No abstract

The International Prognostic Index (IPI) has been the basis for predicting the outcome of diffuse large B cell lymphoma (DLBCL). With the improvement in prognosis with immunochemotherapy the predictive power of the IPI has been questioned. Recently, the enhanced NCCN-IPI has been proposed to provide a better discrimination of risk groups. Additionally, other patient- and disease-specific factors not incorporated in the prognostic algorithms and easily assessed at diagnosis have been shown to impact on the clinical course. The prognostic value of the NCCN-IPI together with other clinical characteristics remains to be widely validated. We aimed to assess the power of NCCN-IPI in risk group discrimination and compare it with IPI, as well as to investigate if the Body Mass Index (BMI), gender, diameter of the largest mass and levels of β2-microglobulin have independent prognostic impact when controlling for NCCN-IPI risk. We conducted a retrospective analysis of consecutive R-CHOP treated de novo DLBCL cases diagnosed between January 2002 and December 2013 in a single center. Kaplan-Meier method was used to estimate the overall (OS) and progression-free survival (PFS) distributions for risk groups. Logrank test with p-value adjustment by Hochberg method was used for pairwise comparisons between risk groups within each index. Independent prognostic impact of BMI, gender, bulky disease and β2-microglobulin was assessed by multivariate analysis using Cox model. 410 patients were included in the analysis, with a median age of 64 (16-89) years; 51% were male. The distribution according to age, stage, extranodal (EN) sites, performance status, β2-microglobulin, BMI and bulky disease (Table 1) were similar to published data. With a median follow-up of 59 months, 5-yr OS and PFS were 69% and 65%, respectively. The distribution of patients according to IPI and NCCN-IPI risk groups is detailed in Table 1. Both scores adequately stratified patients according to risk of progression and death. In our cohort OS and PFS of low and low-intermediate risk groups using the NCCN-IPI were not significantly different. We documented a migration of patients from low (62%) and high (48%) to intermediate risk groups using the NCCN-IPI compared to the IPI. This score allowed a better discrimination between high-intermediate and high-risk disease (pairwise comparison of OS and PFS within NCCN and IPI risk scores of p=0.0087 vs p=0.044 and p=0.032 vs p=0.096, respectively). Only 12 patients classified as intermediate-risk IPI migrated to high NCCN risk group. Of these, 5 relapsed and 6 died (4 of lymphoma). Multivariate analysis showed that bulky disease and β2-microglobulin are independent prognostic factors for OS and PFS when controlling for NCCN-IPI. Using a large series of unselected R-CHOP treated patients, we pursued the validation of the NCCN-IPI as a better prognostic tool for DLBCL. In contrast to the original series, the NCCN-IPI showed limited discrimination capacity for OS and PFS between low- and low-intermediate risk groups. However, the new prognostic index helps to identify particularly poor prognosis patients that might benefit from alternative therapies. We also identified bulky disease and β2-microglobulin as independent prognostic factors in addition to NCCN-IPI and that could be incorporated in future prognostic scores. Additional studies are needed to evaluate the ability of biomarkers to improve the clinical risk stratification systems. Table 1.CharacteristicsValue, n (%)AgeMedian (range), yr64 (16-89)≥60 years241 (59%)Male208 (51%)Performance status (ECOG) ≥2a66 (16%)Stage ≥ III221 (54%)BMIbUnderweight (<18.5)15 (4%)Normal (18.5-24.9)170 (43%)Overweight (25.0-29.9)139 (35%)Obese (>29.9)73 (18%)Bulky disease (>7 cm)c157 (40%)β2 microglobulin >ULNd154 (45%)≥2 EN sites92(23%)BM, CNS, liver/GI or lung involvement136 (33%)IPIe vs NCCN-IPIe (%)Low12 vs 34Low-Int38 vs 26High-Int35 vs 20High18 vs 125-yr OS (%)IPI (low, low-int, high-int, high)87, 70, 59, 50p= 8x10-12NCCN-IPI (low, low-int, high-int, high)88, 82, 59, 47p= 2x10-125-yr PFS (%)IPI (low, low-int, high-int, high)86, 66, 51, 48p= 2x10-11NCCN-IPI (low, low-int, high-int, high)84, 78, 54, 45p= 7x10-10BM: bone marrow, CNS: central nervous system, GI: gastrointestinal, int: intermediate.a: 405 pts, b: 397 pts, c: 391 pts, d: 341 pts, e: 403 pts. Disclosures No relevant conflicts of interest to declare.

