Integrative analysis of cancer multimodality data identifying COPS5 as a novel biomarker of diffuse large B-cell lymphoma.

Preventing, diagnosing, and treating diseases requires accurate clinical biomarkers, which remains challenging. Recently, advanced computational approaches have accelerated the discovery of promising biomarkers from high-dimensional multimodal data. Although machine-learning methods have greatly contributed to the research fields, handling data sparseness, which is not unusual in research settings, is still an issue as it leads to limited interpretability and performance in the presence of missing information. Here, we propose a novel pipeline integrating joint non-negative matrix factorization (JNMF), identifying key features within sparse high-dimensional heterogeneous data, and a biological pathway analysis, interpreting the functionality of features by detecting activated signaling pathways. By applying our pipeline to large-scale public cancer datasets, we identified sets of genomic features relevant to specific cancer types as common pattern modules (CPMs) of JNMF. We further detected COPS5 as a potential upstream regulator of pathways associated with diffuse large B-cell lymphoma (DLBCL). COPS5 exhibited co-overexpression with MYC, TP53, and BCL2, known DLBCL marker genes, and its high expression was correlated with a lower survival probability of DLBCL patients. Using the CRISPR-Cas9 system, we confirmed the tumor growth effect of COPS5, which suggests it as a novel prognostic biomarker for DLBCL. Our results highlight that integrating multiple high-dimensional data and effectively decomposing them to interpretable dimensions unravels hidden biological importance, which enhances the discovery of clinical biomarkers.

A computational approach for deciphering the interactions between proximal and distal gene regulators in GC B-cell response.

Delineating the intricate interplay between promoter-proximal and -distal regulators is crucial for understanding the function of transcriptional mediator complexes implicated in the regulation of gene expression. The present study aimed to develop a computational method for accurately modeling the spatial proximal and distal regulatory interactions. Our method combined regression-based models to identify key regulators through gene expression prediction and a graph-embedding approach to detect coregulated genes. This approach enabled a detailed investigation of the gene regulatory mechanisms for germinal center B cells, accompanied by dramatic rearrangements of the genome structure. We found that while the promoter-proximal regulatory elements were the principal regulators of gene expression, the distal regulators fine-tuned transcription. Moreover, our approach unveiled the presence of modular regulators, such as cofactors and proximal/distal transcription factors, which were co-expressed with their target genes. Some of these modules exhibited abnormal expression patterns in lymphoma. These findings suggest that the dysregulation of interactions between transcriptional and architectural factors is associated with chromatin reorganization failure, which may increase the risk of malignancy. Therefore, our computational approach helps decipher the transcriptional cis-regulatory code spatially interacting.

HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data.

Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell labels are still lacking in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. Here, we present a novel automated cell annotation method that transfers cell type information from a well-labeled scRNA-seq reference to an unlabeled scATAC-seq target, via a parallel graph neural network, in a semi-supervised manner. Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno leverages genome-wide accessibility peak features to facilitate the training process. In addition, HyGAnno reconstructs a reference-target cell graph to detect cells with low prediction reliability, according to their specific graph connectivity patterns. HyGAnno was assessed across various datasets, showcasing its strengths in precise cell annotation, generating interpretable cell embeddings, robustness to noisy reference data and adaptability to tumor tissues.

Epigenetic characterization of housekeeping core promoters and their importance in tumor suppression.

In this research, we elucidate the presence of around 11,000 housekeeping cis-regulatory elements (HK-CREs) and describe their main characteristics. Besides the trivial promoters of housekeeping genes, most HK-CREs reside in promoter regions and are involved in a broader role beyond housekeeping gene regulation. HK-CREs are conserved regions rich in unmethylated CpG sites. Their distribution highly correlates with that of protein-coding genes, and they interact with many genes over long distances. We observed reduced activity of a subset of HK-CREs in diverse cancer subtypes due to aberrant methylation, particularly those located in chromosome 19 and associated with zinc finger genes. Further analysis of samples from 17 cancer subtypes showed a significantly increased survival probability of patients with higher expression of these genes, suggesting them as housekeeping tumor suppressor genes. Overall, our work unravels the presence of housekeeping CREs indispensable for the maintenance and stability of cells.

CoraL: interpretable contrastive meta-learning for the prediction of cancer-associated ncRNA-encoded small peptides.

