Sensitive detection of synthetic response to cancer immunotherapy driven by gene paralog pairs.

Emerging immunotherapies such as immune checkpoint blockade (ICB) and chimeric antigen receptor T-cell (CAR-T) therapy have revolutionized cancer treatment and have improved the survival of patients with multiple cancer types. Despite this success many patients are unresponsive to these treatments or relapse following treatment. CRISPR activation and knockout (KO) screens have been used to identify novel single gene targets that can enhance effector T cell function and promote immune cell targeting and eradication of tumors. However, cancer cells often employ multiple genes to promote an immunosuppressive pathway and thus modulating individual genes often has a limited effect. Paralogs are genes that originate from common ancestors and retain similar functions. They often have complex effects on a particular phenotype depending on factors like gene family similarity, each individual gene's expression and the physiological or pathological context. Some paralogs exhibit synthetic lethal interactions in cancer cell survival; however, a thorough investigation of paralog pairs that could enhance the efficacy of cancer immunotherapy is lacking. Here we introduce a sensitive computational approach that uses sgRNA sets enrichment analysis to identify cancer-intrinsic paralog pairs which have the potential to synergistically enhance T cell-mediated tumor destruction. We have further developed an ensemble learning model that uses an XGBoost classifier and incorporates features such as gene characteristics, sequence and structural similarities, protein-protein interaction (PPI) networks, and gene coevolution data to predict paralog pairs that are likely to enhance immunotherapy efficacy. We experimentally validated the functional significance of these predicted paralog pairs using double knockout (DKO) of identified paralog gene pairs as compared to single gene knockouts (SKOs). These data and analyses collectively provide a sensitive approach to identify previously undetected paralog pairs that can enhance cancer immunotherapy even when individual genes within the pair has a limited effect.

Genome scale CRISPR screens identify actin capping proteins as key modulators of therapeutic responses to radiation and immunotherapy.

Radiotherapy (RT), is a fundamental treatment for malignant tumors and is used in over half of cancer patients. As radiation can promote anti-tumor immune effects, a promising therapeutic strategy is to combine radiation with immune checkpoint inhibitors (ICIs). However, the genetic determinants that impact therapeutic response in the context of combination therapy with radiation and ICI have not been systematically investigated. To unbiasedly identify the tumor intrinsic genetic factors governing such responses, we perform a set of genome-scale CRISPR screens in melanoma cells for cancer survival in response to low-dose genotoxic radiation treatment, in the context of CD8 T cell co-culture and with anti-PD1 checkpoint blockade antibody. Two actin capping proteins, Capza3 and Capg, emerge as top hits that upon inactivation promote the survival of melanoma cells in such settings. Capza3 and Capg knockouts (KOs) in mouse and human cancer cells display persistent DNA damage due to impaired homology directed repair (HDR); along with increased radiation, chemotherapy, and DNA repair inhibitor sensitivity. However, when cancer cells with these genes inactivated were exposed to sublethal radiation, inactivation of such actin capping protein promotes activation of the STING pathway, induction of inhibitory CEACAM1 ligand expression and resistance to CD8 T cell killing. Patient cancer genomics analysis reveals an increased mutational burden in patients with inactivating mutations in CAPG and/or CAPZA3, at levels comparable to other HDR associated genes. There is also a positive correlation between CAPG expression and activation of immune related pathways and CD8 T cell tumor infiltration. Our results unveil the critical roles of actin binding proteins for efficient HDR within cancer cells and demonstrate a previously unrecognized regulatory mechanism of therapeutic response to radiation and immunotherapy.

Genetic variants of phospholipase C-γ2 alter the phenotype and function of microglia and confer differential risk for Alzheimer's disease.

