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Published in final edited form as: Addict Neurosci. 2023 Apr 1;7:100083. doi: 10.1016/j.addicn.2023.100083

Genomic medicine to reduce tobacco and related disorders: Translation to precision prevention and treatment☆

Li-Shiun Chen a,b,*, Timothy B Baker c, Alex Ramsey a,b, Christopher I Amos d,e, Laura J Bierut a,b
PMCID: PMC10434839  NIHMSID: NIHMS1899409  PMID: 37602286

Abstract

Genomic medicine can enhance prevention and treatment. First, we propose that advances in genomics have the potential to enhance assessment of disease risk, improve prognostic predictions, and guide treatment development and application. Clinical implementation of polygenic risk scores (PRSs) has emerged as an area of active research. The pathway from genomic discovery to implementation is an iterative process. Second, we provide examples on how genomic medicine has the potential to solve problems in prevention and treatment using two examples: Lung cancer screening and evidence-based tobacco treatment are both under-utilized and great opportunities for genomic interventions. Third, we discuss the translational process for developing genomic interventions from evidence to implementation by presenting a model to evaluate genomic evidence for clinical implementation, mechanisms of genomic interventions, and patient desire for genomic interventions. Fourth, we present potential challenges in genomic interventions including a great need for evidence in all diverse populations, little evidence on treatment algorithms, challenges in accommodating a dynamic evidence base, and implementation challenges in real world clinical settings. Finally, we conclude that research to identify genomic markers that are associated with smoking cessation success and the efficacy of smoking cessation treatments is needed to empower people of all diverse ancestry. Importantly, genomic data can be used to help identify patients with elevated risk for nicotine addiction, difficulty quitting smoking, favorable response to specific pharmacotherapy, and tobacco-related health problems.

Keywords: Genomics, Polygenic risk, Clinical implementation, Genomic medicine, Precision treatment, Precision prevention, Tobacco, Lung cancer

1. Introduction

1.1. Genomic medicine can enhance prevention and treatment

Advances in genomics have the potential to enhance assessment of disease risk, improve prognostic predictions, and guide treatment development and application. Genomic discoveries may also enable better understanding of both disease and treatment mechanisms and thereby enhance scientific understanding. As such, precision medicine has the potential to meaningfully benefit patient health with personalized prevention and treatment interventions (Fig. 1).

Fig. 1.

Fig. 1.

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Precision prevention and precision treatment.

Having the potential to capture multiple different biological mechanisms and adapt to a dynamic evidence base, genomics is increasingly recognized as an important source of data that can inform predictions regarding medication efficacy or adverse events [1,2]. Given the growing evidence supporting genetic contributions to drug response, the Clinical Pharmacogenetics Implementation Consortium (CPIC), [3,4] an international consortium, was formed to provide consensus guidelines on interpretation and translation of genotype results into actionable prescribing decisions. Genetic information is now included in US Food and Drug Administration-approved labeling for an increasing number of medications. For example, medication Bupropion (antidepressant and smoking cessation aid) pharmacokinetic properties are affected by CYP2D6 [5]. The likelihood of such benefits is growing with our on-going progress in discovering genetic influences on disease, offering increased insights into disease mechanisms of action and metabolism of medications.

1.2. Clinical implementation of polygenic risk scores (PRSs) is an area of active research

Genome-wide association studies (GWAS) have identified an increasing number of genomic variants significantly associated with common complex human diseases, including nicotine dependence and smoking behaviors [6,7] PRSs provide a single measure of genetic association that aggregates risk alleles across the genome. PRS may confer information on risk magnitude that is equivalent to that conferred by clinical risk factors. Proper clinical implementation of PRS is now an important area of active research across many disease areas [8]. Importantly, we note that PRS could be constructed as a risk or protective factor because it is a weighted sum to capture polygenic effects.

Paving the way for implementing genomic medicine, eMERGE is a national network funded by the National Human Genome Research Institute (NHGRI) that combines DNA biorepositories with electronic medical record (EMR) systems for large scale genetic research [9]. Additionally, the National Institutes of Health (NIH)-funded Implementing GeNomics In praTticE (IGNITE) network was established to support the development and investigation of genomic medicine models to enhance the application and implementation of genomic based practices into routine clinical care [10].

