Background: There is little information about cancer‐related cognitive impairment (CRCI) in adolescent and young adults (AYA, 15–39 years old) due to its rare incidence. Here, we present the pre‐treatment (before chemotherapy or radiotherapy) evaluation of cognitive function and ability of AYA with cancer (AYAC) in a multicentered cohort study.Methods: Newly diagnosed AYAC and age‐matched healthy controls (HC) were recruited between 2018 and 2021. The primary outcome was the comparison of pre‐treatment cognitive impairment defined as 2 standard deviations (SDs) below the HC on ≥1 cognitive test, or >1.5 SDs below on ≥2 tests using CANTAB® between AYAC and HC. Secondary outcomes included self‐perceived cognitive ability assessed by FACT‐Cog v3 and biomarkers (inflammatory cytokines and brain‐derived neurotrophic factor [BDNF]).Results: We recruited 74 AYAC (median age = 34) and 118 HC (median age = 32). On objective cognitive testing, we observed three times more AYAC patients performed poorly on at least 2 cognitive tests compared to HC (40.5% vs. 13.6%, p < 0.001). AYAC self‐perceived less degree of cognitive impairment than HC (p < 0.001). However, AYAC perceived a greater impact of cognitive changes on their quality of life compared to HC (p = 0.039). Elevated baseline inflammatory markers (IL‐2, IL‐4, IL‐6, IL‐8, IL‐10 and IFN‐γ) were observed among AYAC compared to HC, and baseline BDNF was lower in AYAC compared to HC. Interaction effects between cancer diagnosis and biomarkers were observed in predicting cognitive function.Conclusion: With the pre‐existence of CRCI and risk factors of neuroinflammation even prior to systemic therapy, AYAC should receive early rehabilitation to prevent further deterioration of cognitive function after initiation of systemic therapies. (ClinicalTrials.gov Identifier: NCT03476070).

PURPOSE: Young adult childhood cancer survivors (YACCSs) are often impacted by cancer-related cognitive impairment (CRCI) and psychological distress. Using the Project Forward Cohort, we evaluated the relationship between CRCI and substance use behaviors. METHODS: YACCSs were surveyed between 2015 and 2018 (N = 1,106, female = 50.8%, Hispanic = 51.5%, median age = 25.5 years). Associations between CRCI and substance use (tobacco, binge drinking, marijuana, prescription drug misuse, and e-cigarette/vaporizer) were examined in multivariate logistic or log-binomial regressions, adjusting for child at diagnosis (0-14 years), years since diagnosis, sex, race/ethnicity, cancer type, and treatment intensity. Mediation analysis was performed to determine opportunities for interventions. RESULTS: CRCI was reported by 144 (13.0%) survivors. The highest prevalence was observed in CNS cancers (25.4%) and leukemia (13.3%) survivors. After covariate adjustment, CRCI was associated with 2.26 times the odds of prior 30-day vaping (95% CI, 1.24 to 4.11; P = .007). Mediators with significant indirect effects in the CRCI-vaping relationship include depressive symptoms (Center for Epidemiological Studies Depression Scale) and having two or more cancer-related late effects ( P < .05). CONCLUSION: CRCI among YACCSs was associated with reports of vaping. Oncologists should screen for vaping behavior if CRCI is apparent. Increasing access to long-term follow-up clinics, addressing physical and mental health issues, and monitoring and educating on vaping and other substance use behaviors is recommended to improve the long-term health of YACCSs.

