PQ1: What are the underlying causes of the unexplained rising incidence in certain early-onset cancers?
Intent: Recent data show an increased incidence of several cancers in people younger than 50 years of age. This includes, but is not limited to, increased incidence of noncardia gastric cancer, colorectal cancer, and breast cancer. Data further suggest that overall cancer incidence may increase by an additional 11%-12% by 2030 in 25- to 39-year-olds and that early onset cancers are mainly sporadic. This PQ seeks applications that investigate the etiologic factors responsible for the alarming increase in sporadic early onset cancers. The nature of early onset cancers suggests that altered gene-environment interactions are a major contributor to increased risk. Several major risk factors documented to have a co-incident increase with early onset cancer have been identified, including higher rates of obesity, changes in diet and food processing, microbial dysbiosis, and early antibiotic exposure. The goal of the PQ is to accelerate research to understand why certain cancers are occurring in younger populations and to identify biomarkers for early detection and better screening approaches for these cancers. Approaches can include epidemiological or clinical cohort studies (retrospective or prospective) to establish risk factors or identify biomarkers, or preclinical studies that are focused on the etiologic factors that affect development of early onset cancer. These studies may include examining how risk factors alter immune function, metabolism, and/or microbial interactions with host that contribute to initiation or progression of early onset cancer. Studies that consider race and ethnicity are encouraged.
Applications focused primarily on germline mutations will be considered nonresponsive.
Background: Early onset cancer is defined as cancer occurring in people younger than 50 years of age. According to recent SEER data, the annual percent change in incidence for certain cancers has been increasing significantly among younger individuals compared with older adults. This increase may reflect changes in lifestyle and environmental exposures, such as changes in food processing or diet, or early and increased antibiotic exposure. Rising prevalence of obesity may also play a role, impacting the microbiota, the immune system, and the inflammatory milieu. If these changes occur during crucial developmental periods, an individual’s cancer risk could be greatly affected. While there are more than 50 recognized hereditary cancer syndromes, the known inherited mutations are evaluated to contribute in only 5-10% of adult cancer patients and this prevalence is not increasing. Rare mutations may be acting in tandem with environmental exposures to increase risk for younger adults. Due to their age group, the populations most affected by early onset cancer are not typically targeted for cancer screening based on current screening recommendations (e.g., colorectal). Understanding the etiology of sporadic early onset cancers and developing effective screening and prevention strategies will require research in multiple domains (e.g., epidemiology, environmental sciences, screening, molecular biology, etc.) and ultimately effective integration of this data.
Feasibility: Given the likelihood that multiple factors contribute to the rising incidence of early onset cancers, successful applications could include investigation of genomic/epigenomic effects of environmental exposures, or novel screening approaches that use either epidemiology or basic biology methods. Although increasing, the incidence of early onset cancers remains rare; therefore, when appropriate, applicants may consider making use of existing cohorts and data repositories. Preclinical studies establishing new models to test critical gene-environment interactions would help define mechanisms of pathogenesis. Studies that identify biomarkers for early detection or that are focused on how changes in screening practices could reduce mortality in this population are also encouraged. Studies investigating the role of risk factors in the context of differences in racial/ethnic distribution would be responsive to this PQ.
PQ2: How does intermittent fasting affect cancer incidence, treatment response, or outcome?
Intent: The intent of this question is to better understand the effects of intermittent fasting (IF) in humans on cancer risk factors, cancer incidence, treatment response, or cancer related outcomes (such as disease recurrence or survival). We highly encourage transdisciplinary and integrative approaches that bridge mechanisms and human research. For the purposes of this PQ, IF involves restricting caloric intake during specific hours of the day or to specific days of the week or month. IF is in contrast to standard fasting, where an individual restricts caloric intake daily, but does not restrict the time during the day when they eat. Successful applications may investigate 1) the relationship between IF and cancer risk factor modification (e.g., weight loss, dietary patterns), 2) the approach to IF (e.g., duration/timing, combination therapy with a nonpharmacologic intervention such as exercise) which leads to optimal cancer outcomes, and/or 3) an individual’s adherence to IF. All applications must examine the direct effect of IF on cancer risk factors, cancer incidence, treatment response, toxicity, and/or other related cancer outcomes and specify the mechanism(s) by which this occurs. Human studies are required, supported when deemed appropriate by preclinical investigations. Clinical trials are welcomed but not required.
