Predicting Response to Hypomethylating Agents in Patients with Myelodysplastic Syndromes (MDS) Using Artificial Intelligence (AI)

Introduction While the hypomethylating agents (HMAs) azacitidine (AZA) and decitabine (DAC) improve cytopenias and prolong survival in MDS patients (pts), response is not guaranteed. Timely identification of non-responders could prevent prolonged exposure to ineffective therapy, thereby reducing toxicities and costs. Currently no widely accepted clinical or genomic models exist to predict response or resistance to HMAs. We developed a clinical model to predict response or resistance to HMA after 90 days of initiating therapy based on changes in blood counts using time series analysis technology similar to the kind used in Apple's Siri or Google Assistant. In the setting of voice recognition, the sequence and context of words determines the meaning of a sentence; similarly, we hypothesized that the pattern of changes in MDS pts' blood counts would predict response or resistance early during treatment. Methods We screened a cohort of 107 pts with MDS (per 2016 WHO criteria) who received HMAs at our institution between February 2005 and July 2013 and had regular CBCs drawn during treatment. Mutations from a panel of 60 genes commonly mutated in myeloid malignancy were included. Responses were assessed after 6 months of therapy per International Working Group (IWG) 2006 criteria. Pts were divided randomly into training (80%) and validation (20%) cohorts. To address the potential for bias due to a small sample size, an oversampling algorithm was used to cluster similar pts based on their CBC data, Revised International Prognostic Scoring System (IPSS-R) score, and % bone marrow blasts at the time of diagnosis. CBC data from the first 90 days of treatment were fed into deep neural network (recurrent neural network) and decision tree algorithms, which were trained to predict whether pts would achieve a response (defined as complete remission (CR), partial remission (PR), or hematologic Improvement (HI)). Area under the curve (AUC) was used to assess model performance. Important features that impact the algorithm's predictions were extracted and plotted. Results 20747 unique data points were used, including CBC, clinical and genomic data. Among 107 pts, 61 (57.0%) received AZA only, 19 (17.8%) DAC only, 4 (3.7%) received both DAC and AZA, and 23 (21.5%) received HMA with an additional agent. Median age was 69 years (range: 37-100 years), and 27 (26.4%) were female. Forty pts (37.4%) were very low/low risk, 32 (29.9%) intermediate, 19 (17.8%) high, and 16 (14.9%) very high risk per IPSS-R. Responses included 23 (22.5%) CR, 2 (1.9%) marrow CR, 4 (3.9%) PR, and 20 (19.6%) HI. The most commonly mutated genes were ASXL1 (17.6%), TET2 (16.7%), SRSF2 (15.7%), SF3B1 (11.8%), RUNX1 (10.8%), STAG2(10.8%), and DNMT3A (10.8%). The median number of mutations per sample was 1 (range, 0-11), and 40 pts (39.2%) had > 3 mutations per sample. When trained using absolute values and changes in CBC values, the model's AUC was 0.95 in the training cohort and 0.83 in the validation cohort. When the cohort was oversampled to 1000 pts, the validation cohort AUC increased to 0.89. Feature extraction algorithms identified increases in MCV and RDW during weeks 2-8 of treatment, increased proportion of lymphocytes, decreased proportion of monocytes, and increased platelet counts during weeks 6-8 as factors favoring response to HMA. The model provides personalized, patient-specific predictions that correlate with blood counts (Figure 1). Conclusions We describe a machine learning model that monitors changes in blood counts during therapy with HMA to predict response or resistance to HMA in MDS pts. Such a model can be used to develop novel trial designs wherein pts predicted to not respond after 90 days of HMA treatment could be assigned to an investigational agent. Conversely, it would help inform the decision to continue HMA therapy in pts predicted to respond. Increasing sample size with oversampling dramatically increased model accuracy; a larger cohort of pts treated at different institutions is currently under development. Disclosures Sekeres: Millenium: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Mukherjee:Partnership for Health Analytic Research, LLC (PHAR, LLC): Consultancy; Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Projects in Knowledge: Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria; McGraw Hill Hematology Oncology Board Review: Other: Editor; Bristol-Myers Squibb: Speakers Bureau. Advani:Glycomimetics: Consultancy, Research Funding; Kite Pharmaceuticals: Consultancy; Amgen: Research Funding; Pfizer: Honoraria, Research Funding; Macrogenics: Research Funding; Abbvie: Research Funding. Maciejewski:Alexion: Consultancy; Novartis: Consultancy. Nazha:Novartis: Speakers Bureau; Tolero, Karyopharma: Honoraria; Abbvie: Consultancy; Jazz Pharmacutical: Research Funding; Incyte: Speakers Bureau; Daiichi Sankyo: Consultancy; MEI: Other: Data monitoring Committee.

Download Full-text

Personalized Transcriptomic Analyses Identify Unique Signatures That Correlate with Genomic Subtypes in Acute Myeloid Leukemia (AML) Using Explainable Artificial Intelligence

Blood ◽

10.1182/blood-2020-139522 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 33-34 ◽

Cited By ~ 1

Author(s):

Yazan Rouphail ◽

Nathan Radakovich ◽

Jacob Shreve ◽

Sudipto Mukherjee ◽

Babal K. Jha ◽

...

Keyword(s):