1.3. Advancing science in two key domains — precision prevention and precision treatment

PRS and other biomarkers related to smoking and smoking-related conditions have potential utility for clinical care. Use of PRS has enabled research to evaluate the relative contribution of genetic and non-genetic factors. For example, recent evidence suggest that the adding PRS for smoking cessation significantly improves the prediction of successful smoking cessation beyond use of clinical predictors and that the adding PRS for lung cancer significantly may improve the prediction of lung cancer beyond use of clinical predictors [11,12].

Advances in genomic knowledge is being translated into personalized approaches for both prevention and treatment. One precision prevention opportunity is focused on the primary prevention of smoking among individuals who have not initiated smoking (most notably children, adolescents, and young adults). Another prevention opportunity is the secondary prevention of smoking-related morbidity and mortality among people with a substantial smoking history (most notably those eligible for lung cancer screening). This approach can enhance primary and secondary cancer prevention by providing personalized feedback (e.g., PRS on lung cancer and persistent smoking) to reduce smoking initiation and motivate smoking cessation attempts and cancer screening.

In contrast, precision treatment opportunities leverage the use of pharmacogenomic biomarkers (genetics of smoking, nicotine metabolism) to personalize treatment algorithms, including recommended dosing and selection of medications to maximize effectiveness, minimize side-effects and increase smoking cessation. By working synergistically with common measures, joint efforts to build PRS pipelines and EHR integration, and lessons learned from implementation in real-world settings, the field is positioned to accelerate the advancement of these related yet distinct areas of investigation.

1.4. From genomic discovery to implementation: an iterative process

In this paper, we present a framework for translating genomic discoveries to clinical interventions and provide examples related to prevention of lung cancer and tobacco treatment. Specifically we will discuss both 1) areas that require new research to support personalized tobacco treatment and 2) real world application research that evaluates the clinical needs and readiness for implementing genomic medicine in real world settings. We suggest taking this two-prong parallel approach based on two major reasons: First, there is a great need to reduce the long lag between published science to clinical care [13,14]. Second, a multilevel genomic intervention has the potential as a novel approach that can enhance the implementation and uptake of evidence-based tobacco treatment that are insufficiently used in clinical settings.

2. Precision prevention

2.1. Problem – lung cancer screening and tobacco treatment are much under-utilized

Annual lung cancer screening with low-dose computed tomography can reduce lung cancer mortality and is currently recommended in the U.S. for asymptomatic adults aged 50 to 80 years who have a 20 pack-year smoking history and have smoked in the past 15 years [15]. Unfortunately, as few as 2.0% of 7.6 million eligible smokers receive lung cancer screening [16]. Primary care physicians are the predominant referral source for lung cancer screening; however, only a minority ordered lung cancer screening for any screen-eligible patients in the past 12 months [17]. Further, most (>70%) of these lung cancer screening-eligible patients are current smokers, yet smoking cessation pharmacotherapy is prescribed in only 8% of patients who smoke [18], and efficacious treatments are underused when prescribed [19,20].

2.2. Translational opportunity - Precision prevention strategies may boost uptake of lung cancer screening and tobacco treatment

Little progress has been made in incorporating precision medicine into behavioral interventions for tobacco treatment and lung cancer. Two findings offer opportunities to support precision prevention in lung cancer screening and tobacco treatment: 1) clinical and genetic factors inform personalized risk for lung cancer and personalized benefit of smoking cessation, providing the basis for decision support and communication tools for physicians [11,21–25] and 2) patients express a high desire for genetic risk feedback, suggesting its potential to activate behavior change [26–28]. A precision risk intervention leveraging polygenic risk and patient receptivity could help close gaps in ordering and patient uptake of lung cancer screening and tobacco treatment.

2.3. Current evidence on precision prevention: what is known and what is needed?

Growing evidence shows that risk stratification can increase efficiency of lung cancer screening beyond use of existing criteria based on smoking status, pack years, and age. There is growing evidence that genetic variants predict lung cancer risk and likelihood of smoking cessation success (Fig. 2) [6,7,11,21–25].

Fig. 2.

Fig. 2.

