Citations

Abstract Patients with rare genetic disorders often wait years for a correct diagnosis, highlighting the urgent need for early and efficient triage for those at risk. Electronic health records (EHRs) are rich in useful information, yet the extent to which this data is sufficient to generate prediagnostic signals is uncertain. We trained machine learning models on billing codes and phenotypes extracted from clinical notes and applied these across eight conditions in a longitudinal cohort of roughly 3 million patient records from the Mayo Clinic, seeking early identification of conditions. The number of patients identified early varied by condition, ranging from 10% to 89% at 99% specificity. Median lead times exceeded one year prior to the first diagnostic billing code for most conditions, including Fabry disease (1,408 days), hereditary angioedema (2,755 days), neurofibromatosis type 1 (1,067 days), and hereditary hemorrhagic telangiectasia (2,320 days). We show that ML models trained on EHR data can prioritize patients for clinician review and confirmatory genetic testing and that the integration of diverse phenotypic data sources provides superior predictive value.

Background. Rare diseases collectively affect an estimated 300 million people worldwide, yet patients wait an average of 4-8 years for a correct diagnosis after visiting multiple physicians, accumulating unexplained findings, and suffering preventable disease progression before a unifying diagnosis is reached. Electronic health records (EHRs) capture this pre-diagnostic trajectory in rich detail, but conventional methods typically collapse years of longitudinal phenotypic signals into a single static snapshot; our approach instead models this temporal structure directly. Methods. We conducted a retrospective cohort study using longitudinal EHR data from approximately 3 million patients at Mayo Clinic Platform_Accelerate. Cases were patients with confirmed diagnoses of 10 rare diseases; controls (~5,200 per disease) had no record of any rare disease. HPO phenotype terms were extracted from clinical notes and laboratory results using a medspaCy NLP pipeline; ICD codes were grouped at three-character level. We developed and trained two temporal models, GRU-Attn and Conformer, that encode patient histories as quarterly time-binned sequences spanning up to 30 years, and compared these against three static baselines (CatBoost, XGBoost, logistic regression). Models were evaluated systematically at prediction horizons h = 1-10 years before diagnosis. Results. In the full held-out cohort benchmark, temporal models outperformed static baselines across the large majority of diseases and prediction horizons, with the advantage widening substantially as the horizon lengthened. At 1-2 years prior to diagnosis, mean AUROC was 0.928 (GRU-Attn) versus 0.871 (CatBoost); at 6-10 years prior to diagnosis, 0.726 versus 0.693, with static models degrading to near-chance for some diseases. Combining HPO and ICD features yielded the strongest performance, with a mean gain of +0.063 AUROC over the best single source at 1-2 years prior to diagnosis. Separately, in a manually curated cohort of 143 confirmed-undiagnosed cases with extended EHR histories, GRU-Attn identified 138 cases before first disease mention and all 143 cases before formal diagnosis at the prespecified 99th-percentile specificity threshold, with median lead times of 1.1-8.3 years and 1.9-21.7 years, respectively. Conclusions. By learning from the temporal trajectory of phenotypic signals rather than their static aggregate, temporal EHR models improved retrospective early detection performance across several rare diseases, most powerfully for conditions where pre-diagnostic findings accrue gradually and heterogeneously over years. These results show that longitudinal EHR trajectories carry rare-disease signal beyond static phenotypic burden and support prospective evaluation of temporal EHR modeling for rare-disease risk stratification.

PURPOSE Precision medicine has revolutionized oncology; however, tumor biomarkers are not reflective of the heterogeneous cancer population. We evaluated NRG Oncology prostate cancer (PCa) clinical trials for demographic differences among patients with optional biospecimen collection (BC) consent and biospecimen submission (BSub). METHODS Data from 19 NRG PCa clinical trials closed before 2015 were analyzed. Patients who consented to BC and completed BSub were evaluated by race, ethnicity, median income, area deprivation index (ADI; categorized as highest v lowest three quartiles), age at enrollment, site, and year of enrollment. T/chi-square tests were used for continuous/categorical variables, respectively, followed by logistic regression. RESULTS Of the 15,648 randomized patients eligible for BC, 11,796 (75%) had specimens submitted. In all, 4,598 (82.2%) of 5,597 eligible patients consented for optional BC in nine clinical trials with a separate BC consent process (consent rates by race/ethnicity: 74.1% Black, 72.8% Hispanic/Latino, 83.8% White). A smaller proportion of Black and Hispanic/Latino patients consented to optional BC compared with those who did not (12.1% v 19.5% Black, P < .0001; 3.5% v 5.8% Hispanic, P = .0006). In univariable logistic regression models, high ADI (more socioeconomic disadvantage) was associated with a decreased likelihood for optional BC consent (odds ratio [OR], 0.67 [95% CI, 0.55 to 0.82]; P = .02), but not a decreased likelihood for BSub (OR, 0.74 [95% CI, 0.53 to 1.04]; P = .08). Multivariable models demonstrated that Black/Hispanic/Latino patients were less likely to consent to optional BC, and Black patients were less likely to have BSub ( P < .05 for all). CONCLUSION White/non-Hispanic patients and those with less socioeconomic disadvantage were more likely to consent to optional BC, whereas Black patients were less likely to have BSub. Targeted solutions are needed to improve biorepository representation so that precision medicine approaches better reflect the cancer population.

