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Purpose:As monotherapies, eribulin (chemotherapy) and pembrolizumab (immunotherapy) have shown promise for patients with metastatic triple-negative breast cancer (mTNBC). This phase Ib/II study examined eribulin plus pembrolizumab as a potential mTNBC treatment in first-line and later-line settings.Patients and Methods:In this open-label, single-arm, phase Ib/II study, eligible patients had mTNBC, measurable disease, and ≤2 prior systemic anticancer therapies in the metastatic setting. Patients were enrolled by number of prior systemic anticancer therapies (stratum 1: 0 vs stratum 2: 1–2) in the metastatic setting and further analyzed by tumor programmed death-ligand 1 (PD-L1) expression status. All patients received intravenous eribulin 1.4 mg/m2 on day 1 and day 8, plus intravenous pembrolizumab 200 mg on day 1, of 21-day cycles. The primary objectives were the safety, tolerability, and objective response rate (ORR) of this combination.Results:The study included 167 patients (phase Ib, n = 7; phase II, n = 160). The most common treatment-emergent adverse events were fatigue (66%), nausea (58%), peripheral sensory neuropathy (41%), alopecia (40%), and constipation (37%). ORRs were 25.8% [95% confidence interval (CI): 15.8–38.0] for stratum 1 (n = 66) and 21.8% (95% CI: 14.2–31.1) for stratum 2 (n = 101). Patients with PD-L1–positive tumors (combined positive score ≥1) had numerically higher ORR than those with PD-L1–negative tumors, particularly in stratum 1 [stratum 1: 34.5% (95% CI: 17.9–54.3) vs 16.1% (95% CI: 5.5–33.7); stratum 2, 24.4% (95% CI: 12.9–39.5) vs 18.2% (95% CI: 8.2–32.7)].Conclusions:Eribulin plus pembrolizumab was generally well tolerated and showed promising antitumor activity in mTNBC. Efficacy outcomes appeared influenced by line of therapy and PD-L1 status.

Supplementary Information

This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care setting. The methodology has previously been successfully applied to a heterogeneous patient population. The protocol details how to acquire the necessary images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The data collection portion of the protocol requires ~25 min of scanning, postprocessing requires 2–3 h, and the model calibration and prediction components require ~10 h per patient depending on tumor size. The response of individual breast cancer patients to neoadjuvant therapy is forecast by application of a biophysical, reaction–diffusion mathematical model to these data. Successful application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor’s size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient’s tumor will respond to therapy. Expertise in image acquisition and analysis, as well as the numerical solution of partial differential equations, is required to carry out this protocol.

PURPOSE To provide standards and practice recommendations specific to telehealth in oncology. METHODS A systematic review of the literature on telehealth in oncology was performed, including the use of technologies and telecommunications systems, and other electronic methods of care delivery and sharing of information with patients. The evidence base was combined with the opinion of the ASCO Telehealth Expert Panel to develop telehealth standards and guidance. Public comments were solicited and considered in preparation of the final manuscript. RESULTS The Expert Panel determined that general guidance on implementing telehealth across general and specialty settings has been published previously and these resources are endorsed. A systematic search for studies on topics specific to oncology resulted in the inclusion of two clinical practice guidelines, 12 systematic reviews, and six primary studies. STANDARDS AND GUIDANCE Standards and guidance are provided for which patients in oncology can be seen via telehealth, establishment of the doctor-physician relationship, role of allied health professionals, role of advanced practice providers, multidisciplinary cancer conferences, and teletrials in oncology. Additional information is available at www.asco.org/standards .

PURPOSE To update evidence-based guideline recommendations to practicing oncologists and others on systemic therapy for patients with human epidermal growth factor receptor 2 (HER2)–positive advanced breast cancer. METHODS An Expert Panel conducted a targeted systematic literature review (for both systemic treatment and CNS metastases) and identified 545 articles. Outcomes of interest included efficacy and safety. RESULTS Of the 545 publications identified and reviewed, 14 were identified to form the evidentiary basis for the guideline recommendations. RECOMMENDATIONS HER2-targeted therapy is recommended for patients with HER2-positive advanced breast cancer, except for those with clinical congestive heart failure or significantly compromised left ventricular ejection fraction, who should be evaluated on a case-by-case basis. Trastuzumab, pertuzumab, and taxane for first-line treatment and trastuzumab deruxtecan for second-line treatment are recommended. In the third-line setting, clinicians should offer other HER2-targeted therapy combinations. There is a lack of head-to-head trials; therefore, there is insufficient evidence to recommend one regimen over another. The patient and the clinician should discuss differences in treatment schedule, route, toxicities, etc during the decision-making process. Options include regimens with tucatinib, trastuzumab emtansine, trastuzumab deruxtecan (if either not previously administered), neratinib, lapatinib, chemotherapy, margetuximab, hormonal therapy, and abemaciclib plus trastuzumab plus fulvestrant, and may offer pertuzumab if the patient has not previously received it. Optimal duration of chemotherapy is at least 4-6 months or until maximum response, depending on toxicity and in the absence of progression. HER2-targeted therapy can continue until time of progression or unacceptable toxicities. For patients with HER2-positive and estrogen receptor–positive or progesterone receptor–positive breast cancer, clinicians may recommend either standard first-line therapy or, for selected patients, endocrine therapy plus HER2-targeted therapy or endocrine therapy alone. Additional information is available at www.asco.org/breast-cancer-guidelines .

PURPOSE To provide updated evidence- and consensus-based guideline recommendations to practicing oncologists and others on the management of brain metastases for patients with human epidermal growth factor receptor 2 (HER2)–positive advanced breast cancer up to 2021. METHODS An Expert Panel conducted a targeted systematic literature review (for both systemic therapy for non-CNS metastases and for CNS metastases of HER2+ guideline updates) that identified 545 articles. Outcomes of interest included overall survival, progression-free survival, and adverse events. RESULTS Of the 545 publications identified and reviewed, six on systemic therapy were identified to form the evidentiary basis for the systemic therapy for CNS metastases guideline recommendations. RECOMMENDATIONS Patients with brain metastases should receive appropriate local therapy and systemic therapy, if indicated. Local therapies include surgery, whole-brain radiotherapy, and stereotactic radiosurgery. Memantine and hippocampal avoidance should be added to whole-brain radiotherapy when possible. Treatments depend on factors such as patient prognosis, presence of symptoms, resectability, number and size of metastases, prior therapy, and whether metastases are diffuse. Other options include systemic therapy, best supportive care, enrollment onto a clinical trial, and/or palliative care. There are insufficient data to recommend for or against performing routine magnetic resonance imaging to screen for brain metastases; clinicians should have a low threshold for magnetic resonance imaging of the brain because of the high incidence of brain metastases among patients with HER2-positive advanced breast cancer. Additional information is available at www.asco.org/breast-cancer-guidelines .

Background: This study evaluates the ability to predict the response of locally advanced breast cancers to neoadjuvant therapy (NAT) using patient-specific magnetic resonance imaging (MRI) data and a biophysical mathematical model. The 3D mathematical model consists of three parts: tumor cell proliferation, tumor spread (diffusion), and treatment. In particular, the tumor cells proliferate according to logistic growth, and the diffusion term is coupled to the mechanical properties of the surrounding fibroglandular and adipose tissues to inform individual tumor growth patterns (specific to each patient’s anatomy). The model’s treatment term accounts for tumor cell reduction according to approximate local drug delivery for each patient. Methods: Patients (N = 21) with intermediate to high grade invasive breast cancers with varying receptor status, who were eligible for NAT as a component of their clinical care, were recruited. Each patient was treated with standard-of-care consisting of one or two NAT regimens in sequence followed by surgical resection of any residual tumor. MRI data are acquired at four time points: 1) prior to initiation of NAT, 2) after 1 cycle of NAT, 3) after 2-4 cycles of NAT, and 4) 1 cycle after scan 3. The MRI data is processed and evaluated using our semi-automated pipeline. Specifically, diffusion-weighted MRI data is utilized to characterize the cellularity throughout the tumor tissue, and dynamic contrast-enhanced (DCE-) MRI data is used to segment the breast tissue and analyze the local drug delivery using pharmacokinetic analysis and population-derived plasma curves of drug concentrations. The model’s predictive ability is assessed using three different strategies. First, the model is calibrated using each patient’s first two scans to enable predictions of the total tumor cellularity, volume, and longest axis that are directly compared to the values measured from their third scan. Second, the model’s predictions for tumor response are compared to the corresponding response evaluation criteria in solid tumors (RECIST) results. Third, the model is re-calibrated using scans 3 and 4 and simulated to the time of surgery to compare the model’s predictions to each patient’s response status determined by surgical pathology. Results: Calibrating the model with MRI data for one cycle of therapy yields predictions strongly correlated with tumor response measured from each patient’s third scan, concordance correlation coefficients of 0.91, 0.90, and 0.86 for total cellularity, volume, and longest axis, respectively (p < 0.01, N = 18). The model’s predictions are significantly (p < 0.01) correlated with tumor response as designated by RECIST for the cohort. Specifically, the model predicts greater percent reduction in the longest axis for the RECIST designated responder group (i.e., complete response and partial response) compared to non-responders. At the time of surgery, the model predicts changes in total tumor cellularity from baseline that are significantly (p < 0.01) correlated with pathological response status—an area under the receiver operator characteristic curve of 0.92 and a sensitivity and specificity of 1.0 and 0.74, respectively. Discussion: These preliminary results suggest that this clinical-mathematical approach can be predictive of tumor response very early in the course of NAT on a patient-specific basis. Moreover, the study was performed in the community-care setting across a heterogenous group of patients, indicating the approach may be practical for wide-spread application. NCI U01 CA174706, NCI U01 CA154602, CPRIT RR160005, ACS-RSG-18-006-01-CCE, R01CA240589, NCI-U24CA226110, CPRIT RR160093 Citation Format: Angela M Jarrett, David A. Hormuth, II, Anum K Syed, Chengyue Wu, John Virostko, Anna G Sorace, Julie C DiCarlo, Jeanne Kowalski, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E Yankeelov. Predicting breast cancer response to neoadjuvant therapies using a mathematical model individualized with patient-specific magnetic resonance imaging data: Preliminary Results [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS13-18.