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Polygenic score and smoking related outcomes.

2.3.1. What is known about precision risk for lung cancer

There is strong evidence that genetic variants predict lung cancer risk [11,23,24,29–35]. Multiple meta-analyses based on the Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium, the largest international consortium for lung cancer, show that patients with high genetic risk (CHRNA5 genotypes or polygenic risk) have a 2-fold increase in risk for lung cancer compared to patients with low genetic risk [11,23,24]. Specifically, patients with high genetic risk receive a lung cancer diagnosis about 6 years earlier compared to those at low risk (age 65 vs. 71) [24,36]. These results demonstrate that an individual’s genetic background can inform the precision of lung cancer risk assessment and the urgency for lung cancer screening and smoking cessation.

2.3.2. What is known about precision benefit of smoking cessation in reducing risk for lung cancer

Multiple large genome-wide meta-analyses, by our team with the INTEGRAL Consortium, demonstrate the benefit of smoking cessation based on each patient’s genotypes [23,24,36]. Specifically, patients with high genetic risk (CHRNA5 genotypes or polygenic risk) are less likely to quit successfully when unassisted with medication. Individuals with high genetic risk can benefit from a 50% reduced cancer risk and a 9-year delay in cancer onset (age 71 vs. 62) if they quit. Patients with low genetic risk have a lower risk of cessation failure, but they too can benefit from quitting with a 50% reduction in cancer risk and a 6-year delay in cancer onset (age 74 vs. 68). These results show that an individual’s genetic background not only predicts differential risk of lung cancer and delay in cancer onset but also predicts substantial benefit of cessation even amongst those at low genetic risk.

2.3.3. What is known about precision risk for persistent smoking (or failed smoking cessation)

Risk for nicotine dependence is not only polygenic, but also pleiotropic across multiple related traits such as neuroticism, alcohol use, depression, and schizophrenia. [37] Large genome-wide meta-analyses identified multiple genetic markers for smoking quantity, and smoking cessation, with the most robust signal near the α5 nicotinic cholinergic receptor gene (CHRNA5) [6,7]. Research found that use of this variant and polygenic risk scores has prognostic significance for likelihood of cessation [6,23,24]. Specifically, those with high-risk genetic variants are more likely to 1) smoke heavily [6,7,38], 2) delay age of quitting smoking [24], 3) have lower success with unassisted quit attempts [39], and 4) benefit from cessation medications [39–48], compared to those without the risk variants. Most importantly, use of polygenic risk scores for risk smoking behaviors significantly increases prediction of smoking cessation beyond use of demographic and clinical predictors such as cigarettes per day, carbon monoxide, Fagerström Test for Nicotine Dependence (FTND), age of initiation, anxiety, and depression [12].

2.3.4. What is needed

First, to enhance the evidence base, it is important to note that Genome-wide association studies (GWAS) on lung cancer and smoking behaviors in non-European populations (e.g., African, Hispanic/Latinx, Asian ancestries) are still rare and much smaller in sample size) [6,29,34,49,50]. Larger datasets in African and other underrepresented populations are much needed to identify the ancestry-specific loci and dissect the heterogeneity across different ancestries. Second, there is a missed opportunity to diversity research in countries with varying policy and culture regarding smoking to gain insights in the genomic-environment interactions. Cross-ancestry or international research is still limited and much needed to understand biology [29,38]. Third, to enable precision prevention, we need research to evaluate the clinical utility of a precision intervention and determine if the precision intervention (vs. usual care) generates better clinical outcomes such as increased tobacco treatment and lung cancer screening among eligible patients by effectively motivating health behavior changes.

3. Precision treatment

3.1. Problem - evidence-based tobacco treatment is under-utilized

Innovative interventions are needed to address gaps in tobacco treatment. Smoking remains a leading cause of premature death, causing ~400,000 deaths annually in the U.S. and >50% of all cancer deaths [51–55]. and smoking cessation significantly reduces this risk [56]. However, the public health impact of smoking cessation treatment is limited by a cascade of factors including a low rate of physician prescription of cessation medication (<20%) [55,57–62], a low rate of patient use of cessation medication (~33%) [55,63,64], and limited medication effectiveness (<30%) [65–69]. Thus, the reach and effectiveness of smoking cessation treatment remains limited despite the wide availability of treatment, clear guidance from clinical practice guidelines and considerable efforts to execute health system changes designed to increase smoking treatment implementation and engagement [60,70,71]. The cumulative effects of these multiple constraints yield a very low expected rate of smoking cessation (~2%) among patients in primary care. Existing strategies have had limited success, despite considerable implementation effort with providers and patients. There is a great need for a multilevel intervention that addresses each of these rate limiting factors.

