599), R-IPSS (n=1618), MD Anderson Scoring System (n=1297), MD Anderson Prognostic Score (n=1584), Dusseldorf Score (n=1234), Mayo (n=1653) and CPSS (n=1281) for each evaluable case. All tested prognostic models were valid and able to predict OS by the KM method and the log-rank test (P<0.0001) (Figure 1). Next, we compared the relative model performance using 1013 complete cases with sufficient data to calculate all risk models using ROC curves and their AUC. ROC curves were calculated for OS at 36 months. The C-index, which evaluates prognostic power across time points, was also used to orthogonally validate the relative prognostic power of each model. The R-IPSS model had the highest AUC (0.694), whereas the Dusseldorf Score model had the lowest (0.635). The difference in AUC between the R-IPSS, IPSS and Dusseldorf Score models was statistically significant (P=0.003), whereas there was no significant difference between any other models tested, suggesting that the majority of models were comparable (Figure 2). Because there was a significant survival difference between MDS-CMML and MPN-CMML, suggesting discordant disease behavior, we parsed our cases by French–American–British category to determine whether a specific model would be superior when considering only these subgroups. However, calculating the AUC of the ROC and the C-index again could not identify a statistically superior model (Figure 3).

Bottom Line: Of note, we found that the majority of CMML patients were classified as World Health Organization CMML-1 and that a 7.5% bone marrow blast cut-point may discriminate prognosis with higher resolution in comparison with the existing 10%.Using random forest survival analysis for variable discovery, we demonstrated that the prognostic power of clinical variables alone is limited.Last, we confirmed the independent prognostic relevance of ASXL1 gene mutations and identified the novel adverse prognostic impact imparted by CBL mutations.

View Article: PubMed Central - PubMed

Affiliation: Department of Malignant Hematology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.

ABSTRACT
Since its reclassification as a distinct disease entity, clinical research efforts have attempted to establish baseline characteristics and prognostic scoring systems for chronic myelomonocytic leukemia (CMML). Although existing data for baseline characteristics and CMML prognostication have been robustly developed and externally validated, these results have been limited by the small size of single-institution cohorts. We developed an international CMML data set that included 1832 cases across eight centers to establish the frequency of key clinical characteristics. Of note, we found that the majority of CMML patients were classified as World Health Organization CMML-1 and that a 7.5% bone marrow blast cut-point may discriminate prognosis with higher resolution in comparison with the existing 10%. We additionally interrogated existing CMML prognostic models and found that they are all valid and have comparable performance but are vulnerable to upstaging. Using random forest survival analysis for variable discovery, we demonstrated that the prognostic power of clinical variables alone is limited. Last, we confirmed the independent prognostic relevance of ASXL1 gene mutations and identified the novel adverse prognostic impact imparted by CBL mutations. Our data suggest that combinations of clinical and molecular information may be required to improve the accuracy of current CMML prognostication.

No MeSH data available.


Related in: MedlinePlus