tbuckley/cabot-search huggingface dataset:
National implementation of an AI-based virtual dietitian for patients with cancer.
Authors: Marissa Lubin Buchan, Susan Bratton, Eliza Jones, Keshav Goel, Ethan Basch
Journal: Journal of Clinical Oncology (2024)

1500 Background: Nutritional status is an established key driver of cancer outcomes and patient experience. However, the workforce of oncology dietitians is unable to meet the needs of the population, and 80% of patients seek but do not receive nutrition support. Advances in artificial intelligence (AI) and machine learning (ML) capabilities afford the opportunity to expand reach and rapidly hone in on clinical and contextual needs to deliver evidence-based nutritional guidance. Methods: To address this unmet need, an AI-based nutrition expert-platform was developed. Interventions were curated by oncology-nutrition experts based on peer-reviewed literature and clinical guidelines to facilitate self-management of cancer side effects and optimize nutrition. A growing database of over 114,000 evidence-based interventions powers Ina, a text-message based virtual nutrition assistant. Interventions are continually enhanced and refined using AI and ML applied to an extensive learning dataset. The tool was implemented nationally in partnership with 25 cancer advocacy organizations. Data on demographics, patient-reported outcomes, and utilization were systematically collected. Results: Between July 2019 and August 2023, 3,310 users from all 50 states registered for the virtual nutrition platform. Based on self-report, users were 73% female; mean age 57 years (18-91); most common cancer types were GU (22%), Breast (21%), Gyn (19%), GI (14%), and Lung (12%), representing nearly 90% of users. Users were medically complex, with 50% reporting Stage 3-4 disease, 64% with metastases, and 50% with 2+ chronic conditions. Nutritional challenges were highly prevalent: 58% were overweight/obese by BMI, 83% reported barriers to good nutrition, and 42% had food allergies/intolerances. Levels of engagement were high: 68% of users texted queries to Ina, 79% completed surveys, and median user retention duration was 8.8 months. Overall, 94% of users reported being satisfied with the platform and 98% found the guidance to be helpful. In an evaluation of outcomes, 84% reported actively using the nutrition advice to guide their diets, 47% used the recommended recipes, and 82% felt the program improved their quality of life. In this cohort with high symptom burden, 88% reported Ina helped them manage their symptoms and 34% noted improved symptom control during participation. Conclusions: Implementation of an evidence-based AI virtual dietitian is feasible, and is reported by patients to be beneficial on diet, quality of life, and symptom control. Ongoing evaluations are assessing impact on other outcomes.
Adaptively learning visual model of surgical resection images via coarse-to-fine–grained strategy for pathological invasiveness prediction in indeterminate stage IA lung adenocarcinoma.
Authors: Lintong Yao, HaiYu Zhou, Linghan Cai, Qiaxuan Li, Daipeng Xie, Hongwei Lin
Journal: Journal of Clinical Oncology (2024)

1563 Background: Indeterminate nodules with high risk of malignancy are suggested to receive video-assisted thoracoscopy biopsy. Frozen section assists surgeons to decide surgical strategies for stage IA lung adenocarcinoma (LUAD) intraoperatively, which is time-consuming and may misdiagnosis due to limited sampling and poor tissue quality. Methods: We prospectively enrolled stage IA LUAD patients underwent complete resection between June 2020 and September 2023 in Guangdong Provincial People's Hospital (GDPH), Affiliated Hospital of Guangdong Medical University, Meizhou People's Hospital. All the resected lung nodules were finally diagnosed as pre-invasive lesion (PIL), minimally invasive adenocarcinoma (MIA) or invasive lung adenocarcinoma (IAC) by FFPE pathological diagnosis. Images of the whole section of the lung nodules were taken by smartphones under natural lighting condition in operating theaters without shadowless surgical lights. Patients received preoperative anti-cancer therapy and with low-quality tumor section images were excluded. Images of nodules section, FS reports, FFPE diagnosis and clinical information were prospectively collected. Predictive artificial intelligence (AI) model was developed by a three-step process, which