2090 Background: A subset of meningiomas has an aggressive clinical course requiring multimodal therapies. Yet, treatment options beyond surgery and radiation are limited. Somatostatin receptor 2 (SSTR2) is uniformly expressed in meningiomas and can be targeted by the radiopharmaceutical agent [ 177 Lu]Lu-DOTA-TATE ( 177 Lu-Dotatate). [ 68 Ga]Ga-DOTA-TATE ( 68 Ga-Dotatate) PET is emerging as a promising imaging modality to assess treatment response in SSTR2-expressing tumors. However, prospective clinical data supporting the use of 177 Lu-Dotatate and 68 Ga-Dotatate in treatment-refractory meningiomas is limited. Methods: In this multicenter, open-label, phase 2 clinical study (NCT03971461), adult patients with progressive intracranial meningiomas received 177 Lu-Dotatate at 7.4 GBq (200 mCi) every 8 weeks for 4 doses. The primary endpoint was 6-month progression-free survival (PFS-6). Up to 5 meningioma lesions per patient were assessed by gadolinium-enhanced T1 MRI every 12 weeks. 68 Ga-Dotatate PET was obtained before/after 177 Lu-Dotatate treatments. Lesional 68 Ga-Dotatate PET activity was assessed relative to liver/spleen or superior sagittal sinus (SSS). Results: 32 patients (female = 21, male = 11) with progressive meningiomas (WHO grade 1 = 7, grade 2 = 24, grade 3 = 1) were enrolled. Median age was 65.5 (range 41-78) years. All patients had at least one prior tumor surgery and at least one course of radiation. Overall, 177 Lu-Dotatate was well tolerated and the most common grade 3/4 adverse events were transient cytopenia in 15 patients (47%). One patient (3%) discontinued treatment due to grade 4 thrombocytopenia. PFS-6 was reached in 22 patients (69%), which includes 5 of 7 (71%) with grade 1 and 17 of 24 (71%) with grade 2 meningiomas. Best radiographic response by MRI was partial response (PR) in 5 (16%) and stable disease (SD) in 20 (63%) patients. Eight patients (16 lesions) were evaluable by paired MRI and 68 Ga-Dotatate PET pre- and post-treatment. Referenced to SSS, 68 Ga-Dotatate avidity reduced by ≥50% in 2 lesions (PR), 25-49% (minor response, MR) in 3 lesions and remained stable ( < 25% reduction and < 25% increase) in 11 lesions. Using liver/spleen as a reference (6 patients, 14 lesions), 68 Ga-Dotatate uptake was reduced in 9 lesions and stable in 5 lesions. 68 Ga-Dotatate PET assessments were generally concordant with MRI-based treatment response assessments. Conclusions: The primary endpoint (PFS-6) was met in this phase 2 study with 69% of patients achieving PFS-6, thereby surpassing established historical benchmarks. 177 Lu-Dotatate was generally well tolerated and deserves further study in patients with advanced meningiomas. Exploratory 68 Ga-DOTATATE PET studies showed decreased uptake in a subset of lesions and bears promise as an imaging biomarker to assess treatment response. Clinical trial information: NCT03971461 .

