Dana–Farber/Harvard Cancer Center



RFA-CA-20-005List of PQs for this FOAEach application must address a specific PQ from the list below, exactly as defined in this FOA. Applications that do not explore issues presented in the Intent Statement of the selected PQ will be considered nonresponsive. In order to facilitate the submission and peer-review processes, PQs are numbered 1-9. However, the order of the numbering of questions is arbitrary and should not be construed to indicate any order of priority or funding potential.PQ1: What are the underlying causes of the unexplained rising incidence in 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.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.PQ3: How do selective pressures affect cell competition and cooperation during cancer initiation and 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.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.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.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 applications 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. Applications 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.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.PQ8:?What strategies improve and sustain coordination of comprehensive healthcare for underserved cancer patients with comorbidities?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.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.Applications that do not explore issues presented in the relevant Intent Statement will be considered nonresponsive. ................
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