Machine Learning ◽

Board Of Directors ◽

Research Funding ◽

State Of The Art ◽

Data Monitoring Committee ◽

Biological Pathways ◽

Machine Learning Algorithms ◽

Advisory Committees ◽

Altered Expression ◽

Individual Level

Background Multi-omic analysis can identify unique signatures that correlate with cancer subtypes. While clinically meaningful molecular subtypes of AML have been defined based on the status of single genes such as NPM1 and FLT3, such categories remain heterogeneous and further work is needed to characterize their genetic and transcriptomic diversity on a truly individualized basis. Further, patients (pts) with NPM1+/FLT3-ITD- AML have a better overall survival compared to patients with NPM1-/FLT3-ITD+, suggesting that these pts could have different transcriptomic signature that impact phenotype, pathophysiology, and outcomes. Many current transcriptome analytic techniques use clustering analysis to aggregate samples and look at relationships on a cohort-wide basis to build transcriptomic signatures that correlate with phenotype or outcome. Such approaches can undermine the heterogeneity of the gene expression in pts with the same signatures. In this study, we took advantage of state of the art machine learning algorithms to identify unique transcriptomic signatures that correlate with AML genomic phenotype. Methods Genomic (whole exome sequencing and targeted deep sequencing) and transcriptomic data from 451 AML pts included in the Beat AML study (publicly available data) were used to build transcriptomic signatures that are specific for AML patients with NPM1+/FLT3-ITD+ compared to NPM1+/FLT3-ITD, and NPM1-/FLT3-ITD-. We chose these AML phenotypes as they have been described extensively and they correlate with clinical outcomes. Results A total of 242 patients (54%) had NPM1-/FLT3-, 35 (8%) were NPM1+/FLT3-, and 47 (10%) were NPM1+/FLT3+. Our algorithm identified 20 genes that are highly specific for NPM1/FLT3ITD phenotype: HOXB-AS3, SCRN1, LMX1B, PCBD1, DNAJC15, HOXA3, NPTXq, RP11-1055B8, ABDH128, HOXB8, SOCS2, HOXB3, HOXB9, MIR503HG, FAM221B, NRP1, NDUFAF3, MEG3, CCDC136, and HIST1H2BC. Interestingly, several of those genes were overexpressed or underexpressed in specific phenotypes. For example, SCRN1, LMX1B, RP11-1055B8, ABDH128, HOXB8, MIR503HG, NRP1 are only overexpressed or underexpressed in patients with NPM1-/FLT3-, while PCBD1, NDUFAF3, FAM221B are overexpressed or underexpressed in pts with NPM1+/FLT3+. These genes affect several important pathways that regulate cell differentiation, proliferation, mitochondrial oxidative phosphorylation, histone modification and lipid metabolism. All these genes had previously been reported as having altered expression in genomic studies of AML, confirming our approach's ability to identify biologically meaningful relationships. Further, our algorithm can provide a personalized explanation of overexpressed and underexpressed genes specific for a given patient, thus identifying targetable pathways for each pt. Figure 1 below shows three pts with the same genotype (NPM1+/FLT3-ITD+) but demonstrate different transcriptomic patterns of overexpression or underexpression that affect different biological pathways. Conclusions We describe the use of a state of the art explainable machine learning approach to define transcriptomic signatures that are specific for individual pts. In addition to correctly distinguishing AML subtype based on specific transcriptomic signatures, our model was able to accurately identify upregulated and downregulated genes that affecte several important biological pathways in AML and can summarize these pathways at an individual level. Such an approach can be used to provide personalized treatment options that can target the activated pathways at an individual level. Disclosures Mukherjee: Partnership for Health Analytic Research, LLC (PHAR, LLC): Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; EUSA Pharma: Consultancy; Celgene/Acceleron: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squib: Honoraria; Aplastic Anemia and MDS International Foundation: Honoraria; Celgene: Consultancy, Honoraria, Research Funding. Maciejewski:Alexion, BMS: Speakers Bureau; Novartis, Roche: Consultancy, Honoraria. Sekeres:BMS: Consultancy; Takeda/Millenium: Consultancy; Pfizer: Consultancy. Nazha:Jazz: Research Funding; Incyte: Speakers Bureau; Novartis: Speakers Bureau; MEI: Other: Data monitoring Committee.

Download Full-text

Machine Learning Prediction for Complete Response to Hypomethylating Agents with or without Additional Agents in Patients with Newly Diagnosed Myelodysplastic Syndrome

Blood ◽

10.1182/blood-2019-130155 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 1720-1720

Author(s):

Koji Sasaki ◽

Guillermo Montalban Bravo ◽

Rashmi Kanagal-Shamanna ◽

Elias Jabbour ◽

Farhad Ravandi ◽

...

Keyword(s):

Machine Learning ◽

Board Of Directors ◽

Research Funding ◽

Area Under The Curve ◽

Complete Response ◽

Learning Model ◽

Hypomethylating Agents ◽

Newly Diagnosed ◽

Advisory Committees ◽

Machine Learning Model

Background: Myelodysplastic syndrome (MDS) is a heterogeneous malignant myeloid neoplasm of hematopoietic stem cells due to cytogenetic alterations and somatic mutations in genes (DNA methylation, DNA repair, chromatin regulation, RNA splicing, transcription regulation, and signal transduction). Hypomethylating agents (HMA) are the standard of care for MDS, and 40-60% of patients achieved response to HMA. However, the prediction for response is difficult due to the nature of heterogeneity and the context of clinical conditions such as the degree of cytopenias and the dependency on transfusion. Machine learning outperforms conventional statistical models for prediction in statistical competitions. Prediction with machine learning models may predict response in patients with MDS. The aim of this study is to develop a machine learning model for the prediction of complete response (CR) to HMA with or without additional therapeutic agents in patients with newly diagnosed MDS. Methods: From November 2012 to August 2017, we analyzed 435 patients with newly diagnosed MDS who received frontline therapy as follows; azacitidine (AZA) (3-day, 5-day, or 7-day) ± vorinostat ± ipilimumab ± nivolumab; decitabine (DAC) (3-day or 5-day) ± vorinostat; 5-day guadecitabine. Clinical variables, cytogenetic abnormalities, and the presence of genetic mutations by next generation sequencing (NGS) were included for variable selection. The whole cohort was randomly divided into training/validation and test cohorts at an 8:2 ratio. The training/validation cohort was used for 4-fold cross validation. Hyperparameter optimization was performed with Stampede2, which was ranked as the 15th fastest supercomputer at Texas Advanced Computing Center in June 2018. A gradient boosting decision tree-based framework with the LightGBM Python module was used after hyperparameter tuning for the development of the machine learning model with training/validation cohorts. The performance of prediction was assessed with an independent test dataset with the area under the curve. Results: We identified 435 patients with newly diagnosed MDS who enrolled on clinical trials as follows: 33 patients, 5-day AZA; 23, 5-day AZA + vorinostat; 43, 3-day AZA; 20, 5-day AZA + ipilimumab; 19 patients, AZA + nivolumab; 7, AZA + ipilumumab + nivolumab; 114, 5-day DAC; 74, 3-day DAC; 4, DAC + vorinostat; 97, 5-day guadecitabine. In the whole cohort, the median age at diagnosis was 68 years (range, 13.0-90.3); 117 (27%) patients had a history of prior radiation or cytotoxic chemotherapy; the median white blood cell count was 2.9 (×109/L) (range, 0.5-102); median absolute neutrophil count, 1.1 (×109/L) (range, 0.0-55.1); median hemoglobin count, 9.5 (g/dL) (range, 4.7-15.4); median platelet count, 63 (×109/L) (range, 2-881); and median blasts in bone marrow, 8% (range, 0-20). Among 411 evaluable patients for the revised international prognostic scoring system, 15 (4%) had very low risk disease; 42 (10%), low risk; 68 (17%), intermediate risk; 124 (30%), high risk; and 162 (39%), very high risk. Overall, 153 patients (53%) achieved CR. Hyperparameter tuning identified the optimal hyperparameters with colsample by tree of 0.175, learning rate of 0.262, the maximal depth of 2, minimal data in leaf of 29, number of leaves of 11, alpha regularization of 0.010, lambda regularization of 2.085, and subsample of 0.639. On the test cohort with 87 patients, the machine learning model accurately predicted response in 65 patients (75%); 53 non-CR among 56 non-CR (95% accuracy); and 12 CR among 31 CR (39% accuracy). The trend of accuracy improvement by iteration (i.e., the number of decision trees) is shown in Figure 1. The area under the curve was 0.761521 in the test cohort. Conclusion: Our machine learning model with clinical, cytogenetic, and NGS data can predict CR to HMA in patients with newly diagnosed MDS. This approach can identify patients who may benefit from HMA therapy with and without additional agents for response, and can optimize the timing of allogeneic stem cell transplant. Disclosures Sasaki: Otsuka: Honoraria; Pfizer: Consultancy. Jabbour:Takeda: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Cyclacel LTD: Research Funding. Ravandi:Cyclacel LTD: Research Funding; Selvita: Research Funding; Menarini Ricerche: Research Funding; Macrogenix: Consultancy, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Xencor: Consultancy, Research Funding. Kadia:Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Bioline RX: Research Funding; Jazz: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Research Funding; BMS: Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Takahashi:Symbio Pharmaceuticals: Consultancy. DiNardo:syros: Honoraria; jazz: Honoraria; agios: Consultancy, Honoraria; celgene: Consultancy, Honoraria; notable labs: Membership on an entity's Board of Directors or advisory committees; medimmune: Honoraria; abbvie: Consultancy, Honoraria; daiichi sankyo: Honoraria. Cortes:Novartis: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Immunogen: Consultancy, Honoraria, Research Funding; Sun Pharma: Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Astellas Pharma: Consultancy, Honoraria, Research Funding; Jazz Pharmaceuticals: Consultancy, Research Funding; Merus: Consultancy, Honoraria, Research Funding; Forma Therapeutics: Consultancy, Honoraria, Research Funding; Daiichi Sankyo: Consultancy, Honoraria, Research Funding; BiolineRx: Consultancy; Biopath Holdings: Consultancy, Honoraria; Takeda: Consultancy, Research Funding. Kantarjian:AbbVie: Honoraria, Research Funding; Cyclacel: Research Funding; Pfizer: Honoraria, Research Funding; Astex: Research Funding; Agios: Honoraria, Research Funding; Jazz Pharma: Research Funding; Daiichi-Sankyo: Research Funding; Novartis: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Immunogen: Research Funding; Takeda: Honoraria; BMS: Research Funding; Ariad: Research Funding; Amgen: Honoraria, Research Funding. Garcia-Manero:Amphivena: Consultancy, Research Funding; Helsinn: Research Funding; Novartis: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding; Merck: Research Funding.

