Brian D. Gonzalez

Purpose: Sleep disturbance is strongly associated with reduced quality of life and increased anxiety and depression. However, research on what contributes to these negative biobehavioral symptoms among hematopoietic cell transplantation (HCT) recipients remains limited. This study sought to understand sleep disturbances experienced by allogeneic HCT survivors from the perspectives of patients, clinicians, and caregivers, to identify key contributors to sleep disturbances across the HCT trajectory. Methods: This cross-sectional qualitative study included semi-structured interviews with a purposive sample of HCT survivors, caregivers, and clinicians. Interview data were analyzed using an applied thematic approach. Results: Participants ( N = 29) were mostly female (69%), White (90%), and non-Hispanic (86%). Clinically relevant time periods emerged in patients’ journeys that mark changes in their sleep patterns: during hospitalization, the isolation period after hospitalization, and at home after isolation (i.e., around day +90). In each period, cross-cutting themes emerged that present the trajectory of patients’ sleep disturbance throughout the HCT process. Patients continued with poor maladaptive sleep habits acquired during hospitalization after returning home. Conclusion: This study expands research on biobehavioral symptoms among HCT recipients, providing context for quantitative findings in the literature and helping to map the sleep trajectory of allogeneic HCT recipients from multiple perspectives. Future research should consider using the patient, clinician, and caregiver perspectives presented in this work to identify points for targeted interventions that improve patients’ sleep and overall quality of life and to advocate for “sleep-friendly” hospital procedures before chronic sleep issues become entrenched.

Background: Treatment‐related symptoms and toxicities are common in Head and Neck Cancer (HNC) patients undergoing chemoradiation (CRT). This underscores the importance of timely interventions and effective symptom and toxicity monitoring strategies to mitigate complications and improve quality of life (QoL). Traditional clinician and care team assessments often underreport patient symptoms, leading to urgent care and emergency room admissions, referred to as health care utilization (HCU). In contrast, electronically captured patient‐reported outcomes (ePROs) are associated with improved survival, better health‐related QoL, and lower HCU. Wearable activity monitors (WAMs) offer an additional method for continuous monitoring, enabling clinicians to detect symptoms and toxicities more efficiently and respond rapidly to patient needs. Methods: 12 HNC patients were assessed with ePROs and WAMs during CRT. Results: HNC patients undergoing CRT are receptive to ePRO and WAM‐based monitoring. Conclusions: HNC patients are receptive to participating in ePROs and WAM studies.

Purpose: Young adult cancer survivors aged 20–30 years are at increased risk for chronic health conditions, influenced by modifiable lifestyle behaviors. Many do not meet the American Cancer Society’s guidelines for physical activity and healthy eating. Guided by Social Cognitive Theory, this qualitative study explored the facilitators and barriers to healthy lifestyle behaviors among a racially and ethnically diverse sample of young adult cancer survivors aged 20–30 years to inform tailored interventions. Methods: Participants were recruited through the Adolescent and Young Adult Oncology Program at Moffitt Cancer Center in Florida, USA. Semi-structured interviews were conducted and analyzed using applied thematic analysis. Transcripts were coded in NVivo 12 Plus and organized into themes reflecting Social Cognitive Theory constructs and intervention preferences. Demographic information was collected via pre-interview surveys. Results: Participants (N = 30) were an average of 26 years (SD = 2.4). Most identified as non-Hispanic (60%), White (60%), unmarried (83%), women (83%), and with annual household income ≥$40,000 (67%). The most common diagnoses were thyroid cancer (20%), lymphoid (17%), and sarcoma (13%). Interviews lasted 30–88 min (M = 52). Five themes emerged: (1) competing demands and time constraints; (2) symptom burden and desire for credible, flexible support; (3) previous habits, self-efficacy, and community building; (4) dual role of social support; and (5) cancer as a catalyst for behavior change. Conclusions: Findings identified facilitators and barriers to physical activity and healthy eating, and intervention preferences among young adult cancer survivors, which can inform the timing, content, and modality of tailored interventions. Implications for Cancer Survivors: Findings can inform tailored, survivor-centered interventions for young adult cancer survivors.

