A Novel Inflammatory Index Is Sufficient to Identify Hemophagocytic Lymphohistiocytosis in Adult Patients with Hematologic Malignancies

Introduction: Hemophagocytic lymphohistiocytosis (HLH) is a life-threatening hyper-inflammatory syndrome which may occur in adults with hematologic malignancies (HM). The diagnosis of HLH in this context (HM-HLH) is hindered by a number of factors. First, the currently used HLH 2004 diagnostic criteria are derived from pediatric patients commonly with HLH-associated genetic lesions, a very different population than adults with cancer. Second, most parameters used for diagnosis of HLH are directly impacted by the underlying HM and may reflect the presence of the malignant clone itself rather than an inflammatory process. Finally, appropriate diagnostic cutoff values for laboratory abnormalities in HM-HLH have not been defined. In this study we determine the diagnostic value of the laboratory components of the HLH 2004 diagnostic criteria and establish optimal cutoffs for the diagnosis of HM-HLH in HM patients. Methods: This is a multicenter, retrospective study of adult patients with a hematologic malignancy in whom sCD25 was measured because of clinically suspected HM-HLH or as part of routine screening of patients with a newly diagnosed hematologic malignancy, between January 2012 and March 2020. We considered patients fulfilling the five of eight of the HLH 2004 diagnostic criteria to have HM-HLH. Patients fulfilling fewer than five criteria were assigned to the HM group. These cohorts were well balanced in terms of disease distribution. We established the optimal cutoffs for laboratory parameters used for the diagnosis of HM-HLH using receiver operating curves (ROC) in a discovery cohort and tested their performance in a validation cohort. In order to improve the results obtained using the individual ROC, we then created a combined ROC using parameters demonstrating the highest individual performance (highest area under the curve (AUC)), in order to develop a diagnostic index. Finally, we examined the performance of each parameter in each cohort by using a contingency table and Chi-square and Fisher's exact test to determine the positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity and likelihood ratio (LR) of disease for each parameter. Results: 212 adults with HM with or without HLH in whom testing for HLH was performed were included in the study. HMs were: B cell lymphoma (41%), T cell lymphoma (26%), Hodgkin lymphoma (9%), acute myeloid leukemia (8%), myelodysplastic syndrome (8%), myeloproliferative neoplasms (5%) and chronic lymphocytic leukemia (4%). 99 (47%) patients had HM-HLH. Despite considerable overlap in laboratory values between the patient groups, all parameters apart from fibrinogen were able to distinguish HM-HLH from HM alone, with ferritin and sCD25 having the greatest discriminatory power. ROC analysis revealed an optimal cutoff value of >5,600 U/mL for sCD25 (sensitivity/specificity 76%/78%, AUC=0.83) and >1,300 ng/ml for ferritin (sensitivity/specificity 76%/76%, AUC=0.83). Combining the two markers to create a novel inflammatory index (HM-INFL) yielded superior diagnostic ability (AUC =0.86). Using HLH 2004 cutoff levels the HM-INFL index had a sensitivity of 94% and NPV of 94% and when using the optimal cutoff levels, it had a specificity of 92% and PPV of 90% (Table 1). Conclusions: HM-INFL is an index comprising only ferritin and sCD25. Using the original HLH 2004 cutoffs the index is an effective screening tool. Using our newly defined cutoff levels obtained by ROC analysis it is highly specific and can be used as a confirmatory test for the diagnosis of HLH in HM patients. These findings also support the hypothesis that HLH in the context of HM is an inflammatory condition associated with immune dysregulation. Disclosures Miller: Foundation Medicines, Inc.: Consultancy. Daver:Daiichi Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Research Funding; Servier: Research Funding; Genentech: Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novimmune: Research Funding; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Trovagene: Research Funding; Fate Therapeutics: Research Funding; ImmunoGen: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Jazz: Consultancy, Membership on an entity's Board of Directors or advisory committees; Trillium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Syndax: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees. Jordan:Sobi: Consultancy.

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Outcomes of Patients with Hematologic Malignancies and COVID-19 Infection: A Report from the ASH Research Collaborative Data Hub

Blood ◽

10.1182/blood-2020-141327 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 7-8

Author(s):

William A. Wood ◽

Donna S. Neuberg ◽

John Colton Thompson ◽

Martin S. Tallman ◽

Mikkael A. Sekeres ◽

...

Keyword(s):

