The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes
Open accessAbstract
PURPOSE: Recurrently mutated genes and chromosomal abnormalities have been identified in myelodysplastic syndromes (MDS). We aim to integrate these genomic features into disease classification and prognostication. METHODS: We retrospectively enrolled 2,043 patients. Using Bayesian networks and Dirichlet processes, we combined mutations in 47 genes with cytogenetic abnormalities to identify genetic associations and subgroups. Random-effects Cox proportional hazards multistate modeling was used for developing prognostic models. An independent validation on 318 cases was performed. RESULTS: We identify eight MDS groups (clusters) according to specific genomic features. In five groups, dominant genomic features include splicing gene mutations ( SF3B1, SRSF2, and U2AF1) that occur early in disease history, determine specific phenotypes, and drive disease evolution. These groups display different prognosis (groups with SF3B1 mutations being associated with better survival). Specific co-mutation patterns account for clinical heterogeneity within SF3B1- and SRSF2-related MDS. MDS with complex karyotype and/or TP53 gene abnormalities and MDS with acute leukemia–like mutations show poorest prognosis. MDS with 5q deletion are clustered into two distinct groups according to the number of mutated genes and/or presence of TP53 mutations. By integrating 63 clinical and genomic variables, we define a novel prognostic model that generates personally tailored predictions of survival. The predicted and observed outcomes correlate well in internal cross-validation and in an independent external cohort. This model substantially improves predictive accuracy of currently available prognostic tools. We have created a Web portal that allows outcome predictions to be generated for user-defined constellations of genomic and clinical features. CONCLUSION: Genomic landscape in MDS reveals distinct subgroups associated with specific clinical features and discrete patterns of evolution, providing a proof of concept for next-generation disease classification and prognosis.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Arber, D. A., Orazi, A., Hasserjian, R., Thiele, J., Borowitz, M. J., Le Beau, M. M., Bloomfield, C. D., Cazzola, M., & Vardiman, J. W. (2016) · Blood · doi:10.1182/blood-2016-03-643544 · Cited by 10,119 · cited 5×
“Despite recent progress in understanding the disease biology, MDS with isolated 5q deletion is the only category defined by a specific genomic abnormality in theWHO classification(2) and only few genotypephenotype associations have been reported until now, mainly referring to the close relationship betweenmutations in…”
BackgroundMethodInfluentialGenomic classification and prognosis in acute myeloid leukemia
Papaemmanuil, E., Gerstung, M., Bullinger, L., Gaidzik, V. I., Paschka, P., Roberts, N. D., Potter, N. E., Heuser, M., Thol, F., Bolli, N., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1516192 · Cited by 4,350
Self-citationFrequent pathway mutations of splicing machinery in myelodysplasia
Yoshida, K., Sanada, M., Shiraishi, Y., Nowak, D., Nagata, Y., Yamamoto, R., Sato, Y., Sato-Otsubo, A., Kon, A., Nagasaki, M., et al. (2011) · Nature · doi:10.1038/nature10496 · Cited by 2,029
Self-citationSomatic Mutations of Calreticulin in Myeloproliferative Neoplasms
Klampfl, T., Gisslinger, H., Harutyunyan, A. S., Nivarthi, H., Rumi, E., Milosevic, J. D., Them, N. C. C., Berg, T., Gisslinger, B., Pietra, D., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1311347 · Cited by 2,003
