Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, "precision medicine," a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.

Cutaneous and pulmonary cancers associated with Bowen's disease

Thirty-one patients with Bowen's disease were studied in a restricted district of Namikata and its neighborhood of Ehime, Japan. Nineteen were alive and 12 were dead; the youngest living patient was 52 years of age. Invasive skin cancers were found in 10 patients and internal malignancies in 10, including 7 patients with pulmonary cancers. Palmoplantar keratosis was present in 25 patients and raindrop-type pigment anomalies in 15. Neutron activation analysis of hair showed only slightly higher arsenic values in patients with Bowen's disease than those in normal controls, though the differences were statistically significant at p less than 0.05. A possible arsenic exposure 43 years previously was considered responsible for the occurrence of neoplasms, though the arsenic route and amount were not determined. Bowen's disease started within 10 years, invasive skin cancers after 20 years, and pulmonary cancers after 30 years following the suspected arsenic exposure.

Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

Artificial intelligence (AI) is being increasingly adopted in medical research and applications. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other countries. Ultrasound (US) imaging is commonly used in an extensive range of medical fields. However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities. The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control. In this review, we would like to introduce the global trends of medical AI research in US imaging from both clinical and basic perspectives. We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and future perspectives towards the clinical application of AI-based US diagnostic support technologies.

Image Segmentation of the Ventricular Septum in Fetal Cardiac Ultrasound Videos Based on Deep Learning Using Time-Series Information

Image segmentation is the pixel-by-pixel detection of objects, which is the most challenging but informative in the fundamental tasks of machine learning including image classification and object detection. Pixel-by-pixel segmentation is required to apply machine learning to support fetal cardiac ultrasound screening; we have to detect cardiac substructures precisely which are small and change shapes dynamically with fetal heartbeats, such as the ventricular septum. This task is difficult for general segmentation methods such as DeepLab v3+, and U-net. Hence, here we proposed a novel segmentation method named Cropping-Segmentation-Calibration (CSC) that is specific to the ventricular septum in ultrasound videos in this study. CSC employs the time-series information of videos and specific section information to calibrate the output of U-net. The actual sections of the ventricular septum were annotated in 615 frames from 421 normal fetal cardiac ultrasound videos of 211 pregnant women who were screened. The dataset was assigned a ratio of 2:1, which corresponded to a ratio of the training to test data, and three-fold cross-validation was conducted. The segmentation results of DeepLab v3+, U-net, and CSC were evaluated using the values of the mean intersection over union (mIoU), which were 0.0224, 0.1519, and 0.5543, respectively. The results reveal the superior performance of CSC.

MDM2 is a potential therapeutic target and prognostic factor for ovarian clear cell carcinomas with wild type TP53

MDM2, a ubiquitin ligase, suppresses wild type TP53 via proteasome-mediated degradation. We evaluated the prognostic and therapeutic value of MDM2 in ovarian clear cell carcinoma. MDM2 expression in ovarian cancer tissues was analyzed by microarray and real-time PCR, and its relationship with prognosis was evaluated by Kaplan-Meier method and log-rank test. The anti-tumor activities of MDM2 siRNA and the MDM2 inhibitor RG7112 were assessed by cell viability assay, western blotting, and flow cytometry. The anti-tumor effects of RG7112 in vivo were examined in a mouse xenograft model. MDM2 expression was significantly higher in clear cell carcinoma than in ovarian high-grade serous carcinoma (P = 0.0092) and normal tissues (P = 0.035). High MDM2 expression determined by microarray was significantly associated with poor progression-free survival and poor overall survival (P = 0.0002, and P = 0.0008, respectively). Notably, RG7112 significantly suppressed cell viability in clear cell carcinoma cell lines with wild type TP53. RG7112 also strongly induced apoptosis, increased TP53 phosphorylation, and stimulated expression of the proapoptotic protein PUMA. Similarly, siRNA knockdown of MDM2 induced apoptosis. Finally, RG7112 significantly reduced the tumor volume of xenografted RMG-I clear cell carcinoma cells (P = 0.033), and the density of microvessels (P = 0.011). Our results highlight the prognostic value of MDM2 expression in clear cell carcinoma. Thus, MDM2 inhibitors such as RG7112 may constitute a class of potential therapeutics.

