Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes

Abstract

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.

Conflict of interest statement

Manja MeggendorferEmployment: MLL Munich Leukemia Laboratory Marianna RossiConsulting or Advisory Role: Pfizer, Celgene, IQvia, Janssen Emanuele AngelucciHonoraria: Celgene, Vertex Pharmaceuticals Incorporated (MA) and CRISPR Therapeutics AG (CH)Consulting or Advisory Role: Novartis, Bluebird BioTravel, Accommodations, Expenses: Janssen-Cilag Massimo BernardiHonoraria: CelgeneConsulting or Advisory Role: PfizerTravel, Accommodations, Expenses: Medac, Amgen, Sanofi, Jazz Pharmaceuticals, BioTest, Abbvie, Takeda Lorenza BorinLeadership: CelgeneSpeakers' Bureau: GenzymeTravel, Accommodations, Expenses: Genzyme Benedetto BrunoHonoraria: Jazz Pharmaceuticals, Novartis, AmgenResearch Funding: Amgen Valeria SantiniHonoraria: Celgene/Bristol-Myers Squibb, Novartis, Janssen-CilagConsulting or Advisory Role: Celgene/Bristol-Myers Squibb, Novartis, Menarini, Takeda, PfizerResearch Funding: CelgeneTravel, Accommodations, Expenses: Janssen-Cilag, Celgene Andrea BacigalupoHonoraria: Pfizer, Therakos, Novartis, Sanofi, Jazz Pharmaceuticals, Riemser, Merck Sharp & Dohme, Janssen-Cilag, Gilead Sciences, Kiadis Pharma, Astellas PharmaConsulting or Advisory Role: Novartis, Kiadis Pharma, Gilead Sciences, Astellas PharmaSpeakers' Bureau: Pfizer, Therakos, Novartis, Sanofi, Riemser, Merck Sharp & Dohme, Adienne, Jazz PharmaceuticalsTravel, Accommodations, Expenses: Sanofi, Therakos, Jazz Pharmaceuticals Maria Teresa VosoHonoraria: Celgene/Jazz, AbbvieConsulting or Advisory Role: Celgene/JazzSpeakers' Bureau: CelgeneResearch Funding: Celgene Esther OlivaHonoraria: Celgene, Novartis, Amgen, Alexion PharmaceuticalsConsulting or Advisory Role: Amgen, Celgene, NovartisSpeakers' Bureau: Celgene, NovartisPatents, Royalties, Other Intellectual Property: Royalties for QOL-E instrument Francesco PassamontiSpeakers' Bureau: Novartis, AOP Orphan Pharmaceuticals Niccolò BolliConsulting or Advisory Role: JanssenSpeakers' Bureau: Celgene, Amgen Alessandro RambaldiHonoraria: Amgen, OmerosConsulting or Advisory Role: Amgen, Omeros, Novartis, Astellas Pharma, Jazz PharmaceuticalsTravel, Accommodations, Expenses: Celgene Wolfgang KernEmployment: MLL Munich Leukemia LaboratoryLeadership: MLL Munich Leukemia LaboratoryStock and Other Ownership Interests: MLL Munich Leukemia Laboratory Shahram KordastiHonoraria: Beckman Coulter, GWT-TUD, Alexion PharmaceuticalsConsulting or Advisory Role: Syneos HealthResearch Funding: Celgene, Novartis Guillermo SanzHonoraria: CelgeneConsulting or Advisory Role: Abbvie, Celgene, Helsinn Healthcare, Janssen, Roche, Amgen, Boehringer Ingelheim, Novartis, TakedaSpeakers' Bureau: TakedaResearch Funding: CelgeneTravel, Accommodations, Expenses: Celgene, Takeda, Gilead Sciences, Roche Pharma AG Armando SantoroConsulting or Advisory Role: Bristol-Myers Squibb, Servier, Gilead Sciences, Pfizer, Eisai, Bayer AG, MSD, Sanofi, ArQuleSpeakers' Bureau: Takeda, Roche, Abbvie, Amgen, Celgene, AstraZeneca, ArQule, Lilly, Sandoz, Novartis, Bristol-Myers Squibb, Servier, Gilead Sciences, Pfizer, Eisai, Bayer AG, MSD Uwe PlatzbeckerHonoraria: Celgene/JazzConsulting or Advisory Role: Celgene/JazzResearch Funding: Amgen, Janssen, Novartis, BerGenBio, CelgenePatents, Royalties, Other Intellectual Property: part of a patent for a TFR-2 antibody (Rauner et al. Nature Metabolics 2019)Travel, Accommodations, Expenses: Celgene Pierre FenauxHonoraria: CelgeneResearch Funding: Celgene Torsten HaferlachEmployment: MLL Munich Leukemia LaboratoryLeadership: MLL Munich Leukemia LaboratoryConsulting or Advisory Role: IlluminaNo other potential conflicts of interest were reported.

