Publications by authors named "Moom R Roosan"

7 Publications

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Utility of Circulating Tumor DNA in Identifying Somatic Mutations and Tracking Tumor Evolution in Patients with Non-small Cell Lung Cancer.

Chest 2021 Apr 17. Epub 2021 Apr 17.

City of Hope Comprehensive Cancer Center, Duarte, CA 91010. Electronic address:

Background: The utility of circulating tumor DNA (ctDNA) in detecting mutations and monitoring treatment response has not been well studied beyond a few actionable biomarkers in non-small cell lung cancer (NSCLC).

Research Question: How does the utility of circulating tumor DNA (ctDNA) compare to that of solid tumor biopsy in non-small cell lung cancer (NSCLC) patients?

Methods: We retrospectively evaluated 370 adult NSCLC patients treated at the City of Hope between November 2015 and August 2019 to assess the utility of ctDNA in mutation identification, survival, concordance with matched tissue samples in thirty-two genes, and tumor evolution.

Results: A total of 1688 somatic mutations were detected in 473 ctDNA samples from 370 NSCLC patients. Of the 473 samples, 177 had at least one actionable mutation with currently available FDA-approved NSCLC therapies. MET and CDK6 amplifications co-occurred with BRAF amplifications (false discovery rates [FDR] < 0.01), and gene-level mutations were mutually exclusive in KRAS and EGFR (FDR = 0.0009). Low cumulative percent ctDNA levels were associated with longer progression-free survival (hazard ratio [HR] 0.56, 95% CI: 0.37-0.85, p = 0.006). Overall survival was shorter in BRAF (HR 2.35, 95% CI: 1.24-4.6, p = 0.009, PIK3CA (HR 2.77, 95% CI: 1.56-4.9, P< 0.001 and KRAS-positive patients (HR 2.32, 95% CI: 1.30-4.1, P= 0.004). Gene-level concordance was 93.8% while the positive concordance rate was 41.6%. More mutations in targetable genes were found in ctDNA than in tissue biopsies. Treatment response and tumor evolution over time were detected in repeated ctDNA samples.

Interpretation: Although ctDNA exhibited similar utility to tissue biopsies, more mutations in targetable genes were missed in tissue biopsies. Therefore, the evaluation of ctDNA in conjunction with tissue biopsies may help to detect additional targetable mutations to improve clinical outcomes in advanced NSCLC.
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http://dx.doi.org/10.1016/j.chest.2021.04.016DOI Listing
April 2021

SARS-CoV-2 early infection signature identified potential key infection mechanisms and drug targets.

BMC Genomics 2021 Feb 18;22(1):125. Epub 2021 Feb 18.

School of Pharmacy, Chapman University, Irvine, CA, 92618, USA.

Background: The ongoing COVID-19 outbreak has caused devastating mortality and posed a significant threat to public health worldwide. Despite the severity of this illness and 2.3 million worldwide deaths, the disease mechanism is mostly unknown. Previous studies that characterized differential gene expression due to SARS-CoV-2 infection lacked robust validation. Although vaccines are  now available, effective treatment options are still out of reach.

Results: To characterize the transcriptional activity of SARS-CoV-2 infection, a gene signature consisting of 25 genes was generated using a publicly available RNA-Sequencing (RNA-Seq) dataset of cultured cells infected with SARS-CoV-2. The signature estimated infection level accurately in bronchoalveolar lavage fluid (BALF) cells and peripheral blood mononuclear cells (PBMCs) from healthy and infected patients (mean 0.001 vs. 0.958; P < 0.0001). These signature genes were investigated in their ability to distinguish the severity of SARS-CoV-2 infection in a single-cell RNA-Sequencing dataset. TNFAIP3, PPP1R15A, NFKBIA, and IFIT2 had shown bimodal gene expression in various immune cells from severely infected patients compared to healthy or moderate infection cases. Finally, this signature was assessed using the publicly available ConnectivityMap database to identify potential disease mechanisms and drug repurposing candidates. Pharmacological classes of tricyclic antidepressants, SRC-inhibitors, HDAC inhibitors, MEK inhibitors, and drugs such as atorvastatin, ibuprofen, and ketoconazole showed strong negative associations (connectivity score < - 90), highlighting the need for further evaluation of these candidates for their efficacy in treating SARS-CoV-2 infection.