Background: Both prolonged reduction in daily caloric intake and IF have been shown to reduce cancer incidence in animal models. Moreover, there is preclinical and limited clinical evidence that fasting leads to improved response to treatment (chemotherapy and immunotherapy) and protection from chemotherapy related adverse effects. Additional studies in humans are needed to confirm these findings and to bridge mechanisms and human studies. Molecular mechanisms have been studied primarily in animal models and a variety of pathways and systems have been identified as potential mediators of IF response, including, but not limited to: synchronization with the circadian rhythm; reductions in insulin, IGF-1, and leptin; autophagy; and, its effects on gut microbiota. There is a need for well-designed human studies, including clinical trials, to test whether these associations are also present in humans. For studies involving individuals with cancer, it is also important to investigate whether these associations differ across cancer sites and tumor types.
In addition to molecular pathways that may explain the benefits of IF, it is important to assess the optimal delivery (e.g., timing) and factors that improve adherence. Adherence to IF is essential to achieve the health benefits associated with reduced cancer risk, enhanced treatment response, and improved cancer related outcomes. Biological (e.g., appetite hormones), behavioral (e.g., self-monitoring, meal-timing, disinhibited eating, sleep), psychosocial (e.g., self-efficacy, perceived hunger/satiety, executive function) and environmental (e.g., built environment) factors can all affect adherence. Assessing not only adherence to IF, but the predictors of adherence and risk factors for non-adherence, will inform strategies to improve long-term (e.g., > 6-12 months) efficacy and the effectiveness of IF interventions.
The intent of this PQ is to support research that bridges mechanisms and human studies to deciphers the mechanisms in humans by which IF favorably influences cancer risk factors, cancer development, response to treatment, or cancer related outcomes, as well as adherence to IF both in the short and long term.
Feasibility: There is convincing preclinical evidence that IF is effective in favorably altering cancer incidence, treatment response, and outcome; however, there are few clinical studies and they have been less consistent. Given existing data, it should be possible to design a clinical trial or other study involving human information and material, informed by animal studies as deemed appropriate, to assess the impact of IF in humans. Among the important features of IF that could be studied are: How do different approaches to IF impact cancer promoters such as IGF-1 and how does this compare to caloric restriction diets? Among patients receiving a certain class of drugs, how does IF decrease treatment toxicity? How does IF decrease tumor recurrence in cancer survivors? How does aligning meal times with circadian rhythms that regulate metabolism improve adherence to IF and enhance the efficacy of the intervention?
PQ3: How do selective pressures affect cell competition and cooperation during cancer initiation or development?
Intent: Cell competition denotes the process by which differences in fitness among neighboring cells results in the loss of less fit cells. Although cell competition has been described in multiple tissues during development and tissue regeneration, its role and regulation in cancer is less well defined and can be tumor suppressive or tumor promoting depending on the context studied. Understanding how cells interact with each other in response to selective pressures to drive competition and cooperation and acquire fitness-enhancing traits that allow cells to out-compete their neighbors may provide opportunities to develop targets for cancer prevention or treatment, including opportunities to manipulate treatment responses. This PQ seeks applications that investigate how cell autonomous or extrinsic selective pressures affect cell competition and cooperation amongst cells of the same lineage during cancer initiation, development, or treatment response and resistance. Applications may involve vertebrate, non-vertebrate and other model systems, including quantitative mechanistic models (e.g., mathematical and simulation models), to demonstrate the dynamics of cell competition and cooperation to selective pressures, provided relevance to human cancer can be demonstrated. Applications focused on in silico models must include some biological validation of the model. Successful applications should be mechanistic and include analyses of cell competition or cooperation within the tumor and its microenvironment or explore how genetic or epigenetic variation affects cancer cell fitness within the context of same cell lineage host-tumor interactions.
Background: A high degree of cellular cooperation and coordination is needed to ensure full functionality of multicellular tissues and organisms. To enact such cooperation and eliminate potentially defective cells that can arise from intrinsic or extrinsic pressures, fitness between neighboring cells of the same lineage must be sensed and consequences levied when a fitness differential is detected. These consequences result in the survival of the more fit cells at the expense and loss of the less fit cells and provide the foundation for cell competition paradigms— i.e., cell-intrinsic surveillance and sensing mechanisms —to explain clonal dynamics within tissues to ensure homeostasis. Insults that trigger cell competition and the processes involved that lead to the elimination of the less fit cells have been studied predominantly in developmental and regenerative model systems. Far less is known about the instigation, action, and regulation of cell competition in cancer models. Indeed, within the context of cancer, cell competition can be tumor suppressive, eliminating mutated cells as they arise, or tumor promoting, eliminating wildtype cells to accommodate cells with “super-fit” characteristics. Furthermore, known triggers for cell competition in model systems include dysregulated cellular metabolism and loss of apico-basal polarity, both considered hallmarks of cancer, suggesting an intimate relationship between cancer development and cell competition.
