Citations

Context.— One objective of the College of American Pathologists/American College of Medical Genetics Cytogenetics Committee is to ensure the accurate detection and clinical interpretation of abnormalities observed by fluorescence in situ hybridization (FISH) analysis when evaluating hematologic neoplasms. Objective.— To report a 20-year performance summary (2005–2024) of FISH challenges focusing on hematologic neoplasms. Design.— A retrospective review was performed from 2005 through 2024 to identify FISH challenges specifically addressing hematologic neoplasms. The overall performance of participants was evaluated to identify potential recurring errors of clinical relevance. Results.— A total of 124 hematologic neoplasm FISH challenges from 2005 to 2024 were provided to participating laboratories, including 45 paper challenges (results and/or images were provided for interpretation purposes), 43 liquid challenges (bone marrow aspirate or peripheral blood), and 36 formalin-fixed paraffin-embedded (FFPE) tissue challenges. Of the 124 challenges, 120 (97%) exceeded the required 80% consensus for grading purposes. Of the 4 failed challenges (3%), 2 (1 liquid, 1 paper) involved the interpretation of atypical FISH signal patterns; 1 (paper) involved the interpretation of FISH signal patterns correlating involvement by myeloid versus lymphoid interphase nuclei; and 1 (liquid) likely involved a specimen processing or handling error that resulted in discordant participant responses. All 36 FFPE tissue challenges exceeded the 80% consensus for grading purposes. Conclusions.— This 20-year retrospective review demonstrates that clinical cytogenomic laboratories have been and continue to be highly proficient in the detection and interpretation of FISH abnormalities associated with hematologic neoplasms.

Background Genes that enhance T-cell function represent promising targets for improving engineered T-cell therapies for cancer. While extensive CRISPR knockout screens have identified key genes enhancing T-cell persistence, employing Sleeping Beauty ( SB ) insertional mutagenesis, which induces both gain-of-function (GOF) and loss-of-function (LOF) mutations via the generation of fusion transcripts with endogenous genes, may uncover additional critical factors that previous approaches have overlooked. Methods We developed transgenic mice carrying D oxycycline (Dox)- i nducible SB mutag e nesis s y stem (DiSBey) in primary T cells. Using DiSBey, we conducted screens for genetic alterations enhancing T-cell persistence under chronic antigen exposure. Specifically, CD8 + T cells from Dox-fed DiSBey mice were subjected to repeated anti-CD3 stimulation over 18 days to mimic chronic antigenic stimulation. We then identified SB transposon genomic insertion sites and corresponding fusion transcripts from the persistent DiSBey CD8 + T cells using enhanced-specificity tagmentation sequencing and RNA sequencing, respectively. Results Under chronic stimulation, SB -mutagenized CD8 + T cells exhibited improved persistence and reduced terminal exhaustion phenotype. Across six independent screens, we identified 38 genes that were recurrently targeted by the SB transposon T2/Onc2 and differentially expressed under chronic anti-CD3 stimulation. T2/Onc2 insertions into Bach2 and Elmo1 were recurrently identified at the genomic level and were associated with altered nascent transcript expression. Bach2 , known as a key regulator of T-cell memory formation and resistance to chronic viral infection-induced exhaustion but less characterized in engineered T cells for cancer therapy, was found to counteract exhaustion in vitro and enhance in vivo tumor persistence in the B16-Ova tumor model. Further, we showed that ectopic Bach2 expression levels influence engineered T-cell differentiation lineage, as low Bach2 overexpression retained more functional progenitor exhausted T cells and exhibited improved therapeutic efficacy. Finally, in human CART19-28ζ cells, BACH2 overexpression enhanced cytotoxicity and tumor control following chronic cancer stimulation. Conclusions Controllable SB mutagenesis using DiSBey mice provides a novel platform for functional screening of genes that improve T-cell phenotypes important for their use as therapies. Our findings highlight a dose-dependent role of BACH2 in enhancing the function of engineered T cells under conditions of chronic antigenic stimulation.

Aims: The primary aim of this study was to determine whether CYP2D6 phenotypes are associated with atomoxetine treatment outcomes in children and adolescents with attention-deficit hyperactivity disorder. Patients and Methods: Patients 21 years and younger with a known CYP2D6 genotype and a prescription for atomoxetine were included in this electronic health record review. The primary outcome was defined as discontinuation of atomoxetine due to a lack of efficacy or due to toxicity. CYP2D6 activity scores were adjusted to account for phenoconversion if patients were concomitantly taking a CYP2D6 inhibitor medication. Results: Of 108 patients that met inclusion criteria, including 13 poor, 30 intermediate, 61 normal, and 4 ultra-rapid CYP2D6 metabolizers after adjusting for phenoconversion. Normal/ultrarapid metabolizers were significantly more likely to discontinue atomoxetine due to lack of efficacy (subdistribution hazard ratio [sHR] = 1.9, p = 0.04), while poor/intermediate metabolizers were significantly more likely to stop due to toxicity (sHR 0.4, p = 0.03). A significant nonlinear effect of adjusted activity score on likelihood of discontinuation due to lack of efficacy ( p < 0.001) or toxicity ( p < 0.001) was identified, where higher CYP2D6 activity scores were associated with more discontinuation due to lack of efficacy and lower activity scores were associated with more discontinuation due to toxicity. When CYP2D6 genotype was available prior to prescribing atomoxetine in intermediate/poor metabolizers, lower median initial doses were observed (0.5 mg/kg/day) compared to those who underwent CYP2D6 genotyping after the initial atomoxetine prescription (0.8 mg/kg/day). Conclusion: Patients with normal or increased CYP2D6 phenotypes may be more likely to discontinue atomoxetine due to a lack of efficacy, while those with reduced function CYP2D6 phenotypes may be more likely to discontinue atomoxetine due to toxicity.