first predict the IAC and non-IAC region at coarse level, and identify fine level features through the high-risk region. The coarse and fine labels were then coordinated through risk ranking loss to reach a final predictive result. Results: We prospectively enrolled 1516 patients with 1638 indetermined lung nodules from preoperative chest CT following the inclusion and exclusion criteria, with 2438 images acquired from intraoperative nodule sections. The included patients have median age of 57 years old and 38.6% male patients. The pathological diagnosis for included lung nodules are 122 PIL nodules, 502 MIA nodules and 1014 stage IA LUAD. A multi-task artificial intelligence based on Coarse-to-Fine-Grained Strategy has been established to classify the indetermined lung nodules during the surgery. For binary classification, AI model reach an AUC of 0.86 in differentiation between IAC or non-IAC. For 3-classification level, AI model reach an AUC of 0.87 in differentiation between PIL, MIA or IAC. We further defined the IAC nodules into G1, G2 and G3 according to IASLC lung adenocarcinoma grading system. For 5-classification level, AI model reach an AUC of 0.86 in differentiation between PIL, MIA, IAC-G1, IAC-G2 and IAC-G3. Conclusions: Our AI models based on images of intraoperative surgical resection could effectively classify indetermined lung nodules, which can assist thoracic surgeons diagnose the nodules rapidly during the surging and decide the following surgical strategies. Clinical trial information: ChiCTR2300075999 .
Effect of aberrant N-glycosylation of PD-L1 on the potency of anti-PD-1 treatments.
Authors: Bar Kaufman, Nofar Erlichman, Tsipi Meshel, Adit Ben-Baruch, Moshe Elkabets, Angel Porgador
Journal: Journal of Clinical Oncology (2024)

e14599 Background: Immune checkpoint inhibitors (ICIs) targeting the PD1/PDL1 axis have revolutionized the way we treat cancer patient. Yet, response rate to these treatments remains low. Post translational modifications of PD-L1 in the form of N-glycosylation play a crucial role in both receptor-ligand interaction and in the potency of PD1/PDL1 ICIs. Understanding the effect of each N-glycosylation site is crucial for patient stratification for treatment groups and may help to increase response rate to anti-PD1/PDL1 ICIs. We recently developed the Immune-Checkpoint Artificial Reporter (IcAR) technology to assess specific ICI potency for target tumors including clinical samples. Using IcAR expressing recombinant human PD1 receptor we can analyze clinical response of patients to PD1-PDL1 blocking drugs and analyze the glycan-modified PDL1 expressing cancer cells. Methods: We have created two cell lines (MDA-MB231 and MCF7) overexpressing PD-L1 in its wildtype form, single mutated glycosylation sites (N35A, N192A, N200A, N219A) and all sites mutated together (Nx4). Using IcAR-PD1 we measured the functional binding of the different mutated cell lines, cell derived xenografts (CDX) and fixed cell samples to evaluate baseline binding. Later we have incubated IcAR-PD1 and mutated cell liens together with multiple clinically used PD1/PDL1 ICIs to evaluate the inhibition of interaction of each ICI. Results: While staining and functional interaction of PDL1 and single mutants is similar, removing all four N-glycosylation sites (Nx4) decreased both the staining of PDL1 and functional binding. Interestingly, blocking capacity of different ICIs was impaired by N35A PDL1 mutation, and was absolute through all concentration (2.5 to 40 ug/mL) when employed on the Nx4 cell lines. While anti-PDL1 ICIs were managed to block PD-1/PD-L1 interaction trough all cell lines and mutants, the inhibitory capacity of anti-PD1 ICIs was impaired by glycosylation, especially on N35. Conclusions: Aberrant glycosylation of PD-L1 impair the potency of anti-PD-1 treatment in cancer cell lines. These results might explain why patients with negative PD-L1 staining (Nx4), might respond to ICIs, and suggest an explanation of mechanism in which patients with high PD-L1 staining (i.e. N35), might not respond to anti-PD-1 treatments. Our results shed a light on the mechanism of ICI resistance and response – using this knowledge patient will be stratify better into treatment groups, getting oncology a step closer to precision immunology.
Cancer clinical trial participation in socioeconomically vulnerable patients: A risk model to aid in targeted interventions.
