Key Points• Independent prognostic impact of biological markers, notably TP53 and SF3B1 mutations, in CLL patients requiring therapy.• NOTCH1 mutation as a predictive factor for reduced benefit from the addition of rituximab to FC chemotherapy.Mutations in TP53, NOTCH1, and SF3B1 were analyzed in the CLL8 study evaluating firstline therapy with fludarabine and cyclophosphamide (FC) or FC with rituximab (FCR) among patients with untreated chronic lymphocytic leukemia (CLL). TP53, NOTCH1, and SF3B1 were mutated in 11.5%, 10.0%, and 18.4% of patients, respectively. NOTCH1 mut and SF3B1 mut virtually showed mutual exclusivity (0.6% concurrence), but TP53 mut was frequently found in NOTCH1 mut (16.1%) and in SF3B1 mut (14.0%) patients. There were few significant associations with clinical and laboratory characteristics, but genetic markers had a strong influence on response and survival. In multivariable analyses, an independent prognostic impact was found for FCR, thymidine kinase (TK) ‡10 U/L, unmutated IGHV, 11q deletion, 17p deletion, TP53 mut , and SF3B1 mut on progression-free survival; and for FCR, age ‡65 years, Eastern Cooperative Oncology Group performance status ‡1, b2-microglobulin ‡3.5 mg/L, TK ‡10 U/L, unmutated IGHV, 17p deletion, and TP53 mut on overall survival. Notably, predictive marker analysis identified an interaction of NOTCH1 mutational status and treatment in that rituximab failed to improve response and survival in patients with NOTCH1 mut . In conclusion, TP53 and SF3B1 mutations appear among the strongest prognostic markers in CLL patients receiving current-standard first-line therapy. NOTCH1 mut was identified as a predictive marker for decreased benefit from the addition of rituximab to FC. This study is registered at www.clinicaltrials.gov as #NCT00281918. (Blood. 2014;123(21):3247-3254)

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.

433 Novel gene mutations have been found in CLL by next generation sequencing including mutations of NOTCH1 and SF3B1 in 5–20% of cases. In initial studies, both have been associated with advanced disease and poor outcome. We assessed the incidence and impact of gene mutations in the CLL8 trial (1st line FC vs. FCR, n=817). TP53 (exons 2–11) was analyzed by a re-sequencing chip (Amplichip, Roche Molecular Systems) with confirmatory Sanger sequencing. NOTCH1 was analyzed by Sanger sequencing exon 34, chr9:139,390,619–139,391,290 (PEST domain). SF3B1 (exons 13–16) was analyzed by DHPLC (WAVE® 3500HT, Transgenomic Inc.) with subsequent Sanger sequencing. Baseline samples were available for analysis of genetic markers in 619 (75.8%) to 645 (78.9%) patients. All markers were available for 573 (70.1%) patients and this cohort was representative of the full trial population. Mutations (mut) were found in TP53, NOTCH1, and SF3B1 in 11.5%, 10.0%, and 18.4%, respectively. At least one mutation was identified in 35.2% patients, while 30.6% had one, 4.4% had two and 0.2% had three mutations. Concurrent NOTCH1mut and SF3B1mut were found in only 0.5% patients. TP53mut was observed in 16.7% of NOTCH1mut cases (p=.528) and in 14.5% of SF3B1mut patients (p=.472). Regarding baseline characteristics, there were significant associations of TP53mut with CIRS>1, unmutated IGHV and 17p-; of NOTCH1mut with Binet A/B, no B-symptoms, unmutated IGHV, and 17p-; and of SF3B1mut with TK>10, and no +12. Regarding response to therapy, TP53mut was significantly associated with refractory disease in both arms (FCR: 25.0% vs. 1.8%, p<.001, FC: 48.4% vs. 7.8%, p<.001,); while NOTCH1mut showed only a trend in the FCR arm (FCR: 10.9% vs. 3.4%, p=.109, FC: 11.9% vs. 12.9%, p=.775); and SF3B1mut did not impact response to therapy (FCR: 3.6% vs. 3.7%, p=1.00, FC: 12.3% vs. 10.9%, p=1.00). At extended follow-up (median 69.97 months), FCR resulted into significantly improved PFS (HR 0.586, p<.001) and OS (HR 0.678, p=.001). TP53mut was associated in both treatment arms with significantly decreased PFS (FC: HR 4.295, p<.001; FCR: HR 3.173 p<.001) and OS (FC: HR 4.642 p<.001; FCR: HR 4.447, p<.001). In contrast, NOTCH1mut was only in the FCR arm associated with significantly decreased PFS (FC: HR 0.931, p=.741; FCR: HR 1.718, p=.013) and a trend to inferior OS (FC: HR 0.854, p=.605; FCR: HR 1.610, p=.112). SF3B1mut was associated in both treatment arms with significantly decreased PFS (FC: HR 1.520, p=.009; FCR: HR 1.463, p=.033) and a trend to inferior OS (FC: HR 1.338, p=.178; FCR: HR 1.305, p=.301). To evaluate the independent prognostic impact, we performed multivariable analyses by Cox regression for PFS and OS including the following variables: treatment, age, sex, stage, ECOG status, B-symptoms, WBC, TK, β2-MG, 11q-, +12, 13q-, 17p-, IGHV, TP53, NOTCH1 and SF3B1. Regarding PFS, the following independent prognostic factors were identified: FCR (HR 0.510, p<.001), TK>10 (HR 1.367, p=.019), IGHV<98% (HR 1.727, p<.001), 11q- (HR 1.536, p<.001), 17p- (HR 2.949 p<.001), TP53mut (HR 2.113 p<.001), and SF3B1mut (HR 1.348, p=.024). Regarding OS, the following independent prognostic factors were identified: FCR (HR 0.701, p=.049), ECOG>0 (HR 2.202, p<.001), TK>10 (HR 2.707, p<.001), IGHV<98% (HR 1.547, p=.055), 17p- (HR 3.546 p<.001) and TP53mut (HR 3.032 p<.001). To identify a predictive impact of gene mutations for a specific treatment effect by the addition of rituximab, we performed multivariable analyses including the treatment arms, the gene mutations and the interaction of both. Regarding PFS, FCR (HR 0.544, p<.001), TP53mut (HR 3.607, p<.001), SF3B1mut (HR 1.355, p=.012) and NOTCH1mut interaction with FCR (HR 1.652, p=.022) were identified as independent factors. Regarding OS, FCR (HR 0.654, p=.002) and TP53mut (HR 4.470, p<.001) were identified as independent factors while NOTCH1mut interaction with FCR (HR 1.331, p=.344) showed a trend. The interaction between NOTCH1mut and FCR treatment is illustrated in univariate PFS analysis, in which the addition of rituximab led to a benefit only among patients without NOTCH1mut (Figure). In conclusion, gene mutations show independent prognostic value for PFS (TP53, SF3B1) and OS (TP53) in patients receiving 1st line FC and FCR treatment. Of note, NOTCH1mut appears to identify a subset of CLL patients that does not benefit from the addition of rituximab to FC. Disclosures: Stilgenbauer: Roche: Consultancy, Honoraria, Research Funding. Patten:Roche: Employment. Wenger:Roche: Employment. Mendila:Roche: Employment. Hallek:Roche: Consultancy, Honoraria, Research Funding.