Haddad, Tufia C.

TPS615 Background: The TAILORx and RxPONDER trials demonstrated that RS identifies many postmenopausal pts with node-neg and node-pos BC and RS ≤25, who do not benefit from addition of ACT to endocrine therapy (ET). Both trials also showed that certain subsets of premenopausal pts (node-neg/high clinical risk/RS 16-20, node-neg/RS 21-25, and node-pos/RS ≤25) benefited from adding ACT to ET. Most premenopausal pts in these trials did not receive ovarian function suppression (OFS) as part of their ET regimen. Given the observed benefit from OFS in high-risk premenopausal pts with HR+/HER2- BC in the SOFT/TEXT trials, many questioned whether all or part of the observed ACT benefit in the TAILORx/RxPONDER trials may have been the result of chemotherapy-induced OFS. To address this question, we developed OFSET, a phase III, multicenter clinical trial comparing OFS+ET v ACT+OFS+ET. Methods: We hypothesize that addition of ACT to OFS+ET is superior to OFS+ET in improving invasive breast cancer-free survival (IBCFS) among premenopausal, early-stage BC pts with HR+/HER2- tumors, and a 21-gene RS between 16-25 (for pN0 pts) and 0-25 (for pN1 pts). Secondary objectives include invasive disease-free survival, overall survival, distant recurrence-free interval, breast cancer-free interval, and health-related quality of life (HRQOL). Pts must be node-neg with RS 16-20 (plus high clinical risk), or RS 21-25, or have 1-3 positive nodes with RS ≤25. Stratification is by nodal status/RS status (pN0 RS 16-25 v pN1 RS 0-15 and pN1 RS 16-25), intent to receive CDK4/6 inhibitor (yes; no), and age (18-39 v ≥40). Pts are randomized after surgery to either OFS+ET or ACT+OFS+ET v ET is an aromatase inhibitor (AI). Choice is per investigator discretion; tamoxifen is allowed if AI is not tolerated or if OFS is incomplete. Radiotherapy will be administered per investigator discretion per protocol guidelines. The HRQOL sub-study will assess differences in severe menopausal symptoms, measured by the FACT ESS-19 score between arms, as well as increased pain severity (PROMIS). Blood and tumor specimens will be collected for future research. Accrual of 3,960 pts is anticipated to be completed in 7 yrs, 7 mos. Per NSABP B-28 and RxPONDER data, 5yr IBCFS of pN1 pts on the ACT+OFS+ET arm is estimated at 92.3%. Based on TAILORx data, 5yr IBCFS of pN0 pts on the ACT arm is ~95%. Assuming 56% of pts to be pN0 and 44% pN1, and a 0.5% annual loss-to-follow-up rate, the definitive analyses to detect a hazard ratio: 0.75 with ACT+OFS+ET v OFS+ET, with one-sided α of 0.025 and 80% power, will require 380 IBCFS events, expected to occur~11 yrs after study initiation. OFSETwas activated Aug 2023. As of 1-6-25, accrual is: 188/3,960. NCT #: NCT05879926. Support: U10CA180868, -80822, UG1CA189867, U24CA196067. Clinical trial information: NCT05879926 .

