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There are two types of timing irregularities seen in pulsars: glitches and timing noise. Both of these phenomena can help us probe the interior of such exotic objects. This article presents a brief overview of the observational and theoretical aspects of pulsar timing irregularities and the main results from the investigations of these phenomena in India. The relevance of such Indian programs for monitoring of young pulsars with the Square Kilometer Array (SKA) is presented, highlighting possible contributions of the Indian neutron star community to the upcoming SKA endeavour., Comment: 7 figure. Accepted for publication in the Journal of Astrophysics and Astronomy : SKA special issue

This paper proposes a Virtual Synchronous Generator (VSG) based frequency regulation scheme for an isolated microgrid. The conventional VSG scheme is modified using self frequency recovery control scheme which reduces the frequency deviation occurred in the system due to any power mismatch between the source and the load. The isolated microgrid considered in this paper consists of a solar photo-voltaic (PV) generator connected parallel to HESS with battery and ultra-capacitor. The modified VSG helps voltage source converter (VSC) to mimic the characteristics of a synchronous generator and improves system inertia using hybrid energy storage system (HESS). HESS act as an energy buffer, which ensures faster DC bus voltage regulation. The solar PV generator operates in maximum power point tracking (MPPT) mode using Perturb and Observe (P&O) algorithm. The performance of modified VSG controller was tested using MATLAB/Simulink and compared with conventional controllers under different system inertia parameters with a step load change and solar PV output fluctuating conditions.

In this study, microstructural and tribological analysis of Aluminium Alloy composites 2024 and 2900 is done. The composite with reinforcements SiC and Al 2 O 3 from 0-20 wt% is prepared by powder metallurgy process. As microwave sintering is the process used the core is much denser than at the surface. Strength of aluminium is increased by percentage of copper alloyed in aluminium matrix. Microstructure of the sample is examined thoroughly using scanning electron microscope. X-ray diffraction patterns are investigated for the elements present in the composites and a detailed check for the intermetallic precipitates and the impurities. A uniform microstructure is obtained by microwave sintering process. Aluminium alloy 2024 and 2900 which is microwave sintered is having round-edged porosities with high ductility and toughness. Tribological studies are carried out using pin on disc wear testing machine. The results indicated that 2900 exhibited excellent frictional coefficient behavior compared to AA 2024.

Background: The optimal treatment strategy for managing Acute Myeloid Leukemia (AML) and the use of reliable and predictive biomarkers to guide selection of cytotoxic chemotherapy regimens among patients with diverse genomic profiles remain unmet needs in the clinic. The combination of MEC [mitoxantrone (MIT), etoposide (VP16), and cytarabine (ARA-C)] is a commonly used regimen for relapsed or refractory AML patients. Unfortunately, many patients do not respond to MEC, and which of the three drug agents matters most for each individual patient is not known. Predictors of response are needed urgently. Methods: The Computational Omics Biology Model (CBM) is a computational multi-omic biology software model created using artificial intelligence heuristics and literature sourced from PubMed to generate a patient-specific protein network map. The aberration and copy number variations from individual cases served as input into the CBM. Disease-biomarkers unique to each patient were identified within patient-specific protein network maps. Biosimulations were conducted on the Cellworks Biosimulation Platform by measuring the effect of chemotherapy on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Biosimulation of drugs was conducted by mapping the interaction of various drug combinations with the patient's genomic and pathway alterations based on signaling pathway mechanisms and their phenotypic consequences. The Cellworks Biosimulation Platform identified unique chromosomal signatures that permit a stratification of patients that are most likely to respond to MIT, VP16, or ARA-C as well as their combinations. 65 AML patients were selected for this study largely based on genomic data published in TCGA and PubMed: ARA-C [N=12, 7 responders (R) & 5 non-responders (NR)]ARA-C + MIT [N=30, 29 R & 1 NR]ARA-C + MIT + VP16 [N=23, 12 R & 11 NR] Results: Of the12 patients treated with ARA-C alone, 5 were predicted to be NR and 7 were predicted to be R. Of the 5 NR, 4 had 5q del which resulted in loss of APC, CSNK1A1 and SLC22A4 (nucleoside carrier) forming the non-response biomarkers for ARA-C. Notably, the biosimulation predicted lenalidomide to be beneficial for these patients. Out of 7 R, 4 patients also had 5q del, but were predicted to be R because of co-occurring aberrations involving CLSPN del, DHODH del, MSH2 del, EP300 del, CREBBP del, MSH6 Del, and RRM2 del. These genes were exclusively present in ARA-C responders. Of 53 patients who received ARA-C + VP16 + MIT or ARA-C + MIT, 41 patients were predicted to be R and 12 patients were predicted to be NR. The genomic aberrations predicted by biosimulation to be associated with response to this regimen include: NPM1-mut, TET2-mut, IDH1-mut, IDH2-mut, RAD17-del, NRAS-mut (Table 1). Notably, CBM predicted 19 of the 41 R had no genomic biomarkers of response to VP16 or MIT, suggesting these patients might have benefited equally from ARA-C alone with less toxicity and cost. Finally, 11/65 patients were predicted NR to MEC treatment. In the biosimulation, treatment failure was associated with high aberration frequencies of KMT2C-mut/del, FLT3 mut, TWIST1 del, LIMK1 del, SNAI2 amp, FNTA amp, and KAT6A amp. Of note, these genomic markers suggested a likelihood of benefit from other therapies, including vincristine, JQ1 and rigosertib. Conclusions: The Cellworks Biosimulation Platform identified novel polygenic biomarkers of response that can be employed to determine the optimal therapy for relapsed AML patients. Biosimulation permits avoidance of cytotoxic drugs with little chance of efficacy and reveals vulnerabilities in each patient's cancer that can be exploited to improve disease control. In AML, biosimulation promises to improve intensive therapy regimens by tailoring chemotherapy to optimize disease control and minimize toxicity. Figure 1 Figure 1. Disclosures Marcucci: Novartis: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings; Abbvie: Other: Speaker and advisory scientific board meetings. Kumar: Cellworks Group Inc.: Current Employment. Castro: Cellworks Group Inc.: Current Employment; Omicure Inc: Consultancy; Caris Life Sciences Inc.: Consultancy; Exact sciences Inc.: Consultancy; Bugworks: Consultancy; Guardant Health Inc.: Speakers Bureau. Grover: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Alam: Cellworks Group Inc.: Current Employment. Azam: Cellworks Group Inc.: Current Employment. Mohapatra: Cellworks Group Inc.: Current Employment. Tyagi: Cellworks Group Inc.: Current Employment. Kumari: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Lunkad: Cellworks Group Inc.: Current Employment. Joseph: Cellworks Group Inc.: Current Employment. G: Cellworks Group Inc.: Current Employment. Chauhan: Cellworks Group Inc.: Current Employment. Basu: Cellworks Group Inc.: Current Employment. Behura: Cellworks Group Inc.: Current Employment. Ghosh: Cellworks Group Inc.: Current Employment. Husain: Cellworks Group Inc.: Current Employment. Mandal: Cellworks Group Inc.: Current Employment. Raman: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Howard: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees.