3.2. Translational opportunity - Precision treatment strategies may boost the implementation and effectiveness of tobacco treatment

Precision treatment has the potential to alter clinical practice and patient behavior. First, precision treatment may increase treatment effectiveness by matching patients with the medication that maximizes efficacy and safety. Evidence from meta-analyses, and our work has laid a foundation for precision treatment as described in details below on precision risk, benefit, and treatment choice [36,39,40,43,72–76]. Second, when physicians receive specific recommendations of precision treatment based on biomarkers, they may be more motivated to prescribe medication that is pharmacogenetically matched to their patient [77–80]. Physicians report concerns over medication effectiveness and safety, so a precision medicine approach that addresses these concerns may increase physician outcome expectancies and prescribing [81,82]. Third, when patients receive personalized information on risks and benefits, and personalized treatment recommendations, they may be more motivated to use medication due to increased perception of benefit with reduced side effects [83–85]. Growing evidence suggests that patients who smoke want gene-guided treatment, which may increase their motivation to use it as prescribed [26,86–88].

3.3. Current evidence on precision treatment: what is known and what is needed

In the past decade, evidence has emerged on the role of genetic markers guiding precision treatment based on genetic and metabolic markers for patients who smoke (Fig. 2) [12,23,24,36,39,40,43,73–76].

3.3.1. What is known about precision treatment choice

A recent review by Panagiotou et al.[72] and several landmark trials [36,39,40,43,73–75], demonstrate that patient response to cessation medication is moderated by two major biomarkers: the nicotine metabolite ratio (NMR) and the genotype of rs16969968 in the nicotinic receptor gene CHRNA5.

Metabolic markers

NMR is a biomarker for nicotine metabolism, which can be dichotomized as “normal ” vs. “slow. ” Mounting evidence, including the 2015 landmark Pharmacogenetics of Nicotine Addiction Treatment (PNAT) trial[74] and a recent review [72], showed that NMR can inform choice of cessation medication. These results suggest nicotine replacement therapy (NRT) for slow metabolizers due to similar effectiveness and lower side effect profiles, and varenicline for normal metabolizers due to superior effectiveness. We and others have replicated this finding and meta-analyzed our recent Genetically Informed Smoking Cessation (GISC) trial and the PNAT trial to confirm that NMR moderates cessation medication efficacy [73,89–91]. In addition, our work and existing evidence has shown the value of CYP2A6 genotypes in predicting nicotine metabolism and smoking cessation. [92–95]

Genetic markers

There is mounting evidence that genotypes predict an individual’s response to a specific medication [39,43]., [96–100] The pharmacogenetic evidence is more convergent for African American patients than it is for White patients. The recent review and other evidence show that CHRNA5 may moderate response to NRT in African American patients[72,75]. Patients with rs16969968-GG genotype had higher abstinence at end-of-treatment/12 weeks and 6 months when receiving NRT vs. placebo. However, evidence on treatment for patients with the GA/AA genotypes was lacking.

Our recent GISC trial yielded evidence of differential treatment response for African American patients according to CHRNA5 rs16969968 genotypes: combination NRT [patch and lozenge] was more effective in patients with GG genotypes, whereas varenicline was more effective for patients with GA/AA genotypes [40]. Thus, this finding agrees with evidence and other trials (demonstrating that NRT is more effective in patients with GG genotypes) [72],[75] and suggests that varenicline is more effective for patients with GA/AA genotypes. If these findings are robust, the majority of African American patients will be able to quit smoking more successfully with combination NRT—a less expensive over the counter medication that has fewer side effects than varenicline, but varenicline will be especially effective with the remaining individuals with GA/AA genotypes (~13% of patients).