Authors: Katherine Szarama, Joseph M. Unger
Journal: Journal of Clinical Oncology (2024)

1529 Background: In patients with cancer, those with lower income are 30% less likely to participate in clinical trials. Lower income individuals face direct and indirect costs that can make participation in a clinical trial prohibitive. However, a broader evaluation of specific behavioral, information, insurance, and area-level variables that could contribute to this disparity has not been conducted. We aimed to develop and validate a model to identify socioeconomically vulnerable patients at high risk of non-participation in clinical trials. Methods: We used data from the Health Information National Trends Survey (HINTS), a national cross-sectional survey about knowledge of, attitudes toward, and use of health-related information. We analyzed HINTS survey databases that included questions about whether patients with cancer participated in a clinical trial (survey years 2014, 2017, and 2020). We examined 21 different demographic, socioeconomic, behavioral, geographic, and health information questions. We derived a risk model to predict clinical trial participation in a random set of 60% of participants using best subset selection with k-fold cross validation. The derived model was validated in the remaining 40% of participants. Logistic regression was used. Results: We examined N=1,023 participants with household income &lt;$75,000, the current median in the U.S. In the training dataset of n=614 participants, a model with 5 variables was identified. Non-Hispanic White patients and patients without a college education, with high levels of distrust, with high levels of anxiety or depression, and from non-urban areas were all at lower risk of trial participation. We summed the adverse risk factors for all individuals; a risk score with 4 levels was constructed based on distribution quartiles. In the independent validation cohort (n=409), each increase in level of adverse risk factors was associated with a 42% reduction in the odds of trial participation (OR=0.58, 95%-CI, 0.40-0.84, p=.004), indicating successful model validation. Among all individuals, trial participation rates decreased from 18.6% to 7.5% to 4.6% to 2.8%, respectively, as the number of adverse risk factors increased (in quartiles) from 0-1 to 2 to 3 to 4-5. Individuals with 4 or 5 risk factors (vs. those with 0-1 factors) were 87% less likely to participate (OR=0.13, 95% CI, 0.05-0.31, p&lt;.0001). Conclusions: We developed and validated a 5-variable risk model that identified a large set of lower income individuals at lower risk of trial participation. In a first-time observation, psychosocial variables were shown to be meaningful predictors of lower trial participation. These findings could aide in the early identification of patients who may benefit from additional support to navigate the treatment trial decision making process, in the name of more equitable participation in trials for all patients.
TriHealth Cancer Institute’s collaboration with the Tempus AI TIME program impact on clinical trial operations and enrollment.
Authors: James F. Maher, Samantha Mallahan, Jason Joseph Claes, Wayne Thompson, Benjamin Kuritzky, Robert Neff, Seerin Viviane Shatavi, Courtney Rice, Karen M. Huelsman, Leanne S. Budde, Faisal Adhami, Megan Shulman, Meghan Degele, Blathnaid Donovan, Danielle Skelly, Emily Patnaude, Christian Clifford, Annie Darmofal, Chelsea Kendall Osterman, Matthew M. Cooney
Journal: Journal of Clinical Oncology (2024)

1553 Background: TriHealth Cancer Institute has the largest personalized medicine program serving Cincinnati. TriHealth’s EMR integration with Tempus AI TIME allows for patient screening using an AI powered software TApp that matches to a broad clinical trial portfolio. Each patient is followed using LINK portal and trials can be activated rapidly via TIME’s operational model. The goal of this collaboration is to increase TriHealth’s patients' access for clinical trials. Methods: The TApp is software utilizing subject data, trial eligibility criteria, and NLP models for AI matching to TIME studies. Unique TApp searches occur whenever there is new patient data or changes to eligibility criteria. All TApp matches are reviewed by Tempus AI nurses followed by confirmatory screening by TriHealth’s staff. Trials were activated using the TIME program’s standardized operational methods including a pre-negotiated rate card, trials agreement, and a central IRB. Data included number of patients screened, unique TApp searches, trial activations, consents, operational metrics, and financial impact. Results: From January 2022 - March 2023, TApp performed 8,653,397 unique searches for 135 TIME trials on TriHealth’s population of 18,823 patients. During this period, 985 trial eligibility criteria were modified and 200,776 patient clinical updates occurred. There were 2,032 potential matches (1,944 interventional, 88 observational) for 45 different trials. Tempus AI nurses spent 389 hours reviewing all 1,944 interventional matches and of these sent 284 to TriHealth for evaluation. For TIME studies, 28 patients consented (18 interventional, 10 observational) and 7 trials were activated (4 interventional, 3 observational). Total trial billable revenue for TIME studies was approximately $241,500 ($34,500 average per trial). Regarding non-TIME trials, 3 patients consented on 3 interventional studies. Activation time for non-TIME trials compared to TIME trials was 186 days versus 37 days. Conclusions: TriHealth’s partnership with TIME evaluated 18,800+ patients for 135 trials. Over 8.6M unique TApp searches resulted in 2,000+ patients screened by nurses. Operationally, TIME trials resulted in a 2.3X increase in the number of trial activations, an 80% reduction in days to activation, and 9.3 fold increase in enrollment compared to non-TIME studies. The use of AI enabled patient screening, combined with a structured trial activation process, improved patient enrollment at a community health system. [Table: see text]
Efficient site selection for clinical trials using simulated annealing.