Background: DNA methyltransferase inhibition (DNMTi) with the hypomethylating agents (HMA) azacitidine (AZA) or decitabine, remains the mainstay of therapy for the majority of high-risk Myelodysplastic Syndromes (MDS) patients. Nevertheless, only 40-50% of MDS patients achieve clinical improvement with DNMTi. There is a need for a predictive clinical approach that can stratify MDS patients according to their chance of benefit from current therapies and that can identify and predict responses to new treatment options. Ideally, patients predicted to be non-responders (NR) could be offered alternative strategies while being spared protracted treatment with HMA alone that has a low likelihood of efficacy. Recently, an intriguing discovery of immune modulation by HMA has emerged. In addition to the benefits of unsilencing differentiation genes and tumor suppressor genes, HMA's reactivate human endogenous retroviral (HERV) genes leading to viral mimicry and upregulation of the immune response as a major mechanism of HMA efficacy. Although the PD-L1/PD1 blockade plus HMA has been recognized as a beneficial combination, there are no established markers to guide decision-making. We report here the utility of immunomic profiling of chromosome 9 copy number status as a significant mechanism of immune evasion and HMA resistance. Methods: 119 patients with known clinical responses to AZA were selected for this study. Publicly available data largely from TCGA and PubMed was utilized for this study. The aberration and copy number variations from individual cases served as input into the Cellworks Computation Omics Biology Model (CBM), a computational biology multi-omic software model, created using artificial intelligence heuristics and literature sourced from PubMed, to generate a patient-specific protein network map. Disease-biomarkers unique to each patient were identified within protein network maps. The Cellworks Biosimulation Platform has the capacity to biosimulate disease phenotypic behavior and was used to create a disease model and then conduct biosimulations to measure the effect of AZA on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Biosimulation of drug response was conducted to identify and predict therapeutic efficacy. Results: Although AZA treatment increased tumor associated antigens and interferon signaling, it also increased PD-L1 expression to inactivate cytotoxic CD8(+) T cells. Copy number alterations of the chromosome 9p region were found to significantly drive PD-L1 expression with multiple genes such as CD274, IFNA1, IFNA2, JAK2, PDCD1LG and KDM4C playing a role in PD-L1 regulation further increasing immune suppression (Figure 1). Among 6 cases of chromosome 9p aberration in this dataset, 9p amp (n=2) were clinical non-responders (NR) while 9p del (n=4) were responders (R) to AZA. In principle, checkpoint immunotherapy could improve outcomes for patients with 9p abnormalities. Additionally, copy number variation loss of key genes located on chromosome 16 involved in antigen processing and presentation such as CIITA, CTCF, IRF8, PSMB10, NLRC5, and SOCS1 were found to negatively impact AZA sensitivity (NR=4; R=0); these patients would also be unlikely to respond to checkpoint immunotherapy. Also, aberrations in melanoma antigen gene (MAGE) family proteins (NR=2; R=O), and STT3A (NR=1; R=5) were found to impact AZA efficacy by decreasing antigen processing on tumor cells. Conclusion: Based on the results from the Cellworks Biosimulation Platform applied to the CBM, copy number variants of chromosome 9p and 16 can be converted into CBM-derived biomarkers for response to checkpoint immunotherapy in combination with HMA. Our results support a future prospective evaluation in larger cohorts of MDS patients. Figure 1 Figure 1. Disclosures Howard: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees. Kumar: Cellworks Group Inc.: Current Employment. Castro: Bugworks: Consultancy; Exact sciences Inc.: Consultancy; Guardant Health Inc.: Speakers Bureau; Cellworks Group Inc.: Current Employment; Caris Life Sciences Inc.: Consultancy; Omicure Inc: Consultancy. Grover: Cellworks Group Inc.: Current Employment. Mohapatra: Cellworks Group Inc.: Current Employment. Kapoor: Cellworks Group Inc.: Current Employment. Tyagi: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Suseela: Cellworks Group Inc.: Current Employment. Pampana: Cellworks Group Inc.: Current Employment. Lala: Cellworks Group Inc.: Current Employment. Singh: Cellworks Group Inc.: Current Employment. Shyamasundar: Cellworks Group Inc.: Current Employment. Kulkarni: Cellworks Group Inc.: Current Employment. Narvekar: Cellworks Group Inc.: Current Employment. Sahni: Cellworks Group Inc.: Current Employment. Raman: Cellworks Group Inc.: Current Employment. Balakrishnan: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Palaniyeppa: Cellworks Group Inc.: Current Employment. Balla: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Marcucci: Abbvie: Other: Speaker and advisory scientific board meetings; Novartis: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings.

