Does Scalp Cooling Have the Same Efficacy in Black Patients Receiving Chemotherapy for Breast Cancer?

LESSONS LEARNED

Despite U.S. Food and Drug Administration approval to reduce alopecia, data on efficacy of scalp cooling in Black patients with cancer are limited by lack of minority representation in prior clinical trials. Scalp cooling devices may have less efficacy in Black patients; additional studies are required to explore the possible causes for this, including hair texture and cap design.

BACKGROUND

The Paxman scalp cooling (SC) device is U.S. Food and Drug Administration (FDA)-approved for prevention of chemotherapy-induced alopecia. Studies report 50%-80% success rates and high patient satisfaction, yet there have been no studies of SC in Black patients. We conducted a phase II feasibility study of Paxman SC with a planned enrollment of 30 Black patients receiving chemotherapy for stage I-III breast cancer.

METHODS

Black patients who planned to receive at least four cycles of chemotherapy with non-anthracycline (NAC) or anthracycline (AC) regimens were eligible. Alopecia was assessed by trained oncology providers using the modified Dean scale (MDS) prior to each chemotherapy session. Distress related to alopecia was measured by the Chemotherapy Alopecia Distress Scale (CADS).

RESULTS

Fifteen patients enrolled in the intervention before the study was closed early because of lack of efficacy. Median MDS and CADS increased after SC, suggesting increased hair loss (p < .001) and alopecia distress (p = .04). Only one participant was successful in preventing significant hair loss; the majority stopped SC before chemotherapy completion because of grade 3 alopecia (>50% hair loss).

CONCLUSION

SC may not be efficacious in preventing alopecia in Black women. Differences in hair thickness, hair volume, and limitations of cooling cap design are possible contributing factors.

Scalp cooling to prevent chemotherapy induced alopecia (CIA) in black patients: Differences in efficacy?

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Background: The Paxman scalp cooling device has been used for over 20 years to prevent CIA, obtaining FDA clearance in the U.S. in 2017. Prior studies reported 50-80% success and high patient satisfaction yet included few or no black patients. In the U.S. this may reflect disparities in access due to cost, awareness, or availability. We opened a prospective observational study combining patient-reported outcomes with clinical assessments of alopecia and planned to deliver scalp cooling to 30 black patients receiving chemotherapy for breast cancer. Methods: Patients who self-identified racially as black, had a new diagnosis of stage I-III breast cancer, and planned to receive chemotherapy with taxane-containing regimens were eligible. Anthracycline (AC) and non-anthracycline (NAC) chemotherapy agents were included; costs for the intervention were covered by Paxman and internal philanthropic funding. Patients who declined scalp cooling were approached for enrollment as controls. Primary endpoints were grade of alopecia as measured by providers and patient self-report using Modified Dean Scale and Visual Analog Scale (VAS) respectively. Hair preservation was defined as <50% hair loss (<grade 2) by Dean and score < 50 on VAS. Secondary endpoints were alopecia by NCI grading scale and psychosocial from CADS and EORTC QLQ BR45 questionnaires. Results: 15 out of 30 planned participants enrolled by February 2020 with interim analysis and hold in accrual due to lack of efficacy. Four patients remain on treatment. Of 11 scalp cooling patients who completed chemotherapy, 0 prevented significant alopecia. Nine discontinued use of scalp cooling before completion (1 due to scheduling, 8 due to >grade 3 alopecia). The 2 patients who used scalp cooling for the duration had >grade 3 alopecia before the last cycle of treatment. Conclusions: Scalp cooling is an important supportive therapy that can reduce chance of alopecia, a bothersome side effect for patients. Our experience indicates decreased efficacy in black patients with both AC and NAC regimens. This is an important negative result to explore. Discussions with the Paxman team and providers with expertise in alopecia are underway to explore contributing factors such as hair thickness, prior hair treatments, and cap design. [Table: see text]

Abstract P2-13-07: A lifestyle intervention program for obese breast cancer survivors using shared appointments, technology, and community partners in an underserved area