The ability to accurately predict response and then rigorously optimize a therapeutic regimen on a patient-specific basis, would transform oncology. Toward this end, we have developed an experimental-mathematical framework that integrates quantitative magnetic resonance imaging (MRI) data into a biophysical model to predict patient-specific treatment response of locally advanced breast cancer to neoadjuvant therapy. Diffusion-weighted and dynamic contrast-enhanced MRI data is collected prior to therapy, after 1 cycle of therapy, and at the completion of the first therapeutic regimen. The model is initialized and calibrated with the first 2 patient-specific MRI data sets to predict response at the third, which is then compared to patient outcomes (N = 18). The model's predictions for total cellularity, total volume, and the longest axis at the completion of the regimen are significant within expected measurement precision (P< 0.05) and strongly correlated with measured response (P < 0.01). Further, we use the model to investigate, in silico, a range of (practical) alternative treatment plans to achieve the greatest possible tumor control for each individual in a subgroup of patients (N = 13). The model identifies alternative dosing strategies predicted to achieve greater tumor control compared to the standard of care for 12 of 13 patients (P < 0.01). In summary, a predictive, mechanism-based mathematical model has demonstrated the ability to identify alternative treatment regimens that are forecasted to outperform the therapeutic regimens the patients clinically. This has important implications for clinical trial design with the opportunity to alter oncology care in the future.

Purpose In MONARCH 1 (NCT02102490), single-agent abemaciclib demonstrated promising efficacy activity and tolerability in a population of heavily pretreated women with refractory HR+, HER2− metastatic breast cancer (MBC). To help interpret these results and put in clinical context, we compared overall survival (OS) and duration of therapy (DoT) between MONARCH 1 and a real-world single-agent chemotherapy cohort. Methods The real-world chemotherapy cohort was created from a Flatiron Health electronic health records-derived database based on key eligibility criteria from MONARCH 1. The chemotherapies included in the cohort were single-agent capecitabine, gemcitabine, eribulin, or vinorelbine. Results were adjusted for baseline demographics and clinical differences using Mahalanobis distance matching (primary analysis) and entropy balancing (sensitivity analysis). OS and DoT were analyzed using the Kaplan–Meier method and Cox proportional hazards regression. Results A real-world single-agent chemotherapy cohort (n = 281) with eligibility criteria similar to the MONARCH 1 population (n = 132) was identified. The MONARCH 1 (n = 108) cohort was matched to the real-world chemotherapy cohort (n = 108). Median OS was 22.3 months in the abemaciclib arm versus 13.6 months in the matched real-world chemotherapy cohort with an estimated hazard ratio (HR) of 0.54. The median DoT was 4.1 months in MONARCH 1 compared to 2.9 months in the real-world chemotherapy cohort with HR of 0.76. Conclusions This study demonstrates an approach to create a real-world chemotherapy cohort suitable to serve as a comparator for trial data. These exploratory results suggest a survival advantage and place the benefit of abemaciclib monotherapy in clinical context.

Objective: To summarize significant research contributions on cancer informatics published in 2019. Methods: An extensive search using PubMed/Medline and manual review was conducted to identify the scientific contributions published in 2019 that address topics in cancer informatics. The selection process comprised three steps: (i) 15 candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of two best papers was conducted by the editorial committee of the Yearbook. Results: The two selected best papers demonstrate the clinical utility of deep learning in two important cancer domains: radiology and pathology. Conclusion: Cancer informatics is a broad and vigorous subfield of biomedical informatics. Applications of new and emerging computational technologies are especially notable in 2019.

Introduction: Tumor forecasting methods for predicting treatment response of individual breast cancer patients to neoadjuvant therapy (NAT) have shown promise in clinical application. Our framework for predicting tumor response integrates quantitative magnetic resonance imaging (MRI) data acquired early in the course of NAT into a mechanism-based, biophysical model that predicts the eventual treatment response of breast tumors. Being able to predict which patient will respond effectively to NAT would have a fundamental and lasting impact on healthcare. However, the ultimate goal is to optimize therapy given the unique characteristics of each patient. Here we show that the detailed combination of advanced image analysis and rigorous mathematical modeling can accurately predict response for the individual patient. Further, we use the model to demonstrate the potential selection of personalized therapeutic regimens. This is accomplished by initializing the mathematical model with patient-specific characteristics and then varying, in silico, a range of treatment plans to achieve the greatest tumor control. Methods: Quantitative MRI was acquired from breast cancer patients (N = 11) at three time points during the course of NAT: 1) prior to NAT, 2) after 1 cycle of their initial chemotherapy, and 3) after the completion of the initial chemotherapy regimen. With these data, we implemented our recently established mechanically coupled, reaction-diffusion model at the tissue scale for predicting breast tumor response to therapy. The 3D model is initialized with patient-specific, diffusion-weighted MRI data characterizing tumor cellularity. Additionally, the model includes a tumor cell reduction term for local drug delivery as estimated from pharmacokinetic analysis of dynamic contrast-enhanced MRI data and population-derived plasma curves of therapeutic concentrations. The model’s predictive ability was assessed using three different measures. Using the first two scans, the model is calibrated and simulated forward to the third scan time to compare the predicted total tumor cellularity, volume, and longest axis to the actual values measured from the patient’s third scan. We then simulate alternate regimens using the same total dose each patient received during their standard regimen, while varying dosages and frequency between their second and third scans. Results: After calibrating the model using the first two imaging time points, the model’s predictions are significantly correlated to the measured tumor burden at scan three with Pearson Correlation Coefficients of 0.93, 0.89, 0.96 (p < 0.01) for total cellularity, total volume, and longest axis, respectively. The model predicts that for the alternative dosing regimens assessed, individual patients could have achieved an additional 0-43% reduction in total cellularity compared to the therapeutic regimens patients actually received. This indicates that standard regimens may not be the most effective for every patient. Discussion and future directions: These results demonstrate that the mathematical model can be predictive of tumor response using data at the earliest times of therapy regimens. The in silico results illustrate how for individual patients (depending on their unique tumor characteristics and vasculature—captured by the calibrated parameters of the model), therapy regimens can be tailored and even optimized (via established optimal control theory methods) to each patient using a mathematical model and simulation studies. The present investigation represents a significant first step towards personalizing patient regimens through quantitative imaging and mathematical modeling. NCI U01 CA174706, NCI U01 CA154602, CPRIT RR160005, ACS-RSG-18-006-01-CCE Citation Format: Angela M Jarrett, David A Hormuth II, Chengyue Wu, John Virostko, Anna G Sorace, Julie C DiCarlo, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E Yankeelov. Optimizing neoadjuvant regimens for individual breast cancer patients generated by a mathematical model utilizing quantitative magnetic resonance imaging data: Preliminary results [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 P2-16-17.

Background: In the FAIRLANE trial (NCT02301988) in early TNBC, adding the oral AKT inhibitor IPAT to neoadjuvant PAC led to numerical increases in rates of pathologic complete response (pCR; primary endpoint) and complete response by MRI in unselected patients, with a numerically greater treatment effect in patients with PIK3CA/AKT1/PTEN-altered tumors [Oliveira et al., Ann Oncol 2019]. The IMpassion130 trial established the efficacy of first-line atezolizumab + nab-PAC in patients with PD-L1-positive advanced TNBC [Schmid et al., NEJM 2018]. Preliminary phase 1b results showed promising activity with a triplet combination of IPAT, atezolizumab, and taxane as first-line therapy for advanced TNBC across PIK3CA/AKT1/PTEN and PD-L1 biomarker subgroups [Schmid et al., AACR 2019]. Using samples from the FAIRLANE trial, PD-L1 expression was characterized to evaluate relationships between IPAT treatment effect and expression of PD-L1 and biomarkers relevant to IPAT. Methods: Pretreatment tumor samples were evaluated for genomic alterations using the FoundationOne® assay (Foundation Medicine). Pretreatment PD-L1 status was determined using the SP142 immunohistochemistry assay (VENTANA Medical Systems), with PD-L1 positivity defined as PD-L1 expression on ≥1% of tumor-infiltrating immune cells. Tumor-infiltrating lymphocytes (TILs) were quantified using the Salgado method [Salgado et al., Ann Oncol 2015]. Gene expression was measured by RNA sequencing. Results: Samples were evaluable for genomic alterations in 144 patients, TILs in 135 patients, gene expression in 111 patients, and PD-L1 expression in 99 patients. The prevalence of PD-L1 positivity was 39%, consistent with the reported prevalence in IMpassion130. Compared with PD-L1-negative tumor samples, PD-L1-positive tumor samples showed significantly higher levels of TILs (mean 37% vs 24%; p=0.004) and significantly higher expression of the genes encoding for PD-L1 (CD274: geometric mean 10.5 vs 3.9 counts per million; p Citation Format: Matthew J Wongchenko, Mafalda Oliveira, Cristina Saura, Paolo Nuciforo, Isabel Calvo, Jay Andersen, José L Passos Coelho, Miguel Gil Gil, Begoña Bermejo, Debra A Patt, Eva Ciruelos, Zhen Shi, Na Xu, Patricia Villagrasa, Aruna Mani, Steven J Isakoff. Expression of PD-L1 is independent of PIK3CA/AKT1/PTEN alterations in triple-negative breast cancer (TNBC) and is not associated with response to ipatasertib (IPAT) plus paclitaxel (PAC) [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 P4-10-23.

Background The purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced breast cancer. Methods Patients with stage II/III breast cancer (N = 28) were enrolled in a multicenter study performed in community radiology settings. Dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI data were acquired at four time points during the course of NAT. Estimates of the vascular perfusion and permeability, as assessed by the volume transfer rate (Ktrans) using the Patlak model, were generated from the DCE-MRI data while estimates of cell density, as assessed by the apparent diffusion coefficient (ADC), were calculated from DW-MRI data. Tumor volume was calculated using semi-automatic segmentation and combined with Ktrans and ADC to yield bulk tumor blood flow and cellularity, respectively. The percent change in quantitative parameters at each MRI scan was calculated and compared to pathological response at the time of surgery. The predictive accuracy of each MRI parameter at different time points was quantified using receiver operating characteristic curves. Results Tumor size and quantitative MRI parameters were similar at baseline between groups that achieved pCR (n = 8) and those that did not (n = 20). Patients achieving a pCR had a larger decline in volume and cellularity than those who did not achieve pCR after one cycle of NAT (p Ktrans, ADC, cellularity, and bulk tumor flow from baseline (pre-treatment) were all significantly greater (p Conclusions Quantitative analysis of DCE-MRI and DW-MRI can be implemented in the community care setting to accurately predict the response of breast cancer to NAT. Dissemination of quantitative MRI into the community setting allows for the incorporation of these parameters into the standard of care and increases the number of clinical community sites able to participate in novel drug trials that require quantitative MRI.