Genetic association studies have demonstrated the critical involvement of the microglial immune response in Alzheimer's disease (AD) pathogenesis. Phospholipase C-gamma-2 (PLCG2) is selectively expressed by microglia and functions in many immune receptor signaling pathways. In AD, PLCG2 is induced uniquely in plaque-associated microglia. A genetic variant of PLCG2, PLCG2P522R, is a mild hypermorph that attenuates AD risk. Here, we identified a loss-of-function PLCG2 variant, PLCG2M28L, that confers an increased AD risk. PLCG2P522R attenuated disease in an amyloidogenic murine AD model, whereas PLCG2M28L exacerbated the plaque burden associated with altered phagocytosis and Aß clearance. The variants bidirectionally modulated disease pathology by inducing distinct transcriptional programs that identified microglial subpopulations associated with protective or detrimental phenotypes. These findings identify PLCG2M28L as a potential AD risk variant and demonstrate that PLCG2 variants can differentially orchestrate microglial responses in AD pathogenesis that can be therapeutically targeted.

Genetic Regulation of Human isomiR Biogenesis.

MicroRNAs play a critical role in regulating gene expression post-transcriptionally. Variations in mature microRNA sequences, known as isomiRs, arise from imprecise cleavage and nucleotide substitution or addition. These isomiRs can target different mRNAs or compete with their canonical counterparts, thereby expanding the scope of miRNA post-transcriptional regulation. Our study investigated the relationship between cis-acting single-nucleotide polymorphisms (SNPs) in precursor miRNA regions and isomiR composition, represented by the ratio of a specific 5'-isomiR subtype to all isomiRs identified for a particular mature miRNA. Significant associations between 95 SNP-isomiR pairs were identified. Of note, rs6505162 was significantly associated with both the 5'-extension of hsa-miR-423-3p and the 5'-trimming of hsa-miR-423-5p. Comparison of breast cancer and normal samples revealed that the expression of both isomiRs was significantly higher in tumors than in normal tissues. This study sheds light on the genetic regulation of isomiR maturation and advances our understanding of post-transcriptional regulation by microRNAs.

Alternative splicing in multiple myeloma is associated with the non-homologous end joining pathway.

Alternative splicing plays a pivotal role in tumorigenesis and proliferation. However, its pattern and pathogenic role has not been systematically analyzed in multiple myeloma or its subtypes. Alternative splicing profiles for 598 newly diagnosed myeloma patients with comprehensive genomic annotation identified primary translocations, 1q amplification, and DIS3 events to have more differentially spliced events than those without. Splicing levels were correlated with expression of splicing factors. Moreover, the non-homologous end joining pathway was an independent factor that was highly associated with splicing frequency as well as an increased number of structural variants. We therefore identify an axis of high-risk disease encompassing expression of the non-homologous end joining pathway, increase structural variants, and increased alternative splicing that are linked together. This indicates a joint pathogenic role for DNA damage response and alternative RNA processing in myeloma.

Intron-Retention Neoantigen Load Predicts Favorable Prognosis in Pancreatic Cancer.

PURPOSE: High tumor mutation burden (TMB) in many cancer types is associated with the production of tumor-specific neoantigens, a favorable outcome and response to immune checkpoint blockade (ICB) therapy. Besides mutation-derived neoantigens, aberrant intron retention also produces tumor neopeptides that could trigger an immune response. The relationship between intron-retention-derived tumor neoantigens (IR-neoAg) and clinical outcomes in pancreatic cancer remains uncertain. Here, we quantify IR-neoAg in pancreatic cancer and evaluate whether IR-neoAg load might serve as a biomarker for selecting patients who may benefit from ICB therapy. METHODS: We developed a computational approach to estimate patient-specific IR-neoAg load from transcriptome data available in The Cancer Genome Atlas pancreatic cancer cohort. Associations between IR-neoAg load and patient overall survival were evaluated using Kaplan-Meier estimates and Cox regression. Differential expression of immune checkpoint and HLA-I genes was evaluated in tumors with high IR-neoAg load. RESULTS: High IR-neoAg load predicted better overall survival in pancreatic cancer, although no association was found for TMB. IR-neoAg load remained a significant prognostic factor after adjusting for patient age, sex, tumor stage and grade, and TMB. Moreover, pancreatic tumors with both high IR-neoAg load and high HLA-I gene expression had similar gene expression profiles as other tumor types that showed response to anti-programmed cell death protein 1 therapy. CONCLUSION: IR-neoAg load is associated with favorable survival in pancreatic cancer. These findings provide strong evidence for considering IR-neoAgs when selecting patients who might benefit from ICB therapy.

Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease.

We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information "impressions," which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single-cell and patient bulk tissue transcriptomic datasets from glioblastoma multiforme (GBM), Alzheimer's disease (AD), and multiple myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on myeloma single-cell transcriptomics identified PHF19high myeloma cells associated with progression. Availability: https://github.com/tsteelejohnson91/DEGAS .

PLCG2 is associated with the inflammatory response and is induced by amyloid plaques in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is characterized by robust microgliosis and phenotypic changes that accompany disease pathogenesis. Accumulating evidence from genetic studies suggests the importance of phospholipase C γ 2 (PLCG2) in late-onset AD (LOAD) pathophysiology. However, the role of PLCG2 in AD is still poorly understood. METHODS: Using bulk RNA-Seq (N=1249) data from the Accelerating Medicines Partnership-Alzheimer's Disease Consortium (AMP-AD), we investigated whether PLCG2 expression increased in the brains of LOAD patients. We also evaluated the relationship between PLCG2 expression levels, amyloid plaque density, and expression levels of microglia specific markers (AIF1 and TMEM119). Finally, we investigated the longitudinal changes of PLCG2 expression in the 5xFAD mouse model of AD. To further understand the role of PLCG2 in different signaling pathways, differential gene expression and co-expression network analyses were performed using bulk RNA-Seq and microglial single-cell RNA-Seq data. To substantiate the human analyses, we performed differential gene expression analysis on wild-type (WT) and inactivated Plcg2 mice and used immunostaining to determine if the differentially expressed genes/pathways were altered by microglial cell coverage or morphology. RESULTS: We observed significant upregulation of PLCG2 expression in three brain regions of LOAD patients and significant positive correlation of PLCG2 expression with amyloid plaque density. These findings in the human brain were validated in the 5xFAD amyloid mouse model, which showed disease progression-dependent increases in Plcg2 expression associated with amyloid pathology. Of note, increased Plcg2 expression levels in 5xFAD mice were abolished by reducing microglia. Furthermore, using bulk RNA-Seq data, we performed differential expression analysis by comparing cognitively normal older adults (CN) with 75th percentile (high) and 25th percentile (low) PLCG2 gene expression levels to identify pathways related to inflammation and the inflammatory response. The findings in the human brain were validated by differential expression analyses between WT and plcg2 inactivated mice. PLCG2 co-expression network analysis of microglial single-cell RNA-Seq data identified pathways related to the inflammatory response including regulation of I-kappaB/NF-kappa B signaling and response to lipopolysaccharide. CONCLUSIONS: Our results provide further evidence that PLCG2 plays an important role in AD pathophysiology and may be a potential target for microglia-targeted AD therapies.

The Impact of Pro-Inflammatory Cytokines on Alternative Splicing Patterns in Human Islets.

Alternative splicing (AS) within the ß cell has been proposed as one potential pathway that may exacerbate autoimmunity and unveil novel immunogenic epitopes in type 1 diabetes (T1D). We employed a computational strategy to prioritize pathogenic splicing events in human islets treated with IL-1ß + IFN-γ as an ex vivo model of T1D and coupled this analysis with a k-mer based approach to predict RNA binding proteins involved in AS. In total, 969 AS events were identified in cytokine-treated islets, with the majority (44.8%) involving a skipped exon. ExonImpact identified 129 events predicted to impact protein structure. AS occurred with high frequency in MHC Class II-related mRNAs, and targeted qPCR validated reduced inclusion of Exon5 in the MHC Class II gene HLA-DMB. Single molecule RNA FISH confirmed increased HLA-DMB splicing in pancreatic sections from human donors with established T1D and autoantibody positivity. Serine and Arginine Rich Splicing Factor 2 was implicated in 37.2% of potentially pathogenic events, including Exon5 exclusion in HLA-DMB. Together, these data suggest that dynamic control of AS plays a role in the ß cell response to inflammatory signals during T1D evolution.