TPS2099 Background: Gliomas are the most common primary central nervous system (CNS) malignancy in adults, accounting for 26.3% of all brain tumors. Care at high volume centers is associated with an overall survival benefit, but access to in-person evaluations can be challenging due to disease-related neurological disability and loss of income. Telehealth represents a convenient and efficient alternative to in-person evaluations, but acceptability and comparative safety of this care delivery modality has not been prospectively evaluated among glioma patients undergoing chemotherapy. Methods: This single-arm non-randomized pragmatic clinical trial evaluates patient satisfaction with, and safety of video-enabled telehealth assessments compared to in-person evaluations for patients with glioma undergoing temozolomide chemotherapy. The study includes adult patients with a diagnosis of glioma requiring adjuvant temozolomide chemotherapy. Participants act as their own controls, alternating between in-person and telehealth assessments while undergoing chemotherapy dosed per standard of care. Monitoring labs are completed locally and transmitted electronically. For participants without access to Wi-Fi or a device (e.g. mobile phone or computer), cellular-enabled tablet devices are provided to facilitate appointments and completion of electronic study components. All participants who travel to in-person appointments are reimbursed for travel expenses. The primary outcome measure is patient satisfaction with care delivered, as measured by institutional Press-Ganey survey scores obtained following telehealth and in-person assessments. A key secondary outcome measure is completion rate of planned oral chemotherapy, tracked using a digital pill diary incorporated into our institutional electronic health record. The digital diary allows real-time tracking of chemotherapy adherence and adverse events experienced by participants. Other secondary outcomes include acute care utilization days following telehealth and in-person visits (defined as emergency department evaluations and days of inpatient stay), neurologic disability as measured by the Neurologic Assessment in Neuro-Oncology (NANO) scale, and disease related quality of life measured by the EORTC QLQ-C30. All participant surveys are self-reported and completed electronically. This decentralized pragmatic clinical trial provides unprecedented, prospective real-world data on utilization of telehealth services compared to in-person visits for patients undergoing chemotherapy for glioma. We expect the data generated to inform the design and conduct of future decentralized interventional neuro-oncology trials. NCT06625047 opened for enrollment in October 2024, 16 of 30 intended participants were accrued as of January 2025. Clinical trial information: NCT06625047 .

e13042 Background: The conventional treatment paradigm for metastatic breast cancer (MBC) is to continue systemic therapy until disease progression. With improved systemic therapy options, some patients (pts) with MBC attain radiographic remission. Tumor-informed circulating tumor (ct)DNA detects molecular disease burden and may guide decision-making. We report on an institutional cohort of pts with MBC in which tumor-informed ctDNA testing was utilized prospectively to guide decisions on systemic therapy or surveillance imaging frequency. Methods: Utilizing an IRB-approved protocol, we retrospectively identified pts monitored with tumor-informed ctDNA using the Signatera™ mPCR-NGS assay (Natera, Inc) as part of routine clinical care in Mayo Clinic, Rochester. We then identified a subset with biopsy-proven MBC who attained radiographic remission of disease following systemic therapy, deemed by the treating medical oncologist as “exceptional responders”. Real-time ctDNA testing was used to inform a decision to either discontinue systemic therapy, treatment de-escalation (remove a component of multi-drug therapy), increase the time interval between restaging scans, or stay off therapy previously stopped due to toxicity . Data on breast cancer characteristics, imaging results, ctDNA test results, and outcomes were collected. Descriptive statistics were used to analyze the data. Results: 10 pts met our study criteria. The median age at MBC diagnosis was 42 years (range 28-61); 5 pts had TNBC, 3 pts had HER2+, and 2 pts with ER+/HER2- MBC. Most pts (60%) were on first or second line therapy (range 1-5). The median duration of the most recent regimen was 27.5 months (range 10-70 months). All pts were without radiographic evidence of active cancer at the time of ctDNA testing with negative results (0 MTM/mL) at the time of clinical decision-making. 4 pts stopped all systemic therapy and proceeded with observation (1 trastuzumab, 1 trastuzumab deruxtecan, 1 pembrolizumab, and 1 talazoparib). 2 pts discontinued a targeted therapy (1 olaparib, 1 abemaciclib) while continuing endocrine therapy. 1 patient stopped targeted therapy (abemaciclib) due to a blood clot, and ctDNA testing guided the decision to not start another CDK4/6 inhibitor while continuing endocrine therapy. In 3 pts the frequency of restaging scans was decreased to either annually or semi-annually. The median follow-up after a ctDNA-informed decision was 11 months (range 1-41 months). At this time, no patient has converted to ctDNA-positivity, experienced resurgence or progression of disease, needed to change systemic therapy or increase imaging frequency. Conclusions: Tumor-informed ctDNA may offer additional confidence to oncologists and pts to deescalate systemic therapy or decrease the interval of imaging frequency in exceptional responders with MBC. Additional data to support this approach is needed.