Background: DNA methyltransferase inhibition (DNMTi) with hypomethylating agents (HMA), azacitidine (AZA) or decitabine (DAC), remains the mainstay of therapy for most high-risk Myelodysplastic syndrome (MDS) patients. However, only 40-50% of MDS patients achieve clinical improvement with DNMTi. Previously, combinations of HMA and histone deacetylase (HDAC) inhibitors have been explored in MDS with varying clinical outcomes. However, the heterogeneity of genomic aberrations in MDS portend widely divergent responses from HDAC inhibition, implying that a predictive clinical decision support tool could select patients most likely to benefit from this combination. We explored the molecular basis of observed clinical response in a group of patients treated with DAC and Valproic-Acid (VPA). Method: 16 MDS patients with known clinical responses to DAC + VPA were selected for study from the Cellworks patient repository. The aberration and copy number variations from individual cases served as input into the Computational Omics Biology Model, a computational multi-omic biology software model largely created using literature sourced from PubMed, to generate a patient-specific protein network map. Disease biomarkers unique to each patient were identified within these maps. The Cellworks Biosimulation Platform has the capacity to biosimulate disease phenotypic behavior and was used to create a patient-specific disease model. Biosimulations were then conducted on each patient-specific disease model to measure the effect of DAC + VPA according to a cell growth score. This score was comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Biosimulation of drug response was conducted to identify and predict therapeutic efficacy. Results: In the biosimulation, VPA is a relatively weak HDAC inhibitor, but it also inhibits GSK3B and in turn increases beta-catenin (CTNNB1) levels. Additionally, monosomy 7 associated with loss of CAV1, HIPK2, and TRRAP also causes high CTNNB1, thereby further contributing to drug resistance. Biosimulation correctly identified that 7 of 8 patients with these genomic findings were clinical non-responders (NR) to VPA, indicating that CTNNB1 status is likely to predict treatment failure from the VPA + HMA combination in this disease. Notably, CTNNB1 levels have been reported to foster an immune-evasive tumor microenvironment resistant to CTL activation. By contrast, high levels of c-MYC predict response to VPA + HMA combination. VPA inhibits MYC transcription and thereby reduces MYC-induced downregulation of p21 through CKS1B. Additionally MYC is a transcriptional regulator of DNMT1 which is degraded after hyperacetylation induced by HDAC3 inhibition suggesting that VPA also enhances DNMT1 turnover. One patient analyzed had trisomy 8 resulting in c-MYC over-expression and responded to HMA + VPA. Additionally, other aberrations enhancing c-MYC transcription such as copy number variant (CNV) loss of MXI1, HHEX, FBXW7, SMAD7 or CNV gain of BRD4, BCL7B led to high clinical response to the combination (Table 1). By comparison to the CTNNB1-driven subset, the impact of VPA on CTNNB1 in the MYC-dominant disease network did not negate the benefit of VPA for these patients. Additionally, the inhibition of GSK3B by VPA leading to diminished FBXW7 and less ubiquitin-mediated turnover of c-MYC was not sufficient to overcome the inhibition of MYC transcription and HDAC3i-mediated turnover. Immune activation has become a recognized mechanism of responsiveness to HMA. However, among patients with upregulated CTNNB1, VPA is likely to further decrease response to treatment. By contrast, among MYC-driven cancers that are typically immune-evasive, VPA appears to be a vital mechanism of overcoming MYC-driven immune evasion. Conclusion: Signaling pathway consequences related to CTNNB1 and c-MYC upregulation predict response to DAC + VPA. Although HMA plus HDAC inhibition can be generally beneficial for MDS, variable mechanisms of action among various HDAC inhibitors and unique patient disease characteristics should be considered for optimal treatment selection. Finally, CTNNB1 emerged from the Cellworks biosimulations as a therapeutically relevant target in MDS that determines whether VPA synergizes or antagonizes the effect of other agents in this challenging subtype of MDS. Figure 1 Figure 1. Disclosures Castro: Caris Life Sciences Inc.: Consultancy; Omicure Inc: Consultancy; Cellworks Group Inc.: Current Employment; Exact sciences Inc.: Consultancy; Guardant Health Inc.: Speakers Bureau; Bugworks: Consultancy. Kumar: Cellworks Group Inc.: Current Employment. Grover: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Kapoor: Cellworks Group Inc.: Current Employment. Agrawal: Cellworks Group Inc.: Current Employment. Sauban: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Basu: Cellworks Group Inc.: Current Employment. Suseela: Cellworks Group Inc.: Current Employment. Kumar: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Kumari: Cellworks Group Inc.: Current Employment. Pampana: Cellworks Group Inc.: Current Employment. Ullal: Cellworks Group Inc.: Current Employment. Azam: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Amara: Cellworks Group Inc.: Current Employment. Sahu: Cellworks Group Inc.: Current Employment. Raveendaran: Cellworks Group Inc.: Current Employment. Veedu: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Howard: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees.

Background: Therapy selection for MDS patients is often based on information considering only cytogenetics and single molecular aberrations and ignoring other patient-specific omics data that could potentially enable more effective treatments. In turn, despite using cytogenetic and molecular-risk stratification and precision medicine, the current overall outcome of MDS patients remains relatively poor. The Cellworks Singula™ report predicts response for physician prescribed treatments using the novel Cellworks Omics Biology Model (CBM) to simulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. Methods: The performance of Singula™ was evaluated in an independent, randomly selected, retrospective cohort of 144 MDS patients aged 28 to 89 years (median 69). The pre-defined Singula™ Classifier utilizes an individual's genomics profile to provide a dichotomous prediction of response or non-responses to a given physician prescribed treatment (PPT). Outcome data for these subjects, including measurement of complete response (CR), were obtained from 42 PubMed publications, each including patient genomics data of either karyotyping, targeted gene panels, and/or whole exome sequencing. Blinded to clinical outcomes, Cellworks utilized these data to generate a Singula™ classifier of responder vs non-responder in this MDS cohort. Statistical analyses, including assessments of accuracy, sensitivity, specificity, negative (NPV) and positive predictive (PPV) values were performed on the merged data to compare the Singula™ predicted response with the actual observed CR. Multivariate logistic regression models of complete response were performed incorporating covariates for patient age, PPT, and the Singula™ Classifier. Results: Table 1 reveals that the pre-defined Singula™ classifier had 90.3% (Exact 95% CI: 84.2%, 94.6%) accuracy in predicting observed patient response from the physician prescribed treatment. In this study, Singula™ was able to accurately identify responders with 90.0% (81.2%, 95.6%) sensitivity. Importantly, Singula™ had 90.6% (80.7%, 96.5%) specificity for the subset of 64 patients (44.4%) that had a non-response. For 32% (17/54) of the non-responders patients, Singula™ provided an alternative Standard of Care treatment therapy, as shown in Table 2. The remaining 37 patients were predicted to be non-responders to all remaining Standard of Care options, so did not have alternate treatment predictions. Assuming at least 4% of these non-responding patients would have responded to the alternative Singula™ prescribed therapy, then these data support that Singula™ improves prediction of CR compared to the original PPT (McNemar's p-value < 0.05). In multivariate logistic regression models of CR that included patient age and prescribed drug therapy, the Singula™ Classifier remained an independent, significant predictor of CR (OR > 100, p-value < 0.0001), while both patient age (p = 0.372) and drug therapy (p = 0.720) fell off the model. Conclusions: Cellworks Singula™ has high accuracy and sensitivity in predicting CR for MDS patient response to physician prescribed therapies. Singula™ also has high specificity in identifying patients who are unlikely to respond to physician prescribed therapies and provides alternative treatment recommendations for these patients. The Singula™ Classifier is an independent and superior predictor of CR compared with other clinical (age) or therapeutic (PPT) factors. Figure Disclosures Stein: Amgen: Consultancy, Speakers Bureau; Stemline: Consultancy, Speakers Bureau. Watson:BioAI Health Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mercy Bioanalytics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; SEER Biosciences, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellworks Group Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellmax Life Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees. Nair:Cellworks Research India Private Limited: Current Employment. Basu:Cellworks Research India Private Limited: Current Employment. Ullal:Cellworks Research India Private Limited: Current Employment. Ghosh:Cellworks Research India Private Limited: Current Employment. Narvekar:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Sahu:Cellworks Research India Private Limited: Current Employment. Prakash:Cellworks Research India Private Limited: Current Employment. Behura:Cellworks Research India Private Limited: Current Employment. Balakrishnan:Cellworks Research India Private Limited: Current Employment. Roy:Cellworks Research India Private Limited: Current Employment. Rajagopalan:Cellworks Research India Private Limited: Current Employment. Alam:Cellworks Research India Private Limited: Current Employment. Parashar:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Christie:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Marcucci:Abbvie: Speakers Bureau; Novartis: Speakers Bureau; Pfizer: Other: Research Support (Investigation Initiated Clinical Trial); Merck: Other: Research Support (Investigation Initiated Clinical Trial); Takeda: Other: Research Support (Investigation Initiated Clinical Trial); Iaso Bio: Membership on an entity's Board of Directors or advisory committees.