Feasibility: Insight is lacking into the frequency of occurrence and mechanisms associated with cell competition, compensation and cooperation across the spectrum of cancers and cancer stages (initiation, development, progression, treatment, resistance, recurrence). Understanding cell competition and cooperation within a cancer context has been limited because of a lack of suitable methods to score and track fitness across cell types and tissues. This is particularly true in vivo, where demonstration of endogenous cell competition mechanisms has been challenging. Comparisons in how intrinsic or extrinsic selection pressures initiate, maintain, or suppress cell competition mechanisms within cancer tissues are not well documented. Although our knowledge is limited about the mechanisms of cell competition and cooperation in response to intrinsic or extrinsic selection pressures in a cancer setting, this PQ is intended to expand our understanding of the clinically relevant mechanisms to identify potential future opportunities for intervention. Successful PQ3 applications should inform our basic understanding of how selective pressures influence fitness and cell competition/cooperation as operative in a cancer setting and identify molecular markers of fitness selection.
PQ4: What mechanisms explain sex differences in cancer incidence, lesion location, or response to therapy?
Intent: Accumulating data suggest that differences in the biology across sexes influence the incidence of cancer types, molecular and histological characteristics, severity, progression trajectories, therapeutic responses, and overall survival of cancer patients. This PQ invites research applications for elucidating molecular and cellular mechanisms underlying sexual dimorphisms in cancer. Applicants may seek to advance our understanding of the etiology of sex-specific differences in cancer to inform targeted prevention efforts. Applicants may also seek to demonstrate how a mechanistic understanding of sex differences can lead to safer and more efficacious sex-specific therapeutic strategies. Applicants may use pre-clinical model systems and/or conduct molecular epidemiology, translational, or clinical studies. Responsive applications must go beyond characterization studies and test specific hypotheses that are not solely attributable to known hormonal differences.
Background: Substantial sexual dimorphisms reported in cancer include its evolutionary forces, epidemiological characteristics, driver genes, oncogenesis, histopathology, growth regulation, immune system, microenvironment components, expression of cytokines, angiogenesis, inflammation, mitochondrial genes, metabolism, pharmacology, toxicology, and associated microbiota. These differences have shown to impact susceptibility, incidence, prognosis, diagnosis, certain affected histologic types, progression, severity, therapeutic responses and clinical outcomes of cancer. However, individual sex effects have not been adequately estimated in making clinical decisions for cancer patients. There is an unmet need for thorough investigation of the biological mechanisms across sexes that would inform development of sex-specific cancer diagnosis, prevention and treatment strategies.
Feasibility: In response to this PQ, investigators are invited to investigate and/or validate clinically-informative mechanisms of sexual dimorphisms in cancer that would “explain sex differences in cancer incidence, lesion location, or response to therapy”. Well-designed mechanistic studies may lead to development of safe and effective interventions that are tailored to target the sexual dimorphisms in certain cancers. Applicants utilizing pre-clinical models are expected to use model systems that recapitulate clinical observations. Cancer etiology studies, translational and clinical cancer research projects are encouraged to enable accelerated development of sex-specific preventive or therapeutic treatment strategies for human benefit. Applications should propose studies beyond molecular characterizations of cancer or beyond canonical sex hormone signaling pathways.
PQ5: What strategies can block or reverse the emergence of new cell lineage states induced by cancer treatments?
Intent: Recent data indicate that drug resistance mechanisms arise in which cancer cells acquire alternative cell lineage(s) in response to treatment to sustain their survival. These resistance mechanisms, termed “lineage plasticity,” “transdifferentiation,” or “pathway indifference” are evident in prostate cancer, melanoma, lung cancer, and other malignancies, and are often associated with poor prognosis in the clinic. The goal of this PQ is to identify strategies to block or reverse the emergence of new cell lineages associated with drug treatment. Strategies are encouraged that are feasible for future clinical trials and based on understanding of the fundamental pathways and molecular drivers of lineage transition.
Applications that propose clinical trials, solely focus on mechanistic studies, or lack translational potential will not be considered responsive.
