scholarly journals Treatment Response and Long-Term Survival in Multiple Myeloma in the GMMG-HD4 Trial - Neither Profit All Molecular Entities Alike, Nor Are Remissions to Different Regimen Equal

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4485-4485
Author(s):  
Anja Seckinger ◽  
Hans Jürgen Salwender ◽  
Hans Martin ◽  
Christof Scheid ◽  
Thomas Hielscher ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
High Risk ◽  
Renal Insufficiency ◽  
Treatment Regimen ◽  
Research Funding ◽  
Advisory Board ◽  
Novel Agents ◽  
Term Survival ◽  
Long Term Survival

Abstract Introduction The inclusion of "novel" agents including proteasome inhibitors or IMiD-derivatives in the treatment of multiple myeloma significantly improves patient survival. Results of several study groups suggest incorporating at least one "novel agent" in first-line treatment before and after high-dose chemotherapy (HDT) followed by autologous stem cell transplantation. Here we address four main questions: First, what determines (excellent) long-term survival for different treatment regimen? Second, can we show benefit of novel agents for all patients and molecular subentities, including low risk? Third, can the prognostic impact of molecular entities be explained by different association with response, proliferation, and renal impairment? Fourth, does it matter regarding long-term survival by which agents, i.e. "old" vs. "new", a response was reached? Patients and Methods Patients were included in the prospective phase III HOVON-65/GMMG-HD4-trial (German part, n=354) randomizing VAD-induction, autologous tandem-transplantation and thalidomide-maintenance vs. PAD-induction, tandem-transplantation and bortezomib-maintenance. Plasma cells after CD138-purification were subjected to interphase fluorescence in-situ hybridization and gene expression profiling using Affymetrix U133 2.0 DNA-microarrays. Median follow-up (time to censoring) was 93 months. Results Low proliferation, revised-ISS I and cyto-ISS I delineate excellent long-term survival (70-75% after eight years, both arms). Molecular entities are associated with proliferation-rate, i.e. higher (del17p13, del8p21, del13q14, 1q21+, t(4;14)) or lower proliferation (hyperdiploidy), and response: bad response/survival in case of del17p, bad response/no survival impact (t(11;14)), and good response/bad survival (1q21+, t(4;14), and del13q), depending on the treatment regimen. Thus, it does not hold true that good response = good survival if patients are substratified according to their molecular background. Renal insufficiency is associated with 1q21+, del17p13, and t(4;14). For patients with ≥1 of the chromosomal aberrations del17p13, t(4;14), 1q21+ (i.e. cytogenetic high risk, 27.5% of patients) or renal insufficiency (10.6%), risk is abrogated; in absence of these risk features, no benefit could be shown. Patients reaching a near complete remission or better (≥nCR) with VAD-based regimen, HDT followed by thalidomide maintenance show significantly better survival compared to those reaching ≥nCR after bortezomib-based induction/HDT followed by bortezomib maintenance treatment. Conclusions Taken together, adversely prognostic molecular entities are associated with proliferation but can show association with better or adverse remission. Bortezomib-based upfront treatment abrogates chromosomal high-risk aberrations and renal insufficiency; however, no long-term survival-benefit is evident for those without these risk factors or low proliferation, i.e. the majority of patients. Responses achieved by different regimen are not equal in transmission in long-term survival. Responses (≥nCR) are not equivalent regarding their biological and prognostic role in patients with different molecular background and different treatment regimen. Disclosures Seckinger: Celgene: Research Funding; Sanofi: Research Funding; EngMab: Research Funding. Salwender:Novartis: Honoraria, Other: travel suppport, Research Funding; Amgen: Honoraria, Other: travel suppport, Research Funding; Bristol-Myers Squibb: Honoraria, Other: travel suppport, Research Funding; Celgene: Honoraria, Other: travel suppport, Research Funding; Takeda: Honoraria; Janssen: Honoraria, Other: travel support, Research Funding. Scheid:Celgene: Honoraria; Janssen: Honoraria. Knauf:Janssen: Consultancy; AbbVie: Consultancy; Celgene: Consultancy, Honoraria; Gilead Sciences: Consultancy; Roche: Consultancy; Amgen: Consultancy, Honoraria; Mundipharma: Consultancy. Duehrsen:AbbVie: Consultancy, Honoraria; Amgen: Research Funding; Janssen: Honoraria; Celgene: Honoraria, Research Funding; Roche: Honoraria, Research Funding; Gilead: Consultancy, Honoraria. Dürig:Roche: Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria; Celgene: Honoraria. Schmidt-Wolf:Janssen: Research Funding; Novartis: Research Funding. Haenel:Novartis: Honoraria; Amgen: Honoraria; Roche: Honoraria; Takeda: Honoraria. Raab:Novartis: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding. Sonneveld:Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding; BMS: Honoraria, Research Funding. Blau:Celgene: Other: Advisory board, Research Funding; Janssen: Other: Advisory board, Research Funding; Amgen: Other: Advisory board; Takeda: Other: Advisory board; Novartis: Other: Advisory boards; BMS: Other: Advisory board. Hillengass:Takeda: Honoraria, Other: Advisory Board; BMS: Honoraria, Other: Advisory Board; Celgene: Consultancy, Honoraria, Other: Advisory Board, Research Funding; amgen: Consultancy, Honoraria, Other: Advisory Board; Sanofi: Research Funding; Janssen: Honoraria, Other: Advisory Board; Novartis: Honoraria, Other: Advisory Board. Weisel:Amgen, BMS, Celgene, Janssen, and Takeda: Honoraria; Amgen, Celgene, Janssen, and Sanofi: Research Funding; Amgen, BMS, Celgene, Janssen, Juno, Sanofi, and Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees. Goldschmidt:Chugai: Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Takeda: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Mundipharma: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Research Funding; Adaptive Biotechnology: Consultancy; ArtTempi: Honoraria. Hose:Celgene: Honoraria, Research Funding; EngMab: Research Funding; Sanofi: Research Funding.

