The resurgence of interest in cancer metabolism has linked alterations in the regulation and exploitation of metabolic pathways with an anabolic phenotype that increases biomass production for the replication of new daughter cells. To support the increase in the metabolic rate of cancer cells, a coordinated increase in the supply of nutrients, such as glucose and micronutrients functioning as enzyme cofactors is required. The majority of co-enzymes are water-soluble vitamins such as niacin, folic acid, pantothenic acid, pyridoxine, biotin, riboflavin and thiamine (Vitamin B1). Continuous dietary intake of these micronutrients is essential for maintaining normal health. How cancer cells adaptively regulate cellular homeostasis of cofactors and how they can regulate expression and function of metabolic enzymes in cancer is underappreciated. Exploitation of cofactor-dependent metabolic pathways with the advent of anti-folates highlights the potential vulnerabilities and importance of vitamins in cancer biology. Vitamin supplementation products are easily accessible and patients often perceive them as safe and beneficial without full knowledge of their effects. Thus, understanding the significance of enzyme cofactors in cancer cell metabolism will provide for important dietary strategies and new molecular targets to reduce disease progression. Recent studies have demonstrated the significance of thiamine-dependent enzymes in cancer cell metabolism. Therefore, this review discusses the current knowledge in the alterations in thiamine availability, homeostasis, and exploitation of thiamine-dependent pathways by cancer cells.

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BACKGROUND: Clinical trials offer novel treatments, which are essential to high quality cancer care. Patients living in rural areas are often underrepresented in clinical trials due to several factors. This study evaluated the association between rurality and interest in clinical trial participation, change in interest, and treatment decision-making style preference. METHODS: This cohort study included patients with cancer receiving oncology care at the University of Alabama at Birmingham from 2017 to 2019. Associations between treatment decisionmaking preference and the interaction between rurality and area deprivation were analyzed using multinomial logistic regression. Initial interest in clinical trial participation and change in interest were analyzed using modified Poisson regressions with robust standard errors. Initial interest model was stratified by Area Deprivation Index (ADI; higher vs. lower disadvantaged). RESULTS: In adjusted models, patients in rural versus urban areas had similar initial interest in clinical trials, both those in higher (40% vs. 50%) and lower disadvantaged settings (54% vs. 62%). Additionally, rural versus urban patients had similar change of clinical trial interest for both those who changed from uninterested-to-interested (31% vs. 26%) and interested-to-uninterested (47% vs. 42%). CONCLUSION: This study compares the interest in clinical trial participation among patients living in rural and urban settings. Lack of interest may be secondary to barriers that patients in rural areas face (e.g., transportation, financial, access). Most rural patients prefer a shared treatment decision-making style, which should be considered when identifying interventions to increase enrollment of underserved rural patients in clinical trials. Cancer 2022;128:3977-3984.

