Genome-Scale Signatures of Gene Interaction from Compound Screens Predict Clinical Efficacy of Targeted Cancer Therapies

CREB1-BCL2 drives mitochondrial resilience in RAS GAP-dependent breast cancer chemoresistance

Triple-negative breast cancer (TNBC) is an aggressive and heterogenous breast cancer subtype. RASAL2 is a RAS GTPase-activating protein (GAP) that has been associated with platinum resistance in TNBC, but the underlying mechanism is unknown. Here, we show that RASAL2 is enriched following neoadjuvant chemotherapy in TNBC patients. This enrichment is specific to the tumour compartment compared to adjacent normal tissues, suggesting that RASAL2 upregulation is tumour-selective. Analyses based on 2D/3D cultures and patient-derived xenograft models reveal that RASAL2 confers cross-resistance to common DNA-damaging chemotherapies other than platinum. Mechanistically, we found that apoptotic signalling is significantly downregulated upon RASAL2 expression. This feature is characterised by substantial alterations in the expression of anti-versus pro-apoptotic factors, pointing to heterogeneous mechanisms. In particular, RASAL2 upregulates BCL2 via activation of the oncogenic transcription co-factor YAP. CREB1, a YAP-interacting protein, was identified as the common transcription factor that binds to the promoter regions of RASAL2 and BCL2, driving their collective expression. A subset of RASAL2 colocalises with BCL2 subcellularly. Both proteins decorate mitochondria, where the high levels of mitochondrial RASAL2-induced BCL2 expression render the organelles refractory to apoptosis. Accordingly, mitochondrial outer membrane permeabilisation assay using live mitochondria from RASAL2-high/chemoresistant tumour cells demonstrated attenuated release of death signal, cytochrome c, when exposed to pro-apoptotic factors BAX and tBID. Similarly, these cells were more resilient towards chemotherapy-induced mitochondrial depolarisation. Together, this work reveals a previously undocumented molecular link between RAS GAP and apoptosis regulation, providing a new mechanistic framework for targeting a subset of chemorefractory tumours.

Integrating bioinformatics with experimental validation unveils immunological and prognostic significance of PVRIG in pan-cancer

The poliovirus receptor-related immunoglobulin domain-containing protein (PVRIG) is a recently identified immune checkpoint receptor predominantly expressed on natural killer and CD8 + T cells. This study investigated the role of PVRIG in the tumor immune microenvironment and its prognostic significance across various cancers. Using bioinformatics analyses, the study revealed that PVRIG expression is associated with immune cell infiltration, immune modulator gene expression, clinical outcomes, CD8 + T cell functionality, and responses to immunotherapies and targeted treatments. Additionally, in vitro and in vivo experiments confirmed that PVRIG plays a critical role in regulating CD8 + T cell functionality. These findings suggest that PVRIG could serve as a biomarker for prognosis and immune infiltration, as well as a promising target for novel cancer immunotherapies.

The prognostic and immune significance of SNHG3 in clear cell renal cell carcinoma

Background

Long non-coding RNA (lncRNA) small nucleolar RNA host gene 3 (SNHG3) has been reported to be involved in the pathological process of a variety of tumors, including clear cell renal cell carcinoma (ccRCC). However, whether SNHG3 can be used as a prognostic biomarker and its correlation with immune infiltration in ccRCC remain unclear, warranting further research. This study aims to explore the relationship between SNHG3 and immune infiltration in ccRCC and confirm the potential of SNHG3 to predict survival of ccRCC patients.

Methods

The Cancer Genome Atlas (TCGA) database was used to assess the expression of SNHG3 in ccRCC, evaluate clinicopathological characteristics, assess prognosis, and conduct functional enrichment analysis. The ccRCC microenvironment and immune infiltration were investigated using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithms, respectively. We additionally investigated the relationships between SNHG3 and immunological checkpoints. Drug sensitivity of SNHG3 was investigated in R. The expression of SNHG3 was verified in the Gene Expression Omnibus (GEO) database, ccRCC cell lines, and tissues. Wound healing and Methylthiazolyldiphenyl-tetrazolium bromide (MTT) assays were used to evaluate tumor cell migration and proliferation. Fluorescence in situ hybridization (FISH) assay was conducted to localize SNHG3 in ccRCC cells.

Results

SNHG3 expression was significantly upregulated in ccRCC cells and tissues and associated with several clinicopathological features and poor prognosis of ccRCC patients. SNHG3 was correlated with immune cells infiltration in ccRCC and exhibited sensitivity to various targeted and chemotherapy drugs. Knockdown of SNHG3 significantly reduced the proliferation and migration of ccRCC. FISH results showed that SNHG3 was located in the cell nucleus.

Conclusions

Overall, this study demonstrates that SNHG3 is a prognostic biomarker correlated with immune infiltration in ccRCC.

Decoupling the correlation between cytotoxic and exhausted T lymphocyte states enhances melanoma immunotherapy response prediction

Cytotoxic T lymphocyte (CTL) and terminal exhausted T lymphocyte (ETL) activities crucially influence immune checkpoint inhibitor (ICI) response. Despite this, the efficacy of ETL and CTL transcriptomic signatures for response prediction remains limited. Investigating this across the TCGA and publicly available single-cell cohorts, we find a strong positive correlation between ETL and CTL expression signatures in most cancers. We hence posited that their limited predictability arises due to their mutually canceling effects on ICI response. Thus, we developed DETACH, a computational method to identify a gene set whose expression pinpoints to a subset of melanoma patients where the CTL and ETL correlation is low. DETACH enhances CTL's prediction accuracy, outperforming existing signatures. DETACH signature genes activity also demonstrates a positive correlation with lymphocyte infiltration and the prevalence of reactive T cells in the tumor microenvironment (TME), advancing our understanding of the CTL cell state within the TME.

Comparative analyses of gene networks mediating cancer metastatic potentials across lineage types

Studies have identified genes and molecular pathways regulating cancer metastasis. However, it remains largely unknown whether metastatic potentials of cancer cells from different lineage types are driven by the same or different gene networks. Here, we aim to address this question through integrative analyses of 493 human cancer cells' transcriptomic profiles and their metastatic potentials in vivo. Using an unsupervised approach and considering both gene coexpression and protein-protein interaction networks, we identify different gene networks associated with various biological pathways (i.e. inflammation, cell cycle, and RNA translation), the expression of which are correlated with metastatic potentials across subsets of lineage types. By developing a regularized random forest regression model, we show that the combination of the gene module features expressed in the native cancer cells can predict their metastatic potentials with an overall Pearson correlation coefficient of 0.90. By analyzing transcriptomic profile data from cancer patients, we show that these networks are conserved in vivo and contribute to cancer aggressiveness. The intrinsic expression levels of these networks are correlated with drug sensitivity. Altogether, our study provides novel comparative insights into cancer cells' intrinsic gene networks mediating metastatic potentials across different lineage types, and our results can potentially be useful for designing personalized treatments for metastatic cancers.