Introduction: Obesity is associated with a 41% increase in all-cause mortality in breast cancer survivors. The majority of patients treated for breast cancer at our center are obese and most patients are racial and ethnic minority women living in low-income neighborhoods. Numerous barriers exist for weight management and physical activity interventions in this patient population. We aimed to assess the feasibility of a lifestyle intervention in 30 obese breast cancer survivors using shared medical appointments and community partnerships. Methods: 30 patients with stage I-III breast cancer with a BMI ≥30 kg/m2 treated in the preceding 5 years were enrolled through medical oncology and breast surgery clinics. All participants were given a Fitbit® to monitor physical activity and a $25 grocery gift card for a shopping trip with a nutritionist. Participants were expected to attend at least 10 group shared medical appointments (SMA) offered weekly on-site. SMA included nutrition education, cooking instruction, exercise classes or survivorship lectures. We collected participant feedback on SMA. Initial study end points were feasibility of intervention delivery measured by number of SMA appointments and physical activity (steps) measured by Fitbit®. The study was divided in three phases. Phase I/II: patients were required to have baseline evaluations, attend 10 SMA, share Fitbit® information, and complete validated eating and health questionnaires (REAP-S and SF-36, respectively) at scheduled time intervals. In phase III, we provided participants with a binder with information on low-cost fitness and nutrition options in patient’s neighborhoods and awarded prizes for high-performing participants. Results: We enrolled 30/30 participants in less than 6 months from opening; 80% were African-American. Three enrolled subjects did not complete more than one SMA. Participants attended an average of 10.2 SMA; attendance ranged from 5-10 participants per session. 63% (n=19) of participants attended the required 10 or more of the required SMA sessions (average compliance with clinic appointments is 50-60%). Participants had an average of -0.18 kg weight loss; 43% (n=13) lost weight and 40% (n=12) gained weight during the intervention (5 participants lost to follow-up or did not have weight changes during intervention). The range of weight loss was 0.1 to 7 kg and weight gain 0.2 to 6.2 kg. Twenty-four participants had consistent Fitbit® steps recorded; 19 increased their average of daily steps and 7 decreased from baseline, however, the steps varied significantly week to week. At baseline, average daily steps was 3,977 (range 200 to 18,432; SD = 4,236 steps) and 5,526 (range 728 to 14,006; SD = 2437 steps) post- intervention. The number of participants who increased steps (n=19) was significantly greater than the number of participants who decreased steps (n=7; p=0.014). The total cost of the intervention was $150 per patient. Challenges to study implementation included collection of Fitbit® data at consistent intervals and the available times for offering SMA; these times precluded enrollment and compliance for patients who work during the day. Conclusions: Our pilot study of a low-cost lifestyle intervention program appears to be feasible and beneficial for obese patients in a largely underserved community. SMA contributed to compliance and had positive feedback; patients expressed high levels of interest and engagement in the intervention. After the intervention participants were motivated to continue with lifestyle modifications and formed a Facebook® page to maintain connections. In the future, the goal is to incorporate this program as part of our survivorship care and expand it to other malignancies and potentially to other sites.

Citation Format: Ilana Schlam, Princess Alintah, Christopher Gallagher, Marc Boisvert, Ami Chitalia, Shruti Tiwari, Patrick Martone, Chiranjeev Dash, Kristi Graves, Asma Dilawari. A lifestyle intervention program for obese breast cancer survivors using shared appointments, technology, and community partners in an underserved area [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-13-07.

Weight change after (neo)adjuvant chemotherapy in a cohort of breast cancer survivors: Trends by race, hormone positivity, and chemotherapy regimen

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Background: It is estimated that over 50% of breast cancer survivors gain weight during treatment; patients receiving chemotherapy are at higher risk for weight gain. Previous studies have reported limited information about weight gain with current chemotherapy regimens. Methods: Individual data were collected from a cohort of 98 breast cancer patients treated with neoadjuvant or adjuvant chemotherapy between 2015 and 2017 at Lombardi Comprehensive Cancer Center. Weight was recorded from baseline visits and ≥ 1 visit following completion of chemotherapy. Regimens were grouped into anthracycline- (AC) and non-anthracycline-based (NAC) chemotherapy. Results: Overall, 49% ( n = 48) of patients gained weight after chemotherapy, though African-American patients demonstrated higher baseline BMI. Patients with ER-positive cancers displayed greater weight gain than hormone-negative counterparts ( p = 0.04); PR- or HER2-status was not associated statistically significant changes in weight ( p = 0.12 and 0.82, respectively). Among patients who did gain weight, NAC was associated with greater weight gain (4.47kg) than AC-based regimens (2.54kg) ( p = 0.03). Conclusions: ER positivity and NAC may serve as independent predictors of weight gain during chemotherapy. Further studies might consider further analyzing these trends to demonstrate additional long-term patterns. Baseline and After Chemotherapy BMI (kg/m2) and Weight Change (kilograms and percentage change) (P* derived from ANOVA). [Table: see text]