陳玫如

Bakr A*, Corte GD#, Veselinov O#, Kelekçi S#, Chen MJM#, Lin YY, Cross A, Syed R, Iacovone M, Sigismondo G, Goyal A, Lutsik P, Weichenhan D, Plass C, Popanda O and Schmezer P.
AT-rich interaction domain protein 1A (ARID1A), a SWI/SNF chromatin remodeling complex subunit, is frequently mutated across various cancer entities. Loss of ARID1A leads to DNA repair defects. Here, we show that ARID1A plays epigenetic roles to promote both DNA double-strand breaks (DSBs) repair pathways, non-homologous end-joining (NHEJ) and homologous recombination (HR). ARID1A is accumulated at DSBs after DNA damage and regulates chromatin loops formation by recruiting RAD21 and CTCF to DSBs. Simultaneously, ARID1A facilitates transcription silencing at DSBs in transcriptionally active chromatin by recruiting HDAC1 and RSF1 to control the distribution of activating histone marks, chromatin accessibility, and eviction of RNAPII. ARID1A depletion resulted in enhanced accumulation of micronuclei, activation of cGAS-STING pathway, and an increased expression of immunomodulatory cytokines upon ionizing radiation. Furthermore, low ARID1A expression in cancer patients receiving radiotherapy was associated with higher infiltration of several immune cells. The high mutation rate of ARID1A in various cancer types highlights its clinical relevance as a promising biomarker that correlates with the level of immune regulatory cytokines and estimates the levels of tumor-infiltrating immune cells, which can predict the response to the combination of radio- and immunotherapy.

Masashi Fujita, Mei-Ju May Chen, Doris Rieko Siwak, Shota Sasagawa, Ayako Oosawa-Tatsuguchi, Koji Arihiro, Atsushi Ono, Ryoichi Miura, Kazuhiro Maejima, Hiroshi Aikata, Masaki Ueno, Shinya Hayami, Hiroki Yamaue, Kazuaki Chayama, Ju-Seog Lee, Yiling Lu, Gordon B. Mills, Han Liang, Satoshi S. Nishizuka & Hidewaki Nakagawa

Primary liver cancer is a heterogeneous disease in terms of its etiology, histology, and therapeutic response. Concurrent proteomic and genomic characterization of a large set of clinical liver cancer samples can help elucidate the molecular basis of heterogeneity and thus serve as a valuable resource for personalized liver cancer treatment. In this study, we perform proteomic profiling of ~300 proteins on 259 primary liver cancer tissues with reverse-phase protein arrays, mutational analysis using whole genome sequencing and transcriptional analysis with RNA-Seq. Patients are of Japanese ethnic background and mainly HBV or HCV positive, providing insight into this important liver cancer subtype. Unsupervised classification of tumors based on protein expression profiles reveal three proteomic subclasses R1, R2, and R3. The R1 subclass is immunologically hot and demonstrated a good prognosis. R2 contains advanced proliferative tumor with TP53 mutations, high expression of VEGF receptor 2 and the worst prognosis. R3 is enriched with CTNNB1 mutations and elevated mTOR signaling pathway activity. Twenty-two proteins, including CDK1 and CDKN2A, are identified as potential prognostic markers. The proteomic classification presented in this study can help guide therapeutic decision making for liver cancer treatment.

Ying Mei, Mei-Ju May Chen, Han Liang & Li Ma
Cutaneous melanoma is the most malignant skin cancer. Biomarkers for stratifying patients at initial diagnosis and informing clinical decisions are highly sought after. Here we classified melanoma patients into three immune subtypes by single-sample gene-set enrichment analysis. We further identified a four-gene tumor immune-relevant (TIR) signature that was significantly associated with the overall survival of melanoma patients in The Cancer Genome Atlas cohort and in an independent validation cohort. Moreover, when applied to melanoma patients treated with the CTLA4 antibody, ipilimumab, the TIR signature could predict the response to ipilimumab and the survival. Notably, the predictive power of the TIR signature was higher than that of other biomarkers. The genes in this signature, SEL1L3, HAPLN3, BST2, and IFITM1, may be functionally involved in melanoma progression and immune response. These findings suggest that this four-gene signature has potential use in prognosis, risk assessment, and prediction of anti-CTLA4 response in melanoma patients.

Fan Yao, Yalan Deng, Yang Zhao, Ying Mei, Yilei Zhang, Xiaoguang Liu, Consuelo Martinez, Xiaohua Su, Roberto R. Rosato, Hongqi Teng, Qinglei Hang, Shannon Yap, Dahu Chen, Yumeng Wang, Mei-Ju May Chen, Mutian Zhang, Han Liang, Dong Xie, Xin Chen, Hao Zhu, Jenny C. Chang, M. James You, Yutong Sun, Boyi Gan & Li Ma

The growing knowledge of ferroptosis has suggested the role and therapeutic potential of ferroptosis in cancer, but has not been translated into effective therapy. Liver cancer, primarily hepatocellular carcinoma (HCC), is highly lethal with limited treatment options. LIFR is frequently downregulated in HCC. Here, by studying hepatocyte-specific and inducible Lifr-knockout mice, we show that loss of Lifr promotes liver tumorigenesis and confers resistance to drug-induced ferroptosis. Mechanistically, loss of LIFR activates NF-κB signaling through SHP1, leading to upregulation of the iron-sequestering cytokine LCN2, which depletes iron and renders insensitivity to ferroptosis inducers. Notably, an LCN2-neutralizing antibody enhances the ferroptosis-inducing and anticancer effects of sorafenib on HCC patient-derived xenograft tumors with low LIFR expression and high LCN2 expression. Thus, anti-LCN2 therapy is a promising way to improve liver cancer treatment by targeting ferroptosis.

