scholarly journals Wide Variation in Use and Interpretation of Gene Mutation Profiling Panels Among Health Care Providers of Patients with Myelodysplastic Syndromes (MDS): Results of a Large Web-Based Survey

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1825-1825 ◽  
Author(s):  
Alexander B Pine ◽  
Nora B Chokr ◽  
Maximilian Stahl ◽  
David P. Steensma ◽  
Mikkael A. Sekeres ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Board Of Directors ◽  
Gene Mutation ◽  
Health Care Providers ◽  
Research Funding ◽  
Specific Gene ◽  
Care Providers ◽  
Advisory Committees ◽  
Web Based ◽  
Mutation Profiling

Abstract Background. Gene mutation profiling is increasingly employed for diagnosis, risk stratification, and clinical management in patients with MDS. However, current World Health Organization MDS classification is still based on histologic findings (with the exception of SF3B1 for MDS-RS), and guidelines generally suggest that clinical decisions be guided by clinico-pathologic risk stratification tools such as Revised International Prognostic Scoring System (IPSS-R). We sought to study beliefs and patterns of practice with respect to gene mutation profiling among health care providers who manage patients with MDS. Methods. A link to a 23-question web-based survey was emailed to members of the Eastern Cooperative Oncology Group (ECOG)-ACRIN Cancer Research Group, Alliance for Clinical Trials in Oncology (Alliance), and the Southwest Oncology Group (SWOG), and the Cancer Trials Support Unit (CTSU) on 5/1/2018 with 6 subsequent weekly reminders. The Qualtrics survey platform was used to record anonymous responses. We used descriptive statistics to analyze the data. No incentive was provided for responses. Results. Of 371 received responses, 262 were received from providers who did not manage MDS patients or lacked analyzable data and therefore were excluded. Of 109 eligible responses, 108 responders were from institutions representing 31 US states (one respondent was from South America). Median age of respondents was 48 years (range, 33-75); 43 (39%) were women. A third of responders (32%) worked at a university hospital, while 25%, 17%, and 5% worked at a community hospital, private practice, or other settings, respectively. While 37% of participants worked at institutions with guidelines for clinical care of MDS patients, 28% reported that their institutional guidelines recommended MDS-specific gene mutation profiling. Such testing was performed at institutions of 13% participants; institutions of 26% of responders tested a general AML panel that included MDS-specific genes. The total number of respondents whose institutions sent out either an MDS-specific gene panel or a general AML gene panel with MDS-specific genes was similar, 25% and 12%, respectively (Fig. 1). Of those who routinely perform molecular testing, 94% do so at diagnosis, 56% at relapse, 33% during preparation for stem cell transplant, 31% after the failure of hypomethylating agents (HMA), 24% during screening for a clinical trial, and 15% at initial treatment (Fig. 2). MDS gene mutation profiling was felt to be most helpful in diagnosis (rarely 11%; sometimes 49%; often 30%; always 9%), risk stratification (sometimes 31%; often 51%; always 15%), and prognosis (sometimes 31%; often 51%; always 14%); its role was more limited in response assessment (never 12%; rarely 25%; sometimes 44%; often 14%) and to predict responses to HMAs (never 5%; rarely 28%; sometimes 52%, often 14%) (Fig. 3). Various types of evidence were used to stratify MDS risk and prognosis: genetic mutations were used by nearly everyone (95%); 70% relied on morphologic findings, while gene expression/transcriptome profiling was used by 40%. Eighty-four percent of responders reported relying on conventional prognostic models like IPSS-R to identify high-risk patients for whom they would consider intensive treatment options. For this purpose, 62% would also rely on mutation profiling, and 32% would also consider higher frequency of gene mutations. While mutations in the p53 pathway were felt to be helpful in terms of risk stratification and treatment decisions by 70% of responders, 43%, 39%, 31%, 26% 23%, 20%, and 3% considered mutations in spliceosome, DNA methylation, transcription factors, histone modification, signaling, RAS pathway, and cohesin genes, respectively, to be useful as well. Approximately 31% of responders were not certain as to which mutations would affect risk stratification and management choices and said they needed to review literature. The respondents also cited multiple limitations to wider clinical use of MDS gene mutation profiling (Fig. 4). Conclusions. Our survey demonstrates widespread use of gene mutation profiling in the management of patients with MDS, but also reveals substantial variability in beliefs, practices, testing logistics, and interpretation of molecular profiling. Our findings emphasize the need for high-quality data to develop consensus evidence-based guidelines for gene profiling of MDS patients. Disclosures Sekeres: Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Bejar:AbbVie/Genentech: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Foundation Medicine: Consultancy; Astex/Otsuka: Consultancy, Honoraria; Modus Outcomes: Consultancy; Takeda: Research Funding; Genoptix: Consultancy. Gore:Celgene: Consultancy, Research Funding. Zeidan:Ariad: Consultancy, Speakers Bureau; Gilead: Consultancy; Incyte: Employment; Celgene: Consultancy; Abbvie: Consultancy; Agios: Consultancy; Novartis: Consultancy; Pfizer: Consultancy.

Hematological Testing Is Not Required with Every Dose of Bortezomib In Patients with Adequate Blood Counts at the Start of Each Cycle

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 1963-1963
Author(s):  
Pooja S. Raibagkar ◽  
Paul G Richardson ◽  
Pieter Sonneveld ◽  
Michael W. Schuster ◽  
David Irwin ◽  
...  
Keyword(s):  
Health Care ◽  
Board Of Directors ◽  
Health Care Providers ◽  
Research Funding ◽  
Standard Dose ◽  
Hematological Toxicity ◽  
Significant Activity ◽  
Care Providers ◽  
Advisory Committees ◽  
Equity Ownership