390 Background: In our team-based approach to care delivery, Advanced Practice Providers (APPs) are a critical component of the care delivery team. In our statewide oncology practice APPs are not available at each site, limiting our ability to provide program visits that enhance patient care and limiting our flexible staffing capacity. APP program visits - Advance Care Planning (ACP), Treatment Review and Coordination (TRC) and Genetics – had limited availability due to the capacity of existing APP staff. We sought to provide both enhancements in patient care with program visits and offer flexible staffing capacity across our statewide practice by using a centralized virtual care clinic model providing high quality specialty care via Telemedicine. Methods: Four APPs were identified and trained to provide full-time telemedicine services statewide under a single collaboration agreement with the Virtual APP Medical Director. Local sites went through a brief onboarding process with the Virtual APP clinic and then submitted requests for appointment coverage by the Virtual APP (VAPP) team. The VAPPs had the same core oncology training, with a few differentiated skills which were matched with coverage requests aligning with their skill sets. To ensure continuity of care, virtual APP clinic notes were visible statewide in the practice EHR, and local providers were alerted by chart message of significant patient concerns. Results: In the first 3 months of service, the VAPPs completed 1,040 appointments for 12 clinics. The VAPPs conducted 50% Program visits and 50% established patient visits (follow-up, on treatment and urgent care). Conducting the Program visits virtually allowed patients to invite family members from any location to join the appointments virtually. The Urgent Care capability was not used frequently in the first 90 days of service, but may still grow in the future. Conclusions: The Virtual APP program ensured oncology patients received high quality, timely care through 1,040 completed visits. This prevented delays in care, resolved staffing challenges, expanded care, and supports virtual care in oncology. This program ensures APPs are available when and where needed and allows them to efficiently serve multiple clinics. The VAPPs provided education, assessment, prevention, and management of toxicities in a flexible manner.

391 Background: While there was broad adoption of telemedicine during the COVID-19 pandemic, optimizing the interaction for patients and the clinical team remained a challenge. We sought to optimize delivery of telemedicine services to provide more efficient and effective patient care. Target areas of concern for improvement were scheduling, staffing, communication, technical challenges with operating the platform, a high cancellation rate, and limited copay collections. Methods: A team of 8 virtual Patient Service Coordinators (VPSCs), 8 virtual Medical Assistants (VMAs), and an RN clinical manager was created to work remotely from home to serve Providers at 8 clinics. VPSCs performed check-in duties, demographics, copay collection and technology trouble-shooting with patients. VMAs performed medical intake (medication reconciliation, depression screenings, and vital signs) with real-time EMR input. VMAs stayed in-touch with patients to communicate Provider delays. Standardized communication pathways connected virtual teams with in-clinic teams. The clinics selected to participate in the TMS program were conducting 29% - 50% of E&M visits by telemedicine. The goal of the TMS program was to reduce stress and burnout, as well as relieve in-clinic staff of telemedicine duties giving them capacity to address in-clinic COVID related staff shortages. Results: The TMS Program supported 15,500 visits (11/15/21 – 5/31/22) and increased upfront expected copay collection from 9% pre-program to 100% post program. The program reduced the time for first contact on video from 18 minutes to 1 minute and reduced the telemedicine cancellation rate by 3%. The supported TM cancellation rate was 7% lower than in-person visit cancellation rate. A geographically distributed work from home team was able to support a 66% increase in visits during inclement weather days which allowed visits to be completed that would have otherwise been canceled due to clinic closures. Additionally, the TMS program relieved workload for in-clinic staff and the VPSC and VMA positions proved highly desirable to the eligible workforce. Conclusions: The TMS Program improved patient connectivity and experience, increased upfront co-pay collection, decreased burden on in-clinic staff, allowed continuity of care during inclement weather, and was an attractive work option for staff. Due to its success, the program moved past pilot phase into an operational program.

This study characterizes the error that results when performing quantitative analysis of abbreviated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data of the breast with the Standard Kety–Tofts (SKT) model and its Patlak variant. More specifically, we used simulations and patient data to determine the accuracy with which abbreviated time course data could reproduce the pharmacokinetic parameters, Ktrans (volume transfer constant) and ve (extravascular/extracellular volume fraction), when compared to the full time course data. SKT analysis of simulated abbreviated time courses (ATCs) based on the imaging parameters from two available datasets (collected with a 3T MRI scanner) at a temporal resolution of 15 s (N = 15) and 7.23 s (N = 15) found a concordance correlation coefficient (CCC) greater than 0.80 for ATCs of length 3.0 and 2.5 min, respectively, for the Ktrans parameter. Analysis of the experimental data found that at least 90% of patients met this CCC cut-off of 0.80 for the ATCs of the aforementioned lengths. Patlak analysis of experimental data found that 80% of patients from the 15 s resolution dataset and 90% of patients from the 7.27 s resolution dataset met the 0.80 CCC cut-off for ATC lengths of 1.25 and 1.09 min, respectively. This study provides evidence for both the feasibility and potential utility of performing a quantitative analysis of abbreviated breast DCE-MRI in conjunction with acquisition of current standard-of-care high resolution scans without significant loss of information in the community setting.

During the coronavirus disease 2019 (COVID-19) pandemic, many of us have faced new challenges as we manage some of the country's most vulnerable citizens, our patients with cancer The telemedicine policy changes and the resultant implementation empowers cancer providers to better serve our patients [Extracted from the article] Copyright of JCO Clinical Cancer Informatics is the property of American Society of Clinical Oncology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )

Purpose The effects of chemotherapy dose intensity on survival in patients with advanced non-small-cell lung cancer (NSCLC) are poorly understood. We retrospectively analyzed dose delays/reduction, relative dose intensity (RDI), and the association between chemotherapy intensity and survival in advanced NSCLC. Methods This retrospective cohort study included adults with advanced lung cancer who received first-line myelosuppressive platinum-based chemotherapy (January 2007–December 2010) in ~ 230 US Oncology Network community practices. Dose delays ≥ 7 days, dose reductions ≥ 15%, and RDI relative to standard regimens were described. Overall survival (OS) was measured using Kaplan-Meier and Cox proportional hazard (PH) models. Results Among 3866 patients with advanced NSCLC, 32.4% experienced dose delays ≥ 7 days, 50.1% experienced dose reductions ≥ 15%, and 40.4% had RDI

Background In MONARCH 1 (NCT02102490), abemaciclib demonstrated promising single-agent activity and tolerability in a population of heavily pretreated women with refractory HR+, HER2- metastatic breast cancer (MBC).1 Confirmed objective response rate (ORR) was 19.7% (95% CI: 13.3, 27.5) and at 18 months minimum follow-up median overall survival (OS) was 22.3 months. Due to the single-arm trial design of MONARCH 1, there is a need to view these results in clinical context relative to available treatment options. This study compared the OS results of abemaciclib in MONARCH 1 vs that in a real-world single-agent chemotherapy cohort with similar patient and disease characteristics. Methods MONARCH 1 study design and key eligibility criteria were previously described.1 The real-world cohort was based on Flatiron Health electronic health records-derived, nationally representative (USA-based) database comprising patient-level structured and unstructured data, curated via technology-enabled abstraction, for patients with MBC between January 1, 2011 through February 28, 2018. A real-world single-agent chemotherapy cohort was created based on the key eligibility criteria of MONARCH 1 and included patients diagnosed with HR+, HER2- MBC who received single-agent chemotherapy (eribulin, capecitabine, gemcitabine, or vinorelbine) following 1-2 prior chemotherapy regimens in the metastatic setting, had an ECOG PS of 0-1, and no prior CDK4 & 6 therapy. The index date was the start of the eligible single-agent chemotherapy, and patients were followed from the index date until date of death, loss to follow-up, or end of the database, whichever occurred earlier. OS results were adjusted using 2 methods (Mahalanobis distance matching and entropy balancing with bootstrapping) to account for baseline demographic and clinical differences between the real-world and trial cohorts. Results A real-world cohort (n=281) with eligibility criteria similar to the MONARCH 1 population (n=132) was identified. A subsequent matching based on Mahalanobis distance was performed to match MONARCH 1 population (n=108) with the real-world cohort (n=108). The matched cohorts demonstrated similar patient and disease characteristics. Median OS was 22.3 months in the abemaciclib arm vs 13.6 months in the matched cohort with an estimated hazard ratio (HR) of 0.54 (95% CI: 0.37, 0.77). Results of a sensitivity analysis performed using entropy balancing were consistent with an adjusted median OS of 12.7 months in the real-world cohort (n=281)with HR of 0.57 (95% CI from bootstrapping: 0.44, 0.78). Conclusion Methodological advances to adjust for potential biases, and improvements in data quality, have evolved enabling the ability to leverage a real-world cohort as an external comparator arm. This study demonstrates the ability to create a real-world chemotherapy cohort suitable to serve as a comparator for MONARCH 1. These exploratory results suggest a survival advantage and adequately place the clinical benefit of abemaciclib monotherapy in clinical context. References Dickler et al, CCR 2017 Citation Format: Rugo H, Dieras V, Cortes J, Patt D, Wildiers H, O'Shaughnessy J, Zamora E, Yardley DY, Carter GC, Sheffield KM, Li L, Andre VA, Derbyshire RE, Li XI, Frenzel M, Huang Y-J, Dickler MN, Tolaney SM. Real-world survival of heavily pretreated patients with refractory HR+, HER2- metastatic breast cancer receiving single-agent chemotherapy - A comparison with MONARCH 1 [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P6-18-19.

