Patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML) are generally older and have more comorbidities. Therefore, identifying personalized treatment options for each patient early and accurately is essential. To address this, we developed a computational biology modeling (CBM) and digital drug simulation platform that relies on somatic gene mutations and gene CNVs found in malignant cells of individual patients. Drug treatment simulations based on unique patient-specific disease networks were used to generate treatment predictions. To evaluate the accuracy of the genomics-informed computational platform, we conducted a pilot prospective clinical study (NCT02435550) enrolling confirmed MDS and AML patients. Blinded to the empirically prescribed treatment regimen for each patient, genomic data from 50 evaluable patients were analyzed by CBM to predict patient-specific treatment responses. CBM accurately predicted treatment responses in 55 of 61 (90%) simulations, with 33 of 61 true positives, 22 of 61 true negatives, 3 of 61 false positives, and 3 of 61 false negatives, resulting in a sensitivity of 94%, a specificity of 88%, and an accuracy of 90%. Laboratory validation further confirmed the accuracy of CBM-predicted activated protein networks in 17 of 19 (89%) samples from 11 patients. Somatic mutations in the TET2, IDH1/2, ASXL1, and EZH2 genes were discovered to be highly informative of MDS response to hypomethylating agents. In sum, analyses of patient cancer genomics using the CBM platform can be used to predict precision treatment responses in MDS and AML patients.
Grafted and ungrafted ‘Primo Red’ tomato (Solanum lycopersicum) transplants were planted at 16-, 20-, and 24-inch spacing in a commercial high tunnel in central New York, USA, to compare yields. ‘Primo Red’ scions were grafted onto ‘Maxifort’ rootstocks and left to heal in a commercial greenhouse facility. Tomatoes were harvested as they ripened, and the weight and number of fruit per plot was recorded and then calculated out to a per-plant basis. Wider plant spacings resulted in higher yields for both grafted and ungrafted plants. However, economic returns remained highest in the highest density (16 inches in-row) spacing with grafted plants. This indicates that growers may not need to adjust density despite additional foliage from grafted plants. Foliar incidence of Botrytis gray mold (Botrytis cinerea) was not significantly different under spacing or grafting treatments. Grafting resulted in higher yields across all plant spacings compared with ungrafted plants. Commercial growers can use this information to make choices on grafting and spacing in high tunnel tomato.
Hypomethylating agents (HMAs) (azacitidine (aza), decitabine (dec)) and lenalidomide (len) are approved agents and used to treat patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML). Despite their widespread use, HMAs fail in the majority of these patients, and len fails in 75% of non-del(5q) MDS. Currently, no method exists to predict disease response, thus the management of MDS and AML patients is challenging. Methods: Patients with AML or MDS were recruited to a clinical trial (NCT02435550) designed to assess predictive values by comparing computer predictions of drug response to actual clinical response. Genomic profiling was conducted by cytogenetics, whole exome sequencing, and array CGH. Genomic results were inputted into a computational software (Cellworks), which generates disease-specific protein network maps using PubMed and other resources. Digital drug simulations were conducted by quantitatively measuring drug effect on a cell growth score (proliferation + viability + apoptosis). Each patient-specific protein network was screened for the extent by which aza, dec or len reduced disease growth in a dose-respondent manner. Treatment was physician’s choice of SOC. Clinical outcomes were prospectively recorded. IWG criteria were used to define response. Western blot assays were performed to validate the predicted protein network perturbations. Fisher’s exact test was used to compare prediction values of the genomics-informed computer method versus empiric drug administration. Results: 88 patients have had all molecular tests and computational modeling performed. Lab validation of computer-predicted, activated protein networks in 19 samples from 13 different patients showed correct prediction of 5 activated networks (Akt2, Akt3, PIK3CA, p38, Erk1/2) in 17 samples, with 89% accuracy. At the time of this report, 26/88 patients were eligible for efficacy evaluation. 8/26 patients showed clinical response to SOC therapy, 18/26 did not. 24/26 outcome predictions were correctly matched to their clinical outcomes, and 2/20 were incorrectly matched, resulting in 92% prediction accuracy, 80% PPV, 100% NPV, 100% sensitivity, and 89% specificity. The accuracy of the genomics-informed computer method was significantly greater than empiric drug administration (p=1.664e-05). New genomic signature rules were discovered to correlate with clinical response after aza, dec or len. Summary: A computational method that models multiple genomic abnormalities simultaneously showed high predictive value of protein network aberrations and clinical outcomes after SOC treatments. The network method uncovered molecular reasons for drug failure and highlighted resistance pathways that could be targeted to recover chemosensitivity. This technology could also be used to establish eligibility criteria for precision enrollment in drug development trials Citation Format: Leylah Drusbosky, Kimberly E. Hawkins, Shireen Vali, Taher Abbasi, Ansu Kumar, Neeraj Kumar Singh, Kabya Basu, Chandan Kumar, Amjad Husain, Caitlin Tucker, Randy A. Brown, Maxim Norkin, John Hiemenz, Jack Hsu, John Wingard, Christopher R. Cogle. iCare 1: A prospective clinical trial to predict treatment response based on mutanome-informed computational biology in patients with AML and MDS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr CT085. doi:10.1158/1538-7445.AM2017-CT085