Abstract Abstract 1963 Introduction: Bortezomib has significant activity in patients with multiple myeloma (MM) but can be associated with hematological toxicity. Based on this initial concern, patients (pts) receiving a standard dose and schedule of Bortezomib, twice a week for two weeks, are typically required to be evaluated for adequate blood counts before each bortezomib infusion. This prerequisite substantially increases the time patients need to be in the clinic putting both patients as well as health care providers at potentially increased inconvenience. In an effort to make bortezomib administration more convenient, we evaluated the need for monitoring complete blood counts (CBC) before each infusion by examining for any predictable changes in blood counts on days 4, 8 and 11, based on day 1 counts. Methods: We investigated the changes in neutophil and platelet counts in pts treated with bortezomib on the phase 3 APEX (Assessment of Proteasome Inhibition for Extending Remission) study. Pts with MM and measurable disease following 1 to 3 prior therapies were randomized to receive bortezomib at 1.3 mg/m2 on days 1, 4, 8, and 11 for eight 3-week cycles, then on days 1, 8, 15, and 22 for three 5-week maintenance cycles, or dexamethasone 40 mg on days 1 to 4, 9 to 12, and 17 to 20 for four 5-week cycles, then days 1 to 4 for five 4-week cycles. Platelet count (Plt) and absolute neutrophil count (ANC) were recorded throughout, and incidences of thrombocytopenia and neutropenia were calculated. Results: Data from all 319 pts receiving bortezomib on this study have been analysed. In this relapsed pt population, 15% of pts had plt counts <100 ×109/L on day 1 of cycle 1. However, by cycle 1 day 11, 43% were below this limit; similarly an ANC <1.5×109/L on day 1 of cycle 1 was observed in 13% and on day 11, in 27%. To evaluate the need for CBC prior to every dose we then analyzed the shift in blood counts from day 1 of cycles 2–4 relative to days 4, 8, and 11 of each cycle. We observed that all pts with Plt ≥100 ×109/L on day 1 of cycles 2, 3, and 4 had Plt ≥20 ×109/L on Day 4, and 8; and 99% on day 11. Importantly none of the patients with plt count > 100 ×109/L on day 8 of cycles 2–4, had their plt count drop below 20 ×109/L on day 11. Similarly pts with ANC ≥ 1.5×109/L on day 1 of each cycles 2–4 had ANC ≥ 0.5 ×109/L on days 4, 8, and 11 in > 99% pts. It is also important to note that responding pts had higher counts on day 1 of each cycle and had a lesser drop in their Plt count and ANC with each treatment. Conclusions: These results suggest that the change in CBC with bortezomib treatment as monotherapy is predictable. Importantly, CBCs done on day 1 of cycle 2 and beyond were able to predict the chance of thrombocytopenia and neutropenia on days 4, 8, and 11. We would suggest that patients with a plt count > 100 ×109/L and ANC > 1.5 ×109/L on day 1 of cycle 2 and beyond may not require rechecking of CBCs on days 4, 8 and 11. Reducing the number of CBC's needed during each cycle may improve patient compliance and save time both for pts and health care providers as well as improve resource utilization. Disclosures: Richardson: Millenium: Membership on an entity's Board of Directors or advisory committees. Sonneveld:Celgene: Membership on an entity's Board of Directors or advisory committees; Johnson & Johnson: Membership on an entity's Board of Directors or advisory committees. Facon:Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Harousseau:Millenium: Membership on an entity's Board of Directors or advisory committees. Lonial:Millennium, Celgene, Bristol-Myers Squibb, Novartis, Onyx: Advisory Board, Consultancy; Millennium, Celgene, Novartis, Onyx, Bristol-Myers Squibb: Research Funding. Reece:Celgene: Honoraria, Research Funding. San Miguel:Celgene: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Janssen-cilag: Membership on an entity's Board of Directors or advisory committees; Millennium: Membership on an entity's Board of Directors or advisory committees. Blade:Millenium: Membership on an entity's Board of Directors or advisory committees. Boccadoro:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen-Cilag: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Neuwirth:Millennium Pharmaceuticals, Inc.: Employment. Esseltine:Millennium Pharmaceuticals, Inc.: Employment; Johnson & Johnson: Equity Ownership. Anderson:Millennium: Consultancy; Celgene: Consultancy; Novartis: Consultancy; Onyx: Consultancy; Merck: Consultancy; Bristol Myers Squibb: Consultancy; Acetylon: Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Munshi:Millennium Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Onyx: Membership on an entity's Board of Directors or advisory committees.

scholarly journals Metaphase Cytogenetics for Risk Stratification in Newly Diagnosed Multiple Myeloma

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 4396-4396
Author(s):  
Patrick Mellors ◽  
Moritz Binder ◽  
Rhett P. Ketterling ◽  
Patricia Griepp ◽  
Linda B Baughn ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Risk Stratification ◽  
Board Of Directors ◽  
Research Funding ◽  
Multivariable Analysis ◽  
Risk Modeling ◽  
Newly Diagnosed ◽  
Advisory Committees ◽  
Cytogenetic Abnormalities ◽  
Improve Risk Stratification