Abstract Background: Glioblastoma recurrence is inevitable despite aggressive surgical resection of contrast enhancing (CE) tumor. Unlike the deep molecular characterization of CE glioblastoma, unresected infiltrating tumor, representing minimal residual disease (MRD) has not been well profiled. Increasingly, clinical trials are being conducted in the MRD setting for glioblastoma patients, necessitating understanding of unresected infiltrating tumor for targeted therapeutic development. Methods: Visium HD spatial transcriptomics was used to profile a cohort of newly diagnosed, treatment-naïve glioblastoma patients that underwent supramaximal resection, with both core and infiltrating in situ components. Spatial profiles generated expression signatures of infiltrative and CE tumor. For in silico prediction of therapeutics targeting the MRD, expression signatures of infiltrative and CE tumor were compared with drug perturbation signatures from the NIH L1000 database using the sRGES method to predict reversal of disease expression patterns. IC50 data from human cell lines were aggregated from the Genomics of Drug Sensitivity in Cancer or individual study data while blood-brain barrier (BBB) penetrance was predicted using the CNS-MPO approach. Findings: Infiltrative tumor expression profiles were enriched for genes corresponding to neural and oligodendrocyte progenitor like cells. In contrast CE glioblastoma displayed mesenchymal and astrocytic programs, suggesting spatial concentration of glioblastoma states within MRD and CE tumor. For CE glioblastoma, proteasome inhibitors and glucocorticoid receptor agonists were predicted to have the highest antitumor activity. In contrast, histone deacetylase inhibitors (HDACi) were highly ranked for MRD while exhibiting low predicted efficacy in CE tumor. Synergy analysis of drug candidates provided multiple dual treatment options for both the CE and MRD contexts. Conclusions: Cumulatively, these data indicate that the resected tumor does not reflect the MRD state, providing clarity on why targeted therapeutics for glioblastoma have not been particularly successful. Spatial profiling of the MRD demonstrates vulnerability to unique drug targets, which if utilized in the correct setting could confer greater survival benefit. Citation Format: Harrshavasan Congivaram, Shashwat Tripathi, Mateo Gomez, Katy McCortney, Ching Man Wai, Ruochen Du, Thomas K. Sears, Jianzhong Zhang, Daniel J. Brat, Craig M. Horbinski, Mark W. Youngblood, Jared T. Ahrendsen, Adam M. Sonabend, Stephen T. Magill, Matthew C. Tate, Maciej S. Lesniak, Sean Sachdev, Timothy Sita, Priya Kumthekar, Karan Dixit, Robin Buerki, Ditte Primdahl, Mustafa Khasraw, John de Groot, David A. Reardon, Rimas V. Lukas, Roger Stupp, Amy B. Heimberger. In silico screening of therapeutics candidates targeting minimal residual disease in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2496.

ABSTRACT Musculoskeletal infection (MSKI) remains a major problem after trauma and elective orthopedic surgery. Chronic MSKI is related to the formation of biofilm, which impairs diagnosis and effective treatments. Therefore, to understand and communicate global standards and best practices, the 2025 International Consensus Meeting (ICM) on MSKI created a Biofilm Section to address crucial aspects of biofilm biology pertaining to its mechanisms of drug resistance and immune evasion, and potential approaches to overcome them. This featured a 2‐year process, with final voting and discussion on May 8–10, 2025, in Istanbul, Turkey. This Consensus Article is the effort of the Biofilm Basic Mechanisms Workgroup, which interpreted the results on ICM questions related to (1) the infectious microenvironment; (2) appropriate inocula in preclinical research; (3) biofilm behavior in infected tissues; and (4) synergy within biofilms and with other comorbidities. Collectively, we find that this field has the necessary research tools to discover the pathophysiology of orthopedic implant‐associated biofilm development and maturation, perform clinically relevant studies in animal models, and elucidate mechanisms that allow opportunistic infections in compromised tissues and patients with other health issues.

Background The efficacy of EVOKE therapy, a closed-loop spinal cord stimulation (SCS) system, has been demonstrated in a double-blinded randomized controlled trial (RCT). RCT evidence, however, may not be generalizable to clinical practice, while limitations of real-world evidence may affect its validity. The aim of this study was to evaluate the effectiveness of EVOKE therapy in routine clinical practice employing a rigorous data collection approach. Methods The ECAP study was a prospective, multicenter, single-arm, pragmatic study that enrolled SCS candidates with chronic, intractable trunk and/or limb pain in 22 US investigational sites. PROMIS-29 questionnaire and objective neurophysiological metrics were collected, and minimal clinically important differences (MCIDs) were used to characterize baseline dysfunction and response to EVOKE therapy both at trial end and maximal analgesic effect post-implant visit. Pre-specified subgroup analyses were conducted based on diagnosis. Results EVOKE therapy observed included a dose ratio of 1.3 and neural dose accuracy of 2.8 μV. Improvements of >1 MCID were observed for all domains and improvements of ≥2 MCIDs were observed for six of the seven domains. Improvements observed at trial end were maintained or improved at post-implant visit, with statistically significant improvement in holistic MCID (MD 0.2, 95% CI 0.1 to 0.4, p=0.004), and a significantly greater proportion of patients considered holistic MCID responders (p=0.047). Subgroup analysis by diagnosis showed similar outcomes. Conclusions EVOKE therapy was associated with clinically meaningful improvements in a real-world chronic pain population across different subgroups and disease pathologies, with effects observed at trial end reproduced at post-implant visit.

