Genomic Biomarkers Predict Response/Resistance to Lenalidomide in Non-Del(5q) Myelodysplastic Syndromes

Abstract Lenalidomide (Len) is FDA approved for the treatment of patients (pts) with lower-risk, transfusion-dependent myelodysplastic syndromes (MDS) with deletion(5q). It is frequently used in lower-risk pts with non-del(5q) MDS, with a transfusion independence response rate of 27%. Identification of pts who may or may not respond to Len can prevent prolonged exposure to ineffective therapy, avoid toxicities, and decrease unnecessary costs. Clinical or genomic data have limited utility in predicting response/resistance to Len. We developed an unbiased framework to study the association of several mutations/cytogenetic abnormalities in predicting response/resistance to Len in non-del(5q) pts, analogous to Netflix or Amazon's recommender system, in which customers who bought products A and B are likely to buy C: pts who have a molecular/cytogenetic abnormalities in gene A, and B are likely to respond or not respond to Len. Clinical and genomic data from pts with MDS or other myeloid malignancies diagnosed according to 2008 WHO criteria between 02/2004 and 06/2015 were analyzed. Next generation targeted deep sequencing panel of 50 genes that are commonly mutated in MDS and myeloid malignancies was included. Association rules using an apriori algorithm were used to study the relationships among multiple genes/cytogenetic abnormalities and response/resistance to Len. Responses included complete and partial remission and hematologic improvement (CR, PR, HI) per IWG 2006 criteria. Pts with stable disease or progressive disease were considered resistant. Association rules are a machine learning algorithm used to identify the association of variables based on their relationships. Rules with the highest confidence (that an association exists) and highest lift (measuring the strength of the association) were chosen. Of 139 pts treated with Len as monotherapy or in combination for at least 2 cycles included, 118 (85%) had MDS and 21 (15%) had other myeloid malignancies. Median age at diagnosis was 69 years (range 20-90 yrs) and 45% were female. Risk stratification by IPSS-R for MDS pts; 51.5 % had very low/low risk, 19.5% intermediate, and 29% high and very high risk disease. Most pts 100 (73%) had non-del(5q) abnormalities, others (39) had del(5q). Cytogenetic abnormalities for the non-del(5q) cohort included 58 pts with normal karyotype (NK), 19 pts with complex karyotype (CK), 4 pts with trisomy 8, 3 pts with del(7q) abnormalities, and 15 pts with other abnormalities. A total of 108 (79%) pts were treated with Len monotherapy. The median duration of treatment was 6 months (range 2- 66 m). Response rates were 46% (n=46) in the non-del(5q) cohort and 74% (n=29) in del(5q). Association rules identified the following combinations of genomic/cytogenetic abnormalities to predict response to Len in non-del(5q): (DDX41, NK) and (MECOM, KDM6A/KDM6B). The combination of the following abnormalities predicted resistance (ASXL1, TET2, NK), (DNMT3A, SF3B1), (TP53, del(5q)+CK), (STAG2, IDH 1/2, NK), (EZH2, NK), (BCOR/ BCORL1, NK), (JAK2, TET2, NK), (U2AF1, +/- ETV6, NK). [Table 1] Only TP53/CK mutations predicted resistance to Len in del(5q) pts. These associations are present in 39% of pts with non-del(5q), and have a specificity of 77%, with a negative predictive value and sensitivity=100%. The algorithm predicted response/resistance to Len with 82% accuracy. The median OS for non-del(5q) pts was 33.2m [95% CI: 19.9, 40.5]. The median OS for responders was 54.8 compared to 24.7 m for non-responders p=.017. The median OS for rules that predicted response was 70.3 m (95% CI: 70.3-NA). The median OS for pts with del(5q) + CK with a TP53 mutation was 9.8m. Several genomic combinations predicted very poor overall survival, including: (ETV6, U2AF1, NK), (BCOR/ BCORL1, NK), (EZH2, NK) , (JAK2, TET2, NK), with median OS of 10.7 m, 7.6 m, 10.8 m and 7.6 m, respectively. [Figure 1] Genomic biomarkers can identify 39% of non-del(5q) MDS pts who may or may not respond to treatment with very high accuracy. Although these abnormalities are only present in a subset of pts, treatment options for these pts can be tailored, by offering alternative therapies to pts with lower-risk disease who may not respond to Len, and preferentially offering Len to those who are more likely to respond. This study highlights how advanced analytic technologies such as machine learning can translate genomic/clinic data into useful clinical tools. Disclosures Sekeres: Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Gerds:Celgene: Consultancy; Apexx Oncology: Consultancy; CTI Biopharma: Consultancy; Incyte: Consultancy. Carraway:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Membership on an entity's Board of Directors or advisory committees; Novartis: Speakers Bureau; Balaxa: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; FibroGen: Consultancy; Jazz: Speakers Bureau; Agios: Consultancy, Speakers Bureau. Santini:Novartis: Honoraria; Amgen: Membership on an entity's Board of Directors or advisory committees; Otsuka: Consultancy; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees. Maciejewski:Ra Pharmaceuticals, Inc: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Apellis Pharmaceuticals: Consultancy; Apellis Pharmaceuticals: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Nazha:MEI: Consultancy.

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Outcome of Lower Risk Non Del 5q MDS after Failure of Erythropoiesis Stimulating Agents (ESA), and Impact of Post-ESA Treatment on Survival: A Retrospective European Study

Blood ◽

10.1182/blood.v126.23.1665.1665 ◽

2015 ◽

Vol 126 (23) ◽

pp. 1665-1665 ◽

Cited By ~ 1

Author(s):

Sophie Park ◽

Jean-François Hamel ◽

Andrea Toma ◽

Charikleia Kelaidi ◽

Maria Campelo Diez ◽

...

