Multi-omics and clustering analyses reveal the mechanisms underlying unmet needs for patients with lung adenocarcinoma and identify potential therapeutic targets.

BACKGROUND: The cancer genome contains several driver mutations. However, in some cases, no known drivers have been identified; these remaining areas of unmet needs, leading to limited progress in cancer therapy. Whole-genome sequencing (WGS) can identify non-coding alterations associated with the disease. Consequently, exploration of non-coding regions using WGS and other omics data such as ChIP-sequencing (ChIP-seq) to discern novel alterations and mechanisms related to tumorigenesis have been attractive these days. METHODS: Integrated multi-omics analyses, including WGS, ChIP-seq, DNA methylation, and RNA-sequencing (RNA-seq), were conducted on samples from patients with non-clinically actionable genetic alterations (non-CAGAs) in lung adenocarcinoma (LUAD). Second-level cluster analysis was performed to reinforce the correlations associated with patient survival, as identified by RNA-seq. Subsequent differential gene expression analysis was performed to identify potential druggable targets. RESULTS: Differences in H3K27ac marks in non-CAGAs LUAD were found and confirmed by analyzing RNA-seq data, in which mastermind-like transcriptional coactivator 2 (MAML2) was suppressed. The down-regulated genes whose expression was correlated to MAML2 expression were associated with patient prognosis. WGS analysis revealed somatic mutations associated with the H3K27ac marks in the MAML2 region and high levels of DNA methylation in MAML2 were observed in tumor samples. The second-level cluster analysis enabled patient stratification and subsequent analyses identified potential therapeutic target genes and treatment options. CONCLUSIONS: We overcome the persistent challenges of identifying alterations or driver mutations in coding regions related to tumorigenesis through a novel approach combining multi-omics data with clinical information to reveal the molecular mechanisms underlying non-CAGAs LUAD, stratify patients to improve patient prognosis, and identify potential therapeutic targets. This approach may be applicable to studies of other cancers with unmet needs.

Mechanism of ERBB2 gene overexpression by the formation of super-enhancer with genomic structural abnormalities in lung adenocarcinoma without clinically actionable genetic alterations.

BACKGROUND: In an extensive genomic analysis of lung adenocarcinomas (LUADs), driver mutations have been recognized as potential targets for molecular therapy. However, there remain cases where target genes are not identified. Super-enhancers and structural variants are frequently identified in several hundred loci per case. Despite this, most cancer research has approached the analysis of these data sets separately, without merging and comparing the data, and there are no examples of integrated analysis in LUAD. METHODS: We performed an integrated analysis of super-enhancers and structural variants in a cohort of 174 LUAD cases that lacked clinically actionable genetic alterations. To achieve this, we conducted both WGS and H3K27Ac ChIP-seq analyses using samples with driver gene mutations and those without, allowing for a comprehensive investigation of the potential roles of super-enhancer in LUAD cases. RESULTS: We demonstrate that most genes situated in these overlapped regions were associated with known and previously unknown driver genes and aberrant expression resulting from the formation of super-enhancers accompanied by genomic structural abnormalities. Hi-C and long-read sequencing data further corroborated this insight. When we employed CRISPR-Cas9 to induce structural abnormalities that mimicked cases with outlier ERBB2 gene expression, we observed an elevation in ERBB2 expression. These abnormalities are associated with a higher risk of recurrence after surgery, irrespective of the presence or absence of driver mutations. CONCLUSIONS: Our findings suggest that aberrant gene expression linked to structural polymorphisms can significantly impact personalized cancer treatment by facilitating the identification of driver mutations and prognostic factors, contributing to a more comprehensive understanding of LUAD pathogenesis.

Comparative analysis of liver resection in Non-B Non-C and hepatitis virus-associated hepatocellular carcinoma.