scholarly journals Conditional Survival Analysis of 816 Multiple Myeloma (MM) Patients Constitutes a Different Way to Provide More Specifically Determined Prognosis

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2091-2091
Author(s):  
Maximilian Schinke ◽  
Inga Promny ◽  
Stefanie Hieke ◽  
Johannes M. Waldschmidt ◽  
Gabriele Ihorst ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Research Funding ◽  
Conditional Survival ◽  
Term Survival ◽  
Long Term Survival ◽  
Prediction Time ◽  
Risk Parameters ◽  
Over Time

Abstract Introduction: Disease monitoring based on genetics or other molecular markers obtained by noninvasive or minimally invasive methods will potentially allow the early detection of treatment response or disease progression in cancer patients. Investigations in order to identify prognostic factors, e.g. patient's baseline characteristics or molecular markers, contributing to long-term survival potentially provide important information for patients with multiple myeloma. Overall survival (OS) is not very informative for patients who already survived one or more years. To better characterize long-term survival respectively long-term survivors, conditional survival (CS) analyses are useful. Conditional survival (CS) describes probabilities of surviving t additional years given they survived s years and provides information, how prognosis evolves over time. We have demonstrated the use of CS in a large data set of multiple myeloma patients with long-term survival which is mandatory for the calculation of CS (Hieke,... Engelhardt, Schumacher. CCR 2015). Methods: We evaluated 816 consecutive multiple myeloma patients treated at our department from 1997 to 2011 with follow-up until the end of 2011. Patients' data were assessed via electronic medical record (EMR) retrieval within an innovative research data warehouse. Our platform, the University of Freiburg Translational Research Integrated Database Environment (U-RIDE), acquires and stores all patient data contained in the EMR at our hospital and provides immediate advanced text searching capacity. We assessed 21 variables including gender, age, stage and admission period. We calculated 5-years CS and stratified 5-years CS according to disease- and host-related risks. Component-wise likelihood-based boosting and variables selected by boosting were investigated in a multivariable Cox model. Results: The OS probabilities at 5- and 10- years were 50% and 25%, respectively. The 5-year CS probabilities remained almost constant over the years a patient had already survived after initial diagnosis (~50%). According to baseline variables, conditional survival estimates showed no gender differences. The estimated 5-year survival probabilities varied substantially, from 25% for patients ages 70 or older to 65% for patients younger than 60 years. Similarly, patients with D&S stage I have an estimated 5-year survival probability of about 75% compared with 40% for patients with D&S stages II and III. Significant risk factors via Cox proportional hazard model were D&S stage II+III, age >70 years, hemoglobin <10g/dl, ß2-MG ≥5.5mg/dl, LDH ≥200U/l. Renal impairment, low albumin and unfavorable cytogenetics increased the risk, but failed to reach significance. Cytogenetics, response, response duration and other risk parameters post treatment are currently included in our assessment. Of note, over the study period, admission of patients <60 years decreased from 60% to 34%, but increased for those ≥70 years from 10% to 35%, respectively, illustrating that not only young and fit, but also elderly patients are increasingly treated within large referral and university centers and that patient cohorts and risks do not remain constant over time. Conclusions: Conditional survival has attracted attention in recent years either in an absolute or relative form where the latter is based on a comparison with an age-adjusted normal population being highly relevant from a public health perspective. In its absolute form, conditional survival constitutes the quantity of major interest in a clinical context. We defined conditional survival by using the fact that the patient is alive at the prediction time s as the conditioning event. Alternatively, one could determine conditional survival, given that the patient is alive and progression-free or alive, but has progression at time s (Zamboni et al. JCO 2010). Analysis of the above and additional variables from diagnosis to prediction time s may refine conditional survival towards an even more specifically determined prognosis; follow-up response and risk parameters most likely further refining these CS analyses. Figure 1. Figure 1. Disclosures Wäsch: MSD: Research Funding; Janssen-Cilag: Research Funding; Comprehensiv Cancer Center Freiburg: Research Funding; German Cancer Aid: Research Funding.

