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Collaborations for Smoking Cessation: The 2019 AACR-Johnson & Johnson Lung Cancer Innovation Science Grants

“Too many people continue to get lung cancer from smoking and lack of early detection,” observes Mary E. Cooley, PhD, RN, FAAN, a nurse scientist at Dana-Farber Cancer Institute and the lead principal investigator of a 2019 AACR-Johnson & Johnson Lung Cancer Innovation Science Grant.

Indeed, lung cancer is the leading cause of cancer death in the United States.1 Its five-year survival rate of only 21.7 percent2 can be partly attributed to the late stage at which most patients are first diagnosed.3 And while over half of all smokers attempt to quit each year, less than eight percent are able to do so successfully on a long-term basis4 and thereby reduce their risk of developing lung cancer. Prevention and interception of this devastating disease are therefore critical.

In recognition of the need to advance the science of lung cancer prevention and interception, the 2019 AACR-Johnson & Johnson Lung Cancer Innovation Science Grants program solicited project proposals prioritizing collaborative, clinically relevant research in expanding domains such as digital therapeutics, smoking cessation biomarker identification, and behavioral phenotyping. Grants were awarded to two multi-institutional teams whose multifaceted projects seek to understand how an individual’s genetics may affect smoking-related behaviors and quit attempts, as well as to develop clinically actionable smoking cessation strategies that will improve patient outcomes.

Mary Cooley
Mary E. Cooley, PhD, RN, FAAN

The team led by Cooley together with Peter Castaldi, MD, MSc, an associate professor of medicine at Harvard Medical School, and Sun S. Kim, PhD, an associate professor of nursing at the University of Massachusetts, Boston, is primarily focused on refining and evaluating a new program to increase rates of smoking cessation and lung cancer screening with low-dose computed tomography (LDCT). In addition to behavioral counseling and nicotine replacement therapy, the program includes a digital therapeutics tool: a storytelling narrative communication video to educate high-risk smokers about the benefits of screening and smoking cessation. This unique video includes testimonials from a diverse group of individuals about their experiences with successfully quitting smoking and undergoing lung cancer screening.

Although the clinical trial for the program is ongoing, Cooley and her team have already received positive feedback from participants in the intervention group. “They have been receptive to lung cancer screening,” Cooley says, “and have shared how helpful the personal stories about lung cancer screening from real people were in making their decision to undergo screening.” Cooley’s team thus appears well on their way to achieving their overarching aim of enhancing lung health and saving lives with their program.

But there is an additional facet to the clinical trial: the collection of DNA samples from consenting participants. With this genome-wide genetic data, Cooley’s team will calculate polygenic risk scores (PRSs), an overall assessment of genetic risk, for smoking behaviors. They will then identify any potential relationship between the scores and smoking cessation outcomes. Finally, they will assess whether the PRSs could have any clinical use and, if so, how to best incorporate such information into smoking cessation interventions. “Not only will we understand more about how genetic risks for smoking impact the effectiveness of smoking cessation,” Cooley explains, “but also how people understand and respond to that kind of information.”

The genetics of smoking behaviors is the principal focus of the other 2019 AACR-Johnson & Johnson Lung Cancer Innovation Science Grant team, led by Paul M. Cinciripini, PhD, professor and Margaret & Ben Love Chair in Clinical Cancer Care in honor of Dr. Charles A. LeMaistre at The University of Texas MD Anderson Cancer Center, and Charles Green, PhD, an associate professor at The University of Texas Health Science Center at Houston.

Paul Cinciripini
Paul M. Cinciripini, PhD

Cinciripini and Green’s team is creating and validating realistic models for the prediction of smoking cessation using machine learning algorithms trained on billions of genetic, brain, behavioral, and pharmacotherapy data points collected from thousands of individuals. “We are using a scaffolding building method towards completion of the algorithm,” Cinciripini explains, “where we start simple, with a few parameters, and carefully make our model more complex by adding more dimensions.”

The team began with the effects of genetic markers and brain response measurements and how they each interact with pharmacological treatment (varenicline, bupropion). They have since identified several genetic markers that appear to predict abstinence behavior. Moreover, they have found that individuals with specific brain profiles (identified by brain activation to relevant stimuli as measured by electroencephalogram [EEG]) differ in their quit rates and response to pharmacotherapy. “Currently, we are making the algorithm more complex by bringing together the genetic and brain measurements to identify how they interact and to evaluate their synergistic effects on smoking,” advises Cinciripini.

As Cinciripini, Green, and their team refine their predictive algorithm to better and more reliably capture the intricate interactions amongst biological determinants of smoking behaviors and treatment interventions, they are eagerly anticipating its future practical use. “In a clinical setting our algorithm could reveal potential outcomes of the course of certain treatment decisions,” Cinciripini says, “which will facilitate a better assessment of risks and benefits that can be communicated to the patient and improve shared decision making.”

The goal is a personalized approach to smoking cessation, with the algorithm identifying the optimal strategy for an individual trying to quit. Cinciripini and Green’s algorithm thus has the potential to notably improve quit rates, which will ultimately save lives.

While the work of both teams funded by the 2019 AACR-Johnson & Johnson Lung Cancer Innovation Science Grants program will assuredly advance the science of lung cancer prevention and interception and improve patient outcomes in the future, the program has had an immediate impact on the teams themselves. “We have each had the opportunity to expand our research in different ways through our collaboration,” says Cooley when asked about the significance of receiving the grant. According to Cinciripini, “This grant provided the resources to bring together an excellent team of collaborators with a diverse set of skills and the expert domain knowledge necessary to build the predictive algorithm.”

Out of one collaboration, that between the AACR and Johnson & Johnson, further collaborations then proliferate, enhancing research and innovation in the field of smoking cessation to the benefit of smokers attempting to quit.

This article was written in recognition of World Lung Cancer Day, which takes place annually on August 1.



3 Wood DE et al. Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2018 Apr;16(4):412-441.
4 Creamer MR et al. Tobacco Product Use and Cessation Indicators Among Adults – United States, 2018. MMWR Morb Mortal Wkly Rep. 2019 Nov 15;68(45):1013-1019