Background: Acute promyelocytic leukemia (APL) is a biologically and clinically distinct subtype of acute myeloid leukemia (AML) with unique molecular pathogenesis, clinical manifestations, and treatment. APL is cytogenetically characterized by a balanced translocation t(15;17) (q24;q21), which involves the retinoic acid receptor alpha (RARA) gene on chromosome 17 and the promyelocytic leukemia (PML) gene on chromosome 15 that results in a PML-RARA fusion gene (PMID: 30575821). The PML-RARA fusion gene is the most critical event involved in the pathogenesis of APL, reported in 99% of APL patients (PMID: 32182684). The fusion confers a selective sensitivity to the targeted drugs, arsenic trioxide (ATO) and all-trans-retinoic acid (ATRA), with response rates over 90% (PMID: 31635329). However, the mechanism of resistance in the minority of non-responders is not well understood. This study used the Cellworks Omics Biology Model (CBM) to predict response to the combination of ATO-ATRA in patients harboring the PML-RARA fusion and identify mechanisms of resistance. Methods: Outcomes of 30 APL patients treated with ATRA or ATRA plus ATO were compared with outcomes predicted by CBM (Table 1). Genomic data from 6 publications (Table 2) derived from whole exome sequencing (WES), targeted next-generation sequencing (NGS), copy number variation (CNV) and/or karyotype data were used. All data was anonymized, de-identified and exempt from IRB review. The available genomic data for each profile was entered into the CBM which generates a patient-specific disease protein network model using PubMed and other online resources. The CBM predicts the patient-specific biomarker and phenotype response of a personalized diseased cell to drug agents, radiation and cell signaling. Disease biomarkers that are unique to each patient were identified within the protein network models. ATO and ATRA were simulated on all 30 patient cases. The treatment impact was assessed by quantitatively measuring the drug's effect on a cell growth score which is a composite of the quantified values for cell proliferation, survival, and apoptosis, along with the simulated impact on each patient-specific disease biomarker score. Each patient-specific model was also digitally screened to identify response to ATO and ATRA. Results: The CBM correctly predicted the response to ATO and ATRA in 28 of 30 cases. The overall prediction accuracy was 93% with a PPV of 100%, NPV of 60%, sensitivity of 93%, and specificity of 100%. In 2 of 30 patients who did not respond to ATO and ATRA, the CBM identified clinically relevant deletions to EZH2, KMT2E, and HIPK2 genes. All three genes are located on chromosome 7 and these non-responders had monosomy 7. Conclusions: The Cellworks Omics Biology Model predicted response to ATO and ATRA in APL patients harboring PML-RARA fusions. Predicting non-response to ATO and ATRA in patients with PML-RARA fusion up-front could prevent ineffective treatment, avoid unnecessary adverse events and reduce treatment costs. Additionally, computational modeling can identify new mechanisms of resistance and suggest alternative regimens for non-responding patients by targeting the patient-specific disease biomarkers unique to each. Disclosures Howard: Servier: Consultancy, Other: Speaker; Boston Scientific: Consultancy; Sanofi: Consultancy, Other: Speaker; EUSA Pharma: Consultancy; Cellworks: Consultancy. Nair:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Tyagi:Cellworks Research India Private Limited: Current Employment. Kumari:Cellworks Research India Private Limited: Current Employment. Prasad:Cellworks Research India Private Limited: Current Employment. Mitra:Cellworks Research India Private Limited: Current Employment. Lala:Cellworks Research India Private Limited: Current Employment. Azam:Cellworks Research India Private Limited: Current Employment. Gupta:Cellworks Research India Private Limited: Current Employment. Mohapatra:Cellworks Research India Private Limited: Current Employment. G:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment.

This paper aims at hardware-in-loop (HIL) implementation of load frequency control (LFC) and automatic voltage regulation (AVR) schemes for a single area power system. The single area power system consists of a synchronous generator based non-reheat thermal area unit serving linear loads. In order to minimize the power mismatch between the generation and load, LFC & AVR control strategies have been developed considering the dynamics of thermal area power system. The control strategies have been developed to operate in a continuous mode of operation using conventional Proportional-Integral (PI) controller. 3%, 8% & 10% variations of load has been considered to validate system performance at various loading levels. HIL implementation has been achieved using dPSACE MicroLabBox which interfaces the controller developed in MATLAB/Simulink platform to the hardware components.

This paper presents an adaptive load-frequency control scheme for a grid-connected solar PV system which supplies a load locally. The grid is meant to complement the solar Photo-Voltaic (PV) system ideally, but because of different loading conditions and intermittent nature of the solar PV system grid support becomes necessary. The PV system is integrated to the synchronous generator based grid via power electronic converters which extracts maximum power from solar PV based using Perturb and Observe (P&O) algorithm to get maximum benefit from the PV system. Integration of renewable energy sources tends to reduce the overall inertia of the grid and hence threaten its stability. Also, perturbations on the load side are known to affect the frequency negatively and now with increased penetration of intermittent renewable energy sources, the frequency problem is expected to be aggravated. The proposed frequency control scheme incorporates a Model Reference Adaptive Controller (MRAC) on the grid side to ensure that the grid frequency is restored within a much smaller time post small disturbances on either the load or the generation side. The proposed system is verified using MATLAB-SIMULINK under wide variation in system conditions.

e16091 Background: Using a 5FU backbone, a variety of combination chemotherapy regimens have been adopted for gastric cancer. However, the genetic heterogeneity of the disease suggests certain drugs would lead to better outcomes for specific patients. In principle, the incorporation of molecular profiling information into treatment selection can generate superior survival for patients. Therefore, we conducted a pilot study using the CBM to identify novel genomic biomarkers of response and resistance to several 5FU-based regimens. Methods: 12 gastric cancer patients treated with 5FU-cisplatin (N = 4), 5FU-VP16 (N = 5) and 5FU-cisplatin-docetaxel (N = 3) were selected from the TCGA database. Mutation and copy number aberrations from each case served as input for CBM to generate patient-specific protein network maps generated from PubMed and other resources. Disease-biomarkers unique to each patient were identified within protein network maps. Drug impact on the disease network was digitally simulated to determine treatment efficacy by measuring effect of chemotherapy on the cell growth score, i.e., a composite of cell proliferation, viability, apoptosis, metastasis, DNA damage and other cancer hallmarks. Effectively, the mechanism of action of each drug was mapped to each patient’s genome and the biological consequences of genomic abnormalities were correlated with response. Results: Of the 12 patients in the study, 10 patients responded to 5FU-based regimens and 2 were non-responders. CBM correctly predicted the therapy response in each therapy segment. Key response criteria were: 5FU-cisplatin - ARID1A (LOF), RAD51 (LOF), MBD1 (LOF), DUT (LOF). 5FU-VP16 - FBXW7 (DEL), RAD51D (DEL), RAD51C (DEL), XRCC2 (DEL), XRCC3 (DEL). 5FU-cisplatin-docetaxel - ARID1A (DEL), CHEK1 (DEL), REV3L (DEL), XRCC3(DEL) and TUBB (AMP). Resistance biomarkers were also identified for these drugs. FBXW7 loss caused cisplatin resistance, but also predicted sensitivity to VP16. Interestingly, all five 5FU-VP16 responders harbored FBXW7 loss. Similarly, in two 5FU-cisplatin-docetaxel non-responders, KIFC1 (GOF), YBX1(AMP), CDC20 (AMP) were key biomarkers for docetaxel resistance. In one of these patients, ATM (DEL), RBBP8 (DEL) and GSTP1 (DEL) predicted response to VP16. Conclusions: The efficacy of individual cytotoxic agents varies in the population. Mutations and copy number changes in DNA checkpoint, DNA repair, and tumor suppressor genes impact responsive to chemotherapy and should be routinely assessed. Notably, 5FU-VP16 combination could have delivered better antitumor efficacy for several non-responders or 25% of the patients in this small cohort. CBM is able to identify both the most active as well inefficacious components of the treatment upfront. We suggest that this approach should be prospectively evaluated in a larger cohort.