Authors: Ian Ruchlin, Vibhor Govil, Deepak Samar, Jitesh Chawla, Pyeush Gurha
Journal: Journal of Clinical Oncology (2024)

1558 Background: Identifying sites with eligible patients for multi-site oncology clinical trials is challenging given increasingly complex inclusion/exclusion criteria, limited site resources, and the need to ensure diversity in trials. This is a complex systems problem with many factors that present nonlinear behaviors. Given manifold combinations of sites, research capabilities, and study characteristics, artificial intelligence solutions may help research sites and sponsors optimize site selection to achieve enrollment, speed, and diversity of goals. We report on one of the first deployments of a multi-AI model solution to optimize site selection for an ongoing clinical trial. Methods: The clinical trials were translated into a standardized digital form expressed as rules fully encompassing the inclusion and exclusion criteria. A patient cohort was created from the criteria using the ConcertAI oncology research database, including site affiliations. The sites had numerical features specific to the protocol indication, such as physician count and projected patient count. Each site was scored by its features with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to create higher alignment to the best matched sites. Feature weights allowed TOPSIS to emphasize desirable attributes in an interpretable way. A simulated annealing algorithm, adept with nonlinear objectives, explored site combinations, yielding a high-scoring, constraint satisfying solution. Independent simulations measured convergence and provided a distribution of solutions. Shapley analysis clarified site contributions to score and constraints. The implementation was compiled and parallelized. Results: Utilizing a Multiple Myeloma clinical trial protocol with 7 criteria, we formed a cohort of 15,881 patients across 1,246 US clinical trial sites. Each site had 12 features. The weights optimized patient diversity while enrolling at least 10 patients per month across all sites to achieve the targeted study accrual timeline. The result was a set of 3 sites enrolling 12 patients per month with 51% diversity, in agreement with the optimal solution obtained through detailed, multi-week, manual analysis. As constraints increased, and exact verification became impractical, convergence was gauged by decreasing variance in independent solutions. Conclusions: Our study introduced a robust, efficient, and interpretable approach to clinical trial site selection. It addressed the challenges posed by increasingly complicated protocols, requirements for study participant diversity, and heterogeneous site features. The multi-AI model scalability underscored its practical utility in streamlining the site selection process for clinical research networks, academic centers, industry sponsored trials, cooperative group studies, and investigator-initiated studies.
Phase I/II study of stereotactic radiation and sacituzumab govitecan with zimberelimab in the management of metastatic triple negative breast cancer with brain metastases.