BACKGROUND: The incidence of non-hepatitis B and non-hepatitis C hepatocellular carcinoma (NBNC-HCC) is increasing in our country. This study assesses the feasibility of employing an identical surgical treatment strategy for resectable NBNC-HCC as that for hepatitis virus-associated HCC (HV-HCC). METHODS: A retrospective analysis (1993-2023) of 1321 curative liver resections for HCC at a single institution was performed. Propensity score matching ensured a balanced comparison of preoperative clinical factors, including tumor status and background liver condition. RESULTS: The proportion of NBNC-HCC cases has gradually increased, reaching up to 70 %. After matching, 294 of 473 NBNC-HCC patients and 294 of 848 HV-HCC patients were compared. Operative outcomes, including operation time, blood loss, type of surgical procedure, and morbidity, were comparable. Long-term outcome analysis showed similar recurrence-free survival (HR: 0.86, 95 % CI: 0.70-1.06, P = 0.167) and overall survival (HR: 0.98, 95 % CI: 0.79-1.23, P = 0.865) for NBNC-HCC. Multivariable analysis identified ICGR15 ≥ 15 %, ALBI grade 2 or 3, aspartate aminotransferase ≥40, tumor size > 5 cm, multiple tumors, macrovascular invasion, and microvascular invasion as independent prognostic factors for overall survival, while hepatitis B or C virus status lost significance. CONCLUSIONS: Despite the increasing incidence of NBNC-HCC, comparable outcomes were achieved between the two groups of matched cohort.

Advances in cancer DNA methylation analysis with methPLIER: use of non-negative matrix factorization and knowledge-based constraints to enhance biological interpretability.

DNA methylation is an epigenetic modification that results in dynamic changes during ontogenesis and cell differentiation. DNA methylation patterns regulate gene expression and have been widely researched. While tools for DNA methylation analysis have been developed, most of them have focused on intergroup comparative analysis within a dataset; therefore, it is difficult to conduct cross-dataset studies, such as rare disease studies or cross-institutional studies. This study describes a novel method for DNA methylation analysis, namely, methPLIER, which enables interdataset comparative analyses. methPLIER combines Pathway Level Information Extractor (PLIER), which is a non-negative matrix factorization (NMF) method, with regularization by a knowledge matrix and transfer learning. methPLIER can be used to perform intersample and interdataset comparative analysis based on latent feature matrices, which are obtained via matrix factorization of large-scale data, and factor-loading matrices, which are obtained through matrix factorization of the data to be analyzed. We used methPLIER to analyze a lung cancer dataset and confirmed that the data decomposition reflected sample characteristics for recurrence-free survival. Moreover, methPLIER can analyze data obtained via different preprocessing methods, thereby reducing distributional bias among datasets due to preprocessing. Furthermore, methPLIER can be employed for comparative analyses of methylation data obtained from different platforms, thereby reducing bias in data distribution due to platform differences. methPLIER is expected to facilitate cross-sectional DNA methylation data analysis and enhance DNA methylation data resources.

AUTOMATIC ACQUISITION OF CT RADIATION DOSE DATA: USING THE DIAGNOSTIC REFERENCE LEVEL FOR RADIATION DOSE OPTIMIZATION.

The present work describes that we try to construct a system that collects dose information that performed CT examination from multiple facilities and unified management. The results of analysis are compared with other National diagnostic reference level (DRL), and the results are fed back to each facility and the cause of the abnormal value is investigated for dose optimization. Medical information collected 139 144 tests from 33 CT devices in 13 facilities. Although the DRL of this study is lower than that of Japan DRL, it was higher than the DRL of each country. When collecting all the examination, it is thought that the variation of the dose due to the error other than the intended imaging site is large. In future, we should continue to collect information in order to DRL renewal and we also think that it is desirable to collect information on physique and detailed scan region as well.

Development and practice of ISMS at a Radiotherapy Hospital by using IHE Integration profiles.

Our hospital is specialized for radiation therapy and has many information devices. Various job categories are working. When we implemented an EMR, we aimed to enforce ISMS by using IHE profiles. To solve the already existed system-related problems, we selected and use some profiles (EUA, PSA, ATNA and PAM). After implementation, we audited and then some findings were pointed out. These findings are being settled by the PDCA cycle. We also found that appropriate IHE profiles were effective in the building of ISMS.

Development of Clinical Database System Specialized for Heavy Particle Therapy.

We have developed a data archiving system for study of charged particle therapy. We required a data-relation mechanism between electronic medical record system (EMR) and database system, because it needs to ensure the information consistency. This paper presents the investigation results of these techniques. The standards in the medical informatics field that we focus on are Integrating the Healthcare Enterprise (IHE) and 2) Health Level-7 (HL7) to archive the data. As a main cooperation function, we adapt 2 integration profiles of IHE as follows, 1) Patient Administration Management (PAM) Profile of IHE-ITI domain for patient demographic information reconciliation, 2) Enterprise Schedule Integration(ESI) profile of IHE-Radiation Oncology domain for order management between EMR and treatment management system(TMS). We also use HL7 Ver2.5 messages for exchanging the follow-up data and result of laboratory test. In the future, by implementation of this system cooperation, we will be able to ensure interoperability in the event of the EMR update.