Citations

2002 Background: Elderly patients with glioblastoma have a poor prognosis with phase III trials reporting median overall survival (OS) and global time to deterioration of 6–9 and 1.2 months respectively. 18F-DOPA (3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine) positron emission tomography (PET) is sensitive and specific for identifying regions of high density and biologically aggressive glioblastoma. Proton beam therapy (PBT) can spare more healthy tissue that may improve quality of life for this patient population. With improved targeting of disease along with dosimetric advantages of PBT, this phase II trial tested whether hypofractionated PBT can offer both improved survival and quality of life. Methods: Patients with newly diagnosed glioblastoma aged ≥65 years without contraindications to 18F-DOPA were eligible. Target volumes were defined by PET (tumor/normal brain SUV ≥2.0) and MRI areas of contrast enhancement including surgical cavity. Patients received dose escalated hypofractionated PBT of 35-40 GyE to PET/MRI over 5-10 treatments with 10 mm margin. Patients received concurrent/adjuvant temozolomide. With an alpha of 0.1, 18 of 39 patients would be required to be alive at 12 months for the study to be considered a success. Kaplan-Meier/Cox regression analysis was performed for progression free survival (PFS) and OS. Toxicities were evaluated with CTCAE v4.0. Patients completed EORTC BN20, QLQ-C30, and MMSE questionnaires. Time to deterioration, defined as a 10-point decrease in the score of the function domain or 10-point increase in score of symptom domain, was evaluated. Results: Between 5/2019-6/2021, 43 patients were enrolled, with 4 patients withdrawing due to progression/insurance denial prior to beginning protocol therapy. The median age was 70.2 yrs. and median follow-up was 12.5 months. MGMT methylation was present in 13 (33%). Tumors were multifocal in 10 (26%) and unifocal in 29 (74%). The primary endpoint was met with median PFS/OS was 6.5/12.9 months for all patients and for MGMT methylated patients 11.9/29.8 months respectively. Grade 3 baseline adjusted treatment related adverse events included 1 (3%) confusion, 2 (5%) fatigue, and 1 (3%) seizure. There were no grade 4/5 events. Multivariate analysis revealed MGMT unmethylated, ECOG 2 and biopsy only patients had worse survival. PET volume was more predictive than MRI volume for OS with PET volume>26 cc having a hazard ratio of 3.7. The median time to global deterioration per QLC-C30 was 6.3 months. Conclusions: This phase II trial incorporating 18F-DOPA PET-guided dose escalated hypofractionated proton beam treatment for elderly patients with glioblastoma met its primary endpoint for improved OS and patient reported quality of life compared to historical controls. Further investigation is warranted. Clinical trial information: NCT03778294 .

e13552 Background: Real-world data from Electronic Health Records (EHR) have been widely used for patient identification to build study cohorts for clinical research. Traditionally, diagnosis codes in the EHR, such International Classification of Diseases (ICD), are used to identify the target patients. However, the accuracy of this approach is dependent on the accuracy of ICD coding, with potential errors especially for tumor types that are frequent locations for metastases (which may contribute to mis-coding). In this study, we attempted to develop a Machine Learning (ML) based approach on EHR data to improve the accuracy of identification of patients with lung cancer. Methods: We used survey respondents in the Enhanced, EHR-facilitated Cancer Symptom Control (E2C2, NCT03892967) cluster-randomized trial at Mayo Clinic as our initial pan-cancer cohort. E2C2 includes adults receiving Medical Hematology/Oncology care for a solid or liquid tumor at Mayo Clinic. We collected cancer diagnoses from the individually abstracted Mayo Clinic Cancer Registry to annotate the cancer type for the patients. Lung cancer related ICD-9 (162.X) and ICD-10 (C34.X) codes were used to build a search query on the Mayo’s EHR to find target patients from the E2C2 cohort, and to investigate the ICD-based lung cancer patient identification performance. Diagnosis, radiation oncology treatment (CPT 77261 - 77799), and antineoplastic drug administration data were collected from EHR as variables. Logistic Regression (LR), Support vector machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB) were selected to build models for lung cancer patient identification. 10-fold cross-validation was implemented to assess the models. Precision, Recall, F1 Score, and area under the curve (AUC) were selected to measure the performance. Results: We collected 13,893 patients with a specific cancer diagnosis, and 1,394 were identified as having lung cancer. The identification performance across different methods is shown. The ICD-based method only had 0.65 precision. It means we collected a lot of false positive cases (other cancer but no lung cancer patient), as we mentioned in the background. SVM gets the best precision results, but its recall and F1 score are not good enough. XGB shows the best F1 Score and AUC, which also means this method achieved the best and most balanced performance. Conclusions: In this study, we found that XGB-based methods achieved the best identification performance for lung cancer. In the future, we will investigate if this is also true for the identification of other cancer types. [Table: see text]

