National Cancer Institute's Cancer and Aging Priorities

Modern improvements in cancer detection and treatment coupled with the implementation of population-based cancer prevention and control strategies have contributed to a sustained decline in overall cancer mortality rates. Although this trend is promising, challenges at the nexus of cancer and aging are, in turn, becoming more prominent. Older adults (age 65 years and older) are the largest growing segment of the U.S. population, and aging into older adulthood is disproportionally associated with the incidence of common cancers. Many survivors of childhood cancer will live for decades after cancer treatment and mature into older age. Strategic investments in aging research will contribute to population health by preserving or improving healthspan and ensuring equitable access to – and benefit from – advances in cancer prevention, control, and population science. This presentation will describe ongoing cancer and aging efforts at the National Cancer Institute, including programmatic priorities and current funding opportunities.

A toolkit for quantification of biological age from blood-chemistry and organ-function-test data: BioAge

Methods to quantify biological aging are emerging as new measurement tools for epidemiology and population science and have been proposed as surrogate measures for healthy lifespan extension in geroscience clinical trials. Publicly available software packages to compute biological aging measurements from DNA methylation data have accelerated dissemination of these measures and generated rapid gains in knowledge about how different measures perform in a range of datasets. Biological age measures derived from blood chemistry data were introduced at the same time as the DNA methylation measures and, in multiple studies, demonstrate superior performance to these measures in prediction of healthy lifespan. However, their dissemination has been slow by comparison, resulting in a significant gap in knowledge. We developed a software package to help address this knowledge gap. The BioAge R package, available for download at GitHub (http://github.com/dayoonkwon/BioAge), implements three published methods to quantify biological aging based on analysis of chronological age and mortality risk: Klemera-Doubal Biological Age, PhenoAge, and homeostatic dysregulation. The package allows users to parametrize measurement algorithms using custom sets of biomarkers, to compare the resulting measurements to published versions of the Klemera-Doubal method and PhenoAge algorithms, and to score the measurements in new datasets. We applied BioAge to safety lab data from the CALERIETM randomized controlled trial, the first-ever human trial of long-term calorie restriction in healthy, non-obese adults, to test effects of intervention on biological aging. Results contribute evidence that CALERIE intervention slowed biological aging. BioAge is a toolkit to facilitate measurement of biological age for geroscience.

Aflatoxin and the aetiology of liver cancer and its implications for Guatemala

During the 60 years since the first scientific reports about a relation between aflatoxin exposure and adverse health consequences, both in animals and humans, there has been a remarkable number of basic, clinical and population science studies characterising the impact of this mycotoxin on diseases such as liver cancer. Many of these human investigations to date have focused on populations residing in Asia and Africa due to the high incidence of liver cancer and high exposures to aflatoxin. These studies formed the basis for the International Agency for Research on Cancer to classify the aflatoxins as Group 1 known human carcinogens. In addition, aflatoxin contamination levels have been used in international commodity trade to set the price of various staples such as maize and groundnuts. While there have been many case-control and prospective cohort studies of liver cancer risk over the years there have been remarkably few investigations focused on liver cancer in Latin America. Our interdisciplinary and multiple institutional collaborative has been developing a long-term strategy to characterise the role of aflatoxin and other mycotoxins as health risk factors in Guatemala and neighbouring countries. This paper summarises a number of the investigations to date and provides a roadmap of our strategies for the near term to discern the emergent aetiology of liver cancer in this region. With these data in hand public health-based prevention strategies could be strategically implemented and conducted to lower the impact of these mycotoxins on human health.

A Social, Behavioral, and Implementation Science Perspective on Convergence Mental Health

Convergence is best approached through a systems science lens because it includes multiple levels of influence and organization and a host of mutually reinforcing elements. Each of these factors requires behavioral and social science research to ensure that convergence is appropriately anchored in the experience of patients and their communities. For example, the continuous assessment of mental state made possible through real-time mobile app recording of voice, movement, and biosignatures will be much less effective if people reject it because of privacy concerns or if this monitoring is not adequately linked to choices for self-care. Patients may need in-person contact with a therapist to choose an appropriate app and in-person boosters to support effective use. Use of the app and its effectiveness accordingly depend on social-behavioral factors. Likewise, the social and behavioral sciences are central for shortening the time between development and translation of mental health treatments and programs. Including the social and behavioral sciences in mental health convergence science suggests the need for broad-scale efforts that link mental health to population science to systems thinking. This effort places mental health within the broader framework of population health and to implementation science for reducing the time from development of a new treatment to its widespread use. The approach has implications for data collection and analysis in that it entails much larger datasets and need for greater computational power.