Identification of a venetoclax-resistance prognostic signature base on 6-senescence genes and its clinical significance for acute myeloid leukemia

Background

Satisfactory responses can be obtained for acute myeloid leukemia (AML) treated by Venetoclax (VEN)-based therapy. However, there are still quite a few AML patients (AMLs) resistant to VEN, and it is critical to understand whether VEN-resistance is regulated by senescence.

Methods

Here, we established and validated a signature for predicting AML prognosis based on VEN resistance-related senescence genes (VRSGs). In this study, 51 senescence genes were identified with VEN-resistance in AML. Using LASSO algorithms and multiple AML cohorts, a VEN-resistance senescence prognostic model (VRSP-M) was developed and validated based on 6-senescence genes.

Results

According to the median score of the signature, AMLs were classified into two subtypes. A worse prognosis and more adverse features occurred in the high-risk subtype, including older patients, non-de novo AML, poor cytogenetics, adverse risk of European LeukemiaNet (ELN) 2017 recommendation, and TP53 mutation. Patients in the high-risk subtype were mainly involved in monocyte differentiation, senescence, NADPH oxidases, and PD1 signaling pathway. The model's risk score was significantly associated with VEN-resistance, immune features, and immunotherapy response in AML. In vitro, the IC50 values of ABT-199 (VEN) rose progressively with increasing expression of G6PD and BAG3 in AML cell lines.

Conclusions

The 6-senescence genes prognostic model has significant meaning for the prediction of VEN-resistance, guiding personalized molecularly targeted therapies, and improving AML prognosis.

Integrated single-cell and bulk sequencing analyses with experimental validation identify the prognostic and immunological implications of CD226 in pan-cancer

PURPOSE

CD226 (DNAM-1) is an activating receptor mainly expressed in CD8 + and NK cells. CD226 deficiency and blockade have been shown to impair tumor suppression, while enhanced CD226 expression positively correlated with the increased efficacy of immune checkpoint blockade (ICB) therapies. However, the detailed function and role of CD226 in pan-cancer are largely unknown and require further in-depth investigation. Therefore, this study aims to investigate the biological functions of CD226, its role in tumor immunity, and its potential to predict prognosis and immunotherapy response in pan-cancer.

METHODS

By taking advantage of single-cell and bulk sequencing analyses, we analyzed the expression profile of CD226, its correlation with patient prognosis, immune infiltration level, immune-related genes, tumor heterogeneity, and stemness in pan-cancer. We also investigated the biological functions of CD226 using gene set enrichment analysis (GSEA) and evaluated its predictive value in response to immunotherapy and small-molecule targeted drugs. In addition, we validated the expression of CD226 in tumor-infiltrating CD8 + and NK cells and studied its association with their functions using a murine B16F10 melanoma model.

RESULTS

CD226 exhibited differential expression across most tumor types, and its elevated expression was associated with improved clinical outcomes in multiple cancer types. CD226 is closely correlated with numerous tumor-infiltrating immune cells, tumor stemness, and heterogeneity in most cancers. Furthermore, based on single-cell sequencing analysis, CD226 expression was found to be higher on effector CD4 + T cells than naïve CD4 + T cells, and its expression level was decreased in exhausted CD8 + T cells relative to effector CD8 + T cells in multiple cancer types. Additionally, flow cytometric analysis demonstrated that CD226 was highly correlated with the function of tumor-infiltrating NK and CD8 + T cells in murine B16F10 melanoma. Moreover, GSEA analysis revealed that CD226 was closely associated with T cell activation, natural killer cell mediated immunity, natural killer cell-mediated cytotoxicity, and T cell receptor signaling pathway. Finally, CD226 showed promising predictive potential for responsiveness to both ICB therapies and various small-molecule targeted drugs.

CONCLUSION

CD226 has shown great potential as an innovative biomarker for predicting patient prognosis, immune infiltration levels, and the function of tumor-infiltrating CD8 + T cells, as well as immunotherapy response. Additionally, our findings suggest that the optimal modification of CD226 expression and function, combined with current ICBs, could be a promising strategy for tumor immunotherapy.

Decoupling the correlation between cytotoxic and exhausted T lymphocyte transcriptomic signatures enhances melanoma immunotherapy response prediction from tumor expression

Background

Cytotoxic T lymphocytes (CTL) play a crucial role in anti-cancer immunity. Progression of CTL to terminal exhausted T lymphocytes (ETL) that overexpress inhibitory receptors can substantially decrease effector cytokines production and diminish cytolytic activity and terminal exhausted T cell cannot be reprogrammed by ICIs in tumor microenvironment (TME). However, while the activity levels of CTL and ETL are considered important determinants of immune checkpoint inhibitors (ICIs) response, it has been repeatedly observed that their predictive power of the latter is quite limited. Studying this conundrum on a large scale across the TCGA cohort, we find that ETL and CTL activity (estimated based on conventional gene signatures in the bulk tumor expression) is strongly positively correlated in most cancer types. We hypothesized that the limited predictive power of CTL activity might result from the high concordance of CTL and ETL activities, which mutually cancels out their individual antagonistic effects on ICI response.

Methods

Consequently, we have set out to identify a set of genes whose expression identifies a subset of patients where the CTL and ETL correlation is diminished, such that the association between these CD8+ T cell states and ICIs response is enhanced.

Results

Analyzing TCGA melanoma bulk gene expression, we identified a set of genes whose over-expression markedly diminishes the CTL and ETL correlation, termed a decoupling signature (DS). Reassuringly, we first find that the correlation between ETL and CTL activities is indeed markedly lower across high scoring DS patients than that observed across low scoring DS patients in numerous independent melanoma ICIs cohorts. Second, indeed, this successful decoupling increases the power of CTL activity in predicting ICIs response in high DS scoring patients. We show that the resulting prediction accuracy is superior to other state-of-art ICI predictive transcriptomic signatures.

Conclusion

The new decoupling score boosts the power of CTL activity in predicting ICIs response in melanoma from the tumor bulk expression. Its use enables a two-step stratification approach, where the response of high scoring DS patient can be predicted more accurately that with extant transcriptomic signatures.