In addition to CHRNA5 (encoding nicotinic receptor) and CYP2A6 (encoding nicotine metabolism), hundreds of genetic variants have been identified for smoking cessation, in large meta-analyses of genome-wide association studies (GWASs) [6]. We have started to integrate multiple genetic markers as predictors for treatment response [43], and our recent work showed the promise of polygenic risk scores in predicting quit success beyond the prediction using clinical factors alone in European Ancestry patients [12,36].

3.3.2. What is needed

First, to enhance the evidence base, these findings require replication, and research is needed on how the genetic marker can be used with other biomarkers such as NMR to enhance treatment assignment. We need genomic and metabolic data for participants in smoking cessation trials to allow the investigation of multiple markers and treatment response. To optimize a treatment algorithm, a single large trial in which FDA approved medications (combination NRT [patch and lozenge] vs. varenicline) can be evaluated and the contributions of multiple biomarkers can be evaluated regarding their main and interactive effects is needed. In most relevant studies, African American individuals constituted only a small portion of the sample. This means that the size for particular subgroups of participants are small for some key comparisons. Small sample size also occurred in recent reviews [67,72,101]. Indeed, smoking cessation trials focused on pharmacotherapy with African Americans are few in number. We need more and large trials to identify treatment algorithms that optimize multiple biomarkers for African American patients who smoke. Second, to enable precision treatment, we need research to evaluate the clinical utility of a precision intervention and determine if the precision intervention (vs. usual care) generates better clinical outcomes such as increased use of tobacco treatment and overall quit success by effectively motivating health behavior changes and matching treatment.

4. Genomic interventions: from evidence to implementation

4.1. A model to evaluate genomic evidence for clinical implementation

The ACCE framework holds that genetic tests be evaluated based on the four main criteria: Analytic validity, Clinical validity, Clinical utility (ACCE), and associated Ethical Legal and Social Implications (ELSI) and other similar criteria for clinical actionability [102–104]. We propose an evaluation model to guide the clinical actions that can be taken in response to a genomic marker (see Fig. 3). Actionability depends on three factors: (I) discriminative power (e.g., analytic validity, accuracy); (II) performance in comparison to other known risk factors (e.g., clinical validity, impact); and (III) improve patient health outcomes (e.g., clinical utility).

Fig. 3.

Fig. 3.

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Model for evaluating a genomic marker as a predictor for clinical application.

Model adapted from ACCE framework by Teutsch SM et al. 2009, and other similar criteria proposed by Klein RJ et al. 2022, Wand H, 2021.

For the use of genomic markers (CHRNA5 genotypes or PRSs for high-risk smoking) for predicting risk of lung cancer occurrence and smoking cessation, current research includes evidentiary support from meta-analyses for analytic validity[6,39,105] and meta-analytic evidence of clinical validity (e.g., use of PRS for high-risk smoking significantly increases the predictive accuracy of smoking cessation success beyond use of demographic and clinical factors) [12,23,24]. However, we need more evidence with regard to clinical utility and we need further investigation of issues around ELSI.

Based on our evaluative model (Fig. 3) for assessing the evidentiary basis for the clinical utility of a genomic marker in predicting treatment choices, we note that there is strong evidence of analytic validity, moderate evidence from meta-analyses and a recent review for clinical validity, but that there is little research regarding the criteria, clinical utility, and ELSI.

4.2. Mechanisms of genomic interventions

Knowledge of personalized genomic risk may motivate behavior change. Studies have examined behavior change following the return of genetic information. Among underserved populations [30], consumers of genomic testing [31], and college students[102] who smoke, returning genetic susceptibility results, including results on nicotine dependence [102], has not been shown to effect psychological or behavioral harms, even among populations at high-risk for adverse reactions. A lack of iatrogenic effects has been supported in multiple meta-analyses [103,104], alleviating concerns about negative behavioral or psychological effects (e.g., sense of fatalism, risk compensation, depression or anxiety) of feedback of genetic risk. Although these meta-analyses confirm a low risk of adverse effects, they report mixed findings on the potential of genetic susceptibility results to motivate positive behavior change. One meta-analysis revealed little to no behavioral change [103], whereas a newer meta-analysis found moderate increases in healthy behaviors [104].