Model-Agnostic Method for Thoracic Wall Segmentation in Fetal Ultrasound Videos

The application of segmentation methods to medical imaging has the potential to create novel diagnostic support models. With respect to fetal ultrasound, the thoracic wall is a key structure on the assessment of the chest region for examiners to recognize the relative orientation and size of structures inside the thorax, which are critical components in neonatal prognosis. In this study, to improve the segmentation performance of the thoracic wall in fetal ultrasound videos, we proposed a novel model-agnostic method using deep learning techniques: the Multi-Frame + Cylinder method (MFCY). The Multi-frame method (MF) uses time-series information of ultrasound videos, and the Cylinder method (CY) utilizes the shape of the thoracic wall. To evaluate the achieved improvement, we performed segmentation using five-fold cross-validation on 538 ultrasound frames in the four-chamber view (4CV) of 256 normal cases using U-net and DeepLabv3+. MFCY increased the mean values of the intersection over union (IoU) of thoracic wall segmentation from 0.448 to 0.493 for U-net and from 0.417 to 0.470 for DeepLabv3+. These results demonstrated that MFCY improved the segmentation performance of the thoracic wall in fetal ultrasound videos without altering the network structure. MFCY is expected to facilitate the development of diagnostic support models in fetal ultrasound by providing further accurate segmentation of the thoracic wall.

Cytogenetic studies in a patient with porokeratosis of Mibelli, multiple cancers and a forme fruste of Werner's syndrome

A 49-year-old man with extensive porokeratosis of Mibelli (PM) developed a squamous cell carcinoma and several carcinomas-in-situ within the lesional skin. The patient also had diabetes mellitus and a short stature with a prematurely aged appearance. The patient's father and two siblings also had PM. The patient died from metastatic squamous cell carcinoma, and at autopsy an adenocarcinoma of the descending colon was also found. Fibroblasts cultured from both the PM-affected and unaffected skin showed chromosomal abnormalities and a decreased lifespan. Cellular sensitivity to ultraviolet rays measured by unscheduled DNA synthesis and colony-forming ability were within normal limits. An association with a forme fruste of Werner's syndrome was suspected.

Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology

With the completion of the International Human Genome Project, we have entered what is known as the post-genome era, and efforts to apply genomic information to medicine have become more active. In particular, with the announcement of the Precision Medicine Initiative by U.S. President Barack Obama in his State of the Union address at the beginning of 2015, "precision medicine," which aims to divide patients and potential patients into subgroups with respect to disease susceptibility, has become the focus of worldwide attention. The field of oncology is also actively adopting the precision oncology approach, which is based on molecular profiling, such as genomic information, to select the appropriate treatment. However, the current precision oncology is dominated by a method called targeted-gene panel (TGP), which uses next-generation sequencing (NGS) to analyze a limited number of specific cancer-related genes and suggest optimal treatments, but this method causes the problem that the number of patients who benefit from it is limited. In order to steadily develop precision oncology, it is necessary to integrate and analyze more detailed omics data, such as whole genome data and epigenome data. On the other hand, with the advancement of analysis technologies such as NGS, the amount of data obtained by omics analysis has become enormous, and artificial intelligence (AI) technologies, mainly machine learning (ML) technologies, are being actively used to make more efficient and accurate predictions. In this review, we will focus on whole genome sequencing (WGS) analysis and epigenome analysis, introduce the latest results of omics analysis using ML technologies for the development of precision oncology, and discuss the future prospects.

Downregulation of METTL6 mitigates cell progression, migration, invasion and adhesion in hepatocellular carcinoma by inhibiting cell adhesion molecules

