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PURPOSE: Health-related quality of life (HRQOL) is an established prognostic factor for mortality; however, it is unclear if HRQOL is predictive of time to disease progression, a particularly meaningful outcome for patients. We examined the association between HRQOL and progression-free survival (PFS) in SWOG Cancer Research Network clinical trials. METHODS: For this secondary analysis, we reviewed all completed SWOG clinical trials to identify those for patients with advanced cancer that incorporated Functional Assessment of Cancer Therapy (FACT) questionnaires at baseline. FACT-Trial Outcome Index (FACT-TOI) was the primary independent variable. Associations between FACT-TOI and other FACT subscores with PFS and overall survival were evaluated via log-rank test and multivariable Cox regression analysis. RESULTS: Three clinical trials met our inclusion criteria: S0027 and S9509 for advanced non–small-cell lung cancer and S0421 for hormone-refractory prostate cancer. Of the 1,527 enrolled patients, 1,295 (85%) had both HRQOL and survival outcomes data available and were included in this analysis. In univariable analysis, we observed a statistically significant gradient effect in all three trials, with higher baseline FACT-TOI scores corresponding to better PFS (S0027, P < .001; S9509, P = .02; and S0421, P < .001). In multivariable analysis, FACT-TOI was significantly associated with PFS in S0027 (hazard ratio [HR] = 0.64; 95% CI, 0.42 to 1.00) but not in S9509 (HR = 0.77; 95% CI, 0.56 to 1.05) or S042 (HR = 0.86; 95% CI, 0.73 to 1.01). FACT-TOI was significantly associated with overall survival in multivariable analysis ( P < .005 in all three trials). CONCLUSION: The association between baseline FACT-TOI scores and survival underscores their potential as a stratification factor in clinical trials.

PURPOSE: The financial toxicity of anticancer drugs is well-documented, but little is known about the costs of drugs used to manage cancer-associated symptoms. METHODS: We reviewed relevant guidelines and compiled drugs used to manage seven cancer-associated symptoms (anorexia and cachexia, chemotherapy-induced peripheral neuropathy, constipation, diarrhea, exocrine pancreatic insufficiency, cancer-associated fatigue, and chemotherapy-induced nausea and vomiting). Using GoodRx website, we identified the retail price (cash price at retail pharmacies) and lowest price (discounted, best-case scenario of out-of-pocket costs) for patients without insurance for each drug or formulation for a typical fill. We describe lowest prices here. RESULTS: For anorexia and cachexia, costs ranged from $5 US dollars (USD; generic olanzapine or mirtazapine tablets) to $1,156 USD (brand-name dronabinol solution) and varied widely by formulation of the same drug or dosage: for olanzapine 5 mg, $5 USD (generic tablet) to $239 USD (brand-name orally disintegrating tablet). For chemotherapy-induced peripheral neuropathy, costs of duloxetine varied from $12 USD (generic) to $529 USD (brand-name). For constipation, the cost of sennosides or polyethylene glycol was CONCLUSION: We highlight the high costs of many symptom control drugs and the wide variation in the costs of these drugs. These findings can guide patient-clinician discussions about cost-effectively managing symptoms, while promoting the use of less expensive formulations when possible.

Patients with gastrointestinal (GI) cancer frequently experience unplanned hospitalizations, but predictive tools to identify high-risk patients are lacking. We developed a machine learning model to identify high-risk patients.In the study, 1341 consecutive patients undergoing GI (abdominal or pelvic) radiation treatment (RT) from March 2016 to July 2018 (derivation) and July 2018 to January 2019 (validation) were assessed for unplanned hospitalizations within 30 days of finishing RT. In the derivation cohort of 663 abdominal and 427 pelvic RT patients, a machine learning approach derived random forest, gradient boosted decision tree, and logistic regression models to predict 30-day unplanned hospitalizations. Model performance was assessed using area under the receiver operating characteristic curve (AUC) and prospectively validated in 161 abdominal and 90 pelvic RT patients using Mann-Whitney rank-sum test. Highest quintile of risk for hospitalization was defined as "high-risk" and the remainder "low-risk." Hospitalizations for high- versus low-risk patients were compared using Pearson's χOverall, 13% and 11% of patients receiving abdominal and pelvic RT experienced 30-day unplanned hospitalization. In the derivation phase, gradient boosted decision tree cross-validation yielded AUC = 0.823 (abdominal patients) and random forest yielded AUC = 0.776 (pelvic patients). In the validation phase, these models yielded AUC = 0.749 and 0.764, respectively (P.001 and P = .002). Validation models discriminated high- versus low-risk patients: in abdominal RT patients, frequency of hospitalization was 39% versus 9% in high- versus low-risk groups (P.001) and 6-month survival was 67% versus 92% (P = .001). In pelvic RT patients, frequency of hospitalization was 33% versus 8% (P = .002) and survival was 86% versus 92% (P = .15) in high- versus low-risk patients.In patients with GI cancer undergoing RT as part of multimodality treatment, machine learning models for 30-day unplanned hospitalization discriminated high- versus low-risk patients. Future applications will test utility of models to prompt interventions to decrease hospitalizations and adverse outcomes.

The NCCN Guidelines for Older Adult Oncology address specific issues related to the management of cancer in older adults, including screening and comprehensive geriatric assessment (CGA), assessing the risks and benefits of treatment, preventing or decreasing complications from therapy, and managing patients deemed to be at high risk for treatment-related toxicity. CGA is a multidisciplinary, in-depth evaluation that assesses the objective health of the older adult while evaluating multiple domains, which may affect cancer prognosis and treatment choices. These NCCN Guidelines Insights focus on recent updates to the NCCN Guidelines providing specific practical framework for the use of CGA when evaluating older adults with cancer.

Purpose Family/caregiver visitation provides critical support for patients confronting cancer and is associated with positive outcomes. However, the COVID-19 pandemic brought historic disruptions including widespread visitation restrictions. Here, we characterize in-depth the visitor policies of NCI-designated comprehensive cancer centers (CCCs) and analyze geographic/temporal patterns across CCCs. Methods The public-facing CCC websites, including archived webpages, were reviewed to abstract initial visitation policies and revisions, including end-of-life (EoL) exceptions and timing of visitation restrictions relative to regional lockdowns. Chi-squared and Fisher’s exact tests were employed to analyze associations between geographic region, timing, and severity of restrictions. Results Most CCCs (n=43, 86%) enacted visitation restrictions between March 15 and April 15, 2020. About half barred all visitors for COVID-negative inpatients (n=24, 48%) or outpatients (n=26, 52%). Most (n=36, 72%) prohibited visitors for patients with confirmed/suspected COVID-19. Most (n=40, 80%) published EoL exceptions but the specifics were highly variable. The median time from initial restrictions to government-mandated lockdowns was 1 day, with a wide range (25 days before to 26 days after). There was no association between timing of initial restrictions and geographic location (p=0.14) or severity of inpatient policies (p=1.0), even among centers in the same city. Outpatient policies published reactively (after lockdown) were more restrictive than those published proactively (p=0.04). Conclusion CCCs enacted strict but strikingly variable COVID-19 visitation restrictions, with important implications for patients/families seeking cancer care. A unified, evidence-based approach to visitation policies is needed to balance proven infection control measures with the needs of patients and families.

