Mayumi Kamada

Brigatinib combined with anti-EGFR antibody overcomes osimertinib resistance in EGFR-mutated non-small-cell lung cancer

Osimertinib has been demonstrated to overcome the epidermal growth factor receptor (EGFR)-T790M, the most relevant acquired resistance to first-generation EGFR–tyrosine kinase inhibitors (EGFR–TKIs). However, the C797S mutation, which impairs the covalent binding between the cysteine residue at position 797 of EGFR and osimertinib, induces resistance to osimertinib. Currently, there are no effective therapeutic strategies to overcome the C797S/T790M/activating-mutation (triple-mutation)-mediated EGFR–TKI resistance. In the present study, we identify brigatinib to be effective against triple-mutation-harbouring cells in vitro and in vivo. Our original computational simulation demonstrates that brigatinib fits into the ATP-binding pocket of triple-mutant EGFR. The structure–activity relationship analysis reveals the key component in brigatinib to inhibit the triple-mutant EGFR. The efficacy of brigatinib is enhanced markedly by combination with anti-EGFR antibody because of the decrease of surface and total EGFR expression. Thus, the combination therapy of brigatinib with anti-EGFR antibody is a powerful candidate to overcome triple-mutant EGFR.

Exome Sequencing Landscape Analysis in Ovarian Clear Cell Carcinoma Shed Light on Key Chromosomal Regions and Mutation Gene Networks

Previous studies have reported genome-wide mutation profile analyses in ovarian clear cell carcinomas (OCCCs). This study aims to identify specific novel molecular alterations by combined analyses of somatic mutation and copy number variation. We performed whole exome sequencing of 39 OCCC samples with 16 matching blood tissue samples. Four hundred twenty-six genes had recurrent somatic mutations. Among the 39 samples, ARID1A (62%) and PIK3CA (51%) were frequently mutated, as were genes such as KRAS (10%), PPP2R1A (10%), and PTEN (5%), that have been reported in previous OCCC studies. We also detected mutations in MLL3 (15%), ARID1B (10%), and PIK3R1 (8%), which are associations not previously reported. Gene interaction analysis and functional assessment revealed that mutated genes were clustered into groups pertaining to chromatin remodeling, cell proliferation, DNA repair and cell cycle checkpointing, and cytoskeletal organization. Copy number variation analysis identified frequent amplification in chr8q (64%), chr20q (54%), and chr17q (46%) loci as well as deletion in chr19p (41%), chr13q (28%), chr9q (21%), and chr18q (21%) loci. Integration of the analyses uncovered that frequently mutated or amplified/deleted genes were involved in the KRAS/phosphatidylinositol 3-kinase (82%) and MYC/retinoblastoma (75%) pathways as well as the critical chromatin remodeling complex switch/sucrose nonfermentable (85%). The individual and integrated analyses contribute details about the OCCC genomic landscape, which could lead to enhanced diagnostics and therapeutic options.

Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features.

BackgroundMachine learning methods are nowadays used for many biological prediction problems involving drugs, ligands or polypeptide segments of a protein. In order to build a prediction model a so called training data set of molecules with measured target properties is needed. For many such problems the size of the training data set is limited as measurements have to be performed in a wet lab. Furthermore, the considered problems are often complex, such that it is not clear which molecular descriptors (features) may be suitable to establish a strong correlation with the target property. In many applications all available descriptors are used. This can lead to difficult machine learning problems, when thousands of descriptors are considered and only few (e.g. below hundred) molecules are available for training.ResultsThe CoEPrA contest provides four data sets, which are typical for biological regression problems (few molecules in the training data set and thousands of descriptors). We applied the same two-step training procedure for all four regression tasks. In the first stage, we used optimized L1 regularization to select the most relevant features. Thus, the initial set of more than 6,000 features was reduced to about 50. In the second stage, we used only the selected features from the preceding stage applying a milder L2 regularization, which generally yielded further improvement of prediction performance. Our linear model employed a soft loss function which minimizes the influence of outliers.ConclusionsThe proposed two-step method showed good results on all four CoEPrA regression tasks. Thus, it may be useful for many other biological prediction problems where for training only a small number of molecules are available, which are described by thousands of descriptors.

