A Review of Genome-Based Precision Medicine Efforts Within the Department of Defense

ABSTRACT Introduction Providing patient-specific clinical care is an expanding focus for medical professionals and researchers, more commonly referred to as personalized or precision medicine. The goal of using a patient-centric approach is to provide safer care while also increasing the probability of therapeutic success through careful consideration of the influence of certain extrinsic and intrinsic human factors in developing the patient care plan. Of increasing influence on patient care is the phenotype and genotype information gathered from employing various next-generation sequencing methods. Guided by and partnered with our civilian colleagues, clinical components within the DoD are embracing and advancing genomic medicine in many facets—from the bench to the bedside—and in many therapeutic areas, from Psychiatry to Oncology. In this PubMed-based review, we describe published clinical research and interventions within the DoD using genome-informed data and emphasize precision medicine efforts in earlier stages of development with the potential to revolutionize the approach to therapeutics. Materials and Methods The new PubMed database was searched for articles published between 2015 and 2020 with the following key search terms: precision medicine, genomic, pharmacogenetic, pharmacogenomic, US military, and Department of Defense. Results Eighty-one articles were retrieved in our initial search. After screening the abstracts for studies that only involved direct testing of (or clinical interaction with) active duty, Reserve, National Guard, or civilian personnel working within the DoD and excluding any epidemiological or microbial isolation studies, seven were included in this review. Conclusion There are several programs and studies within the DoD, which investigate or use gene-based biomarkers or gene variants to deliver more precise clinical assessment and treatment. These genome-based precision medicine efforts aim to optimize the clinical care of DoD beneficiaries, particularly service members in the operational environment.

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Strategies to Integrate Genomic Medicine into Clinical Care: Evidence from the IGNITE Network

Journal of Personalized Medicine ◽

10.3390/jpm11070647 ◽

2021 ◽

Vol 11 (7) ◽

pp. 647

Author(s):

Nina R. Sperber ◽

Olivia M. Dong ◽

Megan C. Roberts ◽

Paul Dexter ◽

Amanda R. Elsey ◽

...

Keyword(s):

Precision Medicine ◽

Program Implementation ◽

Clinical Care ◽

Genomic Medicine ◽

Implementation Strategies ◽

Clinical Decision ◽

Implementing Change ◽

Implementation Outcomes ◽

Planning Framework

The complexity of genomic medicine can be streamlined by implementing some form of clinical decision support (CDS) to guide clinicians in how to use and interpret personalized data; however, it is not yet clear which strategies are best suited for this purpose. In this study, we used implementation science to identify common strategies for applying provider-based CDS interventions across six genomic medicine clinical research projects funded by an NIH consortium. Each project’s strategies were elicited via a structured survey derived from a typology of implementation strategies, the Expert Recommendations for Implementing Change (ERIC), and follow-up interviews guided by both implementation strategy reporting criteria and a planning framework, RE-AIM, to obtain more detail about implementation strategies and desired outcomes. We found that, on average, the three pharmacogenomics implementation projects used more strategies than the disease-focused projects. Overall, projects had four implementation strategies in common; however, operationalization of each differed in accordance with each study’s implementation outcomes. These four common strategies may be important for precision medicine program implementation, and pharmacogenomics may require more integration into clinical care. Understanding how and why these strategies were successfully employed could be useful for others implementing genomic or precision medicine programs in different contexts.

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Indispensability of Clinical Bioinformatics for Effective Implementation of Genomic Medicine in Pathology Laboratories

ACI Open ◽

10.1055/s-0040-1721480 ◽

2020 ◽

Vol 04 (02) ◽

pp. e167-e172

Author(s):

Srikar Chamala ◽

Siddardha Majety ◽

Shesh Nath Mishra ◽

Kimberly J. Newsom ◽

Shaileshbhai Revabhai Gothi ◽

...