Emerging efficacy data have led to the emergency use authorization or approval of COVID-19 vaccines in several countries worldwide. Most trials of COVID-19 vaccines excluded patients with active malignancies, and thus data on the safety, tolerability and efficacy of the vaccines in patients with cancer are currently limited. Given the risk posed by the COVID-19 pandemic, decisions regarding the use of vaccines against COVID-19 in patients participating in trials of investigational anticancer therapies need to be addressed promptly. Patients should not have to choose between enrolling on oncology clinical trials and receiving a COVID-19 vaccine. Clinical trial sponsors, investigators and treating physicians need operational guidance on COVID-19 vaccination for patients with cancer who are currently enrolled or might seek to enrol in clinical trials. Considering the high morbidity and mortality from COVID-19 in patients with cancer, the benefits of vaccination are likely to far outweigh the risks of vaccine-related adverse events. Herein, we provide operational COVID-19 vaccine guidance for patients participating in oncology clinical trials. In our perspective, continued quality oncological care requires that patients with cancer, including those involved in trials, be prioritized for COVID-19 vaccination, which should not affect trial eligibility., Patients with cancer have a high risk of morbidity and mortality from COVID-19. The rapid development of COVID-19 vaccines has provided new hope of mitigating the disease. Herein, the COVID19 and Cancer Clinical Trials Working Group calls for prioritization of patients with cancer, importantly including those participating in oncology clinical trials, for COVID-19 vaccination. The authors also provide operational COVID-19 vaccine guidance for patients participating in oncology clinical trials.

Gallbladder (GB) carcinoma is a relatively rare malignancy and is associated with poor prognosis. Numerous risk factors have been associated with the development of GB carcinoma. GB carcinomas may present as mass lesions replacing the GB, focal or diffuse thickening of the GB wall, and intraluminal mass in the GB. Various benign conditions can mimic GB carcinoma. This article reviews the epidemiology, pathology, clinical findings, imaging features, and management of GB carcinomas.

BackgroundPatients with gastrointestinal (GI) cancers experience a high symptom burden due to the effects of both cancer and treatment. As such, trials assessing symptom burden and supportive interventions are crucial. Here, we characterise the landscape of phase III GI cancer clinical trials and explore study outcomes centred on the patient’s quality of life (QoL).MethodsWe searched ClinicalTrials.gov for phase III randomised controlled trials (RCTs) registered between 2000 and 2017 that are assessing a therapeutic intervention in adult patients with cancer and grouped trials by GI disease sites.ResultsOverall, we identified 76 phase III trials specific to GI cancers that enrolled a total of 53 725 patients. When analysing the primary outcomes measured, the vast majority of studies (n=71, 86%) measured disease-related endpoints such as progression-free survival or overall survival. All trials had a secondary endpoint that measured adverse events, but only 30 trials (39%) included QoL measures as secondary endpoints. Of the 30 trials that included QoL secondary endpoints, only 16 (53%) reported these results. Only five trials (7%) assessed interventions aimed at supportive measures impacting disease-related or treatment-related toxicity. None of the supportive trials included QoL as a primary endpoint and only two of these trials (40%) included QoL as a secondary endpoint.ConclusionsMost GI cancer trials failed to incorporate patient-centred outcomes or QoL measures when studying new interventions. These findings call for greater integration of patient-reported metrics, which may lead to better care and outcomes for patients with GI malignancies.

Context Coronavirus Disease 2019 (COVID-19) has caused unprecedented disruptions to cancer care, including through strict hospital visitation policies. Since a substantial proportion of the U.S. population report a non-English language as their primary language, it is critical that information is disseminated in multiple languages. Objectives To examine the availability of language translations of visitation restrictions on adult National Cancer Institute-designated comprehensive cancer centers (CCCs) Web sites. Methods Cross-sectional analysis of visitation policies abstracted from public-facing Web sites of CCCs in June 2020. Using U.S. Census data, CCC's city and state proportions of self-identifying Hispanic/Latinx population were categorized into three cohorts: low (20%). Results As of June 2020, all 50 CCCs published a COVID-19 visitation policy on their Web site. Of these, 33 (66%) posted policies only in English, whereas 17 (34%) included one or more non-English translations. A minority of CCCs published Spanish language resources, which did not differ based on state or city demographics: for example, only 42% (8 of 19), 10% (1 of 10), and 38% (8 of 21) of CCCs published Spanish language resources in cities with low, moderate, and high Hispanic/Latinx populations, respectively. Conclusion Most CCC's did not publish non-English language translations of their visitor policies. Even in cities and states with larger Hispanic/Latinx populations, most CCCs did not publish resources in Spanish. This study highlights a key opportunity to mitigate communication barriers and deliver culturally competent, patient-centered care.

Background: Opioid-induced constipation (OIC) remains the most common adverse event associated with opioid use. Treatment with more novel and costly agents (such as peripheral μ-opioid receptor antagonists [PAMORAs]) may be indicated in patients with laxative-refractory OIC. Three PAMORAs are U.S. Food and Drug Administration approved for managing OIC-methylnaltrexone (FDA approved in 2008), naloxegol (in 2014), and naldemedine (in 2017). These drugs are indicated only in limited scenarios. Their contemporary patterns of use and burden of spending remain unknown. Objective: To evaluate the trends in use and expenditures for the three PAMORAs approved for treating OIC. Design: Retrospective cross-sectional study using the 2014-2018 Medicare Part D Prescription Drug Event data and the 2018 Part D Prescriber Public Use File. Setting: Prescribers and beneficiaries using PAMORAs. Measurements: The annual spending, number of beneficiaries, number of claims, and spending per beneficiary and claim for each PAMORA. The distribution by prescriber specialty using PAMORA. Results: From 2014 to 2018, aggregate spending on PAMORAs increased, from $13.6 to $150.9 million, and use increased, from 4221 to 72,592 beneficiaries. After FDA approval in 2014, naloxegol overtook methylnaltrexone in the number of users in 2015 and spending in 2016. In 2018, 6989 unique prescribers used any PAMORA. Among them, the most common specialties/professions were family practice (20.2%), internal medicine (18.0%), and nurse practitioner (15.4%). Conclusions: Our findings-significant and increasing expenditure on PAMORAs, and broad use across specialties-serve as a call for defining and implementing appropriate use of PAMORAs.

Chemotherapy-induced peripheral neuropathy (CIPN) is a common and debilitating condition associated with a number of chemotherapeutic agents. Drugs commonly implicated in the development of CIPN include platinum agents, taxanes, vinca alkaloids, bortezomib, and thalidomide analogues. As a drug response can vary between individuals, it is hypothesized that an individual's specific genetic variants could impact the regulation of genes involved in drug pharmacokinetics, ion channel functioning, neurotoxicity, and DNA repair, which in turn affect CIPN development and severity. Variations of other molecular markers may also affect the incidence and severity of CIPN. Hence, the objective of this review was to summarize the known biological (molecular and genomic) predictors of CIPN and discuss the means to facilitate progress in this field.

Background Preclinical studies suggest that combining vandetanib (VAN), a multi-tyrosine kinase inhibitor of rearranged during transfection (RET) proto-oncogene, vascular endothelial growth factor receptor (VEGFR), and epidermal growth factor receptor (EGFR), with everolimus (EV), a mammalian target of rapamycin (mTOR) inhibitor, may improve antitumor activity. We determined the safety, maximum tolerated dose (MTD), recommended phase II dose (RP2D), and dose-limiting toxicities (DLTs) of VAN + EV in patients with advanced solid cancers and the effect of combination therapy on cancer cell proliferation and intracellular pathways. Patients and methods Patients with refractory solid tumors were enrolled in a phase I dose-escalation trial testing VAN (100-300 mg orally daily) + EV (2.5-10 mg orally daily). Objective responses were evaluated using RECIST v1.1. RET mutant cancer cell lines were used in cell-based studies. Results Among 80 patients enrolled, 72 (90%) patients were evaluable: 7 achieved partial response (PR) (10%) and 37 had stable disease (SD) (51%; duration range: 1-27 cycles). Clinical benefit (SD or PR ≥ 6 months) was observed in 26 evaluable patients [36%, 95% confidence intervals (CI) (25% to 49%)]. In 80 patients, median overall survival (OS) was 10.5 months [95% CI (8.5-16.1)] and median progression-free survival (PFS) 4.1 months [95% CI (3.4-7.3)]. Six patients (7.5%) experienced DLTs and 20 (25%) required dose modifications. VAN + EV was safe, with fatigue, rash, diarrhea, and mucositis being the most common toxicities. In cell-based studies, combination therapy was superior to monotherapy at inhibiting cancer cell proliferation and intracellular signaling. Conclusions The MTDs and RP2Ds of VAN + EV are 300 mg and 10 mg, respectively. VAN + EV combination is safe and active in refractory solid tumors. Further investigation is warranted in RET pathway aberrant tumors., Highlights • VAN + EV is safe, active and provides clinical benefit in some patients with refractory solid cancers. • Dual therapy is superior to monotherapy at inhibiting proliferation and intracellular signaling of RET mutant cancer cells. • This study highlights the importance of identifying novel combination therapies to overcome therapeutic resistance. • Next-generation sequencing of advanced solid tumors may inform treatment strategies and guide future drug development.

