Background: Biomarkers with robust analytical and clinical validity can help optimize therapy decisions within clinical trials for patients with breast cancer, particularly if some data on clinical utility also exist. However, little is known about how physicians enrolling in clinical trials view them. Physician comfort with the integral use of conventional and investigational biomarkers for reducing chemotherapy intensity within clinical trials is explored in this study. Method: A convenience sample of academic and community oncologists from across the United States were invited to participate in qualitative interviews that explored their perspectives on the use of biomarkers for the de-escalation of chemotherapy in patients with breast cancer. Purposive sampling techniques were used to identify participants, ensuring even distribution of gender, work setting, and time practicing medical oncology. Interviews were audio-recorded and transcribed. Transcripts were analyzed by two independent coders to identify major themes and exemplary quotes in NVivo. A framework for understanding how providers conceptualize biomarkers was created. Results: There was a total of 39 physicians with a median age of 50; 51% of physicians were academic and 49% were community-based. 44% of oncologists have been in practice for less than 15 years, and 36% and 20% of oncologists were in practice for 15-30 years and over 30 years, respectively. The model on physician level of comfort for biomarker use consisted of 1) standard of care biomarkers, 2) standard biomarkers in newer contexts, and 3) experimental biomarkers with inclusion of additional related subthemes. There was a shared theme among physicians that historical experience with a biomarker made them more comfortable in de-escalation of chemotherapy. The greatest level of physician comfort with biomarker for de-escalation of chemotherapy came with biomarkers used in standard of care (e.g., MammaPrint, Oncotype DX). Themes related to these biomarkers included: strong level of evidence, agreement with NCCN guidelines, and widespread use in the community. For example, one physician stated, “for me to use a prognostic biomarker … typically it’s going to have to at least be within the NCCN guidelines or out there”. Secondly, physicians expressed reasonable confidence with some reluctance in the use of standard of care biomarkers in contexts that differ from where they were initially tested (i.e., use of biomarker in patients with different features or disease biology). These themes included the use of biomarkers in specific subtypes of cancer and when there is less supportive evidence. One physician commented, “It’s just hard to analyze and really know whether [pathCR in ER+ setting] actually holds like it does for other tumor biologies”. There was more hesitation and least comfort with experimental biomarkers (e.g., tumor-infiltrating lymphocytes, circulating tumor DNA). For experimental biomarkers, physicians were primarily concerned with the quality and quantity of evidence supporting their use. Prospective trials were favored over retrospective; however, physicians were accepting if the retrospective study included a large sample, other biomarkers were used in conjunction, or multiple studies confirmed the results. Other themes that emerged regarding experimental biomarkers were their testing in diverse populations and reproducibility. Physicians expressed contentment with experimental biomarkers that were proven in “multiple big enough studies”, were “reproducible and not subjective”, and “demonstrate utility in the patient population that’s relevant”. Conclusion: Biomarkers can be divided into 3 successive levels: 1) standard of care biomarkers, 2) standard biomarkers in newer contexts, and 3) experimental biomarkers. Level of comfort concerning the use of biomarkers for de-escalation of chemotherapy is related to level of evidence for experimental biomarkers. Citation Format: Noon Eltoum, Halle thannickal, Nicole L. Henderson, Lynne I. Wagner, Lauren P. Wallner, Antonio C. Wolff, Gabrielle B. Rocque. The Hierarchy of Biomarkers [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 P6-01-04.

PURPOSE Remote symptom monitoring (RSM) using electronic patient-reported outcomes enables patients with cancer to communicate symptoms between in-person visits. A better understanding of key RSM implementation outcomes is crucial to optimize efficiency and guide implementation efforts. This analysis evaluated the association between the severity of patient-reported symptom alerts and time to response by the health care team. METHODS This secondary analysis included women with stage I-IV breast cancer who received care at a large academic medical center in the Southeastern United States (October 2020-September 2022). Symptom surveys with at least one severe symptom alert were categorized as severe. Response time was categorized as optimal if the alert was closed by a health care team member within 48 hours. Odds ratios (ORs), predicted probabilities, and 95% CIs were estimated using a patient-nested logistic regression model. RESULTS Of 178 patients with breast cancer included in this analysis, 63% of patients identified as White and 85% of patients had a stage I-III or early-stage cancer. The median age at diagnosis was 55 years (IQR, 42-65). Of 1,087 surveys included, 36% reported at least one severe symptom alert and 77% had an optimal response time by the health care team. When compared with surveys that had no severe symptom alerts, surveys with at least one severe symptom alert had similar odds of having an optimal response time (OR, 0.97; 95% CI, 0.68 to 1.38). The results were similar when stratified by cancer stage. CONCLUSION Response times to symptom alerts were similar for alerts with at least one severe symptom compared with alerts with no severe symptoms. This suggests that alert management is being incorporated into routine workflows and not prioritized based on disease or symptom alert severity.