Board Of Directors ◽

Research Funding ◽

Hematologic Malignancy ◽

Disease Status ◽

Hematologic Malignancies ◽

Morbidity And Mortality ◽

Advisory Committees ◽

Presumptive Diagnosis ◽

Blood Cancer ◽

Hematologic Diseases

Introduction: The coronavirus disease 2019 (COVID-19) is an illness resulting from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019. Many patients with blood cancer have underlying immune dysfunction, and many are treated with chemotherapies and immunotherapies that are themselves profoundly immunosuppressive. Additionally, patients with blood cancer are often older, may have comorbid illness including hypertension and diabetes, and may be especially susceptible to complications of COVID-19 include hypercoagulability and thrombosis. For patients with hematologic malignancies, overall risk of morbidity and mortality from COVID-19 infection, and how this risk varies as a function of age, disease status, type of malignancy, and cancer therapy, has not yet been well defined. Methods: The ASH Research Collaborative COVID-19 Registry for Hematology was developed to study features and outcomes of COVID-19 infection in patients with underlying blood disorders, such as hematologic malignancies. The Registry opened for data collection on April 1, 2020. The Registry is a global effort and is housed on a secure data platform hosted by Prometheus Research, an IQVIA company. The Registry collects data from patients of all ages with a current or history of hematological disease, and either a laboratory-confirmed or presumptive diagnosis of SARS-CoV-2 infection. Data are made available and regularly updated on the ASH Research Collaborative website to guide the provider and patient communities. Data presented here are limited to malignant hematologic diseases only. Contributors are individual providers or designees submitting data on behalf of providers. Results: At the time of this analysis, data from 250 patients with blood cancers from 74 sites around the world had been entered into the Registry. The most commonly represented malignancies were acute leukemia (33%), non-Hodgkin lymphoma (27%), and myeloma or amyloidosis (16%). Patients presented with a myriad of symptoms, most frequently fever (73%), cough (67%), dyspnea (50%), and fatigue (40%). Use of COVID-19-directed therapies such as hydroxychloroquine (N=76) or azithromycin (N=59) was common. Overall mortality was 28%. Patients with a physician-estimated prognosis from the underlying hematologic malignancy of less than 12 months at the time of COVID-19 diagnosis and those with relapsed/refractory disease experienced a higher proportion of moderate/severe COVID-19 disease and death. In some instances, death occurred after a decision was made to forego ICU admission in favor of a palliative approach. Conclusions: Taken together, these data support the emerging consensus that patients with hematologic malignancies experience significant morbidity and mortality from COVID-19 infection. However, we see no reason, based on our data, to withhold intensive therapies from patients with underlying hematologic malignancies and favorable prognoses, if aggressive supportive care is consistent with patient preferences. Batch submissions from sites with high incidence of COVID-19 infection are ongoing. The Registry has been expanded to include non-malignant hematologic diseases, and the Registry will continue to accumulate data as a resource for the hematology community. Figure Disclosures Wood: Pfizer: Research Funding; Teladoc/Best Doctors: Consultancy; ASH Research Collaborative: Honoraria. Neuberg:Celgene: Research Funding; Madrigak Pharmaceuticals: Current equity holder in publicly-traded company; Pharmacyclics: Research Funding. Tallman:Amgen: Research Funding; UpToDate: Patents & Royalties; Bioline rx: Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Membership on an entity's Board of Directors or advisory committees; KAHR: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Delta Fly Pharma: Membership on an entity's Board of Directors or advisory committees; Oncolyze: Membership on an entity's Board of Directors or advisory committees; BioSight: Membership on an entity's Board of Directors or advisory committees, Research Funding; Cellerant: Research Funding; Orsenix: Research Funding; ADC Therapeutics: Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Jazz Pharma: Membership on an entity's Board of Directors or advisory committees; Rafael: Research Funding; Glycomimetics: Research Funding; Abbvie: Research Funding. Sekeres:BMS: Consultancy; Takeda/Millenium: Consultancy; Pfizer: Consultancy. Sehn:Karyopharm: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Apobiologix: Consultancy, Honoraria; AstraZeneca: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Acerta: Consultancy, Honoraria; TG therapeutics: Consultancy, Honoraria; Chugai: Consultancy, Honoraria; Servier: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Teva: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; MorphoSys: Consultancy, Honoraria; Merck: Consultancy, Honoraria; Lundbeck: Consultancy, Honoraria; Genentech, Inc.: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Verastem Oncology: Consultancy, Honoraria. Anderson:Janssen: Membership on an entity's Board of Directors or advisory committees; Sanofi-Aventis: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Oncopep and C4 Therapeutics.: Other: Scientific Founder of Oncopep and C4 Therapeutics.; Gilead: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium-Takeda: Membership on an entity's Board of Directors or advisory committees. Goldberg:Dava Oncology: Honoraria; ADC Therapeutics: Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy; Aptose: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding; Celularity: Research Funding; AROG: Research Funding; Aprea: Research Funding. Pennell:Astrazeneca: Consultancy; BMS: Consultancy; Eli Lilly: Consultancy; Amgen: Consultancy; Genentech: Consultancy; Cota: Consultancy; Merck: Consultancy; Inivata: Consultancy; G1 Therapeutics: Consultancy. Niemeyer:Celgene: Consultancy; Novartis: Consultancy. Hicks:Gilead Sciences: Research Funding.

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Financial Hardship Amongst Patients with Hematologic Malignancies: Using the EMR to Streamline and Prioritize Patient-Centered Care

Blood ◽

10.1182/blood-2021-150546 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 661-661

Author(s):

Sandeep S Voleti ◽

Nandita Khera ◽

Carolyn Mead-Harvey ◽

Sikander Ailawadhi ◽

Rafael Fonseca ◽

...

Keyword(s):

Board Of Directors ◽

Cancer Patients ◽

Mayo Clinic ◽

Research Funding ◽

Hematologic Malignancy ◽

Hematologic Malignancies ◽

Cancer Center ◽

Patient Centered ◽

Advisory Committees ◽

Alaskan Native

Abstract Background: Self-reported financial hardship (FH) amongst cancer patients is increasingly becoming a challenge for patients, caregivers, and healthcare providers. FH not only leads to financial struggles, significant lifestyle changes, and emotional distress, but also contributes to treatment noncompliance, affecting clinical outcomes. As treatment costs rise, it is crucial to develop efficient methods to proactively identify and alleviate FH in hematology practice. One potential approach is utilizing automated processes to identify those at highest risk of FH. At Mayo Clinic, screening for FH involves using a single financial strain question 'How hard is it for you to pay for the very basics like food, housing, medical care, and heating?' which all cancer patients answer annually as part of the institution's Social Determinants of Health (SDOH) assessment. Answers are on a five-point scale including not hard at all, not very hard, somewhat hard, hard, and very hard. In this study, we assess the prevalence and predictors for FH (denoted by a response of "Very hard" "Hard" or "Somewhat hard") amongst the Mayo Clinic hematologic malignancy patient population. Our study objective was to determine if this automated process could identify those at risk for FH. Methods: Patients who received care for hematologic malignancies (lymphoma, leukemia, plasma disorders, myelodysplastic/myeloproliferative disorders, and other heme malignancies) at any of the Mayo Clinic cancer centers (Minnesota, Arizona, and Florida) and who had completed the SDOH screen at least once were included in this study. The electronic medical record (EMR) and Mayo Clinic Cancer Registry were utilized to extract demographic and disease variables. Patient's home zip code was used to determine rural/urban residence, distance from cancer center, and the Area Deprivation Index (ADI), a measure of socioeconomic disadvantage based on home zip code (ranging from 1-100, with 100 representing the most disadvantaged). Multivariable logistic regression modeling was used to examine predictor variables for FH in this patient population. Results: The final cohort included 10,024 patients from 2018 to 2020. Median age was 64.6 years (IQR 58.1,73.7), 58% were male, and 79% married. Race/ethnicity composition was 94% White (n=9,268), 2.5% Black (n=246), 0.4% American Indian/Alaskan Native (44), and 4% Hispanic (n=360). Fifty-six percent of patients had Medicare and 41% had commercial insurance. Fifty percent were retired, 40% were working/students, and 72% were urban residents. Mean ADI was 41.2. Fifty-six percent of patients had lymphomas, 23.5% had plasma cell disorders, 8.5% had leukemias, 6.8% had other hematological malignancies, and 5.5% had myelodysplastic/myeloproliferative neoplasms. FH was reported by 12.8% (n=1286) of the patients. Table 1 shows the results of the multivariable model. A significantly higher likelihood of endorsing FH was noted in Hispanic vs non-Hispanics, Black and American Indian/Alaskan Native groups vs whites, Disabled/Unemployed vs working, Medicaid, Medicare, and Self-Pay groups vs commercial insurance, higher ADI (5 th quintile vs 1 st), and myelodysplastic/myeloproliferative disorder and other hematologic malignancy vs lymphoma patients. Older age, being retired, and living farther from the cancer center were associated with significantly less likelihood of endorsing FH. Conclusion: Our study used automated data extraction from the EMR to efficiently identify predictors of FH in hematologic cancer patients. Employing a dichotomized and automated "flag" for FH, particularly if incorporated in the EMR, could ease the identification of SDOH issues, facilitate timely connection to appropriate resources, and help provide better patient-centered care. Figure 1 Figure 1. Disclosures Ailawadhi: Sanofi: Consultancy; Cellectar: Research Funding; Karyopharm: Consultancy; Ascentage: Research Funding; Genentech: Consultancy; Janssen: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Beigene: Consultancy; GSK: Consultancy, Research Funding; AbbVie: Consultancy; Medimmune: Research Funding; Pharmacyclics: Consultancy, Research Funding; Takeda: Consultancy; Amgen: Consultancy, Research Funding; Xencor: Research Funding. Fonseca: OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy; BMS: Consultancy; Mayo Clinic in Arizona: Current Employment; Aduro: Consultancy; AbbVie: Consultancy; GSK: Consultancy; Merck: Consultancy; Juno: Consultancy; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Novartis: Consultancy; Bayer: Consultancy; Celgene: Consultancy; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy; Janssen: Consultancy; Amgen: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy. Griffin: Exact Sciences: Research Funding.

