Citations

Abstract Highly conserved transport protein particle (TRAPP) complexes regulate subcellular trafficking pathways. Accurate protein trafficking has been increasingly recognized to be critically important for normal development, particularly in the nervous system. Variants in most TRAPP complex subunits have been found to lead to neurodevelopmental disorders with diverse but overlapping phenotypes. We expand on limited prior reports on TRAPPC6B with detailed clinical and neuroradiologic assessments, and studies on mechanisms of disease, and new types of variants. We describe 29 additional patients from 18 independent families with biallelic variants in TRAPPC6B. We identified seven homozygous nonsense (n = 12 patients) and eight canonical splice-site variants (n = 17 patients). In addition, we identified one patient with compound heterozygous splice-site/missense variants with a milder phenotype and one patient with homozygous missense variants. Patients displayed non-progressive microcephaly, global developmental delay/intellectual disability, epilepsy and absent expressive language. Movement disorders including stereotypies, spasticity and dystonia were also observed. Brain imaging revealed reductions in cortex, cerebellum and corpus callosum size with frequent white matter hyperintensity. Volumetric measurements indicated globally diminished volume rather than specific regional losses. We identified a reduced rate of trafficking into the Golgi apparatus and Golgi fragmentation in patient-derived fibroblasts that was rescued by wild-type TRAPPC6B. Molecular studies revealed a weakened interaction between mutant TRAPPC6B (c.454C>T, p.Q152*) and its TRAPP binding partner TRAPPC3. Patient-derived fibroblasts from the TRAPPC6B (c.454C>T, p.Q152*) variant displayed reduced levels of TRAPPC6B as well as other TRAPP II complex-specific members (TRAPPC9 and TRAPPC10). Interestingly, the levels of the TRAPPC6B homologue TRAPPC6A were found to be elevated. Moreover, co-immunoprecipitation experiments showed that TRAPPC6A co-precipitates equally with TRAPP II and TRAPP III, while TRAPPC6B co-precipitates significantly more with TRAPP II, suggesting enrichment of the protein in the TRAPP II complex. This implies that variants in TRAPPC6B may preferentially affect TRAPP II functions compared to TRAPP III functions. Finally, we assessed phenotypes in a Drosophila TRAPPC6B-deficiency model. Neuronal TRAPPC6B knockdown impaired locomotion and led to wing posture defects, supporting a role for TRAPPC6B in neuromotor function. Our findings confirm the association of damaging biallelic TRAPPC6B variants with microcephaly, intellectual disability, language impairments, and epilepsy. A subset of patients also exhibited dystonia and/or spasticity with impaired ambulation. These features overlap with disorders arising from pathogenic variants in other TRAPP subunits, particularly components of the TRAPP II complex. These findings suggest that TRAPPC6B is essential for brain development and function, and TRAPP II complex activity may be particularly relevant for mediating this function.

AbstractBackgroundOutcomes for children with relapsed/refractory (R/R) acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) are poor, and new therapies are needed. Pevonedistat is an inhibitor of the NEDD‐8 activating enzyme, a key regulator of the ubiquitin proteasome system that is responsible for protein turnover, with protein degradation regulating cell growth and survival.ProcedureWe evaluated the feasibility, toxicity, and pharmacokinetics (PK) of pevonedistat (20 mg/m2 days 1, 3, 5) in combination with azacitidine, fludarabine, cytarabine (aza‐FLA) in children with R/R AML and MDS (NCT03813147). Twelve patients were enrolled, median age was 13 years (range 1–21). Median number of prior chemotherapeutic regimens was two (range one to five), and two (25%) patients had prior hematopoietic cell transplantation. Diagnoses were AML NOS (n = 10, 83%), acute monocytic leukemia (n = 1), and therapy‐related AML (n = 1).ResultsOverall, three of 12 (25%) patients experienced DLTs. The day 1 mean ± SD (n = 12) Cmax, VSS, T1/2, and CL were 223 ± 91 ng/mL, 104 ± 53.8 L/m2, 4.3 ± 1.2 hours, and 23.2 ± 6.9 L/h/m2, respectively. T1/2, VSS, and Cmax, but not CL, were significantly different between age groups. The overall response rate was 25%, with n = 3 patients achieving a complete remission with incomplete hematologic recovery (CRi).ConclusionsPevonedistat 20 mg/m2 combined with Aza‐FLA was tolerable in children with R/R AML with similar toxicity profile to other intensive AML regimens. However, within the confines of a phase 1 study, we did not observe that the pevonedistat + Aza‐FLA combination demonstrated significant anti‐leukemic activity.