Keyword(s):

Board Of Directors ◽

Lower Risk ◽

Research Funding ◽

Cooperative Groups ◽

Second Line ◽

Advisory Committees ◽

Primary Resistance ◽

Investigational Drugs ◽

Significant Difference ◽

Very High

Abstract Background : Most non-del 5q lower risk MDS patients (pts) are first treated with ESA, with about 50% (generally transient) responses, and second line treatments (TX) including hypomethylating agent (HMA), Lenalidomide (LEN) and investigational drugs are then often proposed, but their effect on overall survival (OS) is unknown. In a previous work on 253 such pts, we found worse OS with early failure to ESA, i.e. primary resistance (RES) or relapse (REL) < 6 months after ESA onset (Kelaidi, Leukemia, 2013), but only few pts had received, after ESA failure, TX other than RBC transfusions. In the present study, we gathered non-del 5q lower risk MDS treated with ESA from several EU MDS cooperative groups, and analyzed their outcome after ESA failure, and the effect of second line TX on survival. Methods : 1611 IPSS low and int-1 (lower risk) non del 5q MDS pts included in the French (GFM), Italian (FISM), Spanish (GESMD), Greek, Düsseldorf and Munich registries between 1997 and 2014, and treated by ESA were studied. Survival was assessed from failure of ESA (i.e. from primary failure evaluated after 12 to 24 weeks of ESA treatment, or from relapse after a response). Progression at ESA failure was defined upon progression to a higher IPSS-R class at ESA failure as compared with ESA onset. Results : At ESA onset, the 1611 pts were reclassified by IPSS-R in 16% very low, 54% low, 13% int, 6% high, 1% very high and 10% ND. HI-E (using IWG 2006 criteria) to ESA treatment was 66.9%, and the median duration of response was 15 months. The cohort of 1038 pts with ESA failure included 521 RES and 517 REL. Median OS was 4.2 years in REL and 3.7 years in RES pts (p=0.56), and no significant difference was seen, even after restricting the analysis to very low and low IPSS-R pts (p=0.81), or when analyzing "early" vs "late" failures, with cut-off points at 6 or 12 months, as we previously reported (Kelaidi, Leukemia, 2013). 336 (32%) pts received second line treatment (TX2) other than RBC transfusions, including HMA in 88 pts, LEN in 169 pts, and other TX (OT) in 79 pts (including 11 chemotherapy, 17 thalidomide, 11 immunosuppressors (ATG, cyclosporine), or investigational drugs), with response rates of 46%, 39% and 33% respectively (p=0.4). 87 pts had a third line TX (mostly a new drug, but also 7 pts who received HMA after LEN, and 33 pts LEN after HMA). Pts treated with LEN as TX2 were younger (median age 70 vs 75 for BSC, and 70 for HMA p<10-4), had more RARS (67% vs 28% for BSC and 27% for HMA, p<10-4), while pts treated with HMA as TX2 had more RAEB-1 (34% vs 10% for BSC and 12% for LEN, p<10-4) and more high and very high IPSS-R at onset of TX2 (48% vs 4.6% for BSC and 3.1% for LEN, p<10-4). Median OS for pts receiving BSC, LEN, HMA and OT as TX 2 was 4.3y, 3.7y (HR 1.1 [0.81-1.50] p=0.5), 2.1y (HR 1.59 [1.12-2.72], p=0.01) and 2.2y (HR1.17 [0.81-1.68], p=0.41) respectively (Figure). However, in a multivariate analysis adjusted on age, gender, and IPSS-R progression at ESA failure, OS difference became not significant. Analysis of AML progression in the different TX2 groups is currently being finalized. C onclusion: In this large multicenter retrospective cohort of non-del 5q lower risk MDS pts having failed ESA treatment, OS from failure was similar in RES and REL pts, contrary to our previous smaller experience. About 1/3 of the pts received second line treatments other than RBC transfusion, mainly LEN or HMA. However, none of those treatments was able to improve OS compared to BSC. Newer treatments are required in this situation, possibly including allogeneic SCT in younger pts. Figure 1. OS since ESA failure according to TX2 (Simon-Makuch method). Figure 1. OS since ESA failure according to TX2 (Simon-Makuch method). Disclosures Park: Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Hospira: Research Funding; Celgene: Research Funding. Off Label Use: Lenalidomide in non del 5q MDS. Santini:celgene, Janssen, Novartis, Onconova: Honoraria, Research Funding. Cony-Makhoul:BMS: Consultancy, Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria, Speakers Bureau. Cheze:Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees. Wattel:PIERRE FABRE MEDICAMENTS: Research Funding; CELGENE: Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding; NOVARTIS: Research Funding, Speakers Bureau; AMGEN: Consultancy, Research Funding. Vey:Celgene: Honoraria; Roche: Honoraria; Janssen: Honoraria. Fenaux:Amgen: Honoraria, Research Funding; Celgene Corporation: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Novartis: Honoraria, Research Funding.

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Genotype-Phenotype Correlations in Patients with Myeloid Malignancies Using Explainable Artificial Intelligence

Blood ◽

10.1182/blood-2020-140023 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 31-32

Author(s):

Nathan Radakovich ◽

Luca Malcovati ◽

Manja Meggendorfer ◽

Mikkael A. Sekeres ◽

Jacob Shreve ◽

...

Keyword(s):

