Squamous cell lung cancer

Anti–PD-1 therapy has transformed the management of NSCLC, but only a subset of patients respond to anti–PD-1 therapy, and responses are rarely curative. Understanding the effects of anti–PD-1 therapy on the composition and functional state of tumor-associated immune subsets can identify mechanisms of response and resistance to anti–PD-1 therapy and provide insights into rational combination strategies to improve upon response rates. Although the effects of anti–PD-1 therapy within the tumor immune microenvironment in preclinical and human subjects have been previously reported, we propose to broadly characterize the effects of anti–PD-1 therapy on the tumor draining lymph nodes (TDLN). Studying the TDLN is particularly valuable because it is the primary sites at which the tumor antigen exposure and the main site of immune T-cell differentiation. To determine the effect of ICI response in the lymph node, we have created an immunocompetent murine model that recapitulates early-stage treatment and outcomes in patients. Additionally, we have characterized TDLN T cells from early-stage cancer patients and have been able to recapitulate our preclinical findings of unique memory T-cell subsets. Our preliminary results of the immune cell type composition, checkpoint expression, and cytokine expression within TDLNs suggest that there are major remodeling events within the TDLN mediated by anti–PD-1 therapy that are critical for conferring effective antitumor long-term immune responses. We demonstrate that unique memory T cell subsets undergo significant activation, expansion, and maturation in response to anti–PD-1 therapy, to shape antitumor programming in TDLNs. Importantly, our proposed study will provide a methodological demonstration in both a murine model and human TDLN that simultaneous uses Flowcytometry, functional immune assays and single cell sequencing that will enable deep interrogation of the immune profile to PD-1 inhibition resistant and responsive patients.

Despite significant progress in the development of novel biomarkers and therapies for lung cancer, such progress has not equitably benefitted all population groups in the United States (US). Racial and ethnic minorities, low income, uninsured, and underinsured individuals with non-small cell lung cancer (NSCLC) continue to experience disparate access to diagnostics tests and treatments, and experience inferior outcomes. These disparities are, at least, partially driven by social context (i.e., social determinants of health). Patients with NSCLC experience high rates of financial hardship and unmet supportive needs, making them a population likely to have high rates of health-related social needs. The prevalence of social needs in patients with NSCLC and their impact on healthcare utilization, cancer outcomes, and quality of life (QOL) remains unknown. To fill this important knowledge gap, we propose an observational, prospective cohort study of 150 patients with unresectable lung cancer receiving first-line systemic therapy. Our study has two primary goals (Aims): 1) Prospectively quantify the prevalence of social needs and determine the associations between social needs, acute care utilization, and QOL; 2) investigate how patient’s social needs influence oncology providers’ clinical decisions for outpatient cancer care of adults with unresectable NSCLC. To achieve these goals, we will conduct serial survey assessments of 150 patients with unresectable NSCLC and conduct semi-structured interviews with 30 oncology providers. This study is significant as it is the first study longitudinally evaluating social needs during cancer therapy in patients with NSCLC. This study will fill a key knowledge gap and inform interventions focused on identifying and supporting patients at risk of social adversity during cancer treatment.

Background: In recent years, an increasing incidence of lung cancer among young patients (<50 years) has been reported in Europe, Asia, and the United States. Young patients with lung cancer are more likely to be women, of Asian or Hispanic descent, have adenocarcinoma histology, and are more likely to present with distant metastases at diagnosis. Although young patients with lung cancer are more likely to be diagnosed at a disruptive time in their lives and face greater mental health and financial challenges than older patients with lung cancer, we lack data regarding their specific psychosocial needs. The lack of inclusion of young patients in survivorship studies has created a knowledge gap that we propose to remedy with the proposed study.

Aim and Hypothesis: The overarching aim of this study is to define the psychosocial needs of young patients with lung cancer and evaluate important cancer care delivery factors, including financial toxicity, gender and racial differences and study participants’ experiences with the healthcare system. We hypothesize that young patients will have greater unmet psychosocial needs and financial toxicity than older patients with lung cancer.

Methods: This study will utilize an expert, multi-disciplinary research team aligned with patient advocates to deploy an explanatory sequential mixed methods design to understand the needs of young patients with lung cancer. We will conduct a cross-sectional two-arm survey regarding psychosocial distress and financial toxicity among a diverse patient population utilizing two validated questionnaires, the Psychosocial Screen for Cancer (PSSCAN-R) questionnaire and the COmprehensive Score for financial Toxicity (COST) assessment. After the quantitative data collection is completed, we will conduct a preliminary analysis that will guide the discussion in the next phase, where we will conduct four focus groups with young patients with lung cancer to expand and obtain in-depth answers about the issues discovered in the survey portion of the study. The study team will collaborate with experienced biostatisticians and qualitative data researchers to conduct the appropriate statistical analysis.

Results and Dissemination Plan: The study results will be submitted to an international conference and later be published in a high-impact journal. In addition, we will disseminate the study results to the lung cancer patient community via a series of webinars; these webinars will be recorded and later shared with patient advocacy groups and lung cancer organizations.

Long-term Outcomes: Our data will yield insights to inform the oncology community of the barriers encountered by young patients with lung cancer and will allow us to develop the first clinical and research program for young patients with lung cancer in the nation.

Though the introduction of immune checkpoint inhibitors (ICI) has improved outcomes for patients with stage III non-small-cell lung cancer, most patients do not have robust tumor responses to ICI and real-world patients discontinue ICI due to toxicity at a higher rate than clinical trial populations. There is an unmet need to develop predictive models to identify patients at risk of poor ICI response, high risk of ICI toxicity, and high risk of non-cancer mortality, as these patients are unlikely to benefit from ICI and may require treatment intensification or de-intensification, selection of alternative therapies, or enrollment on clinical trials. In this proposal we seek to leverage the richness and national scale of Veterans Affairs (VA) clinical data to develop statistical models to predict ICI efficacy, toxicity, and non-cancer mortality. We will evaluate specific hypotheses generated from preclinical research and use machine-learning approaches to identify new predictors from high-dimensional VA healthcare data. These models will be integrated into a unified multistate model, allowing prediction of the individualized overall survival benefit of ICI for each patient. Clinical decision-making based on model predictions will be compared to other decision making strategies using decision-curve analysis. This work will identify novel biomarkers of ICI efficacy and toxicity, validate putative ICI effect modifiers from the preclinical literature, and develop the first unified model for ICI clinical benefit. If successful, this tool can be used for patient counseling, physician decision-making, and identification of patients likely to benefit from alternative therapies, treatment de-escalation, or clinical trial enrollment.