scholarly journals Integration of Multiple Bioassays Using Machine Learning to Identify High-Risk CP-CML Patients Treated with Frontline Imatinib

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1728-1728
Author(s):  
Chung Hoow Kok ◽  
Sakrapee Paisitkriangkrai ◽  
David T Yeung ◽  
Liu Liu ◽  
Verity A Saunders ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Board Of Directors ◽  
Predictive Value ◽  
Research Funding ◽  
Molecular Response ◽  
Advisory Committees ◽  
Term Survival ◽  
Long Term Survival

Abstract Introduction. Imatinib has revolutionised the treatment of chronic phase-chronic myeloid leukemia (CP-CML), with up to 70% of patients (pts) achieving major molecular response (MMR, BCR-ABL1 < 0.1% IS). Achievement of MMR by 2 years (yrs) is associated with an excellent prospect of long term survival. Currently, three baseline prognostic scoring systems - the Sokal, Hasford (Euro) and EUTOS risk scores - have all been used to identify pts with a poor response and/or an adverse prognosis in CP-CML. Recently, the EUTOS long-term survival (ELTS) score is shown to have strong predictive power for overall survival in CML pts. We have previously reported bioassays that have significant value for predicting MMR. Combinations of these biomarkers, together with clinical risk score, may provide a better indicator of high risk pts at the time of diagnosis. Aim. To identify high-risk pts by combining selected predictive bioassay, determine whether the ELTS score is more discriminating, and determine whether it provides additional predictive value when combined with the biomarker score. Methods. Bioassays including CRKL IC50 imatinib (White, Blood, 2005), OCT-1 Activity (OA)(White, JCO, 2010), leves of 39 plasma cytokines (Nievergall, Leukemia, 2016), expression of 20 most prognostic gene by qPCR TLDA (Kok, ASH abstract, 2015), ABCB1 gene expression (Eadie, Leukemia, 2016), KIR2DL5B genotype (Yeung, Blood, 2015), BIM and ASXL1 polymorphisms (Marum, Blood advances, 2017) were used in this study. High-risk by biomarker score (HR) was defined as pts who did not achieve MMR by 2 yrs. 210 TIDEL-II pts (frontline imatinib with early switch to nilotinib for failure to meet optimal time-dependent molecular targets) were used in this study (Yeung, Blood, 2015). Only 201 pts had ELTS scores. The Recursive Partitioning and Regression Trees (rpart) algorithm was used to identify important bioassays in predicting high-risk pts. Fisher's-exact test was used for statistical analysis. Results. In the TIDEL-II cohort, there were 21 high ELTS and 180 low/intermediate ELTS pts. Pts with high ELTS had significantly lower rates of MMR by 2 yrs compared to those pts with low/intermediate ELTS (57% vs 81%, p=0.02). We constructed a predictive model using multiple different bioassays as variables to predict high-risk pts. The rpart based model used in this analysis yielded four variables (IGFBP2 gene expression, KIR2DL5B genotype, OA, and MCP-1 cytokine plasma level) as most important for predicting high-risk pts. The accuracy of the model was 84%. Pts predicted as high-risk (HR, n=27) had significantly lower MMR achievement rate compared to those predicted as low-risk (LR), (26% vs 86%, n=183, p<0.0001, OR:17.3). Importantly, pts with HR had significantly higher rate of blast-crisis progression (15%, n=4/27) compared to those with LR (1.6%, n=3/183, p=0.006, OR:10.4) and pts with high ELTS (5%, n=1/21). Interestingly, there were two categories of HR patient groups based on the model: 1) Patient with high IGFBP2 gene expression and low OA, and 2) pts with low IGFBP2, KIR2DL5B positive genotype and high MCP-1 cytokine level. When combined with ELTS, the bioassays model improved ELTS performance in predicting HR pts. For instance, within the low/intermediate ELTS pts group, our assays could futher distinguish HR pts with inferior MMR (n=20, 2 yrs MMR of 30%) versus LR pts (n=160, 2 yrs MMR 87%). Similarly, pts with high ELTS in combination with HR also had lower MMR rate (n=1/5, 20%) compared to pts with high ELTS in combination with LR (n=11/16, 69%, p=0.11, OR:8.8). Conclusion. We developed a combined bioassays model that is predictive of MMR failure and adverse clinical outcomes for pts who receive optimised frontline imatinib therapy. This model performs well even without adding clinical parameters. Our model has additional predictive value when used together with the ELTS score, and can distinuguish HR pts within the low/intermediate ELTS group, as well as LR patients within the high ELTS category. Further confirmation of the predictive performance of this model, using a large independent patient cohort is now indicated. We postulate that this bioassay-based model could be used, in combination with ELTS, for identifying HR pts who would benefit from intensified therapeutic approaches to obtain optimal clinical outcome. Disclosures Yeung: Amgen: Honoraria; Pfizer: Honoraria; BMS: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Specialised Therapeutics Australia: Honoraria. Yong:Celgene: Research Funding; Novartis: Honoraria, Research Funding; BMS: Honoraria, Research Funding. White:BMS: Research Funding; Novartis: Honoraria, Research Funding. Hughes:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees.