8053 Background: In the US, only 27.4% of lung cancer cases are diagnosed early, with 5-year survival rates of 63% for localized and 27% for late-stage cancers. Despite recommendations since 2012 for screening high-risk individuals with low-dose CT, uptake has been limited, and most lung cancer diagnoses occur after symptoms appear. Similarly, chest x-rays, while commonly used as an initial test to investigate patients with symptomatic suspected lung cancer, have demonstrated limited sensitivities between 50-70% and specificities over 80%. Symptoms often overlap with common conditions, making detection challenging, and studies have identified median delays of 187 days from symptom onset to diagnosis. This prolonged interval presents an opportunity for improvement. This study examines the use of the AI cancer prediction platform, C the Signs, to passively screen for lung cancer by leveraging electronic medical records (EMRs) for early lung cancer detection. Methods: Utilizing the Mayo data platform, we conducted a retrospective analysis of EMR data from 894,409 patients, including 7,395 individuals diagnosed with lung cancer. We assessed the sensitivity and specificity of the AI cancer prediction platform, in identifying patients at risk of lung cancer. Additionally, we compared the timing of lung cancer risk identification by the AI cancer prediction platform with the timing of diagnoses made by physicians to determine whether the platform enabled earlier detection. Results: The AI cancer prediction platform detected 6,749 cases of lung cancer among the 7,395 individuals diagnosed, resulting in an early detection sensitivity of 91.5%. The platform identified 423,249 false positives among the 887,014 patients who did not have lung cancer, leading to a specificity of 52.3%. Additionally, it identified the risk of a lung cancer diagnosis in 26.6% of patients up to five years earlier than the diagnoses made by physicians. Conclusions: This study highlights the potential of leveraging EMR data and AI platforms like C the Signs to enhance early lung cancer detection. Chest X-rays, with their reduced sensitivity for early-stage lesions and reliance on symptom-driven use, remain limited as a screening tool. In contrast, the AI platform achieved a sensitivity of 91.5% and identified 26.6% of cases up to five years earlier than traditional diagnoses. These findings underscore the promise of AI-based platforms as supplementary tools for improving early detection and facilitating timely intervention to enhance patient outcomes.

Abstract Background: Despite recent advances in the treatment of triple negative breast cancer (TNBC) many patients are still suffering from early recurrence, therapy resistance, and organ metastasis 1 to 3 years post-chemotherapy. TNBC cells with stem cell-like properties are resistant to standard of care chemotherapy and are responsible for early tumor relapse and poor outcome. Up-regulation of PD-L1 expression in Cancer Stem Cells (CSCs) has been associated with immune evasion capacity and promotion of their stem-like properties. In this study we identify two druggable stemness target, Aurora-A Kinase (AURKA) and NOTCH3, with positive feedback on intra-tumoral PD-L1 expression and cancer cell plasticity. Methods: MDA-MB 231, MDA-MB 231 LM and SUM149-PT TNBC cells were used both in 2D and 3D environment. Unique TNBC cells were established from metastatic PDX models. TNBC cells were treated with Alisertib (AURKA inhibitor, 50nM) or AV353 (NOTCH3 inhibitor, 200ng). Immunofluorescence and immunoblot were used to compare different biomarker expression. ALDH was measured using Aldefluor kit. Apoptotic cells were quantified in real-time using IncuCyte S3. In vivo studies; 1x106 MDA MB 231/LM cells were injected into the mammary fat pad of NSG-female mice and treated with Alisertib or AV353. Control groups were treated with pacebo. Results: Our study shows that AURKA and NOTCH3 pharmacological blockade reduced nuclear/undruggable PD-L1, that is not achieved clinically by using FDA-approved immune checkpoint inhibitors (ICIs). Significantly, following stemness-targeted therapy with alisertib or AV353, reduction of intra-tumoral PD-L1 resulted in inhibition of tumor growth, decreased levels of CD44 expression, increased CD8+ T-cell tumor infiltration, impairment in the enrichment of ALDHhigh CSCs and enhanced chemosensitivity. In vivo pharmacological blockade of AURKA and NOTCH3 signaling pathways induced inhibition of tumor growth, apoptosis, and impairment of organ metastases. Conclusions: This study demonstrates that selective AURKA and NOTCH3 pharmacological blockade results in the inhibition of cancer cell plasticity that is linked to reduction of undruggable nuclear PD-L1 in TNBC cells. Our findings provide the strong preclinical rationale to develop novel clinical trials for patients with advanced TNBC that are refractory to chemotherapy and show limited response to FDA-approved ICIs. Citation Format: Antonino Bonaventura D'Assoro, Matthew Goetz, Carol Lange, Roberto Leon-Ferre, Tufia Haddad, James Ingle, Annapaola Montagner, Andrew Takchi, Ellie Babadi, Lucio Miele, Urban Lendahl, Urban Lendahl. Pharmacological blockade of AURKA and NOTCH3 oncogenic pathways to inhibit cancer cell plasticity in triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5978.