Keyword(s):

Patient Care ◽

Clinical Laboratory ◽

Clinical Care ◽

Genomic Medicine ◽

Clinical Pathology ◽

Genetic Mutation ◽

Genomic Research ◽

Pathology Laboratory ◽

Clinical Bioinformatics ◽

Effective Implementation

AbstractPatient care is rapidly evolving toward the inclusion of precision genomic medicine when genomic tests are used by clinicians to determine disease predisposition, prognosis, diagnosis, and improve therapeutic decision-making. However, unlike other clinical pathology laboratory tests, the development, deployment, and delivery of genomic tests and results are an intricate process. Genomic technologies are diverse, fast changing, and generate massive data. Implementation of these technologies in a Clinical Laboratory Improvement Amendments-certified and College of American Pathologists-accredited pathology laboratory often require custom clinical grade computational data analysis and management workflows. Additionally, accurate classification and reporting of clinically actionable genetic mutation requires well-curated disease/application-specific knowledgebases and expertise. Moreover, lack of “out of the box” technical features in electronic health record systems necessitates custom solutions for communicating genetic information to clinicians and patients. Genomic data generated as part of clinical care easily adds great value for translational research. In this article, we discuss current and future innovative clinical bioinformatics solutions and workflows developed at our institution for effective implementation of precision genomic medicine across molecular pathology, patient care, and translational genomic research.

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Pharmacogenomics in Pain Management: A Review of Relevant Gene-Drug Associations and Clinical Considerations

Annals of Pharmacotherapy ◽

10.1177/10600280211003875 ◽

2021 ◽

pp. 106002802110038

Author(s):

Emily Brandl ◽

Zachery Halford ◽

Matthew D. Clark ◽

Chris Herndon

Keyword(s):

Clinical Practice ◽

Pain Management ◽

Health Care Professionals ◽

Data Extraction ◽

Clinical Care ◽

Genomic Medicine ◽

Opioid Use Disorder ◽

Patient Specific ◽

Therapeutic Effects ◽

Opioid Use

Objective: To provide an overview of clinical recommendations regarding genomic medicine relating to pain management and opioid use disorder. Data Sources: A literature review was conducted using the search terms pain management, pharmacogenomics, pharmacogenetics, pharmacokinetics, pharmacodynamics, and opioids on PubMed (inception to February 1, 2021), CINAHL (2016 through February 1, 2021), and EMBASE (inception through February 1, 2021). Study Selection and Data Extraction: All relevant clinical trials, review articles, package inserts, and guidelines evaluating applicable pharmacogenotypes were considered for inclusion. Data Synthesis: More than 300 Food and Drug Administration–approved medications contain pharmacogenomic information in their labeling. Genetic variability may alter the therapeutic effects of commonly prescribed pain medications. Pharmacogenomic-guided therapy continues to gain traction in clinical practice, but a multitude of barriers to widespread pharmacogenomic implementation exist. Relevance to Patient Care and Clinical Practice: Pain is notoriously difficult to treat given the need to balance safety and efficacy when selecting pharmacotherapy. Pharmacogenomic data can help optimize outcomes for patients with pain. With improved technological advances, more affordable testing, and a better understanding of genomic variants resulting in treatment disparities, pharmacogenomics continues to gain popularity. Unfortunately, despite these and other advancements, pharmacogenomic testing and implementation remain underutilized and misunderstood in clinical care, in part because of a lack of health care professionals trained in assessing and implementing test results. Conclusions: A one-size-fits-all approach to pain management is inadequate and outdated. With increasing genomic data and pharmacogenomic understanding, patient-specific genomic testing offers a comprehensive and personalized treatment alternative worthy of additional research and consideration.