scholarly journals Long-Term Survival for Myeloma after Autologous Stem Cell Transplantation

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 23-24
Author(s):  
Samer A. Srour ◽  
Qaiser Bashir ◽  
Denái R. Milton ◽  
Yago Nieto ◽  
Rohtesh S. Mehta ◽  
...  
Keyword(s):  
Board Of Directors ◽  
Research Funding ◽  
Advisory Board ◽  
Short Term ◽  
Advisory Committees ◽  
Term Survival ◽  
Long Term Survival ◽  
Grant Support ◽  
Term Survivor

Introduction: Multiple myeloma (MM) remains incurable with only a small proportion of patients surviving over 10 years (long-term survivors) from diagnosis. It has been more than 3 decades since the first autologous stem cell transplantation (ASCT) was performed for MM at our institution. In this study, we sought to determine baseline patient and disease characteristics that are associated with long-term survival after the ASCT. Methods: We included all consecutive patients who received their first ASCT between January 1988 and December 2015. The primary objective was to identify the variables associated with long-term overall survival (&gt;10 years). The control group were patients who survived less than 10 years from their diagnosis data (short-term survivors). Logistic regression models were used to evaluate the association between predictive factors and overall survival of &gt; 10 years. Results: Among 2176 patients who underwent their first ASCT during the study period, 1409 patients met the eligibility criteria. Overall, 392 (28%) patients were long-term survivors (&gt;10 years) and 1017 (72%) were short-term survivors (&lt;10 years). Table 1 shows baseline patient and disease characteristics. Only 24% and 42% of patients in the long-term and short-term survivor groups, respectively, received proteasome inhibitor-based induction therapies. Maintenance therapy was received by 49% and 45% in the short- and long-term survivor groups, respectively (p=0.19). The long-term survivor group was characterized by having higher percentage of patients younger than age 65 (86%) years, and having higher proportions of ISS Stage I (47%), standard-risk cytogenetics (96%), normal LDH (88%) and with serum creatinine &lt;2 mg/d (87%). With a median follow-up of 13 years (range, 10-30 years), the 15-year PFS and OS survival rates in the long-term survivors were 19% (95% CI: 14% - 23%) and 62% (95%: CI 56% - 68%) (Figure 1A). The cumulative incidence rates of relapse at 1, 3, and 5 years in the short-term survival group were 9%, 63%, and 82%, respectively, compared to 2%, 20%, and 40%, respectively, in the long-term survivor group (p&lt;0.001) (Figure 1B). All variables listed in Table 1 were assessed in univariate analysis. Seventy-six percent of patients were relapse-free at 24 months in the long-term survival group, compared to only 32% in the short-term survival group. On multivariable analysis, age, cytogenetic-risk status, race-ethnicity, and duration of remission after ASCT were significant predictors for surviving &gt; 10 years (Table 2). ISS Stage III (vs. Stage I: OR 0.45, 95% CI 0.19-1.09; p=0.08) showed a trend towards surviving ≤ 10 years. Conclusions: ASCT is associated with durable responses and prolonged survival in a subgroup of MM patients irrespective of type of induction therapy and/or use of maintenance therapy. Duration of remission after transplant is the strongest predictor for long-term survival. Age &lt;65 years, being African-American, and standard-risk cytogenetics were also associated with surviving more than 10 years. Disclosures Bashir: Acrotech: Research Funding; StemLine: Research Funding; Celgene: Research Funding; Amgen: Other: Advisory Board; Purdue: Other: Advisory Board; Takeda: Other: Advisory Board, Research Funding; KITE: Other: Advisory Board. Nieto:Affimed: Consultancy, Other: Grant Support; Novartis: Other: Grant Support; Astra Zeneca: Other: Grant Support; Secura Bio: Other: Grant Support. Mehta:CSL Behring: Research Funding; Incyte: Research Funding; Kadmon: Research Funding. Ciurea:Kiadis Pharma: Current equity holder in publicly-traded company, Research Funding. Popat:Bayer: Research Funding; Novartis: Research Funding. Khouri:Bristol Myers Squibb: Research Funding; Pfizer: Research Funding. Kebriaei:Ziopharm: Other: Research Support; Novartis: Other: Served on advisory board; Pfizer: Other: Served on advisory board; Jazz: Consultancy; Kite: Other: Served on advisory board; Amgen: Other: Research Support. Manasanch:GSK: Honoraria; Adaptive Biotechnologies: Honoraria; Merck: Research Funding; Quest Diagnostics: Research Funding; Takeda: Honoraria; BMS: Honoraria; Sanofi: Research Funding; Novartis: Research Funding; Sanofi: Honoraria; JW Pharma: Research Funding. Hosing:NKARTA Inc.: Consultancy. Patel:Janssen: Consultancy, Research Funding; Nektar: Consultancy, Research Funding; Cellectis: Research Funding; Poseida: Research Funding; Oncopeptides: Consultancy; Precision Biosciences: Research Funding; Bristol Myers Squibb: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Takeda: Consultancy, Research Funding. Lee:Regeneron: Research Funding; Amgen: Consultancy, Research Funding; Genentech: Consultancy; Sanofi: Consultancy; Janssen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Genentech: Consultancy; Daiichi Sankyo: Research Funding; Celgene: Consultancy, Research Funding; GlaxoSmithKline: Consultancy, Research Funding. Shpall:Zelluna: Membership on an entity's Board of Directors or advisory committees; Takeda: Other: Licensing Agreement; Adaptimmune: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Magenta: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees. Orlowski:Laboratory research funding from BioTheryX, and clinical research funding from CARsgen Therapeutics, Celgene, Exelixis, Janssen Biotech, Sanofi-Aventis, Takeda Pharmaceuticals North America, Inc.: Research Funding; Founder of Asylia Therapeutics, Inc., with associated patents and an equity interest, though this technology does not bear on the current submission.: Current equity holder in private company, Patents & Royalties; STATinMED Research: Consultancy; Sanofi-Aventis, Servier, Takeda Pharmaceuticals North America, Inc.: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen, Inc., AstraZeneca, BMS, Celgene, EcoR1 Capital LLC, Forma Therapeutics, Genzyme, GSK Biologicals, Ionis Pharmaceuticals, Inc., Janssen Biotech, Juno Therapeutics, Kite Pharma, Legend Biotech USA, Molecular Partners, Regeneron Pharmaceuticals, Inc.,: Honoraria, Membership on an entity's Board of Directors or advisory committees. Champlin:Omeros: Consultancy; Cytonus: Consultancy; Johnson and Johnson: Consultancy; Actinium: Consultancy; Genzyme: Speakers Bureau; Takeda: Patents & Royalties; DKMS America: Membership on an entity's Board of Directors or advisory committees. Qazilbash:Bioclinica: Consultancy; Amgen: Research Funding; Janssen: Research Funding; Angiocrine: Research Funding; Bioline: Research Funding.

