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Most metastatic colorectal cancer (mCRC) patients succumb to refractory disease due to secondary chemotherapy resistance. To elucidate the molecular changes associated with secondary resistance, we recruited 64 patients with mCRC and hepatic metastases before standard first-line chemotherapy between 2014 and 2018. We subjected DNA from primary tumor specimens (P), hepatic metastasis specimens after treatment (M), and liquid biopsies (L) taken prior to (pre), during (intra), and after (post) treatment to next generation sequencing. We performed Nanostring expression analysis in P and M specimens. Comparative bioinformatics and statistical analysis revealed typical mutational patterns with frequent alterations in TP53, APC, and KRAS in P specimens (n = 48). P and pre-L (n = 42), as well as matched P and M (n = 30), displayed a similar mutation spectrum. In contrast, gene expression profiles classified P (n = 31) and M (n = 23), distinguishable by up-regulation of immune/cytokine receptor and autophagy programs. Switching of consensus molecular subtypes from P to M occurred in 58.3% of cases. M signature genes SFRP2 and SPP1 associated with inferior survival, as validated in an independent cohort. Molecular changes during first-line treatment were detectable by expression profiling rather than by mutational tumor and liquid biopsy analyses. SFRP2 and SPP1 may serve as biomarkers and/or actionable targets.

Since the current melanoma clinicopathological staging system remains restricted to predicting survival outcomes, establishing precise prognostic targets is needed. Here, we used gene expression signature (GES) classification and Cox regression analyses to biologically characterize melanoma cells at the single-cell level and construct a prognosis-related gene signature for melanoma. By analyzing publicly available scRNA-seq data, we identified six distinct GESs (named: “Anti-apoptosis”, “Immune cell interactions”, “Melanogenesis”, “Ribosomal biogenesis”, “Extracellular structure organization”, and “Epithelial-Mesenchymal Transition (EMT)”). We verified these GESs in the bulk RNA-seq data of patients with skin cutaneous melanoma (SKCM) from The Cancer Genome Atlas (TCGA). Four GESs (“Immune cell interactions”, “Melanogenesis”, “Ribosomal biogenesis”, and “Extracellular structure organization”) were significantly correlated with prognosis (p = 1.08 × 10−5, p = 0.042, p = 0.001, and p = 0.031, respectively). We identified a prognostic signature of melanoma composed of 45 genes (MPS_45). MPS_45 was validated in TCGA-SKCM (HR = 1.82, p = 9.08 × 10−6) and three other melanoma datasets (GSE65904: HR = 1.73, p = 0.006; GSE19234: HR = 3.83, p = 0.002; and GSE53118: HR = 1.85, p = 0.037). MPS_45 was independently associated with survival (p = 0.002) and was proved to have a high potential for predicting prognosis in melanoma patients.

Some breast cancers have been shown to contain a small fraction of cells characterized by CD44⁺/CD24⁻/low cell-surface antigen profile that have high tumor-initiating potential. In addition, breast cancer cells propagated in vitro as mammospheres (MSs) have also been shown to be enriched for cells capable of self-renewal. In this study, we have defined a gene expression signature common to both CD44⁺/CD24⁻/low and MS-forming cells. To examine its clinical significance, we determined whether tumor cells surviving after conventional treatments were enriched for cells bearing this CD44⁺/CD24⁻/low-MS signature. The CD44⁺/CD24⁻/low-MS signature was found mainly in human breast tumors of the recently identified \"claudin-low\" molecular subtype, which is characterized by expression of many epithelial-mesenchymal-transition (EMT)-associated genes. Both CD44⁺/CD24⁻/low-MS and claudin-low signatures were more pronounced in tumor tissue remaining after either endocrine therapy (letrozole) or chemotherapy (docetaxel), consistent with the selective survival of tumor-initiating cells posttreatment. We confirmed an increased expression of mesenchymal markers, including vimentin (VIM) in cytokeratin-positive epithelial cells metalloproteinase 2 (MMP2), in two separate sets of postletrozole vs. pretreatment specimens. Taken together, these data provide supporting evidence that the residual breast tumor cell populations surviving after conventional treatment may be enriched for subpopulations of cells with both tumor-initiating and mesenchymal features. Targeting proteins involved in EMT may provide a therapeutic strategy for eliminating surviving cells to prevent recurrence and improve long-term survival in breast cancer patients.