Tumor-Infiltrating Immune-Related Long Non-Coding RNAs Indicate Prognoses and Response to PD-1 Blockade in Head and Neck Squamous Cell Carcinoma.

Long non-coding RNAs (lncRNAs) in immune cells play critical roles in tumor cell-immune cell interactions. This study aimed to characterize the landscape of tumor-infiltrating immune-related lncRNAs (Ti-lncRNAs) and reveal their correlations with prognoses and immunotherapy response in head and neck squamous cell carcinoma (HNSCC). We developed a computational model to identify Ti-lncRNAs in HNSCC and analyzed their associations with clinicopathological features, molecular alterations, and immunotherapy response. A signature of nine Ti-lncRNAs demonstrated an independent prognostic factor for both overall survival and disease-free survival among the cohorts from Fudan University Shanghai Cancer Center, The Cancer Genome Atlas, GSE41613, and GSE42743. The Ti-lncRNA signature scores in immune cells showed significant associations with TP53 mutation, CDKN2A mutation, and hypoxia. Inferior signature scores were enriched in patients with high levels of PDCD1 and CTLA4 and high expanded immune gene signature (IGS) scores, who displayed good response to PD-1 blockade in HNSCC. Consistently, superior clinical response emerged in melanoma patients with low signature scores undergoing anti-PD-1 therapy. Moreover, the Ti-lncRNA signature was a prognostic factor independent of PDCD1, CTLA4, and the expanded IGS score. In conclusion, tumor-infiltrating immune profiling identified a prognostic Ti-lncRNA signature indicative of clinical response to PD-1 blockade in HNSCC.

The Impact of Pro-Inflammatory Cytokines on Alternative Splicing Patterns in Human Islets.

Alternative splicing (AS) within the ß cell has been proposed as one potential pathway that may exacerbate autoimmunity and unveil novel immunogenic epitopes in type 1 diabetes (T1D). We employed a computational strategy to prioritize pathogenic splicing events in human islets treated with IL-1ß + IFN-γ as an ex vivo model of T1D and coupled this analysis with a k-mer based approach to predict RNA binding proteins involved in AS. In total, 969 AS events were identified in cytokine-treated islets, with the majority (44.8%) involving a skipped exon. ExonImpact identified 129 events predicted to impact protein structure. AS occurred with high frequency in MHC Class II-related mRNAs, and targeted qPCR validated reduced inclusion of Exon5 in the MHC Class II gene HLA-DMB. Single molecule RNA FISH confirmed increased HLA-DMB splicing in pancreatic sections from human donors with established T1D and autoantibody positivity. Serine and Arginine Rich Splicing Factor 2 was implicated in 37.2% of potentially pathogenic events, including Exon5 exclusion in HLA-DMB. Together, these data suggest that dynamic control of AS plays a role in the ß cell response to inflammatory signals during T1D evolution.

Intron retention-induced neoantigen load correlates with unfavorable prognosis in multiple myeloma.

Neoantigen peptides arising from genetic alterations may serve as targets for personalized cancer vaccines and as positive predictors of response to immune checkpoint therapy. Mutations in genes regulating RNA splicing are common in hematological malignancies leading to dysregulated splicing and intron retention (IR). In this study, we investigated IR as a potential source of tumor neoantigens in multiple myeloma (MM) patients and the relationship of IR-induced neoantigens (IR-neoAg) with clinical outcomes. MM-specific IR events were identified in RNA-sequencing data from the Multiple Myeloma Research Foundation CoMMpass study after removing IR events that also occurred in normal plasma cells. We quantified the IR-neoAg load by assessing IR-induced novel peptides that were predicted to bind to major histocompatibility complex (MHC) molecules. We found that high IR-neoAg load was associated with poor overall survival in both newly diagnosed and relapsed MM patients. Further analyses revealed that poor outcome in MM patients with high IR-neoAg load was associated with high expression levels of T-cell co-inhibitory molecules and elevated interferon signaling activity. We also found that MM cells exhibiting high IR levels had lower MHC-II protein abundance and treatment of MM cells with a spliceosome inhibitor resulted in increased MHC-I protein abundance. Our findings suggest that IR-neoAg may represent a novel biomarker of MM patient clinical outcome and further that targeting RNA splicing may serve as a potential therapeutic strategy to prevent MM immune escape and promote response to checkpoint blockade.