Abstract Objective . To investigate the impact of scan path optimization on the dose accuracy and beam delivery time (BDT) of proton pencil beam scanning in the dose-driven continuous scanning (DDCS). Approach . A diverse set of six clinical plans, representing various spot patterns and treatment sites, was used to evaluate the effectiveness of scan time optimization and scan length optimization. The DDCS dose discrepancy and BDT with optimized scan paths was compared to the default serpentine scan path. Main results . Both scan time optimization and scan path optimization were able to reduce the DDCS dose discrepancy compared to the default serpentine scan path. All plans, except for the layer repainting lung plan, achieved a 2%/2 mm gamma pass rate of over 99% and less than 1% PTV DVH root mean square error (RMSE) through scan path optimization. In the case of the layer repainting lung plan, when compared to the default serpentine scan path, the 2%/2 mm gamma pass rate showed improvements from 91.3% to 93.1% and 95.8%, while the PTV DVH RMSE decreased from 2.1% to 1.7% and 1.1% for scan time optimization and scan length optimization, respectively. Although scan time optimization resulted in shorter total scan times for all plans compared to the default scan path and scan length optimization tended to have longer total scan times. However, due to the short total scan times and their minimal contribution to the total BDT, the impact of scan path optimization on the total BDT was practically negligible. Significance . Both scan time optimization and scan length optimization proved to be effective in minimizing DDCS dose discrepancy. No definitive winner can be determined between these two optimization approaches. Both scan time and scan length optimization had minimal effect on the total BDT.

Across multiancestry groups, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with Parkinson’s disease (PD) and Alzheimer’s disease (AD) versus controls. We demonstrate that the two diseases share the same protective association at the HLA locus. HLA-specific fine-mapping showed that hierarchical protective effects of HLA-DRB1 *04 subtypes best accounted for the association, strongest with HLA-DRB1 *04:04 and HLA-DRB1 *04:07, and intermediary with HLA-DRB1 *04:01 and HLA-DRB1 *04:03. The same signal was associated with decreased neurofibrillary tangles in postmortem brains and was associated with reduced tau levels in cerebrospinal fluid and to a lower extent with increased Aβ42. Protective HLA-DRB1 *04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, however only when acetylated at a lysine (K311), a common posttranslational modification central to tau aggregation. An HLA-DRB1 *04-mediated adaptive immune response decreases PD and AD risks, potentially by acting against tau, offering the possibility of therapeutic avenues.

Abstract Background Tumor cell phenotypes and anti-tumor immune responses are shaped by local metabolite availability, but intratumoral metabolite heterogeneity (IMH) and its phenotypic consequences remain poorly understood. In vitro mechanistic studies have demonstrated that the anti-tumor activity of lymphoid and myeloid cell populations is mediated by metabolite availability and signaling in the TME, raising the possibility that the immune response and metabolism of ccRCC tumors coevolve and jointly influence the likelihood that a patient responds to therapy.. However, both the broad patterns of coordination between metabolite abundance and TME cellular composition, as well as the precise cell populations producing metabolic phenotypes of interest, remain unknown. Methods To study IMH, we multiregionally profiled the metabolome, transcriptome, and genome of 187 tumor/normal regions from 31 clear cell renal cell carcinoma (ccRCC) patients. Using these measurements and additional multimodal metabolomic/transcriptomic profiling of ccRCC and other diseases, we developed computational models that can be used to understand RNA-metabolite covariation and ultimately impute metabolite levels from RNA sequencing data. Results Analysis of intratumoral metabolite-RNA covariation revealed that the immune composition of the microenvironment, and especially the abundance of myeloid cells, drove intratumoral metabolite variation. Motivated by the strength of RNA-metabolite covariation and the clinical significance of RNA biomarkers in ccRCC, we deployed and benchmarked a machine learning method (MIRTH) to impute metabolite levels directly from RNA sequencing data of primary and metastatic ccRCC tumors. We inferred metabolomic profiles from RNA sequencing data of ccRCC patients enrolled in 6 clinical trials, ultimately identifying specific metabolite biomarkers associated with response to anti-angiogenic agents. Conclusions Local metabolic phenotypes therefore emerge in tandem with the immune microenvironment and associate with therapeutic sensitivity. CDMRP DOD Funding: yes

In this article, we present a flexible model for microbiome count data. We consider a quasi‐likelihood framework, in which we do not make any assumptions on the distribution of the microbiome count except that its variance is an unknown but smooth function of the mean. By comparing our model to the negative binomial generalized linear model (GLM) and Poisson GLM in simulation studies, we show that our flexible quasi‐likelihood method yields valid inferential results. Using a real microbiome study, we demonstrate the utility of our method by examining the relationship between adenomas and microbiota. We also provide an R package “fql” for the application of our method.

