Predicting risk of endometrial failure: a biomarker signature that identifies a novel disruption independent of endometrial timing in patients undergoing hormonal replacement cycles

OBJECTIVE

To propose a new gene expression signature that identifies endometrial disruptions independent of endometrial luteal phase timing and predicts if patients are at risk of endometrial failure.

DESIGN

Multicentric, prospective study.

SETTING

Reproductive medicine research department in a public hospital affiliated with private fertility clinics and a reproductive genetics laboratory.

PATIENTS

Caucasian women (n = 281; 39.4 ± 4.8 years old with a body mass index of 22.9 ± 3.5 kg/m2) undergoing hormone replacement therapy between July 2018 and July 2021. Endometrial samples from 217 patients met RNA quality criteria for signature discovery and analysis.

INTERVENTION(S)

Endometrial biopsies collected in the mid-secretory phase.

MAIN OUTCOME MEASURE(S)

Endometrial luteal phase timing-corrected expression of 404 genes and reproductive outcomes of the first single embryo transfer (SET) after biopsy collection to identify prognostic biomarkers of endometrial failure.

RESULTS

Removal of endometrial timing variation from gene expression data allowed patients to be stratified into poor (n = 137) or good (n = 49) endometrial prognosis groups on the basis of their clinical and transcriptomic profiles. Significant differences were found between endometrial prognosis groups in terms of reproductive rates: pregnancy (44.6% vs. 79.6%), live birth (25.6% vs. 77.6%), clinical miscarriage (22.2% vs. 2.6%), and biochemical miscarriage (20.4% vs. 0%). The relative risk of endometrial failure for patients predicted as a poor endometrial prognosis was 3.3 times higher than those with a good prognosis. The differences in gene expression between both profiles were proposed as a biomarker, coined the endometrial failure risk (EFR) signature. Poor prognosis profiles were characterized by 59 upregulated and 63 downregulated genes mainly involved in regulation (17.0%), metabolism (8.4%), immune response, and inflammation (7.8%). This EFR signature had a median accuracy of 0.92 (min = 0.88, max = 0.94), median sensitivity of 0.96 (min = 0.91, max = 0.98), and median specificity of 0.84 (min = 0.77, max = 0.88), positioning itself as a promising biomarker for endometrial evaluation.

CONCLUSION(S)

The EFR signature revealed a novel endometrial disruption, independent of endometrial luteal phase timing, present in 73.7% of patients. This EFR signature stratified patients into 2 significantly distinct and clinically relevant prognosis profiles providing opportunities for personalized therapy. Nevertheless, further validations are needed before implementing this gene signature as an artificial intelligence (AI)-based tool to reduce the risk of patients experiencing endometrial failure.

Lansoprazole Ameliorates Isoniazid-Induced Liver Injury

Isoniazid is a first-line drug in antitubercular therapy. Isoniazid is one of the most commonly used drugs that can cause liver injury or acute liver failure, leading to death or emergency liver transplantation. Therapeutic approaches for the prevention of isoniazid-induced liver injury are yet to be established. In this study, we identified the gene expression signature for isoniazid-induced liver injury using a public transcriptome dataset, focusing on the differences in susceptibility to isoniazid in various mouse strains. We predicted that lansoprazole is a potentially protective drug against isoniazid-induced liver injury using connectivity mapping and an adverse event reporting system. We confirmed the protective effects of lansoprazole against isoniazid-induced liver injury using zebrafish and patients' electronic health records. These results suggest that lansoprazole can ameliorate isoniazid-induced liver injury. The integrative approach used in this study may be applied to identify novel functions of clinical drugs, leading to drug repositioning.

Tumor-immune microenvironment and NRF2 associate with clinical efficacy of PD-1 blockade combined with chemotherapy in lung squamous cell carcinoma

The RATIONALE-307 study (ClinicalTrials.gov: NCT03594747) demonstrates prolonged progression-free survival (PFS) with first-line tislelizumab plus chemotherapy versus chemotherapy in advanced lung squamous cell carcinoma (LUSC; N = 360). Here we describe an immune-related gene expression signature (GES), composed of genes involved in both innate and adaptive immunity, that appears to differentiate tislelizumab plus chemotherapy PFS benefit versus chemotherapy. In contrast, a tislelizumab plus chemotherapy PFS benefit is observed regardless of programmed death ligand 1 (PD-L1) expression or tumor mutational burden (TMB). Genetic analysis reveals that NRF2 pathway activation is enriched in PD-L1positive and TMBhigh patients. NRF2 pathway activation is negatively associated with PFS, which affects efficacy outcomes associated with PD-L1 and TMB status, impairing their predictive potential. Mechanistic studies demonstrate that NRF2 directly mediates PD-L1 constitutive expression independent of adaptive PD-L1 regulation in LUSC. In summary, the GES is an immune signature that might identify LUSC patients likely to benefit from first-line tislelizumab plus chemotherapy.