Cancer-related cognitive impairment (CRCI) is commonly experienced by individuals with non- central nervous system cancers throughout the disease and treatment trajectory. CRCI can have a substantial impact on the functional ability and quality of life of patients and their families. To mitigate the impact, oncology providers must know how to identify, assess, and educate patients and caregivers. The objective of this review is to provide oncology clinicians with an overview of CRCI in the context of adults with non-central nervous system cancers, with a particular focus on current approaches in its identification, assessment, and management.

BACKGROUND: The use of overall survival (OS) as the gold standard primary endpoint (PEP) in metastatic oncologic randomized controlled trials (RCTs) has declined in favor of progression-free survival (PFS) without a complete understanding of the degree to which PFS reliably predicts for OS. METHODS: Using ClinicalTrials.gov, we identified 1,239 phase 3 oncologic RCTs, 260 of which were metastatic solid tumor trials with a superiority-design investigating a therapeutic intervention using either a PFS or OS PEP. Each individual trial was reviewed to quantify RCT design factors and disease-related outcomes. RESULTS: A total of 172,133 patients were enrolled from 1999–2015 in RCTs that utilized PFS (56.2%, 146/260) or OS (43.8%, 114/260) as the PEP. PFS trials were more likely to restrict patient eligibility using molecular criteria (15.1% vs. 4.4%, p=0.005), utilize targeted therapy (80.1% vs. 67.5%, p=0.048), accrue fewer patients (median 495 vs. 619, p=0.03), and successfully meet the trial PEP (66.9% vs. 33.3%, p

Background: Patients with good performance status (PS) tend to be favored in randomized clinical trials (RCTs), possibly limiting the generalizability of trial findings. We aimed to characterize trial-related factors associated with the use of PS eligibility criteria and analyze patient accrual breakdown by PS. Methods: Adult, therapeutic, multiarm phase III cancer-specific RCTs were identified through ClinicalTrials.gov. PS data were extracted from articles. Trials with a PS restriction ECOG score ≤1 were identified. Factors associated with PS restriction were determined, and the use of PS restrictions was analyzed over time. Results: In total, 600 trials were included and 238,213 patients had PS data. Of those trials, 527 studies (87.8%) specified a PS restriction cutoff, with 237 (39.5%) having a strict inclusion criterion (ECOG PS ≤1). Enrollment criteria restrictions based on PS (ECOG PS ≤1) were more common among industry-supported trials (PPConclusions: Use of PS restrictions in oncologic RCTs is pervasive, and exceedingly few patients with poor PS are enrolled. The selective accrual of healthier patients has the potential to severely limit and bias trial results. Future trials should consider a wider cancer population with close toxicity monitoring to ensure the generalizability of results while maintaining patient safety.

Immunotherapy (IO) has altered the therapeutic landscape for multiple cancers. There are emerging data from retrospective studies on a subset of patients who do not benefit from IO, instead experiencing rapid progression with dramatic acceleration of disease trajectory, termed 'hyperprogressive disease' (HPD). The incidence of HPD ranges from 4% to 29% from the studies reported. Biological basis and mechanisms of HPD are currently being elucidated, with one theory involving the Fc region of antibodies. Another group has shown EGFR and MDM2/MDM4 amplifications in patients with HPD. This phenomenon has polarized oncologists who debate that this could still reflect the natural history of the disease. Thus, prospective studies are urgently needed to confirm the underlying biology, predict patients who are susceptible to HPD, and determine the modality of therapy post progression.

Chimeric antigen receptor (CAR) T cell therapy is being increasingly used to treat patients with advanced hematologic malignancies; however, the symptoms related to standard of care CAR T cell therapy during the first year after treatment have not been assessed using patient-reported outcome (PRO) measurements. This study aimed to quantify patients' perspectives of symptom burden and functional status using PROs during the first year after CAR T cell therapy for hematologic malignancies, especially in patients who experienced grade 2-4 toxicities. Sixty patients were enrolled in this observational cross-sectional study at any time during their first 12 months post-treatment. All 60 had received CAR T cell therapy as standard of care at MD Anderson Cancer Center in 2019. PROs were measured using the MD Anderson Symptom Inventory (MDASI), the PROs Measurement Information System 29 (PROMIS-29), the global health tool EQ5D-5L, and the single-item health-related quality of life scale (HRQoL). Twenty-two additional symptoms related to CAR T cell therapy, as identified by an expert panel, were also evaluated. CAR T cell therapy-related toxicities were rated according to the ASTCT consensus grading criteria. The majority of patients (52 of 60; 87%) received axicabtagene ciloleucel (Yescarta). One-third of the patients developed grade 2-4 cytokine release syndrome or neurotoxicity. The first 90 days after infusion represented the most symptomatic period, in which >10% of patients rated 18 symptoms as severe (ie, MDASI symptom score of 7 to 10 on scale of 0 to 10), strongly indicating the need for effective symptom management. Physical functioning, measured by interference on the "general activity" item on the MDASI and this domain on the PROMIS-29, were significantly worse in patients who underwent therapy during the first 30 days compared with those who underwent therapy over 90 days (all P < .05 with the Hochberg step-up procedure), whereas the EQ5D-5L and single-item HRQoL did not detect such differences. Compared with patients who had mild cytokine release syndrome or neurotoxicity (grade 0-1), patients who developed grade 2-4 toxicities persistently reported multiple severe symptoms after 30 days following therapy (all P < .05). Furthermore, although using a different recall period, patient-reported scores on several PROMIS-29 domains were significantly correlated with the scores of corresponding MDASI symptom items. This real-world quantitative PRO symptoms study provides evidence of unique profiles of the physical, psychological, and cognitive symptom burden in patients undergoing CAR T cell therapy that varies within the first year after infusion and demonstrates differences among PRO measurement scales. These results support the need for validation of fit-for-purpose PRO measurements for routinely monitoring symptom and toxicity burdens in CAR T cell therapy care settings.