Epithelial‐mesenchymal transition (EMT) is a reversible and dynamic process hypothesized to be co‐opted by carcinoma during invasion and metastasis. Yet, there is still no quantitative measure to assess the interplay between EMT and cancer progression. Here, we derived a method for universal EMT scoring from cancer‐specific transcriptomic EMT signatures of ovarian, breast, bladder, lung, colorectal and gastric cancers. We show that EMT scoring exhibits good correlation with previously published, cancer‐specific EMT signatures. This universal and quantitative EMT scoring was used to establish an EMT spectrum across various cancers, with good correlation noted between cell lines and tumours. We show correlations between EMT and poorer disease‐free survival in ovarian and colorectal, but not breast, carcinomas, despite previous notions. Importantly, we found distinct responses between epithelial‐ and mesenchymal‐like ovarian cancers to therapeutic regimes administered with or without paclitaxel in vivo and demonstrated that mesenchymal‐like tumours do not always show resistance to chemotherapy. EMT scoring is thus a promising, versatile tool for the objective and systematic investigation of EMT roles and dynamics in cancer progression, treatment response and survival. Synopsis A novel EMT scoring method reveals that EMT status does not unanimously correlate with poorer overall and disease‐free survival. Different EMTed tumours show distinct responses to certain chemotherapeutics, with the potential to stratify patients by EMT status. A novel scoring method was developed based on transcriptomics to universally estimate and compare the Epithelial‐Mesenchymal Transition (EMT) phenotype across cancer types. A spectrum of EMT was established across more than 15 cancers using this EMT scoring method. Correlations of EMT status with poorer overall‐ and disease‐free survival were not unanimously observed in all cancers. Differential and preferential responses of EMTed tumours to certain chemotherapeutics were observed, suggesting the potential to stratify patients by EMT status. Graphical Abstract A novel EMT scoring method reveals that EMT status does not unanimously correlate with poorer overall and disease‐free survival. Different EMTed tumours show distinct responses to certain chemotherapeutics, with the potential to stratify patients by EMT status.

Current staging systems of cancer are mainly based on the anatomical extent of disease. They need refinement by biological parameters to improve stratification of patients for tumor therapy or surveillance strategies. Thanks to developments in genomic, transcriptomic, and big-data technologies, we are now able to explore molecular characteristics of tumors in detail and determine their clinical relevance. This has led to numerous prognostic and predictive gene expression signatures that have the potential to establish a classification of tumor subgroups by biological determinants. However, only a few gene signatures have reached the stage of clinical implementation so far. In this review article, we summarize the current status, and present and future challenges of prognostic gene signatures in three relevant cancer entities: breast cancer, colorectal cancer, and hepatocellular carcinoma.

Background The ongoing COVID-19 outbreak has caused devastating mortality and posed a significant threat to public health worldwide. Despite the severity of this illness and 2.3 million worldwide deaths, the disease mechanism is mostly unknown. Previous studies that characterized differential gene expression due to SARS-CoV-2 infection lacked robust validation. Although vaccines are  now available, effective treatment options are still out of reach. Results To characterize the transcriptional activity of SARS-CoV-2 infection, a gene signature consisting of 25 genes was generated using a publicly available RNA-Sequencing (RNA-Seq) dataset of cultured cells infected with SARS-CoV-2. The signature estimated infection level accurately in bronchoalveolar lavage fluid (BALF) cells and peripheral blood mononuclear cells (PBMCs) from healthy and infected patients (mean 0.001 vs. 0.958; P  < 0.0001). These signature genes were investigated in their ability to distinguish the severity of SARS-CoV-2 infection in a single-cell RNA-Sequencing dataset. TNFAIP3 , PPP1R15A , NFKBIA , and IFIT2 had shown bimodal gene expression in various immune cells from severely infected patients compared to healthy or moderate infection cases. Finally, this signature was assessed using the publicly available ConnectivityMap database to identify potential disease mechanisms and drug repurposing candidates. Pharmacological classes of tricyclic antidepressants, SRC-inhibitors, HDAC inhibitors, MEK inhibitors, and drugs such as atorvastatin, ibuprofen, and ketoconazole showed strong negative associations (connectivity score < − 90), highlighting the need for further evaluation of these candidates for their efficacy in treating SARS-CoV-2 infection. Conclusions Thus, using the 25-gene SARS-CoV-2 infection signature, the SARS-CoV-2 infection status was captured in BALF cells, PBMCs and postmortem lung biopsies. In addition, candidate SARS-CoV-2 therapies with known safety profiles were identified. The signature genes could potentially also be used to characterize the COVID-19 disease severity in patients’ expression profiles of BALF cells.