Introduction: Abnormal metaphase cytogenetics are associated with inferior survival in newly diagnosed multiple myeloma (MM). These abnormalities are only detected in one third of cases due to the low proliferative rate of plasma cells. It is unknown if metaphase cytogenetics improve risk stratification when using contemporary prognostic models such as the revised international staging system (R-ISS), which incorporates interphase fluorescence in situ hybridization (FISH). Aims: The aims of this study were to 1) characterize the association between abnormalities on metaphase cytogenetics and overall survival (OS) in newly diagnosed MM treated with novel agents and 2) evaluate whether the addition of metaphase cytogenetics to R-ISS, age, and plasma cell labeling index (PCLI) improves model discrimination with respect to OS. Methods: We analyzed a retrospective cohort of 483 newly diagnosed MM patients treated with proteasome inhibitors (PI) and/or immunomodulators (IMID) who had metaphase cytogenetics performed prior to initiation of therapy. Abnormal metaphase cytogenetics were defined as MM specific abnormalities, while normal metaphase cytogenetics included constitutional cytogenetic variants, age-related Y chromosome loss, and normal metaphase karyotypes. Multivariable adjusted proportional hazards regression models were fit for the association between known prognostic factors and OS. Covariates associated with inferior OS on multivariable analysis included R-ISS stage, age ≥ 70, PCLI ≥ 2, and abnormal metaphase cytogenetics. We devised a risk scoring system weighted by their respective hazard ratios (R-ISS II +1, R-ISS III + 2, age ≥ 70 +2, PCLI ≥ 2 +1, metaphase cytogenetic abnormalities + 1). Low (LR), intermediate (IR), and high risk (HR) groups were established based on risk scores of 0-1, 2-3, and 4-5 in modeling without metaphase cytogenetics, and scores of 0-1, 2-3, and 4-6 in modeling incorporating metaphase cytogenetics, respectively. Survival estimates were calculated using the Kaplan-Meier method. Survival analysis was stratified by LR, IR, and HR groups in models 1) excluding metaphase cytogenetics 2) including metaphase cytogenetics and 3) including metaphase cytogenetics, with IR stratified by presence and absence of metaphase cytogenetic abnormalities. Survival estimates were compared between groups using the log-rank test. Harrell's C was used to compare the predictive power of risk modeling with and without metaphase cytogenetics. Results: Median age at diagnosis was 66 (31-95), 281 patients (58%) were men, median follow up was 5.5 years (0.04-14.4), and median OS was 6.4 years (95% CI 5.7-6.8). Ninety-seven patients (20%) were R-ISS stage I, 318 (66%) stage II, and 68 (14%) stage III. One-hundred and fourteen patients (24%) had high-risk abnormalities by FISH, and 115 (24%) had abnormal metaphase cytogenetics. Three-hundred and thirteen patients (65%) received an IMID, 119 (25%) a PI, 51 (10%) received IMID and PI, and 137 (28%) underwent upfront autologous hematopoietic stem cell transplantation (ASCT). On multivariable analysis, R-ISS (HR 1.59, 95% CI 1.29-1.97, p < 0.001), age ≥ 70 (HR 2.32, 95% CI 1.83-2.93, p < 0.001), PCLI ≥ 2, (HR 1.52, 95% CI 1.16-2.00, p=0.002) and abnormalities on metaphase cytogenetics (HR 1.35, 95% CI 1.05-1.75, p=0.019) were associated with inferior OS. IR and HR groups experienced significantly worse survival compared to LR groups in models excluding (Figure 1A) and including (Figure 1B) the effect of metaphase cytogenetics (p < 0.001 for all comparisons). However, the inclusion of metaphase cytogenetics did not improve discrimination. Likewise, subgroup analysis of IR patients by the presence or absence of metaphase cytogenetic abnormalities did not improve risk stratification (Figure 1C) (p < 0.001). The addition of metaphase cytogenetics to risk modeling with R-ISS stage, age ≥ 70, and PCLI ≥ 2 did not improve prognostic performance when evaluated by Harrell's C (c=0.636 without cytogenetics, c=0.642 with cytogenetics, absolute difference 0.005, 95% CI 0.002-0.012, p=0.142). Conclusions: Abnormalities on metaphase cytogenetics at diagnosis are associated with inferior OS in MM when accounting for the effects of R-ISS, age, and PCLI. However, the addition of metaphase cytogenetics to prognostic modeling incorporating these covariates did not significantly improve risk stratification. Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Alnylam: Research Funding; Celgene: Research Funding; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding. Kapoor:Celgene: Honoraria; Sanofi: Consultancy, Research Funding; Janssen: Research Funding; Cellectar: Consultancy; Takeda: Honoraria, Research Funding; Amgen: Research Funding; Glaxo Smith Kline: Research Funding. Leung:Prothena: Membership on an entity's Board of Directors or advisory committees; Takeda: Research Funding; Omeros: Research Funding; Aduro: Membership on an entity's Board of Directors or advisory committees. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

The Value of Risk Stratification Tools in Multiple Myeloma (MM) in the Real-World: Validation of the Revised- International Staging System (R-ISS) at Initiation of Treatment and Relevance of the ISS and the R-ISS for Risk Stratification in the Relapsed Setting Using Data from the Czech Registry of Monoclonal Gammopathies (RMG)

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 2418-2418 ◽  
Author(s):  
Roman Hájek ◽  
Jiri Jarkovsky ◽  
Walter Bouwmeester ◽  
Maarten Treur ◽  
DeCosta Lucy ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Board Of Directors ◽  
Real World ◽  
Research Funding ◽  
Risk Group ◽  
Stage I ◽  
Advisory Committees ◽  
Standard Practice ◽  
First Relapse ◽  
Using Data

Abstract The ISS stratifies survival risk in patients with MM based on β2-microglobulin and albumin levels. The R-ISS is an improved stratification tool, which also uses chromosomal abnormalities (CA) and lactate dehydrogenase (LDH). It was developed based on clinical trial data in the first-line setting but, has not been validated outside clinical trials or for use in the relapsed setting. Using data from the RMG, we assessed the real-world validity of the R-ISS at diagnosis. Additionally, as it is standard practice to re-stage patients after first relapse, we explored the value of re-estimating ISS stage in the relapsed setting and exploring the carry on effect of R-ISS from diagnosis. Re-estimation of R-ISS at relapse is not possible as standard practice often does not include CA measurement at first relapse. Assessment of improvement in stratification was based on visual comparison of median OS, hazard ratios (HR) and confidence intervals. Eligible patients were diagnosed with symptomatic MM between May 2007 and April 2016. A Cox regression model and Kaplan-Meier analyses assessed the performance of the ISS and R-ISS for stratifying patients based on survival both at diagnosis and at first relapse. Overall, there were 3027 patients at diagnosis however only 493 were included in these analyses due to unavailable CA values (84% of patients). ISS and R-ISS stage distribution at diagnosis was ISS I 31.2%, II 29.1% and III 39.6%; and R-ISS I 12% II 57% and III 31% (Table 1). Median overall survival (OS) in months (95% confidence interval [CI]), from diagnosis was 73.5 (68.0-NE), 40.5 (31.0-50.0) and 29.0 (20.9-37.2) in patients with ISS stage I, II and III, respectively, and not reached (NR), 46.6 (39.2-54.1) and 26.0 (18.2-33.8) in patients with R-ISS stage I, II and III, respectively. Table 2 shows HR, which indicate OS assessed in alternative ways was significantly different among the three stages for both ISS and R-ISS. R-ISS provided refined stratification than ISS alone, since R-ISS stage III classified patients with higher risk than ISS III alone, (shorter median OS, narrower CI, and stronger HR vs. ISS I and II). From the original sample of 493 patients at diagnosis, only 250 went on to receive further treatment after first relapse. The median OS months (95% CI) after first relapse was 46.4 (32.0-60.8), 22.8 (13.4-32.1) and 14.9 months (9.0-20.8) in patients staged as ISS stage I, II and III at diagnosis, respectively. In patients staged as R-ISS stage I, II and III at diagnosis it was NR, 25.6 (20.8-30.3) and 10.4 (6.7-14.2), respectively. Data to enable re-estimation of ISS and R-ISS at first relapse were available for 187 patients (R-ISS re-stratification was using CA data at diagnosis only). Median OS months (95% CI) from first relapse was 32.2 (15.5-49.0), 25.6 (11.5-39.6) and 10.8 (8.6-13.0) in patients at ISS stage I, II and III, respectively, and 23.3 (NE-NE), 28.4 (20.8-36.0) and 9.7 (6.5-12.9) in patients at R-ISS stage I, II and III, respectively. The HRs comparing OS from first relapse stratified by ISS at diagnosis indicated that re-estimating ISS did not improve stratification (Table 2). For R-ISS, compared with staging patients at diagnosis, staging at first relapse resulted in refined stratification between stage II and III, however assessment of HRs comparing to stage I was difficult owing to small sample sizes. Re-estimation of R-ISS stage at first relapse resulted in 26% of patients having their stage reclassified; the main drivers of reclassification to a lower R-ISS risk group were β2-microglobulin and albumin levels, and to a higher risk group, LDH levels. Our real-world data show that, at diagnosis R-ISS provides refined risk stratification compared with ISS. Further refinement seemed to be added by restaging at first relapse using R-ISS, not ISS however, CA measurements are not currently routinely measured at first relapse, limiting the practical utility of the R-ISS at this stage. Therefore, re-estimating R-ISS stage after first relapse may enable physicians improve estimation of patient prognosis. Both ISS and R-ISS have been developed for use at diagnosis when there is less evidence to predict prognosis, therefore risk stratification after first relapse should also consider other historical patient, disease and treatment factors contributing to improved risk stratification and improved treatment selection and outcomes. Disclosures Hájek: Novartis: Consultancy, Research Funding; Celgene: Research Funding; Amgen: Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Bouwmeester:Amgen: Consultancy. Treur:Amgen: Consultancy. Lucy:Amgen: Employment, Other: Amgen Stock. Campioni:Amgen: Employment, Other: Holds Amgen Stock. Delforge:Celgene: Honoraria; Amgen: Honoraria; Janssen: Honoraria. Raab:BMS: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Research Funding; Novartis: Consultancy, Research Funding. Schoen:Amgen: Employment, Other: Holds Amgen Stock. Szabo:Amgen: Employment, Other: Holds Amgen Stock. Gonzalez-McQuire:Amgen: Employment, Other: Holds Amgen Stock.