Background: Hypomethylating agents (HMAs) (e.g., azacitidine (aza), decitabine (dec)) and lenalidomide (len) are approved agents and used in the treatment of patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML). Despite their widespread use, HMAs fail in the majority of MDS and AML patients, and len fails in 75% of non-del(5q) MDS. Unfortunately, no method exists to predict disease response, thus the management of MDS and AML patients is challenging. Predicting treatment response would improve treatment effectiveness, restrict treatment-related adverse events to those who would benefit, and reduce health care costs. Ideally, patient prediction would be based on disease biology. Aim: To determine the biological and clinical predictive values of a genomics-informed computational biology method in patients with AML and MDS who are treated with aza, dec or len. Methods: Patients with AML or MDS were recruited in a prospective clinical trial (NCT02435550) designed to assess predictive values by comparing computer predictions of treatment response to actual clinical response. Genomic profiling was conducted by conventional cytogenetics, whole exome sequencing (SureSelectXT Clinical Research Exome, Agilent), and array CGH (Agilent). These genomic results were inputted into computational biology software (Cellworks Group), which generates disease-specific protein network maps using PubMed and other online resources. Digital drug simulations were conducted by quantitatively measuring drug effect on a cell growth score, which is a composite of cell proliferation, viability and apoptosis. Each patient-specific protein network map was digitally screened for the extent by which aza, dec or len reduced simulated disease growth in a dose-respondent manner. Treatment was physician's choice based on SOC. Before initiating treatment, treating physicians were masked to the results of whole exome sequencing and computational predictions. Clinical outcomes were prospectively recorded. To be eligible for efficacy assessment, patients must have had at least 4 cycles of HMA treatment or 2 cycles of len treatment. For AML, CR+PR was used to define response (IWG 2003). For MDS, CR+PR+HI was used to define response (IWG 2006). To validate the predicted protein network perturbations, Western blot assays were performed on pertinent pathway proteins. Comparisons of computer-predicted versus actual responses were performed using 2x2 tables, from which prediction values were calculated. Fisher's exact test was used to compare prediction values of the genomics-informed computer method versus empiric drug administration. Results: Between June 2015 and June 2016, 80 patients were recruited. 40/80 (50%) had AML and 40/80 had MDS (50%). The median age was 66 (range 24-91). 44/80 (55%) were treatment-naïve and 36/80 (45%) were treatment-refractory. 99% completed all planned molecular tests and computational analyses. Laboratory validation study of computer-predicted, activated protein networks in 19 samples from 13 different patients showed correct prediction of 5 activated networks (Akt2, Akt3, PIK3CA, p38, Erk1/2) in 17 samples, exhibiting 89% accuracy. At the time of this report, 20/80 patients were eligible for efficacy evaluation. 6/20 patients showed clinical response to SOC therapy, while 14/20 did not achieve clinical response. 18 patients' outcome predictions were correctly matched to their actual clinical outcomes, and 2/20 were incorrectly matched, resulting in 90% prediction accuracy, 75% positive predictive value (PPV), 100% negative predictive value (NPV), 100% sensitivity, and 86% specificity. The accuracy of the genomics-informed computer method was significantly greater than empiric drug administration (p=1.664e-05). New genomic signature rules were discovered to correlate with clinical response after aza, dec or len. Conclusions: A computational method that models multiple genomic abnormalities simultaneously showed high predictive value of protein network perturbations and clinical outcomes after standard of care treatments. The network method uncovered molecular reasons for drug failure and highlighted resistance pathways that could be targeted to recover chemosensitivity. This technology could also be used to establish eligibility criteria for precision enrollment in drug development trials. Disclosures Vali: Cellworks Group: Employment. Abbasi:Cellworks: Employment. Kumar:Cellworks group: Employment. Kumar Singh:Cellworks group: Employment. Basu:Cellworks Group: Employment. Kumar:Cellworks Group: Employment. Husain:Cellworks Group: Employment. Wingard:Ansun: Consultancy; Merck: Consultancy; Fate Therapeutics: Consultancy; Astellas: Consultancy; Gilead: Consultancy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.