Landscape of genetic lesions in 944 patients with myelodysplastic syndromes
Haferlach, T., Nagata, Y., Grossmann, V, Okuno, Y., Bacher, U., Nagae, G., Schnittger, S., Sanada, M., Kon, A., Alpermann, T., et al. (2014) · Leukemia · doi:10.1038/leu.2013.336 · Cited by 1,532
Self-citationSomatic SF3B1 Mutation in Myelodysplasia with Ring Sideroblasts
Papaemmanuil, E., Cazzola, M., Boultwood, J., Malcovati, L., Vyas, P., Bowen, D., Pellagatti, A., Wainscoat, J. S., Hellstrom-Lindberg, E., Gambacorti-Passerini, C., et al. (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1103283 · Cited by 1,222
Self-citationPrognostic Mutations in Myelodysplastic Syndrome after Stem-Cell Transplantation
Lindsley, R. C., Saber, W., Mar, B. G., Redd, R., Wang, T., Haagenson, M. D., Grauman, P. V., Hu, Z.-H., Spellman, S. R., Lee, S. J., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1611604 · Cited by 776
for the International Working Group for MDS Molecular Prognostic Committee, Haase, D., Stevenson, K. E., Neuberg, D., Maciejewski, J. P., Nazha, A., Sekeres, M. A., Ebert, B. L., Garcia-Manero, G., Haferlach, C., et al. (2019) · Leukemia · doi:10.1038/s41375-018-0351-2 · Cited by 259
Self-citation
Cited by
Arber, D. A., Orazi, A., Hasserjian, R. P., Borowitz, M. J., Calvo, K. R., Kvasnicka, H.-M., Wang, S. A., Bagg, A., Barbui, T., Branford, S., et al. (2022) · Blood · doi:10.1182/blood.2022015850 · Cited by 2,945
Self-citationMolecular International Prognostic Scoring System for Myelodysplastic Syndromes
Bernard, E., Tuechler, H., Greenberg, P. L., Hasserjian, R. P., Arango Ossa, J. E., Nannya, Y., Devlin, S. M., Creignou, M., Pinel, P., Monnier, L., et al. (2022) · NEJM Evidence · doi:10.1056/evidoa2200008 · Cited by 867
Self-citationNovel scheme for defining the clinical implications of TP53 mutations in myeloid neoplasia
Bahaj, W., Kewan, T., Gurnari, C., Durmaz, A., Ponvilawan, B., Pandit, I., Kubota, Y., Ogbue, O. D., Zawit, M., Madanat, Y., et al. (2023) · Journal of Hematology & Oncology · doi:10.1186/s13045-023-01480-y · Cited by 45
Self-citationStahl, M., & Zeidan, A. M. (2026) · Blood · doi:10.1182/blood.2025029727 · Cited by 14
Metadata
- Authors
- Bersanelli, M., Travaglino, E., Meggendorfer, M., Matteuzzi, T., Sala, C., Mosca, E., Chiereghin, C., Di Nanni, N., Gnocchi, M., Zampini, M., Rossi, M., Maggioni, G., Termanini, A., Angelucci, E., Bernardi, M., Borin, L., Bruno, B., Bonifazi, F., Santini, V., Bacigalupo, A., Voso, M. T., Oliva, E., Riva, M., Ubezio, M., Morabito, L., Campagna, A., Saitta, C., Savevski, V., Giampieri, E., Remondini, D., Passamonti, F., Ciceri, F., Bolli, N., Rambaldi, A., Kern, W., Kordasti, S., Sole, F., Palomo, L., Sanz, G., Santoro, A., Platzbecker, U., Fenaux, P., Milanesi, L., Haferlach, T., Castellani, G., & Della Porta, M. G.
- Venue
- Journal of Clinical Oncology
- Published
- 2021-04-10
- Volume
- 39
- Issue
- 11
- Pages
- 1223-1233
- ISSN
- 0732-183X (ISSN-L)
- 1527-7755
- OpenAlex
- W3127892484
- Cited by
- 222
- References
- 8
- FWCI
- 22.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes. https://app.cochat.ai/papers/10.1200/jco.20.01659
- The paper
- Bersanelli, M., Travaglino, E., Meggendorfer, M., Matteuzzi, T., Sala, C., Mosca, E., Chiereghin, C., Di Nanni, N., Gnocchi, M., Zampini, M., Rossi, M., Maggioni, G., Termanini, A., Angelucci, E., Bernardi, M., Borin, L., Bruno, B., Bonifazi, F., Santini, V., . . . Della Porta, M. G. (2021). Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes. Journal of Clinical Oncology. https://doi.org/10.1200/jco.20.01659
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