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Utilization of a Targeted Next Generation Sequencing Assay to Identify Copy Number Alterations in Chronic Lymphocytic Leukemia and Monoclonal B-Cell Lymphocytosis

Blood ◽

10.1182/blood-2021-153001 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 4677-4677

Author(s):

Julia E. Wiedmeier ◽

Chantal McCabe ◽

Daniel R. O'Brien ◽

Nicholas J. Boddicker ◽

Rosalie Griffin Waller ◽

...

Keyword(s):

Board Of Directors ◽

Copy Number ◽

Research Funding ◽

Chromosomal Abnormalities ◽

Hematologic Malignancies ◽

Lymphocytic Leukemia ◽

Advisory Committees ◽

Trisomy 12 ◽

Sensitivity Specificity ◽

Generation Sequencing

Abstract Introduction: Chronic lymphocytic leukemia (CLL) is characterized by multiple copy number alterations (CNA) and mutations that are central to disease pathogenesis, prognosis, risk-stratification, and identification of response or resistance to therapies. Fluorescence in situ hybridization (FISH) is gold standard in the clinical laboratory for detecting prognostic CNAs in CLL (e.g. deletion 17p13 (del(17p), deletion 11q23 (del(11q), deletion 13q14 (del(13q), and trisomy 12). Most clinical FISH assays have high specificity and sensitivity, but the technique can detect a limited number of alterations per assay. Importantly, next-generation sequencing (NGS) techniques have become more readily available for clinical applications and are increasingly being used for screening not only mutations, but also copy number abnormalities in multiple genes and chromosomal regions of interest in hematologic malignancies. Here, we evaluated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) using a custom targeted NGS assay for detecting common prognostic chromosomal alterations in CLL and high-count monoclonal B-cell lymphocytosis (MBL), the precursor to CLL. Methods : We designed a SureSelect DNA targeted sequencing panel, covering all exons of 59 recurrently CLL mutated genes and additional amplicons across regions affected by clinically relevant CNAs. All CLL (N=534) and MBL (N=162) patients had pre-treatment peripheral blood mononuclear cells (PBMC) collected within two years of diagnosis. DNA was extracted in cases with purity >80% of CD5+/CD19+ cells. Clinical FISH data was available within 100 days of NGS in all untreated CLL and MBL cases. PatternCNV was used to detect clinically relevant CNAs in chromosomes 11, 12, 13 and 17. We performed a principal component analysis on the CNA calls, excluding chromosomes 11, 12, 13, and 17 to identify clusters of samples. Each cluster was then independently rerun with PatternCNV and the results from chromosomes 11, 12, 13, and 17 were extracted and further analyzed. We excluded samples with low tumor metrics identified by FISH (less than 20% of cells with del(17p), del(11q), trisomy 12 and del(13q)). Results: We sequenced a total of 696 patients of whom 162 were MBL and 534 were untreated CLL. The most commonly mutated genes were NOTCH1 (11.0%), TP53 (8.7%), SF3B1 (7.7%), ATM (4.1%), and CHD2 (3.8%). Based on CNA analyses from the NGS data, we identified 59 (9.1%) individuals with del(17p), 88 (13.4%) individuals with del(11q), 128 (20.0%) individuals with trisomy 12, and 329 (53.0%) individuals with del(13q). All 696 individuals had FISH panels conducted, with 39 (5.6%) individuals with del(17p), 68 (9.8%) individuals with (11q), 119 (17.1%) with trisomy 12, and 295 (42.4%) with del(13q). When we compared our CNA analyses with the FISH data, we found high concordance 95.0% for del(17p), 92.7% del(11p), 94.3% for trisomy 12, and 88.2% for del(13q). For del(17p) we found a sensitivity of 93.9%, specificity of 95.4%, PPV of 52.5%, and NPV of 99.7%. Del(11q) revealed a sensitivity of 88.1%, specificity of 94.0%, PPV of 59.1%, and NPV 98.8%. We found a sensitivity of 93.8%, specificity of 95.6%, PPV 82.0%, and NPV of 98.6% for trisomy 12 and for del(13q) we found a sensitivity of 92.6%, specificity of 90.9%, PPV of 91.7%, and NPV of 93.8%. We found lower PPVs in del(17p) and del(11q) likely due to lower prevalence of these chromosomal abnormalities. Conclusion: Here we show a high sensitivity, specificity, and NPV when comparing targeted sequencing with FISH. FISH panel testing is widely used in clinical practice to characterize highly prognostic chromosomal abnormalities in CLL. Comprehensive genetic profiling with NGS has become increasingly important in the work up of hematologic malignancies and provides additional prognostic and predictive information, including clinically relevant mutations such as TP53, SF3B1, and NOTCH1, tumor mutation load and mutations associated with resistance to chemo-immunotherapy and targeted therapies, such as BTK or BCL2 inhibitors, that FISH cannot offer. We show that NGS can infer clinically relevant CNA in cases without FISH testing while also providing additional clinically relevant information. Figure 1 Figure 1. Disclosures Cerhan: Regeneron Genetics Center: Other: Research Collaboration; Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; NanoString: Research Funding; Genentech: Research Funding. Parikh: Pharmacyclics, MorphoSys, Janssen, AstraZeneca, TG Therapeutics, Bristol Myers Squibb, Merck, AbbVie, and Ascentage Pharma: Research Funding; Pharmacyclics, AstraZeneca, Genentech, Gilead, GlaxoSmithKline, Verastem Oncology, and AbbVie: Membership on an entity's Board of Directors or advisory committees. Kay: Genentech: Research Funding; MEI Pharma: Research Funding; Sunesis: Research Funding; Acerta Pharma: Research Funding; 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; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; CytomX Therapeutics: Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Research Funding; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Targeted Oncology: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Behring: Membership on an entity's Board of Directors or advisory committees.

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Interim FDG PET Imaging in CALGB 50203 Trial of Stage I/II Non-Bulky Hodgkin Lymphoma: Would Using Combined PET and CT Criteria Better Predict Response Than Each Test Alone?,

Blood ◽

10.1182/blood.v118.21.3644.3644 ◽

2011 ◽

Vol 118 (21) ◽

pp. 3644-3644 ◽

Cited By ~ 2

Author(s):

Lale Kostakoglu ◽

Heiko Schoder ◽

Nathan Hall ◽

David J. Straus ◽

Jeffrey L Johnson ◽

...