Background: The oral AKT inhibitor IPAT is being evaluated in cancers with a high prevalence of PI3K/AKT pathway activation. In the PBO-controlled randomized phase II FAIRLANE trial (NCT02301988), adding IPAT to PAC as neoadjuvant therapy for TNBC led to a numerical increase in pathologic complete response (pCR) in unselected patients (17.1% vs 13.3%), with a greater treatment effect in patients with PIK3CA/AKT1/PTEN-altered tumors (17.9% vs 11.8%). The addition of IPAT also led to an increase in complete response (CR) by MRI (27.6% vs 13.3%) that was enhanced in patients with PIK3CA/AKT1/PTEN-altered tumors (39.3% vs 8.8%) [Oliveira, AACR 2018]. We report an exploratory analysis performed to provide better understanding of potential biomarkers for response. Methods: Pretreatment tumor samples were evaluated for genomic alterations using the FoundationOne® (Foundation Medicine) assay (n=144) and gene expression by RNA-Seq (n=92). Samples were classified into TNBC subtypes based on the method developed by Lehmann and Pietenpol [Lehmann, J Clin Invest 2011]. Tumor-infiltrating lymphocytes (TILs) were quantified using the Salgado method [Salgado, Ann Oncol 2015] (n=135). Results: Of 62 patients (43%) with PIK3CA/AKT1/PTEN-altered tumors, 21 had an activating mutation in PIK3CA or AKT1 and 47 had an alteration in PTEN (6 [3 in each arm] had both PIK3CA mutation and PTEN alteration). Although only 3 patients with PIK3CA/AKT1-mutant tumors achieved a pCR, there was an increased rate of MRI CR with the addition of IPAT to PAC [Table]. In patients with PTEN alterations, both pCR rate and MRI CR rate were increased with IPAT. In patients treated with PBO + PAC, all 4 pCR patients evaluable by RNA-Seq were of the immunomodulatory (IM) subtype. However, in the IPAT + PAC arm, pCRs were also seen in patients with basal-like 1 (BL-1), mesenchymal (M), and mesenchymal stem-like (MSL) subtypes. Consistent with this observation, in the PBO + PAC arm, samples from patients achieving a pCR had significantly higher levels of stromal TILs than those from patients who did not have a pCR, while no difference was observed in the IPAT + PAC arm. Response, n (%)PIK3CA/AKT mutation (n=21)PTEN alteration (n=47) IPAT + PAC (n=11)PBO + PAC (n=10)IPAT + PAC (n=21)PBO + PAC (n=26)pCR1 (9%)2 (20%)4 (19%)3 (12%)CR by MRI5 (45%)1 (10%)8 (38%)2 (8%) Conclusions: This retrospective exploratory biomarker analysis of the phase II FAIRLANE trial of neoadjuvant IPAT for TNBC provides insight into the potential heterogeneity of response and resistance to taxane therapy. The results also hint that response to PAC alone is dependent on baseline immune infiltration and that this dependency might be relieved with the addition of AKT inhibition. Citation Format: Wongchenko MJ, Oliveira M, Saura C, Nuciforo P, Calvo I, Andersen J, Passos Coelho JI, Gil Gil M, Bermejo B, Patt DA, Ciruelos E, Singel SM, Maslyar DJ, Xu N, de la Peña L, Baselga J, Gendreau S, Isakoff SJ. Exploratory biomarker analyses of FAIRLANE, a double-blind placebo (PBO)-controlled randomized phase II trial of neoadjuvant ipatasertib (IPAT) + paclitaxel (PAC) for early triple-negative breast cancer (TNBC) [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P2-08-19.

1575 Background: Digital health solutions (DHS) allow for enhanced remote communication between patients and clinical staff and the COVID-19 pandemic has brought these tools to the forefront of care delivery. Once adopted, barriers to adequate utilization still exist. Given the important need to decrease digital divides, and the diversity of patients and care settings across our clinic’s 220 sites of service, we sought to understand how utilization of oncology DHS may be limited among certain populations. Methods: We investigated utilization among cancer patients who enrolled and engaged with a portfolio of DHS between March 1, 2019 and January 15, 2022. This portfolio includes three tools: (1) an electronic patient-reported outcomes (ePRO) remote monitoring program for tracking symptoms and oral adherence, (2) a patient portal (PP) for securely accessing patient health records, and (3) digital education (DE) for patients regarding disease and treatments. ePRO completion rate, average number of PP logins, and DE read rate were used as measures of utilization for each tool, respectively, and compared among patients with different age (< 65 and ≥65 years), language preference [English (EL) or Spanish (SL)], and distance from clinic (non-rural: < 20 miles OR rural: ≥20 miles). Mann-Whitney U and Chi-Square tests were used to compare continuous and categorical variables, respectively. Results: This study included a total of 77,347 unique patients representing 651,004 digital encounters. 9,938 patients engaged in ePRO, 49,771 patients in PP, and 12,044 patients in DE. Engagement across all DHS was high in patients of age group < 65 (ePRO: 72.7%, PP: 79.67% and PE 54.7%) as compared to ≥65 years, but the ePRO completion rate is high in ≥65 age group (59.0% vs 55.6%), whereas no significant difference was observed in the PP login activity and DE read rate. EL patients were significantly (p-value < 0.01) more engaged (ePRO 68% vs. 54%, PP: 80% vs. 62%, DE: 57% vs. 37%) and had higher digital utilization (ePRO completion rate: 57.31% vs 53.23%, average PP logins: 7.48 vs 7.14 and DE read rate: 96.2% vs 90.8%) than SL patients across the DHS. Patients living in rural areas comprised roughly 25% of the population and participated across tools similarly as patients living in non-rural areas (ePRO 67% vs. 69%, PP: 79% vs. 79%, DE: 56.9% vs. 56.8%). Utilization of the portfolio was variable based on rural vs non-rural status (ePRO completion rate: 56.3% vs. 57.4%, average PP logins: 7.9 vs. 7.3, DE read rate: 96.02.7% vs 96.3%). Conclusions: Despite variable engagement based on age, language, and rural status across the portfolio, patients within these populations continue to utilize the DHS. How we understand and explore enhancements to DHS remain under investigation for tool optimization for patient-specific barriers to care.

1507 Background: There is a priority to accelerate the delivery of digital health solutions (DHS) to provide patients with enhanced means for accessing care, but lack of understanding of their utility in certain populations. There are concerns that equitable adoption translate into disparities. We sought to implement a portfolio of DHS across a large practice and characterize engagement across populations to enhance clinical informatics solutions that support care delivery. Methods: This is a retrospective evaluation of cancer patients who engaged with a portfolio of DHS between March 1, 2019 and January 15, 2022. We included four tools with opt-in and opt-out functionality: (1) a care management (CM) platform utilized by clinical staff to manage patient activities, (2) an electronic patient-reported outcomes (ePRO) remote monitoring program for tracking symptoms and oral adherence, (3) a patient portal (PP) for securely accessing patient health records, and (4) digital education (DE) for patients regarding disease and treatments. The engaged population was defined as the number of enrolled patients with at least one (1) record of triage activity, (2) completed ePRO assessment, (3) PP login, and (4) DE read activity, for each tool, respectively. The start of the index period was adjusted based on the first go-live date of each tool. We evaluated factors (age, gender, race/ethnicity, preferred-language, marital status, and distance from clinic) associated with patient engagement using Chi-Square test and multivariate logistic regression. Results: This analysis included a total of 267,375 unique patients. Of the enrolled population per tool, 172,840 (73.6%), 9,938 (67.7 %), 49,771 (79.2%), and 12,044 (56.9%) patients were engaged in CM, ePRO, PP and DE, respectively. The majority (>50%) of engaged patients were female, White and non-Hispanic/Latino, English-language, and aged 61-80 yrs. After adjusting for covariates, we observed that White and non-Hispanic/Latino [(CM: OR 1.15, ePRO OR 1.46, PP: OR 1.48, and DE: OR 1.36) and English-language (CM: OR 1.2, ePRO OR 1.67, PP: OR 1.8 and DE: OR 1.89) patients were significantly (p-value 80 years as compared to reference age of 0-20 years for any digital tools except CM. Conclusions: DHS can be used to support the cancer patient journey and we demonstrated high utilization in an array of sociodemographic variables in our population. However, tools designed and implemented with different populations in mind to reduce staff burden and lessen the digital divide should be further explored.

COVID-19 places unprecedented demands on the oncology ecosystem. The extensive pressure of managing health care during the pandemic establishes the need for rapid implementation of telemedicine. Across our large statewide practice of 640 practitioners at 221 sites of service, an aggressive multidisciplinary telemedicine strategy was implemented in March by coordinating and training many different parts of our healthcare delivery system. From March to September, telemedicine grew to serve 15%-20% of new patients and 20%-25% of established patients, permitting the practice to implement safety protocols and reduce volumes in clinic while continuing to manage the acute and chronic care needs of our patient population. We surveyed practice leaders, queried for qualitative feedback, and established 76% were satisfied with the platform. The common challenges for patients were the first-time use and technology function, and patients were, in general, grateful and happy to have the option to visit their clinicians on a telemedicine platform. In addition to conducting new and established visits remotely, telemedicine allows risk assessments, avoidance of hospitalization, family education, psychosocial care, and improved pharmacy support. The implementation has limitations including technical complexity; increased burden on patients and staff; and broadband access, particularly in rural communities. For telemedicine to improve as a solution to enhance the longitudinal care of patients with cancer, payment coverage policies need to continue after the pandemic, technologic adoption needs to be easy for patients, and broadband access in rural areas needs to be a policy priority. Further research to optimize the patient and clinician experience is required to continue to make progress.

