Citations

Aortic stenosis (AS) requires repeat assessments to optimally time intervention. The utility of laboratory tests to complement clinical and hemodynamic assessments has not been established. We assessed clinical, echocardiographic, and laboratory data in 76 patients with AS or heart valve replacement (HVR), age 77 years, 37% women. Laboratory assessments included von Willebrand factor (VWF) antigen, activity, (Act/Ag, normal > 0.8), VWF multimer distribution (abnormal=loss of highest molecular weight multimers=Mult+), brain natriuretic peptide (BNP, abnormal > 155 pg/ml=BNP+), and platelet function analyzer 100 collagen-ADP closure time (PFA, normal < 121 seconds). Table: Coefficients of correlation, r, and F statistic. Chi-square, and probability. Correlation of BNP to mean gradient in patients with GFR>60 remained poor, r=0.29, while for PFA r = 0.65. Median (IQR) for PFA and BNP for mild, moderate, and severe AS and HVR are shown below. VWF multimers were abnormal in 3/14 mild AS, 6/14 moderate AS, 22/25 severe AS and 4/23 HVR patients. The severity of AS was most strongly associated with VWF multimer abnormality and prolonged PFA closure time, weakly with BNP, and not with VWF Act/Ag. Conclusion: Among various laboratory measures, PFA and VWF multimers are strongly associated with AS severity, and may merit assessment as complementary diagnostic and prognostic biomarkers in AS.

Histology is an important predictor of the behavior of breast cancer. We aim to study the impact of histology on the overall survival (OS) of breast cancer patients. We studied 11,085 breast cancer patients diagnosed with T1–T2 tumors, clinically node-negative and non-metastatic, from 2004 to 2019 included in the National Cancer Database. Kaplan–Meier curves, log-rank tests and Cox regression models were used to study the impact of histology and other variables on OS. In our patient population, 8678 (78.28%) had ductal cancer (IDC), while 2407 (21.71%) had lobular cancer (ILC). ILC patients were significantly more likely to be older, Caucasian, have a lower grade at diagnosis and be hormone receptor-positive compared to IDC patients. There was no statistically significant difference in the 5-year OS of early stage ductal (16.8%) and lobular cancer patients (16.7%) (p = 0.200). Patients of Hispanic and African American origin had worse OS rates compared to non-Hispanic and Caucasian patients, respectively. For node-positive disease, HER2+ tumors and triple-negative tumors, chemotherapy had a positive influence on OS (HR 0.85, 95% CI 0.77–0.93, p = 0.0012). Histology did not have a significant impact on the 5-year OS of early stage breast cancer patients.

OBJECTIVE Chiari malformation type I (CM-I) is a congenital disorder occurring in 0.1% of the population. In symptomatic cases, surgery with posterior fossa decompression (PFD) is the treatment of choice. Surgery is, however, associated with peri- and postoperative complications that may require readmission or renewed surgical intervention. Given the associated financial costs and the impact on patients’ well-being, there is a need for predictive tools that can assess the likelihood of such adverse events. The aim of this study was therefore to leverage machine learning algorithms to develop a predictive model for 30-day readmissions and reoperations after PFD in pediatric patients with CM-I. METHODS This was a retrospective study based on data from the National Surgical Quality Improvement Program–Pediatric database. Eligible patients were those undergoing PFD (Current Procedural Terminology code 61343) for CM-I between 2012 and 2021. Patients undergoing surgery for tumors or vascular lesions were excluded. Unplanned 30-day readmission and unplanned 30-day reoperation were the main study outcomes. Additional outcome data considered included the length of hospital stay, 30-day complications, discharge disposition, and 30-day mortality. Training and testing samples were randomly generated (80:20) to study the 30-day readmission and reoperation using logistic regression, decision tree, random forest (RF), K-nearest neighbors, and Gaussian naive Bayes algorithms. RESULTS A total of 7106 pediatric patients undergoing PFD were included. The median age was 9.2 years (IQR 4.7, 14.2 years). Most of the patients were female (56%). The 30-day readmission and reoperation rates were 7.5% and 3.4%, respectively. Headaches (32%) and wound-related complications (30%) were the most common reasons for 30-day readmission, while wound revisions and evacuation of fluid or blood (62%), followed by CSF diversion-related procedures (28%), were the most common reasons for 30-day reoperation. RF classifiers had the highest predictive accuracy for both 30-day readmissions (area under the curve [AUC] 0.960) and reoperations (AUC 0.990) compared with the other models. On feature importance analysis, sex, developmental delay, ethnicity, respiratory disease, premature birth, hydrocephalus, and congenital/genetic anomaly were some of the variables contributing the most to both RF models. CONCLUSIONS Using a large-scale nationwide dataset, machine learning models for the prediction of both 30-day readmissions and reoperations were developed and achieved high accuracy. This highlights the utility of machine learning in risk stratification and surgical decision-making for pediatric CM-I.