e21211 Background: Cancer management using cytotoxic drugs is hampered by limited efficacy. Hence, a personalized treatment approach matching chemotherapy with appropriate patients remains a persistent and unmet need in the clinic. Genomic heterogeneity among patients creates an opportunity to discern key genomic aberrations and pathways that confer resistance and response to standard treatment options. Pemetrexed, an antifolate, primarily inhibits thymidylate synthase (TYMS) while platinum compounds (carboplatin/cisplatin) trigger DNA damage similar to alkylating agents. When the DNA damage exceeds repair, apoptosis results. We conducted a pilot study using CBM to novel genomic biomarkers of response and resistance to pemetrexed–platinum treatment. Methods: 25 patients who received pemetrexed–platinum therapy were selected from TCGA data. Mutation and copy number aberrations from each case served as input into the CBM (generated from PubMed and other online resources) to create a patient-specific protein network map. Disease-biomarkers unique to each patient were identified within protein network maps. Drug impact on the disease network was digitally simulated to determine treatment efficacy by measuring effect of chemotherapy on the cell growth score, i.e., a composite of cell proliferation, viability, apoptosis, metastasis, DNA damage and other cancer hallmarks. Effectively, the mechanism of action of each drug was mapped to each patient’s genome and biological consequences due to genomic abnormalities were correlated with response. Results: Among the 25 patients, 23 responders (R) and 2 non-responders (NR). The computer simulation correctly predicted response in 19/25 with 76% accuracy, 100% specificity and 73.91% sensitivity. CBM identified co-occurrence of deleted segments of chromosome 6q, 13q, 14q and 17q were responsible for pemetrexed-platinum response. The key genes governing response on these chromosomes included drug transporters and DNA repair pathways. ATP7B (13q) which exports platinum-based drugs was deleted in responders. The loss of REV3L (6q ), MBD1 (6q ), ERCC1 (6q), BRCA2 (13q), BRCA1 (17q), FANCM (14q), RAD51B (14q), XRCC3 (14q ) led to failure of DNA repair. RB1 (13q) del also led to failure of BRG1 mediated DNA repair . Combinations of these repair genes and transporters formed the major response criteria among the 23 responders while these characteristics were absent in non-responders. Conclusions: This pilot study highlights how CBM simulation platform can identify patients for therapy response prediction. Copy number changes impact responsiveness to chemotherapy and should be routinely assessed. We suggest that this approach should be validated prospectively in a larger patient cohort.

7027 Background: ATRA combined with arsenic trioxide revolutionized the treatment of APL. Based on promising in vitro data, several clinical trials evaluated ATRA combinations in non-APL AML, in which some patients seemed to benefit from the addition. Thus, predicting response a priori is imperative to determine the optimal treatment for each patient. The CBM was used to evaluate the impact of initial therapy with ATRA combined with cytarabine, etoposide, idarubicin (ATRA-CEI) to assess the biomarkers responsible for response in adults with AML. Methods: AML patients participating in clinical trial NCT00151242 had their leukemia sequenced as part of the trial, and genomic profiles were used for computational modeling by the CBM, which uses curated data about genomic aberrations from PubMed as input to generate disease-specific protein network maps and predict drug responses. Disease biomarkers unique to each patient were identified using biosimulation. Digital drug simulations were conducted by measuring the effect of ATRA-CEI on a composite cell growth score of cell proliferation, apoptosis and other hallmarks of cancer. ATRA-CEI was mapped to the patient genome along with a mechanism of action and validated based on the genomic profile and its biological consequences. Results: Of 171 patients treated with ATRA-CEI, 107 (63%) responded (R) and 64 did not (NR). A subset of 18 patients with favorable genomic features were found to be NR and their non-response was correctly predicted by CBM in all 18 cases. Mutations of DNMT3A, EZH2, ASXL, FLT-3, and GART amplification emerged as novel biomarkers of ATRA-CEI failure (only 37 of 107 responders (35%) with these findings, compared to 70 of 107 responders (65%) without these findings (p = 0.0027)). DNMT3A, EZH2, ASXL1 loss of function mutations activate FABP5, a key mechanism of ATRA resistance, and also activate ABCC1 (PgP), which reduces the efficacy of etoposide and idarubicin by upregulating MDR1. In general, monosomy 7 is expected to confer ATRA resistance due to the presence of EZH2 and KMT2E gene deletions. Indeed, 18 of 32 patients with monosomy 7 did not respond. However, the 14 who responded had co-occurrence of deletions involving IGFBP3, PMS2, HUS1, CDK5, XRCC2/4, AKR1B10, and others that overcame ATRA resistance associated with monosomy 7 and were identified by CBM. Use of CBM helps avoid unnecessary use of ATRA in patients unlikely to respond (19% of cases) thus reducing toxicity and cost without changing efficacy, and also identifies those likely to respond, even when they have monosomy 7, where non-response is the norm. Conclusions: ATRA benefits a subset of patients with non-APL AML. CBM predicted response using computational modeling of all genetic alternations, which explains its success versus traditional one-gene-one-drug approaches.