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Precision medicine technology reality not hype - The example of model-informed precision dosing

F1000Research ◽

10.12688/f1000research.20489.2 ◽

2019 ◽

Vol 8 ◽

pp. 1709

Author(s):

Thomas M. Polasek ◽

Sepehr Shakib ◽

Amin Rostami-Hodjegan

Keyword(s):

Patient Care ◽

Precision Medicine ◽

Clinical Care ◽

Medical Science ◽

Improve Patient Care ◽

Clinical Workflow ◽

User Friendliness ◽

Clinical Patient ◽

Novel Technologies ◽

Drug Concentrations

Novel technologies labelled as ‘precision medicine’ are targeting all aspects of clinical care. Whilst some technological advances are undeniably exciting, many doctors at the frontline of healthcare view precision medicine as being out of reach for their patients. Model-informed precision dosing (MIPD) is a precision medicine technology that predicts drug concentrations and drug responses based on individual patient characteristics. In this opinion piece, the example of MIPD is used to illustrate eight features of a precision medicine technology less likely to be hyperbole and more likely to improve patient care. Positive features in this regard include: (1) fitting the definition of ‘precision medicine’; (2) addressing a major clinical problem that negatively impacts patient care; (3) a track record of high-quality medical science published via peer-reviewed literature; (4) well-defined clinical cases for application; (5) quality evidence of benefits measured by various clinical, patient and health economic endpoints; (6) strong economic drivers; (7) user friendliness, including easy integration into clinical workflow, and (8) recognition of importance by patients and their endorsement for broader clinical use. Barriers raised by critics of the approach are given to balance the view. The value of MIPD will be decided ultimately by the extent to which it can improve cost-effective patient care.

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Precision medicine technology hype or reality? The example of computer-guided dosing

F1000Research ◽

10.12688/f1000research.20489.1 ◽

2019 ◽

Vol 8 ◽

pp. 1709 ◽

Cited By ~ 1

Author(s):

Thomas M. Polasek ◽

Sepehr Shakib ◽

Amin Rostami-Hodjegan

Keyword(s):

Patient Care ◽

Precision Medicine ◽

Clinical Care ◽

Medical Science ◽

Improve Patient Care ◽

Clinical Workflow ◽

User Friendliness ◽

Clinical Patient ◽

Novel Technologies ◽

Drug Concentrations

Novel technologies labelled as ‘precision medicine’ are targeting all aspects of clinical care. Whilst some technological advances are undeniably exciting, many doctors at the frontline of healthcare view precision medicine as being out of reach for their patients. Computer-guided dosing is a precision medicine technology that predicts drug concentrations and drug responses based on individual patient characteristics. In this opinion piece, the example of computer-guided dosing is used to illustrate eight features of a precision medicine technology less likely to be hyperbole and more likely to improve patient care. Positive features in this regard include: (1) fitting the definition of ‘precision medicine’; (2) addressing a major clinical problem that negatively impacts patient care; (3) a track record of high-quality medical science published via peer-reviewed literature; (4) well-defined clinical cases for application; (5) quality evidence of benefits measured by various clinical, patient and health economic endpoints; (6) strong economic drivers; (7) user friendliness, including easy integration into clinical workflow, and (8) recognition of importance by patients and their endorsement for broader clinical use. Barriers raised by critics of the approach are given to balance the view. The value of computer-guided dosing will be decided ultimately by the extent to which it can improve cost-effective patient care.

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Randomized controlled study of the impact of a participatory patient care plan among primary care patients with common chronic diseases: a one-year follow-up study

BMC Health Services Research ◽

10.1186/s12913-021-06716-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Nina Tusa ◽

Hannu Kautiainen ◽

Pia Elfving ◽

Sanna Sinikallio ◽

Pekka Mäntyselkä

Keyword(s):