Purpose To describe onboarding and utilization of telemedicine across a large statewide community oncology practice and to evaluate trends, barriers, and opportunities in care delivery during the coronavirus disease 2019 pandemic. Methods We describe telemedicine onboarding and utilization across a statewide oncology practice, covering 221 sites of service and more than 650 practitioners. We describe qualitatively the onboarding process of a diverse set of administrative, technical, and clinical partners. We describe quantitatively utilization throughout the practice. We describe a survey conducted to enlighten barriers and opportunities for optimal utilization. Results Multistakeholder education was directed to clinical teams, administrative and technical support staff, and patients through webinars and team meetings. Utilization was high from April through October 2020, representing 15% to 20% of new-patient visits and 20% to 25% of established-patient visits. In a survey offered to all clinicians, 96% of respondents indicated they are using telemedicine, with 33% using it for more than 25% of patient encounters. Among respondents, 59% reported that the use of telemedicine helps expedite diagnosis and treatment more than seeing patients in person in the clinic, 55% of respondents managed urgent issues by telemedicine, 80% believed that patients benefited From urgent assessment by telemedicine, and 57% believed an emergency department visit or a hospitalization was avoided because of a telemedicine visit. Most clinicians reported that patients enjoy benefits of telemedicine because of decreased exposure risk, decreased transportation requirements, and ease of including caregivers in the visit with the treating clinician. The most common barriers to patients accessing telemedicine were technical challenges and broadband access. Despite this, less than 5% of respondents routinely use telephone-only communication, as most typically use bimodal audio/video communication. Many clinicians have expansion ideas on how telemedicine can further expand the longitudinal care delivery for our patient population. Conclusions Telemedicine can be implemented successfully across a large statewide oncology practice and service a high volume of patients. Clinicians utilize telemedicine for new and established patients with minimal dysfunction. Clinicians believe patients benefit From telemedicine For new, established, and urgent care visits. Broadband access functionality should be explored to optimally serve our patient population.

Purpose: As monotherapies, eribulin (chemotherapy) and pembrolizumab (immunotherapy) have shown promise for patients with metastatic triple-negative breast cancer (mTNBC). This phase Ib/II study examined eribulin plus pembrolizumab as a potential mTNBC treatment in first-line and later-line settings. Patients and Methods: In this open-label, single-arm, phase Ib/II study, eligible patients had mTNBC, measurable disease, and ≤2 prior systemic anticancer therapies in the metastatic setting. Patients were enrolled by number of prior systemic anticancer therapies (stratum 1: 0 vs stratum 2: 1–2) in the metastatic setting and further analyzed by tumor programmed death-ligand 1 (PD-L1) expression status. All patients received intravenous eribulin 1.4 mg/m2 on day 1 and day 8, plus intravenous pembrolizumab 200 mg on day 1, of 21-day cycles. The primary objectives were the safety, tolerability, and objective response rate (ORR) of this combination. Results: The study included 167 patients (phase Ib, n = 7; phase II, n = 160). The most common treatment-emergent adverse events were fatigue (66%), nausea (58%), peripheral sensory neuropathy (41%), alopecia (40%), and constipation (37%). ORRs were 25.8% [95% confidence interval (CI): 15.8–38.0] for stratum 1 (n = 66) and 21.8% (95% CI: 14.2–31.1) for stratum 2 (n = 101). Patients with PD-L1–positive tumors (combined positive score ≥1) had numerically higher ORR than those with PD-L1–negative tumors, particularly in stratum 1 [stratum 1: 34.5% (95% CI: 17.9–54.3) vs 16.1% (95% CI: 5.5–33.7); stratum 2, 24.4% (95% CI: 12.9–39.5) vs 18.2% (95% CI: 8.2–32.7)]. Conclusions: Eribulin plus pembrolizumab was generally well tolerated and showed promising antitumor activity in mTNBC. Efficacy outcomes appeared influenced by line of therapy and PD-L1 status.

This report presents the American Society of Clinical Oncology’s (ASCO’s) evaluation of the adaptations in care delivery, research operations, and regulatory oversight made in response to the coronavirus pandemic and presents recommendations for moving forward as the pandemic recedes. ASCO organized its recommendations for clinical research around five goals to ensure lessons learned from the COVID-19 experience are used to craft a more equitable, accessible, and efficient clinical research system that protects patient safety, ensures scientific integrity, and maintains data quality. The specific goals are: (1) ensure that clinical research is accessible, affordable, and equitable; (2) design more pragmatic and efficient clinical trials; (3) minimize administrative and regulatory burdens on research sites; (4) recruit, retain, and support a well-trained clinical research workforce; and (5) promote appropriate oversight and review of clinical trial conduct and results. Similarly, ASCO also organized its recommendations regarding cancer care delivery around five goals: (1) promote and protect equitable access to high-quality cancer care; (2) support safe delivery of high-quality cancer care; (3) advance policies to ensure oncology providers have sufficient resources to provide high-quality patient care; (4) recognize and address threats to clinician, provider, and patient well-being; and (5) improve patient access to high-quality cancer care via telemedicine. ASCO will work at all levels to advance the recommendations made in this report.

PURPOSEWhile the immediate care and access disruptions associated with the COVID-19 pandemic have received growing attention in certain areas, the full range of gaps in cancer screenings and treatment is not yet well understood or well documented throughout the country comprehensively.METHODSThis study used a large medical claims clearinghouse database representing 5%-7% of the Medicare fee-for-service population to characterize changes in the utilization of cancer care services and gain insight into the impact of COVID-19 on the US cancer population, including identification of new patients, gaps in access to care, and disruption of treatment journeys.RESULTSIn March-July 2020, in comparison with the baseline period of March-July 2019, there is a substantial decrease in cancer screenings, visits, therapy, and surgeries, with variation by cancer type and site of service. At the peak of the pandemic in April, screenings for breast, colon, prostate, and lung cancers were lower by 85%, 75%, 74%, and 56%, respectively. Significant utilization reductions were observed in April for hospital outpatient evaluation and management (E&M) visits (−74%), new patient E&M visits (−70%), and established patient E&M visits (−60%). A decrease in billing frequency was observed for the top physician-administered oncology products, dropping in both April (−26%) and July (−31%). Mastectomies were reduced consistently in April through July, with colectomies similarly reduced in April and May and prostatectomies dipping in April and July.CONCLUSIONThe current impact of the COVID-19 pandemic on cancer care in the United States has resulted in decreases and delays in identifying new cancers and delivery of treatment. These problems, if unmitigated, will increase cancer morbidity and mortality for years to come.

PURPOSE: This study assesses the rate of enhancement of breast fibroglandular tissue after administration of a magnetic resonance imaging (MRI) gadolinium-based contrast agent and determine its relationship with response to neoadjuvant therapy (NAT) in women with breast cancer. METHOD: Women with locally advanced breast cancer (N = 19) were imaged four times over the course of NAT. Dynamic contrast-enhanced (DCE) MRI was acquired after administration of a gadolinium-based contrast agent with a temporal resolution of 7.27 sec. The tumor, fibroglandular tissue, and adipose tissue were semi-automatically segmented using a manually drawn region of interest encompassing the tumor followed by fuzzy c-means clustering. The rate and relative intensity of signal enhancement were calculated for each voxel within the tumor and fibroglandular tissue. RESULTS: The rate of fibroglandular tissue enhancement after contrast agent injection declined by an average of 29% over the course of NAT. This decline was present in 16 of the 19 patients in the study. The rate of enhancement is significantly higher in women who achieve pathological complete response (pCR) after both 1 cycle (68% higher, p < 0.05) and after 3–5 cycles of NAT (58% higher; p < 0.05). The relative intensity of fibroglandular enhancement correlates with the rate of enhancement (R(2) = 0.64, p < 0.001) and is higher in women who achieve pCR after both 1 cycle and after 3–5 cycles of NAT (p < 0.05, both timepoints). CONCLUSION: The rate of fibroglandular tissue enhancement declines over the course of therapy, provides novel information not reflected by tumoral measures, and may predict pathological response early in the course of therapy, with smaller declines in enhancement in women who achieve favorable response.

Defining patient-to-patient similarity is essential for the development of precision medicine in clinical care and research. Conceptually, the identification of similar patient cohorts appears straightforward; however, universally accepted definitions remain elusive. Simultaneously, an explosion of vendors and published algorithms have emerged and all provide varied levels of functionality in identifying patient similarity categories. To provide clarity and a common framework for patient similarity, a workshop at the American Medical Informatics Association 2019 Annual Meeting was convened. This workshop included invited discussants from academics, the biotechnology industry, the FDA, and private practice oncology groups. Drawing from a broad range of backgrounds, workshop participants were able to coalesce around 4 major patient similarity classes: (1) feature, (2) outcome, (3) exposure, and (4) mixed-class. This perspective expands into these 4 subtypes more critically and offers the medical informatics community a means of communicating their work on this important topic.

The effects of chemotherapy dose intensity on patient outcomes in advanced cancer are not well understood. We studied the association between chemotherapy relative dose intensity (RDI) and overall survival (OS) among patients with advanced breast or ovarian cancer.This retrospective cohort study included adults with advanced breast or ovarian cancer who received first-line myelosuppressive chemotherapy (January 2007 to December 2010) in US Oncology Network community practices. Dose delays ≥ 7 days, dose reductions ≥ 15%, and RDI relative to standard regimens were described. OS was measured by the Kaplan-Meier method and Cox proportional hazards models.Among 874 patients with advanced breast cancer, 33.2% experienced dose delays ≥ 7 days, 48.7% experienced dose reductions ≥ 15%, and 38.9% had RDI 85%. In the multivariable Cox proportional hazards model, Eastern Cooperative Oncology Group performance status 1/2 versus 0 (hazard ratio [HR] = 1.45; 95% confidence interval [CI], 1.15-1.82) and triple-negative status (HR = 3.14; 95% CI, 1.15-8.62) were significantly associated with mortality. Among 170 patients with advanced ovarian cancer, 43.5% experienced dose delays ≥ 7 days, 48.2% experienced dose reductions ≥ 15%, and 46.5% had RDI 85%. In the multivariable Cox proportional hazards model, dose reductions ≥ 15% (HR = 1.94; 95% CI, 1.09-3.46) and other tumor histology (vs. nonserous adenocarcinoma; HR = 3.55; 95% CI, 1.38-9.09) were significantly associated with mortality.Dose delays, dose reductions, and reduced RDI were common. In advanced breast cancer, health status and triple-negative disease were significantly associated with mortality. In advanced ovarian cancer, dose reductions and tumor histology were significantly associated with mortality. These results can help identify potential risk factors and characterize the effect of chemotherapy dose modification strategies on mortality.

