Updated Safety And Efficacy Data In The Phase 1 Trial Of Patients With Mantle Cell Lymphoma (Mcl) Treated With Bruton Tyrosine Kinase (Btk) Inhibitor Zanubrutinib (Bgb‐3111)

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149513/1/hon55_2630.pd

Anytime Ranking for Impact-Ordered Indexes

The ability for a ranking function to control its own execution time is useful for managing load, reigning in outliers, and adapting to different types of queries. We propose a simple yet effective anytime algorithm for impact-ordered indexes that builds on a score-at-a-time query evaluation strategy. In our approach, postings segments are processed in decreasing order of their impact scores, and the algorithm early terminates when a specified number of postings have been processed. With a simple linear model and a few training topics, we can determine this threshold given a time budget in milliseconds. Experiments on two web test collections show that our approach can accurately control query evaluation latency and that aggressive limits on execution time lead to minimal decreases in effectiveness

Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments

BACKGROUND: Current radiological assessments of (18)fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging data in diffuse large B-cell lymphoma (DLBCL) can be time consuming, do not yield real-time information regarding disease burden and organ involvement, and hinder the use of FDG-PET to potentially limit the reliance on invasive procedures (e.g. bone marrow biopsy) for risk assessment. METHODS: Our aim is to enable real-time assessment of imaging-based risk factors at a large scale and we propose a fully automatic artificial intelligence (AI)-based tool to rapidly extract FDG-PET imaging metrics in DLBCL. On availability of a scan, in combination with clinical data, our approach generates clinically informative risk scores with minimal resource requirements. Overall, 1268 patients with previously untreated DLBCL from the phase III GOYA trial (NCT01287741) were included in the analysis (training: n = 846; hold-out: n = 422). RESULTS: Our AI-based model comprising imaging and clinical variables yielded a tangible prognostic improvement compared to clinical models without imaging metrics. We observed a risk increase for progression-free survival (PFS) with hazard ratios [HR] of 1.87 (95% CI: 1.31–2.67) vs 1.38 (95% CI: 0.98–1.96) (C-index: 0.59 vs 0.55), and a risk increase for overall survival (OS) (HR: 2.16 (95% CI: 1.37–3.40) vs 1.40 (95% CI: 0.90–2.17); C-index: 0.59 vs 0.55). The combined model defined a high-risk population with 35% and 42% increased odds of a 4-year PFS and OS event, respectively, versus the International Prognostic Index components alone. The method also identified a subpopulation with a 2-year Central Nervous System (CNS)-relapse probability of 17.1%. CONCLUSION: Our tool enables an enhanced risk stratification compared with IPI, and the results indicate that imaging can be used to improve the prediction of central nervous system relapse in DLBCL. These findings support integration of clinically informative AI-generated imaging metrics into clinical workflows to improve identification of high-risk DLBCL patients. TRIAL REGISTRATION: Registered clinicaltrials.gov number: NCT01287741. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40644-022-00476-0

A rare case of paediatric astroblastoma with concomitant MN1-GTSE1 and EWSR1-PATZ1 gene fusions altering management

In a case of astroblastoma, methylation analysis was uninformative, with no clustering with known CNS-HGNET-MN1 cases. Whole genome sequencing however identified a novel MN1-GTSE1 gene fusion (image), confirming the diagnosis of astroblastoma, as well as an EWSR1-PATZ1 gene fusion. Whole genome sequencing, alongside methylation profiling and conventional neuropathology, will continue to lead to improved diagnostics and prognostication for children with brain tumours

Mix 'n Match: Integrating Text Matching and Product Substitutability within Product Search

Two products are substitutes if both can satisfy the same consumer need. Intrinsic incorporation of product substitutability - where substitutability is integrated within latent vector space models - is in contrast to the extrinsic re-ranking of result lists. The fusion of text matching and product substitutability objectives allows latent vector space models to mix and match regularities contained within text descriptions and substitution relations. We introduce a method for intrinsically incorporating product substitutability within latent vector space models for product search that are estimated using gradient descent; it integrates flawlessly with state-of-the-art vector space models. We compare our method to existing methods for incorporating structural entity relations, where product substitutability is incorporated extrinsically by re-ranking. Our method outperforms the best extrinsic method on four benchmarks. We investigate the effect of different levels of text matching and product similarity objectives, and provide an analysis of the effect of incorporating product substitutability on product search ranking diversity. Incorporating product substitutability information improves search relevance at the cost of diversity

Ubiquitination regulates PTEN nuclear import and tumor suppression

The PTEN tumor suppressor is frequently affected in cancer cells, and inherited PTEN mutation causes cancer-susceptibility conditions such as Cowden syndrome. PTEN acts as a plasma-membrane lipid-phosphatase antagonizing the phosphoinositide 3-kinase/AKT cell survival pathway. However, PTEN is also found in cell nuclei, but mechanism, function, and relevance of nuclear localization remain unclear. We show that nuclear PTEN is essential for tumor suppression and that PTEN nuclear import is mediated by its monoubiquitination. A lysine mutant of PTEN, K289E associated with Cowden syndrome, retains catalytic activity but fails to accumulate in nuclei of patient tissue due to an import defect. We identify this and another lysine residue as major monoubiquitination sites essential for PTEN import. While nuclear PTEN is stable, polyubiquitination leads to its degradation in the cytoplasm. Thus, we identify cancer-associated mutations of PTEN that target its posttranslational modification and demonstrate how a discrete molecular mechanism dictates tumor progression by differentiating between degradation and protection of PTEN