Keyword(s):

Board Of Directors ◽

Hodgkin Lymphoma ◽

Fdg Pet ◽

Research Funding ◽

Roc Analysis ◽

Stage I ◽

Advisory Committees ◽

Interim Pet ◽

Number Of Patients ◽

Pet Ct

Abstract Abstract 3644 Use of interim PET in Hodgkin Lymphoma (HL) for risk-adapted treatment has been confounded by a lack of standardized criteria for interpretation. The International Harmonisation Project (IHP) criteria (JCO 2007;25:571) have been validated and are widely used for restaging following therapy. Our objective was to validate the IHP criteria for response evaluation based on PET after two cycles and correlate with the “London”criteria and diagnostic CT-based (dCT) lesion size changes. Methods: Pts were accrued prospectively to CALGB 50203, a trial of doxorubicin, vinblastine and gemcitabine (AVG) for initial treatment of stage I-II non-bulky Hodgkin lymphoma (HL). All had FDG PET or PET/CT and a separate high-resolution dCT scan at baseline, after two cycles of AVG (PET-2 and dCT-2) and at the end of therapy. No treatment change was made based on the PET-2 results. Of 99 assessable pts, 88 had both PET-2 and dCT-2. The primary PET-2 interpretation was based on IHP criteria (uptake > mediastinal blood pool/background is positive), a secondary interpretation was performed using the 5 point London criteria (uptake > liver is positive). The percent decrease in the sum of the products of the diameter (%SPPD) was determined between baseline dCT and dCT-2. A receiver operator curve (ROC) analysis was performed to determine the best cut-off for %SPPD to define a positive and negative CT result. The PET-2 and dCT-2 (%SPPD change) data were correlated with progression free survival (PFS). Results: Sixty-four pts (73%) achieved a complete remission (CR)/CR unconfirmed (CRu), 23.9% a partial response (PR), and 3% had stable disease. After a median follow-up of 3.3 years (1.8–5.0 years), 23.9% of patients relapsed/progressed with an estimated 3-year PFS of 0.77 [CI 68,84]. Eleven of 24 (45.8%) PET-2 positive patients relapsed vs. 10 of 64 (15.6%) PET-2 negative patients (p=0.0004). The best cut-off determined from ROC analysis for %SPPD change was 65%. The comparative results for individual evaluation criteria are displayed in the Table. In the PET-2 positive group, a negative dCT-2 increased PFS by 27–35%, suggesting an influence from dCT-2 results. However, in the combinatorial analysis, some of the confidence intervals are large due to small number of patients in each individual group, particularly, when both PET-2 and dCT-2 were positive as well as when PET-2 was positive and dCT-2 was negative. Conclusions: Interim PET/CT after two cycles of chemotherapy using either IHP or London criteria has a high NPV in early stage non-bulky HL. However, the PPV of interim PET needs to be improved to guide clinical management. Combining PET-2 with %SPPD decrease after 2 cycles improves prediction of PFS compared to each test alone. These data provide a proof of concept for risk-adapted clinical trials and further studies are underway to confirm these findings in a larger population. Disclosures: Bartlett: seattle genetics: Research Funding. Cheson:Celphalon: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celegen: Membership on an entity's Board of Directors or advisory committees, Research Funding; GlaxoSmithKline: Research Funding.

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Subset Analysis of Response to Treatment of Chronic Phase CML in a Phase 1 Study of Ponatinib in Refractory Hematologic Malignancies

Blood ◽

10.1182/blood.v118.21.602.602 ◽

2011 ◽

Vol 118 (21) ◽

pp. 602-602 ◽

Cited By ~ 3

Author(s):

Jorge E. Cortes ◽

Hagop M. Kantarjian ◽

Neil Shah ◽

Dale Bixby ◽

Michael J. Mauro ◽

...

Keyword(s):

Board Of Directors ◽

Median Time ◽

Research Funding ◽

Phase 1 ◽

Chronic Phase ◽

Hematologic Malignancies ◽

Employment Equity ◽

Advisory Committees ◽

Mutation Status ◽

Equity Ownership

Abstract Abstract 602 Background: Ponatinib is a potent, oral, pan-BCR-ABL inhibitor active against the native enzyme and all tested resistant mutants, including the uniformly resistant T315I mutation. Initial findings of a phase 1 trial in patients (pts) with refractory hematologic malignancies have been reported. The effect of duration of treatment, prior treatment, and mutation status on response to treatment was examined in CML chronic phase (CP) pts who responded to ponatinib. Methods: An open-label, dose escalation, phase 1 trial of ponatinib in pts with hematologic malignancies is ongoing. The primary aim is to assess the safety; anti-leukemic activity is also being investigated. Pts resistant to prior treatments or who had no standard treatment available were enrolled to receive a single daily oral dose of ponatinib (2 mg to 60 mg). Subset analyses of factors impacting cytogenetic and molecular response endpoints (MCyR and MMR) were performed for pts with CP-CML. Data are presented through April 15, 2011. Results: In total, 81 pts (54% male) received ponatinib. Overall, 43 pts had CP with 34 ongoing at analysis. MCyR was observed as best response in 31/43 (72%), 27 (63%) CCyR. The median time to MCyR was 12 (3 to 104) wks. Response rates were assessed by duration of treatment (1 pt in CCyR at entry was excluded; 6 pts in PCyR had to achieve CCyR). At the 3 month assessment, 22/42 (52%) CP pts achieved MCyR; at 6 months, 24/42 (57%); at 12 months, 29/42 (69%) had MCyR. The impact of prior treatment on response and time to response was assessed. 42 pts (98%) had >2 prior TKIs and 28 (65%) ≥3 prior TKIs, including investigational agents. Of approved TKIs, all pts were previously treated with imatinib, 19 dasatinib or nilotinib after imatinib, and 21 both dasatinib and nilotinib after imatinib. MCyR rate decreased with number of prior TKIs (2 prior TKIs 13/14 [93%], ≥3 prior TKIs 17/28 [61%]) and number of approved TKIs (imatinib followed by dasatinib or nilotinib 17/19 [90%], or by both dasatinib and nilotinib 12/21 [57%]). Time to response was prolonged in pts more heavily treated with prior TKIs. Median time to MCyR increased with the number of prior TKIs and approved TKIs (2 TKIs 12 wks, ≥3 TKIs 32 wks). The effect of mutation status on response and time to response was also evaluated. At entry, 12 pts had the T315I mutation, 15 had other BCR-ABL kinase domain mutations, 12 had no mutations detected, 4 did not allow sequencing. MCyR response rate for CP pts with T315I was 11/12 (92%); for other mutations, 10/15 (67%); and no mutation, 7/12 (58%). Similarly, mutation status had an impact on time to response: median time to MCyR was 12 wks for those with T315I or other mutations and 32 wks in resistant pts with no mutation. All CP patients were evaluable for MMR. At analysis, MMR was 17/43 (40%). MMR rate was inversely related to number of prior TKIs (2 TKIs 10/14 [71%], ≥3 TKIs 6/28 [21%]), approved TKIs (imatinib followed by dasatinib or nilotinib 12/19 [63%], or by both dasatinib and nilotinib 4/21 [19%]), and was higher for T315I pts (7/12, 58%) and those with other mutations (7/15, 47%) compared with no mutation (2/12, 17%). Median time to MMR for CP pts was 97 wks; median time to MMR was shorter for pts who were less heavily treated (2 prior TKIs 24 wks) and those with T315I or other mutations (63 wks). Conclusion: In this subset analysis of the phase 1 data, ponatinib had substantial activity in all subgroups analyzed. Time on treatment, less prior therapy and kinase domain mutations were associated with higher response rates and early responses in CP pts. Cytogenetic responses improved over the first 12 months of treatment and were higher in less heavily treated pts. Disclosures: Cortes: Novartis: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Ariad: Consultancy, Research Funding. Kantarjian:Novartis: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; BMS: Consultancy, Research Funding; ARIAD: Research Funding. Shah:Ariad: Consultancy, Research Funding. Bixby:Novartis: Speakers Bureau; BMS: Speakers Bureau; GSK: Speakers Bureau. Mauro:ARIAD: Research Funding. Flinn:ARIAD: Research Funding. Hu:ARIAD: Employment. Clackson:ARIAD: Employment, Equity Ownership. Rivera:ARIAD: Employment, Equity Ownership. Turner:ARIAD: Employment, Equity Ownership. Haluska:ARIAD: Employment, Equity Ownership. Druker:MolecularMD: OHSU and Dr. Druker have a financial interest in MolecularMD. Technology used in this research has been licensed to MolecularMD. This potential conflict of interest has been reviewed and managed by the OHSU Conflict of Interest in Research Committee and t. Deininger:BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Ariad: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Genzyme: Research Funding. Talpaz:ARIAD: Research Funding.