scholarly journals Evaluation of the International Prognostic Index for Chronic Lymphocytic Leukemia (CLL-IPI) and Validation of a Proposed Novel Risk Model (BALL Score) in Real-World Relapsed/Refractory (R/R) CLL Patients Receiving Idelalisib and Rituximab

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 5485-5485
Author(s):  
Massimo Gentile ◽  
Gianluigi Reda ◽  
Francesca Romana Mauro ◽  
Paolo Sportoletti ◽  
Luca Laurenti ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Board Of Directors ◽  
Research Funding ◽  
Advisory Board ◽  
Low Risk ◽  
Intermediate Risk ◽  
Advisory Committees ◽  
Scientific Advisory Board ◽  
Scientific Advisory

The CLL-IPI score, which combines genetic, biochemical, and clinical parameters, represents a simple worldwide model able to refine risk stratification for CLL patients. This score, developed in the era of chemo-immunotherapy, has not been gauged extensively in R/R-CLL patients treated with novel targeted agents, such as BCR and BCL2 inhibitors. Soumerai et al (Lancet Hematol 2019) assembled a novel risk model for OS in the setting of R/R-CLL receiving targeted therapies in clinical trials. This model, consisting of four accessible markers (β2M, LDH, Hb, and time from initiation of last therapy; BALL score), is able to cluster 3 groups of CLL patients with significantly different OS. This multicenter, observational retrospective study aimed to validate the proposed Soumerai (BALL) and/or CLL-IPI scores for R/R-CLL real-world patients treated with idelalisib and rituximab (IDELA-R). The primary objectives were to determine whether: i) the CLL-IPI retains its prognostic power also in R/R patients treated with IDELA-R; ii) the BALL score is of prognostic value for IDELA-treated R/R-CLL patients, and iii) the BALL score is predictive of PFS. This study, sponsored by Gilead (ISR#IN-IT-312-5339), included CLL patients collected from 12 Italian centers, who received IDELA-R (idelalisib 150 mg b.i.d. and a total of 8 rituximab infusions intravenously) outside clinical trials as salvage therapy with available data for the calculation of the CLL-IPI and BALL scores at the time of treatment start. OS was estimated for all subgroups of both scores. Additionally, risk-specific PFS was assessed. Kaplan-Meier curve, log-rank test, and Cox regression analyses were performed. The prognostic accuracy of the predictive model was assessed by Harrell's C-index. Overall, 120 CLL patients were included in this analysis. The majority of patients were Binet stage B and C (94.2%). The median age was 75 years and 83 cases (69.2%) were male. The median number of previous therapies was 3 (range 1-9) Baseline patient features are listed in Table 1. After a median follow-up of 1.6 years (1 month to 5.8 years), 33 patients had died and 39 experienced an event (death or progression). CLL-IPI scoring (115/120 evaluable cases) indicated that 6 patients (5.2%) were classified as low-risk, 24 (20.9%) as intermediate-risk, 58 (50.4%) as high-risk, and 27 (23.5%) as very high-risk. Stratification of patients according to the CLL-IPI score did not allow prediction of significant differences in OS. Thus, low-risk patients had a 2-year OS probability of 75% (HR=1), with an intermediate-risk of 68% (HR=2.9, 95%CI 0.37-23.3, P=0.3), high-risk of 83% (HR=1.58, 95%CI 0.2-12.5, P=0.66), and very high-risk of 63% (HR=5.9, 95%CI 0.78-45.2, P=0.86). Next, we tested a modified CLL-IPI by assigning a more balanced score to the original CLL-IPI variables (Soumerai et al, Leukemia Lymphoma 2019), partially overlapping previous results. Specifically, modified CLL-IPI high-risk group showed a significantly different OS as compared with intermediate- and low-risk groups. However, differently from the original report no difference was observed between low- and intermediate-risk). According to the BALL score (120/120 evaluable cases), 33 patients (27.5%) were classified as low-risk, 68 (56.7%) as intermediate-risk, and 19 (15.8%) as high-risk. Stratification of patients according to the BALL score predicted significant differences in terms of OS. Thus, low-risk patients had a 2-year OS probability of 92% (HR=1), intermediate-risk of 76% (HR=5.47, 95%CI 1.3-23.7, P=0.023), and high-risk of 54% (HR=15.1, 95%CI 3.4-67, P<0.0001) (Figure 1). Harrell's C-statistic was 0.68 (P<0.001) for predicting OS. To note, BALL score failed to significantly stratify patients in terms of PFS. As for Soumerai et al (Leukemia Lymphoma 2019), the original CLL-IPI score did not retain discriminative power in term of OS in R/R-CLL patients receiving IDELA-R. The modified CLL-IPI failed to stratify low- and intermediate-risk groups, likely due to the number of cases analysed in the current cohort and the heterogeneous IDELA-containing regimens included in the Soumerai study (Soumerai et al, Leukemia Lymphoma 2019). The CLL-IPI was designed for CLL patients treated with first-line chemo-immunotherapy. Herein, we confirm the prognostic power of the BALL score in this real-world series for OS, while losing the predictive impact of patient outcomes in terms of PFS. Disclosures Mauro: Gilead: Consultancy, Research Funding; Jannsen: Consultancy, Research Funding; Shire: Consultancy, Research Funding; Abbvie: Consultancy, Research Funding; Roche: Consultancy, Research Funding. Coscia:Abbvie: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Karyopharm Therapeutics: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding. Varettoni:ABBVIE: Other: travel expenses; Roche: Consultancy; Janssen: Consultancy; Gilead: Other: travel expenses. Rossi:Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Honoraria, Other: Scientific advisory board; Janseen: Honoraria, Other: Scientific advisory board; Roche: Honoraria, Other: Scientific advisory board; Astra Zeneca: Honoraria, Other: Scientific advisory board. Gaidano:AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sunesys: Consultancy, Honoraria; Astra-Zeneca: Consultancy, Honoraria; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.