273 Background: To date, studies of machine learning (ML) algorithms within oncology for mortality prediction have focused on structured electronic health record (EHR) data. Given the complex symptom burden of patients with advanced cancers, ML models may be better suited to identify patterns and interactions between symptom burden and outcomes compared to traditional statistical methods. To that end, in this study, we leverage the patient reported outcomes (PRO) data together with clinical EHR-based variables to assess the performance of ML algorithms to predict mortality in patients with advanced cancers. Methods: We randomly selected 689 patients with advanced cancer who had their first Palliative Care encounter between January 2012 and December 2017. 59 patients were lost to follow-up and were excluded from this analysis. The remaining cohort of 630 patients was split 4:1 randomly into a training and validation set to develop and test a supervised ML algorithm (Extreme Gradient Boosting [XGB] tree) to predict the 6-month mortality. Candidate variables for algorithm development included gender, age, ECOG performance status (PS), number of prior systemic therapies, and scores on the Edmonton Symptom Assessment System (ESAS)-FS, a 12-item PRO measure of physical and psychosocial symptom burden include the composite Physical Symptom Score (PHS), a sum of the physical ESAS symptoms (pain, fatigue, nausea, drowsiness, shortness of breath, appetite, wellbeing, sleep). Results: Overall, 630 patients were included in this 6-month mortality prediction; mean age 59 years, 354 (56%) female; 276 (44%) male. Variables with the most significant impact on the XGB tree mortality prediction were the ESAS symptoms of shortness of breath (1-AUC, 0.295), appetite, ESAS PHS, financial distress, age, and appetite as well as ECOG PS and number of prior systemic therapies. The XGB tree algorithm demonstrated the best overall prediction performance of 6-month mortality in the independent testing set, AUC 0.716 (95% CI 0.63 - 0.81), sensitivity 0.75 (95% CI 0.66 - 0.87), and a positive predictive value 0.67 (95% CI 0.57 - 0.79). Conclusions: Our ML model leveraged PRO-based assessment of symptom burden to correctly identify the majority of patients who died within 6 months. These models are uniquely positioned to not only automatically identify patients at high risk for short-term mortality but also the specific symptoms of concern for clinical intervention. Such models can be applied to available clinical and PRO data to facilitate clinical decision-making. Futures studies on improving model performance with the inclusion of interventions to modify symptom burden are in design.

210 Background: Research shows that healthcare professionals’ personal hopefulness is associated with burnout and life satisfaction, highlighting the value of hope-enhancement interventions. Feldman and Dreher developed a single-session hope intervention, but this has been used rarely with oncology professionals, and only in in-person format (Shah, Ferguson, Corn et al.). Given SWOG Cancer Research Network’s commitment to assessing hope-enhancement approaches and the need for online interventions given COVID-19, we report a feasibility study of a virtual hope workshop in SWOG members. Methods: The workshop was a single 2-hour session delivered live via Zoom to 6-8 participants at a time. The workshop comprises 3 components: A brief lecture on hope and two exercises—a “hope mapping” exercise (aided by a smartphone app created for the workshop) and a hope visualization exercise—both designed to build hope for a life goal of each participant’s choosing. 29 SWOG members participated. A link to post-workshop measures was sent to participants, which they were given a week to complete. Measures included Thanarajasingamet al’s 5-item Was-it-Worth-it (WIWI) measure; Kirkpatrick’s 4-item Training Evaluation Model (TEM; reaction, learning, behavior, results); and an item assessing the degree to which participants believe concepts from the workshop should be integrated into SWOG studies. Results: In all, 25 participants (86%) completed measures. Participants were physicians (n = 8), nurses (n = 4), patient advocates (n = 3), research staff (n = 3), and others (n = 7); mostly female (n = 17), mostly white (n = 18), with a mean age of 55.5 (SD = 13.95). Results for the WIWI items are as follows: “Was it worthwhile to participate in the Hope Workshop?” (23 Yes, 2 No/Undecided/Missing Answer); “If you had to do over, would you participate in the Hope Workshop again?” (22 Yes, 3 No/Undecided/Missing Answer); “Would you recommend participating in the Hope Workshop to others?” (22 Yes, 3 No/Undecided/Missing Answer). Two additional items on the WIWI asked participants to rate on a 3-point scale the degree to which they believe their quality of life had increased due to the workshop (M = 2.52, SD =.51) and their overall experience in the workshop (M = 2.70, SD =.64). Ratings for Kirkpatrick’s TEM items likewise were high, ranging from 6.91 (SD = 1.31) to 7.70 (SD =.70) on an 8-point scale. Finally, participants gave a mean rating of 4.44 (SD =.59) on a 5-point scale to the item “To what degree do you believe it may be useful to integrate concepts from this workshop into SWOG trials/studies?” Conclusions: It is feasible to implement hope-enhancement workshops in an online platform that includes a smartphone app. Data obtained from two validated tools (WIWI instrument and Kirkpatrick’s TEM) attest to an array of positive outcomes. Participants also overwhelmingly advocated integrating hope concepts into SWOG’s research.

153 Background: The long-term symptoms from COVID-19 (C19) infection in pts with cancer is not fully known. To monitor the evolution of this symptom burden over time, we designed and implemented a C19-specific patient-reported outcome (PRO) measure that integrated with a known measure of cancer symptom burden. Methods: Within the institutional initiative on C19 and cancer named Data-Driven Determinants for C19 Oncology Discovery Effort (D3CODE), pts with cancer & PCR-pos C19 are invited to participate in this longitudinal study. Pts complete the EQ-5D-5L, the 13 symptom severity & 6 interference items of the core MD Anderson Symptom Inventory (MDASI)+14 COVID-specific items, all scored on a 0-10 scale, 0 = none, 10 = worst imaginable. Pts complete the survey daily x 14 days from positive test date, then weekly x 3months, then monthly x 2yrs. Demographic and disease information was collected. Psychometric procedures determined validity and reliability of the MDASI-COVID. Results: Between 5/15/20 – 02/14/21, 2154 pts w PCR-confirmed C19 were invited to participate in the longitudinal study. 1282 (60%) pts provided consent and began the longitudinal completion of PRO surveys. Pts were 54.5% Female and 45.5% Male, median age 59 years (range 15 – 92). 1021 (80%) are White/Caucasian, 206 (16%) Hispanic, 113 (9%) African American, and 39 (3%) Asian. The validation analysis of MDASI-COVID instrument included the 1st 600 pts where the mean overall health rating on EQ-5D-5L was 78.3 (SD 19.6), best being 100. Highest mean (M) severity symptoms on the MDASI-COVID were fatigue (M 3.45, SD 2.17), drowsiness (M 2.50, SD 2.89), sleep disturbance (M 2.44, SD 2.99), malaise (M 2.37, SD 3.05), and distress (M 2.27, SD 2.90). Most severe (≥ 7) symptoms) reported were fatigue (21.3% of pts), change in taste (14.8%), change in smell (14.4%), malaise (14.3%), sleep disturbance (14.3%), and drowsiness (14%). showed internal consistency (Cronbach α) of the 27 symptom items was 0.957, of the 6 interference items was 0.937. Mean severity of the 27 symptom items was significantly correlated with overall EQ-5D-5L health rating (correlation = -0.45, P < 0.0005), demonstrating concurrent validity. Mean symptom severity and interference showed known-group validity between pts who required hospitalization (symptom M 2.32, SD 2.09; interference M 3.29, SD 3.02) and those who did not (symptom M 1.69, SD 1.85; interference M 2.20, SD 2.64) (symptom P 0.007; interference P 0.004). Conclusions: We successfully deployed a PRO-based long-term symptom monitoring platform for pts with C19 and cancer. The validation analysis of this novel C19 specific PRO, the MDASI-COVID, aids in the quantification of the global symptom burden in pts with both cancer and COVID-19 infection. Deployment of this measure in the ongoing longitudinal observational cohort allows for in-depth understanding of the long-term symptoms related to C19 and cancer.