Purpose To update evidence-based guideline recommendations for practicing oncologists and others on systemic therapy for patients with human epidermal growth factor receptor 2 (HER2)–positive advanced breast cancer to 2018. Methods An Expert Panel conducted a targeted systematic literature review (for both systemic treatment and CNS metastases) and identified 622 articles. Outcomes of interest included overall survival, progression-free survival, and adverse events. Results Of the 622 publications identified and reviewed, no additional evidence was identified that would warrant a change to the 2014 recommendations. Recommendations HER2-targeted therapy is recommended for patients with HER2-positive advanced breast cancer, except for those with clinical congestive heart failure or significantly compromised left ventricular ejection fraction, who should be evaluated on a case-by-case basis. Trastuzumab, pertuzumab, and taxane for first-line treatment and trastuzumab emtansine for second-line treatment are recommended. In the third-line setting, clinicians should offer other HER2-targeted therapy combinations or trastuzumab emtansine (if not previously administered) and may offer pertuzumab if the patient has not previously received it. Optimal duration of chemotherapy is at least 4 to 6 months or until maximum response, depending on toxicity and in the absence of progression. HER2-targeted therapy can continue until time of progression or unacceptable toxicities. For patients with HER2-positive and estrogen receptor–positive/progesterone receptor–positive breast cancer, clinicians may recommend either standard first-line therapy or, for selected patients, endocrine therapy plus HER2-targeted therapy or endocrine therapy alone. Additional information is available at www.asco.org/breast-cancer-guidelines .

Objective: To summarize significant research contributions on cancer informatics published in 2017. Methods: An extensive search using PubMed/Medline, Google Scholar, and manual review was conducted to identify the scientific contributions published in 2017 that address topics in cancer informatics. The selection process comprised three steps: (i) 15 candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of three best papers was conducted by the editorial board of the Yearbook. Results: Results: The three selected best papers present studies addressing many facets of cancer informatics, with immediate applicability in the research and clinical domains. Conclusion: Cancer informatics is a broad and vigorous subfield of biomedical informatics. Strides in knowledge management, crowdsourcing, and visualization are especially notable in 2017.

Background Quantitative diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) have the potential to impact patient care by providing noninvasive biological information in breast cancer. Purpose/hypothesis To quantify the repeatability, reproducibility, and accuracy of apparent diffusion coefficient (ADC) and T1 -mapping of the breast in community radiology practices. Study type Prospective. Subjects/phantom Ice-water DW-MRI and T1 gel phantoms were used to assess accuracy. Normal subjects (n = 3) and phantoms across three sites (one academic, two community) were used to assess reproducibility. Test-retest analysis at one site in normal subjects (n = 12) was used to assess repeatability. Field strength/sequence 3T Siemens Skyra MRI quantitative DW-MRI and T1 -mapping. Assessment Quantitative DW-MRI and T1 -mapping parametric maps of phantoms and fibroglandular and adipose tissue of the breast. Statistical tests Average values of breast tissue were quantified and Bland-Altman analysis was performed to assess the repeatability of the MRI techniques, while the Friedman test assessed reproducibility. Results ADC measurements were reproducible across sites, with an average difference of 1.6% in an ice-water phantom and 7.0% in breast fibroglandular tissue. T1 measurements in gel phantoms had an average difference of 2.8% across three sites, whereas breast fibroglandular and adipose tissue had 8.4% and 7.5% average differences, respectively. In the repeatability study, we found no bias between first and second scanning sessions (P = 0.1). The difference between repeated measurements was independent of the mean for each MRI metric (P = 0.156, P = 0.862, P = 0.197 for ADC, T1 of fibroglandular tissue, and T1 of adipose tissue, respectively). Data conclusion Community radiology practices can perform repeatable, reproducible, and accurate quantitative T1 -mapping and DW-MRI. This has the potential to dramatically expand the number of sites that can participate in multisite clinical trials and increase clinical translation of quantitative MRI techniques for cancer response assessment. Level of evidence 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Objective: To summarize significant research contributions on cancer informatics published in 2018. Methods: An extensive search using PubMed/Medline, Google Scholar, and manual review was conducted to identify the scientific contributions published in 2018 that address topics in cancer informatics. The selection process comprised three steps: (i) 15 candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook. Results: The four selected best papers present studies addressing many facets of cancer informatics, with immediate applicability in the translational and clinical domains. Conclusion: Cancer informatics is a broad and vigorous subfield of biomedical informatics. Progress in cancer genomics, artificial intelligence, and passively collected data is especially notable in 2018.

189 Background: Digital monitoring strategies that include electronic patient reported outcomes (ePRO) measures to monitor symptoms among cancer patients have been shown to be effective in improving patient outcomes in a large academic setting and across several smaller multi-center trials. However, demonstration of clinical utility in the real-world setting must incorporate patient and provider perspectives of ePRO programs to ensure successful implementation. We sought to understand perceptions among patients and clinicians in ePRO digital symptom monitoring program. Methods: Texas Two-Step is an ongoing hybrid implementation-effectiveness study of Navigating Cancer’s ePRO digital monitoring program at Texas Oncology. Patients initiating new systemic therapy for their cancer diagnosis were introduced to the program by their oncologist and enrolled in the program by nursing staff for weekly reporting of symptoms based on a modified version of NCI’s PRO-CTCAE instrument. Feedback surveys were administered to both patients and clinic staff after 6 months of implementation of the program to evaluate the overall experience with the program. Results: 1040 (23.5%) patients and 215 (12.4%) clinicians completed the feedback survey. Of the patient responders, 90% found the program very or somewhat easy for reporting symptoms, 85% moderately-extremely beneficial for having symptoms addressed, and 84% moderately-extremely interested in utilizing the program for future treatments. Of the clinician responders, 73% indicated that that they had a good understanding of the benefit of the program; 70.6% felt confident in their ability to interpret patients’ ePRO responses; 80.3% felt confident in their ability to discuss the program with patients; 71.2% confident in their ability to counsel patients based on ePRO responses; and 55.3% felt the program enhanced communication with patients. Additionally, 59% of clinicians felt the program was beneficial for patients. Conclusions: Patients have a more favorable perception of the benefit of the ePRO program than clinicians. Methods to reduce staff burden and reinforcement of program benefits during training and implementation are imperative to improve clinical utility and will be studied further as the program is optimized. As implementation occurred during the COVID-19 pandemic, this may impact perceptions regarding the tool.

Purpose: The phase II MONARCH 1 study was designed to evaluate the single-agent activity and adverse event (AE) profile of abemaciclib, a selective inhibitor of CDK4 and CDK6, in women with refractory hormone receptor–positive (HR+), HER2− metastatic breast cancer (MBC). Experimental Design: MONARCH 1 was a phase II single-arm open-label study. Women with HR+/HER2− MBC who had progressed on or after prior endocrine therapy and had 1 or 2 chemotherapy regimens in the metastatic setting were eligible. Abemaciclib 200 mg was administered orally on a continuous schedule every 12 hours until disease progression or unacceptable toxicity. The primary objective of MONARCH 1 was investigator-assessed objective response rate (ORR). Other endpoints included clinical benefit rate, progression-free survival (PFS), and overall survival (OS). Results: Patients (n = 132) had a median of 3 (range, 1–8) lines of prior systemic therapy in the metastatic setting, 90.2% had visceral disease, and 50.8% had ≥3 metastatic sites. At the 12-month final analysis, the primary objective of confirmed objective response rate was 19.7% (95% CI, 13.3–27.5; 15% not excluded); clinical benefit rate (CR+PR+SD≥6 months) was 42.4%, median progression-free survival was 6.0 months, and median overall survival was 17.7 months. The most common treatment-emergent AEs of any grade were diarrhea, fatigue, and nausea; discontinuations due to AEs were infrequent (7.6%). Conclusions: In this poor-prognosis, heavily pretreated population with refractory HR+/HER2− metastatic breast cancer, continuous dosing of single-agent abemaciciclib was well tolerated and exhibited promising clinical activity. Clin Cancer Res; 23(17); 5218–24. ©2017 AACR.

In the information age, we expect data systems to make us more effective and efficient—not to make our lives more difficult! In this article, we discuss how we are using data systems, such as electronic health records (EHRs), to improve care delivery. We illustrate how US Oncology is beginning to use real-world evidence to facilitate trial accrual by automatic identification of eligible patients and how big data and predictive analytics will transform the field of oncology. Some information systems are already being used at the point of care and are already empowering clinicians to improve the care of their patients in real time. Telehealth platforms are being used to bridge gaps that currently exist in expertise, geography, and technical capability. Optimizing virtual collaboration, such as through virtual tumor boards, is empowering communities that are geographically disparate to coordinate care. Informatics methods can provide solutions to the challenging problems of how to manage the vast amounts of data confronting the practicing oncologist, including information about treatment regimens, side effects, and the influence of genomics on the practice of oncology. We also discuss some of the challenges of clinical documentation in the modern era, and review emerging efforts to engage patients as digital donors of their EHR data.

Background This hypothesis-generating trial evaluated neoadjuvant ipatasertib–paclitaxel for early triple-negative breast cancer (TNBC). Patients and methods In this randomized phase II trial, patients with early TNBC (T≥1.5cm, N0–2) were randomized 1:1 to receive weekly paclitaxel 80mg/m2 with ipatasertib 400mg or placebo (days 1–21 every 28days) for 12weeks before surgery. Co-primary end points were pathologic complete response (pCR) rate (ypT0/TisN0) in the intention-to-treat (ITT) and immunohistochemistry phosphatase and tensin homolog (PTEN)-low populations. Secondary end points included pCR rate in patients with PIK3CA/AKT1/PTEN-altered tumors and pre-surgery response rates by magnetic resonance imaging (MRI). Results pCR rates with ipatasertib versus placebo were 17% versus 13%, respectively, in the ITT population (N=151), 16% versus 13% in the immunohistochemistry PTEN-low population (N=35), and 18% versus 12% in the PIK3CA/AKT1/PTEN-altered subgroup (N=62). Rates of overall and complete response (CR) by MRI favored ipatasertib in all three populations (CR rate 39% versus 9% in the PIK3CA/AKT1/PTEN-altered subgroup). Ipatasertib was associated with more grade ≥3 adverse events (32% versus 16% with placebo), especially diarrhea (17% versus 1%). Higher cycle 1 day 8 (C1D8) immune score was significantly associated with better response only in placebo-treated patients. All ipatasertib-treated patients with low immune scores and a CR had PIK3CA/AKT1/PTEN-altered tumors. Conclusions Adding ipatasertib to 12weeks of paclitaxel for early TNBC did not clinically or statistically significantly increase pCR rate, although overall response rate by MRI was numerically higher with ipatasertib. The antitumor effect of ipatasertib was most pronounced in biomarker-selected patients. Safety was consistent with prior experience of ipatasertib–paclitaxel. A T-cell-rich environment at C1D8 had a stronger association with improved outcomes in paclitaxel-treated patients than seen for baseline tumor-infiltrating lymphocytes. This dependency may be overcome with the addition of AKT inhibition, especially in patients with PIK3CA/AKT1/PTEN-altered tumors. ClinicalTrials.gov NCT02301988.