Identification of a core EMT signature that separates basal-like breast cancers into partial- and post-EMT subtypes

Epithelial-mesenchymal transition (EMT) is a cellular plasticity program critical for embryonic development and tissue regeneration, and aberrant EMT is associated with disease including cancer. The high degree of plasticity in the mammary epithelium is reflected in extensive heterogeneity among breast cancers. Here, we have analyzed RNA-sequencing data from three different mammary epithelial cell line-derived EMT models and identified a robust mammary EMT gene expression signature that separates breast cancers into distinct subgroups. Most strikingly, the basal-like breast cancers form two subgroups displaying partial-EMT and post-EMT gene expression patterns. We present evidence that key EMT-associated transcription factors play distinct roles at different stages of EMT in mammary epithelial cells.

Use of a gene expression signature to identify trimetazidine for repurposing to treat bipolar depression

OBJECTIVES

The aim of this study was to repurpose a drug for the treatment of bipolar depression.

METHODS

A gene expression signature representing the overall transcriptomic effects of a cocktail of drugs widely prescribed to treat bipolar disorder was generated using human neuronal-like (NT2-N) cells. A compound library of 960 approved, off-patent drugs were then screened to identify those drugs that affect transcription most similar to the effects of the bipolar depression drug cocktail. For mechanistic studies, peripheral blood mononuclear cells were obtained from a healthy subject and reprogrammed into induced pluripotent stem cells, which were then differentiated into co-cultured neurons and astrocytes. Efficacy studies were conducted in two animal models of depressive-like behaviours (Flinders Sensitive Line rats and social isolation with chronic restraint stress rats).

RESULTS

The screen identified trimetazidine as a potential drug for repurposing. Trimetazidine alters metabolic processes to increase ATP production, which is thought to be deficient in bipolar depression. We showed that trimetazidine increased mitochondrial respiration in cultured human neuronal-like cells. Transcriptomic analysis in induced pluripotent stem cell-derived neuron/astrocyte co-cultures suggested additional mechanisms of action via the focal adhesion and MAPK signalling pathways. In two different rodent models of depressive-like behaviours, trimetazidine exhibited antidepressant-like activity with reduced anhedonia and reduced immobility in the forced swim test.

CONCLUSION

Collectively our data support the repurposing of trimetazidine for the treatment of bipolar depression.

EBF1, MYO6 and CALR expression levels predict therapeutic response in diffuse large B-cell lymphomas

Background

Diffuse large B-cell lymphoma (DLBCL) is a hematological malignancy representing one-third of non-Hodgkin's lymphoma cases. Notwithstanding immunotherapy in combination with chemotherapy (R-CHOP) is an effective therapeutic approach for DLBCL, a subset of patients encounters treatment resistance, leading to low survival rates. Thus, there is an urgent need to identify predictive biomarkers for DLBCL including the elderly population, which represents the fastest-growing segment of the population in Western countries.

Methods

Gene expression profiles of n=414 DLBCL biopsies were retrieved from the public dataset GSE10846. Differentially expressed genes (DEGs) (fold change >1.4, p-value <0.05, n=387) have been clustered in responder and non-responder patient cohorts. An enrichment analysis has been performed on the top 30 up-regulated genes of responder and non-responder patients to identify the signatures involved in gene ontology (MSigDB). The more significantly up-regulated DEGs have been validated in our independent collection of formalin-fixed paraffin-embedded (FFPE) biopsy samples of elderly DLBCL patients, treated with R-CHOP as first-line therapy.

Results

From the analysis of two independent cohorts of DLBCL patients emerged a gene signature able to predict the response to R-CHOP therapy. In detail, expression levels of EBF1, MYO6, CALR are associated with a significant worse overall survival.

Conclusions

These results pave the way for a novel characterization of DLBCL biomarkers, aiding the stratification of responder versus non-responder patients.