Machine Learning ◽

Board Of Directors ◽

Research Funding ◽

Clinical Characteristics ◽

Normal Karyotype ◽

Complex Karyotype ◽

Advisory Committees ◽

Myeloid Malignancies ◽

Disease Subtypes ◽

Undetermined Significance

Background: Myelodysplastic syndromes (MDS) and related myeloid malignancies are highly variable in both their clinical manifestations and underlying genetic abnormalities. While few mutations in myeloid malignancies are considered disease-defining, significant and complex associations between these genes exist and can influence the clinical characteristics and disease phenotype. Here, we took advantage of a large, international cohort of patients with myeloid malignancies to define genotype-phenotype relationships using state of the art machine learning models. Methods Data were collected for patients (pts) from the Cleveland Clinic (CC; 652 pts), Munich Leukemia Laboratory (MLL; 1509 pts), and the University of Pavia in Italy (UP; 536 patients). Clinical data including CBC at time of diagnosis and a genomic panel of 20 commonly mutated genes in myeloid malignancies were analyzed. Gene-gene correlations within disease subtypes, individual genes' co-occurrence or exclusivity within disease subtypes, and the co-occurrence or exclusivity of individual genes with clinically meaningful features including karyotypic abnormalities and severe cytopenias (defined as hemoglobin < 8 g/dL, platelets < 50k/dL, and ANC < 1k/dL) were evaluated using multiple machine learning/correlation/feature extraction algorithms. Results 2697 pts were included, 1630 (60%) with MDS, 399 (15%) with chronic myelomonocytic leukemia (CMML), 142 (5%) with idiopathic cytopenia of undetermined significance (ICUS), 129 (5%) with MDS-MPN overlap syndromes (MDS-MPN), 95 (4%) with primary myelofibrosis (PMF), 93 (3%) with clonal cytopenia of undetermined significance (CCUS), 52 (2%) with essential thrombocythemia (ET), 41 (2%) with polycythemia vera (PV), and 26 (1%) with other myeloproliferative neoplasms (MPNs). The median age at diagnosis for the entire cohort was 70 years [36 - 86]. Of patients with karyotype data available, 1091 pts (50%) had a normal karyotype, 17 (1%) had chromosome 17 abnormalities, 96 (4%) had chromosome 7 abnormalities, 145 (7%) had chromosome 5 abnormalities, and 123 (6%) had a complex karyotype. The most commonly mutated genes were: TET2 (28%), ASXL1 (22%), SF3B1 (22%), SRSF2 (19), JAK2 (11%), DNMT3A (9%), RUNX1 (9%), and U2AF1 (6%) SF3B1 mutations were associated with normal karyotype (NK), age <65 years, ANC >1 k/dL, platelets (plts) >50 k/dL, marrow blasts (MB) <10% and hemoglobin (hb) <8 g/dL. TP53 mutations were associated with complex karyotype, chro 5, 7, or 17 abnormalities. Clinical characteristics were also associated with specific genomic alterations (Figure 1). For example, NK correlated with the presence of SF3B1, ZRSR2, DNMT3A, a higher number of mutations, and absence of TP53, ASXL1, or KRAS; chromosome 5, 7, and 17 abnormalities were associated with a lower mutation number and the presence of TP53 mutations; complex karyotype correlated with the absence of TET2 and SF3B1 and the presence of TP53; age < 65 was associated with the presence of NRAS and JAK2 mutations and the absence of TET2, SF3B1, and SRSF2 mutations; hemoglobin < 8 g/dL positively correlated with mutation number and SF3B1 mutations and negatively correlated with TET2 mutations; ANC < 1 negatively correlated with JAK2, SF3B1, and DNMT3A mutations; platelets < 50k/dL negatively correlated with SF3B1 and JAK2 mutations, and positively correlated with the number of mutations; and MB <10% positively correlated with SF3B1 mutations and negatively correlated with number of mutations and ASXL1, RUNX1, TP53, and STAG2 mutations (Figure 1). Conclusions We applied machine learning techniques to reveal the complex relationships between mutational data and the clinical characteristics of several myeloid malignancies using a large, international patient cohort. In addition to correctly identifying previously described genotype-phenotype relationships, we identified several other intriguing relationships such as the relationship of particular mutations to the development of different cytopenias, demonstrating the potential utility for machine learning approaches in interrogating genomic data. Disclosures Sekeres: BMS: Consultancy; Pfizer: Consultancy; Takeda/Millenium: Consultancy. Gerds:Gilead Sciences: Research Funding; Imago Biosciences: Research Funding; CTI Biopharma: Consultancy, Research Funding; Pfizer: Research Funding; Sierra Oncology: Research Funding; AstraZeneca/MedImmune: Consultancy; Incyte Corporation: Consultancy, Research Funding; Apexx Oncology: Consultancy; Celgene: Consultancy, Research Funding; Roche/Genentech: Research Funding. Mukherjee:Aplastic Anemia and MDS International Foundation: Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Bristol Myers Squib: Honoraria; 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. Maciejewski:Alexion, BMS: Speakers Bureau; Novartis, Roche: Consultancy, Honoraria. Nazha:Novartis: Speakers Bureau; MEI: Other: Data monitoring Committee; Jazz: Research Funding; Incyte: Speakers Bureau.

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Impact of Cytogenetic Abnormalities and Cytogenetic Response to Hypomethylating Agents (HMAs) in Patients (pts) with Lower Risk Myelodysplastic Syndromes (MDS)

Blood ◽

10.1182/blood.v126.23.2877.2877 ◽

2015 ◽

Vol 126 (23) ◽

pp. 2877-2877 ◽

Cited By ~ 1

Author(s):

Guillermo Montalban-Bravo ◽

Guillermo Garcia-Manero ◽

Nicholas J. Short ◽

Koji Sasaki ◽

Mikkael A. Sekeres ◽

...

Keyword(s):