Abstract Objective Transformer-based language models are prevailing in the clinical domain due to their excellent performance on clinical NLP tasks. The generalizability of those models is usually ignored during the model development process. This study evaluated the generalizability of CancerBERT, a Transformer-based clinical NLP model, along with classic machine learning models, i.e., conditional random field (CRF), bi-directional long short-term memory CRF (BiLSTM-CRF), across different clinical institutes through a breast cancer phenotype extraction task. Materials and methods Two clinical corpora of breast cancer patients were collected from the electronic health records from the University of Minnesota (UMN) and Mayo Clinic (MC), and annotated following the same guideline. We developed three types of NLP models (i.e., CRF, BiLSTM-CRF and CancerBERT) to extract cancer phenotypes from clinical texts. We evaluated the generalizability of models on different test sets with different learning strategies (model transfer vs locally trained). The entity coverage score was assessed with their association with the model performances. Results We manually annotated 200 and 161 clinical documents at UMN and MC. The corpora of the two institutes were found to have higher similarity between the target entities than the overall corpora. The CancerBERT models obtained the best performances among the independent test sets from two clinical institutes and the permutation test set. The CancerBERT model developed in one institute and further fine-tuned in another institute achieved reasonable performance compared to the model developed on local data (micro-F1: 0.925 vs 0.932). Conclusions The results indicate the CancerBERT model has superior learning ability and generalizability among the three types of clinical NLP models for our named entity recognition task. It has the advantage to recognize complex entities, e.g., entities with different labels.

ImportanceSystematic data on the association between anticancer therapies and thromboembolic events (TEEs) in patients with COVID-19 are lacking.ObjectiveTo assess the association between anticancer therapy exposure within 3 months prior to COVID-19 and TEEs following COVID-19 diagnosis in patients with cancer.Design, Setting, and ParticipantsThis registry-based retrospective cohort study included patients who were hospitalized and had active cancer and laboratory-confirmed SARS-CoV-2 infection. Data were accrued from March 2020 to December 2021 and analyzed from December 2021 to October 2022.ExposureTreatments of interest (TOIs) (endocrine therapy, vascular endothelial growth factor inhibitors/tyrosine kinase inhibitors [VEGFis/TKIs], immunomodulators [IMiDs], immune checkpoint inhibitors [ICIs], chemotherapy) vs reference (no systemic therapy) in 3 months prior to COVID-19.Main Outcomes and MeasuresMain outcomes were (1) venous thromboembolism (VTE) and (2) arterial thromboembolism (ATE). Secondary outcome was severity of COVID-19 (rates of intensive care unit admission, mechanical ventilation, 30-day all-cause mortality following TEEs in TOI vs reference group) at 30-day follow-up.ResultsOf 4988 hospitalized patients with cancer (median [IQR] age, 69 [59-78] years; 2608 [52%] male), 1869 had received 1 or more TOIs. Incidence of VTE was higher in all TOI groups: endocrine therapy, 7%; VEGFis/TKIs, 10%; IMiDs, 8%; ICIs, 12%; and chemotherapy, 10%, compared with patients not receiving systemic therapies (6%). In multivariable log-binomial regression analyses, relative risk of VTE (adjusted risk ratio [aRR], 1.33; 95% CI, 1.04-1.69) but not ATE (aRR, 0.81; 95% CI, 0.56-1.16) was significantly higher in those exposed to all TOIs pooled together vs those with no exposure. Among individual drugs, ICIs were significantly associated with VTE (aRR, 1.45; 95% CI, 1.01-2.07). Also noted were significant associations between VTE and active and progressing cancer (aRR, 1.43; 95% CI, 1.01-2.03), history of VTE (aRR, 3.10; 95% CI, 2.38-4.04), and high-risk site of cancer (aRR, 1.42; 95% CI, 1.14-1.75). Black patients had a higher risk of TEEs (aRR, 1.24; 95% CI, 1.03-1.50) than White patients. Patients with TEEs had high intensive care unit admission (46%) and mechanical ventilation (31%) rates. Relative risk of death in patients with TEEs was higher in those exposed to TOIs vs not (aRR, 1.12; 95% CI, 0.91-1.38) and was significantly associated with poor performance status (aRR, 1.77; 95% CI, 1.30-2.40) and active/progressing cancer (aRR, 1.55; 95% CI, 1.13-2.13).Conclusions and RelevanceIn this cohort study, relative risk of developing VTE was high among patients receiving TOIs and varied by the type of therapy, underlying risk factors, and demographics, such as race and ethnicity. These findings highlight the need for close monitoring and perhaps personalized thromboprophylaxis to prevent morbidity and mortality associated with COVID-19–related thromboembolism in patients with cancer.

ABSTRACT Colorectal signet ring cell carcinoma is a rare type of colon cancer. Early diagnosis remains challenging because of nonspecific colonoscopy findings, such as diffuse circumferential thickening, stricture, and ulcerations, and the potential absence of typical pathological features in the initial biopsy sample. In this article, we report a 41-year-old man with ulcerating rectosigmoid stricture in the rectosigmoid colon with inconclusive histology. Subsequently, the patient developed small bowel obstruction and was diagnosed with stage 4 colorectal signet ring cell carcinoma with peritoneal carcinomatosis.