Generation of the tumor-suppressive secretome from tumor cells.

Rationale: The progression of cancer cells depends on the soil and building an inhibitory soil might be a therapeutic option. We previously created tumor-suppressive secretomes by activating Wnt signaling in MSCs. Here, we examined whether the anti-tumor secretomes can be produced from tumor cells. Methods: Wnt signaling was activated in tumor cells by overexpressing ß-catenin or administering BML284, a Wnt activator. Their conditioned medium (CM) was applied to cancer cells or tissues, and the effects of CM were evaluated. Tumor growth in the mammary fat pad and tibia in C57BL/6 female mice was also evaluated through µCT imaging and histology. Whole-genome proteomics analysis was conducted to determine and characterize novel tumor-suppressing proteins, which were enriched in CM. Results: The overexpression of ß-catenin or the administration of BML284 generated tumor-suppressive secretomes from breast, prostate and pancreatic cancer cells. In the mouse model, ß-catenin-overexpressing CM reduced tumor growth and tumor-driven bone destruction. This inhibition was also observed with BML284-treated CM. Besides p53 and Trail, proteomics analysis revealed that CM was enriched with enolase 1 (Eno1) and ubiquitin C (Ubc) that presented notable tumor-suppressing actions. Importantly, Eno1 immunoprecipitated CD44, a cell-surface adhesion receptor, and its silencing suppressed Eno1-driven tumor inhibition. A pan-cancer survival analysis revealed that the downregulation of MMP9, Runx2 and Snail by CM had a significant impact on survival outcomes (p < 0.00001). CM presented a selective inhibition of tumor cells compared to non-tumor cells, and it downregulated PD-L1, an immune escape modulator. Conclusions: The tumor-suppressive secretome can be generated from tumor cells, in which ß-catenin presented two opposing roles, as an intracellular tumor promoter in tumor cells and a generator of extracellular tumor suppressor in CM. Eno1 was enriched in CM and its interaction with CD44 was involved in Eno1's anti-tumor action. Besides presenting a potential option for treating primary cancers and metastases, the result indicates that aggressive tumors may inhibit the growth of less aggressive tumors via tumor-suppressive secretomes.

Development and validation of a novel mRNA signature for predicting early relapse in non-small cell lung cancer.

BACKGROUND: Recurrence after initial primary resection is still a major and ultimate cause of death for non-small cell lung cancer patients. We attempted to build an early recurrence associated gene signature to improve prognostic prediction of non-small cell lung cancer. METHODS: Propensity score matching was conducted between patients in early relapse group and long-term survival group from The Cancer Genome Atlas training series (N = 579) and patients were matched 1:1. Global transcriptome analysis was then performed between the paired groups to identify tumour-specific mRNAs. Finally, using LASSO Cox regression model, we built a multi-gene early relapse classifier incorporating 40 mRNAs. The prognostic and predictive accuracy of the signature was internally validated in The Cancer Genome Atlas patients. RESULTS: A total of 40 mRNAs were finally identified to build an early relapse classifier. With specific risk score formula, patients were classified into a high-risk group and a low-risk group. Relapse-free survival was significantly different between the two groups in both discovery (HR: 3.244, 95% CI: 2.338-4.500, P < 0.001) and internal validation series (HR 1.970, 95% CI 1.181-3.289, P = 0.009). Further analysis revealed that the prognostic value of this signature was independent of tumour stage, histotype and epidermal growth factor receptor mutation (P < 0.05). Time-dependent receiver operating characteristic analysis showed that the area under receiver operating characteristic curve of this signature was higher than TNM stage alone (0.771 vs 0.686, P < 0.05). Further, decision curve analysis curves analysis at 1 year revealed the considerable clinical utility of this signature in predicting early relapse. CONCLUSIONS: We successfully established a reliable signature for predicting early relapse in stage I-III non-small cell lung cancer.