Board Of Directors ◽

Lower Risk ◽

Research Funding ◽

Risk Model ◽

Clonal Evolution ◽

Cytogenetic Response ◽

Low Risk ◽

Abnormal Karyotype ◽

Advisory Committees ◽

Cytogenetic Abnormalities

Abstract Introduction: Although pts with lower risk MDS have longer OS and transformation free survival than pts with higher risk disease, a subset of pts with lower risk MDS have higher risk cytogenetics. There is scarce data on the impact of cytogenetic abnormalities on response to HMAs in this patient population, the ability of these agents to induce cytogenetic responses along with the prognostic relevance of clonal evolution in this subset of pts. Methods: Pts with lower risk MDS treated with HMAs between 2012 and 2015 were evaluated. Information regarding baseline cytopenias, prior malignancy or chemotherapy, initial and on therapy bone marrow cytogenetic findings and response to therapy by IWG criteria were collected. Pts were stratified according to IPSS/IPSS-R cytogenetic groups and MDACC Lower risk model. Data regarding time to cytogenetic response, clonal evolution and clinical evolution was also reviewed. Statistical analysis included Chi-squared for categorical variables, T-student for continuous variables and Kaplan-Meier for OS and EFS. Results: A total of 83 pts were evaluated. Pt characteristics are in Table 1. Overall response rate was 61% with 39% CR, 10% CRn, 2% CRp, 1% CRi and 10% pts showing hematological improvement only. No significant difference in response rates (68% vs 61%, p=0.51) to HMAs was observed between pts with or without cytogenetic abnormalities. In pts with abnormal karyotype, 10 (26%) had complete cytogenetic response (CCyR) and 12 (31%) had partial cytogenetic response (PCyR) after a median of 7 months of therapy (range 2-18). Clonal evolution during therapy was observed in 12 (14%) pts after a median time of 8 months, and was associated with loss of response in 6 (50%) pts. There was no correlation between the achievement of a CR and cytogenetic response (p=0.36).The median follow-up was 13 months (2-30 months). Stratification of pts by IPSS or IPSS-R cytogenetic scores did not significantly predict differences for EFS (p=0.31 and p=0.47) nor OS (p=0,52 and p=0.18). By applying the MDACC low-risk scoring system, the 13-month survival rate was 100%, 83%, and 73%, for pts with categories 1, 2, and 3 respectively (p=0.35). No differences in EFS were observed between these groups. The 1-year EFS and OS rates were 79% vs 24% (p<0.001), and 83% vs 67% (p=0.3) for pts with and without any response (Figure 1). Pts achieving CR had 1-year EFS and OS rates of 83% and 92%, respectively. The 1-year EFS and OS rates were 89% vs 50% (p=0.26) and 77% vs 74% (p=0.84), for pts with or without a cytogenetic response. Among pts with morphologic CR, achieving a cytogenetic response did not confer a significant benefit in EFS (100% vs 75%; p=0.69) or OS (88% vs 75%, p=0.91). Acquisition of clonal evolution did not significantly impact EFS (56% vs 31%; p=0.37) nor OS (59% vs 57%, p=0,5). Presence of complex cytogenetics was associated with a trend for a shorter OS (67% vs 80%, p=0,072) and EFS (42% vs 66%, p=0.36). Conclusions: Achieving response to HMA therapy in pts with low-risk MDS is associated with improvement of outcome. Current IPSS or IPSS-R cytogenetic scores do not predict for outcome with HMA therapy. MDACC Lower risk model showed a tendency to better stratify OS of pts with low risk MDS treated with HMA. Cytogenetic evolution does not appear to impact outcome in patient with low-risk MDS treated with HMAs. Table 1. Studied factor Normal Karyotype Abnormal Karyotype Age 68 (44-85) 72 (55-84) SexMaleFemale 33 (70%)14 (30%) 21 (58%)15 (42%) IPSS CategoryLowIntermediate-1 10 (21%)37 (79%) 3 (8%)33 (92%) MDACC Lower risk modelLow (category 1)Intermediate (category 2)High (category 3) 6 (13%)25 (53%)16 (34%) 1 (3%)13 (36%)22 (61%) Mean blood counts at baselineHemoglobinPlateletsWBCANC 10.2g/dL (7.3-14.4)103 x109/L (4-404)6.8 x109/L (0.7-35.2)4.5 x109/L (0.2-23-2) 10.5 g/dL (7.8-15.5)96.x109/L (7-325)5.6 x109/L (1.2-32.2)2.7 x109/L (0.2-16.9) Number of clones at baseline 1 2 (1-4) Baseline % of blasts 5 (0-10) 2 (0-8) Baseline Cytogenetic abnormalities-Ydel(20q)del(11q)del(5q)del(7q) or -7+8OtherComplex Cytogenetics - 2 (6%)2 (6%)2 (6%)5 (14%)5 (14%)6 (17%)14 (39%)6 (17%) Cytogenetics at baseline by IPSSGoodIntermediatePoor 47 (100%) 8 (22%)20 (56%)8 (22%) Cytogenetics at baseline by IPSS-RVery goodGoodIntermediatePoorVery poor 47 (100%) 3 (8%)7 (19%)18 (50%)3 (8%)5 (14%) Prior malignancy 19 (40%) 13 (36%) Therapy related 7 (15%) 9 (25%) Number of Cycles of HMA 9 (2-25) 11 (2-29) Figure 1. Figure 1. Disclosures Sekeres: Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; TetraLogic: Membership on an entity's Board of Directors or advisory committees. Komrokji:Incyte: Consultancy; Celgene: Consultancy, Research Funding; Novartis: Research Funding, Speakers Bureau; Pharmacylics: Speakers Bureau. Steensma:Celgene: Consultancy; Amgen: Consultancy; Incyte: Consultancy; Onconova: Consultancy. DiNardo:Novartis: Research Funding. Pemmaraju:Stemline: Research Funding; Incyte: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding; LFB: Consultancy, Honoraria. Daver:ImmunoGen: Other: clinical trial, Research Funding. Konopleva:Novartis: Research Funding; AbbVie: Research Funding; Stemline: Research Funding; Calithera: Research Funding; Threshold: Research Funding. Cortes:Teva: Research Funding; BMS: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; BerGenBio AS: Research Funding; Pfizer: Consultancy, Research Funding; Ariad: Consultancy, Research Funding; Astellas: Consultancy, Research Funding; Ambit: Consultancy, Research Funding; Arog: Research Funding; Celator: Research Funding; Jenssen: Consultancy.

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p53 Mutant Independently Impacts Risk: Analysis of Deletion 5q, Lower-Risk Myelodysplastic Syndromes (MDS) Patients Treated with Lenalidomide (LEN) in the MDS-004 Study

Blood ◽

10.1182/blood.v124.21.414.414 ◽

2014 ◽

Vol 124 (21) ◽

pp. 414-414 ◽

Cited By ~ 3

Author(s):

Leonie Saft ◽

Jack Shiansong Li ◽

Peter L. Greenberg ◽

Mikkael A. Sekeres ◽

Guillermo F. Sanz ◽

...

Keyword(s):

