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Introduction Chemotherapy administration in real-world cancer care can differ extensively from clinical trials. It is important to understand real-world practice to identify dose reductions, delays, regimen changes and early discontinuations that impact cancer outcomes. Such variables require knowledge of intended regimens, which may not be well-documented in structured data in electronic health records (EHRs). We examined EHR data from the Kaiser Permanente Northern California (KPNC) site of the Optimal Breast Cancer Chemotherapy Dosing (OBCD) study to develop a process to identify each patient’s intended regimen. Methods In this study of women diagnosed and treated with primary stage I-IIIA breast cancer at KPNC from 2006-2019, and ages 18+y at diagnosis, we analyzed treatment patterns using structured EHR data on the drugs, dosages, and dates at which they were administered (from which intervals and total length can be derived). Chemotherapy agents were identified using the NCI’s CANMED database augmented with other sources. We used these data to categorize patients into the 22 drug combinations described in the National Cancer Care Network (NCCN) guidelines for breast cancer treatment. Within these 22 drug combinations, women were then subcategorized into 45 distinct chemotherapy administration schedules, defined as NCCN guideline regimens (NGRs). For this step, algorithms were developed that categorized patients into NGRs if they received the exact regimen described in the guidelines. For the second step, we conducted a manual review of the EHR data for patients who were unable to be categorized. This enabled us to gradually loosen the criteria (in terms of cycle intervals or number of cycles) so patients whose chemotherapy administration aligned closely with NGRs were categorized into each of the 45 NGRs. Clear patterns emerged of regimens that were administered to multiple patients, despite being outside of the NCCN guidelines, which we have defined as non-standard NGRs. For example, in the drug combination TC (cyclophosphamide and docetaxel) the NGR was TC every 21 days for 4 cycles. We found approximately 1 in 10 patients received 6 cycles, which we defined as a non-NGR. For the remaining uncategorized patients, medical chart abstraction was undertaken as a third step, at which point patients were categorized into either existing regimens or new non-NGRs if their intended regimen had not been previously described in the guidelines. Results Among 31,418 women with breast cancer, 12,427 (39.6%) received chemotherapy. We determined the intended chemotherapy regimens for 6,559 (52.8%) receiving the 45 NGRs using EHR data. We further expanded the algorithms through a manual review of the EHR data, which enabled us to categorize 2,977 (24.0%) additional women into their intended regimens. Abstracted medical notes were reviewed for the remaining patients for whom we had not been able to identify the intended regimen. Across both the manual review and abstraction processes, we were able to identify additional non-standard NGR regimens. In total, 9,536 (76.7%) of women were categorized into their intended regimen through the algorithm/manual review process, while 2891 (23.3%) of women underwent medical chart abstraction to identify the intended regimen. Conclusion Here, we describe the challenges and approaches to operationalize complex, real-world data to identify intended chemotherapy regimens at a granularity and scale not seen previously. We are adapting this method at a second OBCD study site, Kaiser Permanente Washington, where all women have undergone medical chart abstraction. We hope this methodology leads to increased feasibility and efficiency of use of large-scale clinical data, in turn improving cancer care delivery, patient outcome evaluation, and other real-world questions. Citation Format: Jenna Bhimani, Kelli O’Connell, Rachael P. Burganowski, Isaac J. Ergas, Marilyn J. Foley, Grace B. Gallagher, Jennifer J. Griggs, Narre Heon, Tatjana Kolevska, Yuriy Kotsurovskyy, Candyce H. Kroenke, Kanichi G. Nakata, Sonia Persaud, Donna R. Rivera, Janise M. Roh, Sara Tabatabai, Emily Valice, Erin J. Bowles, Elisa V. Bandera, Lawrence H. Kushi, Elizabeth D. Kantor. A methodology for using real-world data from electronic health records to assess chemotherapy administration in women with breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-16.

Background: Most cytotoxic drugs are dosed according to body surface area (BSA), yet not all patients receive the full BSA-determined dose. Prior work suggests that dose reduction may occur more frequently in obese patients, likely due to concern about inducing toxicity at high doses. Other factors, such as race/ethnicity, have been suggested to be associated with dosing, yet the factors associated with dose reduction remain poorly understood, with little known about dosing patterns in integrated healthcare delivery systems and how such patterns have changed over time. Methods: We examined chemotherapy dosing in 452 women diagnosed with stage I-IIIA primary breast cancer at Kaiser Permanente Northern California. All study participants were a part of the Pathways Study, diagnosed between 2006-2013, and treated with the common breast cancer regimen, ACT (cyclophosphamide and doxorubicin, followed by paclitaxel). We explored the association between obesity and dose reduction, and further explored other factors in relation to dose reduction, including various sociodemographic characteristics, tumor characteristics, and comorbidities. We assessed dosing using first cycle dose proportion (< 90% expected dose) and average relative dose intensity (ARDI, a metric of dose intensity over the entire course of chemotherapy). Results: Overall, 8% of women received a dose reduction >10% in the first cycle of chemotherapy and 21.2% of patients had an ARDI < 90%. Obesity was a strong predictor of dose reduction, both in the first cycle and across all cycles. In the first cycle, 21.9% of severely obese patients (body mass index, BMI: 35+ kg/m2) were dose reduced, whereas no normal weight patients (BMI: 18.5-< 25 kg/m2) experienced a first cycle dose reduction. Across all cycles, 38.4% of severely obese women had an ARDI < 90%, as compared 12.8% of normal weight women. In logistic regression models adjusted for age, race/ethnicity, and white blood cell count, obese women had 4.1-fold higher odds of receiving a dose reduction of 10% or more over the course of chemotherapy than their normal weight counterparts (95% CI: 1.9, 8.9; p-trend: 0.006). Increasing age was positively associated with dose reduction across the course of chemotherapy, as was the presence of comorbidity. Importantly, dose reduction was less common in later calendar years. Sensitivity analyses revealed that the positive association between obesity and dosing was robust to further adjustment for these other significantly associated factors. Impact: These results offer insight on factors associated with variation in chemotherapy dosing for a common breast cancer treatment regimen. Larger studies are required to evaluate relevance of these findings to other treatment regimens. Further work will be needed to determine whether dose reductions impact breast cancer outcomes. Citation Format: Elizabeth D. Kantor, Kelli O’Connell, Isaac J. Ergas, Emily Valice, Janise M. Roh, Jenna Bhimani, Narre Heon, Jennifer J. Griggs, Jean Lee, Erin J. Bowles, Donna R. Rivera, Tatjana Kolevska, Elisa Bandera, Lawrence H. Kushi. Assessment of Breast Cancer Chemotherapy Dose Reduction in an Integrated Healthcare Delivery System [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-09.