322 Background: Natural language processing (NLP) algorithms can be leveraged to better understand prevailing themes in healthcare conversations. Sentiment analysis, an NLP technique to analyze and interpret sentiments from text, has been validated on Twitter in tracking natural disasters and disease outbreaks. To establish its role in healthcare discourse, we sought to explore the feasibility and accuracy of sentiment analysis on Twitter posts (‘’tweets’’) related to prior authorizations (PAs), a common occurrence in oncology built to curb payer-concerns about costs of cancer care, but which can obstruct timely and appropriate care and increase administrative burden and clinician frustration. Methods: We identified tweets related to PAs between 03/09/2021-04/29/2021 using pre-specified keywords [e.g., #priorauth etc.] and used Twarc, a command-line tool and Python library for archiving Twitter JavaScript Object Notation data. We performed sentiment analysis using two NLP models: (1) TextBlob (trained on movie reviews); and (2) VADER (trained on social media). These models provide results as polarity, a score between 0-1, and a sentiment as ‘’positive’’ (>0), ‘’neutral’’ (exactly 0), or ‘’negative’’ (

55 Background: To date, drugs used for CACS have limited efficacy and may have unwanted side effects. Despite this, clinicians commonly prescribe certain drugs for CACS, adding financial burden to patients through out-of-pocket costs near or at the end of life. We sought to describe the range of costs of the multiple drugs commonly used to manage CACS. Methods: We reviewed oncology, nutrition, and supportive care guidelines (ASCO, ESMO, ESPEN, MASCC) and compiled a list of drugs recommended for CACS. We also included drugs commonly used off-label in clinical practice as identified by study authors. Using GoodRx.com, we extracted available formulations (e.g., tablet [tab] vs capsule [cap] vs oral disintegrating tablet [ODT] vs solution [sol]), and brand name vs generic products for each drug. We identified the average retail price, and the lowest price with coupons, for each formulation using the most commonly used dosage for a 2-week period (typical fill). The average retail price is the cash price for consumers without prescription drug coverage. The lowest price represents a discounted, ‘’best case’’ scenario of out-of-pocket costs for patients without prescription drug coverage. We collected data using the zip code 10065 in May 2021 to describe the range of costs of these drug formulations. Results: We included 7 drugs available in 20 formulations (Table). For a 2-week fill, the least expensive options included generic olanzapine 5 mg tab (once daily) and generic mirtazapine 15 mg tab (once daily), costing $4.50 per fill. Brand-name solutions were costly, ranging from $605.50 (megestrol acetate, Megace ES) to $1,156.30 (dronabinol, Syndros). Costs between formulations of the same drug/dosage varied widely: for olanzapine 5 mg, the lowest price varied from $4.50 [generic, tab] to $238.40 [Zyprexa zydis, brand-name, ODT]. Conclusions: We found that the costs of drugs used for CACS are highly variable, ranging from < $5 to > $1000 per fill. Overall there is limited data supporting use of drugs for managing CACS. Thus, our findings can help guide patient-clinician discussions about the risk/benefit ratio of a prescription for managing CACS, while highlighting the importance of seeking less expensive formulations when possible.[Table: see text]

This cohort study examines the association of the Edmonton Symptom Assessment, including the Global Distress Score, with overall survival rates in patients with advanced cancer.

3112 Background: The advent of tyrosine kinase inhibitors (TKIs) has altered the therapeutic landscape of multiple hematological and solid malignancies. FDA approved starting doses of TKIs are based on the recommended phase 2 dose (RP2D) in clinical trials. Since many of these drugs are continuously dosed, ongoing toxicities may lead to drug reductions and drug discontinuations. Hence in practice, many patients are started on lower doses due to tolerability concerns or are dose-reduced subsequently for toxicity. Herein, we assessed the dosing, drug reduction rates, and drug discontinuation rates among FDA approved TKIs. Methods: We established a database of all FDA approved TKIs from the FDA online label repository. We extracted descriptive data for indications and usage, type of approval, approval dosage, dose reduction recommendation, median age, dose reduction rates, drug discontinuation rates, dose modification, warnings, adverse reactions, clinical trial experience, and geriatric use above 65 yrs and 75 yrs. Results: TKIs were approved for 143 different indications, ranging from 1 indication to 11 indications (eg. Imatinib) from data arising from 7 patients to 2816 patients for a specific indication. Among all TKIs, median dose was 150 mg and average dose was 237 mg; 34 (23.8%) were approved bid vs 71 (49.7%) qd. Specifically for oncology, 54 indications were biomarker based (eg. EGFR mutant NSCLC) and 52 were non-biomarker based for specific diseases (eg. metastatic RCC). Among approved indications range of dose reduction rate (DRR), drug interruption rates (DIR), drug discontinuations rates (DDR) were 0.80-89%, 3%-89%, and 1-39% respectively. Most common reasons for DRR, DIR, and DDR were diarrhea, fatigue, nausea, vomiting, hepatotoxicity, rash, and hypertension. Geriatric use was listed in 99 indications ( > 65 yrs, 6-68%; > 75 yrs, 0.50-24%). Patient reported outcomes were not available. Conclusions: TKIs have a variable dose reduction and drug discontinuation rate. Clinical trials should evaluate multiple dosing regimens and schedules to lessen the toxicity burden and improve QOL in patients. Future studies are warranted to look into flat dosing vs alternative dosing, like weight-based dosing for TKIs, and to report patient reported outcomes.

e13555 Background: Contra to national guidelines, women with ovarian cancer often receive aggressive treatment until the end-of-life. We trained machine learning algorithms to predict mortality within 180 days for women with ovarian cancer. Methods: Data were collected data from a single academic cancer institution in the United States. Women with recurrent ovarian cancer completed biopsychosocial patient-reported outcome measures (PROMs) every 90 days. We randomly partitioned our dataset into training and testing samples with a 2:1 ratio. We used synthetic minority oversampling to reduce class imbalance in the training dataset. We fitted training data to six machine learning algorithms and combined their classifications on the testing dataset into a voting ensemble. We assessed the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) for each algorithm. Results: We recruited 245 patients who completed 1319 assessments. The final voting ensemble performed well across all performance metrics (Accuracy = .79, Sensitivity = .71, Specificity = .80, AUROC = .76). The algorithm correctly identified 25 of the 35 women in the testing dataset who died within 180 days of assessment Conclusions: Machine learning algorithms trained using PROM data offer state-of-the-art performance in predicting whether a woman with ovarian cancer will reach the end-of-life within 180 days. We highlight the importance of PROM data in ML models of mortality. Our model exhibits substantial improvements in prediction sensitivity compared to other similar models trained using electronic health record data alone. This model could inform clinical decision making and improve the uptake of appropriate end-of-life care. Further research is warranted to expand on these findings in a larger, more diverse sample.

12124 Background: Most studies of clinician-patient communication use scales created ad hoc with unknown validity. To provide a standard measure for future studies, we developed and validated a new scale of clinician-reported skills in communicating difficult news: the Self-Efficacy for Medical Communication (SEMC) scale. Methods: Using evidence-based scale development guidelines, we created 16 items sampling a range of communication skills, including “Disclose difficult news in manageable chunks, so the patient is not overwhelmed,” and “Determine how to present information based on the patient’s emotional state.” Items are rated on Likert scales from 1 ( cannot do at all) to 10 ( highly certain can do). We constructed two forms—one assessing communication with patients and one with family—using identical items but replacing “patient” with “family/caregiver.” We examined the convergent and discriminant validity of the SEMC (correlations with similar and dissimilar measures) as well as its reliability and factor structure. A total of 221 clinicians working in oncology settings (physicians, nurses, medical students) completed measures online. Convergent measures included medical communication items from past studies; the Self-Perceived Communication Competence Scale to measure communication ability outside the medical realm; and the General Self-Efficacy and Occupational Self-Efficacy scales to measure overall self-efficacy/confidence. Discriminant measures included the Ten Item Personality Inventory to measure personality factors; the Maslach Burnout Inventory to measure job burnout; and the Satisfaction with Life Scale to measure well-being. Finally, the Marlowe-Crowne Social Desirability (MCSD) scale measured motivation to “look good” in responding to survey questions. Results: Mean scores were similar for the patient (126.36) and family (127.09) forms (max score 160), both with excellent reliability ( alphas =.94,.96, respectively). Because these forms were almost perfectly correlated ( r =.95, p

