A new genomic framework to categorize pediatric acute myeloid leukemia.

Recent studies on pediatric acute myeloid leukemia (pAML) have revealed pediatric-specific driver alterations, many of which are underrepresented in the current classification schemas. To comprehensively define the genomic landscape of pAML, we systematically categorized 887 pAML into 23 mutually distinct molecular categories, including new major entities such as UBTF or BCL11B, covering 91.4% of the cohort. These molecular categories were associated with unique expression profiles and mutational patterns. For instance, molecular categories characterized by specific HOXA or HOXB expression signatures showed distinct mutation patterns of RAS pathway genes, FLT3 or WT1, suggesting shared biological mechanisms. We show that molecular categories were strongly associated with clinical outcomes using two independent cohorts, leading to the establishment of a new prognostic framework for pAML based on these updated molecular categories and minimal residual disease. Together, this comprehensive diagnostic and prognostic framework forms the basis for future classification of pAML and treatment strategies.

Randomized singular value decomposition for integrative subtype analysis of 'omics data' using non-negative matrix factorization.

Integration of multiple 'omics datasets for differentiating cancer subtypes is a powerful technic that leverages the consistent and complementary information across multi-omics data. Matrix factorization is a common technique used in integrative clustering for identifying latent subtype structure across multi-omics data. High dimensionality of the omics data and long computation time have been common challenges of clustering methods. In order to address the challenges, we propose randomized singular value decomposition (RSVD) for integrative clustering using Non-negative Matrix Factorization: intNMF-rsvd. The method utilizes RSVD to reduce the dimensionality by projecting the data into eigen vector space with user specified lower rank. Then, clustering analysis is carried out by estimating common basis matrix across the projected multi-omics datasets. The performance of the proposed method was assessed using the simulated datasets and compared with six state-of-the-art integrative clustering methods using real-life datasets from The Cancer Genome Atlas Study. intNMF-rsvd was found working efficiently and competitively as compared to standard intNMF and other multi-omics clustering methods. Most importantly, intNMF-rsvd can handle large number of features and significantly reduce the computation time. The identified subtypes can be utilized for further clinical association studies to understand the etiology of the disease.

Proposal of a new genomic framework for categorization of pediatric acute myeloid leukemia associated with prognosis.

Recent studies on pediatric acute myeloid leukemia (pAML) have revealed pediatric-specific driver alterations, many of which are underrepresented in the current classification schemas. To comprehensively define the genomic landscape of pAML, we systematically categorized 895 pAML into 23 molecular categories that are mutually distinct from one another, including new entities such as UBTF or BCL11B, covering 91.4% of the cohort. These molecular categories were associated with unique expression profiles and mutational patterns. For instance, molecular categories characterized by specific HOXA or HOXB expression signatures showed distinct mutation patterns of RAS pathway genes, FLT3, or WT1, suggesting shared biological mechanisms. We show that molecular categories were strongly associated with clinical outcomes using two independent cohorts, leading to the establishment of a prognostic framework for pAML based on molecular categories and minimal residual disease. Together, this comprehensive diagnostic and prognostic framework forms the basis for future classification of pAML and treatment strategies.

Network-based integrative clustering of multiple types of genomic data using non-negative matrix factorization.

Identification of novel molecular subtypes of disease using multi-source 'omics data is an active area of on-going research. Integrative clustering is a powerful approach to identify latent subtype structure inherent in the data sets accounting for both between and within data correlations. We propose a new integrative network-based clustering method using the non-negative matrix factorization, nNMF, for clustering multiple types of interrelated datasets assayed on same tumor-samples. nNMF utilizes the consensus matrices generated using the non-negative matrix factorization (NMF) algorithm on each type of data as networks among the patient samples. The multiple networks are then combined, and a comprehensive network structure is created optimizing the strengths of the relationships. A spectral clustering algorithm is then used on the final network data to determine the cluster groups. nNMF is a non-parametric method and therefore prior assumptions on the statistical distribution of data is not required. The application of the proposed nNMF method has been provided with simulated and the real-life datasets obtained from The Cancer Genome Atlas studies on glioblastoma, lower grade glioma and head and neck cancer. nNMF was found to be working competitively with previous methods and sometimes better as compared to previous NMF or model-based method especially when the signal to noise ratio is small. The novel nNMF method allows researchers to utilize such relationships to identify the latent subtype structure inherent in the data so that further association studies can be carried out. The R program for the nNMF will be available upon request.

Characterization and expression of a novel cystatin gene from Schistosoma japonicum.

Cystatins are a family of cysteine protease inhibitors that play a crucial role in the immune evasion from their host and in the adaptation to host defence. Here, we isolated a full-length cDNA sequence inferred to encode a novel cystatin gene from a blood fluke, Schistosoma japonicum. The cDNA, designated SjCystatin, comprised an open reading frame (ORF) of 306 bp, and encoded 101 amino acids with a predicted molecular weight of 11.3 kDa. This predicted protein shared a significant degree of sequence identity with the type I cystatin (stefin) of Schistosoma mansoni and Homo sapiens. These proteins exhibited a typical cystatin topology, including the absence of disulfide bonds and three conserved catalytic motifs, Gly at the N-terminus (Gly(6)), Gln-X-Val-X-Gly motif (Q(49)VVAG(53)) and an LP pair at the C-terminus (L(76)P(77)). The SjCystatin gene spanned 376 bp and contained three exons. The positions of two introns were conserved between the cystatin genes of trematodes and their vertebrate hosts. Reverse transcription polymerase chain reaction confirmed the transcription of SjCystatin in the egg, schistosomula and adult stages of S. japonicum. The encoding ORF region was cloned into pET-28a (+) prokaryotic expression vector. After purification, the recombinant protein SjCystatin (recSjCystatin), expressed in Escherichia coli, was used to immunize animals and produce its specific polyclonal antibody. Western blot analysis revealed that the native SjCystatin was expressed in the egg and adult stages. The enzyme activity assay of the recSjCystatin showed that it inhibited the proteolytic activity of papain. SjCystatin protein was mainly localized on the miracidium within eggs. Immunohistochemistry revealed that SjCystatin mainly localized in the epithelial cells lining the gut as well as the tegument on the surface of adult worms. The conserved genomic DNA structure among cystatin homologues of trematode and their vertebrate host emphasized the characteristics of compatibility between parasites and their hosts. This study provides the first insight into the gene and protein of S. japonicum cystatin and a basis for a further understanding the functions of this gene.