High Risk ◽

Board Of Directors ◽

Lower Risk ◽

Research Funding ◽

Risk Group ◽

Risk Groups ◽

Employment Equity ◽

Advisory Committees ◽

Equity Ownership ◽

Very High

Abstract Introduction: Refined risk-classification of patients (pts) with MDS allows for improved treatment selection for individual pts. The Revised International Prognostic Scoring System (IPSS-R) has recently been validated as a prognostic tool in lower-risk MDS pts with deletion 5q [del(5q)], who were treated with LEN in the MDS-004 study (Sekeres et al. Blood Cancer J 2014; in press). P53 nuclear protein expression, as assessed by immunohistochemistry (IHC), predicted overall survival (OS) and risk of progression to acute myeloid leukemia (AML) in lower-risk MDS pts with del(5q) (Saft et al. Haematologica 2014;99:1041-9). This analysis evaluated the prognostic value of adding p53 IHC to IPSS-R to predict OS and AML progression in pts with lower-risk MDS with del(5q). Methods: In a subset of 85 pts from MDS-004 with bone marrow (BM) biopsies available, p53+ staining (≥ 1% IHC+++ BM cells) was visualized by IHC. Twenty-four pts had missing IPSS-R scores; 1 due to lack of baseline cytogenetic data and 23 because of missing exact BM blast percentage. Thus, 61 pts (42 initially treated with LEN and 19 with placebo) had IPSS-R and p53 IHC data available; 89% of pts in the placebo group crossed over to LEN 5 mg at Week 16. The IPSS-R Very Low and Very High risk groups with < 5 pts were combined with the Low and High risk groups, respectively. AML-free survival (AFS), OS, and time to AML progression within p53 IHC status (p53+ vs p53−), and IPSS-R risk groups were characterized by the Kaplan-Meier method with differences evaluated by the log-rank test. Results: Of 61 pts, 38% were p53+. There was a linear increasing trend in the proportion of pts with p53+ across IPSS-R risk groups from Very Low/Low, Intermediate to High/Very High (29%, 47% and 63%, respectively; Cochran-Armitage trend test P = 0.050). The 3 IPSS-R risk groups significantly predicted AFS and OS (log-rank P < 0.001 for both AFS and OS), but not time to AML progression (P = 0.335). Overall, AFS, OS, and time to AML progression differed significantly between p53+ versus p53− pts (23.9 vs 47.9 months for median AFS, P = 0.003; 27.0 vs 50.6 months for median OS, P = 0.005; and 44.3 months vs not reached [NR] for median time to AML progression,P = 0.003). In the IPSS-R Very Low/Low risk group (n = 38), AFS, OS, and time to AML progression were significantly worse in p53+ versus p53− pts (20.1 vs 63.1 months for median AFS, P = 0.011; 28.4 vs 76.8 months for median OS, P = 0.031; and 65.2 months vs NR for median time to AML progression, P = 0.014). Results for all IPSS-R risk groups in pts with p53 and IPSS-R data are presented in the Figure. The lack of significant differences between p53+ versus p53− pts in the Intermediate and High/Very High risk groups is likely due to the small sample size of these groups. Conclusions: In this exploratory subset analysis of lower-risk MDS pts with del(5q), p53 IHC status in the IPSS-R Very Low/Low risk group significantly impacted AFS, OS, and AML progression. These data support the addition of p53 mutational analysis to prognostic risk assessment which should help inform the selection of appropriate treatment for individual MDS pts with del(5q). These results need to be validated in a large sample set, which will be accomplished as part of the ongoing efforts to include prognostic molecular mutations in future updates of IPSS-R Figure 1 AFS (A), OS (B), and time to AML progression (C) in pts with p53 and IPSS-R data (N = 61) Figure 1. AFS (A), OS (B), and time to AML progression (C) in pts with p53 and IPSS-R data (N = 61) Figure 2 Figure 2. Figure 3 Figure 3. Disclosures Shiansong Li: Celgene Corporation: Employment, Equity Ownership. Greenberg:Celgene: Research Funding; Onconova: Research Funding; GSK: Research Funding; Novartis: Research Funding; KaloBios: Research Funding. Sekeres:Amgen Corp.: Membership on an entity's Board of Directors or advisory committees; Boehringer-Ingelheim Corp.: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees. Dreyfus:Novartis: Honoraria; Celgene: Honoraria. Fenaux:Novartis: Research Funding; Janssen: Research Funding; Celgene: Research Funding. Swern:Celgene: Employment, Equity Ownership. Sugrue:Celgene: Employment, Equity Ownership. Hellstrom-Lindberg:Celgene: Research Funding.

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To Treat or Not to Treat? Understanding Treatment Patterns in Patients with Lower-Risk Myelofibrosis at the Time of Enrollment in the MOST Study

Blood ◽

10.1182/blood-2020-134958 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 49-51

Author(s):

Rami S. Komrokji ◽

Brady L. Stein ◽

Robyn M. Scherber ◽

Patricia Kalafut ◽

Haobo Ren ◽

...

Keyword(s):

Board Of Directors ◽

Lower Risk ◽

Research Funding ◽

Symptom Burden ◽

Risk Groups ◽

Treatment Patterns ◽

Low Risk ◽

Publicly Traded ◽

Advisory Committees ◽

Therapy Initiation

Background: Myelofibrosis (MF) is a chronic Philadelphia chromosome-negative myeloproliferative neoplasm (MPN) characterized by extramedullary hematopoiesis, bone marrow fibrosis, splenomegaly, constitutional symptoms, and diminished quality of life. Treatment decisions may involve a variety of factors including prognosis and symptomatology. Data regarding real-world disease and demographic factors that contribute to therapy initiation and choice in pts with lower risk MF are limited. This analysis of data from the ongoing Myelofibrosis and Essential Thrombocythemia Observational STudy (MOST; NCT02953704) assessed whether these factors differ for lower risk pts who were treated vs untreated at enrollment. Methods: MOST is a longitudinal, noninterventional, prospective, observational study in pts with MF or essential thrombocythemia enrolled at clinical practices within the US. Pts included in the analysis (≥18 y), had low risk MF by the Dynamic International Prognostic Scoring System (DIPSS; Blood. 2010;115:1703), or intermediate-1 (INT-1) risk by age >65 y alone. Pt data were entered into an electronic case report form during usual-care visits over a planned 36-month observation period. Pt-reported symptom burden was assessed using the MPN-Symptom Assessment Form (MPN-SAF); Total Symptom Score (TSS) was calculated (0 [absent] to 100 [worst imaginable]; J Clin Oncol. 2012;30:4098). Data were analyzed with basic descriptive and inferential statistics. Results: Of 233 pts with MF enrolled at 124 sites between 11/29/2016 and 03/29/2019, 205 were included in this analysis; 28 were excluded for being INT-1 risk for reasons other than age. Of the 205 pts, 85 (41.5%) were low- and 120 (58.5%) were INT-1 risk; 56.5% (48/85) and 59.2% (71/120), respectively, were being treated at enrollment. Pt characteristics are listed in Table 1A. Fewer low- vs INT-1 risk pts were JAK2 V617F or MPL positive, and more were CALR positive. The proportion of pts with palpable splenomegaly was similar for treated low- and INT-1 risk pts. In low risk pts, the proportion of pts with palpable splenomegaly was higher in untreated vs treated pts; whereas, in INT-1 risk pts, the opposite was observed (ie, lower proportion in untreated vs treated pts). Blood counts were generally similar across cohorts, except median leukocytes were lower for low risk treated pts and platelet counts were elevated in low- vs INT-1 risk pts. The proportion of pts with comorbidities was similar across cohorts, except for fewer cardiovascular comorbidities in low- vs INT-1 risk pts. Mean TSS was lower in low- vs INT-1 risk pts, but the proportion of pts with TSS ≥20 was greater in treated vs untreated pts in both low- and INT-1 risk groups. Fatigue was the most severe pt-reported symptom in all cohorts. Differences in mean TSS and individual symptom scores between risk groups were not significant (P > 0.05), except itching was worse among INT-1 risk pts (P=0.03). Physician-reported signs and symptoms were generally more frequent for untreated vs treated pts, irrespective of risk (all P > 0.05). Most low risk (69.4%) and INT-1 risk pts (61.2%) who were currently untreated at enrollment had not received any prior MF-directed treatment (Table 1B); the most common prior treatment among currently untreated pts was hydroxyurea (HU) in both risk groups. Of currently treated pts, HU was the most common MF-directed monotherapy at enrollment in low-risk pts, and ruxolitinib was most common in INT-1 risk pts. No low risk pts and few INT-1 risk pts were currently receiving >1 MF-directed therapy at enrollment. Conclusion: These real-world data from pts with MF enrolled in MOST show that a substantial proportion of both low- and INT-1 risk pts who had received treatment before enrollment were not being treated at the time of enrollment. Although watch-and-wait is a therapeutic option, the finding that many of these lower risk pts had in fact received prior therapies suggests an unmet need for effective and tolerable second-line treatment options. Treated pts had greater pt-reported symptom burden vs untreated pts, which suggests that high symptom burden may contribute to the decision for treatment. Prospective studies are needed to evaluate symptom burden change with therapy initiation. In this regard, future analyses of data from MOST are planned to assess the longitudinal evolution of the clinical characteristics, treatment patterns, and management of pts with MF. Disclosures Komrokji: Geron: Honoraria; Agios: Honoraria, Speakers Bureau; AbbVie: Honoraria; Incyte: Honoraria; Novartis: Honoraria; BMS: Honoraria, Speakers Bureau; JAZZ: Honoraria, Speakers Bureau; Acceleron: Honoraria. Stein:Incyte: Research Funding; Kartos: Other: educational content presented; Constellation Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Pharmaessentia: Membership on an entity's Board of Directors or advisory committees. Scherber:Incyte Corporation: Current Employment, Current equity holder in publicly-traded company. Kalafut:Incyte: Current Employment, Current equity holder in publicly-traded company. Ren:Incyte: Current Employment, Current equity holder in publicly-traded company. Verstovsek:Incyte Corporation: Consultancy, Research Funding; Roche: Research Funding; Genentech: Research Funding; Blueprint Medicines Corp: Research Funding; CTI Biopharma Corp: Research Funding; NS Pharma: Research Funding; ItalPharma: Research Funding; Celgene: Consultancy, Research Funding; Gilead: Research Funding; Protagonist Therapeutics: Research Funding; Novartis: Consultancy, Research Funding; Sierra Oncology: Consultancy, Research Funding; PharmaEssentia: Research Funding; AstraZeneca: Research Funding; Promedior: Research Funding.