Figures

FIG 1.

(A) Frequency of mutations and chromosomal abnormalities in the EuroMDS cohort (N = 2,043), stratified according to the type of mutation (missense, nonsense, affecting a splice site, or other). Insertions and deletions (del) were categorized according to whether they resulted in a shift in the codon reading frame (by either 1 or 2 base pairs [bp]) or were in frame. Splicing factor genes were the most frequently mutated (49%), followed by DNA methylation–related genes (37.9%), chromatin and histone modifier genes (31.3%), signaling genes (28.5%), transcription regulation genes (24%), tumor suppressor genes (11.1%), and cohesin complex genes (7.6%). (B) Frequency of recurrently mutated genes and chromosomal abnormalities in the EuroMDS cohort, broken down by MDS subtype according to 2016 WHO criteria. (C) VAF of driver mutations in the EuroMDS cohort, broken down by gene and gene function (boxplots reporting median, 25-75 percentiles, and ranges); VAF of X-linked genes (ATRX, BCOR, BCORL1, PHF6, PIGA, SMC1A, STAG2, UTX, and ZRSR2, highlighted by asterisk in the figure plot) was halved in male patients. (D) Relationship between the number of genomic abnormalities (mutations and chromosomal abnormalities) and outcome (overall survival). MDS, myelodysplastic syndromes; MDS 5q-, MDS with isolated deletion of long arm of chromosome 5; MDS-EB1, MDS with excess of blasts, type 1; MDS-EB2, MDS with excess of blasts, type 2; MDS-MLD, MDS with multilineage dysplasia; MDS-RS-MLD, MDS with ring sideroblasts and multilineage dysplasia; MDS-RS-SLD, MDS with ring sideroblasts and single-lineage dysplasia; MDS-SLD, MDS with single-lineage dysplasia; VAF, variant allele frequencies.

FIG 2.

(A) Probability of overall survival after allogeneic transplantation in the EuroMDS cohort. Patients were stratified according to specific genomic features. A total of 424 cases with complete information about transplant procedures and clinical outcome entered the analysis. (B) Comparison of probability of survival among different genomic-based MDS groups (P values of log-rank test were reported). AML, acute myeloid leukemia; MDS, myelodysplastic syndromes.

FIG 3.

Fraction of explained variation that was attributable to different prognostic factors for overall survival.

FIG 4.