A Distributed International Patient Data Registry for Hairy Cell Leukemia

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 5986-5986
Author(s):  
Leslie A. Andritsos ◽  
Michael R. Grever ◽  
Mirela Anghelina ◽  
Claire E Dearden ◽  
Monica Else ◽  
...  
Keyword(s):  
Board Of Directors ◽  
Hairy Cell Leukemia ◽  
Research Funding ◽  
Patient Data ◽  
Hairy Cell ◽  
Cell Leukemia ◽  
Advisory Committees ◽  
Web Based ◽  
Data Registry ◽  
The Web

Abstract BACKGROUND: The study of rare diseases is limited by the uncommon nature of the conditions as well as the widely dispersed patient populations. Current rare disease registries such as the National Organization of Rare Diseases utilize centralized platforms for data collection; however because of their broad nature, these do not always capture unique, disease specific elements. Hairy Cell Leukemia (HCL) is a rare leukemia globally with approximately 900 new cases diagnosed in the US each year. The HCL Foundation undertook creation of a Patient Data Registry that collects data from multiple HCL Centers of Excellence (COE) around the globe to better understand the complications, treatment outcomes, disease subtypes, comorbid conditions, epidemiology, and quality of life of patients with HCL. METHODS: Investigators at The Ohio State University Department of Biomedical Informatics and Division of Hematology in collaboration with the HCL Foundation developed a Patient Data Registry (PDR) for the longitudinal capture of high quality research data. This system differs from other registries in that it uses a federated( rather than centralized) architecture, wherein data is queried and integrated in an on-demand manner from local registry databases at each participating site. Further, the data collected for use in the registry combines both automated exports from existing electronic health records (EHRs) as well as additional data entered via a set of web-based forms. All manually entered data comes from source documents, and data provenance spanning electronic and manually entered data is maintained via multiple technical measures. Patients may be enrolled at HCL COE, or, if they do not have access to a COE they may enroll via a web-based portal (www.hairycellleukemia.org). At this time due to regulatory requirements the web-based portal is available to US patients only. All data are de-identified (see Figure 1: De-Identification Workflow) which reduces regulatory burden and increases opportunities for data access and re-use. End users have access to data via a project-specific query portal. RESULTS: The Patient Data Registry has been deployed at The Ohio State University, Royal Marsden Hospital, and MD Anderson Cancer Center, and is undergoing deployment at the University of Rochester. Up to 25 international HCL COE may participate. In addition, US patients are actively entering the registry via the web-based portal. To date, 227 patients have been consented to the registry with 119 of these being via the web-based entry point. CONCLUSION: We created an international and web-based patient data registry which will enable researchers to study outcomes in HCL in ways not previously possible given the rarity of the disease. This work was made possible by research funding from the Hairy Cell Leukemia Foundation. Figure De-Identification Workflow Figure. De-Identification Workflow Disclosures Andritsos: Hairy Cell Leukemia Foundation: Research Funding. Anghelina:Hairy Cell Leukemia Foundation: Research Funding. Lele:Hairy Cell Leukemia Foundation: Research Funding. Burger:Pharmacyclics: Research Funding. Delgado:Gilead: Consultancy, Honoraria; Novartis/GSK: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Research Funding; Infinity: Research Funding. Jones:AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics, LLC, an AbbVie Company: Membership on an entity's Board of Directors or advisory committees, Research Funding. Lozanski:Beckman Coulter: Research Funding; Genentech: Research Funding; Stemline Therapeutics Inc.: Research Funding; Boehringer Ingelheim: Research Funding. Montserrat:Morphosys: Other: Expert Testimony; Vivia Biotech: Equity Ownership; Gilead: Consultancy, Other: Expert Testimony; Pharmacyclics: Consultancy; Janssen: Honoraria, Other: travel, accommodations, expenses. Parikh:Pharmacyclics: Honoraria, Research Funding. Park:Genentech/Roche: Research Funding; Amgen: Consultancy; Juno Therapeutics: Consultancy, Research Funding. Robak:Pharmacyclics, LLC, an AbbVie Company: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding. Tam:janssen: Honoraria, Research Funding; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees. Heckler:Hairy Cell Leukemia Foundation: Research Funding. Payne:Hairy Cell Leukemia Foundation: Research Funding.

