“Partner”, “Caregiver”, or “Co-Survivor”—Might the Label We Give the Partners of Cancer Patients Affect the Health Outcome of the Patients and Their Partners?

Having a life partner significantly extends survival for most cancer patients. The label given to the partners of cancer patients may, however, influence the health of not just the patients but their partners. “Caregiver” is an increasingly common label for the partners of patients, but it carries an implicit burden. Referring to partners as “caregivers” may be detrimental to the partnerships, as it implies that the individuals are no longer able to be co-supportive. Recognizing this, there has been some effort to relabel cancer dyads as “co-survivors”. However, many cancer patients are not comfortable being called a “survivor”, and the same may apply to their partners. Cancer survivorship, we argue, could be enhanced by helping keep the bond between patients and their partners strong. This includes educating patients and partners about diverse coping strategies that individuals use when facing challenges to their health and wellbeing. We suggest that preemptive couples’ counselling in cancer centers may benefit both patients and their partners.

A National Survey of Palliative Care Team Compositions

Purpose: It is unclear how well palliative care teams are staffed at US cancer centers. Our primary objective was to compare the composition of palliative care teams between National Cancer Institute (NCI)-designated cancer centers and non-NCI-designated cancer centers in 2018. We also assessed changes in team composition between 2009 and 2018. Methods: This national survey examined the team composition in palliative care programs at all 61 NCI-designated cancer centers and in a random sample of 60 of 1252 non-NCI-designated cancer centers in 2018. Responses were compared to those from our 2009 survey. The primary outcome was the presence of an interprofessional team defined as a palliative care physician, nurse, and psychosocial member. Secondary outcomes were the size and number of individual disciplines. Results: In 2018, 52/61 (85%) of NCI-designated and 27/38 (71%) non-NCI-designated cancer centers in the primary outcome comparison responded to the survey. NCI-designated cancer centers were more likely to have interprofessional teams than non-NCI-designated cancer centers (92% vs 67%; P = .009). Non-NCI-designated cancer centers were more likely to have nurse-led teams (14.8% vs 0.0%; P = .01). The median number of disciplines did not differ between groups (NCI, 6.0; non-NCI, 5.0; P = .08). Between 2009 and 2018, NCI-designated and non-NCI-designated cancer centers saw increased proportions of centers with interprofessional teams (NCI, 64.9% vs 92.0%, P < .001; non-NCI, 40.0% vs 66.7%; P = .047). Conclusion: NCI-designated cancer centers were more likely to report having an interprofessional palliative care team than non-NCI-designated cancer centers. Growth has been limited over the past decade, particularly at non-NCI-designated cancer centers.

Composition of the Current Academic Radiation Oncology Workforce in Comprehensive Cancer Centers

PURPOSE: The landscape of the profession of academic radiation oncology is constantly changing. We sought to determine the demographic makeup of the current academic radiation oncology workforce. MATERIALS AND METHODS: Internet web site searches of the 51 National Cancer Institute–designated Comprehensive Cancer Centers (CCCs) were conducted in September 2019. The Scopus database was subsequently searched in December 2019 to ascertain the h-index for each radiation oncologist. Geographic location was economically stratified (New York, California, Massachusetts, Illinois, and Washington DC) as previously reported. Race and binary sex were attributed by authors using publicly available information. Univariate analysis involved the chi-square test; a multivariable model considered several factors including rank and sex. RESULTS: Of 993 radiation oncologists at CCCs, 53.6% are junior faculty, 24.8% associate professors, and 21.7% full professors. The average radiation oncologist at a CCC has been a physician for 19.7 (standard deviation = 11.3) years; 4.7% (47/993) are under-represented minorities. 24.6% of men and 15.5% of women were full professors, a statistically significant difference ( P = .001). Of the 51 department chairs, 11.8% are women and 5.6% are under-represented minorities. There are fewer female than male program directors in the most economically stratified locations ( P = .02). The mean h-index for all faculty is 17.6 (standard deviation = 16.9), and significantly differs between junior faculty (8.21), associate professors (18.46), and full professors (40.05; P < .0001). It also differs between men (19.35) and women (14.11). On multivariable analysis, sex, academic rank, and a secondary advanced degree were independently significant correlates of h-index. CONCLUSION: Among academic radiation oncologists at CCCs, under 5% are under-represented minorities, men are significantly over-represented among senior faculty, and women have significantly lower h-indices than men.