Background: The use of targeted therapies in cancer over the last decade has revealed various mechanisms of drug resistance, including mutation of the target protein itself, activation of down-stream or parallel pathways to bypass the target inhibition, and more recently, emergence of resistant cells that bear hallmarks of alternate cell lineage. Lineage switching occurs when a subset of cells from the tumor is transcriptionally reprogrammed to have the features of a different cell type and is stably resistant to drug treatment. Examples include the transformation of EGFR-mutant lung adenocarcinoma to small-cell lung cancer following treatment with EGFR tyrosine kinase inhibitors; a switch from androgen-sensitive luminal prostate cancer to an androgen-resistant neuroendocrine cell type after androgen deprivation; and the change of BRAF-mutant melanoma to phenotypically distinct cells following BRAF inhibitor treatment. An understanding of the molecular underpinnings of lineage plasticity in drug resistance may lead to delineation of rational single or combination therapeutic strategies to circumvent or reverse resistance. Notable unresolved questions include: Which therapeutic drugs or drug combinations may lead to the emergence of new cell lineages? Can we adjust the existing treatment regimen to circumvent the emergence of these cells? What drugs or combinations can be applied to block the transition of lineage or target the stably resistant cells induced by anticancer therapy? Are there common developmental or cellular pathways utilized during lineage switching in response to drug treatments? Can emergence of new lineages be reversed?
Feasibility: Responsive applications should emphasize strategies that are clinically feasible. Specific Aim(s) to delineate the molecular underpinnings of plasticity are appropriate in the context of determining strategies for blocking or reversing this plasticity. Animal models should be relevant to clinical disease and recapitulate lineage changes as an underlying drug resistance mechanism. Access to clinical samples from patients will add clinical relevance to the project. Candidate drugs for inducing, preventing or reversing lineage switching should be at an advanced preclinical stage, in clinical investigation, or have received FDA approval. Examples of research activities responsive to this PQ are to: 1) conduct screens to identify drug(s) that can prevent therapy-induced emergence of resistant cell lineage(s) either alone or in combinations; 2) validate the potential pathways regulating lineage plasticity and test the effect of targeting these pathways by drugs in clinically relevant models; and 3) identify new examples of drug-resistance arising from lineage plasticity and develop therapeutic strategies to target the resistance.
PQ6: How can cancer cachexia be reversed?
Intent: Cancer cachexia is associated with many types of cancer, involves dysfunction of multiple tissues and organs systems, and is a significant determinate in patient survival. This PQ seeks proposals that leverage a mechanistic understanding of cancer cachexia and systemic processes to develop treatment strategies designed to reverse cachexia that encompass pre-cachexia, cachexia, and refractory cachexia states. Proposals may provide evidence for interventions that identify those at risk of cachexia and strategies to prevent progression. Successful applications may include objective measures that can be used as a basis for diagnosis across the cachectic spectrum, disease monitoring, and understanding response to new anti-cachectic treatment strategies developed in the project period. Research that seeks to address how cancer cachexia processes can be reversed at its earliest indications are strongly encouraged. Applicants may use pre-clinical model systems and/or human studies.
Background: Our current understanding of cancer cachexia is that it commences early in the malignancy process as subtle deviations in the central nervous, neuroendocrine, and immune systems in conjunction with metabolic imbalances. These changes progress to culminate with systemic dysfunction and deterioration of multiple organ systems, and often manifest in the end-stages as severe muscle catabolism-loss, respiratory distress, cardiac and renal failure. Barriers in developing therapies to combat cancer cachexia include varied disease trajectories and natural history dependent on the underlying type of cancer, patient germline factors (SNPs, age, sex, co-morbidities), and the lack of precise measures of the disease and appropriate pre-clinical models.
An effort to support studies that seek to address how cancer cachexia can be reversed will challenge the dogma that cachexia is an inexorable process. Success on this front can have a significant impact on both prognosis and quality of life for patients with cachexia. Furthermore, because cachectic patients are also less likely to tolerate oncologic-targeted interventions, actions aimed to prevent, impede, or reverse cachexia processes have potential for treatment synergisms with existing anti-cancer regimens.