Transcriptional profiles reveal histologic origin and prognosis across 33 The Cancer Genome Atlas tumor types

Background

In recent years, with the development of transcriptome sequencing, the molecular characteristics of tumors are gradually revealed. Because of the complexity of tumor transcriptome, there is a need to look for the molecular signatures which can be used to evaluate the tissue origin and cell stemness of tumors in order to promote the diagnosis and treatment of tumors.

Methods

Tumor tissue-specific gene sets (TTSGs) consisting of 200 genes were selected using RNA expression data of 9,875 patients from 33 tumor types. t-distributed Stochastic Neighbor Embedding (t-SNE) was used for dimensionality reduction and visualization of TTSGs in each tumor type. To evaluate oncogenic dedifferentiation and loss of cell stemness, Euclidean distance from each sample to a human embryo single-cell RNA-seq dataset (GSE36552) of TTSGs was calculated as TTSGs index indicating dissimilarity of tumors and embryo. TTSGs index was evaluated for prognosis in each tumor type. Two published signature indexes, the mRNA signature index (mRNAsi) and CIBERSORT, were compared to assess the correlation between the TTSGs index with cell stemness and immune microenvironment. Finally, the difference of prognosis, immune microenvironment and radiotherapy outcomes were compared between patients with high and low TTSGs index.

Results

In this study, all 33 tumor types in The Cancer Genome Atlas (TCGA) were embedded into isolated clusters by t-SNE and confirmed by k-nearest neighbors (kNN) algorithm. Clusters of squamous-cell carcinoma were adjacent to each other revealing similar histologic origin. Basal-like breast cancer was separated from luminal and HER-2-amplified subtypes and closed to squamous-cell carcinoma. TTSGs index was related to overall survival outcomes in cancers derived from liver, thyroid, brain, cervical and kidney. There was a positive correlation between mRNAsi and TTSGs index in pan-kidney and pan-neuronal cancers. Furthermore, cell fractions of M2 macrophages and total leukocytes increased in the group with higher TTSGs index. Patients with higher TTSGs index had longer overall survival time and less radiation therapy resistance compared to patients with lower TTSGs index.

Conclusions

The signature of TTSGs is related to tumor expression features that distinguish tumors of different histologic origin using t-SNE. The signature also relates to prognosis of certain kinds of tumors.

Immune-related signature of periodontitis and Alzheimer's disease linkage

Background: Periodontits (PD) and Alzheimer's disease (AD) are both associated with ageing and clinical studies increasingly evidence their association. However, specific mechanisms underlying this association remain undeciphered, and immune-related processes are purported to play a signifcant role. The accrual of publicly available transcriptomic datasets permits secondary analysis and the application of data-mining and bioinformatic tools for biological discovery. Aim: The present study aimed to leverage publicly available transcriptomic datasets and databases, and apply a series of bioinformatic analysis to identify a robust signature of immune-related signature of PD and AD linkage. Methods: We downloaded gene-expresssion data pertaining PD and AD and identified crosstalk genes. We constructed a protein-protein network analysis, applied immune cell enrichment analysis, and predicted crosstalk immune-related genes and infiltrating immune cells. Next, we applied consisent cluster analysis and performed immune cell bias analysis, followed by LASSO regression to select biomarker immune-related genes. Results: The results showed a 3 gene set comprising of DUSP14, F13A1 and SELE as a robust immune-related signature. Macrophages M2 and NKT, B-cells, CD4+ memory T-cells and CD8+ naive T-cells emerged as key immune cells linking PD with AD. Conclusion: Candidate immune-related biomarker genes and immune cells central to the assocation of PD with AD were identified, and merit investigation in experimental and clinical research.

Virus-specific and shared gene expression signatures in immune cells after vaccination in response to influenza and vaccinia stimulation

Background

In the vaccine era, individuals receive multiple vaccines in their lifetime. Host gene expression in response to antigenic stimulation is usually virus-specific; however, identifying shared pathways of host response across a wide spectrum of vaccine pathogens can shed light on the molecular mechanisms/components which can be targeted for the development of broad/universal therapeutics and vaccines.

Method

We isolated PBMCs, monocytes, B cells, and CD8+ T cells from the peripheral blood of healthy donors, who received both seasonal influenza vaccine (within <1 year) and smallpox vaccine (within 1 - 4 years). Each of the purified cell populations was stimulated with either influenza virus or vaccinia virus. Differentially expressed genes (DEGs) relative to unstimulated controls were identified for each in vitro viral infection, as well as for both viral infections (shared DEGs). Pathway enrichment analysis was performed to associate identified DEGs with KEGG/biological pathways.