Download Full-text

Pathologic Misdiagnosis in Children with Suspected Burkitt Lymphoma Associated with Early Mortality

Blood ◽

10.1182/blood-2018-99-116263 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2271-2271

Author(s):

Chloe Stiggelbout ◽

Megan Real-Hall ◽

Innocent Mutyaba ◽

Elizabeth M Krantz ◽

Scott Adams ◽

...

Keyword(s):

Sample Size ◽

Board Of Directors ◽

Burkitt Lymphoma ◽

Research Funding ◽

Early Mortality ◽

Disease Stage ◽

Advanced Stage ◽

Advisory Committees ◽

Kaplan Meier ◽

Stage Disease

Abstract INTRODUCTION Burkitt lymphoma (BL) is one of the most common childhood cancers across sub-Saharan Africa (Walusansa et. al, 2012). Unfortunately, the one-year survival rate of children with BL treated in low- and middle- income countries (LMICs) remains low, compared to higher-resource settings (Howard et al., 2008, Stanley et al., 2016, Buckle et al., 2016). Factors in LMICs contributing to this disparity include inability to give high-dose chemotherapy, lack of supportive care measures, and treatment abandonment (Gopal, 2018). The impact of diagnostic inaccuracies on BL outcome has not been well-studied to date. PURPOSE To determine the frequency and impact of an incorrect histopathologic diagnosis in children with suspected BL presenting to the Uganda Cancer Institute (UCI). METHODS Study Design and Participants -A sample of subjects with available tissue biopsies was selected from a cohort of children presenting to the UCI with suspected BL between July 2012 and July 2017. Laboratory Methods - Formalin fixed, paraffin embedded (FFPE) tumor blocks were obtained from local Ugandan pathology laboratories and sectioned in a single, central Ugandan histology lab. Slides were then shipped to a US-based reference laboratory for front-line evaluation by Hematoxylin and Eosin (H&E) staining, by intentionally streamlined immunohistochemistry (IHC) for CD20, c-Myc, and TdT detection, and by EBER-1 in situ hybridization (ISH) for EBV detection. A diagnosis of BL required the expected H&E appearance and prominent tumor expression of CD20, c-Myc, and EBER-1, with no significant TdT expression. For equivocal cases, additional CD10, CD21, bcl-2, and Ki67 IHC could be employed. Misdiagnosis Definition - A discrepancy between the pathologic diagnosis confirmed by IHC/ISH at the US-based laboratory, and the diagnosis that determined treatment in Uganda. Clinical and Statistical Analysis - Advanced disease stage included Ziegler stage C, D, or AR based on physical exam. Kaplan-Meier and Cox regression analysis were applied to evaluate survival. RESULTS We enrolled 97 participants of with a median age of 7 (interquartile range (IQR) 4-10); 69% were male, 47% had ECOG status 0-1, and 48% had advanced stage disease (though 22% had missing staging information - Table 1). The majority of patients had facial involvement, while less than half of the evaluable patients had abdominal involvement. Twenty percent of biopsies (19/97) were misdiagnosed. Median follow-up time was 7.1 (IQR 1-12) months, during which 68% (13/19) of misdiagnosed patients died, compared to 49% (38/78) of correctly diagnosed patients. The Kaplan Meier estimate of survival among the entire cohort was 42% (95%CI 31-52%); those with and without a misdiagnosis had survivals of 20% (95% CI 5-42%) and 46% (95% CI 34-57%), respectively (Figure 1). The logrank value comparing survival among those with and without a misdiagnosis was 0.0047. CONCLUSIONS BL diagnosis remains challenging in resource-limited areas, with a high misdiagnosis rate of 20% in this cohort. Misdiagnosed patients tended to be younger and to have more advanced stage disease. We observed a significant positive association between misdiagnosis and early mortality. Misdiagnosis likely contributes to poorer BL survival in low-resource settings by increasing the chance of treatment for the wrong tumor type. SIGNIFICANCE Study limitations include relatively small sample size and the potential for selection bias among patients who had tissues samples available; however, the 12-month survival of all patients diagnosed with BL at the UCI during the study period was around 55%, and not markedly different from the 42% seen here. Next steps include a repeat study with a larger sample size. Finally, our novel IHC/ISH diagnostic algorithm, requiring 6 total slides (including 1 control slide to assess RNA quality), worked with high sensitivity and specificity, and will be described separately. Disclosures Real-Hall: Phenopath Laboratories: Employment. Adams:Burkitt Lymphoma Fund for Africa: Membership on an entity's Board of Directors or advisory committees, Research Funding. Uldrick:Celgene: Research Funding; Celgene: Patents & Royalties: 10,001,483 B2; Merck: Research Funding. Casper:Janssen: Consultancy, Research Funding; Up to Date: Patents & Royalties; TempTime: Consultancy, Other: Travel, Accommodation, Expenses; GSK: Other: Travel, Accommodation, Expenses; Roche: Consultancy, Other: Travel, Accommodation, Expenses. McGoldrick:Burkitt Lymphoma Fund for Africa: Membership on an entity's Board of Directors or advisory committees, Research Funding; Seattle Genetics: Employment. Kussick:Phenopath Laboratories: Employment, Equity Ownership.

Download Full-text

Next-Generation Sequencing to Assess the Impact of Low Dose Melphalan on Residual Disease before High-Dose Melphalan and Stem Cell Transplant in Multiple Myeloma

Blood ◽

10.1182/blood-2021-148220 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 4933-4933

Author(s):

Ehsan Malek ◽

Mary Hislop ◽

Leland Metheny ◽

Molly Gallogly ◽

Marcos J.G. de Lima ◽

...

