DNA and RNA Profiles in Machine Learning Algorithm to Predict Which Patients with AML/MDS Will Respond to Venetoclax-Based Therapy

Introduction Venetoclax in combination with hypomethylation agents is currently used as an alternative to standard induction therapy in patients with acute myeloid leukemia (AML) or advanced myelodysplastic syndrome (MDS). This is particularly relevant when patients are not eligible for standard induction therapy due to age or comorbidity. However, little is known about which patients will benefit from such therapy and which patients will not respond. Selecting the right patient for such therapy is very important considering the clinical fragility of these patients. Genomic profiling using DNA and RNA is becoming a standard of care in clinical testing and there is a need to expand on the clinical utility of the generated data for this profiling. We explored the potential of using these genomic DNA and RNA profiles in a machine learning algorithm to predict which patients with AML/MDS will respond to venetoclax-based therapy.

Methods: DNA and RNA from bone marrow samples from 46 patients with AML or advanced high-grade MDS treated with venetoclax (plus either azacitidine or decitabine) were extracted. Using next generation sequencing (NGS), the DNA was sequenced using a 177-gene panel and the RNA was sequenced using a 1408-gene panel. Sequencing was performed using the Illumina NextSeq 550 platform. Ten million reads per sample in a single run were required. Read length was 2 × 150 bp. Expression profiles were generated using Cufflinks. We developed a machine learning algorithm that first selected the relative genes based on performance of each gene with cross-validation and based on stability measures using statistical significance tests. The selected genes were then used to predict response or survival with a k-fold cross-validation procedure (k=12). A naïve Bayesian classifier was constructed on the training of k-1 subsets and tested on the other testing subset. We applied geometric mean naïve Bayesian (GMNB) as the classifier for prediction.

Results: Of the 46 patients included in the study, 18 (39%) were females. The median age was 70.5 years (range 33-84). The most common mutations detected in these patients were: ASXL1 (41%), RUNX1 (26%), DNMT3A (24%), FLT3 (21%), NPM1 (20%), NRAS (20%), IDH1/2 (20%), TET2 (17%), TP53 (15%), and SRSF2 (11%). Of these 46 patients, 17 (37%) achieved complete response (CR). Using machine learning we were able to predict those who achieved CR with AUC of 0.972 (95% CI: 0.914-1.00) (Figure), sensitivity of 94.1% and specificity of 93.1%. To achieve this prediction only 10 genes were necessary. There was no significant difference between responders and non-responders in expression level of BCL2 (P=0.11), BAX (P=0.18), or MCL1 (P=0.54). BCL2L1 (BCL-XL) was significantly higher in non-responders (P=0.006) as compared with responders. TP53 was mutated in seven patients, six of whom were non-responders, but no other statistically significant association between specific mutation and response occurred. With a median follow-up of 13 months, 28 (61%) patients were alive and 18 (39%) were dead. Using a machine learning algorithm, we were able to predict overall survival with AUC of 0.970 (95% CI: 0.923-1.00 ) (Figure), sensitivity of 100% and specificity of 94.4%. The algorithm selected 90 genes for this prediction.

Conclusion: This data shows that bone marrow expression profiling when used in machine learning can provide a valuable and practical approach for predicting response and overall survival to venetoclax-based therapy in patients with AML/MDS. This prediction requires complex data from a large number of genes (10 to 90 genes) that must be incorporated in a machine learning algorithm. While further studies are needed for confirmation, genomic profiling along with machine learning algorithms can be a practical test for selecting a specific therapeutic approach for specific patients with AML/MDS.

Albitar:Genomic Testing Cooperative: Current Employment, Current holder of stock options in a privately-held company. Ip:AstraZeneca: Membership on an entity's Board of Directors or advisory committees; SecuraBio: Membership on an entity's Board of Directors or advisory committees; Seagen: Speakers Bureau; Pfizer: Honoraria; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees. Goy:Xcenda: Consultancy, Honoraria; AbbVie: Consultancy; Medscape: Consultancy, Honoraria; Michael J. Hennessy Associates, Inc.: Consultancy, Honoraria; Vincerx: Honoraria, Other: Scientific Advisory Board; Pharmacyclics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Steering committee, Research Funding; OncLive Peer Exchange: Consultancy, Honoraria; Acerta: Research Funding; Genentech: Research Funding; Hoffmann-La Roche: Research Funding; Infinity Pharmaceuticals: Research Funding; Karyopharm: Research Funding; lloplex: Current holder of stock options in a privately-held company, Honoraria, Membership on an entity's Board of Directors or advisory committees; Rosewell Park: Consultancy, Honoraria; Clinical Advances in Hematology & Oncology: Consultancy, Honoraria; Physicians' Education Resource: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; MorphoSys: Honoraria, Other: Steering Committee, Research Funding; Kite: 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, Other: Steering Committee, Research Funding; AstraZeneca: Honoraria, Other: Steering Committee, Research Funding; Bristol Meyers Squibb: Honoraria, Other: Scientific Advisory Board, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Elsevier Practice Update: Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Incyte: Honoraria, Other: Steering Committee, Research Funding; Seattle Genetics: Research Funding; Verastem: Research Funding; Resilience: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Genomic Testing Cooperative: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Regional Cancer Care Associates: Current Employment; OMI: Current Employment; Cancer Outcome Tracking Analysis: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees. McCloskey:AbbVie, CTI BioPharma, and Novartis: Consultancy; AbbVie, Amgen, Bristol Myers Squibb, Incyte, Jazz Pharmaceuticals, Stemline, and Takeda: Speakers Bureau.