Results

We identified 2,906, 3,888, 681, and 446 DEGs in PBMCs, monocytes, B cells, and CD8+ T cells, respectively, in response to influenza stimulation. Meanwhile, 97, 120, 20, and 10 DEGs were identified as gene signatures in PBMCs, monocytes, B cells, and CD8+ T cells, respectively, upon vaccinia stimulation. The majority of DEGs identified in PBMCs were also found in monocytes after either viral stimulation. Of the virus-specific DEGs, 55, 63, and 9 DEGs occurred in common in PBMCs, monocytes, and B cells, respectively, while no DEGs were shared in infected CD8+ T cells after influenza and vaccinia. Gene set enrichment analysis demonstrated that these shared DEGs were over-represented in innate signaling pathways, including cytokine-cytokine receptor interaction, viral protein interaction with cytokine and cytokine receptor, Toll-like receptor signaling, RIG-I-like receptor signaling pathways, cytosolic DNA-sensing pathways, and natural killer cell mediated cytotoxicity.

Conclusion

Our results provide insights into virus-host interactions in different immune cells, as well as host defense mechanisms against viral stimulation. Our data also highlights the role of monocytes as a major cell population driving gene expression in ex vivo PBMCs in response to viral stimulation. The immune response signaling pathways identified in this study may provide specific targets for the development of novel virus-specific therapeutics and improved vaccines for vaccinia and influenza. Although influenza and vaccinia viruses have been selected in this study as pathogen models, this approach could be applicable to other pathogens.

A Nine-Gene Expression Signature Distinguished a Patient with Chronic Lymphocytic Leukemia Who Underwent Prolonged Periodic Fasting

Background and Objectives: This study aimed to investigate the causes of continuous deep fluctuations in the absolute lymphocyte count (ALC) in an untreated patient with Chronic Lymphocytic Leukemia (CLL), who has had a favorable prognosis since the time of diagnosis. Up until now, the patient has voluntarily chosen to adopt a predominantly vegetarian and fruitarian diet, along with prolonged periods of total fasting (ranging from 4 to 39 days) each year. Materials and Methods: For this purpose, we decided to analyze the whole transcriptome profiling of peripheral blood (PB) CD19+ cells from the patient (#1) at different time-points vs. the same cells of five other untreated CLL patients who followed a varied diet. Consequently, the CLL patients were categorized as follows: the 1st group comprised patient #1 at 20 different time-points (16 time-points during nutrition and 4 time-points during fasting), whereas the 2nd group included only one time point for each of the patients (#2, #3, #4, #5, and #6) as they followed a varied diet. We performed microarray experiments using a powerful tool, the Affymetrix Human Clariom™ D Pico Assay, to generate high-fidelity biomarker signatures. Statistical analysis was employed to identify differentially expressed genes and to perform sample clustering. Results: The lymphocytosis trend in patient #1 showed recurring fluctuations since the time of diagnosis. Interestingly, we observed that approximately 4-6 weeks after the conclusion of fasting periods, the absolute lymphocyte count was reduced by about half. The gene expression profiling analysis revealed that nine genes were statistically differently expressed between the 1st group and the 2nd group. Specifically, IGLC3, RPS26, CHPT1, and PCDH9 were under expressed in the 1st group compared to the 2nd group of CLL patients. Conversely, IGHV3-43, IGKV3D-20, PLEKHA1, CYBB, and GABRB2 were over-expressed in the 1st group when compared to the 2nd group of CLL patients. Furthermore, clustering analysis validated that all the samples from patient #1 clustered together, showing clear separation from the samples of the other CLL patients. Conclusions: This study unveiled a small gene expression signature consisting of nine genes that distinguished an untreated CLL patient who followed prolonged periods of total fasting, maintaining a gradual growth trend of lymphocytosis, compared to five untreated CLL patients with a varied diet. Future investigations focusing on patient #1 could potentially shed light on the role of prolonged periodic fasting and the implication of this specific gene signature in sustaining the lymphocytosis trend and the favorable course of the disease.

Bioinformatic Analysis of Key Regulatory Genes in Adult Asthma and Prediction of Potential Drug Candidates

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.