e13003 Background: The emergence of somatic KMT2C mutations has been associated with resistance to various treatments, including chemotherapy and endocrine therapy in patients with estrogen receptor-positive breast cancers. The impact of these mutations in patients treated with poly-ADP ribose polymerase inhibitors (PARPi) and phosphatidylinositol-3-kinase inhibitors (PI3Ki) is unknown. Methods: Circulating tumor DNA (ctDNA) was obtained from plasma and peripheral blood at different time points from 38 patients prior to treatment with the combination of the PARPi olaparib with the alpha-specific PI3Ki alpelisib (NCT01623349). CtDNA was also collected immediately before the first cycle and at the time of disease progression. CtDNA underwent deep targeted sequencing (tumor-normal pairs, 397 genes (2Mb) at 25,000x somatic and 10,000x germline) at the Broad Institute of MIT and Harvard. Variants that were exonic, non-synonymous, and predicted to lead to loss-of-function were considered mutations. Cohorts were stratified according to the presence or absence of the genomic alteration, and progression-free survival was compared with the Kaplan-Meier method and log-rank test. A Cox regression was performed using age, tumor type, germline BRCA1/2 mutation status, performance status, and number of previous lines of treatment as covariates. Results: The KMT2C gene was one of the 3 genes most frequently mutated in our cohort, affecting 5 patients (15%) after the exclusion of those with variants with non-deleterious alterations. The other 2 commonly mutated genes were TP53 (76%) and PIK3CA (15%). In the comparison of patients based on these most common mutations, we identified that patients with somatic KMT2C mutations (n=5) have longer mPFS than those that were wild type (n=28): mPFS 386 vs. 151 days, log-rank p = 0.025. Cox proportional hazards model accounting for age, tumor type, germline BRCA1/2 mutation status, performance status, and number of previous lines revealed a hazard ratio of 0.16; p = 0.007. All somatic KMT2C mutations were proximal to the SET domain critical for methyltransferase activity. Upon progression, the KMT2C-mutated clones were suppressed to below the detection level of mutation calling algorithms. Analysis of the raw files revealed that the clones were suppressed but not eliminated. Conclusions: While the emergence of KMT2C has been reported to be associated with cancer progression and resistance to different therapies, patients with deleterious KMT2C mutations in the ctDNA may benefit from therapy with PARPi and PI3Ki.

Abstract Objective Social determinants of health (SDoH) play critical roles in health outcomes and well-being. Understanding the interplay of SDoH and health outcomes is critical to reducing healthcare inequalities and transforming a “sick care” system into a “health-promoting” system. To address the SDOH terminology gap and better embed relevant elements in advanced biomedical informatics, we propose an SDoH ontology (SDoHO), which represents fundamental SDoH factors and their relationships in a standardized and measurable way. Material and Methods Drawing on the content of existing ontologies relevant to certain aspects of SDoH, we used a top-down approach to formally model classes, relationships, and constraints based on multiple SDoH-related resources. Expert review and coverage evaluation, using a bottom-up approach employing clinical notes data and a national survey, were performed. Results We constructed the SDoHO with 708 classes, 106 object properties, and 20 data properties, with 1,561 logical axioms and 976 declaration axioms in the current version. Three experts achieved 0.967 agreement in the semantic evaluation of the ontology. A comparison between the coverage of the ontology and SDOH concepts in 2 sets of clinical notes and a national survey instrument also showed satisfactory results. Discussion SDoHO could potentially play an essential role in providing a foundation for a comprehensive understanding of the associations between SDoH and health outcomes and paving the way for health equity across populations. Conclusion SDoHO has well-designed hierarchies, practical objective properties, and versatile functionalities, and the comprehensive semantic and coverage evaluation achieved promising performance compared to the existing ontologies relevant to SDoH.