e21208 Background: Paclitaxel and carboplatin (PC) is used to treat a wide variety of malignancies including gynecologic, breast, lung, and occult primary cancers. In NSCLC, PC led to a substantial improvement in 1-yr survival from 10% (P alone) to approximately 50% seen with the combination. Nevertheless, a large proportion of patients do not respond. An optimal cytotoxic strategy for managing NSCLC and the discovery of chemotherapy biomarkers to guide treatment selection remain unmet needs in the clinic. Cellworks CBM platform identified a unique chromosomal signature which permits a stratification of which patients are most likely to respond to PC treatment. Methods: 22 patients treated with PC were published in TCGA dataset and selected for analysis. The mutation and copy number aberrations from individual cases served as input into the CBM (generated from PubMed and other online resources) to create a patient-specific protein network map. Disease-biomarkers unique to each patient were identified within protein network maps. Digital drug simulations were conducted by measuring effect of PC on a cell growth score comprised of a composite of cell proliferation, apoptosis, and other cancer hallmarks. Drug simulations were systematically conducted to identify and evaluate therapeutic efficacy. The drug combination was mapped to the patient genome along with a rational mechanism of action and validated based on the genomic profile and its biological consequences. Results: Of the 22 patients treated with PC, 13 had clinical responses and 9 were non-responders. The computer simulation correctly predicted response in 16/22 with 72.73% accuracy, 55.56% specificity and 84.62% sensitivity. CBM identified novel amplified segments of Chromosome 11p and 1p were responsible for non-responsiveness to PC. Key genes on these chromosomes were identified belonging to the autophagy, reactive oxygen species (ROS) scavenging, DNA repair, and microtubule polymerization pathways. Amplification of AMBRA1, ATG13 and TRAF6 (11p) led to autophagy upregulation resulting in low ROS level, a well-documented resistance loop for chemotherapy. SIRT3 and CAT (11p), ROS scavenging genes, were also upregulated due to increase in copy number. CTH (1p) is another key enzyme involved in GSH-mediated ROS scavenging and was also upregulated. Biosimulation indicated a low ROS level was the key reason of resistance to PC. Heightened DNA damage repair due FANCF and ZNF143 (11p amp) and USP1 (1p amp), was another cause of PC resistance. These discoveries suggest that a combination of an autophagy inhibitor / BCL2 mimetic might prove useful to reverse PC resistance associated with 11p and 1p amp. Conclusions: This study highlights how CBM simulation platform can help to identify novel patient segments for therapy response prediction and use drug re-purposing to overcome chemotherapy resistance.

Background: Monosomy 7/Del 7 (-7) or its long arm (del(7q)) is one of the most common cytogenetic abnormalities in pediatric and adult myeloid malignancies, particularly in adverse-risk acute myeloid leukemias (AMLs). In general, (-7) is associated with poor response to induction chemotherapy (PMID 12393746). At the same time, not all patients fare poorly so the ability to identify responders and non-responders remains a high priority. Aim: To predict the response for induction chemotherapy in AML patients with (-7) and identify novel genomic signatures of response and resistance. Methods: Genomic data from 13 consecutive patients with (-7) were analyzed using the Cellworks Omics Biology Model (CBM) to generate patient-specific protein network models. All data was anonymized, de-identified and exempt from IRB review. For each model, disease simulations were performed and patients were segregated into HOXA-upregulated and HOXA-downregulated cohorts based on the simulation levels of HOXA5 and HOXA9. Digital drug simulations for induction chemotherapy were accomplished by measuring the impact of drug effect on a cell growth score, a composite of cell proliferation, viability and apoptosis indices. Each patient-specific model was analyzed to identify mechanisms underlying treatment outcomes. Results: 7/13 (54%) of (-7) patients failed to achieve remission after induction chemotherapy (Table 1) which highlighted that (-7) alone does not confer resistance to chemotherapy. CBM identified other genomic alterations that determine chemotherapy response, including DNA repair deficiency genes, mismatch repair (MMR), and homologous recombination repair (HRR) genes. DNA methylation and histone methylation (H3K27me) impacting HOXA gene expression, mainly HOXA5 and HOXA9, were identified as upstream regulators of DNA repair genes. Loss or reduced levels of EZH2 is associated with lower H3K27 methylation and thereby higher expression of HOXA5 and HOXA9 gene targets. High active levels of HOXA correlated with low rates of successful remission induction (22%, n=7) and lower activity levels of HOXA correlated with successful induction chemotherapy (100%, n=4) (Table 1). CBM identified that (-7) results in a decreased expression of EZH2, CARD11, EIF3, PMS2, HUS1, KMT2C (MLL3), CDK5 and IKZF1 genes. Since EZH2 abnormalities alone are not implicated in poor prognosis for AML patients, we sought other aberrations that prevent HOXA upregulation. Using CBM, we identified multiple accompanying aberrations which regulate HOXA genes. Deletions of KAT6A, ASXL1, DNMT3B, DNMT3L genes and high KMT2A-partial tandem duplication correspond to HOXA-upregulation whereas EED amplification, gain of function mutations in DNMT3A, mutations in IDH1/2, MYC and KAT6A amplification and KDM4A deletion result in HOXA-downregulation. Conclusion: Alterations of chromatin regulation have consequences for transcription factors that regulate expression of DNA repair genes. Under conditions where DNA repair is enhanced, induction chemotherapy was 78% less likely to effect remission in (-7) AML patients undergoing induction chemotherapy. Loss of H3K27 methylation associated with loss of PRC2 function by any means resulted in HOXA-upregulation and upregulation of DNA repair genes induced resistance to induction therapy. On the other hand, CBM analysis identified genetic signatures associated with a 100% remission rate from AML induction therapy despite the presence of (-7). Generation of H3K27me caused by PRC2 activation resulting from numerous mechanisms led to HOXA-downregulation and 100% response to induction therapy. Stratification of patients harboring (-7) by HOXA biomarker analysis could inform treatment planning, avoid drug-related adverse events and reduce treatment costs after validation with a larger prospective dataset. Disclosures Castro: Cellworks Group Inc: Consultancy. Watson:Cellworks Group Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellmax Life Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Mercy Bioanalytics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; SEER Biosciences, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; BioAI Health Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees. Kumar:Cellworks Research India Private Limited: Current Employment. Nair:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Sahu:Cellworks Research India Private Limited: Current Employment. Mohapatra:Cellworks Research India Private Limited: Current Employment. G:Cellworks Research India Private Limited: Current Employment. Agarwal:Cellworks Research India Private Limited: Current Employment. Suseela:Cellworks Research India Private Limited: Current Employment. Ganesh:Cellworks Research India Private Limited: Current Employment. Sauban:Cellworks Research India Private Limited: Current Employment. Kumar:Cellworks Research India Private Limited: Current Employment. Raman:Cellworks Research India Private Limited: Current Employment. Singh:Cellworks Research India Private Limited: Current Employment. Basu:Cellworks Research India Private Limited: Current Employment. Lunkad:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Howard:Servier: Consultancy, Other: Speaker; Boston Scientific: Consultancy; Sanofi: Consultancy, Other: Speaker; EUSA Pharma: Consultancy; Cellworks: Consultancy.