TPS3143 Background: Many rare cancers are understudied, and affected patients often have few if any standard treatment options and limited access to clinical trials. Comprehensive genomic profiling (CGP) is a robust tool to efficiently identify the genomic alterations of cancer and can be applied to study rare tumors. The promise of this technology is to simultaneously enhance our understanding of diverse rare tumors and find matched therapies to benefit the individual patient in real-time. The COVID19 pandemic has challenged the “trial-centric” clinical trial infrastructure. Novel “patient-centric” trials are needed, especially for rare cancers. TRACK is a decentralized, patient advocacy-initiated trial that aims to establish whether patients with rare tumors and cancer of unknown primary can benefit from matched molecular therapy as dictated by their CGP results. Methods: This is a national open label, non-randomized, multi-center (includes community and academic centers) pragmatic study in rare tumors ( 6 months; PR; CR), and high-grade toxicities (as available) of matched targeted therapy. Exploratory endpoints include matching rates; understanding genomic correlates of response; concordance rates between CGP from tissue and blood, time-to-treatment failure, and the impact of the cancer treatment from the patient perspective (via ESAS-FS, a validated patient-reported outcomes (PRO) instrument). All eligible and enrolled patients, regardless of whether receiving matched treatment or not, will be followed for a minimum of one year in a uniform way such that the treatment efficacy and outcomes can be assessed in standard formats. The study is open with six patients enrolled at time of submission. Clinical trial information: NCT04504604.

12113 Background: The symptom burden experienced by patients with cancer who contract the COVID-19 (C19) infection remains to be fully understood. To accurately assess this symptom burden, we developed a valid, reliable patient-reported outcome (PRO) measure of C19 symptoms combined with a known measure of cancer symptom burden. Methods: Within the institutional initiative on COVID-19 and cancer named Data-Driven Determinants for COVID-19 Oncology Discovery Effort (D3CODE), patients with cancer and PCR-positive C19 tests were invited to participate in this longitudinal study after providing consent. Pts completed the EQ-5D-5L and the 13 symptom severity and 6 interference items of the core MD Anderson Symptom Inventory (MDASI) plus 14 COVID-specific symptom items generated from literature and expert review. Items were measured on a 0-10 scale, 0 = none to 10 = worst imaginable symptom or interference. Demographic and disease information was collected. Psychometric procedures determined validity and reliability of the MDASI-COVID. Results: 600 pts enrolled, mean age 56.5y (range 20 to 91y). 59% female, 80% white. 78% solid tumors, 19% heme cancers. 12.5% required hospitalization for C19. Median number of days between positive C19 test and PRO completion was 17 days. Mean overall health rating on EQ-5D-5L was 78.3 (SD 19.6), best being 100. Highest mean (M) severity symptoms on the MDASI-COVID were fatigue (M 3.45, SD 2.17), drowsiness (M 2.50, SD 2.89), sleep disturbance (M 2.44, SD 2.99), malaise (M 2.37, SD 3.05), and distress (M 2.27, SD 2.90). Most severe (≥ 7) symptoms) reported were fatigue (21.3% of pts), change in taste (14.8%), change in smell (14.4%), malaise (14.3%), sleep disturbance (14.3%), and drowsiness (14%). Internal consistency (Cronbach α) of the 27 symptom items was 0.957, of the 6 interference items was 0.937. Mean severity of the 27 symptom items was significantly correlated with overall EQ-5D-5L health rating (correlation = -0.45, P < 0.0005), demonstrating concurrent validity. Mean symptom severity and interference showed known-group validity between patients who required C19 hospitalization (symptom M 2.32, SD 2.09; interference M 3.29, SD 3.02) and those who did not (symptom M 1.69, SD 1.85; interference M 2.20, SD 2.64) (symptom P 0.007; interference P 0.004). Conclusions: We have validated a novel PRO, the MDASI-COVID, to quantify the combined symptom burden in patients with cancer and COVID-19. This measure allows longitudinal evaluation of COVID-19 on cancer symptom burden and provide clinicians with an accurate tool for ongoing symptom assessment and management. Longitudinal analysis on long-term symptoms related to COVID-19 and cancer are ongoing.

6510 Background: Most COVID-19 (C19) vaccine trials excluded patients with active cancer. Here, we report our real-world patient-reported and clinical outcomes of BNT162b2 mRNA C19 vaccine in patients with cancer. Methods: Our institutional Data-Driven Determinants for COVID-19 Oncology Discovery Effort (D3CODE) follows a longitudinal observational cohort of pts w cancer getting C19 vaccine. Pts complete a validated PRO tool, MD Anderson Symptom Inventory (MDASI, 13 core, 6 interference plus 17 items of symptoms from prior vaccine trials) pre-dose 1, then daily x 6d, then weekly, then on day of dose 2, then daily x 6d, then weekly x 3w. Demographics, cancer variables, prior immune checkpoint inhibitors (ICI), C19 status pre- & post-vaccine are aggregated via Syntropy platform: Palantir Foundry. Primary outcome is incidence of PRO symptoms bw dose 1 & 2 across AYA 15-39y, mid-age 40-64y & senior 65y+ cohorts. Secondary outcomes include PRO symptom incidence post-dose 2, post-vaccine change in cancer symptoms, post-vaccine symptom severity based on prior ICI, and confirmed C19 > 7 days post-dose 2. First planned 8-wk interim analysis is reported here. Results: 6388 pts w cancer (4973 w mets) received a BNT162b2 vaccine dose (4811 both doses, 1577 received one & await dose 2). Overall, median age 64y (range 16-95y); 382 AYAs, 2927 mid-age, 3079 seniors (65-70y n = 1158, 70-79y n = 1521, 80-89y n = 378, 90y+ n = 22). 4099 (64%) are White, 823 (13%) AA, 791 (12%) Hispanic, 441 (7%) Asians. Primary cancers: breast (1397), GU (821), heme (775), thoracic/HN (745), and CRC (385). Prior to dose 1, 1862 had no prior systemic tx while 4526 pts did including 3243 who had only non-IO tx (chemo, targeted tx), 1,283 had immunotherapy including 857 who had ICIs prior to dose 1. Patient-reported symptoms after C19 Vaccine: Of 6388 pts, 4714 (74% response rate, median age 67y, range 16-95y) completed 16485 PRO surveys. After 2 doses, seniors reported lower mean scores vs mid-age or AYAs on 22 of 36 symptoms including injection site pain, palpitations, itch, rash, malaise, fevers/chills, arthralgia, myalgia, headache, pain, fatigue, nausea, disturbed sleep, distress (p < 0.05). Pts w prior ICIs had higher severity of itch, rash (p < 0.05) from baseline after both dose 1 & 2 vs pts without systemic tx. Post dose 1, pts with prior ICI had higher increase in fatigue, malaise, itch, rash, myalgia, anorexia from their baseline vs pts without systemic tx (p < 0.05). C19 Outcomes: Of 6388 pts, 616 had a C19 test at any time post-dose 1: 23 (0.36%) tested positive of whom 20 (0.3%) were between dose 1 & 2; two (0.031%) were within 7 days post-dose 2, and one patient (0.016%) tested positive 16 days after dose 2, requiring admission. Conclusions: This real-world observational cohort demonstrates post-vaccine symptom burden and outcomes in patients with cancer. Second interim analysis is planned at 16 weeks.