349 Background: Biomarkers are regularly utilized to select treatment within cancer clinical trials. However, there remains a lack of understanding regarding physician perspectives on what data is needed for physicians to comfortably use these markers to escalate or de-escalate chemotherapy. Methods: Semi-structured qualitative interviews with medical oncologists from different academic and community-based cancer centers were conducted to investigate perspectives on the utilization of biomarkers to de-escalate chemotherapy. Key topics explored included: (1) physician preference for biology-based (e.g. genomic profiles) vs. response-based (e.g. complete pathologic response) biomarkers, and (2) importance of personal familiarity with biomarkers. Interviews were audio-recorded and transcribed. Two independent coders analyzed transcripts using a constant comparative method in NVivo to identify major themes. Analysis was stratified by practice-type to elucidate differences between oncologists at academic and community practices. Results: Of the 39 participating physicians, 51% practiced in an academic setting and 49% practiced in a community setting. The majority of physicians (67% overall, 77% community, 59% academic) did not have a preference for biology-based vs. response-based biomarkers, if the data is equally strong and clinical use is appropriate for the clinical context (e.g. patient subtype). Many physicians were reassured by achieving a real-time therapeutic response, with 23% of physicians preferring response-based biomarkers. One physician stated, “I am still more comfortable with a real-time, well-validated biomarker, response marker, than I am with an overall predictive marker for a population”. In contrast, 10% (all academic) preferred biology-based biomarkers. One physician commented “I think the biology is probably more attractive because that potentially allows you to avoid treatment, whereas pathCR they've already had to get treatment to get there”. The majority of academic physicians (55%) felt that strong data was more important than personal familiarity with regards to implementation of novel biomarkers, as noted by one who stated, “As long as there's good data, I don't care.” 15% of community physicians shared a similar view. The majority of community physicians (54%) voiced familiarity to be more important in their comfort with biomarker use as noted by one physician who stated, “I think things I’m already familiar with, I'm more inclined to feel good about”. 18% of academic physicians held a similar perspective. Conclusions: Academic and community physicians’ perspectives regarding use of novel biomarkers overlap, with multiple factors playing a role in how these biomarkers are used in decision-making. Future research is needed to understand the impact of biomarker selection on clinical trial enrollment.

341 Background: Remote symptom monitoring (RSM) using electronic patient reported outcomes (ePROs) allow for patients with cancer to communicate symptoms to their clinical team between clinic visits. Prior randomized control trials of RSM focused on advanced cancer, and less data are available for patient with early stage cancers. The University of Alabama at Birmingham (UAB) implemented RSM for early stage (I-III) and advanced stage (IV) patients on active treatment. This study evaluates nurses’ real-world response time to alerts by varying severity and by patients cancer stages. Methods: This study included women with stage I-IV breast cancer who received care at UAB from October 2020 through May 2022. The program was first implemented in the breast clinic allowing for larger patient numbers with early and advanced stage breast cancer. A composite score for symptom severity is automatically calculated in the Carevive® platform for moderate, severe, or worsening symptoms using patient responses for frequency, severity, and interference. The nurse receives an alert if a symptom is moderate or severe. Surveys with at least one severe alert were categorized as severe and response time was categorized as optimal if the survey was closed within 48 hours (goal time for phone message follow-up). Odds ratios (OR), predicted probabilities, and 95% confidence intervals (CI) were estimated using a patient nested logistic regression evaluating time to response comparing surveys with at least one severe alert notification to those with no severe, adjusting for age at enrollment, race, cancer stage, provider who closed the surveys, and quarter from study start and date. An interaction between severity and cancer stage was evaluated. Results: Of 137 patients included in this study, 64% were White; 86% were diagnosed with early-stage breast cancer. The median age at diagnosis was 54 (27-79). Of 802 surveys included, 38% reported at least one severe symptom and 70% had an optimal response time. Similar results were seen when stratified by early vs. advanced stage with 39% and 38% reporting at least one severe alert and 68% and 71% an optimal response time, respectively. In our adjusted analysis, when compared with surveys that had no severe alerts, surveys with at least one severe alert had similar odds of having an optimal response time (OR, 1.29; 95%CI, 0.88, 1.89). No significant interaction between severity and stage was observed on the odds of optimal response time. Conclusions: Response times to alerts were similar regardless of the severity of the alert and cancer stage, suggesting alert management is incorporated into routine workflows and not prioritized based on disease or alert severity. Additional research is needed to understand factors contributing to non-optimal response times.

Background Tumor biomarkers are regularly used to guide breast cancer treatment and clinical trial enrollment. However, there remains a lack of knowledge regarding physicians’ perspectives towards biomarkers and their role in treatment optimization, where treatment intensity is reduced to minimize toxicity. Methods Thirty-nine academic and community oncologists participated in semi-structured qualitative interviews, providing perspectives on optimization approaches to chemotherapy treatment. Interviews were audio-recorded, transcribed, and analyzed by 2 independent coders utilizing a constant comparative method in NVivo. Major themes and exemplary quotes were extracted. A framework outlining physicians’ conception of biomarkers, and their comfortability with their use in treatment optimization, was developed. Results In the hierarchal model of biomarkers, level 1 is comprised of standard-of-care (SoC) biomarkers, defined by a strong level of evidence, alignment with national guidelines, and widespread utilization. Level 2 includes SoC biomarkers used in alternative contexts, in which physicians expressed confidence, yet less certainty, due to a lack of data in certain subgroups. Level 3, or experimental, biomarkers created the most diverse concerns related to quality and quantity of evidence, with several additional modulators. Conclusion This study demonstrates that physicians conceptualize the use of biomarkers for treatment optimization in successive levels. This hierarchy can be used to guide trialists in the development of novel biomarkers and design of future trials.