Keyword(s):

Sample Size ◽

Board Of Directors ◽

Research Funding ◽

Disease Burden ◽

Residual Disease ◽

Tumor Burden ◽

High Dose ◽

Cell Transplant ◽

Size Estimation ◽

Advisory Committees

Abstract High-Dose Melphalan (HDM) followed by stem cell transplant (SCT) remains the standard-of-care for transplant-eligible patients newly-diagnosed with multiple myeloma (MM). However, ~1/3 of patients relapse <2 years after undergoing HDM-SCT, indicating that melphalan-sensitivity is limited to a subset of patients and is currently not predictable. Currently, models that predict melphalan-resistance before proceeding to transplant are lacking. Rather, transplant-eligibility is defined mostly based on adequate organ function and performance status. Therefore, there is an urgent and unmet clinical need to develop strategies that accurately predict melphalan sensitivity among MM patients prior to HDM-SCT and save melphalan-resistant patients from undergoing this highly morbid procedure, if no demonstrable benefit is expected from it. Traditional disease-measurement methods based on International Myeloma Working Group (IMWG) criteria rely on the secretory function of myeloma cells and measure monoclonal protein levels. Following induction therapy, pre-transplant monoclonal protein levels are usually very low, and further reduction in myeloma secretory function are not detectable. In addition, the long half-life of monoclonal proteins makes assessing short-term disease changes problematic. Methods to accurately detect minor changes in disease burden following a low dose of melphalan (LDM) as a marker of melphalan sensitivity are needed to better predict patient responses to LDM. Next-generation sequencing (NGS), is an alternative approach that may allow for the highly sensitive, rapid, real-time detection of minuscule changes in tumor volume that are not influenced by the long half-life of monoclonal proteins. Here, we propose to use NGS-based tumor assessment to evaluate changes in disease volume following LDM before proceeding to HDM-SCT. Evidence is lacking to determine whether a single LDM generates a decrease in myeloma burden that is measurable by NGS. Our central hypothesis is that NGS of bone marrow aspirates from newly-diagnosed, post-induction transplant-eligible MM patients will provide a method to precisely determine the effect of LDM on disease burden. ClonoSEQ assay is an FDA-cleared, highly sensitive, specific, and standardized method to detect and monitor MRD, in MM patients. clonoSEQ leverages the power of NGS and offers an accurate and reliable way to assess how disease burden changes over time in response to treatment. Therefore, we propose a proof-of-principle study to assess the validity of this strategy and to provide essential data for future trial design investigating individualized approaches based on NGS sequencing and low doses of therapeutic agents. We will test the central hypothesis that LDM, administered at 16 mg/m 2, generates a detectable reduction in tumor burden measured by NGS. A detectable reduction in tumor burden is defined as a ≥ 20% decrease in NGS clonal count in at least 30% of subjects. We will administer propylene glycol-free melphalan formulation (EVOMELA) due to greater stability upon reconstitution than AlKERAN formulation in order to diminish the variability in the effective administered dose. The primary and secondary objectives and endpoints of the study are listed in Table-1,2. Statistical Considerations: Clonoseq detects measurable residual disease at the level of a single cell given sufficient sample input. The specific hypothesis of this pilot trial is LDM produces a measurable disease reduction that is readily detectable by clonoSEQ with at least a 20% reduction in at least 30% of patients. Assuming a 100% yield for VJD clonal sequencing and calibration efficacy by clonoSEQ, the sample size required to test the null hypothesis of 5% patients with positive MRD test against alternative 30% patients with positive MRD test is 16 patients. The sample size estimation is using two-sided chi-square test with 80% power. The sample size estimation is n = 21, when power = 90% based on one sample Binomial distribution theory. We will assume 20% failure rate for VJD clonal sequencing and calibration efficacy by clonoSEQ. Therefore, by enrolling 20 patients, we expect that at least 16 patients will have MRD assessable by NGS method. Figure 1 Figure 1. Disclosures Malek: BMS: Honoraria, Research Funding; Amgen: Honoraria; Bluespark Inc.: Research Funding; Sanofi: Other: Advisory Board; Cumberland Inc.: Research Funding; Takeda: Honoraria; Janssen: Other: Advisory board ; Medpacto Inc.: Research Funding. Metheny: Incyte: Speakers Bureau; Pharmacosmos: Honoraria. de Lima: Miltenyi Biotec: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees.

Download Full-text

External Validation and Revision of Thrombosis Lymphoma /Throly/ Score

Blood ◽

10.1182/blood-2018-99-115568 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 140-140 ◽

Cited By ~ 1

Author(s):

Darko Antic ◽

Natasa Milic ◽

Biljana Mihaljevic ◽

Bruce Cheson ◽

Mayur Narkhede ◽

...

Keyword(s):

At Risk ◽

Board Of Directors ◽

Predictive Value ◽

Research Funding ◽

Validation Cohort ◽

External Validation ◽

Advisory Committees ◽

Final Model ◽

Risk Patients ◽

Reduced Mobility

Abstract Introduction Lymphoma patients are at increased risk of thromboembolic events (TE), however, thromboprophylaxis in these patients is largely under utilized. Actual guidelines recommend different models for thromboembolic risk estimation in cancer patients. Proposed models are of limited use in lymphoma patients as their development is not based on specific characteristics for this patient population. Previously, we developed and internally validated a simple model, based on individual clinical and laboratory patient characteristics that would classify lymphoma patients at risk for a TE. The variables independently associated with the risk for thromboembolism were: previous venous and/or arterial events, mediastinal involvement, BMI>30 kg/m2, reduced mobility, extranodal localization, development of neutropenia and hemoglobin level < 100g/L. For patients classified at risk in derivation cohort (n=1236), the model revealed positive predictive value of 25.1%, negative predictive value of 98.5%, sensitivity of 75.4%, and specificity of 87.5%. The diagnostic performance measures retained similar values in the internal validation cohort (n=584). The aim of this study was to perform external validation of the previously developed thrombosis lymphoma (Throly) score. Methods The study population included patients with a confirmed diagnosis of non-Hodgkin lymphoma (NHL), Hodgkin lymphoma (HL), and chronic lymphocytic leukemia (CLL)/ small lymphocytic lymphoma (SLL) from 8 lymphoma centers from USA, France, Spain, Croatia, Austria, Switzerland, Macedonia, and Jordan. During 2015 to 2016, data were prospectively collected for venous TE events from time of diagnosis to 3 months after the last cycle of therapy for newly diagnosed and relapsed patients who had completed a minimum of one chemotherapy cycle. The score development and validation were done according to TRIPOD suggested guidelines. Sensitivity analyses were carried out to test the model robustness to possible different settings, according to in/out patient settings and according to different countries included. Results External validation cohort included 1723 patients, similar to the developed group and consisted of 467 indolent NHL, 647 aggressive NHL, 235 CLL/SLL and 366 HL patients, out of which 121 (7%) patients developed venous thromboembolic events. For patients classified at risk in external validation cohort, the model resulted in positive and negative predictive values of 17% and 93%, respectively. Based on new available information from this large prospective cohort study this model was revised to include the following variables: diagnosis/clinical stage, previous VTE, reduced mobility, hemoglobin level < 100g/L and presence of vascular devices. In the new score we divided patients in two groups: low risk patients, score value ≤ 2; and high risk patients, score value > 2. For patients classified at risk by the revised model, the model produced positive predictive value of 22%, negative predictive value of 96%, sensitivity of 51%, and specificity of 72%. In sensitivity analysis, the final model proved its robustness in different settings of major importance for lymphoma patients. The final model presented good discrimination and calibration performance. Concordance C statistics was 0.794 (95% CI 0.750-0.837). Conclusions Revised Thrombosis Lymphoma - ThroLy score is more specific for lymphoma patients than any other available score targeting thrombosis risk in solid cancer patients. We included biological characteristic of lymphoma, indolent vs aggressive, as well as data about dissemination of disease, localized vs advanced stage, reflecting specificity of lymphomas comparing to other types of cancer. Also, we pointed out significance of central vascular devices as risk factor having considered the role of vascular damage during insertion as a potential trigger for activation of the clotting cascade. This score is user friendly for daily clinical practice and provides a very good predictive power to identify patients who are candidates for pharmacological thromboprophylaxis. Disclosures Cheson: AbbVie, Roche/Genentech, Pharmacyclics, Acerta, TG Therapeutics: Consultancy. Ghielmini:Roche: Consultancy, Honoraria, Research Funding, Speakers Bureau. Jaeger:Gilead: Consultancy, Honoraria, 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; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; AOP Orphan: Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; MSD: Research Funding; Bioverativ: Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Honoraria; Mundipharma: Membership on an entity's Board of Directors or advisory committees; Takeda-Millenium: Membership on an entity's Board of Directors or advisory committees; Takeda-Millenium: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Infinity: Membership on an entity's Board of Directors or advisory committees.