11001 Background: Few studies have used comprehensive validated measures to investigate the incidence and impact of workplace sexual harassment experienced by physicians (and none, to our knowledge, by oncologists). Methods: We conducted a cross-sectional survey of ASCO’s Research Survey Pool with targeted social media outreach to examine the prevalence and types of sexual harassment (SH) experienced by oncologists. Using the Sexual Experiences Questionnaire (SEQ), we measured their work experience of three SH forms (gender harassment [GH], unwanted sexual attention [USA], sexual coercion [SC]) in the past year by institutional insiders (peers/superiors) and patients/families separately. Controlling for race, career stage & specialty, multivariable (MV) regression models assess the impact of SH (independent variable) on 4 dependent variables of mental health (MHI5), perceptions of workplace safety (single item), job satisfaction (MOAQ) & 3-item measure of turnover intentions (in non-trainees). Results: Of 271 respondents, 250 were physicians in practice and 21 were residents/fellows; 153 (56%] were women, 168 (62%) practiced in academic settings & 227 (84%) were medical oncologists. SH by peers/superiors was reported by 189 (70%) overall, including 80% of women and 56% of men (p

TPS12136 Background: Patients w advanced cancer participating in Phase I trials carry a high symptom burden from cancer and prior therapies. Our prior work shows patients on phase I trials w multiple active symptoms impacting their immediate quality of life with implications on toxicities and clinical outcomes on subsequent therapy. To identify an effective scalable approach to comprehensive symptom management for patients w adv cancer on phase I trials, we leveraged the increased technology use to design a technology-enhanced symptom management and palliative care intervention (TEC). Methods: Patients w adv cancer seen in the phase I clinic will be given the Edmonton Symptom Assessment System (ESAS), a validated patient-reported outcomes (PRO) tool of common cancer symptoms to identify those with a high symptom burden defined as ≥4 out of 10 on >1 ESAS symptom and a Global Distress Score (GDS) of ≥20. The GDS, a validated score of overall symptom intensity derived from the ESAS, is comprised of 6 physical (pain, fatigue, nausea, drowsiness, appetite, shortness of breath) & 2 psychosocial symptoms (depression, anxiety), and overall wellbeing. TEC is an innovative patient-centered care program of strategic vigorous symptom management where standard-of-care clinic visits are complemented by proactive symptom monitoring between clinic visits remotely and through provider-initiated calls. In this pilot randomized study, we will determine the effect sizes of High-Intensity TEC (HI-TEC; q3day remote PRO assessments w preset provider-initiated call bw visits), Low-Intensity TEC (LO-TEC; q5day remote PRO assessments w preset provider-initiated call bw visits), and Standard Palliative Care (no preset provider contact bw visits). Our guiding hypothesis is that a comprehensive, proactive, technology-enhanced symptom management program led by a Palliative Care team can mitigate the high symptom burden of patients with advanced cancers enrolling in phase I trials. The primary objective assesses the effect size of each TEC intervention on the GDS measure of symptom burden prior to C1D1 on phase I trial. Our working hypothesis is that HI-TEC and LO-TEC will be associated with a lower overall symptom burden signifying symptom optimization prior to starting on a phase I trial. Secondary objectives aim to estimate the effect size of TEC on the following: Symptom burden over 12 weeks on a phase I trial using ESAS, quality of life using FACIT-Sp, PRO-CTCAE and patient satisfaction using FAMCARE-P13. clinical outcomes at 6 months including OS, treatment outcomes (interruptions, dose reductions, discontinuation, time on trial) and quality metrics for end-of-life (EOL) (chemotherapy in the last 14 days of life, ICU admit in last 30 days of life, death without hospice or < 3d of hospice). Qualitatively assessment of patients’ + caregivers’ perceptions of receiving TEC-based cancer care. Clinical trial information: NCI-2020-07465.

e24129 Background: Few studies have examined the coping mechanisms among patients with advanced cancer seen by palliative care. The objective of the study was to evaluate coping strategies in advanced cancer patients and identity risk factors for maladaptive coping. Methods: The authors conducted a secondary analysis of a cross-sectional survey on chemical coping. We prospectively enrolled patients with advanced cancer from a Supportive Care Clinic and documented the patient demographics, symptom expression (Edmonton Symptom Assessment System), Zubrod performance status, substance use history including tobacco, and coping strategies (the Brief COPE Questionnaire). Univariate and multivariate analyses were performed to identify risk factors for the use of maladaptive coping strategies. Results: Among 399 patients, the most common malignancies were gastrointestinal (21%) and breast (19%). Cancer patients frequently incorporated adaptive coping strategies including acceptance (86.7%), emotional support (79.9%), religion (69.4%), active coping (62.4%), instrumental support (48.4%), positive reframing (48.6%), planning (49.6%), and infrequently, humor (18.5%). Common maladaptive strategies included self-distraction (36.6%) and venting (14.5%), while self-blame (6.3%), denial (4.5%), behavioral disengagement (1.8%), and substance use (1.0%) were infrequently reported. On univariate analysis, venting was significantly associated with anxiety and depression, female gender, and tobacco use; and self-distraction was significantly associated with younger age, gender, depression, dyspnea, and a post-secondary education (P

Patients ≥65 years with cancer remain underrepresented in clinical trials, particularly in phase I clinical trials. We analyzed the clinical course of patients ≥65 years treated on phase I clinical trials with particular emphasis on toxicities. We identified 347 consecutive patients ≥65 years with advanced cancer in our phase I clinic from 01/2004-12/2009 and analyzed disease characteristics, toxicities, survival and response. Overall, 251 patients received a targeted agent, of whom 241 (96%) received an investigational, non-FDA-approved drug. Clinical benefit (complete response + partial response + stable disease ≥ 6 months) was noted in 61 patients (18%). Eighty-nine patients (26%) had grade 3/4 toxicity, commonly hematologic, including 6 dose-limiting toxicities and 1 treatment-related death ( 1, prior radiation, ≥5 prior treatments; p 1, thrombocytosis, >2 metastatic sites, and elevated lactate dehydrogenase (p

Background Age disparities among cancer clinical trial participants are pervasive and worsening over time. Identification of factors associated with age disparities is critical to improve enrollment of older patients on trials. The incidence and impact of trial eligibility criteria that exclude patients on the basis of age remains opaque. Methods ClinicalTrials.gov was queried for completed oncologic randomized controlled trials (RCTs). Phase 3 RCTs assessing a therapeutic intervention among adult cancer patients were included. Trial eligibility criteria were assessed using the ClinicalTrials.gov website as well as trial publications and protocol documentation. Results Seven hundred and forty-two trials met inclusion criteria, with a total combined enrollment of 449,720 patients. Upper age restriction enrollment criteria were identified for 10.1% of RCTs; the median age cutoff for restricted trials was 72 years (interquartile range 70–80 years). Linear regression modeling revealed decreasing incidence of age restriction criteria over time, at a rate of −1.1% annually (p = .03); trials initiating enrollment in 2002–2005, for example, had a 16.1% rate of age-restrictive eligibility criteria, compared with 7.6% for trials initiating enrollment in 2010–2014. Conclusion Use of eligibility criteria that explicitly exclude patients on the basis of age appears to be decreasing with time. Future efforts should aim to better characterize the relationship between eligibility criteria (such as those that exclude patients on the basis of specific organ function) and their association with age disparities among enrolled patients.

Emerging modalities of communication have transformed the landscape of information dissemination particularly in the context of health care. Within oncology, stakeholders in all roles have formed both role-specific and multidisciplinary communities within the modalities of social media. Two platforms with particularly high adoption and penetration within oncologic practice for clinicians and general oncologic care as well as for patients and the community have been Facebook and Twitter. On both platforms, patients have come together to form disease-specific (or even mutation-specific) groups ripe with discussion on all aspects of their cancer, including disease and treatment symptoms, novel therapeutics, and clinical trial participation. Similarly, clinicians have united within professional communities to facilitate collaboration and community building in this rapidly changing medical practice landscape. Here, we investigate the current state of stakeholder engagement within social media and review strategies and platforms to maximize the impact of social media for patients and clinicians.

The use of artificial intelligence (AI) has become an integral part of patient care, but there are concerns about the impact of non-human decision assistance on patient outcomes. This commentary focuses on how AI can assist oncologists and benefit patients.