Importance Normothermic regional perfusion (NRP) is an emerging recovery modality for transplantable allografts from controlled donation after circulatory death (cDCD) donors. In the US, only 11.4% of liver recipients who are transplanted from a deceased donor receive a cDCD liver. NRP has the potential to safely expand the US donor pool with improved transplant outcomes as compared with standard super rapid recovery (SRR). Objective To assess outcomes of US liver transplants using controlled donation after circulatory death livers recovered with normothermic regional perfusion vs standard super rapid recovery. Design, Setting, and Participants This was a retrospective, observational cohort study comparing liver transplant outcomes from cDCD donors recovered by NRP vs SRR. Outcomes of cDCD liver transplant from January 2017 to May 2023 were collated from 17 US transplant centers and included livers recovered by SRR and NRP (thoracoabdominal NRP [TA-NRP] and abdominal NRP [A-NRP]). Seven transplant centers used NRP, allowing for liver allografts to be transplanted at 17 centers; 10 centers imported livers recovered via NRP from other centers. Exposures cDCD livers were recovered by either NRP or SRR. Main Outcomes and Measures The primary outcome was ischemic cholangiopathy (IC). Secondary end points included primary nonfunction (PNF), early allograft dysfunction (EAD), biliary anastomotic strictures, posttransplant length of stay (LOS), and patient and graft survival. Results A total of 242 cDCD livers were included in this study: 136 recovered by SRR and 106 recovered by NRP (TA-NRP, 79 and A-NRP, 27). Median (IQR) NRP and SRR donor age was 30.5 (22-44) years and 36 (27-49) years, respectively. Median (IQR) posttransplant LOS was significantly shorter in the NRP cohort (7 [5-11] days vs 10 [7-16] days; P &amp;lt; .001). PNF occurred only in the SRR allografts group (n = 2). EAD was more common in the SRR cohort (123 of 136 [56.1%] vs 77 of 106 [36.4%]; P = .007). Biliary anastomotic strictures were increased 2.8-fold in SRR recipients (7 of 105 [6.7%] vs 30 of 134 [22.4%]; P = .001). Only SRR recipients had IC (0 vs 12 of 133 [9.0%]; P = .002); IC-free survival by Kaplan-Meier was significantly improved in NRP recipients. Patient and graft survival were comparable between cohorts. Conclusion and Relevance There was comparable patient and graft survival in liver transplant recipients of cDCD donors recovered by NRP vs SRR, with reduced rates of IC, biliary complications, and EAD in NRP recipients. The feasibility of A-NRP and TA-NRP implementation across multiple US transplant centers supports increasing adoption of NRP to improve organ use, access to transplant, and risk of wait-list mortality.