Long‐term survival of two patients with recurrent high‐risk neuroblastoma

2021 ◽  
Author(s):  
Tomonobu Sato ◽  
Kazuya Hara ◽  
Go Ohba ◽  
Hiroshi Yamamoto ◽  
Akihiro Iguchi
Keyword(s):  
High Risk ◽  
Term Survival ◽  
Long Term Survival

Long term survival in multiple myeloma: a single institution experience in underprivileged circumstances

2021 ◽  
pp. 1-6
Author(s):  
Lorena Bojalil-Alvarez ◽  
Morie A. Gertz ◽  
Elizabeth Garcia-Villaseñor ◽  
José Antonio Fernández-Gutiérrez ◽  
Oscar Alfonso Reyes-Cisneros ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Single Institution ◽  
Term Survival ◽  
Long Term Survival

scholarly journals Conventional Immunochemotherapy (R-CHOEP) Vs High-Dose Immunochemotherapy (R-MegaCHOEP) in Younger Patients with High-Risk Aggressive B-Cell Lymphoma: 10-Year Long-Term Follow-up of a German Lymphoma Alliance (GLA) Study

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1589-1589
Author(s):  
Fabian Frontzek ◽  
Marita Ziepert ◽  
Maike Nickelsen ◽  
Bettina Altmann ◽  
Bertram Glass ◽  
...  
Keyword(s):  
High Risk ◽  
Board Of Directors ◽  
Research Funding ◽  
Advisory Board ◽  
High Dose ◽  
Advisory Committees ◽  
Scientific Advisory Board ◽  
Scientific Advisory