Comprehensive bioinformatic analysis of the expression and prognostic significance of TSC22D domain family genes in adult acute myeloid leukemia

BACKGROUND

TSC22D domain family genes, including TSC22D1-4, play a principal role in cancer progression. However, their expression profiles and prognostic significance in adult acute myeloid leukemia (AML) remain unknown.

METHODS

The online databases, including HPA, CCLE, EMBL-EBI, GEPIA2, BloodSpot, GENT2, UCSCXenaShiny, GSCALite, cBioportal, and GenomicScape, utilized the data of TCGA and GEO to investigate gene expression, mutation, copy number variation (CNV), and prognostic significance of the TSC22D domain family in adult AML. Computational analysis of resistance (CARE) was used to explore the effect of TSC22D3 expression on drug response. Functional enrichment analysis of TSC22D3 was performed in the TRRUST Version 2 database. The STRING, Pathway Commons, and AnimalTFDB3.0 databases were used to investigate the protein-protein interaction (PPI) network of TSC22D3. Harmonizome was used to predict target genes and kinases regulated by TSC22D3. The StarBase v2.0 and CancermiRNome databases were used to predict miRNAs regulated by TSC22D3. UCSCXenaShiny was used to investigate the correlation between TSC22D3 expression and immune infiltration.

RESULTS

Compared with normal adult hematopoietic stem cells (HSCs), the expression of TSC22D3 and TSC22D4 in adult AML tissues was markedly up-regulated, whereas TSC22D1 expression was markedly down-regulated. The expression of TSC22D1 and TSC22D3 was significantly increased in adult AML tissues compared to normal adult tissues. High TSC22D3 expression was significantly associated with poor overall survival (OS) and event-free survival (EFS) in adult AML patients. Univariate and multivariate Cox analysis showed that overexpression of TSC22D3 was independently associated with adverse OS of adult AML patients. High TSC22D3 expression had a adverse impact on OS and EFS of adult AML patients in the chemotherapy group. TSC22D3 expression correlated with drug resistance to BCL2 inhibitors. Functional enrichment analysis indicated that TSC22D3 might promote AML progression. MIR143-3p sponging TSC22D3 might have anti-leukemia effect in adult AML.

CONCLUSIONS

A significant increase in TSC22D3 expression was observed in adult AML tissues compared to normal adult HSCs and tissues. The prognosis of adult AML patients with high TSC22D3 expression was unfavorable, which could severe as a new prognostic biomarker and potential target for adult AML.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

Six-Transmembrane Epithelial Antigen of Prostate 4: An Indicator of Prognosis and Tumor Immunity in Hepatocellular Carcinoma

Purpose

The six-transmembrane epithelial antigen of prostate 4 (STEAP4) has been linked to tumor progression via its involvement in inflammatory responses, oxidative stress, and metabolism. However, STEAP4 has rarely been studied in hepatocellular carcinoma (HCC). We explored STEAP4 expression associated with tumor prognosis to understand its role in tumor biology in HCC.

Patients and Methods

STEAP4 mRNA and protein expressions were primarily analyzed using bioinformatics tools based on The Cancer Genome Atlas database to understand the expression pattern, molecular mechanism, prognostic impact, and association with immune cell infiltration. We further investigated the association between STEAP4 protein expression and clinicopathological parameters and their predictive value in HCC patients using immunohistochemical staining of tissue microarrays.

Results

The expression of STEAP4 mRNA and protein in HCC tissues was significantly lower than in normal liver tissues. Reduced expression of STEAP4 was linked to advanced HCC stages, poor recurrence-free survival (RFS), and overall survival. Furthermore, reduced STEAP4 expression was a significant predictor of worse RFS in univariate and multivariate analyses in the immunohistochemical cohort. GO, KEGG, and GSEA analyses revealed that STEAP4 is related to numerous biological processes and pathways, including drug metabolism, DNA replication, RNA metabolism, and immune response. In terms of the immune system, the decreased level of STEAP4 was correlated with the immunosuppressive microenvironment.

Conclusion

Our data indicated that reduced STEAP4 expression was significantly associated with tumor aggressiveness and poor prognosis, possibly because of its link to various biological processes and induction of HCC immune evasion. Therefore, STEAP4 expression may serve as a potential prognostic biomarker for cancer progression and immunity, as well as a therapeutic target in HCC.

EZH2 as a Prognostic Factor and Its Immune Implication with Molecular Characterization in Prostate Cancer: An Integrated Multi-Omics in Silico Analysis

Prostate cancer (PCa) is a type of potentially fatal malignant tumor. Immunotherapy has shown a lot of potential for various types of solid tumors, but the benefits have been less impressive in PCa. Enhancer of zeste homolog 2 (EZH2) is one of the three core subunits of the polycomb repressive complex 2 that has histone methyltransferase activity, and the immune effects of EZH2 in PCa are still unclear. The purpose of this study was to explore the potential of EZH2 as a prognostic factor and an immune therapeutic biomarker for PCa, as well as the expression pattern and biological functions. All analyses in this study were based on publicly available databases, mainly containing Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), UCSCXenaShiny, and TISIDB. We performed differential expression analysis, developed a prognostic model, and explored potential associations between EZH2 and DNA methylation modifications, tumor microenvironment (TME), immune-related genes, tumor mutation burden (TMB), tumor neoantigen burden (TNB), and representative mismatch repair (MMR) genes. We also investigated the molecular and immunological characterizations of EZH2. Finally, we predicted immunotherapeutic responses based on EZH2 expression levels. We found that EZH2 was highly expressed in PCa, was associated with a poor prognosis, and may serve as an independent prognostic factor. EZH2 expression in PCa was associated with DNA methylation modifications, TME, immune-related genes, TMB, TNB, and MMR. By gene set enrichment analysis and gene set variation analysis, we found that multiple functions and pathways related to tumorigenesis, progression, and immune activation were enriched. Finally, we inferred that immunotherapy may be more effective for PCa patients with low EZH2 expression. In conclusion, our study showed that EZH2 could be a potentially efficient predictor of prognosis and immune response in PCa patients.