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Metaphase Cytogenetics for Risk Stratification in Newly Diagnosed Multiple Myeloma

Blood ◽

10.1182/blood-2019-122291 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 4396-4396

Author(s):

Patrick Mellors ◽

Moritz Binder ◽

Rhett P. Ketterling ◽

Patricia Griepp ◽

Linda B Baughn ◽

...

Keyword(s):

Multiple Myeloma ◽

Risk Stratification ◽

Board Of Directors ◽

Research Funding ◽

Multivariable Analysis ◽

Risk Modeling ◽

Newly Diagnosed ◽

Advisory Committees ◽

Cytogenetic Abnormalities ◽

Improve Risk Stratification

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

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Dynamic Epigenetic Landscapes Define Multiple Myeloma Progression and Drug Resistance

Blood ◽

10.1182/blood-2020-142872 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 32-33

Author(s):

Rafael Renatino-Canevarolo ◽

Mark B. Meads ◽

Maria Silva ◽

Praneeth Reddy Sudalagunta ◽

Christopher Cubitt ◽

...

Keyword(s):

Gene Expression ◽

Drug Resistance ◽

Disease Progression ◽

Board Of Directors ◽

Research Funding ◽

Chromatin Accessibility ◽

Refractory Disease ◽

Advisory Committees ◽

Cytogenetic Abnormalities ◽

Genome Wide

Multiple myeloma (MM) is an incurable cancer of bone marrow-resident plasma cells, which evolves from a premalignant state, MGUS, to a form of active disease characterized by an initial response to therapy, followed by cycles of therapeutic successes and failures, culminating in a fatal multi-drug resistant cancer. The molecular mechanisms leading to disease progression and refractory disease in MM remain poorly understood. To address this question, we have generated a new database, consisting of 1,123 MM biopsies from patients treated at the H. Lee Moffitt Cancer Center. These samples ranged from MGUS to late relapsed/refractory (LR) disease, and were comprehensively characterized genetically (844 RNAseq, 870 WES, 7 scRNAseq), epigenetically (10 single-cell chromatin accessibility, scATAC-seq) and phenotypically (537 samples assessed for ex vivo drug resistance). Mutational analysis identified putative driver genes (e.g. NRAS, KRAS) among the highest frequent mutations, as well as a steady increase in mutational load across progression from MGUS to LR samples. However, with the exception of KRAS, these genes did not reach statistical significance according to FISHER's exact test between different disease stages, suggesting that no single mutation is necessary or sufficient to drive MM progression or refractory disease, but rather a common "driver" biology is critical. Pathway analysis of differentially expressed genes identified cell adhesion, inflammatory cytokines and hematopoietic cell identify as under-expressed in active MM vs. MGUS, while cell cycle, metabolism, DNA repair, protein/RNA synthesis and degradation were over-expressed in LR. Using an unsupervised systems biology approach, we reconstructed a gene expression map to identify transcriptomic reprogramming events associated with disease progression and evolution of drug resistance. At an epigenetic regulatory level, these genes were enriched for histone modifications (e.g. H3k27me3 and H3k27ac). Furthermore, scATAC-seq confirmed genome-wide alterations in chromatin accessibility across MM progression, involving shifts in chromatin accessibility of the binding motifs of epigenetic regulator complexes, known to mediate formation of 3D structures (CTCF/YY1) of super enhancers (SE) and cell identity reprograming (POU5F1/SOX2). Additionally, we have identified SE-regulated genes under- (EBF1, RB1, SPI1, KLF6) and over-expressed (PRDM1, IRF4) in MM progression, as well as over-expressed in LR (RFX5, YY1, NBN, CTCF, BCOR). We have found a correlation between cytogenetic abnormalities and mutations with differential gene expression observed in MM progression, suggesting groups of genetic events with equivalent transcriptomic effect: e.g. NRAS, KRAS, DIS3 and del13q are associated with transcriptomic changes observed during MGUS/SMOL=>active MM transition (Figure 1). Taken together, our preliminary data suggests that multiple independent combinations of genetic and epigenetic events (e.g. mutations, cytogenetics, SE dysregulation) alter the balance of master epigenetic regulatory circuitry, leading to genome-wide transcriptional reprogramming, facilitating disease progression and emergence of drug resistance. Figure 1: Topology of transcriptional regulation in MM depicts 16,738 genes whose expression is increased (red) or decreased (green) in presence of genetic abnormality. Differential expression associated with (A) hotspot mutations and (B) cytogenetic abnormalities confirms equivalence of expected pairs (e.g. NRAS and KRAS, BRAF and RAF1), but also proposes novel transcriptomic dysregulation effect of clinically relevant cytogenetic abnormalities, with yet uncharacterized molecular role in MM. Figure 1 Disclosures Kulkarni: M2GEN: Current Employment. Zhang:M2GEN: Current Employment. Hampton:M2GEN: Current Employment. Shain:GlaxoSmithKline: Speakers Bureau; Amgen: Speakers Bureau; Karyopharm: Research Funding, Speakers Bureau; AbbVie: Research Funding; Takeda: Honoraria, Speakers Bureau; Sanofi/Genzyme: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Adaptive: Consultancy, Honoraria; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Siqueira Silva:AbbVie: Research Funding; Karyopharm: Research Funding; NIH/NCI: Research Funding.