Download Full-text

Phase II Trial of Ixazomib and Dexamethasone Versus Ixazomib, Dexamethasone and Lenalidomide, Randomized with NFKB2 Rearrangement. (Proteasome Inhibitor NFKB2 Rearrangement Driven Trial, PINR)

Blood ◽

10.1182/blood-2018-99-119337 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2011-2011

Author(s):

Leon Bernal-Mizrachi ◽

Craig E. Cole ◽

Leonard Heffner ◽

Ajay K Nooka ◽

Ravi Vij ◽

...

Keyword(s):

Partial Response ◽

Board Of Directors ◽

Interim Analysis ◽

Proteasome Inhibitor ◽

Research Funding ◽

Treated Patient ◽

Data Monitoring Committee ◽

Caspase 8 ◽

Open Label ◽

Advisory Committees

Abstract Introduction: Proteasome inhibitors (PI) and immunomodulatory drugs have become the backbone of therapy for multiple myeloma (MM). The oral boron-containing selective and reversible proteasome inhibitor ixazomib has shown to induce deep and durable responses (Kumar RK et la, Blood 2016. 128(20):2415-2422). Triplets containing ixazomib, has shown to be more efficacious than doublet regimens in the relapse setting (Moreau P, et al. N Engl J Med 2016. 374:1621-1634).However, to date there is not companion diagnostics capable of predicting PI response. We have recently discovered that MM patients resistant to PI lack of the ankyrin (ANK) and death domain (DD) present in the 3'-end of NFKB2. Loss of NFKB2 3'end frequently resulted from a structural rearrangements. We found that NFKB2-ANK and -DD are crucial at initiating bortezomib's apoptotic signal by facilitating caspase-8 activation. Activation of this caspase resulted from NFKB2 interaction with FADD/caspase-8/p62 (unpublished data). Based on this findings, we design this study to examine the efficacy of NFKB2 break apart FISH to predict the response to the duplet ixazomib and dexamethasone (Id) vs ixazomib, lenalidomide and dexamethasone (IRd) in early relapse MM patients (1-3 lines of therapy). Methods: In this phase 2 biomarker driven open label trial, relapse patients with <4 lines of therapy were randomized to ixazomib 4 mg weekly 3 of 4 weeks and 40 mg weekly dexamethasone vs Id plus 25 mg of lenalidomide daily days 21/28, based on the status of NFKB2 rearrangement in plasma cells. Patients were screened for NFKB2 rearrangement was detected by NFKB2 break apart FISH. Patients without NFKB2 rearrangement received Id and patients with NFKB2 rearrangement were subsequently randomized in a 1:1 fashion to receive Id or IRd. The primary endpoint is to compare the response rate of Id or IRd at 4 cycles according to the rearrangement status of NFKB2 Results: At the moment of interim analysis, 26 patients have achieved 4 cycles of treatment. All treatment groups (NFKB2 FISH [-] Id, n=16, NFKB2 FISH [+] Id, n=6 and IRd, n=4) received a median of 2 prior lines of therapy. A higher ORR was observed in NFKB2 FISH negative treated with Id compared with NFKB2 FISH positive (56.3% CI:29.9%-80.3% vs. 16.7% CI:0.4%-64.1%, P=0.16), including significantly higher rates of ≥very good partial response, ≥ partial response, ≥ minimal response (12%, 37.5%, 6% vs. 0%, 0%, 16%, respectively). ORR for IRd arm is for now 25% CI:0.6%-80.6%. Patients that continue treatment after cycle 4 that achieve minimal response or better improved their depth of response in 6% in Id treated NFKB2 FISH negative patients and 25% of IRd treated NFKB2 FISH negative patients. The most common (≥10%) grade 2/4 include pneumonia 20% (Id treated NFKB2 FISH negative), thrombocytopenia 16% (Id NFKB2 FISH positive), neutropenia 60% (IRd NFKB2 FISH positive. Treatment discontinuations only occurred in 1 Id treated patient (5%). Conclusion: Interim analysis demonstrate a trend higher efficacy of ixazomib with dexamethasone in MM patients with negative NFKB2 break-apart FISH compared to those with a positive test. Efficacy and toxicity of the triplet regimen are comparable to what is seen in Tourmaline 1 trial. This study was registered at www.clinicaltrials.gov as # NCT02765854. Disclosures Bernal-Mizrachi: Takeda Pharmaceutical Company: Research Funding; Kodikaz Therapeutic Solutions: Consultancy, Equity Ownership. Cole:Cancer Support Community myeloma advisory board: Membership on an entity's Board of Directors or advisory committees; University of Michigan: Employment. Heffner:ADC Therapeutics: Research Funding; Kite Pharmaceuticals: Research Funding; Pharmacyclics: Research Funding; Genentech: Research Funding. Nooka:GSK: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Spectrum Pharmaceuticals: 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; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen pharmaceuticals: 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; Adaptive technologies: Consultancy, Membership on an entity's Board of Directors or advisory committees. Vij:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jansson: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharma: 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, Research Funding. Kelkar:Empire Genomics: Employment. Lonial:Amgen: Research Funding. Kaufman:Karyopharm: Other: data monitoring committee; Abbvie: Consultancy; Roche: Consultancy; BMS: Consultancy; Janssen: Consultancy.

Download Full-text

Prognostic Significance of Bone Marrow Cellularity in the Outcome of Patients with Myelodysplastic Syndromes Treated with Azacyitidine: A Retrospective Analysis from the Hellenic MDS Study Group

Blood ◽

10.1182/blood-2019-131003 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 5417-5417

Author(s):

Elena E. Solomou ◽

Alexandra Kourakli ◽

Anna Vardi ◽

Ioannis Kotsianidis ◽

Panagiotis Zikos ◽

...