Multiple Myeloma Patients Treated at Academic Centers Have Improved Survival Outcomes

Background Treatment at academic cancer centers (ACs) has been associated with improved outcomes across hematologic malignancies, including acute myeloid leukemia and non-Hodgkin lymphoma. ACs offer the benefit of high treatment volume in addition to enrollment in clinical trials, involvement in post-graduate education, and expanded access to diagnostic and treatment related services. Though studies on multiple myeloma (MM) have demonstrated a survival benefit with treatment at both high-volume centers and at NCCN designated cancer centers, this is the largest study to date examining the benefit of academic centers. Methods The National Cancer Database was utilized to obtain data on patients diagnosed with MM between 2004-2017 for which data on treatment facility type was available. Using the Commission on Cancer facility categories, patients treated at ACs were compared to those treated at non-academic centers (NACs), including small and large volume community cancer centers. Demographic and treatment characteristics were compared between centers, with median overall survival (OS) assessed by Kaplan Meier. Cox regression analysis was used to asses the HR for OS by facility type, and adjusted on multivariate analysis for age, sex, race, insurance, time to treatment, and use of autologous transplant. Results Of the 179,769 MM patients available, 42.4% were treated at ACs (p&lt;0.05). Patients treated at ACs were younger than those treated at NACs (mean age 64.7 years vs. 69.2 years, p&lt;0.05) and patients &gt; 75 years of age were more often treated at NACs (35.6% vs. 20.3%, p&lt;0.05). ACs were more likely to treat Black and other minority patients, with Black patients representing 23.5% vs. 18%, and other minorities 5.6% vs. 3.6% of patients treated at ACs vs. NACs, respectively; all p&lt;0.05. Academic centers were more likely to treat uninsured patients (6.3% vs. 4.1%), patients on Medicaid (7.9% vs. 5.5%) as well as privately insured patients (41.5% vs. 29.6%),( all p&lt;0.05). The majority of Medicare patients were treated at NACs (60.8%), p&lt;0.05. The time from diagnosis to treatment was longer at ACs, at 32.4 vs. 26.5 days (p&lt;0.05), and patients were more likely to receive autologous stem cell transplant as first line treatment at ACs (6.3% vs 2.2%, p&lt;0.05). While clinical trial data is limited in the NCDB, the majority of the 495 patients treated under a clinical trial were treated at ACs (393 vs 102, or 0.5% vs 0.1% of patients treated at ACs vs NACs, p&lt;0.05). Median OS at ACs was significantly longer than at NACs, with median OS of 67.8 months (95% CI 66.89-68.79 months) compared to 38.6 months (95% CI 38.15-39.13 months) at NACs, p&lt;0.05. One year OS was 75% vs 61%, 5-year OS 48% vs 33%, and 10-year OS of 22% vs 12% at ACs vs NACs, respectively; p&lt;0.05. When adjusted for age, gender, race, insurance, time to treatment, and use of autologous transplant on Cox Regression analysis, the improvement in OS remained. Patients treated at AC had hazard ratio of 0.77 for all-cause mortality (95% CI 0.756-0.784) when referenced to NACs on multivariate analysis (p&lt;0.05). Conclusion Patients with MM had significantly improved survival when treated at academic centers compared to all other facility types. The improvement in OS remained when controlled for available treatment and demographic features. Multiple factors, including specialized care, trial enrollment, and early access to autologous stem cell transplant may contribute to these improvements. Further investigations into the factors contributing to such disparities are required to standardize care and improve overall outcomes. Figure 1 Figure 1. Disclosures Tantravahi: CTI BioPharma: Research Funding; Novartis: Research Funding; BMS: Research Funding; Abbvie Inc.: Research Funding; Karyopharm Therapeutics Inc.: Consultancy, Honoraria, Research Funding. Sborov: SkylineDx: Consultancy; GlaxoSmithKline: Consultancy; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sanofi: Consultancy.