This was the first study to assess patient-reported outcome (PRO) trajectories among patients with relapsed/refractory multiple myeloma (RRMM) receiving standard of care chimeric antigen receptor T-cell therapy (CAR-T) and to compare PRO trajectories by treatment group, idecabtagene vicleucel (ide-cel) vs. ciltacabtagene autoleucel (cilta-cel). Participants completed health-related quality of life (HRQOL) and symptom surveys at enrollment/baseline (pre-lymphodepletion), infusion day(D)0, D7, D14, D21, D30, D60, and D90. Piecewise growth curve models assessed PRO trajectories pre-D7 and post-D7. Among 99 participants, ide-cel recipients ( n = 49) were older than cilta-cel recipients ( n = 50) (median 73 vs. 64 years, p < 0.001). Many PROs worsened pre-D7 and improved post-D7, including overall HRQOL, physical well-being, functional well-being, fatigue, physical function, and social function ( p -values < 0.05). Several PROs were stable pre-D7 and improved post-D7, including anxiety, sleep disturbance, pain interference, and pain intensity ( p -values < 0.05). There were differences between groups in the trajectories of social well-being pre-D7 ( p = 0.01) and cognitive function post-D7 ( p = 0.001). Patients with RRMM receiving standard of care CAR-T had similar PRO trajectories regardless of CAR-T type, with most PROs initially worsening or stable before significantly improving post-treatment. Future studies should investigate potential differences by treatment for social well-being and cognitive function as well as PRO trajectories beyond D90 post-treatment.

Background: Addressing disparities in prostate cancer requires inclusive research that recruits racially and ethnically diverse participants. This article describes the recruitment procedures and participant characteristics of a biospecimen-based study involving African American (AA), Hispanic, and non-Hispanic White (NHW) prostate cancer survivors. Methods: AA participants were recruited through an existing biobank, whereas Hispanic and NHW participants were recruited from Moffitt Cancer Center and the University of Miami. Eligible survivors were approached via mail/phone/in person. Consented participants completed paper or electronic patient-reported outcomes measures (English or Spanish) and provided a saliva sample. Recruitment outcomes and participant characteristics were summarized by racial/ethnic group. Genetic ancestry was estimated using admixture analyses based on the 1000 Genomes Project. Results: Of the 1,862 survivors approached, 648 (35%) consented. Consent rates were highest among AA (56%), followed by NHW (37%) and Hispanic (20%) participants. Among those enrolled, 632 participants (mean age = 64 at diagnosis) completed surveys and self-identified as AA (n = 251), Hispanic (n = 130), or NHW (n = 251). Among those who provided saliva samples (n = 567), genetic admixture analysis revealed that AA participants predominantly shared African ancestry [mean (M) = 82%], NHW participants showed mostly European ancestry (M = 95%), and Hispanic participants displayed a heterogeneous mix of European (M = 67%), African (M = 16%), and admixed American (M = 13%) ancestry. Conclusions: Our inclusive recruitment approaches yielded a racially and genetically diverse cohort of prostate cancer survivors. Impact: These findings support the feasibility of recruiting diverse populations and integrating self-reported and genomic data to advance research on cancer survivorship disparities.

With the increasing use of immune checkpoint modulators and subsequent immune‐related adverse events, a validated patient‐reported measure is needed to assess the most common, distressing, and clinically useful symptomatic immune‐related adverse events. Through a rigorous multistep process of item generation, item selection, and measure evaluation, the brief Functional Assessment of Cancer Therapy – Immune Checkpoint Modulator 17‐item Symptom Index is now optimized, initially validated, and ready for use in research and clinical care.