Mu-En Chen, Mong-Hsun Tsai, Hsiang-Ting Huang, Ching-Chu Tsai, Mei-Ju Chen, Da-Syuan Yang, Teng-Zhi Yang, John Wang & Rong-Nan Huang
We have assembled and annotated the first egg transcriptome for F. taiwana, a biting midge. Our results suggest that down-regulation of the laccase2 and DCE/yellow genes might be the mechanism responsible for the NaCl-induced inhibition of melanization of F. taiwana eggs.

Over the past decade, high-throughput molecular profiling technologies have revolutionized cancer research. Petabytes of omics data (e.g., genomic, transcriptomic, proteomic, epigenomic, and metabolic data) have been generated from thousands of patients, animal models, and cell line samples, especially through some large consortium projects such as The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). These rich, high-throughput cancer omics data have provided an unprecedented opportunity to characterize cancer-related molecular mechanisms and identify biomarkers and therapeutic targets systematically. However, this tidal wave of molecular data also presents a major challenge for cancer researchers in analyzing the data and obtaining meaningful biological and clinical insights effectively. This is particularly true for a large proportion of researchers who have no or limited bioinformatics and statistical expertise. Many efforts have been made to overcome this challenge. First, many programming languages with specially designed modules or libraries to allow easy analysis and visualization of omics data have been developed. However, these tools still require users to acquire some programming skills, such as Python, R, and Perl, which is not feasible for most experimental researchers. Second, many web-based and stand-alone bioinformatics databases and applications have been developed to allow users to explore and analyze cancer omics data through a user-friendly, interactive interface. But these bioinformatics tools usually focus on one specific type of molecular data, provide only predefined analysis, and do not allow the customization of analytic and visualization tasks.

TMPRSS2 is an important membrane-anchored serine protease involved in human prostate cancer progression and metastasis. A serine protease physiologically often comes together with a cognate inhibitor for execution of proteolytically biologic function; however, TMPRSS2’s cognate inhibitor is still elusive. To identify the cognate inhibitor of TMPRSS2, in this study, we applied co-immunoprecipitation and LC/MS/MS analysis and isolated hepatocyte growth factor activator inhibitors (HAIs) to be potential inhibitor candidates for TMPRSS2. Moreover, the recombinant HAI-2 proteins exhibited a better inhibitory effect on TMPRSS2 proteolytic activity than HAI-1, and recombinant HAI-2 proteins had a high affinity to form a complex with TMPRSS2. The immunofluorescence images further showed that TMPRSS2 was co-localized to HAI-2. Both KD1 and KD2 domain of HAI-2 showed comparable inhibitory effects on TMPRSS2 proteolytic activity. In addition, HAI-2 overexpression could suppress the induction effect of TMPRSS2 on pro-HGF activation, extracellular matrix degradation and prostate cancer cell invasion. We further determined that the expression levels of TMPRSS2 were inversely correlated with HAI-2 levels during prostate cancer progression. In orthotopic xenograft animal model, TMPRSS2 overexpression promoted prostate cancer metastasis, and HAI-2 overexpression efficiently blocked TMPRSS2-induced metastasis. In summary, the results together indicate that HAI-2 can function as a cognate inhibitor for TMPRSS2 in human prostate cancer cells and may serve as a potential factor to suppress TMPRSS2-mediated malignancy.

In recent years, tremendous efforts have been made to systematically characterize the molecular profiles of tumor tissues from individuals with cancer, laying a critical foundation for elucidating the molecular basis of tumorigenesis and developing biomarker-based diagnostic, prognostic and therapeutic approaches. In particular, cancer genomic data at the DNA or RNA level are being accumulated at an unprecedented speed. However, it remains to be a big challenge in cancer research to systematically understand causality and mechanisms underlying the behaviors of cancer cells. To address it, perturbation experiments are a very powerful approach in which the cells are first modulated by perturbagens and the downstream consequences are then monitored. Recently, large-scale compendia of the phenotypic and cellular effects of perturbed cancer cell lines have been established. However, similar resources for the proteomic responses of perturbed cancer cell lines have yet to be established. Reverse-phase protein arrays (RPPAs) is a powerful targeted functional proteomics approach to studying cancer mechanisms, biomarkers and therapies. This quantitative antibody-based assay is able to assess a large number of protein markers in many samples in a cost-effective, sensitive manner.

The increasing omics data present a daunting informatics challenge. DrBioRight, a natural language-oriented and artificial intelligence-driven analytics platform, enables the broad research community to perform analysis in an intuitive, efficient, transparent, and collaborative way. The emerging next-generation analytics will maximize the utility of omics data and lead to a new paradigm for biomedical research.

Perturbation biology is a powerful approach to modeling quantitative cellular behaviors and understanding detailed disease mechanisms. However, large-scale protein response resources of cancer cell lines to perturbations are not available, resulting in a critical knowledge gap. Here we generated and compiled perturbed expression profiles of ∼210 clinically relevant proteins in >12,000 cancer cell line samples in response to ∼170 drug compounds using reverse-phase protein arrays. We show that integrating perturbed protein response signals provides mechanistic insights into drug resistance, increases the predictive power for drug sensitivity, and helps identify effective drug combinations. We build a systematic map of “protein-drug” connectivity and develop a user-friendly data portal for community use. Our study provides a rich resource to investigate the behaviors of cancer cells and the dependencies of treatment responses, thereby enabling a broad range of biomedical applications.