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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.

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A Phase 1 Study of Sea-CD70 in Myeloid Malignancies

Blood ◽

10.1182/blood-2020-136203 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 23-24

Author(s):

Ahmed Aribi ◽

Anjali S Advani ◽

William Donnellan ◽

Amir T. Fathi ◽

Marcello Rotta ◽

...

Keyword(s):

Board Of Directors ◽

Immune Evasion ◽

Dose Escalation ◽

Research Funding ◽

Phase 1 ◽

Steering Committee ◽

Advisory Board ◽

Open Label ◽

Advisory Committees ◽

Myeloid Malignancies

Background SEA-CD70 is being developed in myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). Current treatment options are limited for patients (pts) with relapsed or refractory (r/r) MDS or r/r AML and outcomes remain poor. SEA-CD70 is an investigational humanized, non-fucosylated monoclonal antibody targeting CD70. Expression of CD70 is limited in normal tissue, but is aberrantly expressed on malignant myeloid blasts while absent from healthy hematopoietic progenitor cells. CD70 and its ligand, CD27, may play a role in malignant blast cell survival and/or tumor immune evasion. SEA-CD70 uses a novel sugar-engineered antibody (SEA) platform to produce a non-fucosylated antibody with enhanced effector function. The proposed mechanism of action of SEA-CD70 includes elimination of CD70 positive cells via enhanced antibody-dependent cellular cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP), and mediation of complement-dependent cytoxicity (CDC). Additionally, SEA-CD70 has the potential to block the interaction of CD70 with CD27, which may disrupt signals that enhance blast proliferation and survival and may modulate the immune system to limit immune evasion and increase antigen specific T cell responses. Methods SGNS70-101 is a phase 1, open-label, multicenter, dose-escalation, and cohort expansion study designed to establish the safety, tolerability, and preliminary activity of SEA-CD70 in pts with myeloid malignancies (NCT04227847). Dose escalation is ongoing. In dose escalation, pts must have r/r MDS with 5-20% blasts which has failed prior treatment with a hypomethylating agent (HMA), and have no other therapeutic options known to provide clinical benefit for MDS. After conclusion of dose escalation, monotherapy expansion cohorts will be opened for pts with MDS and for pts with AML. Primary objectives are to evaluate the safety and tolerability, and to determine the maximum tolerated dose (MTD) or recommended expansion dose of SEA-CD70. Secondary objectives are to assess antitumor activity, PK, and immunogenicity of SEA-CD70. Once dose escalation is complete and the recommended monotherapy dose is identified, combination cohorts will be considered in AML and MDS. The study is currently enrolling with sites opening in the US and EU. Disclosures Aribi: Seattle Genetics: Consultancy. Advani:OBI: Research Funding; Takeda: Research Funding; Novartis: Consultancy, Other: advisory board; Pfizer: Honoraria, Research Funding; Kite: Other: Advisory board/ honoraria; Amgen: Consultancy, Other: steering committee/ honoraria, Research Funding; Seattle Genetics: Other: Advisory board/ honoraria, Research Funding; Immunogen: Research Funding; Glycomimetics: Consultancy, Other: Steering committee/ honoraria, Research Funding; Macrogenics: Research Funding; Abbvie: Research Funding. Donnellan:Kite Pharma/Gilead: Research Funding; Janssen: Research Funding; Karyopharm Therapeutics: Research Funding; AstraZeneca: Research Funding; Astex Pharmaceuticals: Research Funding; Incyte: Research Funding; MedImmune: Research Funding; TCR2 Therapeutics: Research Funding; Genentech: Research Funding; PTC Therapeutics: Consultancy, Research Funding; Pfizer: Research Funding; Daiichi Sankyo: Research Funding; Bristol-Myers Squibb: Research Funding; Amgen: Consultancy; Abbvie: Consultancy, Research Funding; Bellicum Pharmaceuticals: Research Funding; CTI Biopharma: Research Funding; Celgene: Research Funding; Celularity: Research Funding; Forma Therapeutics: Research Funding; Forty Seven: Research Funding; Takeda: Research Funding; H3 Biomedicine: Research Funding; Ryvu Therapeutics: Research Funding; Seattle Genetics: Consultancy, Research Funding. Fathi:Astellas: Consultancy; Agios: Consultancy, Research Funding; Amphivena: Consultancy, Honoraria; AbbVie: Consultancy; Pfizer: Consultancy; Daiichi Sankyo: Consultancy; Celgene: Consultancy, Research Funding; Forty Seven: Consultancy; Jazz: Consultancy, Honoraria; Kite: Consultancy, Honoraria; NewLink Genetics: Consultancy, Honoraria; Novartis: Consultancy; PTC Therapeutics: Consultancy; Takeda: Consultancy; TrovaGene: Consultancy; Amgen: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Blue Print Oncology: Consultancy; Boston Biomedical: Consultancy; Kura: Consultancy; Trillium: Consultancy; Seattle Genetics: Consultancy, Research Funding. Rotta:Merck: Speakers Bureau; Jazz Pharma: Speakers Bureau. Vachani:Blueprint: Consultancy; CTI Biopharma: Consultancy; Daiichi Sankyo: Consultancy; Incyte: Consultancy, Research Funding; Jazz: Consultancy; Astellas: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy; Abbvie: Consultancy. Yang:AROG: Research Funding; Protagonist: Research Funding; Jannsen: Research Funding; AstraZeneca: Research Funding. Ho:Seattle Genetics: Current Employment, Current equity holder in publicly-traded company. Garcia-Manero:Novartis: Research Funding; Helsinn Therapeutics: Consultancy, Honoraria, Research Funding; Merck: Research Funding; Jazz Pharmaceuticals: Consultancy; Onconova: Research Funding; Amphivena Therapeutics: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Acceleron Pharmaceuticals: Consultancy, Honoraria; AbbVie: Honoraria, Research Funding; Astex Pharmaceuticals: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; H3 Biomedicine: Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.