Background. In addition to clinical considerations (e.g., age, de novo vs secondary disease, comorbidities), therapy selection for AML patients is often based on information considering only cytogenetics and/or molecular aberrations and ignoring other patient-specific omics information that could potentially enable selection of more effective treatments. In turn, despite using cytogenetic and molecular-risk stratification, the current overall outcome of AML patients remains relatively poor. The Cellworks Singula™ report predicts clinical response to physician-prescribed treatments using the novel Cellworks Omics Biology Model (CBM) that simulate in silico downstream molecular effects on cell signaling and survival of drug treatments in patient-specific diseased cells. Methods. The performance of Singula™ was evaluated in a cohort of 474 AML patients aged 2 to 85. The pre-defined Singula™ Classifier utilizes individual patients' next-generation sequencing (NGS) profiles to provide a dichotomous prediction of response or non-response to the physician prescribed treatments. The clinical outcome data for these subjects, i.e., complete response (CR) and overall survival (OS), were obtained from the TCGA and other 144 PubMed publications, each including also information on patients' cytogenetics, targeted gene mutations, and/or whole exome sequencing. Blinded to clinical outcomes, Cellworks utilized the cytogenetic and molecular data to generate a Singula™ predicted response (i.e., CR vs non-response) classification for each patient. Statistical analyses, including assessments of accuracy, sensitivity, specificity, and negative (NPV) and positive predictive (PPV) values were performed to compare the Singula™ predicted clinical response to the actual observed clinical response. Kaplan-Meier curves, associated log rank tests and median OS are provided for patients stratified by Singula™ predicted response. Multivariate Cox proportional hazards regression was used to further test the hypothesis that Singula™ is an independent predictor for OS once adjusted for patient age and actual prescribed treatment. Results. Data are summarized in Table 1. The Singula™ classifier had 92.3% (90.6%, 95.3%) accuracy in predicting correctly observed patient complete response to the prescribed treatment. with 97.3% (95.0%, 98.8%) sensitivity. Singula™ had 83.3% (76.1%, 89.1%) specificity for the non-responder patients (n=138; 29.1%). For each of the non-responders, Singula™ provided an alternative treatment therapy predicted to produce clinical response. Assuming at least 2% of the non-responders would have responded to the alternative Singula™ prescribed treatment, Singula™ improves CR rates compared to the original physician prescribed treatment (McNemar's p-value < 0.05). Figure 1 provides the Kaplan-Meier curves of Singula-predicted responders vs non-responders for a subset of 292 subjects that had OS data available. In multivariate Cox proportional hazards models, the Singula Classifier remained a significant predictor of overall survival (HR = 2.171, p-value < 0.0001) once adjusted for patient age and physician prescribed treatment. Conclusions. Cellworks Singula™ has high accuracy and sensitivity in predicting CR for AML patient. Singula also has high specificity in identifying patients who are unlikely to respond physician and may prescribed potentially effective therapies. The Singula™ predicted responders have a significantly longer OS than the predicted non responders. Thus, Cellworks Singula™ can accurately predict AML response, be used to validate or reject a physician's therapy selection decision and, eventually, provide alternative treatment recommendations. Disclosures Marcucci: Novartis: Speakers Bureau; Abbvie: Speakers Bureau; Iaso Bio: Membership on an entity's Board of Directors or advisory committees; Takeda: Other: Research Support (Investigation Initiated Clinical Trial); Pfizer: Other: Research Support (Investigation Initiated Clinical Trial); Merck: Other: Research Support (Investigation Initiated Clinical Trial). Watson:Mercy Bioanalytics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; SEER Biosciences, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; BioAI Health Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellmax Life Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cellworks Group Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees. Nair:Cellworks Research India Private Limited: Current Employment. Basu:Cellworks Research India Private Limited: Current Employment. Ullal:Cellworks Research India Private Limited: Current Employment. Ghosh:Cellworks Research India Private Limited: Current Employment. Narvekar:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Sahu:Cellworks Research India Private Limited: Current Employment. Amara:Cellworks Research India Private Limited: Current Employment. Pampana:Cellworks Research India Private Limited: Current Employment. Roy:Cellworks Research India Private Limited: Current Employment. Rajagopalan:Cellworks Research India Private Limited: Current Employment. Alam:Cellworks Research India Private Limited: Current Employment. Parashar:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Christie:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Stein:Stemline: Consultancy, Speakers Bureau; Amgen: Consultancy, Speakers Bureau.

Background: AML is a heterogeneous hematological cancer, characterized by the clonal expansion of myeloid blasts in the peripheral blood, bone marrow and other tissues. Among adults, AML is the leading cause of leukemia-associated death. Patients are often elderly and have comorbid conditions. Response to remission induction therapy varies by biologic subtype and by the drugs used for induction, but responses to each therapy are not predictable, even within specific biologic subgroups. To improve prognostication and identify new therapeutic options, Cellworks developed the Cellworks Omics Biology Model (CBM) that relies on multi-omics inputs from malignant cells to predict response and identify potential therapies tailored to the unique mechanisms of each patient's disease. Aim: To predict the response to induction chemotherapy regimens in AML patients and identify predictive genomic signatures, pathways, and personalized treatment options for refractory patients. Method: 57 AML patients with known therapy response were selected from 8 PubMed publications (Table 1). The cohort was split into 3 groups with CBFB-MYH11 fusion (n=19), RUNX1-RUNX1T1 fusion (n=10) and CEBPα del (n=28). All data was anonymized, de-identified and exempt from IRB review. NCCN offers specific recommendations for AML patients with these mutations and they are associated with high rates of remission after induction therapy. The available genomic data for each profile was processed using Cellworks' CBM to generate an AML subtype-specific protein network map using published information from PubMed and other online resources permitting patient-specific biomarkers to be mapped to sensitivity and resistance pathways. Drugs selected from a digital drug library were simulated for each patient across the three cohorts. Benefit was assessed by measuring each drug's effect on a cell growth score, a composite of proliferation, viability, and apoptosis indices. Results: Of 57 patients with favorable-risk AML, 6 (11%) experienced induction failure. 1/19 patients harboring CBFB-MYH11 fusion failed to respond to induction therapy (Table 2) and CBM identified co-occurring gene aberrations responsible for resistance in the patient with resistant disease (ID:757637), who harbored trisomy 6 with amplification of GSTA1, GSTA2, GSTA4, DEK, TFAP2A, NFYA and EHMT2 causing dysregulation of pathways involved in treatment failure. CBM also identified 3 prospective therapies that target this patient's specific biomarkers (Table 3). Similarly, 2/10 patients with RUNX1-RUNX1T1 fusion failed to respond to induction therapy. CBM identified a loss of function mutation in EZH2 causing increased levels of HOXA5 and HOXA9 as key orchestrators of treatment failure (Patient ID: 757805). Furthermore, CBM also identified 3 prospective therapies for this patient, targeting patient-specific resistance mechanisms (Table 3). In the CEBPα cohort, 3/28 patients did not respond to induction therapy (Table 2). For Patient ID:755125, CBM identified a DNMT3A loss of function mutation and consequential gain in the levels of HOXA5 and HOXA9 as key orchestrators of treatment failure. Again, CBM identified 3 novel therapies for this patient targeting patient-specific biomarkers (Table 3). Conclusion: Even in biological subgroups of AML expected to have sensitive disease, induction failure is not rare. Fortunately, the CBM could identify causes of failure and suggest alternative therapies based on co-occurring genomic abnormalities to mitigate the ineffectiveness of standard induction regimens in patients with resistant disease despite their favorable biology. The identification of patient-specific resistance mechanisms characterizes a new therapeutic imperative founded on deep molecular diagnosis that promises to enhance disease outcomes, inform treatment planning, avoid adverse events from ineffective therapies, and reduce costs. Also, computational modeling identifies alternate therapy regimens for refractory patients by incorporating patient-specific disease biomarkers. For all the non-responders, CBM identified 3 potential therapies across each fusion subgroup. Although these combinations need further validation in the clinic, the Cellworks CBM biosimulation platform allows for precision medicine approaches that target patient-specific disease biomarkers and treatment resistance mechanisms. Disclosures Castro: Cellworks Group Inc: Consultancy. Nair:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Kumar:Cellworks Research India Private Limited: Current Employment. Agarwal:Cellworks Research India Private Limited: Current Employment. Suseela:Cellworks Research India Private Limited: Current Employment. Gopi:Cellworks Research India Private Limited: Current Employment. Ganesh:Cellworks Research India Private Limited: Current Employment. Shyamasundar:Cellworks Research India Private Limited: Current Employment. Kulkarni:Cellworks Research India Private Limited: Current Employment. Sauban:Cellworks Research India Private Limited: Current Employment. Balla:Cellworks Research India Private Limited: Current Employment. Chafekar:Cellworks Research India Private Limited: Current Employment. Choudhury:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Howard:Boston Scientific: Consultancy; Cellworks: Consultancy; EUSA Pharma: Consultancy; Sanofi: Consultancy, Other: Speaker; Servier: Consultancy, Other: Speaker.