This chapter presents data pertaining specifically to pulmonary and CNS-related metastases from colorectal carcinoma. Most studies on these unusual sites of metastatic disease do not differentiate between rectal and colonic cancers. The data would suggest in small series that selected surgical resection in combination with adjuvant chemoradiation may lead to improved survival in these settings. There are prognostic factors which are associated with a better outcome based upon the locale of the tumor, the performance status, a longer disease-free interval and control of the primary without extrametastatic disease.

Emerging efficacy data have led to the emergency use authorization or approval of COVID-19 vaccines in several countries worldwide. Most trials of COVID-19 vaccines excluded patients with active malignancies, and thus data on the safety, tolerability and efficacy of the vaccines in patients with cancer are currently limited. Given the risk posed by the COVID-19 pandemic, decisions regarding the use of vaccines against COVID-19 in patients participating in trials of investigational anticancer therapies need to be addressed promptly. Patients should not have to choose between enrolling on oncology clinical trials and receiving a COVID-19 vaccine. Clinical trial sponsors, investigators and treating physicians need operational guidance on COVID-19 vaccination for patients with cancer who are currently enrolled or might seek to enrol in clinical trials. Considering the high morbidity and mortality from COVID-19 in patients with cancer, the benefits of vaccination are likely to far outweigh the risks of vaccine-related adverse events. Herein, we provide operational COVID-19 vaccine guidance for patients participating in oncology clinical trials. In our perspective, continued quality oncological care requires that patients with cancer, including those involved in trials, be prioritized for COVID-19 vaccination, which should not affect trial eligibility.

12044 Background: While safety and dose-finding remain the primary objective of Phase 1 trials, the potential for clinical benefit has taken a greater meaning in the last decade with the novel therapies. With data from phase I trials being submitted for regulatory approval, the finer details of these studies are under even more scrutiny: in particular, do the trial participants reflect the general patient population for whom the drug may be indicated? To that end, we investigated age-based enrollment on phase I clinical trials over time. Methods: We queried a prospectively maintained database at a major phase I trials center to identify eligible patients and demographic + clinical variables including phase I trial characteristics, age at date of enrollment into 3 age-based cohorts: AYA ages 15-39y, mid-age 40-64y, older adults aged 65y+. We calculated descriptive statistics, and explored correlations (Pearson/Spearman) and associations (linear regression) between age and independent variables. Results: Over a 3-year period (1/1/17 to 12/31/19), we identified 6267 pts enrolled on 338 phase I trials. Median overall age 58.4y (range 15.5-95.1y). 729 (12%, median age 34.8y) were AYA, 3652 (58%, median age 55.4y) mid-age and 1886 (30%, median 70y) older adults, of whom 870 pts were aged 70-79y and 76 pts aged 80y+ (18 being >85y). There was no association b/w senior participation and year of enrollment (2017 31%, 2018 29%, 2019 30%, b/w age and type of therapy (i.e. targeted vs immunotherapy, etc.) or b/w age and # of drugs given on trial (single agent vs combo) (all p > 0.05). Conclusions: Older adults remain underrepresented on phase I trials esp. when compared to incidence of cancer in that age group (30% enrollment vs 60% incidence), a discordance more staggering in the oldest old pts (85y+; only 18 pts enrolled over 3 yrs when compared to 140,690 pts 85y+ w a new cancer dx in just 2019). Once enrolled, older adults received similar types of phase I therapies with comparable number of drugs as compared to middle age patients, i.e. older adults were just as likely to get immunotherapy or targeted therapy as well mono- vs combo therapy as mid-age pts. [Table: see text]

e24147 Background: Early referral to palliative care (PC) for patients w/ adv cancer is supported by compelling evidence from large RCTs demonstrating a lower symptom burden, higher QOL, and increased OS. However, these studies reflect pts who have self-selected to accept a PC referral and attend a PC visit. WHO/NCCN/ASCO guidelines support early integration of PC. We sought to characterize the referral patterns in our heme/onc practice to identify and mitigate the barriers to early PC adoption in the community setting. We began a concerted effort to discuss early PC referral w/ pts recently diagnosed w/ adv cancer at the time systemic therapy was initiated. Methods: To ensure real-world applicability of this study, we identified a large satellite clinic of a major academic center w/ access to PC on the main campus (located 20 miles from the satellite clinic). We retrospectively reviewed new pts age 18y+ w/ adv cancer, characterized PC referrals and outcomes. Using qualitative methodology, we identified pt-reported barriers to accepting PC care. Results: 407 new pts were seen; 168 w/ benign heme, 145 w/ early cancers, 94 w/ adv cancers. Of the 94 pts w/ adv cancers, 25 pts had one-time 2ndopinion visits, and 16 pts were not candidates for, or did not desire cancer treatment and directly enrolled on hospice. Our analysis cohort consisted of 53 pts w/ adv cancer pursuing life prolonging therapy. At initiation of treatment, 57 % (n=30) were not offered a PC referral, 22% (n=12) received a PC referral and attended the appt, however 21% (n=11) received a PC referral but did not attend the appt. A qualitative analysis of the 11 patients referred to early PC who did not attend the appt revealed; 5 patients scheduled an appt but did not attend (3-unknown reason, 2-hospitalized during appt, 1-lack of transportation), 2 pts were unreachable, and 4 pts were contacted but declined to schedule stating: “I feel pressured” “I want to hear what other treatment options I have” “I want to be treated first and then see if I need it” "I am overwhelmed with too many new doctors and visits". Conclusions: Despite the benefit of early PC referral in pts w/ adv cancer, we identified a considerable gap in its adoption in our community practice despite access to proximate PC clinic. Further studies are under design to address institutional and pt-related factors to improve real-world adoption of this critical service.

12021 Background: Despite compelling data supporting their use, patient reported outcomes (PROs) are not widely integrated into routine cancer care. In our Palliative Care (PC) practice, all patients complete the Edmonton Symptom Assessment Scale (ESAS), a simple, validated 10-item PRO tool which uses a 0 to 10 rating of 10 common symptoms (pain, fatigue, nausea, drowsiness, appetite, sleep, dyspnea, well-being, anxiety & depression). Our team has previously validated the Global Distress Score (GDS), a sum of 9 physical + psychosocial ESAS items. Here, we studied the implementation of the GDS as a streamlined way to capture the overall symptom burden while providing prognostic value. Methods: We queried a PC database for patients w metastatic cancer at time of 1st PC visit. GDS was calculated & grouped into 3 cohorts based on previous work & clinical experience: high (GDS of 35+), Moderate (16-34) or Low (0-15). Overall Survival was defined as time from 1st PC visit date to death. Regression analysis, ANOVA and t-tests were conducted. Results: 333 patients met the inclusion criteria: median age 62.4y (range 20.5-88.4y), 25 AYA (15-39y), 169 mid age (35-64y), 140 seniors (65y+); 190 female 143 male; median prior therapies 2 (range 0-11), 227 patients were in 2nd line + above therapy. Median ECOG PS 2; 124 patients w ECOG PS 3 & 33 w ECOG PS 4. 262 patients had died at time of analysis. Lower OS was associated w higher GDS (r 0.21, P < 0.001). OS in Low, Mod, High GDS cohorts was 13.1m, 7.9m, & 3.7m, respectively (p < 0.001). There were no sig OS difference between 3 age cohorts (AYA 5.2m, mid age 6m, seniors 5.4m, p0.56). Conclusions: Higher GDS score was associated with a clinically significant decrease in overall survival highlighting the potential of the ESAS as a PRO tool in prognostication and clinical decision making for patients with advanced cancers with a high symptom burden. In the realm of increasingly complex PRO instruments, the ESAS represents a simple, well-validated tool which, in our studies and 25 years of clinical experience, takes the patient less than a minute to complete, with subscores such as the GDS which carry a highly prognostic utility for patients with advanced cancers.