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Measurement of PTCH1 Expression at Diagnosis Is an Appropriate Tool for Tyrosine Kinase Inhibitors Selection in Chronic Myeloid Leukemia Patients in Chronic Phase

Blood ◽

10.1182/blood.v126.23.2791.2791 ◽

2015 ◽

Vol 126 (23) ◽

pp. 2791-2791

Author(s):

Juan Manuel Alonso-Dominguez ◽

Felipe Casado ◽

MariaTeresa Gómez Casares ◽

Ismael Buno ◽

Francisca Ferrer-Marin ◽

...

Keyword(s):

Board Of Directors ◽

Research Funding ◽

First Line ◽

Advisory Committees ◽

Expression Ratio ◽

Predictive Values ◽

Free Survival ◽

Exact Test ◽

Sensitivity Specificity ◽

Prognostic Power

Abstract Imatinib treatment has radically changed the prognosis of patients with CML. However, around 23-32% of patients discontinue this therapy due to lack of efficacy. Second generation TKI are available, which exhibit greater potency, so there is scope to further improve the strategy of selection of the appropriate TKI in the first line setting. Measurement of PTCH1 expression at diagnosis has been proposed as a useful strategy to tailor first line therapy as patients with low PTCH1 expression showed a worse outcome. Signalling via SMO is inhibited by non-Hedgehog ligated PTCH1 in Hedgehog pathway. SMO and PTCH1/SMO expression ratio has also been related to response to imatinib. Our aim was to corroborate imatinib outcome prediction in a different cohort and compare the prognostic power of PTCH1, SMO and PTCH1/SMO. We have retrospectively studied 101 pre-treatment samples of patients who received first-line imatinib from 14 Spanish centres. Clinical data were recorded in the Spanish CML Registry (RELMC). Informed consent was signed by every patient. Predesigned assays for PTCH1, SMO and GUSB (control gene) were used in single qPCR reactions in duplicates and run in an ABI 7900. Receiver operating characteristic (ROC) curves were plotted for PTCH1, SMO and PTCH1/SMO expression ratio and the area under curve (AUC) was used to compare its capacity to predict imatinib failure free survival (IFFS). For the measurement with higher AUC a threshold was set to divide patients with high and low expression. TKI failure was defined as loss of CCyR, progression to advanced phase disease, death or change in treatment from imatinib due to lack of efficacy. Secondary endpoints were: probability of achieving <10% BCRABL/ABL at 3 months, probability of achieving CCyR; probability of achieving MMR, progression free survival (PFS) and overall survival related to CML (CML OS). TFFS, CCyR, MMR and CML OS were analyzed by Kaplan-Meier analysis and log-rank test. Fishers exact test was employed to analyze the relationship with <10% BCRABL/ABL at 3 months and PFS. All analysis were carried out in an intention-to-treat basis. Age, Sokal, and EUTOS scores were introduced with categorised PTCH1 expression in a forward stepping Cox regression analysis for prediction of IFFS. Sensitivity, specificity and negative predictive values for prediction of IFFS were calculated. Patient median follow-up was 33 months (2-151). 13 patients (12.9%) showed imatinib failure. The AUC of PTCH1, SMO and PTCH1/SMO expression ratio were 0.72, 0.55 and 0.71. A PTCH1 expression of 0.026 was used as cut-off. Low and high PTCH1 expression groups had a 10 year rates of IFFS of 64% vs 95% (p=0.01), CCyR at 1 year of 91% vs 93% (p=0.261) and MMR at 12 months of 53% vs 81% (p=0.022). Median time of the entire cohort of achievement of CCyR was 6 months. Fishers exact test for achievement of <10% BCRABL/ABL at 3 months was significant (p=0.021). Three patients who progressed to accelerated or blastic phase and two of them who died from CML were included in the low expression group but no significant results were obtained due to the low number of events. PTCH1 expression was the unique independent predictor of IFFS in the multivariate analysis (p=0.023, HR=5.8(1.3-26)). Sensitivity, specificity and negative predictive values were 84.6%, 55.7 and 96.1%. We have confirmed PTCH1 expression prognostic power and found a greater predictive capacity than SMO and PTCH1/SMO expression ratio. Compared to previous PTCH1 studies this is a more real-to-life cohort, extracted from tertiary and secondary hospitals and the results confirm PTCH1 expression can be applied to very different clinical settings (previous studies had been performed in a cohort from a national CML reference hospital). Maybe the greater prognostic power of PTCH1 expression reflects its biological role in CML expands further than controlling SMO activity. Therefore PTCH1 could be used as a therapeutic target instead of SMO inhibitors which have shown poor results and high toxicity in early phase clinical trials. A reference standard, similarly as made for BCRABL/ABL1 measurement, could be developed with a level of PTCH1 expression equivalent to the cut-off established in this study. In this way PTCH1 expression could be implemented in the clinical setting. Figure 1. Figure 1. Disclosures García-Gutierrez: Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Ariad: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Martinez-Lopez:Janssen: Honoraria; Bristol-Meyer Squibb: Honoraria; Novartis: Honoraria, Research Funding; Celgene: Honoraria.