Application of Artificial Intelligence Methods to Pharmacy Data for Cancer Surveillance and Epidemiology Research: A Systematic Review

PURPOSE The implementation and utilization of electronic health records is generating a large volume and variety of data, which are difficult to process using traditional techniques. However, these data could help answer important questions in cancer surveillance and epidemiology research. Artificial intelligence (AI) data processing methods are capable of evaluating large volumes of data, yet current literature on their use in this context of pharmacy informatics is not well characterized. METHODS A systematic literature review was conducted to evaluate relevant publications within four domains (cancer, pharmacy, AI methods, population science) across PubMed, EMBASE, Scopus, and the Cochrane Library and included all publications indexed between July 17, 2008, and December 31, 2018. The search returned 3,271 publications, which were evaluated for inclusion. RESULTS There were 36 studies that met criteria for full-text abstraction. Of those, only 45% specifically identified the pharmacy data source, and 55% specified drug agents or drug classes. Multiple AI methods were used; 25% used machine learning (ML), 67% used natural language processing (NLP), and 8% combined ML and NLP. CONCLUSION This review demonstrates that the application of AI data methods for pharmacy informatics and cancer epidemiology research is expanding. However, the data sources and representations are often missing, challenging study replicability. In addition, there is no consistent format for reporting results, and one of the preferred metrics, F-score, is often missing. There is a resultant need for greater transparency of original data sources and performance of AI methods with pharmacy data to improve the translation of these results into meaningful outcomes.

Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute–funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders’ titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was “intersections between informatics, data science, and population science.” We conclude with a discussion on “hot topics” on the horizon for cancer informatics.

INTEREST GROUP SESSION—GEROSCIENCE: METHODS FROM BENCH TO POPULATION SCIENCE TO INFORM CONSTRUCTION OF GEROSCIENCE CLINICAL TRIALS

We are on the cusp of a revolution in aging science. It has matured to the point where geroscience trials will test interventions in humans which alter aging mechanisms to lengthen healthspan and possibly lifespan. This goal is unprecedented in clinical trial design, and it requires retooling the clinical trial toolbox. Traditionally, trials are constructed around a single disease; interventions target a narrow part of a defined biological pathway involving only one molecule, tissue, or organ; events are well known intermediate endpoints and clinically-defined hard outcomes; and follow up may be short and historically informed based on prior trials. Geroscience trials by design target aging mechanisms which, when altered, are likely to have pleiotropic effects that modify several biologic pathways; efficacy and safety signals may require integration across multiple levels of biologic organization; intermediate endpoints are not agreed upon; and follow up timelines are undefined. In this symposium, we provide guidance on the design of geroscience trials using examples that span from bench to population science. Dr. LeBrasseur will discuss screening senolytic compounds across models of age-associated decline and advancing their candidacy as interventions. Dr. Justice will detail a framework for biomarker selection in geroscience trials, focusing on a trial of metformin as an example. Dr. Sanders will illustrate how observational data can inform phenotype use in clinical trials. Dr. Levine will explain translating omics data for use in geroscience trials, focusing on epigenomics. We expect additional discussion to hasten development of well-designed geroscience trials.

Data as promise: Reconfiguring Danish public health through personalized medicine

‘Personalized medicine’ might sound like the very antithesis of population science and public health, with the individual taking the place of the population. However, in practice, personalized medicine generates heavy investments in the population sciences – particularly in data-sourcing initiatives. Intensified data sourcing implies new roles and responsibilities for patients and health professionals, who become responsible not only for data contributions, but also for responding to new uses of data in personalized prevention, drawing upon detailed mapping of risk distribution in the population. Although this population-based ‘personalization’ of prevention and treatment is said to be about making the health services ‘data-driven’, the policies and plans themselves use existing data and evidence in a very selective manner. It is as if data-driven decision-making is a promise for an unspecified future, not a demand on its planning in the present. I therefore suggest interrogating how ‘promissory data’ interact with ideas about accountability in public health policies, and also with the data initiatives that the promises bring about. Intensified data collection might not just be interesting for what it allows authorities to do and know, but also for how its promises of future evidence can be used to postpone action and sidestep uncomfortable knowledge in the present.