Promising Prognostic Values of Sequential Gene Expression Profiling in Multiple Myeloma Patients Undergoing Various Systemic Therapy

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 5648-5648
Author(s):  
Yazan Migdady ◽  
Taiga Nishihori ◽  
Rachid Baz ◽  
Ram Thapa ◽  
Youngchul Kim ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Risk Stratification ◽  
Board Of Directors ◽  
Research Funding ◽  
Risk Classification ◽  
Testing Time ◽  
Advisory Committees ◽  
Cutoff Value ◽  
Genetics Research

Abstract Background: A commercially available gene expression profiling (GEP) model is an important risk stratification method in multiple myeloma. GEP is helpful in dividing myeloma into low and high risk based on a pre-determined threshold, however, the value of sequential GEP testing on patients receiving sequential anti-myeloma therapies has yet to be fully determined. Methods: Fifty four myeloma patients at Moffitt Cancer Center underwent two sequential GEPs based a dedicated 70-gene panel using an Affymetrixmicroarray platform (MyPRSTM) from bone marrow aspirate samples during the course of their disease. Myeloma was characterized based on the risk classification (cutoff of score 45.2), risk score (0-100), differences in the score/classification, and molecular subtyping. Pearson correlation analysis was performed to correlate GEP scores at two time points. Overall survival (OS) was estimated using the Kaplan-Meier method from the time of first GEP analysis and OS curves were compared using the log-rank test. Multivariate Cox proportional hazards regression models were built for OS. Results: Majority were male (67%) and hadDurie-Salmon stage 2 or 3 disease (83%).Immunophenotypeswere IgG (56%), IgA (22%) and light chain (20%). At first GEP testing (Time 1), 20% (11/54) were high risk and the median score was 28.6 (range, 10.8-66.5) with frequency of molecular subtypes being cycle family (CD) 1&2 28%,hyperdiploidy(HY) 24%, low bone disease (LB) 22%, proliferation (PR) 17%, and MMSET associated (MS) 9%. There were no MAF associated cases in this cohort. At second GEP testing (Time 2), 28% (15/54) were high risk with a median score of 33.5 (range, 11.4-96.3). Pearson correlation coefficient for the GEP scores of Times 1 & 2 was r=0.77 (p<0.001). Risk group for eight patients (15%) changed with 6 moving from low to high risk and 2 with high to low risk. Molecular subtype for 18 patients (33%) changed with most frequent pattern being LB to PR (n=5) followed by PR to LB (n=3). Median OS for the entire group was 21.4 months. In this cohort, there were no statistical differences in OS based on risk stratification at Time 1 (p=0.187, log-rank), change in risk stratification (p=0.131), and molecular subtypes at Time 1 (p=0.09). When this cohort of patients was re-categorized based on the median GEP score (28.58), OS was significantly worse for those with scores higher than the median (n=27 (50%), p<0.001). With a new cutoff value of 30, patients with a GEP score≥ 30 had inferior OS either based on first GEP (Time 1; n=24 (44%), p=0.02; Figure 1) or second (Time 2; n=33 (61%), p=0.04; Figure 2). With the cutoff value of 30 as a new threshold, patients who had high scores for both Times 1 and 2 (n=24) had worse OS compared to those with both low scores (n=21) and those moved from low to high scores (n=9: p=0.017). When patients with GEP score differences between Times 1 and 2 of more than 1 standard deviation (n=4 moving from low to high with a total n=12) were compared to the rest, there were no statistical differences in OS (p=0.296). In multivariate analyses using the GEP score, score difference, molecular subtype and risk classification, GEP score was the only significant predictive factor for inferior OS (hazard ratio 1.1, 95% confidence interval:1.03-1.18, p=0.006). Conclusions: Serial GEP analysis on myeloma patients demonstrates longitudinal changes in risk scores, molecular subtyping and risk classification suggesting clonal evolution or modifications of overall transcribed genetic signature with systemic therapy. GEP risk score may potentially provide graded risk assessment rather than dichotomous categorization as different risk threshold may be found based on observation of a new cutoff value predictive of OS. Further analysis of gene expression patterns is ongoing to elucidate risk stratification based on molecular signatures and to elucidate potential treatment associated profiles. Figure 1 Figure 1. Disclosures Nishihori: Novartis: Research Funding; Signal Genetics: Research Funding. Baz:Novartis: Research Funding; Millennium: Research Funding; Merck: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Signal Genetics: Research Funding; Karyopharm: Research Funding; Bristol-Myers Squibb: Research Funding. Van Laar:Signal Genetics, Inc.: Employment. Bender:Signal Genetics, Inc.: Employment. Alsina:Takeda/Millennium: Research Funding; Novartis: Research Funding; Signal Genetics: Consultancy; Amgen/Onyx: Consultancy, Speakers Bureau. Shain:Takeda/Millennium: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Signal Genetics: Research Funding; Amgen/Onyx: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Speakers Bureau.

scholarly journals Profound Impact of Sample Processing Delay on Gene Expression of Multiple Myeloma Plasma Cells

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2996-2996
Author(s):  
Tobias Meissner ◽  
Anja Seckinger ◽  
Kari Hemminki ◽  
Uta Bertsch ◽  
Asta Foersti ◽  
...  
Keyword(s):  
Gene Expression ◽  
Risk Stratification ◽  
Board Of Directors ◽  
Research Funding ◽  
Plasma Cells ◽  
University Of Arkansas ◽  
Medical Sciences ◽  
Advisory Committees ◽  
Travel Grant ◽  
The Uk