A novel care paradigm may transform precision tobacco treatment. Genetic information may boost physician prescribing, motivate patients’ use of medication, and enhance medication effectiveness. However, additional research is needed to inform the implementation of a new precision care paradigm that is theory-driven, addresses clinician and patient barriers, and integrated in healthcare settings and practices, factors that are critical to behavior change efforts [106,107].

Drawing from the teachable moment hypothesis in the healthcare context, precision prevention for smoking enables multiple pathways to increase engagement and motivation for behavior change, including 1) communicating personalized risk of lung cancer and the potential benefits of lung cancer screening and tobacco treatment, 2) increasing urgency for action alongside positively-framed, action-oriented messages, and 3) reducing self-stigmatizing views. Translating these genomic findings into effective motivational or behavior change strategies is a promising frontier for engaging both patients and physicians in lung cancer screening and tobacco treatment actions [47,93–96].

While there is emerging evidence to support translation of precision prevention into lung cancer screening and tobacco treatment clinical practice, extant evidence now permits rigorous tests of the utility of incorporating genetic information into motivational interventions for screening and treatment in real-world settings. Such efforts could address the need to drive the uptake of lung cancer screening and tobacco treatment at point-of-care [97,98]. Enriching behavioral interventions with genetic prognostic information may prompt and guide physician ordering and motivate and facilitate patient uptake of annual lung cancer screening and tobacco treatment [93,94].

4.3. Patient desire and the promise of genomic intervention

Research suggest that patients have high demand for personalized genetics [29–32]. We found that patients who smoke strongly endorsed the importance of receiving genetic risk results, in particular to learn the following: risk for developing lung cancer (96%) and guidance regarding smoking cessation (95%) [29].

Patients report increased motivation to use personalized tobacco treatment [28]: In our recent work, 85% of current smokers reported high interest in receiving genetic testing results, with a significantly higher proportion desiring to take medication when they were informed that the likelihood of medication benefit was above average, compared to below average [28]. These data suggest that personalized genetic information on risk and risk modification might motivate smokers for positive behavior change such as use of tobacco treatment [28].

Existing research including ours suggests that smokers not only desire knowledge of personalized risk, but also report that such knowledge will motivate cancer prevention behaviors such as seeking tobacco treatment [28–32]. The robust desire and perceived importance of personalized genetic data suggested strong demand among smokers, motivating the development and evaluation of returning personalized genetic risk as an intervention to promote health behaviors such as smoking cessation. In our recent work to motivate patients who smoke for positive behavior change, we designed RiskProfile as an intervention to communicate personalized genetic risk for developing lung cancer and difficulty in quitting smoking [30–32]. RiskProfile demonstrates high acceptability and utility as a behavior change tool to reduce smoking among individuals who smoke [27]. In a proof-of-concept trial, we found that 69% of participants reported increased readiness to quit after receiving RiskProfile, and 65% reported reducing their cigarette smoking. Overall, 77% reported making at least one smoking-related behavior change (quit attempt, fewer cigarettes, tried cessation medication) [86]. Importantly, we found that patients of high genetic risk substantially reduced their smoking (defeating the bias of “genetic determinism”); likewise, patients of low genetic risk also reduced their smoking (defeating the bias of a “health certificate effect”) [86]. These results showed the potential of a genetically informed intervention for behavior change.

5. Potential challenges in genomic interventions

Genomic medicine is encouraged by advances in genomic discoveries, but hampered by multiple problems: 1) It is critical to enhance evidence in all diverse populations with studies with larger sample sizes, linkage between genomic and phenotypic data, and robust measures. 2) We need to develop and evaluate treatment algorithms that include both genomic and non-genomic factors. 3) Genomic interventions need to be based on most current evidence with a mechanism to incorporate ongoing advances in the knowledge base. 4) Implementation challenges of genomic interventions depends on cost-effectiveness, level of integration with health informatics, and co-design with stakeholders in the healthcare systems.