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Response to Treatment Among SF3B1 Mutated Myelodysplastic Syndromes (MDS): A Case-Control Study from the MDS Clinical Research Consortium (MDS CRC)

Blood ◽

10.1182/blood.v126.23.1697.1697 ◽

2015 ◽

Vol 126 (23) ◽

pp. 1697-1697 ◽

Cited By ~ 2

Author(s):

Rami S. Komrokji ◽

Amy E. DeZern ◽

Katrina Zell ◽

Najla H. Al Ali ◽

Eric Padron ◽

...

Keyword(s):

Board Of Directors ◽

Lower Risk ◽

Research Funding ◽

Somatic Mutations ◽

Response To Treatment ◽

Wild Type ◽

Advisory Committees ◽

Baseline Characteristics ◽

Matching Criteria

Abstract Introduction Somatic mutations in SF3B1 ,a gene encoding a core component of RNA splicing machinery, have been identified in patients (pts) with myelodysplastic syndrome (MDS). The SF3B1 mutation (MT) is more commonly detected in pts with ring sideroblasts (RS) morphology and is associated with favorable outcome. The pattern of response among SF3B1 mutated MDS pts to available treatment options, including erythropoiesis stimulating agents (ESA), hypomethylating agents (HMA) and lenalidomide is not known. The distinct underlying disease biology among such pts may alter response to treatment. Methods Pts treated at MDS CRC institutions with MT vs wild-type SF3B1 (WT) controls were matched 1:2. Matching criteria were age at diagnosis, year of diagnosis and International Prognostic Scoring System (IPSS) category at diagnosis. IPSS category was split into two groups (Low or Int-1 vs. Int-2 or High). Matching was performed using the R package by calculating a propensity score, which was then used to determine the two most similar WT SF3B1 patients for each SF3B1-mutated pt, without replacement. Additionally, to be included in the population, pts also had to have been treated with one of the following: ESAs, HMA, or lenalidomide. Response to treatment was evaluated by international Working Group criteria (IWG 2006) and classified as response if hematological improvement or better was achieved (HI+). Survival was calculated from date of treatment until date of death or last known follow-up, unless otherwise noted. Results: We identified 48 Pts with MT and 96 matched controls. Table 1 summarizes baseline characteristics comparing MT vs WT SF3B1 cohorts. SF3B1 MT was detected more often in association with RS, as expected. The majority of pts had lower-risk disease by IPSS and revised IPSS (IPSS-R). Pts with MT had higher platelets than controls. The most common concomitant somatic mutations observed were TET2 (30%), DNMT3A (21%), and ASXL1 (7%). Median follow-up time from diagnosis was 35 months (mo). Median overall survival (OS) from diagnosis was significantly longer for patients with SF3B1 MT (108.5 mo (68.8, NA)) than wild-type controls (28.3 mo (22.3, 36.4); p < 0.001). Patients with an SF3B1 MT had a decreased hazard of death (hazard ratio [HR]: 0.49 (95% confidence limits [95% CL]: 0.29, 0.84); p = 0.009) ESA was the first line therapy for 43 pts (88%) with MT and 55 WT Pts (56%). For ESA treated pts, 14 out 40 MT Pts responded (35%) compared to 9/56 among WT Pts (16%), p 0.032. Among those treated with HMA therapy, 5 out 21 (24%) MT pts responded compared to 11/46 (24%) WT Pts (p 0.99). Finally, for Pts treated with lenalidomide 4/16 (25%) and 4/21 (19%) responded among SF3B1 MT and WT Pts respectively, p 0.7. Conclusions SF3B1 somatic mutation in MDS is commonly associated with RS, lower risk disease, and better OS. Pts with SF3B1 mutation had higher response to ESA compared WT SF3B1. No difference in response to HMA or lenalidomide was observed compared to WT patients. Response rates to lenalidomide and HMA were low in both MT patients and controls. Biologically rational therapies are needed that target this molecular disease subset. Table 1. Baseline characteristics SF3B1 MT (n=48) SF3B1 WT (n=96) P value Age median 65 67 0.6 Gender male 29 (60%) 64(67%) 0.5 Race White 44/45 (98%) 83/90 (92%) 0.34 WHO classification RA RARS RCMD RARS-T Del5 q RAEB-I RAEB-II MDS-U MDS/MPN CMML 3 24 8 4 1 3 3 2 0 0 6 9 17 2 6 10 9 3 11 9 IPSS Low Int-1 Int-2 High 29 (60%) 16 (33%) 3 (6%) 0 21 (22%) 69 (72%) 4 (4%) 2 (2%) < 0.001 IPSS-R Very low Low Intermediate High Very High 15 (31%) 26 (54%) 5 (10%) 2 (4%) 0 11 (11%) 37 (39%) 26 (27%) 18 (19%) 4 (4%) <0.001 Lab values (mean) Hgb Platelets ANC myeloblasts 9.7 274 2.63 1 9.6 108 1.92 2 0.46 <0.001 0.04 0.05 Disclosures Komrokji: Novartis: Research Funding, Speakers Bureau; Celgene: Consultancy, Research Funding; Incyte: Consultancy; Pharmacylics: Speakers Bureau. Padron:Novartis: Speakers Bureau; Incyte: Research Funding. List:Celgene Corporation: Honoraria, Research Funding. Steensma:Incyte: Consultancy; Amgen: Consultancy; Celgene: Consultancy; Onconova: Consultancy. Sekeres:Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; TetraLogic: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees.

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