PAX5, a master regulator of B cell development and maintenance, is one of the most common targets of genetic alterations in B-cell acute lymphoblastic leukemia (B-ALL).
PAX5
alterations consist of ...copy number variations (whole gene, partial, or intragenic), translocations, and point mutations, with distinct distribution across B-ALL subtypes. The multifaceted functional impacts such as haploinsufficiency and gain-of-function of PAX5 depending on specific variants have been described, thereby the connection between the blockage of B cell development and the malignant transformation of normal B cells has been established. In this review, we provide the recent advances in understanding the function of PAX5 in orchestrating the development of both normal and malignant B cells over the past decade, with a focus on the
PAX5
alterations shown as the initiating or driver events in B-ALL. Recent large-scale genomic analyses of B-ALL have identified multiple novel subtypes driven by
PAX5
genetic lesions, such as the one defined by a distinct gene expression profile and
PAX5
P80R mutation, which is an exemplar leukemia entity driven by a missense mutation. Although altered
PAX5
is shared as a driver in B-ALL, disparate disease phenotypes and clinical outcomes among the patients indicate further heterogeneity of the underlying mechanisms and disturbed gene regulation networks along the disease development. In-depth mechanistic studies in human B-ALL and animal models have demonstrated high penetrance of
PAX5
variants alone or concomitant with other genetic lesions in driving B-cell malignancy, indicating the altered
PAX5
and deregulated genes may serve as potential therapeutic targets in certain B-ALL cases.
B-acute lymphoblastic leukemia (B-ALL) consists of dozens of subtypes defined by distinct gene expression profiles (GEPs) and various genetic lesions. With the application of transcriptome sequencing ...(RNA-seq), multiple novel subtypes have been identified, which lead to an advanced B-ALL classification and risk-stratification system. However, the complexity of analyzing RNA-seq data for B-ALL classification hinders the implementation of the new B-ALL taxonomy. Here, we introduce MD-ALL (Molecular Diagnosis of ALL), an integrative platform featuring sensitive and accurate B-ALL classification based on GEPs and sentinel genetic alterations from RNA-seq data. In this study, we systematically analyzed 2,955 B-ALL RNA-seq samples and generated a reference dataset representing all the reported B-ALL subtypes. Using multiple machine learning algorithms, we identified the feature genes and then established highly sensitive and accurate models for B-ALL classification using either bulk or single-cell RNA-seq data. Importantly, this platform integrates multiple aspects of key genetic lesions acquired from RNAseq data, which include sequence mutations, large-scale copy number variations, and gene rearrangements, to perform comprehensive and definitive B-ALL classification. Through validation in a hold-out cohort of 974 samples, our models demonstrated superior performance for B-ALL classification compared with alternative tools. Moreover, to ensure accessibility and user-friendly navigation even for users with limited or no programming background, we developed an interactive graphical user interface for this MD-ALL platform, using the R Shiny package. In summary, MD-ALL is a user-friendly B-ALL classification platform designed to enable integrative, accurate, and comprehensive B-ALL subtype classification. MD-ALL is available from https://github.com/gu-lab20/MD-ALL.