Per- and polyfluoroalkyl substances (PFAS) are widespread pollutants and classified as potentially carcinogenic to humans. Although consumption of fish, seafood, and their byproducts is a known source of dietary PFAS exposure, little is known about the association between use of fish oil supplements and PFAS. Here, we examine associations between fish oil supplement use and serum PFAS concentrations.This analysis includes adults, ages 25 years of age and older, surveyed as part of the National Health and Nutrition Examinations Survey (NHANES). Outcomes include five serum PFAS compounds: perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorohexane sulphonic acid (PFHxS) and perfluorodecanoic acid (PFDA). To determine the association between fish oil use and log-transformed PFAS concentrations, survey-weighted linear regression was used to estimate multivariate-adjusted ratios between supplement-users' and non-users' geometric mean serum PFAS concentrations.No association was observed between fish oil use and PFAS. While results did not vary substantially by age, gender, study cycle, there was some indication of a potential inverse association in subgroups of interest. Specifically, an inverse association was observed between fish oil supplement use and PFOS levels in older adults, females, and in early calendar years; an inverse association was also observed between fish oil and PFNA in females and early calendar years.While fish oil users did not experience increased serum PFAS, there was an unexpected inverse association in some population subgroups. Further research will be needed to better understand whether this pattern reflects true differences, chance, or bias.

Introduction Drug treatment for cancer has changed dramatically over the past decade with many new drugs often with multiple applications. More recently, the detailed pathway for approval from the National Institute for Health and Care Excellence (NICE) in the UK has been simplified. To explore how these changes have impacted on systemic anti-cancer therapy tumour site-specific prescribing and workload activities, we have reviewed the prescribing records for 2014–2018 in a UK cancer network. Methods Information about the numbers of new systemic anti-cancer therapy drugs and NICE approvals were obtained from print editions of the British National Formulary (BNF) and the NICE website. Data on the numbers of new chemotherapy courses and individual treatment-related attendances were obtained from the cancer network Chemocare electronic prescribing system. Results During the five-year study period, there were 49 new systemic anti-cancer therapy drugs for all tumour types, and a total of 65 NICE technology approvals for solid tumour indications. Overall numbers of treatment courses increased by 40.7% and total treatment-related visits by 80.6%. There was a wide variation across tumour types with the highest number of increased visits seen for melanoma (349.3%) and prostate cancer (242.3%), but in contrast, no appreciable increases were seen for lower gastrointestinal cancers or small cell lung cancer. Conclusion The study confirms the major impact of the arrival of new drug technology and positive NICE appraisals on increasing systemic anti-cancer therapy prescribing and chemotherapy unit activity. The data in this study may be of help in planning for future service delivery planning and workforce configurations.

For many tumours there is a lack of randomised data from which we can guide systemic treatments. Although gene expression profiling along with proteomics has led to advances in diagnosis, classification and prognosis, our ability to target many cancers has been further limited due to a lack of therapeutic options. The use of patient-derived xenograft (PDX) models in the setting of a rare malignancy is discussed here by Kamili et al, with the successful establishment of new model systems.

Background Predictive preclinical models play an important role in the assessment of new treatment strategies and as avatar models for personalised medicine; however, reliable and timely model generation is challenging. We investigated the feasibility of establishing patient-derived xenograft (PDX) models of high-risk neuroblastoma from a range of tumour-bearing patient materials and assessed approaches to improve engraftment efficiency. Methods PDX model development was attempted in NSG mice by using tumour materials from 12 patients, including primary and metastatic solid tumour samples, bone marrow, pleural fluid and residual cells from cytogenetic analysis. Subcutaneous, intramuscular and orthotopic engraftment were directly compared for three patients. Results PDX models were established for 44% (4/9) of patients at diagnosis and 100% (5/5) at relapse. In one case, attempted engraftment from pleural fluid resulted in an EBV-associated atypical lymphoid proliferation. Xenogeneic graft versus host disease was observed with attempted engraftment from lymph node and bone marrow tumour samples but could be prevented by T-cell depletion. Orthotopic engraftment was more efficient than subcutaneous or intramuscular engraftment. Conclusions High-risk neuroblastoma PDX models can be reliably established from diverse sample types. Orthotopic implantation allows more rapid model development, increasing the likelihood of developing an avatar model within a clinically useful timeframe.