Conclusions: Thus, using the 25-gene SARS-CoV-2 infection signature, the SARS-CoV-2 infection status was captured in BALF cells, PBMCs and postmortem lung biopsies. In addition, candidate SARS-CoV-2 therapies with known safety profiles were identified. The signature genes could potentially also be used to characterize the COVID-19 disease severity in patients' expression profiles of BALF cells.
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http://dx.doi.org/10.1186/s12864-021-07433-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7889713PMC
February 2021

Artificial Intelligence-Powered Smartphone App to Facilitate Medication Adherence: Protocol for a Human Factors Design Study.

JMIR Res Protoc 2020 Nov 9;9(11):e21659. Epub 2020 Nov 9.

Department of Pharmacy Practice, School of Pharmacy, Chapman University, Irvine, CA, United States.

Background: Medication Guides consisting of crucial interactions and side effects are extensive and complex. Due to the exhaustive information, patients do not retain the necessary medication information, which can result in hospitalizations and medication nonadherence. A gap exists in understanding patients' cognition of managing complex medication information. However, advancements in technology and artificial intelligence (AI) allow us to understand patient cognitive processes to design an app to better provide important medication information to patients.

Objective: Our objective is to improve the design of an innovative AI- and human factor-based interface that supports patients' medication information comprehension that could potentially improve medication adherence.

Methods: This study has three aims. Aim 1 has three phases: (1) an observational study to understand patient perception of fear and biases regarding medication information, (2) an eye-tracking study to understand the attention locus for medication information, and (3) a psychological refractory period (PRP) paradigm study to understand functionalities. Observational data will be collected, such as audio and video recordings, gaze mapping, and time from PRP. A total of 50 patients, aged 18-65 years, who started at least one new medication, for which we developed visualization information, and who have a cognitive status of 34 during cognitive screening using the TICS-M test and health literacy level will be included in this aim of the study. In Aim 2, we will iteratively design and evaluate an AI-powered medication information visualization interface as a smartphone app with the knowledge gained from each component of Aim 1. The interface will be assessed through two usability surveys. A total of 300 patients, aged 18-65 years, with diabetes, cardiovascular diseases, or mental health disorders, will be recruited for the surveys. Data from the surveys will be analyzed through exploratory factor analysis. In Aim 3, in order to test the prototype, there will be a two-arm study design. This aim will include 900 patients, aged 18-65 years, with internet access, without any cognitive impairment, and with at least two medications. Patients will be sequentially randomized. Three surveys will be used to assess the primary outcome of medication information comprehension and the secondary outcome of medication adherence at 12 weeks.

Results: Preliminary data collection will be conducted in 2021, and results are expected to be published in 2022.

Conclusions: This study will lead the future of AI-based, innovative, digital interface design and aid in improving medication comprehension, which may improve medication adherence. The results from this study will also open up future research opportunities in understanding how patients manage complex medication information and will inform the format and design for innovative, AI-powered digital interfaces for Medication Guides.

International Registered Report Identifier (irrid): PRR1-10.2196/21659.
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http://dx.doi.org/10.2196/21659DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683257PMC
November 2020

The inclusion of health data standards in the implementation of pharmacogenomics systems: a scoping review.

Pharmacogenomics 2020 11 30;21(16):1191-1202. Epub 2020 Oct 30.

Assistant Professor, School of Pharmacy, Department of Pharmacy Practice, Chapman University, Irvine, CA 92618, USA.

Despite potential benefits, the practice of incorporating pharmacogenomics (PGx) results in clinical decisions has yet to diffuse widely. In this study, we conducted a review of recent discussions on data standards and interoperability with a focus on sharing PGx test results among health systems. We conducted a literature search for PGx clinical decision support systems between 1 January 2012 and 31 January 2020. Thirty-two out of 727 articles were included for the final review. Nine of the 32 articles mentioned data standards and only four of the 32 articles provided solutions for the lack of interoperability. Although PGx interoperability is essential for widespread implementation, a lack of focus on standardized data creates a formidable challenge for health information exchange. Standardization of PGx data is essential to improve health information exchange and the sharing of PGx results between disparate systems. However, PGx data standards and interoperability are often not addressed in the system-level implementation.
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http://dx.doi.org/10.2217/pgs-2020-0066DOI Listing
November 2020

Correction: Pharmacogenomics cascade testing (PhaCT): a novel approacre econd screench for preemptive pharmacogenomics testing to optimize medication therapy.