Personalized prediction of overall survival using a multistate prognostic model including clinical and genomic features and their interactions in two patients from the EuroMDS cohort (labeled as patient A and patient B), both classified as MDS with multilineage dysplasia according to 2016 WHO classification and belonging to low-risk group according to age-adjusted revised version of International Prognostic Scoring System (IPSS-R). Using currently available prognostication, both patients are predicted to have an indolent clinical course without significant risk of disease evolution and death (in the EuroMDS cohort, Kaplan-Meier curves show a median survival of 79 months for low-risk age-adjusted IPSS-R). When looking at mutational profile, driver mutations involved different splicing factor genes in these patients: patient A carries SF3B1 mutation, whereas patient B presents SRSF2 mutation. We then calculated expected survival by using the novel genomic-based prognostic model (exponential survival curves are reported in the figure). Patient A was classified into genomic-based group 6, and patient B was classified into group 5. Accordingly, the estimation of life expectancy is now significantly different in these two patients, as underlined by the slope of the two exponential curves. The model predicts a better probability of survival for patient A (with SF3B1 mutation) with respect to patient B (with SRSF2 mutation), thus reflecting more precisely the observed clinical outcome. In fact, patient B died 16 months after the diagnosis as a result of leukemic evolution, whereas patient A was still alive without evidence of disease progression after 60 months of follow-up. IPSS-R fails to capture such a difference in clinical outcome. The interpretation of the predicted survival curves by genomic-based predictive model is meaningful also considering that we are in the context of a cohort of elderly patients: patient A (age 78 years) has a 30% survival probability at the age of 80, whereas patient B (age 73 years) has a 30% survival probability at the age of 74.

FIG A1.

Genomic groups in EuroMDS cohort (N = 2,043) and their relationship with WHO category (defined according to 2016 classification criteria) and overall survival. According to a Bayesian clustering algorithm (Dirichlet processes), patients are classified into eight distinct genomic groups on the basis of the presence or specific mutations and/or chromosomal abnormalities: Group 0, MDS without specific genomic profile; Group 1, MDS with SF3B1 mutations and co-existing mutations in other genes (ASXL1 and RUNX1); Group 2, MDS with TP53 mutations and/or complex karyotype; Group 3, MDS with SRSF2 and concomitant TET2 mutations; Group 4, MDS with U2AF1 mutations associated with deletion of chromosome 20q and/or abnormalities of chromosome 7; Group 5, MDS with SRSF2 mutations with co-existing mutations in other genes (ASXL1, RUNX1, IDH2, and EZH2); Group 6, MDS with isolated SF3B1 mutations (or associated with mutations of TET2 and/or JAK/STAT pathways genes); Group 7, MDS with AML-like mutation patterns (DNMT3A, NPM1, FLT3, IDH1, and RUNX1 genes). These genomic MDS groups significantly differ in WHO MDS categories distribution and in cumulative probability of survival. AML, acute myeloid leukemia; BM, bone marrow; MDS, myelodysplastic syndromes; MDS-EB1, MDS with excess of blasts, type 1; MDS-EB2, MDS with excess of blasts, type 2; MDS-MLD, MDS with multilineage dysplasia; MDS-RS-MLD, MDS with ring sideroblasts and multilineage dysplasia; MDS-RS-SLD, MDS with ring sideroblasts and single-lineage dysplasia; MDS-SLD, MDS with single-lineage dysplasia; PB, peripheral blood; WHO, World Health Organization.

FIG A2.

Extrapolation of genomic landscape of MDS genomic groups through Bayesian Networks, applied to the whole MDS cohort. The size of each node accounts for the number of correspondent genomic or cytogenetic alterations. The color of each link reflects odds ratio (shades of brown represent mutual exclusivity while shades of green color degree co-occurrence). The thickness of edges grows with increasing significance of mutual exclusivity/co-occurrence between alterations. MDS, myelodysplastic syndromes.

FIG A3.

Wide-ranging genomic heterogeneity of 2016 WHO categories within MDS genomic groups. MDS, myelodysplastic syndromes; MDS-EB1, MDS with excess of blasts, type 1; MDS-EB2, MDS with excess of blasts, type 2; MDS-MLD, MDS with multilineage dysplasia; MDS-RS-MLD, MDS with ring sideroblasts and multilineage dysplasia; MDS-RS-SLD, MDS with ring sideroblasts and single-lineage dysplasia; MDS-SLD, MDS with single-lineage dysplasia.

FIG A4.

Diagram to correctly classify MDS patients into the appropriate genomic group according to individual profile. AML, acute myeloid leukemia; MDS, myelodysplastic syndromes.