5.1. Need more evidence in all diverse populations

We need research with large sample sizes, genomic data linked with highly granular phenotypic data, and robust measures such as specific pharmacotherapy, adherence, and bioverified abstinence outcomes. For example, current evidence in African American patients is based on only a few small trials, and research has not explored how biomarkers can be used together and with non-biologic variables to enhance cessation outcomes. Unfortunately, little research has focused on African American patients even though they have disproportionally high rates of tobacco-related cancer and cancer mortality [108], and despite evidence that smoking cessation treatments are often less effective in African American patients than in other groups [109–112]. A well-powered trial with African American patients who smoke is needed to develop biomarker-informed treatment, so that it can then be experimentally evaluated as a precision treatment algorithm in this under-studied population to reduce smoking-related disparities.

5.2. Little evidence on treatment algorithms

Precision treatment algorithms will likely include both metabolic and genetic markers. Work has started to explore the use of NMR in guiding treatment. However, rapidly emerging genomic evidence suggests the potential value of including genetic markers in precision treatment. First, genetic data are increasingly available via EHR with multiple national initiatives on genomic medicine and consumer genomics; thus, gene-based interventions are likely to be highly scalable [1,2]. Second, genetic data have the potential to capture multiple distinct biological pathways (both nicotinic receptor gene function and nicotine metabolism) and adapt to a dynamic evidence base. Third, genetic data may be uniquely informative in personalizing an individual’s risk, benefit, and recommended treatment, capturing multiple mechanisms to optimize treatment success. Given the evidence, evaluating the use of both metabolic and genetic markers in a multilevel intervention using behavior change theories may advance the field of tobacco treatment.

Research is needed to develop and evaluate different treatment algorithms to optimize smoking cessation success: 1) a non-biologic algorithm (using non-biologic predictors such as demographics, smoking quantity, and past treatment history), 2) a biologic algorithm (using either or both NMR and genetic markers), and 3) a hybrid algorithm comprising both non-biologic predictors and biomarkers. For example, our recent work showed the promise of polygenic risk scores in predicting quit success beyond the prediction using clinical factors alone in European Ancestry patients [12],[36] Statistical approaches should be used to identify treatment algorithms and evaluate the utility of these algorithms with regard to optimal prediction of abstinence. The outcomes of algorithms will be appraised with regard to prediction of successful smoking cessation and associated costs (e.g., biomarker assays, medication, time). Development and validation of treatment algorithms to incorporate new evidence will lay the foundation for up to date genomic medicine [113].

5.3. Challenge in accommodating a dynamic evidence base

A mechanism to incorporate a rapidly evolving evidence base is needed. One major challenge of precision treatment is the rapidly evolving evidence that identifies novel biomarkers in treatment. For example, our recent publication suggests the potential of polygenic risk scores in guiding treatment in the future [12]. Despite the presence of actionable precision treatment findings, the dynamic evidence base and perception that superior data are on the horizon has had a stifling effect on implementation [114,115]. We propose research to implement genomic medicine using state-of-the-art, biology-based metabolic and genetic markers that provide robust evidence that will be updated over time to inform both prevention and treatment. This effort will reduce the time lag from evidence to implementation, especially when precision medicine applies an active dynamic evidence base.

5.4. Implementation challenges/clinical utility

Precision tobacco treatment using genomic or biomarkers in healthcare settings may face multiple barriers. First, patients may not be willing to delay treatment due to genomic or biomarker testing. Second, current primary care clinic settings may not permit easy access to genomic or biomarker testing without significant changes in workflows. Some early work has started to explore assessment of biomarkers as part of smoking cessation treatments in research and clinical settings [39,40,74,116]. Early data on metabolism-informed treatment also suggests high feasibility and acceptability of biomarker testing in clinical settings [116]. In our work of returning smoking-related genetic results, conducted in both in-person and virtual visits, we found a high patient completion rate of biomarker assays (>98%) that was robust across categories of age and race [86,117]. Yet, turnaround time for genomic and biomarker assays remain a major challenge. To overcome the multilevel implementation challenges for genomic medicine and empower patients and physicians in making precision decisions, implementation strategies leveraging health informatics and learning health systems may be helpful [118–121].