Although mutation profiling of defined genes is recommended for classification of acute myeloid leukemia (AML) patients, screening of targeted gene panels using next-generation sequencing (NGS) is not always routinely used as standard of care. The objective of this study was to prospectively assess whether extended molecular monitoring using NGS adds clinical value for risk assessment in real-world AML patients. We analyzed a cohort of 268 newly diagnosed AML patients. We compared the prognostic stratification of our study population according to the European LeukemiaNet recommendations, before and after the incorporation of the extended mutational profile information obtained by NGS. Without access to NGS data, 63 patients (23%) failed to be stratified into risk groups. After NGS data, only 27 patients (10%) failed risk stratification. Another 33 patients were re-classified as adverse-risk patients once the NGS data was incorporated. In total, access to NGS data refined risk assessment for 62 patients (23%). We further compared clinical outcomes with prognostic stratification, and observed unexpected outcomes associated with FLT3 mutations. In conclusion, this study demonstrates the prognostic utility of screening AML patients for multiple gene mutations by NGS and underscores the need for further studies to refine the current risk classification criteria.

Standard approach to advanced stage FL patients in need of treatment is immunochemotherapy followed by rituximab maintenance (RM). Maintenance improves disease control after first line and overall survival in the relapse setting. The relative benefits of maintenance after first or subsequent treatment have not been directly compared. Purpose: To compare the clinical outcome of patients receiving RM after first-line treatment with those who received RM for the first time after second line. Patients and Methods: We retrospectively analyzed the outcomes (time to next treatment, TTNT, and progression free survival, PFS) of a single center series of advanced FL patients in remission after first or second treatment who received RM. Demographic and clinical characteristics before first or second induction (age, gender, FLIPI and FLIPI2) as well as induction chemotherapy, response rate, PFS and TTNT were retrieved from clinical charts. Patient characteristics at the beginning of induction were compared by Chi-squared test and time-to-event outcomes evaluated by the Kaplan Meyer method and compared by log rank test, with and without stratification for FLIPI; we calculated the adjusted hazard ratio controlled for FLIPI using Cox regression; p‹0.05 was considered significant. Results: Among 371 advanced FL patients diagnosed and treated in a single center between 2001 and 2013, we identified 83 (45% male) who responded to first or second induction and received RM between 2005 and 2015 (59 after first line - M1 group - and 24 after second line - M2 group; 375 mg/m2 every 2 or 3 months for 2 years, respectively). M2 patients were older than M1 (median age 63 versus 55 yo). Other characteristics before induction were similar between the two groups, including FLIPI (low 14% and 13%, intermediate 42% and 33%, high 42% and 42% for M1 and M2, respectively; p=0.93) and FLIPI2 risk group distribution (low 10% and 4% , intermediate 49% and 38%, high 36% and 38% for M1 and M2, respectively; p=0.62). Median time from diagnosis was 5.1 years in M2 patients; M1 patients were treated at a median of 1.9 months after diagnosis. First line treatment in the M1 group was mostly RCHOP (76%) while similar proportions of M2 pts received RCHOP (38%) and RCVP (46%) as second line. 20/24 of M2 pts (83%) had received Rituximab as part of first line treatment. CR/CRu rates after induction were comparable (48% and 42% in M1 and M2 pts respectively; p=0.17). After maintenance, with 13 pts not yet evaluable for response, ORR was 68% in M1 and 50% in M2 (p= 0.15). The frequency of CR/CRu was similar for first and second line patients (43% and 42% in M1 and M2, respectively) with 10/31 (32%) and 4/11 (36%) PR patients converting to CR after M1 and M2, respectively. At a median follow-up of 2.3 in M1 and 2.5 years in M2, relapse/progression occurred in 16 (27%) and 8 (33%) patients, respectively. A new treatment was started in 19% and 29% of patients in the M1 and M2 groups. Accordingly, TTNT after M1 (not reached) was significantly longer than after M2 (33.2 months), p=0.04. This difference was not significant in stratified analysis by FLIPI. However, when controlling for FLIPI, the hazard of subsequent therapy in M2 was twice as M1 (HR=2.1; 95% CI: 0.8-5.4). Two-year PFS was also superior in M1 (80% [95% CI 70-92%] versus 62% [95% CI 44-87%] in M2) although not statistically significant (p= 0.19). Indeed, there was a 50% increase in the risk of progression or death adjusted for FLIPI in M2 compared to M1 (HR=1.5; 95%CI: 0.6-3.8). Conclusion: In this series, although patients receiving RM both after first and second induction therapy benefited from the treatment, longer treatment-free intervals and disease control were seen in the first line setting. The benefits of repeating Rituximab maintenance have not been prospectively evaluated; in countries where economic constraints impose restrictions to its repeated use, our results suggest that advanced FL patients should preferentially receive RM after first line. Disclosures Silva: Roche Pharmaceutics: Consultancy.