Validation of FLIPI1, FLIPI2, PRIMA and POD24 in Patients with Extranodal Follicular Lymphoma: A Cohort from Two Cancer Centers in Peru

Background Follicular lymphoma (FL) is the second most common non-Hodgkin lymphoma subtype, being nodal involvement its main characteristic. However, data about Extranodal (EN) involvement is not reported in Latin American patients. Our aim was to evaluate the clinical features, treatment patterns outcomes of Peruvian patients with ENFL from two cancer centers and validate FLIPI1, FLIPI2, PRIMA and POD-24 prognostic index in our cohort. Methods: This is a retrospective study, including all patients with a pathological diagnosis of FL grade 1 to 3A treated at the National Institute of Neoplastic Diseases and Oncosalud, both in Lima, Peru from 2010 to 2019. All cases were reviewed by specialized pathologists. Baseline clinical and pathological data were collected. Responses were assessed based on the Lugano criteria. Overall survival (OS) was estimated using the Kaplan-Meier method. Differences were compared with the log-rank test. Results A total of 86 patients were evaluated. The median age was 61 years (30-91), 43% were male, 20% had bulky disease (≥6 cm in diameter), 51% had stage III/IV disease, 31% had hemoglobin &lt;12 g/dl, 11% had serum albumin &lt;3 g/dl, 25% had elevated serum LDH, 31% had B2-microglobulin ≥3,5 mg/l, 27% had bone marrow involvement and 19% had lymph node sites &gt;4. The most frequent EN sites were gastrointestinal, bone marrow, cutaneous and breast with 22%, 21% and 9%, and 6%% respectively. Low, intermediate and high-risk FLIPI1 was seen in 56%, 23% and 21% of patients, respectively. Low, intermediate and high-risk FLIPI2 was seen in 28%, 58% and 14% of patients, respectively. Low, intermediate, and high PRIMA was seen in 57%, 10% and 33%, respectively. 55 patients (64%) received any treatment, 47% received CHOP ± rituximab (R), 16% CVP ± R, 22% radiotherapy alone, 9% CHOP, 15% other treatments. Response data were available in 44 patients with complete response in 45%, partial response in 43% and no response in 12%, for an overall response rate of 88%. From patients who received CHOP/CVP ± R, 13% patients had disease progression within 24 months of first treatment initiation (POD24). For the entire cohort (N=88) the median follow-up time was 2.1 years (interquartile range [IQR] 0.08-11.3), median overall survival was 8.25 years (IQR 4.4-not reached [NR]). 5y OS was71.7% (95% CI 55.3-82.9), figure 1. For the FLIPI group 5y OS for low, intermediate and high FLIPI were 86.9% (95% CI 64.7-95.6), 60.1% (95% CI 24.4-83.2) and 75.7% (95% CI 30.4-93.7), respectively (p=0.07; Figure 2). For the FLIPI2 group (N=58) 5y OS for low, intermediate and high FLIPI2 were 79.5% (95% CI 39.3-94.5), 77% (95% CI 55.3-89.2) and 86% (95% CI 33.4-97.8), respectively (p=0.86). For the PRIMA group (N=38) 5y OS for low, intermediate and high PRIMA was 86% (95%55.6-96.6), 100% (95% CI 100), and 80% (95% CI 42-95), respectively (p=0.80). Patients who had and did not have POD24 had median OS of NR (IQR 0.6-NR) and NR (IQR 2.69-NR), respectively. 5y OS for patients who had and did not have POD24 was 71.2 % (95% CI 48-85) and 75% (12.7-96), respectively (p&lt;0.87). Conclusion: Peruvian patients with ENFL showed a higher rate of female patients. Gastrointestinal involvement was the most common primary site. The OS rates is similar to our nodal involvement cohort. Chemoimmunotherapy is the standard approach to FL patients, which is associated with high rates of overall response. In our study FLIPI, FLIPI2, PRIMA and POD24 were not predictors for OS, but larger cohorts and longer follow-up are needed to find more accurate predictors of survival in these patients. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Stakeholder Perceptions of Barriers to Diverse Acute Myeloid Leukemia Clinical Trial Enrollment at Comprehensive Cancer Centers