6. Conclusion and recommendation

The use of genomic markers in medical treatment is increasing for conditions such as cancer care and nicotine dependence. Research is identifying increasing numbers of genomic markers that are associated with smoking cessation success and the efficacy of smoking cessation treatments. Further, adding genomic data to typical clinical and epidemiological study designs has proven to be informative and important. The potential of genetically informed treatment makes it increasingly important that investigators collect biological samples within clinical trials and integrate their analysis and interpretation with the goals of the trial. For example, genomic data can be used to understand smoking cessation, response to tobacco treatment, tobacco-related health problems, and personalized benefit of annual lung cancer screening. In addition, genomic data can be used to help identify patients with elevated risk for nicotine addiction, difficulty quitting smoking, favorable response to specific pharmacotherapy, and tobacco-related health problems.

These results encourage smoking cessation researchers to engage in research that evaluates such topics as: 1) Enhancing the evidence on the extent to which the benefits of genomic interventions generalize across healthcare sites and patient populations. 2) Building the treatment algorithm that incorporates both biological and non-biological predictors. 3) Starting an iterative process to translate science to interventions. 4) Evaluating clinical utility of genomic interventions and the extent to which such interventions can be implemented consistently and effectively over time in real world healthcare outside of formal research studies.

A major challenge in identifying genomic biomarkers will be to obtain adequate sample sizes that cover diverse ancestry groups. Consortium-based approaches will likely be necessary to yield real successes. For pharmacogenomic studies, meta-analysis of data from individual smoking cessation trials will be crucial and will require comparable trial designs and outcomes [122–124]. Importantly, identifying an optimal pharmacogenomic strategy is highly complex, as treatment trials vary in study designs, the type and intensity of the counseling treatment provided to all groups including the placebo arm, subject inclusion/exclusion criteria, and other experimental methods.

To foster collection of high-quality genomic data in clinical studies, we recommend: (1) genetically informed study designs, (2) biological samples (collection requirements, storage, and analysis with a focus on genomic data) collected as a routine part of healthcare with patients who smoke, (3) participant consent and genomic data sharing requirements for Institutional Review Board (IRB) approvals, and (4) collaborative consortia with harmonization on phenotype characterization for meta-analyses with details described in recent publications by the SRNT Genomics and Omics workgroup [123,124].

We recommend to start an iterative process for translating genomic discovery to implementation. A mechanism to incorporate a rapidly evolving evidence base is needed. One major challenge of precision treatment is the evolving evidence that identifies novel biomarkers in treatment. The dynamic evidence base and perception that superior data are on the horizon has had a stifling effect on implementation [114,115]. This effort will reduce the time lag from evidence to implementation, especially when precision medicine applies an active dynamic evidence base.

To facilitate implementation of genomic interventions, we need research to evaluate clinical utility. Most importantly, we need research to 1) evaluate the needs of patients and physicians, 2) develop/test interventions based on genomic knowledge and user input, and 3) evaluate physician and patient-level effects of genomic interventions in healthcare settings.

In summary, we recommend research to enable the translational roadmap where people of diverse ancestry can make well-informed decisions about their genomic data and health decisions including prevention and treatment [125]. Our goal is to develop not only knowledge bases for genomic medicine, but also strategies for implementing genomic medicine in clinical care in a learning health system to realize precision medicine in reducing tobacco and related disorders.

Source of funding

This work was supported by the National Institutes of Health (P30 CA091842-19S5 (LSC), P50 CA244431 (LSC, AR), R01DA038076 (LSC), R01CA268030 (LSC, AR), R01DA056050 (LSC,AR), R34 DA052928 (AR), U19 CA203654 (LSC, CIA, LJB), P01 CA180945 (TBB); and Alvin J. Siteman Cancer Center Investment Program 5129 - Barnard Trust and The Foundation of Barnes Jewish Hospital Cancer Frontier Fund.

Footnotes

Declaration of Competing Interest

Dr. Bierut is listed as inventor on issued U.S. patent 8080,371, “Markers for Addiction” covering the use of certain SNPs in determining the diagnosis, prognosis, and treatment of addiction. All other authors declare no potential conflict of interest. Dr. Baker has a Glaxo-Wellcome Chair in the Department of Medicine.

☆

Submission to: NIDA special issue on Biomarkers for Smoking Cessation and Treatment to Addiction Neuroscience.

Data availability

No data was used for the research described in the article.

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