Keyword(s):

Bone Marrow ◽

Overall Survival ◽

Board Of Directors ◽

Research Funding ◽

Hypomethylating Agents ◽

Advisory Committees ◽

Female Ratio ◽

Bone Marrow Cellularity ◽

Marrow Cellularity ◽

Male To Female

Introduction: Clinical trials in patients with high risk myelodysplastic syndromes (MDS) have shown that these patients benefit from the available hypomethylating agents 5-azacytidine and decitabine. The majority of these patients display hypercellular bone marrow, but a small proportion despite the excess of blasts, exhibit marrow hypocellularity (<30% cellularity). Data are limited for the efficacy and safety of treatment with hypomethylating agents in this patient subgroup. In the present study we examined the effect of bone marrow cellularity in the overall survival in patients with MDS treated with azacitidine. Patients & Methods: This is a retrospective multicenter study from the Hellenic National MDS Registry (EAKMYS) on behalf of the Hellenic MDS Study Group. Between 1.1.2009 and 31.12.2018 a total of 1161 MDS patients who have received treatment with azacytidine have been registered. Complete patient information and follow-up were available for 989 patients, and all these have been included in the final analysis. Statistical analysis was performed and overall survival (OS) was evaluated, using Kaplan-Meier estimates (GraphPad Prism software, CA). A p value less than 0.05 was considered statistically significant. Results: Forty nine patients had a hypocellular bone marrow (hMDS), representing the 4.95% of the whole patient population. Of these patients 39 were men (5.3% of all men included in the study) and 10 were women representing the 2.98% of all women enrolled (male to female ratio 3.9). In the non-hypoplastic group, 750 were men and 358 were women (male to female ratio 2.09). The median age at diagnosis for the hMDS group was 70.8 years, compared to 72.8 years in the non-hypoplastic group. The IPSS-R prognostic risk categorization included 15 hMDS patients in the low group, 9 in the intermediate, 14 in the high and 11 in the very high risk group. Twenty-six patients (53%) of the hMDS group had bone marrow blasts between 10 and 20%, and the remaining 23 (47%) had 5-10% blasts. The patients with hMDS received an average of 10 cycles of azacytidine treatment during the follow-up period (range 2-29 cycles). The outcomes tested were overall survival and progression to AML. The median overall survival of patients with hMDS, following azacytidine treatment start, was not significantly different from the median survival of patients with non-hypoplastic MDS [20 months versus 16 months in the non-hypoplastic group (95% CI of ratio: 0.839 to 1.863). The survival curves were not significantly different between the hMDS and non-hypoplastic MDS group (p=0.32, Figure 1). Progression to AML was also evaluated. Eleven (22.4 %) hMDS patients showed disease progression to AML. Patients with hMDS had significantly prolonged estimated median time to AML transformation, compared to the non-hypoplastic MDS population (31.7 versus 22 months respectively, p<0.001). There were not any major safety issues among patients with hMDS, despite the increased RBC and Platelet transfusion needs. The infectious episodes and the hospitalization courses did not differ significantly between the hMDS and the non-hypoplastic group. Discussion and Conclusive remarks: In this retrospective study, in which a large number of MDS patients was analyzed, we showed that bone marrow cellularity does not affect the outcome in patients treated with azacyitidine. Patients with hMDS show statistically significant slower AML progression compared to non-hypoplastic MDS. Bone marrow cellularity should not be a contraindication for using hypomethylating agents as a therapeutic option, and this type of treatment can be used safely, when indicated, also in patients with hMDS. Disclosures Pappa: Amgen: Research Funding; Gilead: Honoraria, Research Funding; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene / GenesisPharma: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Research Funding, Speakers Bureau; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Vassilakopoulos:Merck: Honoraria; Takeda Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Genesis Pharmaceuticals: 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; Astellas: Honoraria; Winmedica: Honoraria; Servier: Membership on an entity's Board of Directors or advisory committees. Symeonidis:Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; MSD: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tekeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding; Celgene: Honoraria, 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; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Download Full-text

Effect of Romiplostim on Health-Related Quality of Life in Children with Immune Thrombocytopenia and Associated Burden in Their Parents: Results from a Phase 3, Randomized, Double-Blind, Placebo-Controlled Study

Blood ◽

10.1182/blood.v126.23.37.37 ◽

2015 ◽

Vol 126 (23) ◽

pp. 37-37 ◽

Cited By ~ 2

Author(s):

Susan D Mathias ◽

Xiaoyan Li ◽

Melissa Eisen ◽

Nancy Carpenter ◽

Ross D Crosby ◽

...

Keyword(s):

Sample Size ◽

Board Of Directors ◽

Research Funding ◽

Stock Ownership ◽

Platelet Response ◽

Phase 3 ◽

Advisory Committees ◽

Parental Burden ◽

Response Status ◽

Parent Proxy

Abstract Background: Immune thrombocytopenia (ITP) is an autoimmune disorder characterized by low platelet count levels and increased risk of bleeding. Symptomatic ITP in children can have a negative impact on their health-related quality of life (HRQoL) and increase their parents' burden. The effect of romiplostim (a thrombopoietin receptor agonist) on HRQoL and parental burden was evaluated in a phase 3 study of children with ITP. Methods: In a phase 3, randomized, double-blind, placebo-controlled study on efficacy and safety of romiplostim, children (<18 years) with ITP ≥ 6 months were randomized to weekly romiplostim or placebo for 24 weeks. The Kids' ITP Tool (KIT), a psychometrically-valid disease-specific HRQoL instrument (Klaassen Ped Blood Cancer2007), was administered to children and/or their parents at baseline, weeks 8, 16, and 25. All three KIT versions were used: Child self-report (to assess HRQoL of children ≥7 years), Parent/Proxy (to assess HRQoL of children <7 years via parent proxy), and Parent self-report (to assess impact of children's ITP on parental burden, for children of all ages). Each KIT version contains 26 items, summarized in a single score ranging from 0 to 100. Higher Child or Parent/Proxy KIT scores reflect better HRQoL of a child with ITP, and higher Parent KIT scores reflect less parental burden. Among efficacy endpoints of the study, overall platelet response was defined as achieving a weekly platelet response (platelet count ≥ 50 x 109/L) for ≥ 4 weeks during weeks 2 to 25, and durable platelet response was defined as achieving a weekly platelet response for ≥ 6 weeks during weeks 18 through 25. As exploratory endpoints of the study, changes in KIT scores from baseline to each follow-up assessment were estimated separately by treatment group (romiplostim or placebo) and by overall/durable platelet response status (yes/no). A mixed effects repeated measures analysis was conducted to estimate the difference in changes of Child and Parent KIT scores between romiplostim group and placebo group, controlling for baseline score, child's age, child's gender, and child's race (analysis of Parent/Proxy data was not conducted due to small sample size). Results: Sixty-two patients were enrolled and randomized to receive romiplostim (42 patients) and placebo (20 patients). Mean age was 9.6 years (range: 3-17, 16 patients <7 years), 57% were female, and 66% were white. Overall and durable platelet response was achieved by 34 and 24 patients, respectively. In general, changes in KIT scores by treatment group and overall platelet response status showed numerically greater and more often statistically significant improvements from baseline to each assessment for children receiving romiplostim (vs placebo) and for platelet responders (vs non-responders) (see Tables 1 and 2). Results based on durable response status were similar to those based on overall response status (data not shown). In the mixed effects analysis, greater improvement from baseline to week 8/16/25 on Parent KIT score was found in the romiplostim group vs placebo (by approximately 8 points, p-value<0.05); no significant difference was found between groups for Child KIT score. Conclusion: Romiplostim treatment is associated with reduced parental burden (measured by Parent KIT score). In some instances sample sizes were small for other KIT versions; therefore, results should be interpreted with caution. Table 1. Mean Change from Baseline in KIT Scores by Treatment Arm KIT Version Assessment week (sample size for romiplostim, placebo) Romiplostim Mean (95% CI) PlaceboMean (95% CI) Child 8 (n=28,11) 16 (n=27,10) 25 (n=28,11) 9 (4, 15) 11 (5, 16) 14 (7, 20) 9 (1, 18) 8 (-3, 20) 10 (-1, 20) Parent/Proxy 8 (n=8,2) 16 (n=8,3) 25 (n=9,3) -0.9 (-7, 5) -0.4 (-12, 11) 8 (2, 13) -40 (-108, 23) -1 (-86, 84) -10 (-80, 59) Parent 8 (n=40,16) 16 (n=39,17) 25 (n=37,16) 13 (10, 17) 15 (10, 21) 18 (12, 23) 4 (-6, 13) 12 (4, 20) 13 (4, 22) Table 2. Mean Change from Baseline in KIT Scores by Overall Platelet Response KIT Version Assessment week (sample size for responders, non-responders) Responders Mean (95% CI) Non RespondersMean (95% CI) Child 8 (n=23,17) 16 (n=22,16) 25 (n=23,16) 11 (4, 18) 11 (4, 18) 16 (8, 24) 4 (-5, 12) 8 (1, 15) 8 (1, 15) Parent/Proxy 8 (n=7,5) 16 (n=8,5) 25 (n=8,6) 0.9 (-7, 9) 4 (-10, 18) 9 (1, 17) -15 (-44, 13) -4 (-31, 22) -3 (-23, 17) Parent 8 (n=30,26) 16 (n=30,26) 25 (n=29,24) 11 (7, 14) 14 (7, 20) 17 (10, 24) 10 (3, 18) 15 (9, 21) 15 (9, 21) Disclosures Mathias: Amgen: Research Funding. Li:Amgen: Employment, Other: Stock Ownership. Eisen:Amgen Inc: Employment, Other: stock ownership. Carpenter:Amgen: Employment, Other: Stock Ownership. Crosby:Amgen: Research Funding. Blanchette:Bayer Healthcare: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Octapharma: Other: Data Safety Monitoring Board; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Baxter Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Data Safety Monitoring Board, Research Funding; Novo Nordisk: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Download Full-text