RNA modifications have attracted increasing interest in recent years because they have been frequently implicated in various human diseases, including cancer, highlighting the importance of dynamic post‑transcriptional modifications. Methyltransferase‑like 6 (METTL6) is a member of the RNA methyltransferase family that has been identified in many cancers; however, little is known about its specific role or mechanism of action. In the present study, we aimed to study the expression levels and functional role of METTL6 in hepatocellular carcinoma (HCC), and further investigate the relevant pathways. To this end, we systematically conducted bioinformatics analysis of METTL6 in HCC using gene expression data and clinical information from a publicly available dataset. The mRNA expression levels of METTL6 were significantly upregulated in HCC tumor tissues compared to that in adjacent non‑tumor tissues and strongly associated with poorer survival outcomes in patients with HCC. CRISPR/Cas9‑mediated knockout of METTL6 in HCC cell lines remarkably inhibited colony formation, cell proliferation, cell migration, cell invasion and cell attachment ability. RNA sequencing analysis demonstrated that knockout of METTL6 significantly suppressed the expression of cell adhesion‑related genes. However, chromatin immunoprecipitation sequencing results revealed no significant differences in enhancer activities between cells, which suggests that METTL6 may regulate genes of interest post‑transcriptionally. In addition, it was demonstrated for the first time that METTL6 was localized in the cytosol as detected by immunofluorescence analysis, which indicates the plausible location of RNA modification mediated by METTL6. Our findings provide further insight into the function of RNA modifications in cancer and suggest a possible role of METTL6 as a therapeutic target in HCC.

Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine

The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.

Medical Professional Enhancement Using Explainable Artificial Intelligence in Fetal Cardiac Ultrasound Screening

Diagnostic support tools based on artificial intelligence (AI) have exhibited high performance in various medical fields. However, their clinical application remains challenging because of the lack of explanatory power in AI decisions (black box problem), making it difficult to build trust with medical professionals. Nevertheless, visualizing the internal representation of deep neural networks will increase explanatory power and improve the confidence of medical professionals in AI decisions. We propose a novel deep learning-based explainable representation "graph chart diagram" to support fetal cardiac ultrasound screening, which has low detection rates of congenital heart diseases due to the difficulty in mastering the technique. Screening performance improves using this representation from 0.966 to 0.975 for experts, 0.829 to 0.890 for fellows, and 0.616 to 0.748 for residents in the arithmetic mean of area under the curve of a receiver operating characteristic curve. This is the first demonstration wherein examiners used deep learning-based explainable representation to improve the performance of fetal cardiac ultrasound screening, highlighting the potential of explainable AI to augment examiner capabilities.

Successful dietary control of tyrosinemia II

A Japanese girl, 2 years, 8 months of age, with palmoplantar keratosis and dendritic corneal opacities, showed increased tyrosine levels in the plasma, urine, and cerebrospinal fluid. The mental and physical growth was not retarded. The hepatorenal functions were within normal limits. Electron microscopically, the epidermal keratinocytes showed increased tonofibrils and no structures suggestive of tyrosine crystals. Cytosol and mitochondrial tyrosine aminotransferase (TAT) activities of the liver were greatly decreased, while p-hydroxyphenyl pyruvate oxidase (p-HPPO) activity was not decreased. The plasma tyrosine levels were controlled for 3 years with low phenylalanine-tyrosine diet.

The histone methyltransferase WHSC1 is regulated by EZH2 and is important for ovarian clear cell carcinoma cell proliferation

Background

Wolf-Hirschhorn syndrome candidate gene-1 (WHSC1), a histone methyltransferase, has been found to be upregulated and its expression to be correlated with expression of enhancer of zeste homolog 2 (EZH2) in several cancers. In this study, we evaluated the role of WHSC1 and its therapeutic significance in ovarian clear cell carcinoma (OCCC).

Methods

First, we analyzed WHSC1 expression by quantitative PCR and immunohistochemistry using 23 clinical OCCC specimens. Second, the involvement of WHSC1 in OCCC cell proliferation was evaluated by MTT assays after siRNA-mediated WHSC1 knockdown. We also performed flow cytometry (FACS) to address the effect of WHSC1 on cell cycle. To examine the functional relationship between EZH2 and WHSC1, we knocked down EZH2 using siRNAs and checked the expression levels of WHSC1 and its histone mark H3K36m2 in OCCC cell lines. Finally, we checked WHSC1 expression after treatment with the selective inhibitor, GSK126.

Results

Both quantitative PCR and immunohistochemical analysis revealed that WHSC1 was significantly overexpressed in OCCC tissues compared with that in normal ovarian tissues. MTT assay revealed that knockdown of WHSC1 suppressed cell proliferation, and H3K36me2 levels were found to be decreased in immunoblotting. FACS revealed that WHSC1 knockdown affected the cell cycle. We also confirmed that WHSC1 expression was suppressed by EZH2 knockdown or inhibition, indicating that EZH2 is upstream of WHSC1 in OCCC cells.