Background Early menarche is an established risk factor for breast cancer but its molecular contribution to tumor biology and prognosis remains unclear. Methods We profiled transcriptome-wide gene expression in breast tumors (N = 846) and tumor-adjacent normal tissues (N = 666) from women in the Nurses’ Health Studies (NHS) to investigate whether early menarche (age < 12) is associated with tumor molecular and prognostic features in women with breast cancer. Multivariable linear regression and pathway analyses using competitive gene set enrichment analysis were conducted in both tumor and adjacent-normal tissue and externally validated in TCGA (N = 116). Subgroup analyses stratified on ER-status based on the tumor were also performed. PAM50 signatures were used for tumor molecular subtyping and to generate proliferation and risk of recurrence scores. We created a gene expression score using LASSO regression to capture early menarche based on 28 genes from FDR-significant pathways in breast tumor tissue in NHS and tested its association with 10-year disease-free survival in both NHS (N = 836) and METABRIC (N = 952). Results Early menarche was significantly associated with 369 individual genes in adjacent-normal tissues implicated in extracellular matrix, cell adhesion, and invasion (FDR ≤ 0.1). Early menarche was associated with upregulation of cancer hallmark pathways (18 significant pathways in tumor, 23 in tumor-adjacent normal, FDR ≤ 0.1) related to proliferation (e.g. Myc, PI3K/AKT/mTOR, cell cycle), oxidative stress (e.g. oxidative phosphorylation, unfolded protein response), and inflammation (e.g. pro-inflammatory cytokines IFN α and IFN γ ). Replication in TCGA confirmed these trends. Early menarche was associated with significantly higher PAM50 proliferation scores (β = 0.082 [0.02–0.14]), odds of aggressive molecular tumor subtypes (basal-like, OR = 1.84 [1.18–2.85] and HER2-enriched, OR = 2.32 [1.46–3.69]), and PAM50 risk of recurrence score (β = 4.81 [1.71–7.92]). Our NHS-derived early menarche gene expression signature was significantly associated with worse 10-year disease-free survival in METABRIC (N = 952, HR = 1.58 [1.10–2.25]). Conclusions Early menarche is associated with more aggressive molecular tumor characteristics and its gene expression signature within tumors is associated with worse 10-year disease-free survival among women with breast cancer. As the age of onset of menarche continues to decline, understanding its relationship to breast tumor characteristics and prognosis may lead to novel secondary prevention strategies.

Asthma is a common chronic disease that is characterized by respiratory symptoms including cough, wheeze, shortness of breath, and chest tightness. The underlying mechanisms of this disease are not fully elucidated, so more research is needed to identify better therapeutic compounds and biomarkers to improve disease outcomes. In this present study, we used bioinformatics to analyze the gene expression of adult asthma in publicly available microarray datasets to identify putative therapeutic molecules for this disease. We first compared gene expression in healthy volunteers and adult asthma patients to obtain differentially expressed genes (DEGs) for further analysis. A final gene expression signature of 49 genes, including 34 upregulated and 15 downregulated genes, was obtained. Protein–protein interaction and hub analyses showed that 10 genes, including POSTN, CPA3, CCL26, SERPINB2, CLCA1, TPSAB1, TPSB2, MUC5B, BPIFA1, and CST1, may be hub genes. Then, the L1000CDS2 search engine was used for drug repurposing studies. The top approved drug candidate predicted to reverse the asthma gene signature was lovastatin. Clustergram results showed that lovastatin may perturb MUC5B expression. Moreover, molecular docking, molecular dynamics simulation, and computational alanine scanning results supported the notion that lovastatin may interact with MUC5B via key residues such as Thr80, Thr91, Leu93, and Gln105. In summary, by analyzing gene expression signatures, hub genes, and therapeutic perturbation, we show that lovastatin is an approved drug candidate that may have potential for treating adult asthma.

