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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

Prognostic significance of genetic data in the international CMML database. The frequency and distribution of mutations is shown using the cbioportal oncoprinter for two clinically relevant subgroups and the number of cases contributed from each center (a and b). The KM survival analysis for (c) ASXL1, (d) CBL, (e) RUNX1, (f) SRSF2, (g) TET2, (h) SETBP1, (i) NRAS, (j) JAK2 and (k) EZH2. The number of evaluable cases for each gene and P-value from log-rank test is shown.
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fig5: Prognostic significance of genetic data in the international CMML database. The frequency and distribution of mutations is shown using the cbioportal oncoprinter for two clinically relevant subgroups and the number of cases contributed from each center (a and b). The KM survival analysis for (c) ASXL1, (d) CBL, (e) RUNX1, (f) SRSF2, (g) TET2, (h) SETBP1, (i) NRAS, (j) JAK2 and (k) EZH2. The number of evaluable cases for each gene and P-value from log-rank test is shown.

Mentions: The genetic landscape and its prognostic relevance have been explored in CMML.25, 26, 27, 28 It is recognized that nonsense and frame-shift mutations of ASXL1 are adversely prognostic, and the presence of these mutations has now been incorporated in two distinct CMML prognostic models.14, 15 As such, we wished to explore the prognostic significance of ASXL1 and other recurrent genetic mutations in our data set. Because sequence practice patterns were different among contributing institutions, we next confirmed whether our combined data reflected that of published cohorts in the literature. To address this, we identified two cohorts of patients across several institutions that were profiled for more than four clinically significant genes as shown in Figure 5. Encouragingly as expected, mutational frequencies and mutual exclusivities in signaling mutations in these representative subgroups were similar to those reported from other published cohorts.7, 12, 28 After confirming this, we explored the prognostic relevance of ASXL1 (n=561), TET2 (n=369), SRSF2 (n=487), RUNX1 (n=377), EZH2 (n=323), NRAS (n=367), CBL (n=374) and JAK2 (n=789) in all evaluable cases comprising the most frequently mutated genes in CMML. In the context of 23 clinical variables, we were able to confirm the known prognosis significance of ASXL1 (P<0.0001) and additionally demonstrated that CBL (P=0.0001) and RUNX1 (P=0.0001) had similar prognostic significance in our data set. After correction for hemoglobin, circulating blasts, platelets and karyotype, we identified ASXL1 (P=0.0114) and CBL (P=0.003) mutations as independently prognostic (Supplementary Table S3).


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)

Prognostic significance of genetic data in the international CMML database. The frequency and distribution of mutations is shown using the cbioportal oncoprinter for two clinically relevant subgroups and the number of cases contributed from each center (a and b). The KM survival analysis for (c) ASXL1, (d) CBL, (e) RUNX1, (f) SRSF2, (g) TET2, (h) SETBP1, (i) NRAS, (j) JAK2 and (k) EZH2. The number of evaluable cases for each gene and P-value from log-rank test is shown.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: Prognostic significance of genetic data in the international CMML database. The frequency and distribution of mutations is shown using the cbioportal oncoprinter for two clinically relevant subgroups and the number of cases contributed from each center (a and b). The KM survival analysis for (c) ASXL1, (d) CBL, (e) RUNX1, (f) SRSF2, (g) TET2, (h) SETBP1, (i) NRAS, (j) JAK2 and (k) EZH2. The number of evaluable cases for each gene and P-value from log-rank test is shown.
Mentions: The genetic landscape and its prognostic relevance have been explored in CMML.25, 26, 27, 28 It is recognized that nonsense and frame-shift mutations of ASXL1 are adversely prognostic, and the presence of these mutations has now been incorporated in two distinct CMML prognostic models.14, 15 As such, we wished to explore the prognostic significance of ASXL1 and other recurrent genetic mutations in our data set. Because sequence practice patterns were different among contributing institutions, we next confirmed whether our combined data reflected that of published cohorts in the literature. To address this, we identified two cohorts of patients across several institutions that were profiled for more than four clinically significant genes as shown in Figure 5. Encouragingly as expected, mutational frequencies and mutual exclusivities in signaling mutations in these representative subgroups were similar to those reported from other published cohorts.7, 12, 28 After confirming this, we explored the prognostic relevance of ASXL1 (n=561), TET2 (n=369), SRSF2 (n=487), RUNX1 (n=377), EZH2 (n=323), NRAS (n=367), CBL (n=374) and JAK2 (n=789) in all evaluable cases comprising the most frequently mutated genes in CMML. In the context of 23 clinical variables, we were able to confirm the known prognosis significance of ASXL1 (P<0.0001) and additionally demonstrated that CBL (P=0.0001) and RUNX1 (P=0.0001) had similar prognostic significance in our data set. After correction for hemoglobin, circulating blasts, platelets and karyotype, we identified ASXL1 (P=0.0114) and CBL (P=0.003) mutations as independently prognostic (Supplementary Table S3).

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