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Clonal Hematopoiesis Prevalence Increases throughout Treatment of Newly Diagnosed Multiple Myeloma Patients

Blood ◽

10.1182/blood-2021-152025 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 1091-1091

Author(s):

Tarek H. Mouhieddine ◽

Chidimma Nzerem ◽

Robert A. Redd ◽

Andrew Dunford ◽

Matthew Joseph Leventhal ◽

...

Keyword(s):

Multiple Myeloma ◽

Board Of Directors ◽

Peripheral Blood ◽

Research Funding ◽

Hematologic Malignancy ◽

Newly Diagnosed ◽

Advisory Committees ◽

Clonal Hematopoiesis ◽

First Time ◽

Time Point

Abstract Background: Recent studies have identified clinical and genomic factors contributing to worse clinical outcomes in patients with multiple myeloma (MM). Clonal hematopoiesis (CH) reflects the presence of somatic driver mutations in the blood or marrow of otherwise asymptomatic individuals. Using a variant allele frequency (VAF) cutoff of 2%, we recently reported CH in 21.6% of MM patients at the time of autologous stem cell transplant (ASCT) and found it was associated with shorter overall survival (OS) and progression-free survival (PFS) in those who did not receive maintenance therapy with an immunomodulatory drug (IMiD). However, this finding was based on a single tertiary center and only included MM patients who received ASCT. Methods: We studied a larger cohort of 986 newly diagnosed MM cases. Whole-exome sequencing (WES) data of peripheral blood and bone marrow samples of 986 MM patients (523 transplanted and 463 non-transplanted) from the Multiple Myeloma Research Foundation (MMRF) Clinical Outcomes in MM to Personal Assessment of Genetic Profile (CoMMpass, NCT0145429) study were analyzed. Both peripheral blood and tumor samples were analyzed to filter out myeloma mutations that could be contaminating the peripheral blood. Given the lower depth of coverage compared to prior targeted sequencing studies, small clones with a VAF below 2% were not detected. Altogether, the WES samples had a total depth of coverage of 117.68X. All data were analyzed using R version 3.5.0 (R Core Team). Results: Among the total cohort, 113 CH mutations were detected in 101/986 (10.24%) patients. CH was detected in 42/523 (8.03%) transplanted patients, compared to 59/463 (12.74%) non-transplanted patients. The most commonly mutated genes were DNMT3A, TET2, ASXL1, PPM1D, and TP53. The median age of the cohort was 63 years (range: 27 - 93), 60% were male, and median follow-up was 3.9 years (95% CI: 3.7 - 4.0). The presence of CH was associated with age (69 vs. 62 years, P < 0.001). As expected, the median age of transplanted patients was lower (60 vs. 67 years) than in the non-transplanted group, which likely explains the higher prevalence of CH detected in the non-transplanted group. CH was associated with recurrent bacterial infections (P = 0.01) and increased cardiovascular disease (P = 0.006), but not with cerebrovascular disease (P = 0.74) or coagulopathies (P = 0.65). There was a trend towards worse PFS in non-ASCT patients with CH who were not treated with IMiDs (1.8 years) compared to non-CH IMiD-treated patients (2.7 years) (P < 0.001). A CH effect on PFS was not detected in ASCT patients. OS was not different in those with or without CH in both ASCT and non-ASCT groups. 8 (0.8%) patients developed a second hematologic malignancy. CH at the time of MM diagnosis was not associated with an increased risk of developing a second hematologic malignancy (P = 0.58). To determine whether CH clones emerged or evolved during treatment, we examined serial samples from 52 patients (36 ASCT patients and 16 non-transplanted patients) with sequential samples. The median time between the first and second time point was 3.1 years (range: 1.0 - 5.4 years). At the first time point, only 3/52 (5.8%) patients had CH, but that number increased to 13/52 (25.0%) at the second time point. Five out of the 13 (38%) were non-transplanted patients. All but 1 patient were exposed to IMiDs. The most common emerging mutated gene was DNMT3A, found in 7 patient samples at the second time point, compared to 2 patients at the first time point. Conclusion: Using WES in a large cohort of newly diagnosed MM patients, we detected CH in 10.2% (VAF ≥ 2%) of patients. CH and non-IMiD treatment confers a shorter PFS in non-transplanted MM patients. However, throughout IMiD-based treatment, MM patients tend to acquire and/or expand previously undetected CH clones, particularly DNMT3A. The clinical significance of this clonal expansion during therapy is yet to be elucidated, and for now, this observation does not yet change clinical management. Figure 1 Figure 1. Disclosures Steensma: Novartis: Current Employment. Ebert: Deerfield: Research Funding; GRAIL: Consultancy; Exo Therapeutics: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Skyhawk Therapeutics: Membership on an entity's Board of Directors or advisory committees. Soiffer: NMPD - Be the Match, USA: Membership on an entity's Board of Directors or advisory committees; Gilead, USA: Other: Career Development Award Committee; Rheos Therapeutics, USA: Consultancy; Kiadis, Netherlands: Membership on an entity's Board of Directors or advisory committees; Juno Therapeutics, USA: Other: Data Safety Monitoring Board; Precision Biosciences, USA: Consultancy; Jazz Pharmaceuticals, USA: Consultancy; Jasper: Consultancy; Takeda: Consultancy. Sperling: Adaptive: Consultancy. Getz: Scorpion Therapeutics: Consultancy, Current holder of individual stocks in a privately-held company, Membership on an entity's Board of Directors or advisory committees; IBM, Pharmacyclics: Research Funding. Ghobrial: AbbVie, Adaptive, Aptitude Health, BMS, Cellectar, Curio Science, Genetch, Janssen, Janssen Central American and Caribbean, Karyopharm, Medscape, Oncopeptides, Sanofi, Takeda, The Binding Site, GNS, GSK: Consultancy.

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Impact of the COVID-19 Pandemic on in-Person Visit Rates Among Patients with Hematologic Malignancies in the United States

Blood ◽

10.1182/blood-2021-153625 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 1930-1930

Author(s):

Gaurav Goyal ◽

Krystal W. Lau ◽

Xiaoliang Wang ◽

Amy J. Davidoff ◽

Scott F. Huntington ◽

...