NcRNA-encoded small peptides (ncPEPs) have recently emerged as promising targets and biomarkers for cancer immunotherapy. Therefore, identifying cancer-associated ncPEPs is crucial for cancer research. In this work, we propose CoraL, a novel supervised contrastive meta-learning framework for predicting cancer-associated ncPEPs. Specifically, the proposed meta-learning strategy enables our model to learn meta-knowledge from different types of peptides and train a promising predictive model even with few labeled samples. The results show that our model is capable of making high-confidence predictions on unseen cancer biomarkers with only five samples, potentially accelerating the discovery of novel cancer biomarkers for immunotherapy. Moreover, our approach remarkably outperforms existing deep learning models on 15 cancer-associated ncPEPs datasets, demonstrating its effectiveness and robustness. Interestingly, our model exhibits outstanding performance when extended for the identification of short open reading frames derived from ncPEPs, demonstrating the strong prediction ability of CoraL at the transcriptome level. Importantly, our feature interpretation analysis discovers unique sequential patterns as the fingerprint for each cancer-associated ncPEPs, revealing the relationship among certain cancer biomarkers that are validated by relevant literature and motif comparison. Overall, we expect CoraL to be a useful tool to decipher the pathogenesis of cancer and provide valuable information for cancer research. The dataset and source code of our proposed method can be found at https://github.com/Johnsunnn/CoraL.

Multi-omics computational analysis unveils the involvement of AP-1 and CTCF in hysteresis of chromatin states during macrophage polarization.

Macrophages display extreme plasticity, and the mechanisms and applications of polarization and de-/repolarization of macrophages have been extensively investigated. However, the regulation of macrophage hysteresis after de-/repolarization remains unclear. In this study, by using a large-scale computational analysis of macrophage multi-omics data, we report a list of hysteresis genes that maintain their expression patterns after polarization and de-/repolarization. While the polarization in M1 macrophages leads to a higher level of hysteresis in genes associated with cell cycle progression, cell migration, and enhancement of the immune response, we found weak levels of hysteresis after M2 polarization. During the polarization process from M0 to M1 and back to M0, the factors IRFs/STAT, AP-1, and CTCF regulate hysteresis by altering their binding sites to the chromatin. Overall, our results show that a history of polarization can lead to hysteresis in gene expression and chromatin accessibility over a given period. This study contributes to the understanding of de-/repolarization memory in macrophages.

Comprehensive comparison of gene expression diversity among a variety of human stem cells.

Several factors, including tissue origins and culture conditions, affect the gene expression of undifferentiated stem cells. However, understanding the basic identity across different stem cells has not been pursued well despite its importance in stem cell biology. Thus, we aimed to rank the relative importance of multiple factors to gene expression profile among undifferentiated human stem cells by analyzing publicly available RNA-seq datasets. We first conducted batch effect correction to avoid undefined variance in the dataset as possible. Then, we highlighted the relative impact of biological and technical factors among undifferentiated stem cell types: a more influence on tissue origins in induced pluripotent stem cells than in other stem cell types; a stronger impact of culture condition in embryonic stem cells and somatic stem cell types, including mesenchymal stem cells and hematopoietic stem cells. In addition, we found that a characteristic gene module, enriched in histones, exhibits higher expression across different stem cell types that were annotated by specific culture conditions. This tendency was also observed in mouse stem cell RNA-seq data. Our findings would help to obtain general insights into stem cell quality, such as the balance of differentiation potentials that undifferentiated stem cells possess.

Qualitative differences in disease-associated MEK mutants reveal molecular signatures and aberrant signaling-crosstalk in cancer.

Point-mutations of MEK1, a central component of ERK signaling, are present in cancer and RASopathies, but their precise biological effects remain obscure. Here, we report a mutant MEK1 structure that uncovers the mechanisms underlying abnormal activities of cancer- and RASopathy-associated MEK1 mutants. These two classes of MEK1 mutations differentially impact on spatiotemporal dynamics of ERK signaling, cellular transcriptional programs, gene expression profiles, and consequent biological outcomes. By making use of such distinct characteristics of the MEK1 mutants, we identified cancer- and RASopathy-signature genes that may serve as diagnostic markers or therapeutic targets for these diseases. In particular, two AKT-inhibitor molecules, PHLDA1 and 2, are simultaneously upregulated by oncogenic ERK signaling, and mediate cancer-specific ERK-AKT crosstalk. The combined expression of PHLDA1/2 is critical to confer resistance to ERK pathway-targeted therapeutics on cancer cells. Finally, we propose a therapeutic strategy to overcome this drug resistance. Our data provide vital insights into the etiology, diagnosis, and therapeutic strategy of cancers and RASopathies.