Abstract Background Although the epidermal growth factor receptor (EGFR) is a frequent oncogenic driver in glioblastoma (GBM), efforts to therapeutically target this protein have been largely unsuccessful. The present preclinical study evaluated the novel EGFR inhibitor WSD-0922. Methods We employed flank and orthotopic patient-derived xenograft models to characterize WSD-0922 and compare its efficacy to erlotinib, a potent EGFR inhibitor that failed to provide benefit for GBM patients. We performed long-term survival studies and collected short-term tumor, plasma, and whole-brain samples from mice treated with each drug. We utilized mass spectrometry to measure drug concentrations and spatial distribution and to assess the impact of each drug on receptor activity and cellular signaling networks. Results WSD-0922 inhibited EGFR signaling as effectively as erlotinib in in vitro and in vivo models. While WSD-0922 was more CNS penetrant than erlotinib in terms of total concentration, comparable concentrations of both drugs were measured at the tumor site in orthotopic models, and the concentration of free WSD-0922 in the brain was significantly less than the concentration of free erlotinib. WSD-0922 treatment provided a clear survival advantage compared to erlotinib in the GBM39 model, with marked suppression of tumor growth and most mice surviving until the end of the study. WSD-0922 treatment preferentially inhibited phosphorylation of several proteins, including those associated with EGFR inhibitor resistance and cell metabolism. Conclusions WSD-0922 is a highly potent inhibitor of EGFR in GBM, and warrants further evaluation in clinical studies.

After a virtual congress in 2021 and a previous absence in 2020 because of the coronavirus disease 2019 pandemic, the 27th Annual Congress of the International Liver Transplantation Society was held from May 4 to 7, 2022, in a hybrid format in Istanbul, with 1123 (58% on-site) liver transplant professionals from 61 countries attending the meeting. The hybrid format successfully achieved a balance of much yearned-for “in-person interaction” and global online participation. Almost 500 scientific abstracts were presented. In this report, the Vanguard Committee aims to present a summary of key invited lectures and selected abstracts for the liver transplant community.

Understanding of the evolution of metazoans from their unicellular ancestors is a fundamental question in biology. In contrast to fungi which utilize the Mon1–Ccz1 dimeric complex to activate the small GTPase RAB7A, metazoans rely on the Mon1–Ccz1–RMC1 trimeric complex. Here, we report a near-atomic resolution cryogenic-electron microscopy structure of the Drosophila Mon1–Ccz1–RMC1 complex. RMC1 acts as a scaffolding subunit and binds to both Mon1 and Ccz1 on the surface opposite to the RAB7A-binding site, with many of the RMC1-contacting residues from Mon1 and Ccz1 unique to metazoans, explaining the binding specificity. Significantly, the assembly of RMC1 with Mon1–Ccz1 is required for cellular RAB7A activation, autophagic functions and organismal development in zebrafish. Our studies offer a molecular explanation for the different degree of subunit conservation across species, and provide an excellent example of how metazoan-specific proteins take over existing functions in unicellular organisms.

Background: The role of minute ventilation/carbon dioxide production ( / CO2) slope, a ventilation efficiency marker, in predicting short-term and long-term health outcomes for patients with nonsmall-cell lung cancer (NSCLC) undergoing lung resection has not been well investigated. Material and Methods: This prospective cohort study consecutively enrolled NSCLC patients who underwent a presurgical cardiopulmonary exercise test from November 2014 to December 2019. The association of / CO2 slope with relapse-free survival (RFS), overall survival (OS), and perioperative mortality was evaluated using the Cox proportional hazards and logistic models. Covariates were adjusted using propensity score overlap weighting. The optimal cut-off point of the E/ CO2 slope was estimated using the receiver operating characteristics curve. Internal validation was completed through bootstrap resampling. Results: A cohort of 895 patients [median age (interquartile range), 59 (13) years; 62.5% male] was followed for a median of 40 (range, 1–85) months. Throughout the study, there were 247 relapses or deaths and 156 perioperative complications. The incidence rates per 1000 person-years for relapses or deaths were 108.8 and 79.6 among patients with high and low E/ CO2 slopes, respectively [weighted incidence rate difference per 1000 person-years, 29.21 (95% CI, 7.30–51.12)]. A E/ CO2 slope of greater than or equal to 31 was associated with shorter RFS [hazard ratio for relapse or death, 1.38 (95% CI, 1.02–1.88), P=0.04] and poorer OS [hazard ratio for death, 1.69 (1.15–2.48), P=0.02] compared to a lower / CO2 slope. A high E/ CO2 slope increased the risk of perioperative morbidity compared with a low E/ CO2 slope [odds ratio, 2.32 (1.54–3.49), P<0.001]. Conclusions: In patients with operable NSCLC, a high E/ CO2 slope was significantly associated with elevated risks of poorer RFS, OS, and perioperative morbidity.