Objectives/Goals: Mayo Clinic’s Clinical Trials Beyond Walls™ (CTBW) program collaborates with study teams to implement decentralized elements in clinical trials, enabling participation from home or local settings. In cancer treatment trials, traditional paper pill diaries are replaced with real-time digital tracking solutions to monitor chemotherapy adherence. Methods/Study Population: The CTBW team developed a solution to deliver electronic pill diaries to research participants using the electronic health record (EHR) system Epic and patient portal MyChart1. The solution includes a portal message to remind participants to take chemotherapy. Medication dose, date taken, and reasons for missed doses (e.g., “I forgot” or “side effects were bothersome”) are captured. An automated in-basket notification system alerts the study team when predefined conditions are met. Configurable medication schedules ensure diaries are sent according to the prescribed frequency. Reports were generated to allow study teams to monitor all participant diaries. Results/Anticipated Results: The CTBW team implemented this digital pill diary in neuro-oncology trial NCT066250472. We anticipate the pill diary in the patient portal will enhance chemotherapy adherence by capturing real-time data in a platform widely used by Mayo Clinic patients. This patient-reported data is stored in the EHR, where it is accessible to providers and study teams. This allows for continuous monitoring, which facilitates a streamlined review of potential adverse events, improved compliance visibility, and timely treatment adjustments compared to paper-based or external solutions. The system also streamlines data entry, reducing human error and eliminating manual transcription. The created language and workflow templates allow the CTBW to scale this approach to future cancer trials Discussion/Significance of Impact: Decentralized clinical trial participants may never visit Mayo Clinic, making digital recording essential. The EHR-based digital pill diary enables continuous monitoring within a familiar system for providers and patients, increasing study team visibility, and allowing for earlier intervention in cases of non-compliance or adverse events.

371 Background: Prostate cancer is the most common malignancy in men globally and remains among the top five leading causes of cancer-related deaths. Emerging evidence suggests that early identification of symptoms can detect localized disease thus enabling opportunities for curative intent treatments. This study evaluates the use of the AI-powered prediction model, C the Signs, to passively screen for prostate cancer by leveraging data from electronic medical records (EMRs), offering a novel pathway for identifying high-risk individuals. Methods: A retrospective analysis was conducted using the Mayo Data Platform, encompassing 418,477 male patient records, of which 16,835 were diagnosed with prostate cancer. C the Signs identified patients at risk based solely on their EMR data, utilizing AI-driven pattern recognition. Sensitivity and specificity analyses were performed to assess the prediction model’s accuracy. Additionally, we examined the time-to-diagnosis advantage for patients flagged by the model compared to traditional physician clinical diagnoses. Results: C the Signs demonstrated a sensitivity of 83.3% and a specificity of 52.5% for identifying patients at risk of prostate cancer. Notably, 31.8% of prostate cancer cases were identified at risk up to five years earlier by the model compared to traditional physician clinical diagnosis. Conclusions: The integration of AI-driven prediction models like C the Signs into prostate cancer screening pathways provides an opportunity to enhance early detection, particularly in symptomatic individuals. Compared to prostate-specific antigen (PSA) testing, the model achieved equivalent sensitivity and 38% higher specificity, positioning it as a valuable companion tool for improving patient outcomes.

