Citations

Pontocerebellar hypoplasia (PCH) is a group of rare neurodevelopmental disorders with limited diagnostic and therapeutic options. Mutations in WDR11, a subunit of the FAM91A1 complex, have been found in patients with PCH-like symptoms; however, definitive evidence that the mutations are causal is still lacking. Here, we show that depletion of FAM91A1 results in developmental defects in zebrafish similar to that of TBC1D23, an established PCH gene. FAM91A1 and TBC1D23 directly interact with each other and cooperate to regulate endosome-to-Golgi trafficking of KIAA0319L, a protein known to regulate axonal growth. Crystal structure of the FAM91A1–TBC1D23 complex reveals that TBC1D23 binds to a conserved surface on FAM91A1 by assuming a Z-shaped conformation. More importantly, the interaction between FAM91A1 and TBC1D23 can be used to predict the risk of certain TBC1D23-associated mutations to PCH. Collectively, our study provides a molecular basis for the interaction between TBC1D23 and FAM91A1 and suggests that disrupted endosomal trafficking underlies multiple PCH subtypes.

ABSTRACT How cell metabolism regulates DNA repair is incompletely understood. Here, we define a GTP-mediated signaling cascade that links metabolism to DNA repair and has significant therapeutic implications. GTP, but not other nucleotides, regulates the activity of Rac1, a guanine nucleotide-binding protein, which promotes the dephosphorylation of serine 323 on Abl-interactor 1 (Abi-1) by protein phosphatase 5 (PP5). Dephosphorylated Abi-1, a protein previously not known to activate DNA repair, promotes nonhomologous end joining. In patients and mouse models of glioblastoma, Rac1 and dephosphorylated Abi-1 mediate DNA repair and resistance to standard-of-care genotoxic treatments. The GTP–Rac1–PP5–Abi-1 signaling axis is not limited to brain cancer, as GTP supplementation promotes DNA repair and Abi-1-S323 dephosphorylation in nonmalignant cells and protects mouse tissues from genotoxic insult. This unexpected ability of GTP to regulate DNA repair independently of deoxynucleotide pools has important implications for normal physiology and cancer treatment. SIGNIFICANCE: A newly described GTP-dependent signaling axis is an unexpected link between nucleotide metabolism and DNA repair. Disrupting this pathway can overcome cancer resistance to genotoxic therapy while augmenting it can mitigate genotoxic injury of normal tissues. This article is featured in Selected Articles from This Issue, p. 5

PURPOSE Patients with Down syndrome (DS) and B-ALL experience increased rates of relapse, toxicity, and death. We report results for patients with DS B-ALL enrolled on Children's Oncology Group trials between 2003 and 2019. METHODS We analyzed data for DS (n = 743) and non-DS (n = 20,067) patients age 1-30 years on four B-ALL standard-risk (SR) and high-risk trials. RESULTS Patients with DS exhibited more frequent minimal residual disease (MRD) ≥0.01% at end induction (30.8% v 21.5%; P < .001). This difference persisted at end consolidation only in National Cancer Institute (NCI) high-risk patients (34.0% v 11.7%; P < .0001). Five-year event-free survival (EFS) and overall survival (OS) were significantly poorer for DS versus non-DS patients overall (EFS, 79.2% ± 1.6% v 87.5% ± 0.3%; P < .0001; OS, 86.8% ± 1.4% v 93.6% ± 0.2%; P < .0001), and within NCI SR and high-risk subgroups. Multivariable Cox regression analysis of the DS cohort for risk factors associated with inferior EFS identified age >10 years, white blood count >50 × 103/μL, and end-induction MRD ≥0.01%, but not cytogenetics or CRLF2 overexpression. Patients with DS demonstrated higher 5-year cumulative incidence of relapse (11.5% ± 1.2% v 9.1% ± 0.2%; P = .0008), death in remission (4.9% ± 0.8% v 1.7% ± 0.1%; P < .0001), and induction death (3.4% v 0.8%; P < .0001). Mucositis, infections, and hyperglycemia were significantly more frequent in all patients with DS, while seizures were more frequent in patients with DS on high-risk trials (4.1% v 1.8%; P = .005). CONCLUSION Patients with DS-ALL exhibit an increased rate of relapse and particularly of treatment-related mortality. Novel, less-toxic therapeutic strategies are needed to improve outcomes.

Abstract Introduction: First pass effect (FPE), achievement of complete recanalization (mTICI 2c/3) with a single pass, is a significant predictor of favorable outcomes for endovascular treatment (EVT) in large vessel occlusion stroke (LVO). However, data concerning the impact on functional outcomes and predictors of FPE in medium vessel occlusions (MeVO) are scarce. Patients and Methods: We conducted an international retrospective study on MeVO cases. Multivariable logistic modeling was used to establish independent predictors of FPE. Clinical and safety outcomes were compared between the two study groups (FPE vs non-FPE) using logistic regression models. Good outcome was defined as modified Rankin Scale 0–2 at 3 months. Results: Eight hundred thirty-six patients with a final mTICI ⩾ 2b were included in this analysis. FPE was observed in 302 patients (36.1%). In multivariable analysis, hypertension (aOR 1.55, 95% CI 1.10–2.20) and lower baseline NIHSS score (aOR 0.95, 95% CI 0.93–0.97) were independently associated with an FPE. Good outcomes were more common in the FPE versus non-FPE group (72.8% vs 52.8%), and FPE was independently associated with favorable outcome (aOR 2.20, 95% CI 1.59–3.05). 90-day mortality and intracranial hemorrhage (ICH) were significantly lower in the FPE group, 0.43 (95% CI, 0.25–0.72) and 0.55 (95% CI, 0.39–0.77), respectively. Conclusion: Over 2/3 of patients with MeVOs and FPE in our cohort had a favorable outcome at 90 days. FPE is independently associated with favorable outcomes, it may reduce the risk of any intracranial hemorrhage, and 3-month mortality.

