Abstract
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.