Predicting protein-peptide binding residues via interpretable deep learning.

SUMMARY: Identifying the protein-peptide binding residues is fundamentally important to understand the mechanisms of protein functions and explore drug discovery. Although several computational methods have been developed, most of them highly rely on third-party tools or complex data preprocessing for feature design, easily resulting in low computational efficacy and suffering from low predictive performance. To address the limitations, we propose PepBCL, a novel BERT (Bidirectional Encoder Representation from Transformers) -based contrastive learning framework to predict the protein-peptide binding residues based on protein sequences only. PepBCL is an end-to-end predictive model that is independent of feature engineering. Specifically, we introduce a well pre-trained protein language model that can automatically extract and learn high-latent representations of protein sequences relevant for protein structures and functions. Further, we design a novel contrastive learning module to optimize the feature representations of binding residues underlying the imbalanced dataset. We demonstrate that our proposed method significantly outperforms the state-of-the-art methods under benchmarking comparison, and achieves more robust performance. Moreover, we found that we further improve the performance via the integration of traditional features and our learnt features. Interestingly, the interpretable analysis of our model highlights the flexibility and adaptability of deep learning-based protein language model to capture both conserved and non-conserved sequential characteristics of peptide-binding residues. Finally, to facilitate the use of our method, we establish an online predictive platform as the implementation of the proposed PepBCL, which is now available at http://server.wei-group.net/PepBCL/. AVAILABILITY AND IMPLEMENTATION: https://github.com/Ruheng-W/PepBCL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes.

Currently, the population dynamics of preclonal cancer cells before clonal expansion of tumors has not been sufficiently addressed thus far. By focusing on preclonal cancer cell population as a Darwinian evolutionary system, we formulated and analyzed the observed mutation frequency among tumors (MFaT) as a proxy for the hypothesized sequence read frequency and beneficial fitness effect of a cancer driver mutation. Analogous to intestinal crypts, we assumed that sample donor patients are separate culture tanks where proliferating cells follow certain population dynamics described by extreme value theory (EVT). To validate this, we analyzed three large-scale cancer genome datasets, each harboring > 10000 tumor samples and in total involving > 177898 observed mutation sites. We clarified the necessary premises for the application of EVT in the strong selection and weak mutation (SSWM) regime in relation to cancer genome sequences at scale. We also confirmed that the stochastic distribution of MFaT is likely of the Fréchet type, which challenges the well-known Gumbel hypothesis of beneficial fitness effects. Based on statistical data analysis, we demonstrated the potential of EVT as a population genetics framework to understand and explain the stochastic behavior of driver-mutation frequency in cancer genomes as well as its applicability in real cancer genome sequence data.

A bacterial small RNA regulates the adaptation of Helicobacter pylori to the host environment.

Long-term infection of the stomach with Helicobacter pylori can cause gastric cancer. However, the mechanisms by which the bacteria adapt to the stomach environment are poorly understood. Here, we show that a small non-coding RNA of H. pylori (HPnc4160, also known as IsoB or NikS) regulates the pathogen's adaptation to the host environment as well as bacterial oncoprotein production. In a rodent model of H. pylori infection, the genomes of bacteria isolated from the stomach possess an increased number of T-repeats upstream of the HPnc4160-coding region, and this leads to reduced HPnc4160 expression. We use RNA-seq and iTRAQ analyses to identify eight targets of HPnc4160, including genes encoding outer membrane proteins and oncoprotein CagA. Mutant strains with HPnc4160 deficiency display increased colonization ability of the mouse stomach, in comparison with the wild-type strain. Furthermore, HPnc4160 expression is lower in clinical isolates from gastric cancer patients than in isolates derived from non-cancer patients, while the expression of HPnc4160's targets is higher in the isolates from gastric cancer patients. Therefore, the small RNA HPnc4160 regulates H. pylori adaptation to the host environment and, potentially, gastric carcinogenesis.