Authors: Kamran A. Ahmed, Youngchul Kim, Michelle DeJesus, Daniel Eli Oliver, Matthew Mills, Rajesh Nair, Avan J. Armaghani, Ricardo L Costa, Kimberley T Lee, Loretta S. Loftus, Tracey L. O'Connor, Hatem Hussein Soliman, Aixa Elena Soyano Muller, Michael A. Vogelbaum, Peter A. J. Forsyth, John Arrington, Marilin Rosa, Michael Yu, Hyo S. Han
Journal: Journal of Clinical Oncology (2024)

TPS1140 Background: Patients with triple negative breast cancer (TNBC) are at a high risk to develop brain metastases. The prognosis and quality of life for triple negative brain-metastatic breast cancer remains very poor. Treatment options are limited and improvement in efficacy is needed. Sacituzumab govitecan (SG) has shown blood brain barrier (BBB) penetration in preclinical and clinical settings with anti-tumor activity in the phase III ASCENT Trial (NCT02574455). Radiotherapy can open the BBB and has been shown to be safe in combination with anti-PD-1 therapy in our previous trial (NCT03807765). Therefore, combining radiation with the anti-PD-1 monoclonal antibody, zimberelimab, and SG may provide a synergistic anti-tumor approach. We hypothesize the use of SG and zimberelimab with stereotactic radiosurgery (SRS) among patients with metastatic TNBC with brain metastases will be safe and improve progression free survival compared to treatment with SG alone. Methods: The study is designed as a single-arm, nonrandomized, open-label, phase I/II trial of SG and zimberelimab with SRS among patients with metastatic TNBC with brain metastases. The primary endpoint will be neurologic toxicity defined by CTCAE v5 criteria (phase I) and 12 month PFS (phase II). TNBC patients ≥ 18 years old with ≤ 15 brain metastases with at least one measurable lesion ≥ 0.5 cm per RANO-BM criteria will be enrolled. Treatment will be initiated with SRS followed 1 week later by SG on days 1 and 8 (10 mg/kg) with zimberelimab on day 1 (360 mg IV) repeated every 3 weeks. Follow-up imaging response assessments will be conducted at q9 week intervals. An interim analysis will be performed after 21 patients are enrolled. An additional 10 patients will be enrolled if interim futility criteria are not met. Clinical trial information: NCT06238921 .
Unraveling the role of CTLA-4 variants in acute kidney injury after ICI treatments.
Authors: Yanfei Wang, Chenxi Xiong, Jiang Bian, Qianqian Song, Jing Su
Journal: Journal of Clinical Oncology (2024)

e14682 Background: Immune checkpoint inhibitors (ICIs) often result in various immune-related adverse events (irAEs), which limit their therapeutic application and pose challenges to clinical decision-making. Among these irAEs, the incidence rate of ICI-related acute kidney injury (ICI-AKI) is on the rise. Understanding the genetic predisposition to ICI-AKI can inform personalized treatment plans, thereby minimizing risks and optimizing outcomes. This study aims to interrogate risk loci for AKI patients to gain insights into ICI-related AKI in cancer patients and to support the design of more personalized cancer therapies. Methods: This study comprehensively integrates meta-analysis, replication genome-wide association study (GWAS), fine-mapping, and mendelian randomization (MR) to explore potential genetic variants related with ICI-AKI. We first conducted a meta-analysis of 6 ICI pharmacogenomics studies, aggregating effect sizes from these studies using both fixed-effect and random-effects models to address heterogeneity and identified 186 significant genotypes. Subsequently, a GWAS was carried out over the 186 candidate genotypes in an independent cohort from the NIH All of Us program (N = 230,013) using logistic regression, identifying14,773 patients who developed AKI. To identify causal variants, we implemented fine-mapping techniques to further refine our genotype selection. Results: Our study identified genetic variants associated with an increased risk of AKI. Among these, the variant with the strongest association was located at the cytotoxic T lymphocyte antigen 4 (CTLA-4) gene locus, rs3087243 (chr2: 203874196, G&gt;A,T). This genotype exhibited a significant inverse relationship with AKI risk (beta = -8.19e-2, P = 2e-11), suggesting that reduced activity of the CTLA-4 gene may be linked to an elevated risk of developing AKI. Our findings indicate that cancer patients with this genotype may have a lower risk of ICI-related AKI after immunotherapy. In our future study, we will continue investigating significant SNPs including rs3087243 in cancer patients for their ICI-AKI risk. Conclusions: We identified an AKI-tolerant genotype, rs3087243. Patients with this genotype may be safter from developing ICI-related AKI after immunotherapy treatment. This work also highlights the research potential of All of Us clinicogenomics data.