Introduction Disparities in clinical trial enrollment have the potential to bias findings, limit generalizability, misdirect drug development, and reduce equitable access to novel therapy. Given the unique care delivery and clinical trial patterns of acute leukemia (AML), barriers to trial enrollment for underrepresented groups at the Comprehensive Cancer Centers (CCCs), where patients are often treated, may be distinct. Characterization of these barriers has been limited, preventing the development of interventions to overcome this disparity for AML. Methods We conducted an exploratory qualitative project to characterize barriers to trial enrollment and facilitators to overcome them through a series of focus groups and individual interviews. Participants were persons with AML who identified from one or more underrepresented groups and AML physicians, nurse practitioners, and physician assistants at three Dana-Farber/Harvard Cancer Center hospitals. A moderator guide was developed based on literature review and iteratively revised by study investigators. The questions enabled an open-ended multi-level (patient, provider, trial, institution, societal) exploration of five main domains, including familiarity and experience with clinical trials, factors influencing participation, participant concerns and enrollment supports, and facilitators to address enrollment barriers and disparities. Focus groups and interviews occurred through video conference or phone and were audio recorded and professionally transcribed. The interdisciplinary research team conducted a multi-step thematic analysis guided by framework analysis, and included prefigured and emergent codes. The team-based analysis synthesized data within and across participant types and focused on the identification of key patterns and prevalent themes related to barriers to clinical trial enrollment. Coding and analysis were assisted by NVivo 12 software. Results presented below highlight barriers that were considered relevant to the unique care delivery patterns of AML and clinically significant. Results 37 participants (19 patients; 18 providers) were recruited to focus groups (30) or interviews (7) between 5/6-6/28/2021. Participant characteristics are shown in Table 1. Patient participants were generally familiar with clinical trials. Despite limited explicit expression of enrollment hesitancy, both patients and providers noted multiple potential barriers to clinical trial enrollment specific to AML, including the rapidity from diagnosis to treatment; the inpatient setting of therapy; a lack of knowledge regarding incidence and enrollment demographics and their differences; restrictive trial design factors (e.g., hospital stays, mutation specificity); and concerns over the appropriateness and efficacy of a given trial relative to individual disease risk. Key enrollment barriers reported by participants are shown in Figure 1. Participant-identified facilitators to address these barriers are shown in Figure 2. Facilitators to overcome barriers specific to AML that were considered potentially impactful included: peri-enrollment peer support for patients, continuance of telehealth visits during trial participation, feedback and training to increase provider awareness and understanding of disparities and diverse communities, trial design augmentation to minimize restrictive barriers (centralized laboratory draws, multiple hospitalizations, white blood cell count and organ function criteria) and increase inclusivity (multiple language and culturally competent consent), and bolstering inter-institutional AML program partnerships to increase diverse patient referral. Conclusions Stakeholder-reported barriers to diverse AML clinical trial enrollment at CCCs include multiple disease-specific characteristics that reflect the unique care pattern of AML, such as concerns about restrictive trial design characteristics, the need to be inpatient, and the requirement to make decisions quickly after diagnosis. Several barriers and potential interventions at each level were considered impactful by both providers and patients. These formative data suggest a novel multi-level approach for overcoming AML enrollment disparities at CCCs is needed. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.