INPP5D expression is associated with risk for Alzheimer's disease and induced by plaque-associated microglia.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, robust microgliosis, neuroinflammation, and neuronal loss. Genome-wide association studies recently highlighted a prominent role for microglia in late-onset AD (LOAD). Specifically, inositol polyphosphate-5-phosphatase (INPP5D), also known as SHIP1, is selectively expressed in brain microglia and has been reported to be associated with LOAD. Although INPP5D is likely a crucial player in AD pathophysiology, its role in disease onset and progression remains unclear. We performed differential gene expression analysis to investigate INPP5D expression in AD and its association with plaque density and microglial markers using transcriptomic (RNA-Seq) data from the Accelerating Medicines Partnership for Alzheimer's Disease (AMP-AD) cohort. We also performed quantitative real-time PCR, immunoblotting, and immunofluorescence assays to assess INPP5D expression in the 5xFAD amyloid mouse model. Differential gene expression analysis found that INPP5D expression was upregulated in LOAD and positively correlated with amyloid plaque density. In addition, in 5xFAD mice, Inpp5d expression increased as the disease progressed, and selectively in plaque-associated microglia. Increased Inpp5d expression levels in 5xFAD mice were abolished entirely by depleting microglia with the colony-stimulating factor receptor-1 antagonist PLX5622. Our findings show that INPP5D expression increases as AD progresses, predominantly in plaque-associated microglia. Importantly, we provide the first evidence that increased INPP5D expression might be a risk factor in AD, highlighting INPP5D as a potential therapeutic target. Moreover, we have shown that the 5xFAD mouse model is appropriate for studying INPP5D in AD.

CGPE: an integrated online server for Cancer Gene and Pathway Exploration.

SUMMARY: Cancer Gene and Pathway Explorer (CGPE) is developed to guide biological and clinical researchers, especially those with limited informatics and programming skills, performing preliminary cancer-related biomedical research using transcriptional data and publications. CGPE enables three user-friendly online analytical and visualization modules without requiring any local deployment. The GenePub HotIndex applies natural language processing, statistics and association discovery to provide analytical results on gene-specific PubMed publications, including gene-specific research trends, cancer types correlations, top-related genes and the WordCloud of publication profiles. The OnlineGSEA enables Gene Set Enrichment Analysis (GSEA) and results visualizations through an easy-to-follow interface for public or in-house transcriptional datasets, integrating the GSEA algorithm and preprocessed public TCGA and GEO datasets. The preprocessed datasets ensure gene sets analysis with appropriate pathway alternation and gene signatures. The CellLine Search presents evidence-based guidance for cell line selections with combined information on cell line dependency, gene expressions and pathway activity maps, which are valuable knowledge to have before conducting gene-related experiments. In a nutshell, the CGPE webserver provides a user-friendly, visual, intuitive and informative bioinformatics tool that allows biomedical researchers to perform efficient analyses and preliminary studies on in-house and publicly available bioinformatics data. AVAILABILITY AND IMPLEMENTATION: The webserver is freely available online at https://cgpe.soic.iupui.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice.