Discriminative random field approach to prediction of protein residue contacts

Understanding of interactions of proteins is important to reveal networks and functions of molecules. Many investigations have been conducted to analyze interactions and contacts between residues. It is supported that residues at interacting sites have co-evolved with those at the corresponding residues in the partner protein to keep the interactions between the proteins. Therefore, mutual information (MI) between residues calculated from multiple sequence alignments of homologous proteins is considered to be useful for identifying contact residues in interacting proteins. In our previous work, we proposed a prediction method for protein-protein interactions using mutual information and conditional random fields (CRFs), and confirmed its usefulness. The discriminative random field (DRF) is a special type of CRFs, and can recognize some specific characteristic regions in an image. Since the matrix consisted of mutual information between residues in two interacting proteins can be regarded as an image, we propose a prediction method for protein residue contacts using DRF models with mutual information. To validate our method, we perform computational experiments for several interactions between Pfam domains. The results suggest that the proposed DRF-based method with MI is useful for predicting protein residue contacts compared with that using the corresponding Markov random field (MRF) model.

Prediction of protein-RNA residue-base contacts using two-dimensional conditional random field with the lasso

BackgroundTo uncover molecular functions and networks in biological cellular systems, it is important to dissect interactions between proteins and RNAs. Many studies have been performed to investigate and analyze interactions between protein amino acid residues and RNA bases. In terms of interactions between residues in proteins, it is generally accepted that an amino acid residue at interacting sites has coevolved together with the partner residue in order to keep the interaction between residues in proteins. Based on this hypothesis, in our previous study to identify residue-residue contact pairs in interacting proteins, we made calculations of mutual information (M I) between amino acid residues from some multiple sequence alignment of homologous proteins, and combined it with a discriminative random field (DRF) approach, which is a special type of conditional random fields (CRFs) and has been proved useful for the purpose of extracting distinguishing areas from a photograph in the image processing field. Recently, the evolutionary correlation of interactions between residues and DNA bases has also been found in certain transcription factors and the DNA-binding sites.ResultsIn this paper, we employ more generic two-dimensional CRFs than such DRFs to predict interactions between protein amino acid residues and RNA bases. In addition, we introduce labels representing kinds of amino acids and bases as local features of a CRF. Furthermore, we examine the utility of L1-norm regularization (lasso) for the CRF. For evaluation of our method, we use residue-base interactions between several Pfam domains and Rfam entries, conduct cross-validation, and calculate the average AUC (Area under ROC Curve) score. The results suggest that our CRF-based method using mutual information and labels with the lasso is useful for further improving the performance, especially provided that the features of CRF are successfully reduced by the lasso approach.ConclusionsWe propose simple and generic two-dimensional CRF models using labels and mutual information with the lasso. Use of the CRF-based method in combination with the lasso is particularly useful for predicting the residue-base contacts in protein-RNA interactions.

Prediction of protein-protein interaction strength using domain features with supervised regression.

Proteins in living organisms express various important functions by interacting with other proteins and molecules. Therefore, many efforts have been made to investigate and predict protein-protein interactions (PPIs). Analysis of strengths of PPIs is also important because such strengths are involved in functionality of proteins. In this paper, we propose several feature space mappings from protein pairs using protein domain information to predict strengths of PPIs. Moreover, we perform computational experiments employing two machine learning methods, support vector regression (SVR) and relevance vector machine (RVM), for dataset obtained from biological experiments. The prediction results showed that both SVR and RVM with our proposed features outperformed the best existing method.

Association between homologous recombination repair gene mutations and response to oxaliplatin in pancreatic cancer

Objectives We aimed to examine the association between homologous recombination repair (HRR)-related gene mutations and efficacy of oxaliplatin-based chemotherapy in patients with pancreatic ductal adenocarcinoma (PDAC). Results Non-synonymous mutations in HRR-related genes were found in 13 patients and only one patient had a family history of pancreatic cancer. Eight patients with HRR-related gene mutations (group A) and nine without HRR-related gene mutations (group B) received oxaliplatin-based chemotherapy. Median progression-free survival after initiation of oxaliplatin-based chemotherapy was significantly longer in group A than in group B (20.8 months vs 1.7 months, p = 0.049). Interestingly, two patients with inactivating HRR-related gene mutations who received FOLFIRINOX as first-line treatment showed exceptional responses with respect to progression-free survival for > 24 months. Materials and Methods Complete coding exons of 12 HRR-related genes (ATM, ATR, BAP1, BRCA1, BRCA2, BLM, CHEK1, CHEK2, FANCA, MRE11A, PALB2, and RAD51) were sequenced using a Clinical Laboratory Improvement Amendment-certified multiplex next-generation sequencing assay. Thirty consecutive PDAC patients who underwent this assay between April 2015 and July 2017 were included. Conclusions Our results suggest that inactivating HRR-related gene mutations are predictive of response to oxaliplatin-based chemotherapy in patients with PDAC.