Keyword(s):

Board Of Directors ◽

Research Funding ◽

Hematologic Malignancies ◽

Publicly Traded ◽

Advisory Committees ◽

Visit Rate ◽

Line Of Therapy ◽

Monthly Visit ◽

All Diseases ◽

Privately Held

Abstract Background/objectives: The COVID-19 pandemic led to a dramatic reduction of in-person medical care in the general population; however, impacts have not been well-characterized for patients with hematologic malignancies. This study assessed the impact of COVID-19 on healthcare delivery for patients with hematologic malignancies with documented active treatment. Methods: Patients from the nationwide Flatiron Health electronic health record (EHR)-derived de-identified database with confirmed diagnosis of AML, DLBCL, FL, MCL, CLL or MM, and age ≥ 18 years at initial diagnosis were included. To be included in the study, documented receipt of at least one systemic, non-maintenance line of therapy between March 1, 2016 - February 28, 2021 was required. Patients were categorized into treatment types within lines of therapy: Oral therapy (OralTx); outpatient infusions (OutPtTx); and inpatient infusions, including hematopoietic transplants and CAR-T cell therapy (InPtTx). Monthly visit rates were calculated as the number of visits (telemedicine or in-person [in-clinic treatment administration, vitals, and/or labs]) per active patient per 30-day standardized month. Only visits occurring within a line of therapy were included (i.e. during active therapy, excluding surveillance). Telemedicine was only available for abstraction during the pandemic period. We used time-series forecasting methods on pre-pandemic monthly visit rate data (March 2016 - February 2020) to estimate projected counterfactual visit rates between March 2020 - February 2021 (expected in-person visit rates if the pandemic had not occurred) for all diseases combined, each disease, and each treatment type. Differences between projected and actual monthly visit rates during the pandemic period were considered statistically significant and related to the pandemic if the actual visit rate was outside of the 95% prediction interval (PI) surrounding the projected estimate. Results: A total of 22,559 patients were included in this analysis (6,241 OralTx, 14,501 OutPtTx, 7,675 InPtTx): 4,069 AML, 3,641 DLBCL, 2,004 FL, 1,899 MCL, 4,574 CLL and 6,701 MM. There was a gradual downward trend in in-person visit rates across all diseases over the study period (March 2016 - February 2021, Figure) and general visit frequencies were lower for OralTx and higher for OutPtTx and InPtTx overall. For all diseases combined, early pandemic months (March - May 2020) saw an 18% (95% PI 8.9% - 25%) reduction in in-person visit rates averaged across OralTx and OutPtTx, with the projected rate being 1.5 (95% PI 1.3 - 1.6) visits per patient per month, compared to an actual rate of 1.2. Reductions in the in-person visit rates were significant for all 3 treatment types for MM, for OralTx for CLL, and for OutPtTx for MCL and CLL. Telemedicine visit rates were greatest for patients who received OralTx, followed by OutPtTx, then InPtTx, with greater use in the early pandemic months and subsequent decrease in later months. All in-person visit rates increased close to predicted rates in the later half of the pandemic period. Conclusions: In treatment of hematologic malignancies, overall documented in-person visit rates for patients on OralTx and OutPtTx significantly decreased during early pandemic months, but returned close to the projected rates later in the pandemic. There were no significant reductions in the overall in-person visit rate for patients on InPtTx. Variability in these trends by disease type was observed, with significant reductions in in-person visits impacting MM, CLL, and MCL. Figure. Visit rates over time according to treatment category Figure 1 Figure 1. Disclosures Lau: Roche: Current equity holder in publicly-traded company; Flatiron Health Inc: Current Employment. Wang: Roche: Current equity holder in publicly-traded company; Flatiron Health: Current Employment. Davidoff: AbbVie: Other: Family member consultancy; Amgen: Consultancy. Huntington: Bayer: Honoraria; Thyme Inc: Consultancy; Novartis: Consultancy; Flatiron Health Inc.: Consultancy; Genentech: Consultancy; SeaGen: Consultancy; Servier: Consultancy; AstraZeneca: Consultancy, Honoraria; TG Therapeutics: Research Funding; DTRM Biopharm: Research Funding; AbbVie: Consultancy; Pharmacyclics: Consultancy, Honoraria; Celgene: Consultancy, Research Funding. Calip: Pfizer: Research Funding; Roche: Current equity holder in publicly-traded company; Flatiron Health Inc: Current Employment. Shah: AstraZeneca: Research Funding; Seattle Genetics: Research Funding; Epizyme: Research Funding. Stephens: CSL Behring: Consultancy; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Consultancy; Celgene: Consultancy; JUNO: Research Funding; Mingsight: Research Funding; Abbvie: Consultancy; Arqule: Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding; Epizyme: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees; Innate Pharma: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding. Miksad: Flatiron Health Inc: Current Employment, Current holder of individual stocks in a privately-held company; Roche: Current equity holder in publicly-traded company. Parikh: GNS Healthcare: Current holder of individual stocks in a privately-held company; Onc.AI: Current holder of individual stocks in a privately-held company; Humana: Honoraria, Research Funding; Nanology: Honoraria; Thyme Care: Honoraria; Flatiron Health Inc: Honoraria. Takvorian: Pfizer: Research Funding; Genentech: Consultancy. Neparidze: GlaxoSmithKline: Research Funding; Janssen: Research Funding; Eidos Therapeutics: Membership on an entity's Board of Directors or advisory committees. Seymour: Flatiron Health Inc: Current Employment; Janssen: Membership on an entity's Board of Directors or advisory committees; Roche: Current equity holder in publicly-traded company; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees.

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Evaluating Hematologist's Knowledge of CAR T-Cell Therapy in Hematologic Malignancies

Blood ◽

10.1182/blood-2018-99-115036 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2269-2269

Author(s):

Lauren Willis ◽

Sara R. Fagerlie ◽

Sattva S. Neelapu

Keyword(s):