Abstract Introduction: Gene expression profiling (GEP) has significantly contributed to the elucidation of the molecular heterogeneity of multiple myeloma plasma cells (MMPC) and only recently it has been recommended for risk stratification. Prior to GEP MMPC need to be enriched resulting in an inability to immediately freeze bone marrow aspirates or use RNA stabilization reagents. As a result in multi-center MM trials sample processing delay due to shipping may be an important confounder of molecular analyses and risk stratification based on GEP data. In order to determine the impact of "shipping delay" on MMPC gene expression we analyzed a set of 573 newly diagnosed German MM patients including 230 in-house and 343 shipped samples. Materials and Methods: We included publicly available GEP data of newly diagnosed MM patients treated in the GMMG HD4 and MM5 trials. All samples had been processed in a central laboratory in Heidelberg and include 85 HD4 and 145 MM5 in-house and 97 HD4 and 246 MM5 shipped samples. Prediction of sample status was done on publicly available GEP, including data from the UK, UAMS and MMRC. Differential gene expression was assessed using empirical Bayes statistics in linear models for microarray data. Predictor for shipment status was generated on the MM5 cohort using prediction analysis for microarrays. Pathway enrichment analysis was done using WebGestalt. Risk signatures and molecular subgroups were obtained as previously described. Fisher's exact test was used to compare the subgroup distribution between cohorts. If applicable, results were corrected for multiple testing using the Benjamini-Hochberg method. In all statistical tests, an effect was considered statistically significant if the P-value of its corresponding statistical test was not greater than 5%. Results: Applying the Goeman's global teston the MM5 set showed that "shipping delay" significantly impacted global gene expression (P <0.001). Compared to 145 in-house samples, we detected 3301 down-regulated and 3501 up-regulated genes in 246 shipped samples. For 4280 genes we confirmed differential expression in an independent set of 85 in-house and 97 shipped samples. Of these genes 2040 had a >1.5-fold and 826 a >2-fold difference in expression level. Differentially expressed genes were enriched in processes like ribosome biogenesis, cell cycle, and apoptosis. We observed significantly lower proliferation rates in shipped samples (P <0.001). We did not detect significant differences in the distribution of molecular subgroups between in-house and shipped samples in the combined set of HD4 and MM5. Among GEP based risk predictors the IFM-15 seemed to underestimate high risk in shipped samples, whereas the GEP70 and the EMC-92 gene signatures were more robust. In order to provide a tool to assess the "shipping effect" in public repositories, we generated a 17-gene predictor for shipped samples with a 10-fold cross validation error rate of 0.06 for the training set and an error rate of 0.15 for the validation set. Applying the predictor to further publicly available data sets we detected the "shipping effect" signature in 11% of cases of the UAMS set, 94% of the UK set and 57% of the MMRC set. Conclusion: Our study shows that "shipping delay" widely influences gene expression of MMPC with different impact on molecular classification and risk stratification. Based on available data, currently no clear circumvention of the shipping impact on MMPC can be recommended. It should be avoided if possible or at least be taken into account. Disclosures Seckinger: Takeda: Other: Travel grant. Salwender:Celgene: Honoraria; Janssen Cilag: Honoraria; Bristol Meyer Sqibb: Honoraria; Amgen: Honoraria; Novartis: Honoraria. Goldschmidt:Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Millenium: Honoraria, Research Funding, Speakers Bureau; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen-Cilag: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Chugai: Honoraria, Research Funding, Speakers Bureau; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees. Morgan:MMRF: Honoraria; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; University of Arkansas for Medical Sciences: Employment; CancerNet: Honoraria; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees; Weismann Institute: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees. Hose:Takeda: Other: Travel grant; EngMab AG: Research Funding. Weinhold:Janssen Cilag: Other: Advisory Board; University of Arkansas for Medical Sciences: Employment.

scholarly journals Platelet Transcriptome Yields Progressive Markers in Chronic Myeloproliferative Neoplasms and Identifies Putative Targets of Therapy

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1469-1469
Author(s):  
Zhu Shen ◽  
Wenfei Du ◽  
Cecelia Perkins ◽  
Lenn Fechter ◽  
Vanita Natu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Growth Factor ◽  
Risk Stratification ◽  
Board Of Directors ◽  
Research Funding ◽  
Myeloproliferative Neoplasms ◽  
Rna Seq ◽  
Advisory Committees ◽  
Chronic Myeloproliferative Neoplasms ◽  
Healthy Donors

Abstract Predicting disease progression remains a particularly challenging endeavor in chronic degenerative disorders and cancer, thus limiting early detection, risk stratification, and preventive interventions. Here, profiling the spectrum of chronic myeloproliferative neoplasms (MPNs), as a model, we identify the blood platelet transcriptome as a proxy strategy for highly sensitive progression biomarkers that also enables prediction of advanced disease via machine learning algorithms. Using RNA sequencing (RNA-seq), we derive disease-relevant gene expression in purified platelets from 120 peripheral blood samples constituting two time-separated cohorts of patients diagnosed with one of three MPN subtypes at sample acquisition - essential thrombocythemia, ET (n=24), polycythemia vera, PV (n=33), and primary or post ET/PV secondary myelofibrosis, MF (n=42), and healthy donors (n=21). The MPN platelet transcriptome reveals an incremental molecular reprogramming that is independent of patient driver mutation status or therapy and discriminates each clinical phenotype. Differential markers in each of ET, PV and MF also highlight candidate genes as potential mediators of the pro-thrombotic and pro-fibrotic phenotypes in MPNs. In ET and PV, a strong thromboinflammatory profile is revealed by the upregulation of several interferon inducible transmembrane genes (IFITM2, IFITM3, IFITM10, IFIT3, IFI6, IFI27L1, IFI27L2), interleukin receptor accessory kinases/proteins (IRAK1, IL15, IL1RAP, IL17RC) and several solute carrier family genes (SLC16A1, SLC25A1, SLC26A8, SLC2A9) as glucose and other metabolic transport proteins, and coagulation factor V (F5). In MF, fibrosis-specific markers were identified by an additional focused comparison of MF patients versus ET and PV, showing increased expression of several pro-fibrotic growth factors (FGFR1, FGFR3, FGFRL1), matrix metalloproteinases (MMP8, MMP14), vascular endothelial growth factor A (VEGFA), insulin growth factor binding protein (IGFBP7), and cell cycle regulators (CCND1, CCNA2, CCNB2, CCNF). Also, focusing on the JAK-inhibitor ruxolitinib/RUX-specific signatures, we not only confirm previous observations on its anti-inflammatory and immunosuppressive effects (e.g. downregulation in our RUX-treated cohort of IL1RAP, CXCR5, CPNE3, ILF3) but also identify new gene clusters responsive to RUX - e.g. inhibition of type I interferon (e.g. IFIT1, IFIT2, IFI6), chromatin regulation (HIST2H3A/C, HIST1H2BK, H2AFY, SMARCA4, SMARCC2), epigenetic methylation in mitochondrial genes (ATP6, ATP8, ND1-6 and NDUFA5), and other proliferation, and proteostasis-associated markers as putative targets for MPN-directed therapy. Mechanistic insights from our data highlight impaired protein homeostasis as a prominent driver of MPN evolution, with a persistent integrated stress response. Preliminary ex vivo data on MPN patient bone-marrow-derived CD34+ cells and cultured megakaryocytes validate a proteostasis-focused subset of our peripheral platelet RNA-seq signatures. Further leveraging this substantive dataset, and in particular a progressive expression gradient across MPN, we develop a machine learning model (Lasso-penalized regression) predictive of the advanced subtype MF at high accuracy and under two conditions of validation: i) temporal Stanford internal (AUC-ROC of 0.96) and ii) geographic external cohorts (AUC-ROC of 0.97, using independently published data of an additional n=25 MF and n=46 healthy donors). Lasso-derived signatures offer a robust core set of &lt; 5 MPN progression markers. Together, our platelet transcriptome snapshot of chronic MPNs demonstrates a methodological avenue for disease risk stratification and progression beyond genetic data alone, with potential utility in a wide range of age-related disorders. Part of the work contributing to this abstract has been posted as a preprint at this link: https://www.biorxiv.org/content/10.1101/2021.03.12.435190v2 Figure 1 Figure 1. Disclosures Gotlib: Blueprint Medicines: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Deciphera: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Research Funding; Kartos: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; PharmaEssentia: Honoraria, Membership on an entity's Board of Directors or advisory committees; Cogent Biosciences: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Chair for the Eligibility and Central Response Review Committee, Research Funding; Allakos: Consultancy.