Conclusions

WHSC1 overexpression induced cell growth and its expression is, at least in part, regulated by EZH2. Further functional analysis will reveal whether WHSC1 is a promising therapeutic target for OCCC.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5638-9) contains supplementary material, which is available to authorized users.

Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics

The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.

Repression of the PRELP gene is relieved by histone deacetylase inhibitors through acetylation of histone H2B lysine 5 in bladder cancer

BACKGROUND

Proline/arginine-rich end leucine-rich repeat protein (PRELP) is a member of the small leucine-rich proteoglycan family of extracellular matrix proteins, which is markedly suppressed in the majority of early-stage epithelial cancers and plays a role in regulating the epithelial-mesenchymal transition by altering cell-cell adhesion. Although PRELP is an important factor in the development and progression of bladder cancer, the mechanism of PRELP gene repression remains unclear.

RESULTS

Here, we show that repression of PRELP mRNA expression in bladder cancer cells is alleviated by HDAC inhibitors (HDACi) through histone acetylation. Using ChIP-qPCR analysis, we found that acetylation of lysine residue 5 of histone H2B in the PRELP gene promoter region is a marker for the de-repression of PRELP expression.

CONCLUSIONS

These results suggest a mechanism through which HDACi may partially regulate the function of PRELP to suppress the development and progression of bladder cancer. Some HDACi are already in clinical use, and the findings of this study provide a mechanistic basis for further investigation of HDACi-based therapeutic strategies.

Nitric oxide is involved in the inhibitory action of cholecystokinin octapeptide (CCK-OP) on proximal colonic motility

To determine whether nitric oxide (NO) is a possible mediator in the inhibitory action of CCK-octapeptide (CCK-OP) on circular muscle contractions of the rat proximal colon, contractile activities of the circular muscle were recorded in the proximal colon of unrestrained conscious rats in the fasting state using a miniature strain gauge force transducer and an implantable telemetry system. Regular and rhythmic phasic contractions were observed during the fasted condition, similar to the myoelectric migrating complex seen in intestinal contractions of the fasting dog. These phasic contractions were almost completely inhibited after intraperitoneal (i.p.) administration of CCK-OP at a dose of 15 microg/kg body weight. N(omega)-nitro-arginine, methyl ester (L-NAME), at doses of 20 and 200 mg/kg i.p. administered prior to i.p. injection of CCK-OP, prevented the inhibitory action on the fasting phasic contractions. The degree of prevention was dose-dependent. 100 mg/kg body weight i.p. injection of L-arginine inhibited the circular muscle contractions. The same dose of D-arginine had no action on contractions of the circular muscle of the proximal colon in the fasted rat. From these data, we conclude that NO is one possible mediator in the inhibitory mechanism of CCK-OP on smooth muscle motor activity of the rat proximal colon in vivo.

Epigenetic Mechanisms Underlying COVID-19 Pathogenesis

In 2019, a novel severe acute respiratory syndrome called coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was reported and was declared a pandemic by the World Health Organization (WHO) in March 2020. With the advancing development of COVID-19 vaccines and their administration globally, it is expected that COVID-19 will converge in the future; however, the situation remains unpredictable because of a series of reports regarding SARS-CoV-2 variants. Currently, there are still few specific effective treatments for COVID-19, as many unanswered questions remain regarding the pathogenic mechanism of COVID-19. Continued elucidation of COVID-19 pathogenic mechanisms is a matter of global importance. In this regard, recent reports have suggested that epigenetics plays an important role; for instance, the expression of angiotensin I converting enzyme 2 (ACE2) receptor, an important factor in human infection with SARS-CoV-2, is epigenetically regulated; further, DNA methylation status is reported to be unique to patients with COVID-19. In this review, we focus on epigenetic mechanisms to provide a new molecular framework for elucidating the pathogenesis of SARS-CoV-2 infection in humans and of COVID-19, along with the possibility of new diagnostic and therapeutic strategies.

Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research

In recent years, the diversity of cancer cells in tumor tissues as a result of intratumor heterogeneity has attracted attention. In particular, the development of single-cell analysis technology has made a significant contribution to the field; technologies that are centered on single-cell RNA sequencing (scRNA-seq) have been reported to analyze cancer constituent cells, identify cell groups responsible for therapeutic resistance, and analyze gene signatures of resistant cell groups. However, although single-cell analysis is a powerful tool, various issues have been reported, including batch effects and transcriptional noise due to gene expression variation and mRNA degradation. To overcome these issues, machine learning techniques are currently being introduced for single-cell analysis, and promising results are being reported. In addition, machine learning has also been used in various ways for single-cell analysis, such as single-cell assay of transposase accessible chromatin sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq) analysis, and multi-omics analysis; thus, it contributes to a deeper understanding of the characteristics of human diseases, especially cancer, and supports clinical applications. In this review, we present a comprehensive introduction to the implementation of machine learning techniques in medical research for single-cell analysis, and discuss their usefulness and future potential.

Integrative analysis reveals early epigenetic alterations in high-grade serous ovarian carcinomas

High-grade serous ovarian carcinoma (HGSOC) is the most lethal gynecological malignancy. To date, the profiles of gene mutations and copy number alterations in HGSOC have been well characterized. However, the patterns of epigenetic alterations and transcription factor dysregulation in HGSOC have not yet been fully elucidated. In this study, we performed integrative omics analyses of a series of stepwise HGSOC model cells originating from human fallopian tube secretory epithelial cells (HFTSECs) to investigate early epigenetic alterations in HGSOC tumorigenesis. Assay for transposase-accessible chromatin using sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq), and RNA sequencing (RNA-seq) methods were used to analyze HGSOC samples. Additionally, protein expression changes in target genes were confirmed using normal HFTSECs, serous tubal intraepithelial carcinomas (STICs), and HGSOC tissues. Transcription factor motif analysis revealed that the DNA-binding activity of the AP-1 complex and GATA family proteins was dysregulated during early tumorigenesis. The protein expression levels of JUN and FOSL2 were increased, and those of GATA6 and DAB2 were decreased in STIC lesions, which were associated with epithelial-mesenchymal transition (EMT) and proteasome downregulation. The genomic region around the FRA16D site, containing a cadherin cluster region, was epigenetically suppressed by oncogenic signaling. Proteasome inhibition caused the upregulation of chemokine genes, which may facilitate immune evasion during HGSOC tumorigenesis. Importantly, MEK inhibitor treatment reversed these oncogenic alterations, indicating its clinical effectiveness in a subgroup of patients with HGSOC. This result suggests that MEK inhibitor therapy may be an effective treatment option for chemotherapy-resistant HGSOC.

Genome-Wide Chromatin Analysis of FFPE Tissues Using a Dual-Arm Robot with Clinical Potential

Although chromatin immunoprecipitation and next-generation sequencing (ChIP-seq) using formalin-fixed paraffin-embedded tissue (FFPE) has been reported, it remained elusive whether they retained accurate transcription factor binding. Here, we developed a method to identify the binding sites of the insulator transcription factor CTCF and the genome-wide distribution of histone modifications involved in transcriptional activation. Importantly, we provide evidence that the ChIP-seq datasets obtained from FFPE samples are similar to or even better than the data for corresponding fresh-frozen samples, indicating that FFPE samples are compatible with ChIP-seq analysis. H3K27ac ChIP-seq analyses of 69 FFPE samples using a dual-arm robot revealed that driver mutations in EGFR were distinguishable from pan-negative cases and were relatively homogeneous as a group in lung adenocarcinomas. Thus, our results demonstrate that FFPE samples are an important source for epigenomic research, enabling the study of histone modifications, nuclear chromatin structure, and clinical data.

A case of a surviving co-twin diagnosed with porencephaly and renal hypoplasia after a single intrauterine fetal death at 21 weeks of gestation in a monochorionic monoamniotic twin pregnancy

Monochorionic monoamniotic (MM) twin pregnancy carries a high risk of intrauterine fetal death (IUFD). Single IUFD in an MM twin pregnancy prior to 22 weeks of gestation has been reported to be strongly correlated with double twin demise. To our knowledge, there are no case reports on the natural course of a surviving co-twin in an MM twin pregnancy resulting in live birth after a single IUFD prior to 22 weeks of gestation. Here, we report a case of a surviving co-twin, after a single IUFD at 21 weeks of gestation in a MM twin pregnancy, with an antenatal diagnosis of renal hypoplasia and severe neurological damage leading to porencephaly, and live birth at 36 weeks of gestation.

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.

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.