Transcriptome sequencing (RNA-seq) is widely used to detect gene rearrangements and quantitate gene expression in acute lymphoblastic leukemia (ALL), but its utility and accuracy in identifying copy ...number variations (CNVs) has not been well described. CNV information inferred from RNA-seq can be highly informative to guide disease classification and risk stratification in ALL due to the high incidence of aneuploid subtypes within this disease. Here we describe RNAseqCNV, a method to detect large scale CNVs from RNA-seq data. We used models based on normalized gene expression and minor allele frequency to classify arm level CNVs with high accuracy in ALL (99.1% overall and 98.3% for non-diploid chromosome arms, respectively), and the models were further validated with excellent performance in acute myeloid leukemia (accuracy 99.8% overall and 99.4% for non-diploid chromosome arms). RNAseqCNV outperforms alternative RNA-seq based algorithms in calling CNVs in the ALL dataset, especially in samples with a high proportion of CNVs. The CNV calls were highly concordant with DNA-based CNV results and more reliable than conventional cytogenetic-based karyotypes. RNAseqCNV provides a method to robustly identify copy number alterations in the absence of DNA-based analyses, further enhancing the utility of RNA-seq to classify ALL subtype.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Let λ be a reflexive Banach sequence lattice and
X
be a Banach lattice. In this paper, we show that the positive injective tensor product
λ
⊗
⌣
|
ε
|
X
is a Grothendieck space if and only if
X
is a ...Grothendieck space and every positive linear operator from
λ
* to
X
** is compact.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Oncogenic mutations in RAS genes, like KRAS
or NRAS
, trap Ras in the active state and cause myeloproliferative disorder and T cell leukemia (T-ALL) when induced in the bone marrow via Mx1CRE. The ...RAS exchange factor RASGRP1 is frequently overexpressed in T-ALL patients. In T-ALL cell lines overexpression of RASGRP1 increases flux through the RASGTP/RasGDP cycle. Here we expanded RASGRP1 expression surveys in pediatric T-ALL and generated a RoLoRiG mouse model crossed to Mx1CRE to determine the consequences of induced RASGRP1 overexpression in primary hematopoietic cells. RASGRP1-overexpressing, GFP-positive cells outcompeted wild type cells and dominated the peripheral blood compartment over time. RASGRP1 overexpression bestows gain-of-function colony formation properties to bone marrow progenitors in medium containing limited growth factors. RASGRP1 overexpression enhances baseline mTOR-S6 signaling in the bone marrow, but not in vitro cytokine-induced signals. In agreement with these mechanistic findings, hRASGRP1-ires-EGFP enhances fitness of stem- and progenitor- cells, but only in the context of native hematopoiesis. RASGRP1 overexpression is distinct from KRAS
or NRAS
, does not cause acute leukemia on its own, and leukemia virus insertion frequencies predict that RASGRP1 overexpression can effectively cooperate with lesions in many other genes to cause acute T-ALL.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
An early warning flood forecasting system that uses machine-learning models can be utilized for saving lives from floods, which are now exacerbated due to climate change. Flood forecasting is carried ...out by determining the river discharge and water level using hydrologic models at the target sites. If the water level and discharge are forecasted to reach dangerous levels, the flood forecasting system sends warning messages to residents in flood-prone areas. In the past, hybrid Long Short-Term Memory (LSTM) models have been successfully used for the time series forecasting. However, the prediction errors grow exponentially with the forecasting period, making the forecast unreliable as an early warning tool with enough lead time. Therefore, this research aimed to improve the accuracy of flood forecasting models by employing real-time monitoring network datasets and establishing temporal and spatial links between adjacent monitoring stations. We evaluated the performance of the LSTM, the Convolutional Neural Networks LSTM (CNN-LSTM), the Convolutional LSTM (ConvLSTM), and the Spatio-Temporal Attention LSTM (STA-LSTM) models for flood forecasting. The dataset, employed for validation, includes hourly discharge records, from 2012 to 2017, on six stations of the Humber River in the City of Toronto, Canada. Experiments included forecasting for both 6 and 12 h ahead, using discharge data as input for the past 24 h. The STA-LSTM model’s performance was superior to the CNN-LSTM, the ConvLSTM, and the basic LSTM models when the forecast time was longer than 6 h.
In this paper, we introduce the notion of bicomplex partial metric space and prove some common fixed point theorems. The presented results generalize and expand some of the literature’s well-known ...results. An example and application on bicomplex partial metric space is given.
We evaluate clinical significance of recently identified subtypes of acute lymphoblastic leukemia (ALL) in 598 children treated with minimal residual disease (MRD)-directed therapy. Among the 16 ...B-ALL and 8 T-ALL subtypes identified by next generation sequencing,
, high-hyperdiploid and
-rearranged B-ALL had the best five-year event-free survival rates (95% to 98.4%);
, PAX5alt, T-cell, ETP, iAMP21, and hypodiploid ALL intermediate rates (80.0% to 88.2%); and
,
-like and
-like and
-rearranged ALL the worst rates (64.1% to 76.2%). All but three of the 142 patients with day-8 blood MRD <0.01% remained in remission. Among new subtypes, intensified therapy based on day-15 MRD≥1% improved outcome of
-rearranged,
-like, and
-rearranged ALL, and achievement of day-42 MRD<0.01% did not preclude relapse of PAX5alt,
-rearranged and
-like ALL. Thus, new subtypes including
-rearranged, PAX5alt,
-like,
-like,
-rearranged and
-rearranged ALL have important prognostic and therapeutic implications.
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