Whole genome sequencing analysis identifies recurrent structural alterations in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is the predominant type of esophageal cancer in the Asian region, including Japan. A previous study reported mutational landscape of Japanese ESCCs by using exome sequencing. However, somatic structural alterations were yet to be explored. To provide a comprehensive mutational landscape, we performed whole genome sequencing (WGS) analysis of biopsy specimens from 20 ESCC patients in a Japanese population. WGS analysis identified non-silent coding mutations of TP53, ZNF750 and FAT1 in ESCC. We detected six mutational signatures in ESCC, one of which showed significant association with smoking status. Recurrent structural variations, many of which were chromosomal deletions, affected genes such as LRP1B, TTC28, CSMD1, PDE4D, SDK1 and WWOX in 25%-30% of tumors. Somatic copy number amplifications at 11q13.3 (CCND1), 3q26.33 (TP63/SOX2), and 8p11.23 (FGFR1) and deletions at 9p21.3 (CDKN2A) were identified. Overall, these multi-dimensional view of genomic alterations improve the understanding of the ESCC development at molecular level and provides future prognosis and therapeutic implications for ESCC in Japan.

RNA-sequencing reveals positional memory of multipotent mesenchymal stromal cells from oral and maxillofacial tissue transcriptomes.

BACKGROUND: Multipotent mesenchymal stromal cells (MSCs) can be isolated from numerous tissues and are attractive candidates for therapeutic clinical applications due to their immunomodulatory and pro-regenerative capacity. Although the minimum criteria for defining MSCs have been defined, their characteristics are known to vary depending on their tissue of origin. RESULTS: We isolated and characterized human MSCs from three different bones (ilium (I-MSCs), maxilla (Mx-MSCs) and mandible (Md-MSCs)) and proceeded with next generation RNA-sequencing. Furthermore, to investigate the gene expression profiles among other cell types, we obtained RNA-seq data of human embryonic stem cells (ESCs) and several types of MSCs (periodontal ligament-derived MSCs, bone marrow-derived MSCs, and ESCs-derived MSCs) from the Sequence Reads Archive and analyzed the transcriptome profile. We found that MSCs derived from tissues of the maxillofacial region, such as the jaw bone and periodontal ligament, were HOX-negative, while those derived from other tissues were HOX-positive. We also identified that MSX1, LHX8, and BARX1, an essential regulator of craniofacial development, were strongly expressed in maxillofacial tissue-derived MSCs. Although MSCs may be divided into two distinct groups, the cells originated from over the neck or not, on the basis of differences in gene expression profile, the expression patterns of all CD antigen genes were similar among different type of MSCs, except for ESCs. CONCLUSIONS: Our findings suggest that MSCs from different anatomical locations, despite meeting general characterization criteria, have remarkable differences in gene expression and positional memory. Although stromal cells from different anatomical sources are generally categorized as MSCs, their differentiation potential and biological functions vary. We suggested that MSCs may retain an original tissue memory about the developmental process, including gene expression profiles. This could have an important impact when choosing an appropriate cell source for regenerative therapy using MSCs.

HHEX promotes myeloid transformation in cooperation with mutant ASXL1.

Additional sex combs-like 1 (ASXL1), an epigenetic modulator, is frequently mutated in myeloid neoplasms. Recent analyses of mutant ASXL1 conditional knockin (ASXL1-MT-KI) mice suggested that ASXL1-MT alone is insufficient for myeloid transformation. In our previous study, we used retrovirus-mediated insertional mutagenesis, which exhibited the susceptibility of ASXL1-MT-KI hematopoietic cells to transform into myeloid leukemia cells. In this screening, we identified the hematopoietically expressed homeobox (HHEX) gene as one of the common retrovirus integration sites. In this study, we investigated the potential cooperation between ASXL1-MT and HHEX in myeloid leukemogenesis. Expression of HHEX enhanced proliferation of ASXL1-MT-expressing HSPCs by inhibiting apoptosis and blocking differentiation, whereas it showed only modest effect in normal HSPCs. Moreover, ASXL1-MT and HHEX accelerated the development of RUNX1-ETO9a and FLT3-ITD leukemia. Conversely, HHEX depletion profoundly attenuated the colony-forming activity and leukemogenicity of ASXL1-MT-expressing leukemia cells. Mechanistically, we identified MYB and ETV5 as downstream targets for ASXL1-MT and HHEX by using transcriptome and chromatin immunoprecipitation-next-generation sequencing analyses. Moreover, we found that expression of ASXL1-MT enhanced the binding of HHEX to the promoter loci of MYB or ETV5 via reducing H2AK119ub. Depletion of MYB or ETV5 induced apoptosis or differentiation in ASXL1-MT-expressing leukemia cells, respectively. In addition, ectopic expression of MYB or ETV5 reversed the reduced colony-forming activity of HHEX-depleted ASXL1-MT-expressing leukemia cells. These findings indicate that the HHEX-MYB/ETV5 axis promotes myeloid transformation in ASXL1-mutated preleukemia cells.