Clinical sequencing using a next-generation sequencing-based multiplex gene assay in patients with advanced solid tumors

Advances in next‐generation sequencing (NGS) technologies have enabled physicians to test for genomic alterations in multiple cancer‐related genes at once in daily clinical practice. In April 2015, we introduced clinical sequencing using an NGS‐based multiplex gene assay (OncoPrime) certified by the Clinical Laboratory Improvement Amendment. This assay covers the entire coding regions of 215 genes and the rearrangement of 17 frequently rearranged genes with clinical relevance in human cancers. The principal indications for the assay were cancers of unknown primary site, rare tumors, and any solid tumors that were refractory to standard chemotherapy. A total of 85 patients underwent testing with multiplex gene assay between April 2015 and July 2016. The most common solid tumor types tested were pancreatic (n = 19; 22.4%), followed by biliary tract (n = 14; 16.5%), and tumors of unknown primary site (n = 13; 15.3%). Samples from 80 patients (94.1%) were successfully sequenced. The median turnaround time was 40 days (range, 18–70 days). Potentially actionable mutations were identified in 69 of 80 patients (86.3%) and were most commonly found in TP53 (46.3%), KRAS (23.8%), APC (18.8%), STK11 (7.5%), and ATR (7.5%). Nine patients (13.0%) received a subsequent therapy based on the NGS assay results. Implementation of clinical sequencing using an NGS‐based multiplex gene assay was feasible in the clinical setting and identified potentially actionable mutations in more than 80% of patients. Current challenges are to incorporate this genomic information into better therapeutic decision making.

Predicting protein-RNA residue-base contacts using two-dimensional conditional random field

Understanding of interactions between proteins and RNAs is essential to reveal networks and functions of molecules in cellular systems. Many studies have been done for analyzing and investigating interactions between protein residues and RNA bases. For interactions between protein residues, it is supported that residues at interacting sites have co-evolved with the corresponding residues in the partner protein to keep the interactions between the proteins. In our previous work, on the basis of this idea, we calculated mutual information (MI) between residues from multiple sequence alignments of homologous proteins for identifying interacting pairs of residues in interacting proteins, and combined it with the discriminative random field (DRF), which is useful to extract some characteristic regions from an image in the field of image processing, and is a special type of conditional random fields (CRFs). In a similar way, in this paper, we make use of mutual information for predicting interactions between protein residues and RNA bases. Furthermore, we introduce labels of amino acids and bases as features of a simple two-dimensional CRF instead of DRF. To evaluate our method, we perform computational experiments for several interactions between Pfam domains and Rfam entries. The results suggest that the CRF model with MI and labels is more useful than the CRF model with only MI.

Improving conditional random field model for prediction of protein-RNA residue-base contacts

BackgroundFor understanding biological cellular systems, it is important to analyze interactions between protein residues and RNA bases. A method based on conditional random fields (CRFs) was developed for predicting contacts between residues and bases, which receives multiple sequence alignments for given protein and RNA sequences, respectively, and learns the model with many parameters involved in relationships between neighboring residue-base pairs by maximizing the pseudo likelihood function.MethodsIn this paper, we proposed a novel CRF-based model with more complicated dependency relationships between random variables than the previous model, but which takes less parameters for the sake of avoidance of overfitting to training data.ResultsWe performed cross-validation experiments for evaluating the proposed model, and took the average of AUC (area under receiver operating characteristic curve) scores. The result suggests that the proposed CRF-based model without using L1-norm regularization (lasso) outperforms the existing model with and without the lasso under several input observations to CRFs.ConclusionsWe proposed a novel stochastic model for predicting protein-RNA residue-base contacts, and improved the prediction accuracy in terms of the AUC score. It implies that more dependency relationships in a CRF could be controlled by less parameters.