Transcriptomic discovery of a theranostic signature (SERPINE1/MMP3/COL1A1/SPP1) for head and neck squamous cell carcinomas and identification of antrocinol as a candidate drug

Head and neck squamous cell carcinomas (HNSCC) are prevalent malignancies with a disappointing prognosis, necessitating the search for theranostic biomarkers for better management. Based on a meta-analysis of transcriptomic data containing ten clinical datasets of HNSCC and matched nonmalignant samples, we identified SERPINE1/MMP3/COL1A1/SPP1 as essential hub genes as the potential theranostic biomarkers. Our analysis suggests these hub genes are associated with the extracellular matrix, peptidoglycans, cell migration, wound-healing processes, complement and coagulation cascades, and the AGE-RAGE signaling pathway within the tumor microenvironment. Also, these hub genes were associated with tumor-immune infiltrating cells and immunosuppressive phenotypes of HNSCC. Further investigation of The Cancer Genome Atlas (TCGA) cohorts revealed that these hub genes were associated with staging, metastasis, and poor survival in HNSCC patients. Molecular docking simulations were performed to evaluate binding activities between the hub genes and antrocinol, a novel small-molecule derivative of an anticancer phytochemical antrocin previously discovered by our group. Antrocinol showed high affinities to MMP3 and COL1A1. Notably, antrocinol presented satisfactory drug-like and ADMET properties for therapeutic applications. These results hinted at the potential of antrocinol as an anti-HNSCC candidate via targeting MMP3 and COL1A1. In conclusion, we identified hub genes: SERPINE1/MMP3/COL1A1/SPP1 as potential diagnostic biomarkers and antrocinol as a potential new drug for HNSCC.

Inhibition of lysyl oxidase-like 2 overcomes adhesion-dependent drug resistance in the collagen-enriched liver cancer microenvironment

The tumor microenvironment (TME) is considered to be one of the vital mediators of tumor progression. Extracellular matrix (ECM), infiltrating immune cells, and stromal cells collectively constitute the complex ecosystem with varied biochemical and biophysical properties. The development of liver cancer is strongly tied with fibrosis and cirrhosis that alters the microenvironmental landscape, especially ECM composition. Enhanced deposition and cross-linking of type I collagen are frequently detected in patients with liver cancer and have been shown to facilitate tumor growth and metastasis by epithelial-to-mesenchymal transition. However, information on the effect of collagen enrichment on drug resistance is lacking. Thus, the present study has comprehensively illustrated phenotypical and mechanistic changes in an in vitro mimicry of collagen-enriched TME and revealed that collagen enrichment could induce 5-fluorouracil (5FU) and sorafenib resistance in liver cancer cells through hypoxia-induced up-regulation of lysyl oxidase-like 2 (LOXL2). LOXL2, an enzyme that facilitates collagen cross-linking, enhances cell adhesion-mediated drug resistance by activating the integrin alpha 5 (ITGA5)/focal adhesion kinase (FAK)/phosphoinositide 3-kinase (PI3K)/rho-associated kinase 1 (ROCK1) signaling axis. Conclusion: We demonstrated that inhibition of LOXL2 in a collagen-enriched microenvironment synergistically promotes the efficacy of sorafenib and 5FU through deterioration of focal adhesion signaling. These findings have clinical implications for developing LOXL2-targeted strategies in patients with chemoresistant liver cancer and especially for those patients with advanced fibrosis and cirrhosis.

ATG101-related signature predicts prognosis and therapeutic option in hepatocellular carcinoma

Autophagy plays a critical role in tumor pathogenesis. However, autophagy-related signature in Hepatocellular carcinoma (HCC) has not been revealed yet. We quantified the levels of various cancer hallmarks and identified ATG101 as the major risk factor for overall survival in HCC. A robust ATG101-related gene signature (ATS) for prognosis was constructed using a combination of bioinformatic and statistical approaches. Additionally, genetic and immunological properties were measured between ATS-high and ATS-low groups. The ATS signature was associated with shortened overall survival in HCC patients independently of clinicopathological characteristics. ATS status defines an inflamed yet exhausted tumor microenvironment, in which the activities of the exhausted CD8+ or CD4+ T cells were strongly associated with ATS. The ATS signature predicts the drug resistance to the immunotherapy, thus a combination of targeted therapy and immunotherapy might be suitable for ATS-high patients. This work shed light on the function of ATG101-related genes in HCC and revealed that the ATS signature may be a useful prognostic biomarker for differentiating molecular and immunological features and predicting probable response to the therapy.

Methylation-Mediated Silencing of RBP7 Promotes Breast Cancer Progression through PPAR and PI3K/AKT Pathway

Retinoid-binding protein7 (RBP7) is a member of the cellular retinol-binding protein (CRBP) family, which is involved in the pathogenesis of breast cancer. The study aims to illustrate the prognostic value and the potential regulatory mechanisms of RBP7 expression in breast cancer. Bioinformatics analysis with the TCGA and CPTAC databases revealed that the mRNA and protein expression levels of RBP7 in normal were higher compared to breast cancer tissues. Survival analysis displayed that the lower expression of RBP7, the worse the prognosis in ER-positive (ER+) breast cancer patients. Genomic analysis showed that low expression of RBP7 correlates with its promoter hypermethylation in breast cancer. Functional enrichment analysis demonstrated that downregulation of RBP7 expression may exert its biological influence on breast cancer through the PPAR pathway and the PI3K/AKT pathway. In summary, we identified RBP7 as a novel biomarker that is helpful for the prognosis of ER+ breast cancer patients. Promoter methylation of RBP7 is involved in its gene silencing in breast cancer, thus regulating the occurrence and development of ER+ breast cancer through the PPAR and PI3K/AKT pathways.

The established chemokine-related prognostic gene signature in prostate cancer: Implications for anti-androgen and immunotherapies

Background

Prostate cancer (PCa) was one of the most common malignancies among men, while the prognosis for PCa patients was poor, especially for patients with recurrent and advanced diseases.

Materials and methods

Five PCa cohorts were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases, and the biochemical recurrence (BCR)-related chemokine genes were identified by LASSO-Cox regression. The chemokine-related prognostic gene signature (CRPGS) was established, and its association with PCa patients’ clinical, pathological and immune characteristics was analyzed. The association between CRPGS and PCa patients’ responses to androgen deprivation therapy (ADT) and immunotherapy was analyzed. The CRPGS was compared with other previously published molecular signatures, and the CRPGS was externally validated in our real-world AHMU-PC cohort.

Results

Four recurrence-free survival (RFS)-related chemokine genes (CXCL14, CCL20, CCL24, and CCL26) were identified, and the CRPGS was established based on the four identified chemokine genes, and TCGA-PRAD patients with high riskscores exhibited poorer RFS, which was validated in the GSE70768 cohort. The CRPGS was associated with the clinical, pathological, and immune characteristics of PCa patients. Low-risk PCa patients were predicted to respond better to ADT and immunotherapy. By comparing with other molecular signatures, the CRPGS could classify PCa patients into two risk groups well, and the CRPGS was associated with the m6A level, as well as TP53 and SPOP mutation status of PCa patients. In the AHMU-PC cohort, the CRPGS was associated with the advanced pathology stage and Gleason score.