T Cell ◽

Cell Therapy ◽

Board Of Directors ◽

Research Funding ◽

Hematologic Malignancies ◽

Advisory Committees ◽

T Cell Therapy ◽

Car T Cell ◽

Car T Cell Therapy ◽

Car T

Abstract Background: The objective of this study was to assess current clinical practices of hematologist/oncologist (hem/onc) specialists related to chimeric antigen receptor (CAR) T-cell therapy in hematologic malignancies, in order to identify knowledge, competency, and practice gaps and barriers to optimal care. Methods: A continuing medical education (CME)-certified clinical practice assessment consisting of 25 multiple choice questions was developed to measure knowledge, skills, attitudes, and competence of hem/onc specialists regarding CAR T-cell therapy. The survey instrument was made available online to physicians without monetary compensation or charge. Respondent confidentiality was maintained, and responses were de-identified and aggregated prior to analyses. The activity launched on December 22, 2017 with global distribution, and participant responses are still being collected at the time of abstract submission. Results: At the time of this report there are 192 hem/onc activity participants, collection is on-going. Demographics are listed in Table 1 and levels of confidence and barriers to incorporating CAR T-cell therapy are listed in Table 2.Foundational KnowledgeSub-optimal knowledge was demonstrated in the area of CAR components, dosing, and FDA-approved indications.Over half (61%) could not correctly identify the components of a CAR construct (antigen-specific domain and the signaling domain).Almost half (45%) of the participants did not recognize that currently approved CAR T-cell therapies are dosed as a single infusion.25% demonstrated inaccurate knowledge by recommending patients wait 4 weeks after CAR T-cell infusion before driving.Over half (62%) of participants could not identify the FDA-approved indication for axicabtagene ciloleucel.Knowledge of Clinical Trial DataVery low awareness of efficacy data seen with various CAR T-cell products used to treat R/R B-cell ALL (ELIANA trial), R/R DLBCL (ZUMA-1, JULIET, TRANSCEND trials).Only 32% identified the correct CR/CRi rate seen with tisagenlecleucel in the ELIANA trial.Only 25% correctly identified the CR rate seen with axicabtagene ciloleucel in the ZUMA-1 trial.Only 32% demonstrated knowledge of the 6-month DFS rate for patients in the JULIET trial that had a CR at 3 months.Only 25% identified the association between the dose of JCAR017 and response rates from the TRANSCEND trial.Knowledge and Competence Managing Adverse EventsLack of competence recognizing and treating CAR T-cell associated adverse events such as cytokine release syndrome (CRS) and neurotoxicity.Almost half (44%) could not identify signs of CRS associated with CAR T-cell therapy and 43% lack knowledge that elevated serum C-reactive protein (CRP) is associated with the highest level of CRS (in patients with lymphoma receiving axicabtagene ciloleucel).41% could not identify that the mechanism of tocilizumab is to block IL-6 signaling.Over a third (35%) were unable to identify signs/symptoms/causes of neurotoxicity associated with CAR T-cell therapy.More than half of the learners (54%) could not identify the appropriate role of corticosteroid therapy after CAR T-cell administration in managing CRS and neurotoxicity. Conclusions: This activity found knowledge and competence deficits for hem/onc practitioners related to using CAR T-cell therapy for the treatment of patients with hematologic malignancies. Additionally, the activity demonstrated large gaps in confidence discussing CAR T-cell therapy with patients/families and managing adverse events. There is sub-optimal awareness of CAR T-cell foundational knowledge, clinical trial data, and recognition of common therapy related adverse events and management strategies. Additional education is needed to improve the knowledge, competence, and confidence of academic and community hem/onc specialists who care for patients with hematologic malignancies receiving CAR T-cell therapy as well as strategies for integrating novel agents into clinical practice. Disclosures Neelapu: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Cellectis: Research Funding; Poseida: Research Funding; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta: Research Funding; Karus: Research Funding; Bristol-Myers Squibb: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees; Unum Therapeutics: Membership on an entity's Board of Directors or advisory committees; Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.

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Exome Array Analyses Identify Low-Frequency Germline Variants Associated with Increased Risk of AML in a HLA-Matched Unrelated Donor Blood and Marrow Transplant Population

Blood ◽

10.1182/blood.v128.22.42.42 ◽

2016 ◽

Vol 128 (22) ◽

pp. 42-42

Author(s):

Alyssa I. Clay ◽

Theresa Hahn ◽

Qianqian Zhu ◽

Li Yan ◽

Leah Preus ◽

...

Keyword(s):

Board Of Directors ◽

Research Funding ◽

De Novo ◽

Association Studies ◽

Low Frequency ◽

Hematologic Malignancies ◽

Unrelated Donor ◽

Advisory Committees ◽

De Novo Aml ◽

Normal Cytogenetics

Abstract Both genome wide association studies (GWAS) of common variation and exome wide association studies (EXWAS) of rare variation have successfully identified disease susceptibility variants for a variety of diseases. One GWAS of inherited susceptibility to Acute Myeloid Leukemia (AML) has been conducted, but no EXWAS have been performed to measure risk of AML attributable to low-frequency constitutional genetic variation. We performed the first EXWAS of risk of AML as a nested case-control study in the DISCOVeRY-BMT (Determining the Influence of Susceptibility Conveying Variants Related to one-Year mortality after BMT) cohorts. The DISCOVeRY-BMT parent study examined transplant-related mortality in leukemia patients undergoing unrelated donor allogeneic BMT. To identify low frequency variants and genes contributing to increased susceptibility to AML we used genotype data from the Illumina HumanExome BeadChip typed in the DISCOVeRY-BMT cohorts; the HumanExome BeadChip contains 242,901 variants, which are mainly protein-coding variants. The optimal sequence kernel association test (SKAT-O) was used to analyze gene-level associations with risk of AML. These gene-based tests evaluate the cumulative effects of multiple single gene variants on risk of AML. Analyses were performed in all European American AML cases and two subtypes: 1) de novo AML, 2) de novo AML with normal cytogenetics. Models were adjusted for age at transplant and principal components to control for population stratification. For gene-based tests at least 2 variants with minor allele frequency (MAF) ≤ 5%, were required to be present in the gene. This yielded a total of 13,687 genes tested, and a Bonferroni corrected significance level of P<3.65 x 10-6. Association tests were performed in 1,189 AML cases reported to CIBMTR 2000-08 (Cohort 1) and 327 AML cases reported to CIBMTR from 2009-11 (Cohort 2). Controls in Cohorts 1 (n=1,986) and 2 (n= 515) were 10/10 HLA-matched unrelated donors who passed a comprehensive medical exam and deemed healthy. We used metaSKAT to combine Cohorts 1 and 2 and obtain p-values of association with AML. We present the results of gene-level tests significant in both cohorts. The likely pathogenicity of these variants was determined in silico using SIFT, PolyPhen and MutationTaster. Patient characteristics are in Table 1. DNMT3A, on chromosome 2, was associated in the gene-based test with risk of AML (Pmeta=1.70x10-9, Table 2). Three missense variants at MAF <1% comprise both overall AML and de novo AML gene-based association: exm177559 (Asn->Ser), exm177507 (Arg->His), and exm177543 (Arg->Trp). Normal cytogenetics de novo AML gene-based assocations consisted of only 2 of these variants: exm177559 and exm177507 (Table 2). While prevalence of exm177507 is <1% for all AML cases, in de novo AML with normal cytogenetics the MAF was higher at 3%. The other 2 variants had a MAF<1% irrespective of subtype. Somatically, DNMT3A is most frequently mutated in hematologic malignancies, with >30% of de novo AML cases with a normal karyotype and >10% of MDS patients having DNMT3A mutations. Although these are germline gene associations all three of the variants found have been reported somatically in hematologic malignancies. In 200 AML cases from The Cancer Genome Atlas (TCGA) p.R882H (represented as exm177507 on the exome chip) was a frequent somatic mutation (25%). Exm177543 (p.R635W) and exm177559 (p.N501S) are reported in the Catalogue of Somatic Mutations in Cancer (COSMIC) as somatic mutations involved in hematopoietic and lymphoid tissue in both cell lines and humans. Exm177507 and exm177543 show evidence of pathogenicity in all three in silico tools, while exm177559 was reported as deleterious and disease causing by Sift and MutationTaster, respectively. Our results show that multiple potentially pathogenic missense germline variants in DNMT3A comprise the gene-based association with AML, specifically de novo AML with normal cytogenetics. Given the functional nature of these variants it is possible germline risk stratification could be informative in determining AML risk, and subsequently development of AML harboring DNMT3A mutations. Confirmation of these findings in additional cohorts could have implications for individualized risk screening, prediction and prognosis. Additional cytogenetic subgroup analyses, including treatment-related AML, are underway. Disclosures Hahn: Novartis: Equity Ownership; NIH: Research Funding. McCarthy:Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Onyx: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees; The Binding Site: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees; Gamida Cell: Honoraria, Membership on an entity's Board of Directors or advisory committees. Sucheston-Campbell:NIH/NCI: Research Funding.

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