Introduction: Chemoimmunotherapy with R-CHOP is the standard of care for newly diagnosed diffuse large B cell lymphoma (DLBCL) leading to high cure rates although treatment-related toxicities are significant among older adults. Reduced dose R-CHOP has been proposed to balance efficacy and safety, but there is considerable ambiguity in "whom" and "by how much" with the best available data on those >80y [Peyrade et al 2011]. In this study, we sought to document the variability in chemotherapy dosing among unselected older adults with DLBCL treated with R-CHOP at a large, urban academic center in the Deep South. In addition, we sought to determine how baseline clinical features impacted tolerance of treatment. Methods: We conducted a single-institution retrospective review of all adults with newly diagnosed DLBCL initiating chemoimmunotherapy, predominantly R-CHOP, between 1/2015 and 2/2021 at the University of Alabama at Birmingham. For the current analysis, the cohort was limited to patients >70y. We extracted information on baseline clinico-demographic variables (age, sex, race, albumin, ECOG performance status [ECOG PS], Charlson comorbidity index [CCI], cancer type, stage, Revised International Prognostic Index score) and treatment intensity (type and dose of chemotherapy at 1 st cycle). Dose reduction was defined as any pre-planned reduction in cyclophosphamide, vincristine, or doxorubicin from standard CHOP dosing for cycle 1 of treatment. The primary outcome of interest was ≥grade 3 treatment-related toxicity (using CTCAE v5.0) occurring up to 30 days from the last dose of R-CHOP; secondary outcomes included dose modifications (dose delays >7d, dose reductions and treatment discontinuation) and health care utilization (unplanned emergency department visits and hospitalizations). Given high missingness for ECOG PS, we performed multiple imputation with chained equations producing 10 imputed datasets. Finally, we built a multivariate logistic regression model to identify baseline characteristics associated with severe toxicity; putative risk factors included age, sex, ECOG PS, CCI, albumin and treatment intensity. We pooled regression coefficients across imputed datasets using Rubin's rules. All hypothesis testing were two-sided, and the level of significance was chosen as 0.05. Results: Of 287 patients with DLBCL initiating R-CHOP during the study period, 101 (35%) patients were included in our cohort. The median age was 76y (IQR 73-79), with 55% males and 85% whites. Stage IV disease was present in 33% of patients, and 11% had double or triple-hit subtype. Of those with non-missing ECOG PS (55%), 38% had ECOG PS ≥2, and 29% had a CCI ≥2 [Table 1]. Overall, 55% of patients received planned reduced dose treatment. Interestingly, 51% of patients <80y received reduced dose R-CHOP, whereas 32% of patients ≥80y still received full dose R-CHOP. Over half of patients (54%) experienced ≥grade 3 toxicity (42% hematologic toxicity, 27% non-hematologic toxicity). Dose delays (12%), reductions (12%), or discontinuations (17%) were frequent, and over a third (35%) of patients had unplanned hospitalizations during treatment. In a multivariate logistic regression model, reduced dose R-CHOP (Odds Ratio, OR 0.34; 95% CI 0.12-0.94; p value 0.038) was associated with decreased risk of ≥grade 3 toxicity, and CCI ≥2 (OR 5.35; 95% CI 1.58-18.25; p value 0.007) was associated with increased risk of ≥grade 3 toxicity. Age, albumin, and ECOG PS were not associated with risk of toxicity [Table 2]. Overall, only 11% of the observed inter-individual variability in toxicity could be explained by the above potential risk factors. Conclusions: Among older adults >70y with DLBCL, planned dose reductions based on anticipated tolerability were frequent, yet over half of patients experienced severe treatment-related toxicity. Treatment intensity and comorbidity burden were significantly associated with increased risk of toxicity, but age, albumin, and performance status were not. Overall, pre-treatment factors only explained about 11% of the observed variability in toxicity suggesting that a significant proportion of inter-individual variability in severe toxicity is unexplained by traditional evaluation. Future studies should examine the role of geriatric assessment and altered body composition in older adults to identify those at increased risk of severe toxicity from chemoimmunotherapy. Figure 1 Figure 1. Disclosures Narkhede: Genentech/Roche: Research Funding; Genmab: Other: Medical writing support, Research Funding; TG Therapeautics: Research Funding; Gilead: Research Funding. Mehta: Affirmed; Kite/Gilead; Roche-Genetech; Celgene/BMS; Oncotartis; Innate Pharmaceuticals; Seattle Genetics; Incyte; Takeda; Fortyseven Inc/Gilead; TG Therapeutics; Merck; Juno Pharmaceuticals/Bristol Myers Squibb: Research Funding; Seattle Genetics; Incyte; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Seattle Genetics; Incyte; TG Therapeutics: Consultancy. Giri: PackHealth: Research Funding; CareVive: Honoraria, Research Funding.

110 Background: Patients living in rural or disadvantaged settings are historically underrepresented in clinical trials. This study sought to understand associations between neighborhood characteristics and both interest in clinical trial participation and decision-making style preference. Methods: This cross-sectional study used patient-reported outcome data from patients with cancer treated at the University of Alabama at Birmingham from January 2017 to May 2019. Rural-Urban Commuting Area Codes (RUCA) scores were used to determine rurality of patient residence. Area Deprivation Index (ADI) values (range 0-100) were used to identify patients living in the most disadvantaged (top 15%) census block groups. The Control Preferences Scale captured decision-making preference. Likelihood of interest in clinical trial participation by rurality and neighborhood disadvantage was estimated using risk ratios (RR) and 95% confidence intervals (CI) from modified Poisson regression models. Multinomial regression was used to calculate RRs and 95% CIs estimating likelihood of preferred decision-making style by rurality and neighborhood disadvantage. Models were adjusted for age, sex, race, cancer type, cancer stage, ECOG performance status, and phase of care. Results: Of 1005 patients with cancer, mean age was 67 (SD 11), 68% were female, and 74% white. Gynecologic cancer (32%) was the most prevalent diagnosis, followed by hematologic (20%) and breast (15%) cancer. Of this sample, 16% of patients lived in a rural setting and 18% lived in a disadvantaged neighborhood. Interest in clinical trial participation was no different for patients living in rural vs. urban (RR 0.93, 95% CI 0.73-1.17) or disadvantaged vs. non-disadvantaged neighborhoods (RR 0.88, 95% CI 0.69-1.13). Patients living in rural vs. urban settings trended toward increased likelihood of preferring physician- to patient-driven decision-making (RR 1.67, 95% CI 0.95-2.94). Patients living in disadvantaged vs. non-disadvantaged neighborhoods trended toward increased likelihood of preferring physician- to patient-driven decision-making (RR 1.39, 95% CI 0.82-2.35). Conclusions: Though clinical trial participation interest was similar, patients with cancer living in rural vs. urban settings trended toward increased likelihood of preferring physician- vs. patient-driven decision-making. Opportunities exist for providers to engage historically underrepresented patients for trial participation.