Background NRF2 is the key regulator of oxidative stress in normal cells and aberrant expression of the NRF2 pathway due to genetic alterations in the KEAP1 (Kelch-like ECH-associated protein 1)-NRF2 (nuclear factor erythroid 2 like 2)-CUL3 (cullin 3) axis leads to tumorigenesis and drug resistance in many cancers including head and neck squamous cell cancer (HNSCC). The main goal of this study was to identify specific genes regulated by the KEAP1-NRF2-CUL3 axis in HNSCC patients, to assess the prognostic value of this gene signature in different cohorts, and to reveal potential biomarkers. Methods RNA-Seq V2 level 3 data from 279 tumor samples along with 37 adjacent normal samples from patients enrolled in the The Cancer Genome Atlas (TCGA)-HNSCC study were used to identify upregulated genes using two methods (altered KEAP1-NRF2-CUL3 versus normal, and altered KEAP1-NRF2-CUL3 versus wild-type). We then used a new approach to identify the combined gene signature by integrating both datasets and subsequently tested this signature in 4 independent HNSCC datasets to assess its prognostic value. In addition, functional annotation using the DAVID v6.8 database and protein-protein interaction (PPI) analysis using the STRING v10 database were performed on the signature. Results A signature composed of a subset of 17 genes regulated by the KEAP1-NRF2-CUL3 axis was identified by overlapping both the upregulated genes of altered versus normal (251 genes) and altered versus wild-type (25 genes) datasets. We showed that increased expression was significantly associated with poor survival in 4 independent HNSCC datasets, including the TCGA-HNSCC dataset. Furthermore, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and PPI analysis revealed that most of the genes in this signature are associated with drug metabolism and glutathione metabolic pathways. Conclusions Altogether, our study emphasizes the discovery of a gene signature regulated by the KEAP1-NRF2-CUL3 axis which is strongly associated with tumorigenesis and drug resistance in HNSCC. This 17-gene signature provides potential biomarkers and therapeutic targets for HNSCC cases in which the NRF2 pathway is activated.

Background Accumulating evidence has suggested that cancer progression and therapeutic response depend on both tumor epithelium (EPI) and tumor microenvironment (TME). However, the dependency of clinical outcomes on the tumor EPI vs. the TME has neither been clearly defined nor quantified. Methods We classified 2373 colorectal cancer (CRC) tumors into the consensus molecular subtypes (CMS1-4) and generated the 10-gene TME S and the 10-gene EPI S signatures as the serendipitous derivatives of the most (positively vs. negatively) correlated genes of a highly-prognostic, ~ 500-gene signature we previously identified. Distinct TME vs. EPI cellular features of the signature genes were identified by CIBERSORT deconvolution and validated by scRNASEQ in an independent public dataset. Results The TME S signature was strongly associated with the immune/stromal TME-rich CMS1/CMS4 subtypes that portended worse survival, whereas the EPI S signature was predominantly related to the TME-poor, epithelial CMS2/CMS3 classes that portended better survival. Multivariable Cox regression analysis against 29 TME-related signatures revealed that the TME S signature was the most strikingly impacted by the “Cancer-associated fibroblasts” signature (HR: 10.87 vs. 0.13, both P  < 0.0001). Moreover, the TME S score was strongly correlated with EMT, SRC activation and MEK inhibitor resistance in 2373 CRC tumors (Spearman r  = 0.727, 0.802, 0.824, respectively), which was validated in two independent CRC datasets ( n  = 626 and n  = 566). By contrast, the EPI S score was the dominant force in associating with longer progression free survival in cetuximab-treated metastatic CRC patients derived from two independent clinical trials (Logrank trend P  = 0.0005/ n  = 80; P  = 0.0013/ n  = 44). This finding was further validated in a large real-world clinico-genomics dataset with EGFR inhibitor therapy, which demonstrated that higher EPI S scores were associated with increased overall survival (EGFRi, Logrank trend P  < 0.0001 / n  = 2343) and time on treatment (cetuximab, P  = 0.003/ n  = 953; panitumumab, P  < 0.0001/ n  = 1307). Conclusions Here we identified a pair of new, distinct 10-gene signatures (the EPI S vs. the TME S ) capable of distinguishing the cellular contribution of the tumor EPI vs. the TME in determining CRC prognosis and therapeutic outcomes. With targeted approaches emerging to address both tumor epithelial cells and the TME, the EPI S vs. TME S signature scores may have a novel biomarker role to permit optimization of CRC therapy by identifying sensitive vs. resistant subpopulations.