Quality Of Life ◽

Patient Care ◽

Usual Care ◽

Care Plan ◽

Health Related Quality ◽

Related Quality ◽

Health Related ◽

Disease Specific

Abstract Backround Chronic diseases and multimorbidity are common in the ageing population and affect the health related quality of life. Health care resources are limited and the continuity of care has to be assured. Therefore it is essential to find demonstrable tools for best treatment practices for patients with chronic diseases. Our aim was to study the influence of a participatory patient care plan on the health-related quality of life and disease specific outcomes related to diabetes, ischemic heart disease and hypertension. Methods The data of the present study were based on the Participatory Patient Care Planning in Primary Care. A total of 605 patients were recruited in the Siilinjärvi Health Center in the years 2017–2018 from those patients who were followed up due to the treatment of hypertension, ischemic heart disease or diabetes. Patients were randomized into usual care and intervention groups. The intervention consisted of a participatory patient care plan, which was formulated in collaboration with the patient and the nurse and the physician during the first health care visit. Health-related quality of life with the 15D instrument and the disease-specific outcomes of body mass index (BMI), low density lipoprotein cholesterol (LDL-C), hemoglobin A1c (HbA1C) and blood pressure were assessed at the baseline and after a one-year follow-up. Results A total of 587 patients with a mean age of 69 years were followed for 12 months. In the intervention group there were 289 patients (54% women) and in the usual care group there were 298 patients (50% women). During the follow-up there were no significant changes between the groups in health-related quality and disease-specific outcomes. Conclusions During the 12-month follow-up, no significant differences between the intervention and the usual care groups were detected, as the intervention and the usual care groups were already in good therapeutic equilibrium at the baseline. Trial registration ClinicalTrials.gov Identifier: NCT02992431. Registered 14/12/2016

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Bayesian adaptive design of early-phase clinical trials for precision medicine based on cancer biomarkers

The International Journal of Biostatistics ◽

10.1515/ijb-2021-0009 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Shinjo Yada

Keyword(s):

Neural Network ◽

Clinical Trials ◽

Precision Medicine ◽

Early Phase ◽

Patient Specific ◽

Specific Gene ◽

Cancer Type ◽

Background Characteristics ◽

Number Of Patients ◽

Early Phase Clinical Trials

Abstract Cancer tissue samples obtained via biopsy or surgery were examined for specific gene mutations by genetic testing to inform treatment. Precision medicine, which considers not only the cancer type and location, but also the genetic information, environment, and lifestyle of each patient, can be applied for disease prevention and treatment in individual patients. The number of patient-specific characteristics, including biomarkers, has been increasing with time; these characteristics are highly correlated with outcomes. The number of patients at the beginning of early-phase clinical trials is often limited. Moreover, it is challenging to estimate parameters of models that include baseline characteristics as covariates such as biomarkers. To overcome these issues and promote personalized medicine, we propose a dose-finding method that considers patient background characteristics, including biomarkers, using a model for phase I/II oncology trials. We built a Bayesian neural network with input variables of dose, biomarkers, and interactions between dose and biomarkers and output variables of efficacy outcomes for each patient. We trained the neural network to select the optimal dose based on all background characteristics of a patient. Simulation analysis showed that the probability of selecting the desirable dose was higher using the proposed method than that using the naïve method.

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It’s Time to Talk to Prehospital Providers: Feedback Disparities among Ground-Based Emergency Medical Services Providers and its Impact on Job Satisfaction

Prehospital and Disaster Medicine ◽

10.1017/s1049023x21000601 ◽

2021 ◽

pp. 1-9

Author(s):

Sarayna S. McGuire ◽

Anuradha Luke ◽

Aaron B. Klassen ◽

Lucas A. Myers ◽

Aidan F. Mullan ◽

...

Keyword(s):