Glioblastoma (GBM) is a devastating cancer with a dismal prognosis. Current treatment includes temozolomide (TMZ), which is more effective in about 50% of GBMs that have O 6 - methylguanine DNA methyltransferase ( MGMT ) promoter methylation. MGMT is a DNA repair protein that reverses TMZ-induced DNA damage. EGFR is a prime oncogene in GBM. Here, we report that EGFR inhibition induced the down-regulation of MGMT in GBM cells, revealing a previously unidentified link between EGFR signaling and response to TMZ. EGFR inhibition led to activation of two transcription factors, activator protein-1 (AP-1), which repressed MGMT transcription, and nuclear factor κB (NF-κB), which up-regulated MGMT transcription. EGFR inhibition also induced AP-1–mediated transcription of miR-616. miR-616 inhibited both MGMT translation and NF-κB activation. Thus, the overall effect of EGFR inhibition was down-regulation of MGMT expression. In addition, we provided an explanation for the prior failure of clinical trials that used concomitant EGFR tyrosine kinase inhibitors (TKIs) and TMZ. TMZ up-regulated MGMT, and concomitant treatment with EGFR TKIs and TMZ failed to down-regulate MGMT. However, pretreatment with EGFR TKIs followed by TMZ efficiently down-regulated MGMT and enhanced TMZ sensitivity in experimental models. Posttreatment tumor tissues from two clinical trials were used to validate these findings. We demonstrated that EGFR inhibitors induced down-regulation of MGMT in posttreatment resected tumor tissues from patients with GBM and the failure of EGFR inhibition to down-regulate MGMT if TMZ was used concomitantly. These data support using EGFR TKIs before TMZ treatment as a therapeutic approach in MGMT unmethylated GBM.

Background/Objectives: To develop and evaluate a deep learning-based quality control system for Lumbar Spinal Digital Radiographs (LSDR), designed to automate and improve their evaluation and reduce reliance on manual reviews. Methods: This retrospective study utilized a deep learning workflow comprising image segmentation, feature extraction, and a classification model. The dataset, including anteroposterior (AP) and lateral (LAT) X-ray images, was expanded through data augmentation techniques. Four U-Net-based models were assessed: standard U-Net, Swin-UNet, Attention U-Net, and Attention U-Net with the weight map, with the latter selected for its superior performance. Extracted features, such as brightness, contrast, and anatomical positioning, were used in an XGBoost classifier, which was evaluated using mean intersection over union (mIoU), accuracy, sensitivity, specificity, and AUC. Results: The Attention U-Net with weighted attention outperformed the other models, achieving high mIoU scores in both AP and LAT views. The XGBoost classifier achieved the best performance in classifying images as “qualified” or “unqualified,” with an AUC of approximately 0.9, high accuracy, and balanced sensitivity and specificity. This approach effectively addressed class imbalances and improved model accuracy compared to traditional machine learning models such as MLP and SVM. Conclusions: The developed automated quality control system demonstrated potential for enhancing image quality, enhancing diagnostic reliability, and optimizing clinical workflow efficiency.

Abstract Endocytosis regulates receptor trafficking and signaling, yet its role in colorectal cancer (CRC) remains unclear. We analyzed transcriptomic data from 15,025 CRC tumors to evaluate gene expression in clathrin-mediated endocytosis (CME) and the endosomal sorting complexes required for transport (ESCRT) pathway. Pathway-level signatures were compared across consensus molecular subtypes (CMS) and examined in relation to oncogenic signaling programs, while individual endocytosis-related genes were evaluated for associations with clinical outcomes in an independent randomized trial cohort. Expression of CME and ESCRT pathways varied by CMS, with the highest expression in CMS4 and the lowest in CMS3. Endocytosis signatures correlated with multiple oncogenic signaling pathways, including MAPK, TGF-β, WNT, PI3K, NOTCH, and angiogenesis, suggesting broad links between endocytic activity and tumor biology. In the randomized phase III CALGB/SWOG 80405 trial, low expression of AP2M1 —a core adaptor in the CME complex—was significantly associated with longer overall survival and progression-free survival in patients treated with anti-EGFR agents, but not in those receiving anti-VEGF agents. These findings suggest that endocytic trafficking may be associated with oncogenic signaling and differential therapeutic benefit in patients with metastatic CRC. The endocytosis pathway reflects biologically relevant heterogeneity in CRC and may help guide therapeutic stratification.

Background: Integrating machine learning (ML) with population pharmacokinetic (PPK) modeling may improve therapeutic drug monitoring predictions. Methods: Tacrolimus trough concentrations from lung transplant patients were split into a training data set (1151 concentrations in 80 patients) and a testing data set (224 concentrations in 20 patients). A PPK model was developed using NONMEM, followed by the development of 10 ML models to fit individual pharmacokinetic parameters from the PPK model with Bayesian forecasting. The best performing ML model was selected as the final model. Both the final PPK and ML models were compared for prediction performance. A web-based dashboard was established with R-shiny to recommend dosing regimens based on patient data. Results: In the PPK model, postoperative days, hematocrit, aspartate aminotransferase, tacrolimus daily dose, coadministered voriconazole or posaconazole, and the CYP3A5*3 genotype were identified significant covariates on the clearance. The Cubist ML model outperformed the PPK model, showing lower root mean squared error for tacrolimus concentrations in the testing data set. An online web-based precision dosing dashboard was created, accessible at (https://tac-dose-ml.shinyapps.io/shiny/). Conclusions: Integrating ML with PPK modeling could yield superior tacrolimus concentration predictions for lung transplant patients, offering an efficient alternative to Bayesian forecasting. The online dashboard provides rapid, individualized dosing recommendations.