Introduction: The R-MegaCHOEP trial showed that dose-escalation of conventional chemotherapy necessitating autologous stem cell transplantation (ASCT) does not confer a survival benefit for younger patients (pts) with high-risk aggressive B-cell lymphoma in the Rituximab era (Schmitz et al., Lancet Oncology 2012; 13, 1250-1259). To describe efficacy and toxicity over time and document the long-term risks of relapse and secondary malignancy we present the 10-year follow-up of this study. Methods: In the randomized, prospective phase 3 trial R-MegaCHOEP younger pts aged 18-60 years with newly diagnosed, high-risk (aaIPI 2-3) aggressive B-cell lymphoma were assigned to 8 cycles of CHOEP (cyclophosphamide, doxorubcine, vincristine, etoposide, prednisone) or 4 cycles of dose-escalated high-dose therapy (HDT) necessitating repetitive ASCT both combined with Rituximab. Both arms were stratified according to aaIPI, bulky disease, and center. Primary endpoint was event-free survival (EFS). All analyses were calculated for the intention-to-treat population. This follow-up report includes molecular data based on immunohistochemistry (IHC) and fluorescent in situ hybridization (FISH) for MYC (IHC: 31/92 positive [40-100%], FISH: 14/103 positive), BCL2 (IHC: 65/89 positive [50-100%], FISH: 23/111 positive) and BCL6 (IHC: 52/86 positive [30-100%], FISH: 34/110 positive) and data on cell of origin (COO) classification according to the Lymph2CX assay (GCB: 53/88; ABC: 24/88; unclassified: 11/88). Results: 130 pts had been assigned to R-CHOEP and 132 to R-MegaCHOEP. DLBCL was the most common lymphoma subtype (~80%). 73% of pts scored an aaIPI of 2 and 27% an aaIPI of 3. 60% of pts had an initial lymphoma bulk and in 40% more than 1 extranodal site was involved. After a median observation time of 111 months, EFS at 10 years was 57% (95% CI 47-67%) in the R-CHOEP vs. 51% in the R-MegaCHOEP arm (42-61%) (hazard ratio 1.3, 95% CI 0.9-1.8, p=0.228), overall survival (OS) after 10 years was 72% (63-81%) vs. 66% (57-76%) respectively (p=0.249). With regard to molecular characterization, we were unable to detect a significant benefit for HDT/ASCT in any subgroup analyzed. In total, 16% of pts (30 pts) relapsed after having achieved a complete remission (CR). 23% of all relapses (7 pts) showed an indolent histology (follicular lymphoma grade 1-3a) and 6 of these pts survived long-term. In contrast, of 23 pts (77%) relapsing with aggressive DLBCL or unknown histology 18 pts died due to lymphoma or related therapy. The majority of relapses occurred during the first 3 years after randomization (median time: 22 months) while after 5 years we detected relapses only in 5 pts (3% of all 190 pts prior CR). 11% of pts were initially progressive (28 pts) among whom 71% (20 pts) died rapidly due to lymphoma. Interestingly, the remaining 29% (8 pts) showed a long-term survival after salvage therapy (+/- ASCT); only 1 pt received allogeneic transplantation. The frequency of secondary malignancies was very similar in both treatment arms (9% vs. 8%) despite the very high dose of etoposide (total 4g/m2)in the R-MegaCHOEP arm. We observed 2 cases of AML and 1 case of MDS per arm. In total 70 pts (28%) have died: 30 pts due to lymphoma (12%), 22 pts therapy-related (11 pts due to salvage therapy) (9%), 8 pts of secondary neoplasia (3%), 5 pts due to concomitant disease (2%) and 5 pts for unknown reasons. Conclusions: This 10-year long-term follow-up of the R-MegaCHOEP trial confirms the very encouraging outcome of young high-risk pts following conventional chemotherapy with R-CHOEP. High-dose therapy did not improve outcome in any subgroup analysis including molecular high-risk groups. Relapse rate was generally low. Pts with aggressive relapse showed a very poor long-term outcome while pts with indolent histology at relapse survived long-term. Secondary malignancies occurred; however, they were rare with no excess leukemias/MDS following treatment with very high doses of etoposide and other cytotoxic agents. Supported by Deutsche Krebshilfe. Figure Disclosures Nickelsen: Roche Pharma AG: Membership on an entity's Board of Directors or advisory committees, Other: Travel Grants; Celgene: Membership on an entity's Board of Directors or advisory committees, Other: Travel Grant; Janssen: Membership on an entity's Board of Directors or advisory committees. Hänel:Amgen: Honoraria; Celgene: Other: advisory board; Novartis: Honoraria; Takeda: Other: advisory board; Roche: Honoraria. Truemper:Nordic Nanovector: Consultancy; Roche: Research Funding; Mundipharma: Research Funding; Janssen Oncology: Consultancy; Takeda: Consultancy, Research Funding; Seattle Genetics, Inc.: Research Funding. Held:Roche: Consultancy, Other: Travel support, Research Funding; Amgen: Research Funding; Acrotech: Research Funding; MSD: Consultancy; Bristol-Myers Squibb: Consultancy, Other: Travel support, Research Funding. Dreyling:Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: scientific advisory board, Research Funding, Speakers Bureau; Bayer: Consultancy, Other: scientific advisory board, Speakers Bureau; Celgene: Consultancy, Other: scientific advisory board, Research Funding, Speakers Bureau; Mundipharma: Consultancy, Research Funding; Gilead: Consultancy, Other: scientific advisory board, Speakers Bureau; Novartis: Other: scientific advisory board; Sandoz: Other: scientific advisory board; Janssen: Consultancy, Other: scientific advisory board, Research Funding, Speakers Bureau; Acerta: Other: scientific advisory board. Viardot:Kite/Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria; F. Hoffmann-La Roche Ltd: Honoraria, Membership on an entity's Board of Directors or advisory committees. Rosenwald:MorphoSys: Consultancy. Lenz:Gilead: Consultancy, Honoraria, Research Funding, Speakers Bureau; AstraZeneca: Consultancy, Honoraria, Research Funding; Agios: Research Funding; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Bayer: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Roche: Employment, Honoraria, Research Funding, Speakers Bureau; BMS: Consultancy. Schmitz:Novartis: Honoraria; Gilead: Honoraria; Celgene: Equity Ownership; Riemser: Consultancy, Honoraria.