Repeatability and reproducibility of magnetization transfer magnetic resonance imaging of the breast, and the ability of this technique to assess the response of locally advanced breast cancer to neoadjuvant therapy (NAT), are determined. Reproducibility scans at 3 different 3 T scanners, including 2 scanners in community imaging centers, found a 16.3% difference (n = 3) in magnetization transfer ratio (MTR) in healthy breast fibroglandular tissue. Repeatability scans (n = 10) found a difference of &sim, 8.1% in the MTR measurement of fibroglandular tissue between the 2 measurements. Thus, MTR is repeatable and reproducible in the breast and can be integrated into community imaging clinics. Serial magnetization transfer magnetic resonance imaging performed at longitudinal time points during NAT indicated no significant change in average tumoral MTR during treatment. However, histogram analysis indicated an increase in the dispersion of MTR values of the tumor during NAT, as quantified by higher standard deviation (P = .005), higher full width at half maximum (P = .02), and lower kurtosis (P = .02). Patients' stratification into those with pathological complete response (pCR, n = 6) at the conclusion of NAT and those with residual disease (n = 9) showed wider distribution of tumor MTR values in patients who achieved pCR after 2&ndash, 4 cycles of NAT, as quantified by higher standard deviation (P = .02), higher full width at half maximum (P = .03), and lower kurtosis (P = .03). Thus, MTR can be used as an imaging metric to assess response to breast NAT.

e13021 Background: Palbociclib (PA) has been approved for HR+/HER2–advanced/metastatic breast cancer (mBC) in combination with an aromatase inhibitor (AI) or fulvestrant for more than 6 years. Regardless of the labeled recommended starting dose of 125mg/day, some patients initiate palbociclib at lower doses in routine practice. This study described real-world starting dose, patient characteristics, and effectiveness outcomes of first line PA+ AI for mBC in the US clinical setting. Methods: We conducted a retrospective analysis of Flatiron Health’s nationwide longitudinal electronic health records, which came from over 280 cancer clinics representing more than 2.2 million actively treated cancer patients in the US. Between February 2015 and September 2018, 813 HR+/HER2– mBC women initiated PA+AI as first-line therapy and had ≥ 3 months of potential follow-up. Patients were followed from start of PA+AI to December 2018, death, or last visit, whichever came first. Real-world progression-free survival (rwPFS) was defined as the time from the start of PA+AI to death or disease progression. Real-world tumor response (rwTR) was assessed based on the treating clinician’s assessment of radiologic evidence for change in burden of disease over the course of treatment. Multivariate analyses were performed to adjust for demographic and clinical characteristics. Results: Of 813 eligible patients, 68.3% were white, median age was 65.0 years, and 42.9% had visceral disease (lung and/or liver). Median duration of follow-up was 21.0 months. 805 patients had records of PA starting dose, with 125mg and 75/100mg/day being 86.5% and 13.5%, respectively. Patients who started at 75/100mg/day were more likely to be ≥75 years than those who started at 125mg/day (38.5% vs 17.1%). Other baseline and disease characteristics were generally evenly distributed. Patients who started at 125mg/day had longer median rwPFS (27.8 vs 18.6 months, adjusted HR=0.74, 95%CI=0.52-1.05) and higher rwTR (54.0% vs. 40.4%) than those patients who started 100/75mg/day (adjusted OR=1.76, 95%CI=1.13-2.74). Table presents results in detail. Conclusions: Most patients in this study initiated palbociclib at 125mg/day and dose adjustment was similar regardless of starting dose. These real-world findings may support initiation of palbociclib at a dose of 125mg/day in combination with AI for the first-line treatment of HR+/HER2- mBC. [Table: see text]

1578 Background: Value-based care models such as the Oncology Care Model incentivize practices to reduce hospitalizations and emergency department (ED) visits. Texas Oncology found that most ED visits occurred during regular business hours. Prolonged patient call back times were consistently rated poorly on satisfaction surveys and often led to ED visits for symptoms that could be managed in our offices. We partnered with Navigating Cancer (NC) to implement an electronic patient management technology solution. Methods: For each of our 200 locations, call volume was estimated based on clinic volume. We then reallocated or hired dedicated triage nurses and operators. Incoming calls were entered into the NC dashboard by operators as incidents which were routed based on symptom priority following system generated prompts. Incident volumes and resolution times were tracked. We instituted PDSA cycles at all locations with a goal of less than 90-minute resolution of symptom-related incidents Utilizing the electronic dashboard allowed us to continue this initiative during the COVID-19 public health emergency as our staff could work remotely. Nurses were able to document if a potential ED visit was avoided. These data points allowed our practice to establish comprehensive and strategic actions plans for quality improvement. Results: We finalized implementation of the system in February of 2020. Total incidents for 2020 were over 1 million, averaging over 5000 per location. Resolution time for all incidents started at 3.2 hours pre-implementation and improved to 2.2 hours in December of 2020. Resolution times for symptom-related incidents started at 2.3 hours pre-implementation and ended at 1.5 hours in December of 2020 with over 60% resolved under one hour. 8% of symptom-related incidents resulted in definite or probable ED avoidances by nursing assessment. Shortness of breath, vomiting, chills, and weakness were the top symptom types addressed for ED avoidances. Conclusions: An electronic patient management solution with PDSA cycles of quality improvement can markedly reduce call back times, especially for symptom related calls. We believe managing symptoms in a timely fashion will lower ED visits and hospitalizations as well as improve patient satisfaction. We will report on these outcomes once available.[Table: see text]

12103 Background: Among patients receiving chemotherapy, symptomatic adverse event monitoring with electronic patient-reported outcomes (ePRO) is associated with improved clinical outcomes, satisfaction, and compliance with therapy. Standard approaches for ePRO implementation are not established warranting evaluation in community cancer practices. Objective: Evaluate implementation of ePRO symptom monitoring across a large multi-site community oncology practice network. Methods: Patients initiating a new systemic therapy at one of 210 practice sites in the Texas Oncology Practice were invited to use in the Navigating Cancer ePRO platform, with rolling implementation from July-December 2020. Participating patients received a weekly prompt by text message or email (patient choice) to self-report common symptoms and well-being via web or smartphone. Severe self-reported symptoms triggered a real-time notification alert to nursing triage to address the symptom. Enrollment and compliance were systematically tracked weekly with evaluation of barriers and facilitators to adoption and sustainability. Results: 4375 patients planning systemic treatment enrolled and participated, with baseline characteristics are shown in Table 1. 73% (1841/2522) of enrolled patients with follow up completed at least one ePRO assessment, and among these individuals, 65% (8762/25061) of all available weekly ePRO assessments were completed. Over a 10-week period, compliance with weekly symptom reporting declined from 72% to 52%. Patients on oral therapy had higher compliance rates overall. Barriers currently being addressed include lack of a second reminder text/email prompt, inconsistent discussion of reported ePROs by clinicians at visits, and COVID-related changes in workflow. Facilitators included patient and staff engagement on the importance of PROs for symptom management. Conclusions: ePROs can be effectively implemented in community oncology practice. Utilization of ePROs is high, but diminishes over time without attention to barriers. Ongoing work to address barriers and optimize compliance are underway.[Table: see text]

Background: Neratinib (NERLYNX®) is an irreversible pan-HER inhibitor that significantly improves invasive disease-free survival (iDFS) compared with placebo when given as extended adjuvant therapy in patients with HER2-positive (HER2+) early breast cancer after trastuzumab-based adjuvant therapy. In the phase 3 ExteNET trial, an absolute iDFS benefit of 2.5% and distant disease-free survival (DDFS) benefit of 1.7% were observed with neratinib after 5 years’ follow-up. As reflected in the approved indication by the European Medicines Agency (EMA), patients with hormone receptor-positive (HR+) disease who initiated neratinib treatment within 1 year of completing trastuzumab (HR+/≤1 year) experienced an absolute iDFS benefit of 5.1% and DDFS benefit of 4.7% at 5 years. In HR+/≤1 year patients with residual disease after neoadjuvant therapy, absolute 5-year iDFS and DDFS benefits of 7.4% and 7.0%, respectively, were observed. Here we report the final protocol-defined, event-driven analysis of overall survival (OS) from ExteNET, and provide descriptive analyses of subgroups of primary interest according to the EU label and current clinical practice in early-stage HER2+ disease. Methods: ExteNET was a multicenter, randomized, double-blind, placebo-controlled phase 3 trial of women with early-stage HER2+ breast cancer who had completed neoadjuvant or adjuvant trastuzumab plus chemotherapy (NCT00878709). Patients were randomly assigned to oral neratinib 240 mg/day or placebo for 1 year. Hazard ratios (HR) for OS were estimated from Cox proportional hazards models, and survival rates by the Kaplan-Meier method. The OS analysis was event-driven and powered for the intention-to-treat (ITT) population with a target of 248 events. Descriptive analyses were performed in the HR+/≤1 year subgroup per the approved indication in the EU, and in higher-risk patients, i.e. HR+/≤1 year who have residual disease after neoadjuvant therapy [i.e. those who did not achieve a pathologic complete response (pCR)]. Cut-off date: July 2019. Results: 2840 patients were randomized to study treatment (1420 per group). After a median follow-up of 8.1 years, 127 (8.9%) and 137 (9.6%) patients in the neratinib and placebo ITT groups had died, respectively. The 8-year OS rates were 90.1% (95% CI, 88.3–91.6) in the neratinib group and 90.2% (95% CI, 88.4–91.7) in the placebo group (absolute difference at 8 years -0.1%; stratified HR=0.95; 95% CI, 0.75–1.21; p=0.6914). A positive trend was seen in the prespecified HR+ subgroup (n=1631; absolute difference at 8 years 1.5%; HR=0.80; 95% CI, 0.58–1.12), and within this population, descriptive analyses suggested greater benefits with neratinib in the HR+/≤1 year subgroup (n=1334; absolute difference at 8 years 2.1%; HR=0.79; 95% CI, 0.55–1.13) and in the HR+/≤1 year subset with no pCR after neoadjuvant therapy (n=295; absolute difference at 8 years 9.1%; HR=0.47; 95% CI, 0.23–0.92). No new safety signals were reported with this long-term follow-up to 8 years. Conclusions: In this final OS analysis of ExteNET, there were fewer deaths with neratinib than placebo in the ITT population, but the results did not reach statistical significance. Analyses showed greater OS improvements with neratinib in subgroups including HR+/≤1 year, and HR+/≤1 year with residual disease after neoadjuvant therapy. These findings are consistent with the results based on the primary endpoint of iDFS, and support the use of neratinib in clinical practice in these patients. Citation Format: Frankie Ann Holmes, Beverly Moy, Suzette Delaloge, Stephen Chia, Bent Ejlertsen, Janine Mansi, Hiroji Iwata, Michael Gnant, Mark Buyse, Carlos Barrios, Tajana Silovski, Robert Separovic, Anna Bashford, Angel Guerrero-Zotano, Neelima Denduluri, Debra Patt, Erhan Gokmen, Ira Gore, John Smith, Richard Bryce, Feng Xu, Alvin Wong, Miguel Martin, Arlene Chan. Continued efficacy of neratinib in patients with HER2-positive early-stage breast cancer: Final overall survival analysis from the randomized phase 3 ExteNET trial [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD3-03.

