Londhe, Anil

Abstract Background Patients with high-risk, newly diagnosed multiple myeloma (HR-NDMM) who are ineligible for autologous stem cell transplant (ASCT) have limited first-line treatment options. Recent meta-analyses evaluating the impact of incorporating daratumumab in the backbone regimen on progression-free survival (PFS) have found mixed results in these patients. Materials and Methods A pooled analysis of patient-level data for ASCT-ineligible patients with HR-NDMM [ie, del(17p), t(4;14), t(14;16)] from the MAIA and ALCYONE trials; stratified by study identifier and adjusting for cytogenetic abnormality subtype, baseline performance status, International Staging System stage, myeloma type, and renal impairment; was conducted. Impact of daratumumab on PFS and rates of complete response or better (≥CR), minimal residual disease (MRD)-negative CR, very good partial response or better (≥VGPR), and overall response (ORR) was compared to control. Results Among 101 patients in the daratumumab and 89 patients in the control cohort, median follow-up was 43.7 months. Daratumumab reduced the risk of progression or death by 41% (adjusted hazard ratio for PFS [95% confidence interval (CI)] = 0.59 [0.41-0.85]) versus control. At 36 months, the estimated proportion of patients who did not progress and were still alive was 41.3% in the daratumumab and 19.9% in the control cohort. Rates of ≥CR (41.6% vs. 22.5%), MRD-negative CR (24.8% vs. 5.6%), ≥VGPR (75.2% vs. 46.1%), and ORR (92.1% vs. 74.2%) were higher for daratumumab versus control. Conclusion These findings demonstrate that incorporation of daratumumab in frontline treatment regimens reduced the risk of progression or death and improved response rates among ASCT-ineligible HR-NDMM patients.

Abstract Introduction: Several prognostic models for overall survival (OS) in chronic lymphocytic leukemia (CLL) have been developed, but none were derived for patients (pts) with relapsed or refractory (R/R) CLL treated in the era of novel agents. We used a comprehensive approach to derive and validate a novel risk model for OS in 2,475 pts with R/R CLL who received therapy in randomized phase 3 trials evaluating ibrutinib (IBR), idelalisib (IDELA), and venetoclax (VEN) vs control arms, or the Mayo Clinic CLL Database (MCCD). Methods: In collaboration with Janssen, Gilead, Pharmacyclics, and Genentech/Roche, the analysis included 2,475 pts from 6 multicenter randomized phase 3 trials or the MCCD. Trials included were: IBR plus bendamustine-rituximab (BR) vs placebo plus BR (HELIOS: NCT01611090), IBR vs ofatumumab (RESONATE: NCT01578707), IDELA plus BR vs placebo plus BR (Study 115: NCT01569295); IDELA plus rituximab vs placebo plus rituximab (Study 116: NCT01539512); IDELA plus ofatumumab vs ofatumumab (Study 119: NCT01659021); VEN plus rituximab vs BR (MURANO: NCT02005471). All pts had R/R CLL and required treatment by iwCLL criteria. The model-building dataset (n=969) included pts from HELIOS and RESONATE, and was randomly assigned to the IBR/chemoimmunotherapy (CIT) training dataset (n=727) and IBR/CIT internal-validation dataset(n=242). Three independent external validation datasets included IDELA/CIT (n=897), VEN/CIT (n=389), and MCCD (n=220). We applied univariate and multivariate analyses (MVA) to 28 candidate clinical/laboratory and genetic prognostic factors to derive the risk model in the training dataset. The primary endpoint was OS. We assessed the need for separate models for targeted agents by interactions with treatment in univariate analyses. We proceeded to develop a single model for all pts with R/R CLL, as only one candidate factor (del(13q), interaction p value <0.1) demonstrated a marginal differential effect. We evaluated cutoffs for dichotomized covariates and for definition of risk groups after the final factors were selected. We fit a Cox regression on the training and internal/external validation datasets using the risk categories as the covariate to test the difference among the groups, and calculated the C-statistic as a measure of discrimination. Results: Of 11 selected factors with a significant univariate effect on OS (p<0.1), 6 were independently prognostic in the MVA of OS (p<0.05): Baseline β2-microglobubulin (B2M), lactate dehydrogenase (LDH), hemoglobin (HGB), IGHV mutational status, number of prior therapies, and time from initiation of last therapy. We excluded number of prior therapies from the model, reasoning that its optimal cutoff might change with advances in CLL therapies. IGHV did not remain significant (p=0.0592) in MVA of 5 remaining prognostic factors. Notably, del(17p) was not independently prognostic for OS. The final risk model consisted of 4 prognostic factors: B2M ≥5 mg/L, LDH elevated, HGB <110 mg/L for women or <120 mg/L for men, and time from initiation of last therapy <24 months. Because hazard ratios for each factor were similar (range, 0.55-0.68) with overlapping confidence intervals, 1 point was assigned to each factor. The resulting score separated pts into low (score 0-1), intermediate (score 2-3), and high risk (score 4) groups. Our risk model was prognostic for OS in the IBR/CIT training dataset (C-statistic 0.74 [95% CI 0.60-0.85]; log-rank p<0.0001) and was validated in the IBR/CIT internal validation (C-statistic 0.79 [95% CI 0.56-0.97]; log-rank p<0.0001), as well as in all 3 external validations: IDELA/CIT dataset (CS=0.71, 95% CI 0.59-0.81; log-rank p<0.001); VEN/CIT dataset (CS=0.76, 95% CI 0.66-0.85; log-rank p=0.01); MCCD (CS=0.61, 95% CI 0.56-0.66; log-rank p<0.001). Conclusions: We present the first validated risk model for OS in R/R CLL in the era of targeted therapies. Our model consists of 4 readily available factors (B2M ≥5 mg/L, LDH elevated, HGB <110 mg/L for women or <120 mg/L for men, and time from initiation of last therapy <24 months), identifies 3 prognostic groups with significantly different OS, and has been validated for pts treated with all FDA approved targeted therapies: IBR, IDELA, and VEN. This identifies a well-defined cohort of pts with R/R disease with an unmet clinical need who are suitable for prospective trials targeting these pts. An exploratory analysis regarding prediction of MRD is ongoing. Figure. Figure. Disclosures Darif: Jannsen: Employment. Londhe:Jannsen: Employment. Xing:Gilead Sciences, Inc.: Employment. Mun:Genentech: Employment, Equity Ownership. Kay:Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Acerta: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees. Shanafelt:Mayo Clinic: Patents & Royalties: Physician Well-being Index, Medical Student Well-being Index, Well-being index; GlaxoSmithKline: Research Funding; Genentech: Research Funding; Pharmacyclics: Research Funding; Janssen: Research Funding; Abbvie: Research Funding; Celgene: Research Funding. Furman:Loxo Oncology: Consultancy; Pharmacyclics LLC, an AbbVie Company: Consultancy; Sunesis: Consultancy; TG Therapeutics: Consultancy; Verastem: Consultancy; Gilead: Consultancy; Incyte: Consultancy, Other: DSMB; Acerta: Consultancy, Research Funding; Genentech: Consultancy; Janssen: Consultancy; AbbVie: Consultancy. Hillmen:Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Pharmacyclics: Research Funding; Alexion Pharmaceuticals, Inc: Consultancy, Honoraria; Novartis: Research Funding; Gilead Sciences, Inc.: Honoraria, Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Acerta: Membership on an entity's Board of Directors or advisory committees. Sharman:Acerta: Consultancy, Research Funding; Pharmacyclics, an AbbVie Company: Consultancy, Research Funding. Seymour:AbbVie: Consultancy, Honoraria, Research Funding; Celgene: Consultancy; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Genentech Inc: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Research Funding. Jones:Celgene: Employment, Equity Ownership. Ferrante:Jannsen: Employment. Dreiling:Gilead: Employment. Mobasher:F. Hoffmann-La Roche Ltd: Other: Ownership interests non-PLC; Genentech Inc: Employment. Stark:Genentech: Employment. Reddy:Actinium Pharmaceuticals: Employment, Equity Ownership; Pharmacyclics: Employment. Howes:Janssen: Employment. James:Pharmacyclics: Employment. Bhargava:Gilead: Employment. Zelenetz:Novartis/Sandoz: Consultancy; Abbvie: Research Funding; Celgene: Consultancy; Amgen: Consultancy; Gilead: Consultancy, Research Funding; AstraZeneca: Consultancy; Genentech/Roche: Consultancy, Research Funding.