PURPOSE: Patients receiving systemic therapy (ST) for non–small cell lung cancer (NSCLC) experience toxicities that negatively affect patient outcomes. This study aimed to test an approach for prospectively collecting patient-reported outcome (PRO) data, wearable sensor data (WSD), and clinical data, and develop a machine learning (ML) algorithm to predict health care utilization, specifically urgent care (UC) visits. MATERIALS AND METHODS: Patients with NSCLC completed the PROMIS-57 PRO quality-of-life measure and wore a Fitbit to monitor patient-generated health data from ST initiation through day 60. Demographic and clinical data were abstracted from the medical record. ML explainable models on the basis of Bayesian Networks (BNs) were used to develop predictive models for UC visits. RESULTS: Patients in the training data set (N = 58) were age 69 years on average (range, 35-89) and mostly female (57%), White (88%), and non-Hispanic (95%). Initial BN models trained on demographic and clinical data demonstrated moderate predictive accuracy on cross-validation for UC visits before ST (AUC, 0.72 [95% CI, 0.57 to 0.80]) and during ST (AUC, 0.81 [95% CI, 0.63 to 0.89]). Incorporating PRO and WSD during ST yielded enhanced models with significantly improved performance (final AUC, 0.86 [95% CI, 0.76 to 0.95]) via DeLong test ( P < .001). CONCLUSION: Multidimensional data sources, including demographic, clinical, PRO, and WSD, can enhance ML predictive models to elucidate complex, interactive factors influencing health care utilization during the first 60 days of ST. Use of explainable ML to predict and prevent treatment toxicities and health care utilization could improve patient outcomes and enhance the quality of cancer care delivery.

Background: Cancer-related fatigue (fatigue) is a common and persistent symptom after cancer treatment, yet the role of genetic susceptibility remains unclear. Methods: We used data from a prospective cohort study called the ColoCare Study, conducted over 5 US sites and Germany. Fatigue was assessed at 5 time points using the European Organisation for the Research and Treatment of Cancer Core Quality of Life Questionnaire fatigue subscale and analyzed as (1) a binary summary measure of the trajectory from diagnosis into survivorship (defined as severe: yes/no), (2) a mean score across all time points, and (3) the highest (ie, worst) score across all time points. We genotyped samples using the Illumina Infinium Global Diversity Array kit with imputation using the National Institutes of Health TOPMed reference panel to conduct a genome-wide association study. The Sum of Single Effects was used to identify independent secondary signals. Transcriptome-wide association studies using the S-PrediXcan and MultiXcan methods were conducted to examine genetic regulation of gene expression. The COLOC package assessed whether variants identified in the genome-wide association study influence gene expression through colocalization analysis. Results: Among 1219 participants, 31.0% experienced severe fatigue over the course of their disease. A locus near LINC02505 on chromosome 4 was associated with severe fatigue (rs6531463; odds ratio = 3.25, P = 3.88 × 10−8). When modeling mean fatigue levels, strongly associated variants were identified in or near NEK10 and SLC4A7. Integrative analyses linked the predicted expression of NEK10 in liver tissue to risk of fatigue (P < 4.36 × 10−6). Colocalization analysis identified genetic loci and gene expression near NEK10 (posterior probabilities >0.9). Conclusions: This study identified novel genetic loci associated with fatigue in patients with colorectal cancer and may be useful for identifying high-risk individuals for preventative strategies.

Background/Objectives: Pancreatic cancer (PC) is a diagnosis with a poor prognosis which can be associated with significant distress and may hinder a patient’s ability to understand treatment details. Educating patients based on their learning preferences (LPs) and emotions may allow for personalized, enhanced care. Methods: This prospective project enrolled patients with non-metastatic PC. Phase 1 utilized the Learning Preference Barometer (LPB) and Emotional Journey Barometer (EJB), which are digital instruments co-designed by CANCER101 (C101) and the Health Collaboratory, to assess patient LPs and emotional states. Phase 2 provided information prescriptions aligned with LPs through C101’s Prescription to Learn^® (P2L) platform. Collected data included demographics, treatment, LPs (auditory, kinesthetic, linguistic, visual), patient engagement with P2L, and patient emotional states with qualitative verbal validation. Descriptive variables were used to report outcomes. Results: Primary LPs in the 47 participating patients were as follows: linguistic 45%, visual 34%, auditory 11%, and kinesthetic 9%, with secondary preferences in the majority (53%). Those patients (66%) who accessed P2L had linguistic and visual preferences; the majority accessed 1- 2 resources out of the 25 provided. Resources accessed aligned to 88% of patient LPs. The majority of patients (60%) initiated treatment prior to initial EJB, and 40% were treatment naive. Common baseline emotions were optimistic (47% vs. 36%, respectively), satisfied (11% vs. 25%), acceptance (11% vs. 11%), and overwhelmed (5% vs. 11%). Conclusions: Assessing LPs and emotional state allows for personalized patient education and clinical encounters for PC patients. Future work includes examining the effects of personalized approaches on patient satisfaction, decision-making, health outcomes, and the overall patient–clinician relationship.