SGN-CD33A Plus Hypomethylating Agents: A Novel, Well-Tolerated Regimen with High Remission Rate in Frontline Unfit AML

Blood ◽

10.1182/blood.v126.23.454.454 ◽

2015 ◽

Vol 126 (23) ◽

pp. 454-454 ◽

Cited By ~ 14

Author(s):

Amir T. Fathi ◽

Harry P Erba ◽

Jeffrey E Lancet ◽

Eytan M Stein ◽

Roland B. Walter ◽

...

Keyword(s):

Board Of Directors ◽

Research Funding ◽

Remission Rate ◽

Intensive Therapy ◽

Advisory Board ◽

Hypomethylating Agents ◽

Employment Equity ◽

Advisory Committees ◽

Low Intensity ◽

Equity Ownership

Abstract Background Older patients with AML who are not candidates for intensive therapy are typically treated with hypomethylating agents (HMAs) or other low intensity therapy. HMAs have been shown to upregulate CD33 and to increase sensitivity to cytotoxic chemotherapy by decreasing apoptotic threshold in tumor cells. SGN-CD33A (or 33A) is a CD33-directed antibody conjugated to 2 molecules of a pyrrolobenzodiazepine (PBD) dimer. Upon binding, 33A is internalized and transported to the lysosomes where PBD dimer is released via proteolytic cleavage of the linker, crosslinking DNA, and leading to cell death. In preclinical studies combining 33A with an HMA (azacitidine and decitabine), synergy has been demonstrated in multidrug resistant AML models (Sutherland ASH 2014). Methods A combination cohort in a phase 1 study (NCT01902329) was designed to evaluate the safety, tolerability, pharmacokinetics (PK), and anti-leukemic activity of 33A in combination with an HMA. Eligible patients (ECOG 0-1) must have previously untreated CD33-positive AML, and have declined intensive therapy. A single dose level of 33A, 10 mcg/kg, was administered outpatient IV every 4 weeks on the last day of HMA (azacitidine or decitabine [5 day regimen], standard dosing). Patients with clinical benefit may continue treatment until relapse or unacceptable toxicity. Investigator assessment of response is per IWG criteria; CRi requires either platelet count of ≥100,000/µL or neutrophils of ≥1,000/µL (Cheson 2003). Results To date, 24 patients (63% male) with a median age of 77 years (range, 66-83) have been treated with the combination therapy. 42% of patients had adverse cytogenetics (MRC), 23 patients were treatment naïve and 1 patient had received prior low intensity therapy for MDS. At baseline, patients had a median of 60% BM blasts (range, 2%-90%) and a median of WBC of 2.2 (range, 1-132). At the time of this interim analysis, patients were on treatment for a median of 13.5+ weeks with 17 patients continuing treatment; no DLTs have been reported. Grade 3 or higher adverse events (AE) reported in >20% of patients were fatigue (54%), febrile neutropenia (46%), anemia (25%), neutropenia (25%), and thrombocytopenia (21%). Other treatment-emergent AEs regardless of relationship to study treatment reported in ˃20% of patients were nausea (29%), decreased appetite (25%), and constipation (21%). Thirty- and 60-day mortality rates are 0% and 4% respectively with no treatment-related deaths reported. Fifteen of the 23 efficacy evaluable patients (65%) achieved CR (5) or CRi (10). Remissions were generally obtained after 2 cycles of treatment and were observed in many patients with adverse risk including underlying myelodysplasia (6/8, 75%) and adverse cytogenetics (8/9, 89%). Median OS has not been reached with 20 patients alive at the time of this data cut. Conclusions The combination of 33A with HMA appears to be well-tolerated, active, and has no identified off-target toxicities. Activity with the combination compares favorably with historical experience with HMAs alone in this patient population. The CR+CRi rate of 65% in AML patients with poor risk factors with the observed low 60-day mortality (4%) are particularly encouraging. These promising data warrant further evaluation in future trials. Disclosures Fathi: Agios Pharmaceuticals: Other: Advisory Board participation; Merck: Other: Advisory Board participation; Seattle Genetics: Other: Advisory Board participation, Research Funding. Off Label Use: SGN-CD33A is an investigational agent being studied in patients with CD33-positive AML. SGN-CD33A is not approved for use.. Erba:GlycoMimetics; Janssen: Other: Data Safety & Monitoring Committees; Sunesis;Pfizer; Daiichi Sankyo; Ariad: Consultancy; Millennium/Takeda; Celator; Astellas: Research Funding; Seattle Genetics; Amgen: Consultancy, Research Funding; Novartis; Incyte; Celgene: Consultancy, Patents & Royalties. Lancet:Seattle Genetics: Consultancy; Pfizer: Research Funding; Boehringer-Ingelheim: Consultancy; Kalo-Bios: Consultancy; Amgen: Consultancy; Celgene: Consultancy, Research Funding. Stein:Seattle Genetics, Inc.: Membership on an entity's Board of Directors or advisory committees; Agios: Membership on an entity's Board of Directors or advisory committees. Walter:Pfizer, Inc.: Consultancy; Covagen AG: Consultancy; AstraZeneca, Inc.: Consultancy; CSL Behring: Research Funding; AbbVie, Inc.: Research Funding; Amgen: Research Funding; Amphivena Therapeutics, Inc.: Consultancy, Research Funding; Seattle Genetics, Inc.: Consultancy, Research Funding. DeAngelo:Incyte: Consultancy; Amgen: Consultancy; Pfizer: Consultancy; Ariad: Consultancy; Bristol Myers Squibb: Consultancy; Novartis: Consultancy; Celgene: Consultancy; Agios: Consultancy. Faderl:Celator: Research Funding; Ambit: Research Funding; BMS: Research Funding; Astellas: Research Funding; Karyopharm: Consultancy, Research Funding; Seattle Genetics, Inc.: Research Funding; JW Pharma: Consultancy; Celgene: Consultancy, Research Funding, Speakers Bureau; Pfizer: Research Funding; Onyx: Speakers Bureau. Jillella:Seattle Genetics, Inc.: Research Funding. Bixby:Seattle Genetics, Inc.: Research Funding. Kovacsovics:Seattle Genetics, Inc.: Research Funding. O'Meara:Seattle Genetics, Inc: Employment, Equity Ownership. Kennedy:Seattle Genetics, Inc.: Employment, Equity Ownership. Stein:Amgen: Speakers Bureau.