Abstract Background: Patients (pts) with CLL/SLL who use unmutated IGHV (u-CLL) have a less favorable outcome with standard chemotherapy than pts who use mutated IGHV (m-CLL). Ibrutinib (ibr) inhibits B-cell receptor signaling through Bruton’s tyrosine kinase and has robust clinical activity against CLL. This integrated analysis of 3 phase 3 studies examined the impact of IGHV status on ibr- and comparator (comp)-treated pts. Methods: Pooled data from 3 studies of ibr (420 mg/d) in CLL/SLL pts (RESONATE: relapsed/refractory [R/R] pts, ibr vs ≤ 24 weeks ofatumumab; RESONATE-2: treatment-naïve [TN] pts ≥ 65 yrs of age [no del17p], ibr vs ≤ 12 cycles chlorambucil; HELIOS: R/R pts [no del17p], ≤ 6 cycles bendamustine-rituximab with ibr vs placebo) were analyzed by IGHV status. Analyses included Kaplan-Meier method (overall survival [OS]; progression-free survival [PFS]); log-rank test (group comparisons); and Cox multivariate analysis (prognostic factors; Table). Results: For 491 ibr- (366 u-CLL, 125 m-CLL) and 494 comp-treated (351 u-CLL, 143 m-CLL) pts with IGHV data, baseline characteristics were similar for u-CLL vs m-CLL, except for the proportions of pts who were TN (17 vs 31%), had bulky disease (60 vs 37%), or had CLL cells with del11q (32 vs 16%). Median follow up was 21.4 mos for ibr and 20.6 mos for comp. PFS and OS were similar for u-CLL and m-CLL ibr-treated pts; u-CLL had poorer outcomes than m-CLL in comp-treated pts, which remained after adjustment for prognostic factors (Table). Ibr treatment was more effective than comp regardless of IGHV status. Incidence of adverse events (AEs) was similar between groups. For ibr vs comp pts (u-CLL/m-CLL): serious AEs: 49% (50%/47%) vs 36.5% (37%/35%); AEs that led to discontinuation: 12% (11%/15%) vs 13% (13%/14%); death within 30 days of last dose: 5% (6%/3%) vs 5% (6%/2%). Conclusions: U-CLL was an adverse predictor of outcome for comp-treated, but not for ibr-treated pts. Table.Efficacy Outcomes in Ibr- and Comparator-Treated Patients by IGHV Mutation StatusIbrutinib (N=491)Comparator (N =494)u-CLL (n=717)m-CLL (n=268)u-CLL(n=366): m-CLL(n=125)u-CLL(n=351): m-CLL(n=143)Ibr(n=366): Comp(n=351)Ibr (n=125): Comp (n=143)PFS24-month rate HR78%:81%10%:32%78%:10%81%:32%0.93a1.99a0.12b0.21bp = 0.78p &lt; 0.0001p &lt; 0.0001p &lt;0.0001Adjusted HRc1.02a1.94a0.11b0.17bp = 0.93p &lt; 0.0001p &lt; 0.0001p &lt; 0.0001OS24-month rate HR88%:89%78%:87%88%:78%89%:87%0.95a1.96a0.49b0.85b,ep = 0.86p = 0.02p &lt; 0.01p = 0.66Adjusted HRc1.21a2.10a0.49bNAp =0.57p = 0.01p &lt; 0.001Response ratesu-CLLm-CLLu-CLLm-CLLIbrCompIbrCompORR, %d8990475189479051p = 0.96p = 0.36p &lt; 0.0001p &lt; 0.0001CR, %d221546224156p = 0.10p = 0.44p &lt; 0.0001p = 0.01CI, confidence interval; CR, complete response; HR, hazard ratio; ORR, overall response rate; NA, not applicable. aHR &lt; 1 favors unmutated. bHR &lt; 1 favors ibrutinib. cMultivariate Cox model adjusted for age, sex, current Rai stage, baseline ECOG performance status score, del11q, del17p, number of prior therapies, and cytopenias. dp value is from a Cochran-Mantel-Haenszel chi-square test. eInsufficient number of events prevents meaningful analysis, only 13 and 16 events observed in ibr and comp groups, respectively, and 46 (32%) pts in comp arm crossed over to ibr arm; interpret results with caution. Citation Format: Thomas J. Kipps, Graeme Fraser, Steven Coutre, Jennifer R. Brown, Jacqueline C. Barrientos, Paul M. Barr, John C. Byrd, Susan M. O'Brien, Marie-Sarah Dilhuydy, Stephen Devereux, Ulrich Jaeger, Carol Moreno, Paula Cramer, Stephan Stilgenbauer, Asher A. Chanan-Khan, Michelle Mahler, Mariya Salman, Mei Cheng, Anil Londhe, Joi Ninomoto, Angela Howes, Danelle James, Michael Hallek. Unmutated IGHV is not an adverse predictor of outcome to therapy with ibrutinib in patients with chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr CT158. doi:10.1158/1538-7445.AM2017-CT158