A systematic sequencing-based approach for microbial contaminant detection and functional inference.

BACKGROUND: Microbial contamination poses a major difficulty for successful data analysis in biological and biomedical research. Computational approaches utilizing next-generation sequencing (NGS) data offer promising diagnostics to assess the presence of contaminants. However, as host cells are often contaminated by multiple microorganisms, these approaches require careful attention to intra- and interspecies sequence similarities, which have not yet been fully addressed. RESULTS: We present a computational approach that rigorously investigates the genomic origins of sequenced reads, including those mapped to multiple species that have been discarded in previous studies. Through the analysis of large-scale synthetic and public NGS samples, we estimate that 1000-100,000 contaminating microbial reads are detected per million host reads sequenced by RNA-seq. The microbe catalog we established included Cutibacterium as a prevalent contaminant, suggesting that contamination mostly originates from the laboratory environment. Importantly, by applying a systematic method to infer the functional impact of contamination, we revealed that host-contaminant interactions cause profound changes in the host molecular landscapes, as exemplified by changes in inflammatory and apoptotic pathways during Mycoplasma infection of lymphoma cells. CONCLUSIONS: We provide a computational method for profiling microbial contamination on NGS data and suggest that sources of contamination in laboratory reagents and the experimental environment alter the molecular landscape of host cells leading to phenotypic changes. These findings reinforce the concept that precise determination of the origins and functional impacts of contamination is imperative for quality research and illustrate the usefulness of the proposed approach to comprehensively characterize contamination landscapes.

Genomic analysis of pancreatic juice DNA assesses malignant risk of intraductal papillary mucinous neoplasm of pancreas.

Intraductal papillary mucinous neoplasm (IPMN) of pancreas has a high risk to develop into invasive cancer or co-occur with malignant lesion. Therefore, it is important to assess its malignant risk by less-invasive approach. Pancreatic juice cell-free DNA (PJD) would be an ideal material in this purpose, but genetic biomarkers for predicting malignant risk from PJD are not yet established. We here performed deep exome sequencing analysis of PJD from 39 IPMN patients with or without malignant lesion. Somatic alterations and copy number alterations (CNAs) detected in PJD were compared with the histologic grade of IPMN to evaluate their potential as a malignancy marker. Somatic mutations of KRAS, GNAS, TP53, and RNF43 were commonly detected in PJD of IPMNs, but no association with the histologic grades of IPMN was found. Instead, mutation burden was positively correlated with the histologic grade (r = 0.427, P = 0.015). We also observed frequent copy number deletions in 17p13 (TP53) and amplifications in 7q21 and 8q24 (MYC) in PJDs. The amplifications in 7q21 and 8q24 were positively correlated with the histologic grade and most prevalent in the cases of invasive carcinoma (P = 0.002 and 7/11; P = 0.011 and 6/11, respectively). We concluded that mutation burden and CNAs detected in PJD may have potential to assess the malignant progression risk of IPMNs.

Analyzing the 3D chromatin organization coordinating with gene expression regulation in B-cell lymphoma.

BACKGROUND: Eukaryotes compact chromosomes densely and non-randomly, forming three-dimensional structures. Alterations of the chromatin structures are often associated with diseases. In particular, aggressive cancer development from the disruption of the humoral immune system presents abnormal gene regulation which is accompanied by chromatin reorganizations. How the chromatin structures orchestrate the gene expression regulation is still poorly understood. Herein, we focus on chromatin dynamics in normal and abnormal B cell lymphocytes, and investigate its functional impact on the regulation of gene expression. METHODS: We conducted an integrative analysis using publicly available multi-omics data that include Hi-C, RNA-seq and ChIP-seq experiments with normal B cells, lymphoma and ES cells. We processed and re-analyzed the data exhaustively and combined different scales of genome structures with transcriptomic and epigenetic features. RESULTS: We found that the chromatin organizations are highly preserved among the cells. 5.2% of genes at the specific repressive compartment in normal pro-B cells were switched to the permissive compartment in lymphoma along with increased gene expression. The genes are involved in B-cell related biological processes. Remarkably, the boundaries of topologically associating domains were not enriched by CTCF motif, but significantly enriched with Prdm1 motif that is known to be the key factor of B-cell dysfunction in aggressive lymphoma. CONCLUSIONS: This study shows evidence of a complex relationship between chromatin reorganization and gene regulation. However, an unknown mechanism may exist to restrict the structural and functional changes of genomic regions and cognate genes in a specific manner. Our findings suggest the presence of an intricate crosstalk between the higher-order chromatin structure and cancer development.