Download Full-text

Central Nervous System Involvement By Mantle Cell Lymphoma

Blood ◽

10.1182/blood-2021-148447 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 2426-2426

Author(s):

Nicole McLaughlin ◽

Jonas Paludo ◽

Yucai Wang ◽

David J. Inwards ◽

Nora Bennani ◽

...

Keyword(s):

Board Of Directors ◽

Research Funding ◽

Data Monitoring Committee ◽

Advisory Board ◽

Control Group ◽

Cns Involvement ◽

Extranodal Involvement ◽

Advisory Committees ◽

Scientific Advisory Board ◽

Scientific Advisory

Abstract Background: While extranodal involvement by mantle cell lymphoma (MCL) is relatively common, involvement of the central nervous system (CNS) is rare (<5% of cases), with limited treatment options. We report the outcomes of 36 patients (pts) with CNS involvement compared to 72 matched control MCL pts without CNS involvement. Methods: MCL pts with CNS involvement seen at Mayo Clinic between 1/1995-9/2020 were identified using the Mayo Data Explorer tool. CNS involvement was defined by tissue biopsy confirmed CNS MCL, CSF analysis demonstrating lymphoma cells, and/or neuroimaging findings compatible with CNS involvement. A 2:1 control group of MCL pts without CNS involvement, matched by age (+/- 2 years) and year of diagnosis (+/- 1 year), was selected among all MCL cases. Medical records were reviewed for baseline characteristics, treatment modalities, and outcomes. Kaplan-Meier method was used for time to event analysis. Wilcoxon test was used to compare continuous variables and Chi square test was used for categorical variables. Results: Out of 1,753 pts with MCL, 36 (2%) had evidence of CNS involvement, including 4 pts with CNS involvement at initial MCL diagnosis. Baseline characteristics of pts with CNS involvement (CNS MCL group) and those without CNS involvement (control group) are shown in Table 1. At MCL diagnosis, non-CNS extranodal involvement was seen in 30 (83%) pts in the CNS MCL group (24 pts with 1 site and 6 pts with ≥ 2 sites), with bone marrow being the most common extranodal site of involvement (n=24, 67%). For the control group, 54 (75%) pts had extranodal involvement (44 pts with 1 site and 10 pts with ≥ 2 sites), and bone marrow was also the most common extranodal site of involvement (n=50, 69%). Notably, advanced stage disease (stage 3-4) was more commonly seen in the CNS MCL group (n=32, 97%) than in the control group (n=59, 83%) (p=0.04) at MCL diagnosis. Blastoid variant was present in a higher proportion of pts in the CNS MCL group (n=11, 31%) compared to the control group (n=8, 11%) (p=0.02). The CNS MCL group also presented with a higher median serum LDH at diagnosis (239 U/L [range 153-1901] vs. 187 U/L [range 124-588], p=0.02), and higher Ki-67 (40% [range 15-100] vs. 30% [range 10-90], p=0.04) compared to the control group. The most common frontline treatment regimen was anthracycline-based therapies (i.e. R-CHOP, Nordic regimen, R-hyperCVAD) for both groups (58% in CNS MCL group and 56% in control group). 14 (39%) pts in the CNS MCL group underwent autologous stem cell transplant in CR1 vs. 31 pts (43%) in the control group. Similar use of rituximab maintenance was seen in both groups (31% in CNS MCL group and 25% in control group). Median total lines of therapy from initial MCL diagnosis was 3 (range 1-9) in CNS MCL group and 2 (range 1-9) in the control group. The median follow-up from MCL diagnosis was 134 months (95% CI:119-163) for the entire cohort. Median OS from MCL diagnosis was 50.3 months (95% CI: 20.9-71.1) for the CNS MCL group compared to 97.1 months (95% CI: 82.6-192.7; p=<0.001) for the control group (Figure 1). Median time from MCL diagnosis to CNS involvement was 25 months (range 0-167). Median OS from CNS involvement was 4.7 months (95% CI: 2.3-6.7). At last follow up, 31 (86%) pts were deceased from the CNS MCL group, compared to 38 (52%) pts in the control group. For the CNS MCL group, the causes of death were CNS lymphoma in 10 (32%) pts, systemic lymphoma in 9 (29%) pts, treatment-related complication in 7 (23%) pts, and other/unknown in 5 (16%) pts. For the control group, the causes of death were systemic lymphoma in 15 (39%) pts, treatment-related in 2 (5%) pts, and other/unknown in 21 (55%) pts. Conclusion: In pts with MCL, CNS involvement is associated with worse outcomes as evident by a shorter median OS from initial MCL diagnosis (50 months vs. 97 months). Involvement of the CNS by lymphoma is an important contributor for the shorter OS as suggested by the median OS of only 5 months from CNS involvement. Advanced stage, blastoid variant, elevated LDH, and elevated Ki67 at MCL diagnosis were features more commonly seen in the CNS MCL cohort. Validation of risk factors at initial MCL diagnosis associated with CNS involvement and exploring the role of CNS prophylaxis are important topics for further investigation. Figure 1 Figure 1. Disclosures Paludo: Karyopharm: Research Funding. Wang: Novartis: Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Research Funding; InnoCare: Research Funding; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Research Funding. Bennani: Purdue Pharma: Other: Advisory Board; Daichii Sankyo Inc: Other: Advisory Board; Kyowa Kirin: Other: Advisory Board; Vividion: Other: Advisory Board; Kymera: Other: Advisory Board; Verastem: Other: Advisory Board. Nowakowski: Celgene, MorphoSys, Genentech, Selvita, Debiopharm Group, Kite/Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene, NanoString Technologies, MorphoSys: Research Funding. Witzig: Karyopharm Therapeutics, Celgene/BMS, Incyte, Epizyme: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene/BMS, Acerta Pharma, Kura Oncology, Acrotech Biopharma, Karyopharm Therapeutics: Research Funding. Habermann: Seagen: Other: Data Monitoring Committee; Tess Therapeutics: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Ansell: Bristol Myers Squibb, ADC Therapeutics, Seattle Genetics, Regeneron, Affimed, AI Therapeutics, Pfizer, Trillium and Takeda: Research Funding.

Download Full-text