54 Background: The incidence of colorectal cancer is increasing rapidly, particularly amongst younger people. Despite a screening program being widely available, most patients are diagnosed with cancer through symptomatic presentation. Leveraging electronic medical records (EMRs) through clinical decision support platforms offers potential for identifying high-risk individuals before clinical suspicion develops. This study aims to explore the challenges associated with early colorectal cancer diagnosis and evaluate the effectiveness of the cancer case finding feature of a cancer management platform called C the Signs. Methods: A retrospective analysis was conducted using data from the Mayo Data Platform, comprising records of 894,275 patients, including 7,348 diagnosed with colorectal cancer. C the Signs was employed to identify patients at risk of colorectal cancer based on EMR data. Sensitivity and specificity analyses were performed to assess the platform's performance in identifying high-risk patients. Additionally, the study evaluated the temporal discrepancy in colorectal cancer diagnoses between those flagged by C the Signs and those detected by primary care physicians. Results: The C the Signs screening platform had a sensitivity of 93.8% and a specificity of 19.7% in identifying patients at risk of colorectal cancer. Notably, 29.4% of patients with colorectal cancer were identified as being at risk up to 5 years earlier by C the Signs compared to diagnoses made by primary care physicians, highlighting the platform's potential for early detection. Conclusions: Early identification of high-risk individuals holds promise for improving patient outcomes and reducing the burden of colorectal cancer. Whilst the specificity of the C the Signs platform for colorectal cancer is relatively low, the sensitivity is comparable to the most favorable rates reported for colonoscopy. In addition, if C the Signs was used in conjunction with a relatively cheap screening test such as Faecal Immunochemical Testing, this would significantly drive up the specificity whilst maintaining the sensitivity. Further studies are needed to assess its efficacy in conjunction with tests available in primary care.

Abstract BACKGROUND Central nervous system (CNS) tumors are frequently characterized by physical disabilities, and treatment is often associated with side effects, as well as financial toxicity and loss of income owing to disease-related neurological deficits. Treatment at centers with specialized neuro-oncology care is associated with an overall survival benefit. However, access to such centers can be limited for many patients. Telehealth services represent an option for improving access to specialized care. During the COVID-19 pandemic public health emergency (PHE), regulatory and reimbursement changes provided patients improved access to neuro-oncology by telehealth. METHODS We retrospectively analyzed our institutional utilization of telehealth visits in the neuro-oncology vs solid tumor oncology practice after the COVID-19 pandemic. RESULTS Between July 2020 and April 2021, telehealth represented 24.1% of all neuro-oncology visits compared to 13.3% for all solid tumor groups. Between May 2021 and April 2023, telehealth visits remained frequent in neuro-oncology compared to solid tumor groups (34.6% vs 15.2%). Utilization rates declined across the practice in the year following expiration of the COVID-19 PHE in May 2023, though remained high for neuro-oncology at 34.1% vs 15.2% for solid tumor groups. Overall, telehealth visit utilization for neuro-oncology has remained more than double that of the oncology practice as a whole. CONCLUSIONS The striking difference between telehealth visit utilization in neuro-oncology compared with general medical oncology indicates that patients with CNS tumors, and those that provide care for these patients, have come to depend on this mode of care delivery. Even after expiration of the COVID-19 PHE, telehealth use remains high in neuro-oncology at our institution. Prospective evaluation to determine the safety of and clinical outcomes associated with telehealth visits for individuals with CNS tumors are critically needed. Such data may promote regulatory and reimbursement reform enabling broader adoption, service sustainability, and improved access to specialized care.

Purpose of review This review underscores the critical role and challenges associated with the widespread adoption of artificial intelligence in cancer care to enhance disease management, streamline clinical processes, optimize data retrieval of health information, and generate and synthesize evidence. Recent findings Advancements in artificial intelligence models and the development of digital biomarkers and diagnostics are applicable across the cancer continuum from early detection to survivorship care. Additionally, generative artificial intelligence has promised to streamline clinical documentation and patient communications, generate structured data for clinical trial matching, automate cancer registries, and facilitate advanced clinical decision support. Widespread adoption of artificial intelligence has been slow because of concerns about data diversity and data shift, model reliability and algorithm bias, legal oversight, and high information technology and infrastructure costs. Summary Artificial intelligence models have significant potential to transform cancer care. Efforts are underway to deploy artificial intelligence models in the cancer practice, evaluate their clinical impact, and enhance their fairness and explainability. Standardized guidelines for the ethical integration of artificial intelligence models in cancer care pathways and clinical operations are needed. Clear governance and oversight will be necessary to gain trust in artificial intelligence-assisted cancer care by clinicians, scientists, and patients.