Conclusions

The identified chemokine genes and CRPGS were associated with the prognosis of PCa, which could predict PCa patients’ responses to anti-androgen and immunotherapies.

Integrative Bioinformatics Analysis Reveals CHEK1 and UBE2C as Luminal A Breast Cancer Subtype Biomarkers

In light of the limited number of targetable oncogenic drivers in breast cancer (BRCA), it is important to identify effective and druggable gene targets for the treatment of this devastating disease. Herein, the GSE102484 dataset containing expression profiling data from 683 BRCA patients was re-analyzed using weighted gene co-expression network analysis (WGCNA). The yellow module with the highest correlation to BRCA progression was screened out, followed by functional enrichment analysis and establishment of a protein–protein interaction (PPI) network. After further validation through survival analysis and expression evaluation, CHEK1 and UBE2C were finally identified as hub genes related to the progression of BRCA, especially the luminal A breast cancer subtype. Notably, both hub genes were found to be dysregulated in multiple types of immune cells and closely correlated with tumor infiltration, as revealed by Tumor Immune Estimation Resource (TIMER) along with other bioinformatic tools. Construction of transcription factors (TF)-hub gene network further confirmed the existence of 11 TFs which could regulate both hub genes simultaneously. Our present study may facilitate the invention of targeted therapeutic drugs and provide novel insights into the understanding of the mechanism beneath the progression of BRCA.

YAP1 maintains active chromatin state in head and neck squamous cell carcinomas that promotes tumorigenesis through cooperation with BRD4

Analysis of The Cancer Genome Atlas and other published data of head and neck squamous cell carcinoma (HNSCC) reveals somatic alterations of the Hippo-YAP pathway in approximately 50% of HNSCC. Better strategies to target the YAP1 transcriptional complex are sought. Here, we show that FAT1, an upstream inhibitor of YAP1, is mutated either by missense or by truncating mutation in 29% of HNSCC. Comprehensive proteomic and drug-screening studies across pan-cancer models confirm that FAT1-mutant HNSCC exhibits selective and higher sensitivity to BRD4 inhibition by JQ1. Epigenomic analysis reveals an active chromatin state in FAT1-mutant HNSCC cells that is driven by the YAP/TAZ transcriptional complex through recruitment of BRD4 to deposit active histone marks, thereby maintaining an oncogenic transcriptional state. This study reveals a detailed cooperative mechanism between YAP1 and BRD4 in HNSCC and suggests a specific therapeutic opportunity for the treatment of this subset of head and neck cancer patients.

KIF2C is a Biomarker Correlated With Prognosis and Immunosuppressive Microenvironment in Human Tumors

Kinesin superfamily member 2C (KIF2C) is an essential regulator of the cell cycle and its aberrant expression can promote tumor progression. However, the mechanism of KIF2C in pan-cancer is unclear.Data were obtained from public databases, including The Cancer Genome Atlas (TCGA), UALCAN, TIMER and CellMiner. The data came from public databases such as The Cancer Genome Atlas (TCGA), UALCAN, TIMER, and CellMiner. We analyzed the correlation of KIF2C with expression, prognosis, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repairs (MMR), immune infiltration and anticancer drug sensitivity by R language.KIF2C was highly expressed in several tumors and correlated with poor prognosis. KIF2C expression was significantly correlated with TMB, MSI, MMRs, and immune checkpoint genes, and with the level of immune cell infiltration such as tumor-associated macrophage (TAM), cancer-associated fibroblasts (CAFs), myeloid-derived suppressor cells (MDSCs) and Tregs. The GO and KEGG results suggest that KIF2C is involved in immune regulation in addition to cell cycle regulation.In addition, KIF2C is associated with DNA methylation, m6A modifications and m7G modifications. Our data suggest that KIF2C is a prognostic biomarker linked to immunosuppression, targeting KIF2C may improve the outcome of immunotherapy. Our findings indicate that KIF2C is a prognostic biomarker associated with immunosuppression, and that targeting KIF2C may improve the outcome of immunotherapy.

TET1 mutations as a predictive biomarker for immune checkpoint inhibitors in colon adenocarcinoma

BACKGROUND

The ten-eleven translocation 1 (TET1), which is essential for active DNA demethylation, plays a multifaceted role in the pathogenesis of colorectal cancer. The study has demonstrated the association of TET1 mutations with a high response to immune checkpoint inhibitors (ICIs) in diverse cancers. However, the relationship between TET1 mutations and the response to ICIs in colon cancer is still lacking.

METHODS

The prognosis, predictive markers, immune characteristics, mutation number of DNA damage repair (DDR) pathways, pathway enrichment, and drug sensitivity conditions were all compared between TET1-mutated and wild-type patients with colon adenocarcinoma (COAD).

RESULTS

The overall survival of patients with TET1 mutations in the ICI-treated cohort was significantly longer than those without (p = 0.0059). Compared with the wild-type patients, TET1-mutated patients had higher tumor mutational burden and neoantigen load, enhanced abundance of tumor-infiltrating immune cells, increased expression of immune-related genes, and mutation number of DDR pathways. Additionally, the patients with TET1 mutations were found to be more sensitive to lapatinib and 5-fluorouracil.

CONCLUSION

These findings suggest that TET1 mutations may serve as a potential biomarker for the response to ICIs in COAD patients.

Butyrophilin-like 9 expression is associated with outcome in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most prevalent non-small cell lung cancer (NSCLC). Patients with LUAD have a poor 5-year survival rate. The use of immune checkpoint inhibitors (ICIs) for the treatment of LUAD has been on the rise in the past decade. This study explored the prognostic role of butyrophilin-like 9 (BTNL9) in LUAD.

METHODS

Gene expression profile of buytrophilins (BTNs) was determined using the GEPIA database. The effect of BTNL9 on the survival of LUAD patients was assessed using Kaplan-Meier plotter and OncoLnc. Correlation between BTNL9 expression and tumor-infiltrating immune cells (TILs) was explored using TIMER and GEPIA databases. Further, the relationship between BTNL9 expression and drug response was evaluated using CARE. Besides, construction and evaluation of nomogram based on BTNL9 expression and TNM stage.