Abstract BACKGROUND While most meningiomas are classified as low-grade tumors with indolent behavior, a subset is characterized by a more aggressive clinical course and patients undergo repeated resections and courses of radiation. Once these therapies fail, prognosis is poor and progression-free survival (PFS) rates at 6 months (PFS-6) are estimated to only be 43.6% for WHO grade 1 and 38% for WHO grade 2/3 meningiomas. Effective medical therapies are limited. Somatostatin receptor 2 (SSTR2) is expressed in a majority of meningiomas and represents a suitable treatment target. [177Lu]Lu-DOTA-TATE (177Lu-Dotatate) is an SSTR2-targeted radioligand therapy, established as standard of care in the treatment of certain neuroendocrine tumors. Two ongoing non-randomized studies of 177Lu-Dotatate in meningiomas have reported encouraging efficacy compared to historical benchmarks. METHODS/STUDY DESIGN The MOMENTUM-1 study (RTOG 3523, NCT#06955169) is a multicenter, randomized, open-label study of 177Lu-Dotatate in adult patients with progressive intracranial WHO grade 1-3 meningiomas. Up to 130 eligible subjects are randomized in a 2:1 fashion to 177Lu-Dotatate at 7.4 GBq (200 mCi) every 4 weeks for 4 (+2 optional) doses or standard of care (SOC) medical therapy as defined by the protocol and deemed appropriate by the local investigator. Treatment responses are assessed by gadolinium-enhanced MRI brain and [68Ga]Ga-DOTA-TATE (68Ga-Dotatate) PET imaging. At time of progression, cross-over from the SOC to the interventional treatment arm is allowed. The primary endpoint is PFS. Secondary endpoints include PFS-6, OS-12, OS, response assessments (ORR, DCR), safety/tolerability, and patient-reported outcomes. RESULTS/CONCLUSION MOMENTUM-1 is a multicenter, randomized, open-label, phase II study of 177Lu-Dotatate in adult patients with progressive WHO grade 1-3 meningioma. This clinical trial is expected to start enrolling patients in the United States in the third quarter of 2025. Details on study design, eligibility criteria for subject enrollment and study site eligibility criteria will be presented.

Abstract BACKGROUND [68Ga]Ga-DOTA-TATE (68Ga-Dotatate) PET is a promising imaging modality for post-operative meningioma delineation and outcome assessments following stereotactic radiosurgery. However, its utility for response assessment following other meningioma therapies, including somatostatin receptor 2 (SSTR2) targeting [177Lu]Lu-DOTA-TATE (177Lu-Dotatate) therapy is insufficiently understood. Here we report the 68Ga-Dotatate PET results, prospectively obtained in patients with treatment-refractory meningioma who enrolled in a phase 2, open-label clinical trial (NCT03971461) evaluating the radiopharmaceutical 177Lu-Dotatate. METHODS Patients underwent 68Ga-Dotatate PET during screening and 24±2 weeks following treatment with 177Lu-Dotatate at 7.4 GBq (200 mCi) every 8 weeks for 4 doses. Up to 5 meningioma lesions per patient were assessed by gadolinium-enhanced T1 MRI and 68Ga-Dotatate PET using lesional Krenning score and lesional to superior sagittal sinus (SSS) maximum standardized uptake value (SUVmax) ratio (SUVR (lesion/SSS)). RESULTS Eight patients with 16 lesions were assessed. By RANO criteria, the best radiographic response was stable disease (SD) in 7 patients (14 lesions) and partial response (PR) in 1 patient (2 lesions). Krenning score was assessed in 14 lesions (Krenning 4/3/2/1 in 3/4/5/2 lesions). Following 177Lu-Dotatate, Krenning score was reduced by ≥1 point in 9 and remained stable in 5 lesions. SUVR (lesion/SSS) was evaluated in 16 lesions. Compared to baseline, SUVR (lesion/SSS) at 6 months was partially reduced (PR, ≥50%) in 2 lesions, minimally reduced (MR, &lt;50% and ≥25%) in 3 lesions, and stable (SD, &lt;25% reduction and &lt;25% increase) in 11 lesions. CONCLUSIONS 68Ga-Dotatate PET is a promising imaging biomarker in patients with advanced meningioma. Treatment with 177Lu-Dotatate led to substantial reduction in SUVR (lesion/SSS) in a subset of patients. In contrast to other reports, 68Ga-Dotatate assessments were generally concordant with post-gad T1 based MRI assessments per RANO. Updated results in an expanded group of patients will be presented.