Pharmacogenomics J 2021 Feb;21(1):106

Department of Pharmacy Practice, School of Pharmacy, Chapman University, School of Pharmacy, Irvine, CA, USA.

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http://dx.doi.org/10.1038/s41397-020-00183-8DOI Listing
February 2021

Pharmacogenomics cascade testing (PhaCT): a novel approach for preemptive pharmacogenomics testing to optimize medication therapy.

Pharmacogenomics J 2021 02 25;21(1):1-7. Epub 2020 Aug 25.

Department of Pharmacy Practice, School of Pharmacy, Chapman University, School of Pharmacy, Irvine, CA, USA.

The implementation of pharmacogenomics (PGx) has come a long way since the dawn of utilizing pharmacogenomic data in clinical patient care. However, the potential benefits of sharing PGx results have yet to be explored. In this paper, we explore the willingness of patients to share PGx results, as well as the inclusion of family medication history in identifying potential family members for pharmacogenomics cascade testing (PhaCT). The genetic similarities in families allow for identifying potential gene variants prior to official preemptive testing. Once a candidate patient is determined, PhaCT can be initiated. PhaCT recognizes that further cascade testing throughout a family can serve to improve precision medicine. In order to make PhaCT feasible, we propose a novel shareable HIPAA-compliant informatics platform that will enable patients to manage not only their own test results and medications but also those of their family members. The informatics platform will be an external genomics system with capabilities to integrate with patients' electronic health records. Patients will be given the tools to provide information to and work with clinicians in identifying family members for PhaCT through this platform. Offering patients the tools to share PGx results with their family members for preemptive testing could be the key to empowering patients. Clinicians can utilize PhaCT to potentially improve medication adherence, which may consequently help to distribute the burden of health management between patients, family members, providers, and payers.
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http://dx.doi.org/10.1038/s41397-020-00182-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7840503PMC
February 2021

Glucocorticoids rapidly activate cAMP production via G to initiate non-genomic signaling that contributes to one-third of their canonical genomic effects.

FASEB J 2020 02 27;34(2):2882-2895. Epub 2019 Dec 27.

Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USA.

Glucocorticoids are widely used for the suppression of inflammation, but evidence is growing that they can have rapid, non-genomic actions that have been unappreciated. Diverse cell signaling effects have been reported for glucocorticoids, leading us to hypothesize that glucocorticoids alone can swiftly increase the 3',5'-cyclic adenosine monophosphate (cAMP) production. We found that prednisone, fluticasone, budesonide, and progesterone each increased cAMP levels within 3 minutes without phosphodiesterase inhibitors by measuring real-time cAMP dynamics using the cAMP difference detector in situ assay in a variety of immortalized cell lines and primary human airway smooth muscle (HASM) cells. A membrane- impermeable glucocorticoid showed similarly rapid stimulation of cAMP, implying that responses are initiated at the cell surface. siRNA knockdown of G virtually eliminated glucocorticoid-stimulated cAMP responses, suggesting that these drugs activate the cAMP production via a G protein-coupled receptor. Estradiol had small effects on cAMP levels but G protein estrogen receptor antagonists had little effect on responses to any of the glucocorticoids tested. The genomic and non-genomic actions of budesonide were analyzed by RNA-Seq analysis of 24 hours treated HASM, with and without knockdown of G . A 140-gene budesonide signature was identified, of which 48 genes represent a non-genomic signature that requires G signaling. Collectively, this non-genomic cAMP signaling modality contributes to one-third of the gene expression changes induced by glucocorticoid treatment and shifts the view of how this important class of drugs exerts its effects.
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http://dx.doi.org/10.1096/fj.201902521RDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7027561PMC
February 2020