BACKGROUND: Colon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from pathological indicators. Transcription factors function as critical regulators in all aspects of cell life, but transcription factors-based biomarkers for colon cancer prognosis were still rare and necessary. METHODS: We implemented an innovative process to select the transcription factors variables and evaluate the prognostic prediction power by combining the Cox PH model with the random forest algorithm. We picked five top-ranked transcription factors and built a prediction model by using Cox PH regression. Using Kaplan-Meier analysis, we validated our predictive model on four independent publicly available datasets (GSE39582, GSE17536, GSE37892, and GSE17537) from the GEO database, consisting of 925 colon cancer patients. RESULTS: A five-transcription-factors based predictive model for colon cancer prognosis has been developed by using TCGA colon cancer patient data. Five transcription factors identified for the predictive model is HOXC9, ZNF556, HEYL, HOXC4 and HOXC6. The prediction power of the model is validated with four GEO datasets consisting of 1584 patient samples. Kaplan-Meier curve and log-rank tests were conducted on both training and validation datasets, the difference of overall survival time between predicted low and high-risk groups can be clearly observed. Gene set enrichment analysis was performed to further investigate the difference between low and high-risk groups in the gene pathway level. The biological meaning was interpreted. Overall, our results prove our prediction model has a strong prediction power on colon cancer prognosis. CONCLUSIONS: Transcription factors can be used to construct colon cancer prognostic signatures with strong prediction power. The variable selection process used in this study has the potential to be implemented in the prognostic signature discovery of other cancer types. Our five TF-based predictive model would help with understanding the hidden relationship between colon cancer patient survival and transcription factor activities. It will also provide more insights into the precision treatment of colon cancer patients from a genomic information perspective.

Highly robust model of transcription regulator activity predicts breast cancer overall survival.

BACKGROUND: While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes. METHODS: Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome. RESULT: We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients. CONCLUSION: Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression.

Sestrin 3 Protects Against Diet-Induced Nonalcoholic Steatohepatitis in Mice Through Suppression of Transforming Growth Factor ß Signal Transduction.

Sestrin 3 (Sesn3) belongs to the three-member sestrin protein family. Sestrins have been implicated in antioxidative stress, adenosine monophosphate-activated protein kinase and mammalian target of rapamycin signal transduction, and metabolic homeostasis. However, the role of Sesn3 in the development of nonalcoholic steatohepatitis (NASH) has not been previously studied. In this work, we generated Sesn3 whole-body knockout and liver-specific transgenic mice to investigate the hepatic function of Sesn3 in diet-induced NASH. With only 4 weeks of dietary treatment, Sesn3 knockout mice developed severe NASH phenotype as characterized by hepatic steatosis, inflammation, and fibrosis. Strikingly, after 8-week feeding with a NASH-inducing diet, Sesn3 transgenic mice were largely protected against NASH development. Transcriptomic analysis revealed that multiple extracellular matrix-related processes were up-regulated, including transforming growth factor ß (TGF-ß) signaling and collagen production. Further biochemical and cell biological analyses have illustrated a critical control of the TGF-ß-mothers against decapentaplegic homolog (Smad) pathway by Sesn3 at the TGF-ß receptor and Smad3 levels. First, Sesn3 inhibits the TGF-ß receptor through an interaction with Smad7; second, Sesn3 directly inhibits the Smad3 function through protein-protein interaction and cytosolic retention. Conclusion: Sesn3 is a critical regulator of the extracellular matrix and hepatic fibrosis by suppression of TGF-ß-Smad3 signaling.

Sesn3 deficiency promotes carcinogen-induced hepatocellular carcinoma via regulation of the hedgehog pathway.

Sestrin 3 (Sesn3) belongs to a small protein family that has been implicated in multiple biological processes including anti-oxidative stress, anti-aging, cell signaling, and metabolic homeostasis. However, the role of Sesn3 in hepatocellular carcinoma (HCC) remains unclear. Here we generated a Sesn3 knockout mouse model and induced HCC development by a combination of a single dose of diethylnitrosamine and chronic feeding of a choline deficient-high fat diet. After 6 months of the dietary treatment, Sesn3 knockout mice developed more severe HCC with higher levels of alpha-fetoprotein, arginase 1, and cytokeratin 19, but also higher metastatic rates than wild-type mice. Histological analysis revealed elevated extracellular matrix and cancer stem cell markers including Acta2, Cd44, and Cd133. Signaling analysis showed activated IL6-Stat3 and Akt pathways. Biochemical and microscopic analyses uncovered a novel inhibitory regulation of Gli2, a downstream transcription factor of the hedgehog signaling, by Sesn3. Two of the Gli2-regulated genes - Pdgfrb and Cd44 were upregulated in the Sesn3-deficient liver tissue. In conclusion, our data suggest that Sesn3 plays a critical tumor suppressor role in the liver partly through the inhibition of the hedgehog signaling.