e19502 Background: Despite using cytogenetic and molecular-risk stratification and precision medicine, the current overall outcome of AML patients remains relatively poor. Therapy selection is often based on information considering only cytogenetics and single molecular aberrations and ignoring other patient-specific omics data that could potentially enable more effective treatments. The Cellworks Singula™ report predicts response for physician prescribed therapies (PPT) using the novel Cellworks Omics Biology Model (CBM) to simulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. We test the hypothesis that Singula is a more accurate predictor of patient-specific therapy response than PPT. Methods: Singula’s ability to predict response was evaluated in an independent, randomly selected, retrospective cohort of 494 AML patients aged 2 to 85 years (median 54) treated with PPT. Patient omics data was available from PubMed. The accuracy of Singula was compared to that of PPT using McNemar’s test to account for the correlation between Singula and PPT. Multivariate logistic regression modeled complete response (CR) as a function of patient age, PPT, and Singula against any non-response (NR). Likelihood ratio tests were performed to further validate if Singula provides predictive information beyond PPT or patient age. Similar analyses were performed for overall survival (OS) using proportional hazards regression. Results: Singula was a better predictor for CR than PPT (McNemar’s χ2 = 72.0, p-value < 0.0001), with an overall accuracy of 88.5% (95% CI: 85.3%, 91.1%) compared to 70.2% (95% CI: 66.0%, 74.2%) for PPT. Singula exhibited a sensitivity and specificity of 97.1% and 68.0%, respectively. In multivariate regression analysis, Singula (p < 0.0001) remained an independent predictor for CR after adjusting for patient age (p = 0.0329) while PPT became not significant (p = 0.75). Singula was also an independent predictor for OS (p < 0.0001) after adjusting for patient age (p = 0.0018) and PPT (p = 0.0011). For all 100 true negatives, Singula generated alternative standard of care therapy selections with predicted clinical response. Conclusions: Singula is a superior independent predictor for CR and OS compared to PPT in AML patients. The Singula report can also validate therapy selection, correctly identify non-responders to PPT and further provide alternative therapy selections.

Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations, and splice variants identified in cancer cells are translated. Herein, we apply a proteogenomic data integration tool (QUILTS) to illustrate protein variant discovery using whole genome, whole transcriptome, and global proteome datasets generated from a pair of luminal and basal-like breast-cancer-patient-derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS sample process replicates defined here as an independent tandem MS experiment using identical sample material. Despite analysis of over 30 sample process replicates, only about 10% of SNVs (somatic and germline) detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (

The footprint left by development in the Great Lakes–St. Lawrence River basin is fundamentally linked to the size, structure, and distribution of the human population across the region. Regional demographic conditions and trends over time provide insight into both why the basin looks as it does today and how it might change in the future. We present analyses of population size and distribution, age structure, immigration, and urban land use within the Great Lakes basin, and across six sub-basins, for the period 1960 to 2011. Since 1960, the population within the basin has increased (reaching 48.5 million people in 2011), become older (a result of both decreasing fertility and increasing life expectancy), and more diverse (as immigrants make up a higher share of the population). A booming population on the Canadian side stands in stark contrast to low growth or even population decline on the US side. Urban sprawl continues to outpace population growth in many areas, even for major US cities experiencing population decline. Looking toward the future, we present three scenarios of change for population, land use, and societal values in 2063 and highlight the role that societal values play in shaping the state of the basin. Ultimately, the state of the Great Lakes basin will be the outcome of many complex drivers of change, where demographics are only one entry-point to understanding potential future states.

Peptide and protein identification via tandem mass spectrometry (MS/MS) lies at the heart of proteomic characterization of biological samples. Several algorithms are able to search, score, and assign peptides to large MS/MS datasets. Most popular methods, however, underutilize the intensity information available in the tandem mass spectrum due to the complex nature of the peptide fragmentation process, thus contributing to loss of potential identifications. We present a novel probabilistic scoring algorithm called Context-Sensitive Peptide Identification (CSPI) based on highly flexible Input-Output Hidden Markov Models (IO-HMM) that capture the influence of peptide physicochemical properties on their observed MS/MS spectra. We use several local and global properties of peptides and their fragment ions from literature. Comparison with two popular algorithms, Crux (re-implementation of SEQUEST) and X!Tandem, on multiple datasets of varying complexity, shows that peptide identification scores from our models are able to achieve greater discrimination between true and false peptides, identifying up to ∼25% more peptides at a False Discovery Rate (FDR) of 1%. We evaluated two alternative normalization schemes for fragment ion-intensities, a global rank-based and a local window-based. Our results indicate the importance of appropriate normalization methods for learning superior models. Further, combining our scores with Crux using a state-of-the-art procedure, Percolator, we demonstrate the utility of using scoring features from intensity-based models, identifying ∼4-8 % additional identifications over Percolator at 1% FDR. IO-HMMs offer a scalable and flexible framework with several modeling choices to learn complex patterns embedded in MS/MS data.

Despite the increasing interest in climate change policy in the US, little systematic research has been conducted on the willingness of individuals to change their behaviour to mitigate the problem. Understanding behavioural change is critical if federal and local governments intend to implement programmes requiring actions to mitigate and adapt to climate change. This understudied aspect of climate change policy is addressed by quantitatively examining the degree to which residents living in the US are willing to alter their behaviour to mitigate climate change impacts, and by identifying the major factors contributing to this willingness. Based on a national survey, the reported willingness of individuals to alter behaviours is explained, using the components of risk, individual stress, capacity and ecological values. The findings indicate that specific personal traits and contextual characteristics trigger a significantly greater willingness to change longstanding behavioural patterns. These insights i...