RESULTS

BTNL9 expression was downregulated in LUAD and was associated with a poor probability of 1, 3, 5-years overall survival (OS). In addition, BTNL9 expression was regulated at epigenetic and post-transcriptional modification levels. Moreover, BTNL9 expression was significantly positively correlated with ImmuneScore and ESTIMATEScore. Furthermore, BTNL9 expression was positively associated with infiltration levels of B cells, CD4+ T cells, and macrophages. Kaplan-Meier analysis showed that BTNL9 expression in B cells and dendritic cells (DCs) was significantly associated with OS. BTNL9 expression was significantly positively correlated with CARE scores.

CONCLUSIONS

These findings show that BTNL9 is a potential prognostic biomarker for LUAD. Low BTNL9 expression levels associated with low infiltration levels of naïve B cells, and DCs in the tumor microenvironment are unfavorable for OS in LUAD patients.

Computational biology approaches for mapping transcriptional regulatory networks

Transcriptional Regulatory Networks (TRNs) are mainly responsible for the cell-type- or cell-state-specific expression of gene sets from the same DNA sequence. However, so far there are no precise maps of TRNs available for each cell-type or cell-state, and no ideal tool to map those networks clearly and in full from biological samples. In this review, major approaches and tools to map TRNs from high-throughput data are presented, depending on the type of methods or data used to infer them, and their advantages and limitations are discussed. After summarizing the main principles defining the topology and structure–function relationships in TRNs, an overview of the extensive work done to map TRNs from bulk transcriptomic data will be presented by type of methodological approach. Most recent modellings of TRNs using other types of molecular data or integrating different data types, including single-cell RNA-sequencing and chromatin information, will then be discussed, before briefly concluding with improvements expected to come in the field.

MPI-based bioinformatic analysis and co-inhibitory therapy with mannose for oral squamous cell carcinoma

Mannose induces tumor cell apoptosis and inhibits glucose metabolism by accumulating intracellularly as mannose 6-phosphate while the drug sensitivity of tumors is negatively correlated with mannose phosphate isomerase gene (MPI) expression. In this study, we performed a first attempt to explore the relationship between the targeted gene MPI and immune infiltration and genetic and clinical characteristics of head and neck squamous carcinoma (HNSC) using computational algorithms and bioinformatic analysis, and further to verify the co-inhibition effects of mannose with genotoxicity, immune responses, and microbes dysbiosis in oral squamous cell carcinoma (OSCC) in vitro and in vivo. Our results found that patients with lower MPI expression had higher survival rate. The enhancement of MPI expression was in response to DNA damage gene, and ATM inhibitor was verified as a potential drug with a synergistic effect with mannose on HSC-3. In the HNSC, infiltrated immunocytes CD8+ T cell and B cell were the significantly reduced risk cells, while IL-22 and IFN-γ showed negative correlation with MPI. Finally, mannose could reverse immunophenotyping caused by antibiotics in mice, resulting in the decrease of CD8+ T cells and increase of myeloid-derived suppressor cells (MDSCs). In conclusion, the MPI gene showed a significant correlation with immune infiltration and genetic and clinical characteristics of HNSC. The treatment of ATM inhibitor, immune regulating cells of CD8+ T cells and MDSCs, and oral microbiomes in combination with mannose could exhibit co-inhibitory therapeutic effect for OSCC.

Identification of Radiotherapy-Associated Genes in Lung Adenocarcinoma by an Integrated Bioinformatics Analysis Approach

Radiotherapy (RT) plays an important role in the prognosis of lung adenocarcinoma (LUAD) patients, but the radioresistance (RR) of LUAD is still a challenge that needs to be overcome. The current study aimed to investigate LUAD patients with RR to illuminate the underlying mechanisms. We utilized gene set variation analysis (GSVA) and The Cancer Immunome Atlas (TCIA) database to characterize the differences in biological functions and neoantigen-coding genes between RR and radiosensitive (RS) patients. Weighted Gene co-expression network analysis (WGCNA) was used to explore the relationship between RT-related traits and hub genes in two modules, i.e., RR and RS; two representative hub genes for RR (MZB1 and DERL3) and two for RS (IFI35 and PSMD3) were found to be related to different RT-related traits. Further analysis of the hub genes with the Lung Cancer Explorer (LCE), PanglaoDB and GSVA resources revealed the differences in gene expression levels, cell types and potential functions. On this basis, the Tumor and Immune System Interaction Database (TISIDB) was used to identify the potential association between RR genes and B cell infiltration. Finally, we used the Computational Analysis of Resistance (CARE) database to identify specific gene-associated drugs for RR patients and found that GSK525762A and nilotinib might be promising candidates for RR treatment. Taken together, these results demonstrate that B cells in TME may have a significant impact on the RT and that these two drug candidates, GSK525762A and nilotinib, might be helpful for the treatment of RR patients.

Spike-in normalization for single-cell RNA-seq reveals dynamic global transcriptional activity mediating anticancer drug response

The transcriptional plasticity of cancer cells promotes intercellular heterogeneity in response to anticancer drugs and facilitates the generation of subpopulation surviving cells. Characterizing single-cell transcriptional heterogeneity after drug treatments can provide mechanistic insights into drug efficacy. Here, we used single-cell RNA-seq to examine transcriptomic profiles of cancer cells treated with paclitaxel, celecoxib and the combination of the two drugs. By normalizing the expression of endogenous genes to spike-in molecules, we found that cellular mRNA abundance shows dynamic regulation after drug treatment. Using a random forest model, we identified gene signatures classifying single cells into three states: transcriptional repression, amplification and control-like. Treatment with paclitaxel or celecoxib alone generally repressed gene transcription across single cells. Interestingly, the drug combination resulted in transcriptional amplification and hyperactivation of mitochondrial oxidative phosphorylation pathway linking to enhanced cell killing efficiency. Finally, we identified a regulatory module enriched with metabolism and inflammation-related genes activated in a subpopulation of paclitaxel-treated cells, the expression of which predicted paclitaxel efficacy across cancer cell lines and in vivo patient samples. Our study highlights the dynamic global transcriptional activity driving single-cell heterogeneity during drug response and emphasizes the importance of adding spike-in molecules to study gene expression regulation using single-cell RNA-seq.