scholarly journals Geriatric Assessment Predicts Non-Fatal Toxicities and Survival for Intensively Treated Elderly Acute Myeloid Leukemia: A Prospective Study

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 222-222
Author(s):  
Gi June Min ◽  
Byung Sik Cho ◽  
Sung-Soo Park ◽  
Silvia Park ◽  
Young-Woo Jeon ◽  
...  
Keyword(s):  
Physical Function ◽  
Board Of Directors ◽  
Research Funding ◽  
Prognostic Value ◽  
Prediction Models ◽  
Survival Outcomes ◽  
Survival Prediction ◽  
Intensive Chemotherapy ◽  
Advisory Committees ◽  
Web Based

Abstract Introduction Geriatric assessment (GA) typically refers to a multidimensional evaluation designed to evaluate an older person's functional ability, physical health, cognition, psychological health, nutritional status, and social support. The purpose of GA is to develop time-efficient and straightforward tools to evaluate multiple patient characteristics, which may be predictive of treatment outcomes of elderly acute myeloid leukemia (eAML) patients treated with intensive chemotherapy. Given that there have been few prospective studies with conflicting results, we performed a single-center prospective observational cohort study (#KCT0002172) investigating the prognostic value of multiparameter GA domains for eAML patients' tolerance and survival outcomes after intensive chemotherapy. Patients and methods Newly diagnosed eAML patients aged over 60 years who received intensive chemotherapy (n=105) were prospectively enrolled between November 2016 and December 2019. The median age was 64 years (range, 60-75), and they were all considered fit for intensive chemotherapy, with adequate performance and organ function. All the enrolled patients were administered various questionnaires for pretreatment GA and functional evaluation, which included evaluation for social and nutritional support, cognition, depression, distress, and physical function. Results Of the 105 enrolled patients, 93% had an Eastern Cooperative Oncology Group performance score of 1 and received intensive chemotherapy. Among them, between 32.4% and 69.5% of patients met the criteria for impairment on each GA domain. Physical impairment measured by the Short Physical Performance Battery (SPPB) was significantly associated with non-fatal toxicities of Grade III-IV severe infection (odds ratio (OR) 3.000, 95% confidence interval (CI), 1.159-7.788, p=0.024) and acute renal failure (OR 3.891, 95% CI, 1.329-11.39, p=0.013). Cognitive dysfunction measured by the Mini-Mental Status Examination- Korean version of CERAD Assessment Packet was significantly associated with a higher risk of Grade III-IV infection (OR 2.667, 95% CI, 1.025-6.939, p=0.044) and prolonged hospitalization (OR 4.208, 95% CI, 1.485-4.229, p=0.005). Reduced physical function measured by the SPPB and depressive symptoms measured by the Korean version of Short form Geriatric Depressive Scale (SGDS-K) were predictive of worse overall survival (OS; hazard ratio (HR) 1.917, 95% CI, 1.074-3.420, p=0.027 and HR 1.902, 95% CI, 1.005-3.602, p=0.048). SPPB impairment was also significantly related to higher treatment-related mortality (TRM; HR 2.023, 95% CI, 11.057-3.874, p=0.033). Furthermore, gait or sit-and-stand speed, a component of SPPB, was the single most powerful tool to predict survival outcomes of both OS (HR 2.766, 95% CI, 1.471-5.200, p=0.002 and HR 3.615, 95% CI, 1.868-6.999, p&lt;0.001) and TRM (HR 2.461, 95% CI, 1.233-4.913, p=0.011 and HR 3.814, 95% CI, 1.766-8.237, p&lt;0.001). We reconfirmed the prognostic value of preexisting survival prediction models, Wheatley index scores, and web-based AML scores, contrasting to the lack of significance of Ferrara criteria. The addition of SPPB/SGDS-K or gait (or sit-and-stand) speed/SGDS-K improved the predictability of the Wheatley index and web-based AML scores with 69% and 90% relative increases in predictive power for survival, respectively. Conclusions We prospectively demonstrated the prognostic value of physical and psychological assessment by GA for survival outcomes in intensively treated eAML patients. Gait or sit-and-stand speed was the single most powerful tool to identify frailty and predict survival outcomes. The prognostic value of preexisting survival prediction models, Wheatley index scores, and AML scores was reconfirmed.. The addition of measures for physical function and depression improved the predictability of those prediction models for survival. Cognitive and physical impairment were able to identify non-fatal toxicities during intensive chemotherapy in eAML patients. Our data will facilitate the incorporation of GA measures into validated survival prediction models to determine initial treatment for eAML patients in routine clinical care and clinical trials. Further studies are warranted to determine the best ways to adjust the care provided for frail patients to improve treatment tolerance and outcomes. Disclosures Kim: Novartis: Research Funding; BMS: Research Funding; Pfizer: Research Funding; ILYANG: Research Funding; Takeda: Research Funding. Lee: Alexion, AstraZeneca Rare Disease: Honoraria, Membership on an entity's Board of Directors or advisory committees. Kim: AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; AIMS Biosciense: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; AML-Hub: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Astellas: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; BL & H: Research Funding; BMS & Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Boryung Pharm Co.: Consultancy; Daiichi Sankyo: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Handok: Consultancy, Honoraria; LG Chem: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Consultancy, Honoraria; Pintherapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Honoraria, Speakers Bureau; SL VaxiGen: Consultancy, Honoraria; VigenCell: Consultancy, Honoraria.

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