Feasibility: Applications may involve pre-clinical model systems and/or human studies with the objective of demonstrating how cancer cachexia can be reversed from the perspective of the multiple tissues and organ systems affected by cachectic processes. It is expected that diverse expertise in physiology, oncology, nutrition, and treatment development would be represented by the applicant team. Successful PQ6 applications should utilize objective measures as a basis for diagnosis across the cachectic spectrum, and/or monitoring of response to anti-cachectic therapies in the context of the treatment mechanism-of-action and site of action. Interventions designed to halt progression based on defined endpoints (e.g., orthogonal biomarkers) in patients identified during the pre-cachexic state are acceptable. How the study results might impact future trials or practice must be made explicit.
PQ7: What methods can be developed to integrate patient-generated health data into electronic health records?
Intent: Patient-generated health data (PGHD) are health-related data created, recorded by, or gathered directly from patients. Examples include patient-reported data (e.g., health-related quality of life, health status, and health behaviors), as well as passively collected biometrics (e.g., heart rate and skin temperature). PGHD has the potential to support or inform numerous aspects of cancer care, such as monitoring patient symptoms between visits, personalizing care recommendations, and identifying those at increased risk for poor outcomes (e.g., treatment discontinuation). Few data standards exist for the integration of PGHD into electronic health records (EHRs). Additionally, best practices for use of PGHD in cancer care settings is limited. This PQ calls for: (1) development and evaluation of methods to successfully integrate accurate, interpretable, and time-sensitive PGHD data into EHRs and clinical workflows, and (2) research that combines PGHD with EHR clinical data (e.g., clinical history, cancer histology, genomic data) to better predict and monitor cancer-related outcomes. The intent of this question is to support new analytic and data science methods to improve the capture and use of PGHD sources to inform cancer care.
Applications will be considered non-responsive if they only propose PGHD integration methods without a related cancer-focused research question targeting patient and/or clinician decision-making, patient care, healthcare utilization, or health outcomes.
Background: Precision medicine approaches to cancer treatment require the ability to capture individual patient-level variation in clinical history, molecular features, and lifestyle factors to tailor treatment and care decisions. Due to the rapid increase in the sophistication and wide-spread use of consumer health technologies, such as mobile and wearable devices, the quality and quantity of PGHD available for individual patients has increased exponentially. While PGHD is beginning to be integrated into EHR systems, meaningful barriers exist relative to the systematic collection and use of such data. Current evidence regarding successful approaches to PGHD integration has been limited to feasibility studies, small-scale research, and basic documentation in EHRs. Additionally, challenges exist regarding how to understand PGHD clinically (e.g., day-to-day fluctuations in biometric data exist and can be monitored, but little is known about whether these variations are clinically interpretable and useful). Research to improve integration across PGHD collection platforms and to enhance cancer-relevant interpretation of PGHD trends would allow for real-time clinical decision support, earlier identification of poor outcomes and risk of heath events, and ultimately improve the quality of life of cancer patients.
Feasibility: In response to this PQ, investigators may propose new methods to integrate PGHD into EHRs or leverage existing technologies and data models. Applications are encouraged that examine integration and use of longitudinal PGHD in healthcare delivery settings. Studies examining collaborative approaches to incorporate perspectives of patients, providers, and healthcare systems may also be proposed. Studies that investigate challenges related to data capture from different mobile sources are also encouraged, particularly as they pertain to missing data and associated bias. Projects may also focus on methods to establish clinically relevant interpretation and prediction or applied work such as: data visualization, examining complex clinical workflow integration and decision support, or prediction modeling.
Responsive applications may include, but are not limited to, the development of methods enabling the following:
- Integration of multiple PGHD and clinical EHR data elements across timeframes;
- Advanced patient matching, data aggregation, and resource linking to enable analyses across PGHD, clinical, and healthcare systems data;
- Use of machine learning or other data science methods to model high-volume PGHD health and symptom trajectories in EHR-based patient cohorts;
- Real-time integration of PGHD with EHRs to inform clinical decision making and improved prediction of cancer-relevant events or outcomes.
PQ8: What strategies improve and sustain the coordination of comprehensive healthcare for underserved cancer patients with comorbid conditions?
Intent: The presence of multiple chronic diseases in a patient has a profound impact on health, healthcare utilization, and associated costs. This PQ calls for studies that develop and test intervention strategies for improving coordination of comprehensive healthcare for underserved populations with comorbidities who undergo cancer treatment or are cancer survivors. Specifically, interventions should aim to improve teamwork and coordination among those engaged in supporting the health and care of underserved cancer patients and survivors with comorbidities. Multilevel interventions are encouraged to target modifiable characteristics at two or more levels that include the patient, caregiver, provider, healthcare teams, clinics, delivery organizations, and community. Research applications may examine the association of interventions with a range of outcomes among underserved cancer patients or survivors, and should aim to identify the mechanisms (e.g., teamwork processes, cognitive states) by which multilevel relationships occur. Applications should apply and evaluate strategies within a healthcare setting. For this question, underserved populations include NIH-designated health disparity populations.