Deep generative neural network for accurate drug response imputation

Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tumor microenvironment, has been shown playing a significant role in shaping the actual treatment outcome. In this study, we develop a deep variational autoencoder (VAE) model to compress thousands of genes into latent vectors in a low-dimensional space. We then demonstrate that these encoded vectors could accurately impute drug response, outperform standard signature-gene based approaches, and appropriately control the overfitting problem. We apply rigorous quality assessment and validation, including assessing the impact of cell line lineage, cross-validation, cross-panel evaluation, and application in independent clinical data sets, to warrant the accuracy of the imputed drug response in both cell lines and cancer samples. Specifically, the expression-regulated component (EReX) of the observed drug response achieves high correlation across panels. Using the well-trained models, we impute drug response of The Cancer Genome Atlas data and investigate the features and signatures associated with the imputed drug response, including cell line origins, somatic mutations and tumor mutation burdens, tumor microenvironment, and confounding factors. In summary, our deep learning method and the results are useful for the study of signatures and markers of drug response.

Strongly preserved modules between cancer tissue and cell line contribute to drug resistance analysis across multiple cancer types

The existence and emergence of drug resistance in tumor cells is the main burden of cancer treatment. Most cancer drug resistance analyses are based entirely on cell line data and ignore the discordance between human tumors and cell lines, leading to biased preclinical model transformation. Based on cancer tissue data in TCGA and cancer cell line data in CCLE, this study identified and excluded non-preserved module (NP module) between cancer tissue and cell lines. We used strongly preserved module (SP module) for clinically relevant drug resistance analysis and identified 2068 "cancer-drug-module" pairs of 7 cancer types and 212 drugs based on data in GDSC. Furthermore, we identified potentially ineffective combination therapy (PICT) from multiple perspectives. Finally, we found 1608 sets of predictors that can predict drug response. These results provide insights and clues for the clinical selection of effective chemotherapy drugs to overcome cancer resistance in a new perspective.

Identification of Prognostic Stromal-Immune Score-Based Genes in Hepatocellular Carcinoma Microenvironment

A growing amount of evidence has suggested the clinical importance of stromal and immune cells in the liver cancer microenvironment. However, reliable prognostic signatures based on assessments of stromal and immune components have not been well-established. This study aimed to identify stromal-immune score–based potential prognostic biomarkers for hepatocellular carcinoma. Stromal and immune scores were estimated from transcriptomic profiles of a liver cancer cohort from The Cancer Genome Atlas using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select prognostic genes. Favorable overall survivals and progression-free interval were found in patients with high stromal score and immune score, and 828 differentially expressed genes were identified. Functional enrichment analysis and protein–protein interaction networks further showed that these genes mainly participated in immune response, extracellular matrix, and cell adhesion. MMP9 (matrix metallopeptidase 9) was identified as a prognostic tumor microenvironment–associated gene by using LASSO and TIMER (Tumor IMmune Estimation Resource) algorithms and was found to be positively correlated with immunosuppressive molecules and drug response.

A self-consistent probabilistic formulation for inference of interactions

Large molecular interaction networks are nowadays assembled in biomedical researches along with important technological advances. Diverse interaction measures, for which input solely consisting of the incidence of causal-factors, with the corresponding outcome of an inquired effect, are formulated without an obvious mathematical unity. Consequently, conceptual and practical ambivalences arise. We identify here a probabilistic requirement consistent with that input, and find, by the rules of probability theory, that it leads to a model multiplicative in the complement of the effect. Important practical properties are revealed along these theoretical derivations, that has not been noticed before.

Methods and resources to access mutation-dependent effects on cancer drug treatment

In clinical cancer treatment, genomic alterations would often affect the response of patients to anticancer drugs. Studies have shown that molecular features of tumors could be biomarkers predictive of sensitivity or resistance to anticancer agents, but the identification of actionable mutations are often constrained by the incomplete understanding of cancer genomes. Recent progresses of next-generation sequencing technology greatly facilitate the extensive molecular characterization of tumors and promote precision medicine in cancers. More and more clinical studies, cancer cell lines studies, CRISPR screening studies as well as patient-derived model studies were performed to identify potential actionable mutations predictive of drug response, which provide rich resources of molecularly and pharmacologically profiled cancer samples at different levels. Such abundance of data also enables the development of various computational models and algorithms to solve the problem of drug sensitivity prediction, biomarker identification and in silico drug prioritization by the integration of multiomics data. Here, we review the recent development of methods and resources that identifies mutation-dependent effects for cancer treatment in clinical studies, functional genomics studies and computational studies and discuss the remaining gaps and future directions in this area.

The transcriptomic response of cells to a drug combination is more than the sum of the responses to the monotherapies

Our ability to discover effective drug combinations is limited, in part by insufficient understanding of how the transcriptional response of two monotherapies results in that of their combination. We analyzed matched time course RNAseq profiling of cells treated with single drugs and their combinations and found that the transcriptional signature of the synergistic combination was unique relative to that of either constituent monotherapy. The sequential activation of transcription factors in time in the gene regulatory network was implicated. The nature of this transcriptional cascade suggests that drug synergy may ensue when the transcriptional responses elicited by two unrelated individual drugs are correlated. We used these results as the basis of a simple prediction algorithm attaining an AUROC of 0.77 in the prediction of synergistic drug combinations in an independent dataset.

Insights into DNA-drug interactions in the era of omics

Despite the rise of sophisticated new targeting strategies in cancer chemotherapy, many classic DNA-binding drugs remain on the front line of the therapy against cancer. Based on examples primarily from the author's laboratory, this article reviews the capabilities of several DNA-binding drugs to alter gene expression. Research is ongoing about the molecular bases of the inhibition of gene expression and how alteration of the cellular transcriptome can commit cancer cells to die. The development of a variety of omic techniques allows us to gain insights into the effect of antitumor drugs. Genome-wide approaches provide unbiased genomic data that can facilitate a deeper understanding of the cellular response to DNA-binding drugs. Moreover, the results of large-scale genomic studies are gathered in publicly available databases that can be used in developing precision medicine in cancer treatment.

Genetic Interactions and Tissue Specificity Modulate the Association of Mutations with Drug Response

In oncology, biomarkers are widely used to predict subgroups of patients that respond to a given drug. Although clinical decisions often rely on single gene biomarkers, machine learning approaches tend to generate complex multi-gene biomarkers that are hard to interpret. Models predicting drug response based on multiple altered genes often assume that the effects of single alterations are independent. We asked whether the association of cancer driver mutations with drug response is modulated by other driver mutations or the tissue of origin. We developed an analytic framework based on linear regression to study interactions in pharmacogenomic data from two large cancer cell line panels. Starting from a model with only covariates, we included additional variables only if they significantly improved simpler models. This allows to systematically assess interactions in small, easily interpretable models. Our results show that including mutation-mutation interactions in drug response prediction models tends to improve model performance and robustness. For example, we found that TP53 mutations decrease sensitivity to BRAF inhibitors in BRAF-mutated cell lines and patient tumors, suggesting a therapeutic benefit of combining inhibition of oncogenic BRAF with reactivation of the tumor suppressor TP53. Moreover, we identified tissue-specific mutation-drug associations and synthetic lethal triplets where the simultaneous mutation of two genes sensitizes cells to a drug. In summary, our interaction-based approach contributes to a holistic view on the determining factors of drug response.

Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery

Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, and therefore coined as oncogene and non-oncogene addictions. The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, but the identification of potentially synergistic drugs at scale remains challenging. Here we propose a gene-expression-based approach, which uses the recurrent perturbation-transcript regulatory relationships inferred from a large compendium of chemical and genetic perturbation experiments across multiple cell lines, to engender a testable hypothesis for combination therapies. These transcript-level recurrences were distinct from known compound-protein target counterparts, were reproducible in external datasets, and correlated with small-molecule sensitivity. We applied these recurrent relationships to predict synergistic drug pairs for cancer and experimentally confirmed two unexpected drug combinations in vitro. Our results corroborate a gene-expression-based strategy for combinatorial drug screening as a way to target non-mutated genes in complex diseases.

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Compound signatures targeting non-oncogene addiction for combinatorial drug discovery

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These signatures are reproducible and linked to cancer hallmarks and drug sensitivity

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Two synergistic drug combinations are experimentally confirmed in vitro

Systematic identification of non-coding pharmacogenomic landscape in cancer

Emerging evidence has shown long non-coding RNAs (lncRNAs) play important roles in cancer drug response. Here we report a lncRNA pharmacogenomic landscape by integrating multi-dimensional genomic data of 1005 cancer cell lines and drug response data of 265 anti-cancer compounds. Using Elastic Net (EN) regression, our analysis identifies 27,341 lncRNA-drug predictive pairs. We validate the robustness of the lncRNA EN-models using two independent cancer pharmacogenomic datasets. By applying lncRNA EN-models of 49 FDA approved drugs to the 5605 tumor samples from 21 cancer types, we show that cancer cell line based lncRNA EN-models can predict therapeutic outcome in cancer patients. Further lncRNA-pathway co-expression analysis suggests lncRNAs may regulate drug response through drug-metabolism or drug-target pathways. Finally, we experimentally validate that EPIC1, the top predictive lncRNA for the Bromodomain and Extra-Terminal motif (BET) inhibitors, strongly promotes iBET762 and JQ-1 resistance through activating MYC transcriptional activity.

Challenging transcription by DNA-binding antitumor drugs

Cancer has been associated with altered gene expression. Therefore, transcription and its regulation by transcription factors are considered key points to be explored in the pursuit of more efficient antitumor agents. This paper reviews the effects of DNA-binding drugs on the interaction between transcription factors and DNA, and it discusses recent advances in the understanding of the mechanisms by which small compounds interfere with the activity of transcription factors and gene expression. Many DNA-binding drugs, some of them in clinical use, can compete with a variety of transcription factors for their preferred binding sites in gene promoters, or they can covalently modify DNA, thus preventing transcription factors from recognizing their binding sites. On the other hand, transcription factor activity can be impaired through modification of the protein factors or their complexes. Several "omic" tools have been developed to explore the genome-wide changes in gene expression induced by DNA-binding drugs, which reveal details of the mechanisms of action. Transcriptomic profiles obtained from drug-treated cells and of samples collected from patients upon treatment provide insights into the in vivo mechanisms of drug action related to the inhibition of gene transcription. The information available about the molecular structure and mechanisms of action of both transcription factors and DNA-binding drugs, together with the new opportunities provided by functional genomics, should encourage the development of new more-selective DNA-binding antitumor drugs to target a single gene with little effect on others.

PATRI, a Genomics Data Integration Tool for Biomarker Discovery

The availability of genomic datasets in association with clinical, phenotypic, and drug sensitivity information represents an invaluable source for potential therapeutic applications, supporting the identification of new drug sensitivity biomarkers and pharmacological targets. Drug discovery and precision oncology can largely benefit from the integration of treatment molecular discriminants obtained from cell line models and clinical tumor samples; however this task demands comprehensive analysis approaches for the discovery of underlying data connections. Here we introduce PATRI (Platform for the Analysis of TRanslational Integrated data), a standalone tool accessible through a user-friendly graphical interface, conceived for the identification of treatment sensitivity biomarkers from user-provided genomics data, associated with information on sample characteristics. PATRI streamlines a translational analysis workflow: first, baseline genomics signatures are statistically identified, differentiating treatment sensitive from resistant preclinical models; then, these signatures are used for the prediction of treatment sensitivity in clinical samples, via random forest categorization of clinical genomics datasets and statistical evaluation of the relative phenotypic features. The same workflow can also be applied across distinct clinical datasets. The ease of use of the PATRI tool is illustrated with validation analysis examples, performed with sensitivity data for drug treatments with known molecular discriminants.

Next-generation sequencing in drug development: target identification and genetically stratified clinical trials

Next-generation sequencing (NGS) enabled high-throughput analysis of genotype-phenotype relationships on human populations, ushering in a new era of genetics-informed drug development. The year 2017 was remarkable, with the first FDA-approved gene therapy for cancer (Kymriah™) and for inherited diseases (LUXTURNA™), the first multiplex NGS panel for companion diagnostics (MSK-IMPACT™) and the first drug targeting a genetic signature rather than a disease (Keytruda^®). We envision that population-scale NGS with paired electronic health records (EHRs) will become a routine measure in the drug development process for the identification of novel drug targets, and that genetically stratified clinical trials could be widely adopted to improve power in precision-medicine-guided drug development.

Big Data Approaches for Modeling Response and Resistance to Cancer Drugs

Despite significant progress in cancer research, current standard-of-care drugs fail to cure many types of cancers. Hence, there is an urgent need to identify better predictive biomarkers and treatment regimes. Conventionally, insights from hypothesis-driven studies are the primary force for cancer biology and therapeutic discoveries. Recently, the rapid growth of big data resources, catalyzed by breakthroughs in high-throughput technologies, has resulted in a paradigm shift in cancer therapeutic research. The combination of computational methods and genomics data has led to several successful clinical applications. In this review, we focus on recent advances in data-driven methods to model anticancer drug efficacy, and we present the challenges and opportunities for data science in cancer therapeutic research.