PURPOSE: There is a lack of systematic solutions to manage supportive care issues in racial/ethnic minorities (REM) receiving treatment for cancer. We developed and implemented an electronic patient-reported outcome (ePRO)–driven symptom management tool led by oncology pharmacists in a majority-minority cancer center located in Southern California. This study was designed to evaluate the implementation outcomes of our multilevel intervention. METHODS: This was a prospective, pragmatic, implementation study conducted between July 2021 and June 2023. Newly diagnosed adult patients with cancer receiving intravenous anticancer therapies completed symptom screening using ePRO that consists of the Patient-Reported Outcomes Measurement Information System measures at each infusion visit during the study. ePRO results were presented to an oncologist pharmacist for personalized symptom management and treatment counseling. The RE-AIM framework was used to guide implementation outcomes. Differences in symptom trajectories and clinical outcomes between groups were tested using generalized estimating equations. RESULTS: We screened 388 patients of whom 250 were enrolled (acceptance rate: 64.4%), with 564 assessments being completed. The sample consisted of non-Hispanic White (NHW, 42.4%), Hispanic/Latinx (H/L, 30.8%), and non-Hispanic Asian (20.4%), with one (21.6%) of five participants preferring speaking Spanish. Compared with NHW, H/L participants had greater odds of reporting mild to severe pain interference (odds ratio [OR], 1.91 [95% CI, 1.18 to 3.08]; P = .008) and nausea and vomiting (OR, 2.08 [95% CI, 1.21 to 3.58]; P = .008), and higher rates of urgent care utilization (OR, 1.92 [95% CI, 1.04 to 3.61]; P = .04) within 30 days. Nausea and vomiting (n = 131, 23.2%), pain (n = 91, 16.1%), and fatigue (n = 72, 12.8%) were most likely to be intervened, with 90% of the participants expressing satisfaction across all visits. CONCLUSION: Our multilevel ePRO-driven intervention led by oncology pharmacists helps facilitate symptom assessments and management and potentially reduce health disparities among REM.

Cognitive impairment due to cancer and its therapy is a major concern among cancer patients and survivors. Extracellular vesicle (EVs) composition altered by cancer and chemotherapy may affect neurological processes such as neuroplasticity, potentially impacting the cognitive abilities of cancer patients and survivors. We investigated the EV proteome of breast cancer patients with and without cognitive impairment following anthracycline-based chemotherapy from longitudinally collected plasma. EVs were cup-shaped and positive for Flotillin-1 and TSG-101. We identified 517 differentially expressed EV proteins between the cognitive impaired and non-impaired groups during and post-chemotherapy. The observed decreased expression of p2X purinoceptor, cofilin-1, ADAM 10, and dynamin-1 in the plasma EVs of the cognitive impaired group may suggest alterations in the mechanisms underlying synaptic plasticity. The reduced expression of tight junction proteins among cognitive-impaired patients may imply weakening of the blood-brain barrier. These EV protein signatures may serve as a fingerprint that underscores the mechanisms underlying cognitive impairment in cancer patients and survivors.

Introduction: Adverse drug events (ADEs) are associated with poor outcomes and increased costs but may be prevented with prediction tools. With the National Institute of Health All of Us (AoU) database, we employed machine learning (ML) to predict selective serotonin reuptake inhibitor (SSRI)-associated bleeding. Methods: The AoU program, beginning in 05/2018, continues to recruit ≥ 18 years old individuals across the United States. Participants completed surveys and consented to contribute electronic health record (EHR) for research. Using the EHR, we determined participants who were exposed to SSRIs (citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, vortioxetine). Features (n = 88) were selected with clinicians’ input and comprised sociodemographic, lifestyle, comorbidities, and medication use information. We identified bleeding events with validated EHR algorithms and applied logistic regression, decision tree, random forest, and extreme gradient boost to predict bleeding during SSRI exposure. We assessed model performance with area under the receiver operating characteristic curve statistic (AUC) and defined clinically significant features as resulting in > 0.01 decline in AUC after removal from the model, in three of four ML models. Results: There were 10,362 participants exposed to SSRIs, with 9.6% experiencing a bleeding event during SSRI exposure. For each SSRI, performance across all four ML models was relatively consistent. AUCs from the best models ranged 0.632–0.698. Clinically significant features included health literacy for escitalopram, and bleeding history and socioeconomic status for all SSRIs. Conclusions: We demonstrated feasibility of predicting ADEs using ML. Incorporating genomic features and drug interactions with deep learning models may improve ADE prediction.