Abstract 2143: Improved Long-Term Survival in a High Risk Population Following Drug Eluting Stent Availability

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
Maheer Gandhavadi ◽  
Kendrick A Shunk ◽  
Edward J McNulty
Keyword(s):  
High Risk ◽  
Independent Predictor ◽  
Drug Eluting Stent ◽  
High Risk Population ◽  
Term Survival ◽  
Risk Population ◽  
Long Term Survival ◽  
Drug Eluting ◽  
The Impact

Background Data regarding the impact of drug eluting stent (DES) use on long-term outcomes outside trial populations are limited. Methods 1,547 consecutive patients underwent stent implantation from January 2000 until December 2006 at the San Francisco Veterans Affairs Medical Center. To assess the impact of DES availability on mortality, that population was partitioned into a pre-DES cohort (N=591) and a post-DES availability cohort (N=956). Kaplan-Meier survival curves for the two cohorts were compared. Results The entire population was relatively high risk: 37% had diabetes, 38% a reduced ejection fraction, and 53% a prior MI or elevated troponin prior to the procedure. Median follow up was 4.7 years for the pre-DES cohort and 1.8 years for the post-DES cohort. DES were used in 83% of procedures in the post-DES cohort. Survival improved significantly in the post-DES cohort (P = .04, Log Rank)(see Figure ). Baseline characteristics, procedural variables and discharge medications were analyzed in a Cox proportional hazards model (see Table ). DES use was an independent predictor of improved survival (Hazard Ratio for death 0.52, 95% CI .28–.95). Conclusions In an unselected, high risk population, long-term survival improved following the availability of drug eluting stents. After adjusting for potential confounding factors, DES use was an independent predictor of improved survival. Independent Predictors of Death in all 1,547 Patients

Thirty-Five Year Follow-up Analysis of Follicular Lymphoma Patients Treated through the Nebraska Lymphoma Study Group: Prognostic Factor Analysis and Outcomes

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 7-8
Author(s):  
Amulya Yellala ◽  
Elizabeth R. Lyden ◽  
Heather Nutsch ◽  
Avyakta Kallam ◽  
Kai Fu ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Follicular Lymphoma ◽  
Treatment Regimen ◽  
Research Funding ◽  
Study Group ◽  
Secondary Malignancies ◽  
Lymphoma Study Group