ImportanceFrontotemporal lobar degeneration (FTLD) is relatively rare, behavioral and motor symptoms increase travel burden, and standard neuropsychological tests are not sensitive to early-stage disease. Remote smartphone-based cognitive assessments could mitigate these barriers to trial recruitment and success, but no such tools are validated for FTLD.ObjectiveTo evaluate the reliability and validity of smartphone-based cognitive measures for remote FTLD evaluations.Design, Setting, and ParticipantsIn this cohort study conducted from January 10, 2019, to July 31, 2023, controls and participants with FTLD performed smartphone application (app)–based executive functioning tasks and an associative memory task 3 times over 2 weeks. Observational research participants were enrolled through 18 centers of a North American FTLD research consortium (ALLFTD) and were asked to complete the tests remotely using their own smartphones. Of 1163 eligible individuals (enrolled in parent studies), 360 were enrolled in the present study; 364 refused and 439 were excluded. Participants were divided into discovery (n = 258) and validation (n = 102) cohorts. Among 329 participants with data available on disease stage, 195 were asymptomatic or had preclinical FTLD (59.3%), 66 had prodromal FTLD (20.1%), and 68 had symptomatic FTLD (20.7%) with a range of clinical syndromes.ExposureParticipants completed standard in-clinic measures and remotely administered ALLFTD mobile app (app) smartphone tests.Main Outcomes and MeasuresInternal consistency, test-retest reliability, association of smartphone tests with criterion standard clinical measures, and diagnostic accuracy.ResultsIn the 360 participants (mean [SD] age, 54.0 [15.4] years; 209 [58.1%] women), smartphone tests showed moderate-to-excellent reliability (intraclass correlation coefficients, 0.77-0.95). Validity was supported by association of smartphones tests with disease severity (r range, 0.38-0.59), criterion-standard neuropsychological tests (r range, 0.40-0.66), and brain volume (standardized β range, 0.34-0.50). Smartphone tests accurately differentiated individuals with dementia from controls (area under the curve [AUC], 0.93 [95% CI, 0.90-0.96]) and were more sensitive to early symptoms (AUC, 0.82 [95% CI, 0.76-0.88]) than the Montreal Cognitive Assessment (AUC, 0.68 [95% CI, 0.59-0.78]) (z of comparison, −2.49 [95% CI, −0.19 to −0.02]; P = .01). Reliability and validity findings were highly similar in the discovery and validation cohorts. Preclinical participants who carried pathogenic variants performed significantly worse than noncarrier family controls on 3 app tasks (eg, 2-back β = −0.49 [95% CI, −0.72 to −0.25]; P &amp;lt; .001) but not a composite of traditional neuropsychological measures (β = −0.14 [95% CI, −0.42 to 0.14]; P = .32).Conclusions and RelevanceThe findings of this cohort study suggest that smartphones could offer a feasible, reliable, valid, and scalable solution for remote evaluations of FTLD and may improve early detection. Smartphone assessments should be considered as a complementary approach to traditional in-person trial designs. Future research should validate these results in diverse populations and evaluate the utility of these tests for longitudinal monitoring.

Abstract Multiple myeloma (MM) is an incurable plasma cell (PC) cancer in which all patients are expected to relapse. Cytotoxic agents such as high-dose melphalan (HDM), which is used as a myeloablative agent prior to autologous stem cell transplant (ASCT) in MM, drive therapy-induced senescence (TIS) in non-cancerous tissues. We hypothesized that HDM similarly activates TIS pathways in surviving MM cells and this response may correlate with patient outcomes post-ASCT. To test this, we developed an in vitro model in which 5TGM1 mouse MM cells are cultured with 10uM melphalan (HDM) or vehicle (Veh) for 6 hours, followed by co-culture in normal media with primary mouse bone marrow stromal cells. Cultures were maintained for 10 days and imaged to quantify cumulative population doublings (CPDs). 5TGM1 were then isolated by FACS to measure telomere-associated DNA damage foci (TAFs), sensitivity to senolytic drugs, and senescence gene expression (RT-qPCR and scRNA-seq). To translate these studies, scRNA-seq was used to analyze CD138+ PCs from MM patient bone marrow post-ASCT. Additionally, longitudinal bone biopsies from MM patients at diagnosis and post-ASCT were stained for CD138+ PCs; PC burden was quantified using artificial intelligence assisted histology (HALO). HDM-5TGM1 exhibited stable growth arrest (decreased CPDs) and sustained DNA damage (increased TAFs) compared to Veh-5TGM1. HDM-5TGM1 were also significantly more sensitive to senolytic ablation (Dasatinib+Quercetin or Fisetin). HDM-5TGM1 showed increased senescence (Cdkn1a, Cdkn1c, Glb1), anti-apoptosis (Bcl2l1), and senescence associated secretory phenotype (Ccl5, Icam1, Mmp13) genes, as well as myeloid markers found to be increased in MM dormancy (Axl, Fcer1g, Mpeg1). Analysis by scRNA-seq revealed that HDM-5TGM1 were G0/G1 arrested, and gene set enrichment analysis (GSEA) confirmed upregulation of senescence pathways with downregulation of DNA replication and cell cycle pathways. Consistent with RT-qPCR results, these cells exhibited myeloid gene expression. Analysis of patient CD138+ PCs post-ASCT by scRNA-seq also revealed a myeloid-like cluster. Of interest, of the 491 genes upregulated in this cluster, 268 were also upregulated in HDM-5TGM1. CD138+ PCs could also be detected in patient bone biopsies post-ASCT, although PC burden was significantly reduced post-ASCT. Surprisingly, the percent reduction in PC burden post-ASCT was greater in relapsed (≤3 years) vs non-relapsed patients. Further, in relapsed patients, PC burden post-ASCT positively correlated with time in remission. Altogether, these findings demonstrate that HDM can induce senescence features and a myeloid-dormancy signature in surviving MM cells. This phenotype is associated with longer durable response, suggesting that activation of TIS may be a strategy to extend PFS. Further, senolytic therapy may be a novel approach to eliminate dormant MM cells and prevent relapse. Citation Format: Angelo Jose Guilatco, Marta Diaz-del-Castillo, Gabriel Alvares Borges, Neal I. Sannuli, Megan L. Ritting, Syed Mohammed Aalam, Nagarajan Kannan, Tamar Tchkonia, James L. Kirkland, Yi Lin, Taxiarchis Kourelis, Matthew T. Drake, Thomas Levin Andersen, Megan Weivoda. Melphalan-treated multiple myeloma cells exhibit a senescent-like dormancy phenotype [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2958.