It is well known that the microbiome data are ridden with outliers and have heavy distribution tails, but the impact of outliers and heavy-tailedness has yet to be examined systematically. This paper investigates the impact of outliers and heavy-tailedness on differential abundance analysis (DAA) using the linear models for the differential abundance analysis (LinDA) method and proposes effective strategies to mitigate their influence. The presence of outliers and heavy-tailedness can significantly decrease the power of LinDA. We investigate various techniques to address outliers and heavy-tailedness, including generalizing LinDA into a more flexible framework that allows for the use of robust regression and winsorizing the data before applying LinDA. Our extensive numerical experiments and real-data analyses demonstrate that robust Huber regression has overall the best performance in addressing outliers and heavy-tailedness.

AbstractBackgroundRecent therapeutic advances and screening technologies have improved survival among patients with lung cancer, who are now at high risk of developing second primary lung cancer (SPLC). Recently, an SPLC risk‐prediction model (called SPLC‐RAT) was developed and validated using data from population‐based epidemiological cohorts and clinical trials, but real‐world validation has been lacking. The predictive performance of SPLC‐RAT was evaluated in a hospital‐based cohort of lung cancer survivors.MethodsThe authors analyzed data from 8448 ever‐smoking patients diagnosed with initial primary lung cancer (IPLC) in 1997–2006 at Mayo Clinic, with each patient followed for SPLC through 2018. The predictive performance of SPLC‐RAT and further explored the potential of improving SPLC detection through risk model‐based surveillance using SPLC‐RAT versus existing clinical surveillance guidelines.ResultsOf 8448 IPLC patients, 483 (5.7%) developed SPLC over 26,470 person‐years. The application of SPLC‐RAT showed high discrimination area under the receiver operating characteristics curve: 0.81). When the cohort was stratified by a 10‐year risk threshold of ≥5.6% (i.e., 80th percentile from the SPLC‐RAT development cohort), the observed SPLC incidence was significantly elevated in the high‐risk versus low‐risk subgroup (13.1% vs. 1.1%, p < 1 × 10–6). The risk‐based surveillance through SPLC‐RAT (≥5.6% threshold) outperformed the National Comprehensive Cancer Network guidelines with higher sensitivity (86.4% vs. 79.4%) and specificity (38.9% vs. 30.4%) and required 20% fewer computed tomography follow‐ups needed to detect one SPLC (162 vs. 202).ConclusionIn a large, hospital‐based cohort, the authors validated the predictive performance of SPLC‐RAT in identifying high‐risk survivors of SPLC and showed its potential to improve SPLC detection through risk‐based surveillance.Plain Language Summary Lung cancer survivors have a high risk of developing second primary lung cancer (SPLC). However, no evidence‐based guidelines for SPLC surveillance are available for lung cancer survivors. Recently, an SPLC risk‐prediction model was developed and validated using data from population‐based epidemiological cohorts and clinical trials, but real‐world validation has been lacking. Using a large, real‐world cohort of lung cancer survivors, we showed the high predictive accuracy and risk‐stratification ability of the SPLC risk‐prediction model. Furthermore, we demonstrated the potential to enhance efficiency in detecting SPLC using risk model‐based surveillance strategies compared to the existing consensus‐based clinical guidelines, including the National Comprehensive Cancer Network.

Abstract Introduction . Dispersion in an accelerator quantifies the deviation of the proton trajectory when there is a momentum deviation. We present for the first time a safe method of measuring dispersion in the clinic, using a scintillator detector and the momentum deviations within a spill. This is an important accelerator quantity as we found that this is the reason behind the large dose fluctuation in our absolute dosimetry measurement. Methods . Dispersions are measured for nine energies in a Hitachi ProBeat system at three locations (isocenter and at two profile monitors) and at two gantry angles (0 and 90 degrees) by first measuring the spot position and momentum drift within a spill. The spot position drift is measured by the XRV-4000 at the isocenter, and by the two profile monitors located at 0.57 and 2.27 m from the isocenter. The momentum drift is calculated from the intra-spill range drift which is measured using the Ranger accessory. The dispersion at isocenter and its gradient are calculated using the weighted least square regression on the measured dispersions at the three locations. A constraint is formulated on the dispersion and its gradient to ensure minimal intra-spill spot position deviation around the isocenter. Results . The measured intra-spill range and spot positional drift at isocenter are less than 0.25 mm and 0.7 mm respectively. The momentum spread calculated from the range drift are less than 0.08%. The dispersion at the isocenter ranged from 0.50 to 4.30 m and the zero-crossing happens upstream of isocenter for all energies. 2 of the 9 energies (168.0 and 187.5 MeV) violated the constraint and has an intra-spill spot positional deviation greater than 1.0 within 5 cm from the isocenter. Conclusion . This measurement is recommended as part of commissioning and annual quality assurance for accelerator monitoring and to ensure intra-spill spot deviations remain low.

The digitization of medical records and expanding electronic health records has created an era of “Big Data” with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive. Fortunately, the growth of artificial intelligence (AI) may be a solution to handle large datasets. In particular, computer vision is a field within AI that analyzes images and extracts information. Computer vision has impressive capabilities and can be applied to rheumatologic conditions, necessitating a need to understand how computer vision works. In this article, we provide an overview of AI in rheumatology and conclude with a five step process to plan and conduct research in the field of computer vision. The five steps include (1) project definition, (2) data handling, (3) model development, (4) performance evaluation, and (5) deployment into clinical care.