Background Follicular lymphoma (FL) is the second most common type of non-Hodgkin lymphoma (NHL) and most common of the clinically indolent NHLs. Although often considered an incurable disease, overall survival has increased significantly with refinement in diagnostic techniques and the addition of rituximab. The course of FL is quite variable and presence of symptoms, organ dysfunction, cytopenias, aggressiveness of tumor are all taken into consideration when deciding individual treatment. In this study, we evaluated a large patient cohort with FL treated over a 35 year period for progression free survival (PFS), overall survival (OS) based on FLIPI score, tumor grade, and treatment regimen and also looked at causes of late failures. Methods We evaluated 1037 patients (pts) from the Nebraska Lymphoma Study Group that were diagnosed with FL between the years of 1983-2020. Descriptive statistics were stratified according to age, histological subtype, treatment regimen, FLIPI category, presence and type of secondary malignancy. PFS was calculated from the time of diagnosis to progression or death and OS was the time from diagnosis to death from any cause. PFS and OS were plotted as Kaplan-Meier curves with statistically significant p&lt;0.05. Results The median age at diagnosis and treatment was 61 years (yrs, range 17-91). A total of 9.1% were characterized as FLIPI high risk, 37.8% intermediate risk, and 33.6% low risk, 19.5% unavailable. Among the histological grade, 23.1% had FL- grade 1, 30.2% FL-2, 27.3% FL-3A, 2.5 % FL-3B and 16.9 % Composite Lymphoma. Anthracycline + rituximab was given in 24.5% of pts, whereas 43.8% of pts received an anthracycline based regimen without rituximab, 9.8% received rituximab without an anthracycline and 10.6% received neither of these agents. 6.75% (70 pts) were later found to have secondary malignancies of which 11 pts had myelodysplastic syndrome, 10 pts had acute leukemia and 9 pts had lung cancer. With a median follow up of 9.2 yrs and a maximum of 36 yrs, 29.7% (308 pts) had not relapsed. The median PFS across all groups was 4.6 yrs (Fig 1) and OS was 12.1 yrs. Median OS was significantly longer in patients that received rituximab at 16.1 yrs as compared to patients that did not receive rituximab at 9.89 yrs (Fig 2). PFS was 8.6 yrs, 3.6 yrs and 2.1 yrs and OS was 15.1 yrs, 11.7 yrs and 4.9 yrs in FLIPI low, intermediate and high risk groups respectively (p=&lt;0.001) (Fig 3), suggesting that survival was influenced by FLIPI score. Median PFS in FL-3B and FL-3A was 9.2 yrs and 5.2 yrs respectively which is longer than 4.7 yrs and 4.2 yrs for FL-1 and FL-2 (p=0.24). OS in FL-3A and FL-3B subgroups was 10.8 yrs while it was 11.6 yrs and 14.3 yrs in FL-2 and FL-1 (P=0.081). PFS is significantly longer at 10.6 yrs in pts treated with both anthracycline and rituximab containing regimen as compared to 5.3 yrs in pts treated with rituximab alone and 3.05 yrs in pts that had only anthracycline based regimen (p=&lt;0.001) (Fig 4). The median OS also was significantly higher in the combination regimen group at 18.8 yrs as compared to 11.3 yrs in rituximab only group and 9 yrs in anthracycline based regimen group (p=&lt;0.001). When pts with FL-3A and FL-3B were grouped together and stratified according to treatment regimen, the group that received anthracycline and rituximab combination has highest PFS and OS at 13.3 yrs and 18.8 yrs (p&lt;0.001). when pts with FL-3A were analyzed separately and stratified by treatment regimen, the results of PFS and OS were similar and statistically significant. However, of the 24 pts in FL-3B group, analysis revealed that PFS and OS was longer in anthracycline based regimen only group, however results were not statistically significant. Among the pts that relapsed/died after 10 years (n=190), the cause of death was relapsed lymphoma in 13.7%, unknown in 55.8%, secondary malignancies in 4.2%, treatment related in 2.6% and not related to disease in 23.7%. A total of 278 pts survived &gt; 10 yrs, and of these pts, 119 (30%) had not relapsed at the last follow up. Conclusion The addition of rituximab to standard anthracycline based chemotherapy has resulted in significant improvements in the PFS and OS rates of FL. These results also support the prognostic value of the FLIPI in patients treated in the rituximab era. Late relapses after 10 yrs from disease can occur, but 11.5% of patients had not relapsed with long term follow up. Secondary malignancies are also an important consideration in the long term survivors. Disclosures Lunning: Acrotech: Consultancy; TG Therapeutics: Research Funding; Novartis: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Curis: Research Funding; Beigene: Consultancy, Honoraria; Aeratech: Consultancy, Honoraria; Bristol Meyers Squibb: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria; Legend: Consultancy; Verastem: Consultancy, Honoraria; ADC Therapeutics: Consultancy. Armitage:Trovagene/Cardiff Oncology: Membership on an entity's Board of Directors or advisory committees; Samus Therapeutics: Consultancy; Ascentage: Consultancy. Vose:Bristol-Myers Squibb: Research Funding; Karyopharm Therapeutics: Consultancy, Honoraria; Seattle Genetics: Research Funding; Allogene: Honoraria; AstraZeneca: Consultancy, Honoraria, Research Funding; Kite, a Gilead Company: Honoraria, Research Funding; Wugen: Honoraria; Novartis: Research Funding; Celgene: Honoraria; Incyte: Research Funding; Roche/Genetech: Consultancy, Honoraria, Other; Verastem: Consultancy, Honoraria; Miltenyi Biotec: Honoraria; Loxo: Consultancy, Honoraria, Research Funding; Janssen: Honoraria; Epizyme: Honoraria, Research Funding; AbbVie: Consultancy, Honoraria.

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