Job Satisfaction ◽

Patient Care ◽

Emergency Medical Services ◽

Performance Feedback ◽

Life Support ◽

Clinical Care ◽

Medical Services ◽

The Past ◽

Emergency Medical ◽

Overall Job Satisfaction

Abstract Objective: Performance feedback on clinical care and patient outcomes is a cornerstone of medical education, yet it remains lacking in the prehospital environment. Research seeking to establish the quantity of feedback provided to Emergency Medical Services (EMS) has been limited and studies focused on complimentary feedback or how feedback relates to EMS job satisfaction are lacking. The objectives of this study were to measure the frequency and nature of feedback received by EMS agencies and to identify the importance of receiving feedback as it relates to EMS job satisfaction. Methods: This was an anonymous, survey-based study of twenty-nine Basic Life Support (BLS) and fifteen Advanced Life Support (ALS) agencies located in Southeastern Minnesota (USA). Descriptive statistics and Fisher exact tests were used. The study was deemed exempt by the Mayo Clinic Institutional Review Board. Results: Ninety-four responses were included from nineteen different EMS agencies, including sixty-one (64.9%) paramedics and thirty-three (35.1%) emergency medical technicians (EMTs). One-half of all respondents reported that they had not received any type of feedback in the past 30 days, while another 43.6% of respondents indicated that they had only received feedback one to three times in the same time period. Twenty (60.6%) EMTs reported receiving no feedback in the past 30 days, compared with twenty-seven (44.3%) paramedics (P = .123). Of respondents receiving feedback, 65.9% reported never or rarely receiving positive reinforcing feedback and 60.6% reported never or rarely receiving constructive criticism or feedback regarding something that did not go well with patient care or transport. The majority of respondents were dissatisfied with the quantity (86.1%) and quality (73.4%) of feedback received. An overwhelming majority (93.6%) indicated that feedback on patient care or outcomes was important in influencing their overall job satisfaction. This high importance was maintained across all demographic groups. Conclusion: Within the cohort of survey respondents, a paucity of feedback received by EMS personnel is a source of dissatisfaction for EMS providers. Feedback on patient care strongly relates to overall job satisfaction. These findings suggest system-wide opportunities for structured feedback processes, focusing upon both quality and quantity of delivered feedback, to improve both patient care and staff satisfaction.

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Trailblazing Precision Medicine in Europe: a joint view by Genomic Medicine Sweden and the Centers for Personalized Medicine, ZPM, in Germany

Seminars in Cancer Biology ◽

10.1016/j.semcancer.2021.05.026 ◽

2021 ◽

Author(s):

Albrecht Stenzinger ◽

Anders Edsjö ◽

Carolin Ploeger ◽

Mikaela Friedman ◽

Stefan Fröhling ◽

...

Keyword(s):

Personalized Medicine ◽

Precision Medicine ◽

Genomic Medicine

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Immunotherapy as a Precision Medicine Tool for the Treatment of Prostate Cancer

Cancers ◽

10.3390/cancers13020173 ◽

2021 ◽

Vol 13 (2) ◽

pp. 173

Author(s):

Maria Adamaki ◽

Vassilios Zoumpourlis

Keyword(s):

Prostate Cancer ◽

Precision Medicine ◽

Checkpoint Inhibitors ◽

Invasive Disease ◽

Patient Specific ◽

Treatment Alternative ◽

Substantial Evidence ◽

Significant Survival ◽

Localized Disease ◽

Specific Antigens

Prostate cancer (PCa) is the most frequently diagnosed type of cancer among Caucasian males over the age of 60 and is characterized by remarkable heterogeneity and clinical behavior, ranging from decades of indolence to highly lethal disease. Despite the significant progress in PCa systemic therapy, therapeutic response is usually transient, and invasive disease is associated with high mortality rates. Immunotherapy has emerged as an efficacious and non-toxic treatment alternative that perfectly fits the rationale of precision medicine, as it aims to treat patients on the basis of patient-specific, immune-targeted molecular traits, so as to achieve the maximum clinical benefit. Antibodies acting as immune checkpoint inhibitors and vaccines entailing tumor-specific antigens seem to be the most promising immunotherapeutic strategies in offering a significant survival advantage. Even though patients with localized disease and favorable prognostic characteristics seem to be the ones that markedly benefit from such interventions, there is substantial evidence to suggest that the survival benefit may also be extended to patients with more advanced disease. The identification of biomarkers that can be immunologically targeted in patients with disease progression is potentially amenable in this process and in achieving significant advances in the decision for precision treatment of PCa.

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