Background: Though cancer prevalence and mortality are decreasing among the general population, these gains have not yet reached underserved cancer patients with comorbidities: cardiovascular disease, stroke, type-2 diabetes, obesity and arthritis. Up to 60% of cancer patients undergoing comprehensive care treatment also require consideration of one or more comorbid conditions. The number of comorbidities is higher among underserved populations and is expected to rise in future years with more older adults and the earlier onset of diabetes among young and middle age populations. Patients with comorbidities require more vigilant monitoring or coordination among a wider range of providers and more frequent adjustments in care plans or goals. Medication management and reconciliation, clinical care management of comorbidities during cancer treatment, as well as information sharing among multiple providers requires detailed coordination. Clinicians need strategies of care coordination to manage these complex patients who are frequent users of the healthcare system.
Feasibility: This question calls for approaches that investigate interactions between biological, behavioral, socioeconomic, cultural, and environmental determinants of health. Applications should propose interventions that address comorbid diseases from the time of cancer diagnosis, during treatment, and into survivorship. Management of comorbidities during cancer diagnosis, treatment, or survivorship often involves a team-of-teams comprised of a range of healthcare professionals, including oncology, primary care, subspecialists, nursing, allied health, mental health, rehabilitative care, social work, lay or family caregivers, and community groups. The proposed approaches must align with individual patient preferences, goals and needs, as well as the goals of the provider and the healthcare system.
Responsive research projects may focus on one or more of the following areas:
- For those with early-stage cancers that have the potential for high cure rates with surgery and/or radiation, interventions should focus on improving the cure rates and quality of life.
- Understand types of information exchanges that optimize patient and survivor care, such as between primary and specialty care providers.
- Test alternative survivorship care pathways to the traditional oncologist-led model, among cancer patients who are at lower risk of poor outcomes. Examples include, but are not limited to primary care-led, nurse-led or self-management pathways. Determination of appropriateness of alternative pathway(s) must consider pre-existing comorbidities other than cancer, as well as the patient’s functional status, ability to self-manage, and other factors.
- Multilevel interventions that use health information technology as a tool or solution to facilitate data sharing and care coordination activities (e.g., care planning, medication reconciliation, referral tracking, and follow-up appointment tracking) among patients and survivors.
- Multilevel interventions that test team-based delivery models to improve the coordination and delivery of both guideline-concordant cancer-related and comorbid related care (e.g., engagement of primary care and/or subspecialty care into cancer treatment planning or decision making).
- Multilevel interventions that test community-based strategies focused on modifiable social determinants of health (e.g. housing instability, food insecurity, transportation needs, financial hardship). Interventions must include healthcare teams, healthcare systems and community organizations.
- Multilevel interventions that address the specific needs of older adult cancer patients and survivors who have multiple comorbid conditions (e.g. integration of geriatric assessment, overuse of cancer therapies).
PQ9: What methods can be developed to effectively study small or rare populations relevant to cancer research?
Intent: Small and rare populations present significant challenges for cancer research across the biological scale, including molecular, cellular, tumor, or human population levels. This PQ encourages innovative scientific approaches that may include the development of novel study designs, statistical approaches, or computational tools for describing, analyzing, or monitoring small or rare populations, as well as interpreting the effects of interventions or exposures in small or rare populations. As part of the application, the usefulness of the methods developed must be demonstrated through application to one or more cancer-relevant questions and may utilize data at or across any of the aforementioned levels. Validation of the method is encouraged where appropriate.
Background: Recent interest in single cell analysis, rare cell populations, rare tumors or tumor subtypes has generated a need for new methods to address research questions robustly in these areas. More robust methods are also needed to address the health of rare and small populations and to understand the impact of rare risk factors.
Feasibility: Studies that address this question may develop new methods or leverage existing methods in novel combinations. Proposed methods may involve novel approaches to statistical analysis, mathematical modeling, geospatial techniques or other quantitative and qualitative methods designed to improve inference about small and rare populations. Proposals should include efforts to demonstrate that the methods generate valid parameter estimates or perform well according to appropriate validation approaches.