Background: Pertuzumab was FDA-approved in 6/2012 for use in first-line in combination with trastuzumab (H) and docetaxel for patients (pts) with HER2-positive mBC. This retrospective study investigated the clinical characteristics, treatment patterns, safety, and outcomes for pts with HER2-positive mBC who received a P-containing regimen in first-line in US community oncology practices. Methods: This study utilized iKnowMed electronic health records, Claims Data Warehouse, and Social Security Death Index. Pts with HER2-positive mBC, who received a P-containing regimen between 6/2012 and 6/2014 and were followed through 12/2014, had ≥2 visits within the McKesson Specialty Health/US Oncology Network, and were not on clinical trials during the study period, were eligible. Results: Of the 322 pts who received a P-containing regimen in the first-line setting, 25% were ≥65 years of age, 63% were post-menopausal, 61% had hormone receptor-positive mBC, 84% had a performance status of 0 or 1, and 76% had a Charlson Comorbidities Index of 0. Twenty-one percent of pts had 1 site of metastasis noted, 32% had 2 sites, and 47% had 3 or more sites. Pts with de novo mBC made up 40% of this cohort. Of the pts with recurrent mBC, over 60% received H in the early-stage BC setting. In the first-line mBC setting, 93% of the 322 pts received H+P+taxane, and 7% received H+P with other chemotherapy agent(s). Common adverse events reported included: fatigue (49%), diarrhea (44%), nausea (33%), peripheral neuropathy (33%), neutropenia (24%), and rash (22%). Further analyses including outcomes of these 322 pts will be presented. Conclusions: First-line P was given in combination with H and chemotherapy agent(s) (93% taxane). No new safety signals were observed. More details on the clinical characteristics, specific treatment patterns, and safety will be presented, along with the progression-free survival of these pts receiving first-line P-containing therapy in a real-world setting. Citation Format: Robert N, Goertz H-P, Asmar L, Tseng W-Y, Jiao X, Portera C, Yoo B, Patt D, Antao V. Pertuzumab (P) use in first-line HER2-positive metastatic breast cancer (mBC) in US community oncology practices: Treatment patterns and outcomes. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P4-14-13.

Background: Oral stomatitis is a frequent adverse event (AE) associated with mTOR-inhibitor therapy, and can impact treatment adherence. In the BOLERO-2 trial in patients (pts) with hormone receptor-positive (HR+) metastatic breast cancer (MBC) treated with exemestane (EXE) plus everolimus (EVE), the incidence of all-grade stomatitis or related AEs was 67%, with 24% and 8% of pts developing Grade (G) 2 and G3 stomatitis or related AEs, respectively (Perez et al ASCO 2013 Abst 7029). In BOLERO-2, 24% of pts required EVE dose reduction for stomatitis (Rugo et al Ann Oncol 2014;25:808). The current study evaluated 2 different steroid-based mouth rinses for the prevention or amelioration of oral stomatitis in pts with MBC treated with EVE. Methods: This prospective randomized phase II study enrolled postmenopausal pts (planned accrual=100) with HR+ MBC within the US Oncology Network who were initiating therapy with an aromatase inhibitor plus EVE (10 mg/day). Eligible pts were randomized, blinded, 1:1 to prophylactic treatment with 1 of 2 oral rinses (ARM 1: MMW 480 ml recipe: 320 mL oral Benadryl, 2 g Tetracycline, 80 mg Hydrocortisone, 40 mL Nystatin suspension, water; or ARM 2: Prednisolone 15mg/5mL oral solution). Pts were instructed to swish/expectorate 10 ml of the assigned rinse 4 x daily starting with Day 1 of EVE treatment, for a total of 12 wks. The primary objective was to determine the incidence of G≥2 stomatitis during the first 12 wks of treatment. Secondary objectives included assessment of AEs (all grades), determination of the percentage of pts requiring dose interruption and/or dose reduction of EVE or discontinuation of therapy due to toxicity, and evaluation of the impact of the oral rinses on the duration and severity of stomatitis. Results: As of April 16, 2015, a total of 48 pts have been randomized and 47 pts have received treatment, with a mean time on mouth rinse of 68 days (range 2-84 days). Median age was 65 yrs (range 31-82 yrs). Twelve patients developed an oral AE and the incidence of all-grade stomatitis was 25% (n=12/48). The incidence of G1 stomatitis was 17% (8/48), G2 stomatitis was 8% (4/48) and there were no G3 events. The 4 G2 stomatitis AEs occurred within the first 30 days of treatment. One pt (1/48; 2%) required EVE dose delay. One pt developed oral candidiasis while on the steroid mouth rinse and no pts have stopped the steroid mouth rinse therapy due to mouth rinse-related toxicity. Conclusion: These preliminary data are the first from a prospective trial to provide evidence of a reduced incidence of mTOR-associated stomatitis with prophylactic use of a steroid mouth rinse. The 25% incidence of all-grade and 8%/0% incidence of G2/3 stomatitis compare favorably with the 67% and 24%/8% incidence of all-grade and G2/3 stomatitis, respectively, in BOLERO-2. These preliminary data also demonstrated the safety and tolerability of these 2 steroid mouth rinses. The incidence of stomatitis on each study arm will be available when accrual is completed. The prophylactic use of steroid mouth rinses substantially decreases the incidence of G2/3 stomatitis and the need for EVE dose interruption/reduction. Citation Format: Jones VL, Jensen LL, McIntyre KJ, Oommen SP, Patt DA, Cortas TE, Harris RP, Wilks ST, Fox P, O'Shaughnessy JA. Evaluation of miracle mouthwash (MMW) plus hydrocortisone versus prednisolone mouth rinses as prophylaxis for everolimus-associated stomatitis: Preliminary results of a randomized phase II study. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P1-15-06.

Introduction: We show that the combination of quantitative magnetic resonance imaging (MRI) and mathematical modeling can accurately predict tumor response for individual patients, and we demonstrate the selection of personalized therapeutic regimens using our mathematical model to vary, in silico, a range of clinically feasible treatment plans to achieve the greatest tumor control. Methods: Breast cancer patients (N = 18) were scanned by MRI at three time points during neoadjuvant therapy (NAT): 1) prior to NAT, 2) after one cycle of their initial NAT regimen, and 3) after the completion of their initial NAT regimen. Specifically, diffusion-weighted MRI data characterizing tumor cellularity was collected to estimate tumor cell burden, and dynamic contrast-enhanced MRI data was collected to estimate the local drug delivery using pharmacokinetic analysis and population-derived plasma curves of the administered chemotherapies. To predict tumor response, we calibrated our tissue scale, 3D, biophysical mathematical model to each patient's MRI data set using their first two scans. Using the calibrated, patient-specific parameters, the model was projected forward to the third scan time. The predicted total tumor cellularity, volume, and longest axis were compared to the actual values measured from the patient's third scan. Following evaluation of the model's predictive ability, we then employed the model to identify potentially superior, clinically relevant, alternative dosing regimens for each patient. The alternative regimens were defined using the same total amount of drug each patient received during their standard regimen, while varying dosages and frequency between their second and third scans. Statistical analysis was completed with Pearson correlation and Wilcoxon signed rank test. Results: The model's predictions of tumor response are significantly correlated to the measured tumor burden at the time of scan 3 (r2 > 0.88, p < 0.01) for total cellularity, total volume, and longest axis. For the alternative dosing regimens assessed, the model predicted that individual patients could have achieved, on average, an additional 21% (0-46%) reduction in total cellularity. The optimal dosing regimens chosen by the model were predicted to significantly outperform standard regimens for tumor control (p < 0.001). Conclusions: These results suggest that the mathematical model can be predictive of tumor response by MRI in the clinical setting using data at the earliest times of therapy. With in silico studies, we illustrate how therapeutic regimens can be selected for individual patients for better tumor control, revealing that standard regimens may not be the most effective for every patient. These results represent a first step towards mathematically-based, personalized patient regimens. Future work aims to optimize therapy regimens via established optimal control theory methods. Citation Format: Angela M. Jarrett, Ernesto A. Lima, David A. Hormuth, Chengyue Wu, John Virostko, Anna G. Sorace, Julie C. DiCarlo, Debra Patt, Boone Goodgame, Sarah Avery, Thomas E. Yankeelov. Patient-specific neoadjuvant regimens for breast cancer identified via image-driven mathematical modeling [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5485.