Abstract We used deep learning (DL) to predict multiple genetic biomarkers of colorectal cancer (CRC) from routine histopathology images, aiming to identify morphological changes associated with the mutational profile of tumors. A new Transformer-based prediction model was developed which can simultaneously predict multiple biomarkers from a single histopathology image. We compared the performance of the multi-target approach to conventional single-target models. We analyzed five patient cohorts from the Genetics and Epidemiology of CRC Consortium (GECCO) with colorectal cancer (N=1,385 patients). Our model was trained on an internal data set (N=739) to predict the presence of &gt;200 genetic alterations, focusing on genes with non-silent mutations and mutational signatures, which were assessed using targeted sequencing. We used a 7-fold cross validation and validated the model on an independent, external data set (N=646). Morphological features of microsatellite instability (MSI) are detectable by DL models in histopathology images, making MSI a potential confounding factor. Thus, we also assessed whether our model can predict genetic alterations regardless of MSI status, or whether the model identifies the MSI phenotype. For the majority of biomarkers, the multi-target transformer reached a higher prediction performance as measured by the mean Area Under the Receiver Operating Characteristic (AUROC) Curve and lower standard deviation than single-target DL models. For the external validation set, the mean AUROC of the multi-target versus the single-target model was 0.78 (+/- 0.01) versus 0.72 (+/- 0.06) for BRAF mutations, 0.88 (+/- 0.01) versus 0.86 (+/- 0.03) for hypermutation status, 0.94 (+/- 0.01) versus 0.91 (+/- 0.02) for MSI, 0.86 (+/- 0.01) versus 0.80 (+/- 0.05) for RNF43 mutations and 0.72 (+/- 0.02) versus 0.69 (+/- 0.05) for TP53 mutations, respectively. However, our analysis of the individual prediction scores suggests that the model does not identify the phenotype of the alterations themselves. Instead, the model seems to determine the status of specific genetic alterations based on their correlation with MSI. Consequently, biomarkers which are associated with microsatellite stability have prediction scores negatively correlated with predicted MSI scores. Conversely, biomarkers which are associated with MSI have prediction scores positively correlated with predicted MSI scores. Our results demonstrate potential benefits of multi-target DL models in improving the predictive power and efficacy of histology-based biomarker extraction compared to single-target DL models. However, our results crucially highlight the importance of considering potential confounders, such as MSI status in CRC, and emphasize the need for extensive analysis beyond AUROC in studies focusing on DL-based biomarker detection. Citation Format: Marco Gustav, Marko van Treeck, Zunamys I. Carrero, Chiara M. Loeffler, Nic G. Reitsam, Bruno Märkl, Asier Rabasco Meneghetti, Lisa A. Boardman, Amy J. French, Ellen L. Goode, Andrea Gsur, Stefanie Brezina, Marc J. Gunter, Neil Murphy, Paul Limburg, Stephen Thibodeau, Sebastian Foersch, Robert Steinfelder, Tabitha Harrison, Ulrike Peters, Amanda Phipps, Jakob N. Kather. Assessing microsatellite instability dominance in colorectal cancer phenotype: A multi-study initiative using multi-target transformers for genomic biomarker prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4924.