Mutational Intratumor Heterogeneity is a Complex and Early Event in the Development of Adult T-cell Leukemia/Lymphoma.

The clonal architecture of tumors plays a vital role in their pathogenesis and invasiveness; however, it is not yet clear how this clonality contributes to different malignancies. In this study we sought to address mutational intratumor heterogeneity (ITH) in adult T-cell leukemia/lymphoma (ATL). ATL is a malignancy with an incompletely understood molecular pathogenesis caused by infection with human T-cell leukemia virus type-1 (HTLV-1). To determine the clonal structure through tumor genetic diversity profiles, we investigated 142 whole-exome sequencing data of tumor and matched normal samples from 71 ATL patients. Based on SciClone analysis, the ATL samples showed a wide spectrum of modes over clonal/subclonal frequencies ranging from one to nine clusters. The average number of clusters was six across samples, but the number of clusters differed among different samples. Of these ATL samples, 94% had more than two clusters. Aggressive ATL cases had slightly more clonal clusters than indolent types, indicating the presence of ITH during earlier stages of disease. The known significantly mutated genes in ATL were frequently clustered together and possibly coexisted in the same clone. IRF4, CCR4, TP53, and PLCG1 mutations were almost clustered in subclones with a moderate variant allele frequency (VAF), whereas HLA-B, CARD11, and NOTCH1 mutations were clustered in subclones with lower VAFs. Taken together, these results show that ATL displays a high degree of ITH and a complex subclonal structure. Our findings suggest that clonal/subclonal architecture might be a useful measure for prognostic purposes and personalized assessment of the therapeutic response.

Biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses.

Predictive biomarkers are important for selecting appropriate patients for particular treatments. Comprehensive genomic, transcriptomic, and pharmacological data provide clues for understanding relationships between biomarkers and drugs. However, it is still difficult to mine biologically meaningful biomarkers from multi-omics data. Here, we developed an approach for mining multi-omics cell line data by integrating joint non-negative matrix factorization (JNMF) and pathway signature analyses to identify candidate biomarkers. The JNMF detected known associations between biomarkers and drugs such as BRAF mutation with PLX4720 and HER2 amplification with lapatinib. Furthermore, we observed that tumours with both BRAF mutation and MITF activation were more sensitive to BRAF inhibitors compared to tumours with BRAF mutation without MITF activation. Therefore, activation of the BRAF/MITF axis seems to be a more appropriate biomarker for predicting the efficacy of a BRAF inhibitor than the conventional biomarker of BRAF mutation alone. Our biomarker discovery scheme represents an integration of JNMF multi-omics clustering and multi-layer interpretation based on pathway gene signature analyses. This approach is also expected to be useful for establishing drug development strategies, identifying pharmacodynamic biomarkers, in mode of action analysis, as well as for mining drug response data in a clinical setting.

Generation of tumor antigen-specific murine CD8+ T cells with enhanced anti-tumor activity via highly efficient CRISPR/Cas9 genome editing.

Immunotherapies have led to the successful development of novel therapies for cancer. However, there is increasing concern regarding the adverse effects caused by non-tumor-specific immune responses. Here, we report an effective strategy to generate high-avidity tumor-antigen-specific CTLs, using Cas9/single-guide RNA (sgRNA) ribonucleoprotein (RNP) delivery. As a proof-of-principle demonstration, we selected the gp100 melanoma-associated tumor antigen, and cloned the gp100-specific high-avidity TCR from gp100-immunized mice. To enable rapid structural dissection of the TCR, we developed a 3D protein structure modeling system for the TCR/antigen-major histocompatibility complex (pMHC) interaction. Combining these technologies, we efficiently generated gp100-specific PD-1(-) CD8+ T cells, and demonstrated that the genetically engineered CD8+ T cells have high avidity against melanoma cells both in vitro and in vivo. Our methodology offers computational prediction of the TCR response, and enables efficient generation of tumor antigen-specific CD8+ T cells that can neutralize tumor-induced immune suppression leading to a potentially powerful cancer therapeutic.