Decoding Connectivity Map-based drug repurposing for oncotherapy

The rising global burden of cancer has driven considerable efforts into the research and development of effective anti-cancer agents. Fortunately, with impressive advances in transcriptome profiling technology, the Connectivity Map (CMap) database has emerged as a promising and powerful drug repurposing approach. It provides an important platform for systematically discovering of the associations among genes, small-molecule compounds and diseases, and elucidating the mechanism of action of drug, contributing toward efficient anti-cancer pharmacotherapy. Moreover, CMap-based computational drug repurposing is gaining attention because of its potential to overcome the bottleneck constraints faced by traditional drug discovery in terms of cost, time and risk. Herein, we provide a comprehensive review of the applications of drug repurposing for anti-cancer drug discovery and summarize approaches for computational drug repurposing. We focus on the principle of the CMap database and novel CMap-based software/algorithms as well as their progress achieved for drug repurposing in the field of oncotherapy. This article is expected to illuminate the emerging potential of CMap in discovering effective anti-cancer drugs, thereby promoting efficient healthcare for cancer patients.

Blastoid B-Cell Neoplasms: Diagnostic Challenges and Solutions

Blastoid B-cell neoplasms mainly include B-lymphoblastic leukemia/lymphoma (B-ALL), blastoid mantle cell lymphoma, and high-grade B-cell lymphoma with blastoid morphologic features (blastoid HGBL). Distinguishing blastoid HGBL from B-ALL can be challenging and we previously developed six-point flow cytometry-focused and three-point immunohistochemistry-focused scoring systems to aid in differential diagnosis. However, the six-point scoring system was derived from bone marrow cases and occasional cases may have a misleading score using either system. In this study, we assessed 121 cases of blastoid-HGBL (37 BM and 84 extramedullary) to validate the six-point scoring system in all tissue types and to further compare the two scoring systems. Compared with 47 B-ALL cases enriched for CD34-negative neoplasm, the 121 blastoid-HGBL cases showed distinctive pathologic features. The six-point scoring system showed a sensitivity of 100%. A comparison of the two scoring systems in blastoid HGBL (n = 64) and B-ALL (n = 37) showed a concordance score rate of 88%. Thirteen cases showed misleading scores, including five HGBL and eight B-ALL, and the diagnosis was further validated by gene transcriptome profiling. Twelve of thirteen cases had discordant scores between the two scoring systems. Simultaneous employment of both scoring systems improved the accuracy of classification of blastoid B-cell neoplasms to 99%. In conclusion, the previously defined six-point scoring system showed an excellent performance regardless of the tissue origin. Using both scoring systems together improves the accuracy of classification of blastoid B-cell neoplasms. Cases with discordant scores between the two scoring systems were extremely challenging neoplasms and classification required correlation with all available clinical and genetic features.

NETest: serial liquid biopsies in gastroenteropancreatic NET surveillance

Objective

Up to now, serial NETest measurements in individuals assessing the disease course of gastroenteropancreatic neuroendocrine tumors (GEPNETs) at long-term follow-up and treatment response were not studied.

Design

The study was a longitudinal validation study of serial NETest measurements - a blood-based gene expression signature - in 132 patients with GEPNETs on therapy or watch-and-wait strategy.

Methods

Serial samples were collected during 46 (range: 6-71) months of follow-up. NETest scores were compared with Response Evaluation Criteria in Solid Tumors version 1.1-defined treatment response (e.g. no evidence of disease (NED), stable disease (SD) or progressive disease (PD)).

Results

Consecutive NETest scores fluctuated substantially (range: 0-100) over time in individuals with SD (n = 28) and NED (n = 30). Follow-up samples were significantly higher in SD (samples 3-5) and NED subgroups (samples 2-5) compared with baseline results, without changes in imaging. In 82% of untreated patients with PD, consecutive NETest scores consistently remained high. In patients undergoing systemic treatment, the median pre-treatment NETest score in treatment-responders was 76.5 (n = 22) vs 33 (n = 12) in non-responders (P = 0.001). Patients with low pre-treatment scores had 21 months reduced progression-free survival (10 vs 31 months; P = 0.01). The accuracy of the NETest for treatment response prediction was 0.73 (P = 0.009).

Conclusion

In patients not undergoing treatment, consecutive low NETest scores are associated with indolent behavior. Patients who develop PD exhibit elevated scores. Elevated results have important predictive value for treatment responsiveness and could be used for individualizing decisions on systemic therapy. The clinical value of follow-up NETest scores for patients who choose to watch and wait requires further study.

Serial Analysis of Gene Mutations and Gene Expression during First-Line Chemotherapy against Metastatic Colorectal Cancer: Identification of Potentially Actionable Targets within the Multicenter Prospective Biomarker Study REVEAL

Simple Summary

The emergence of resistant cells remains a major obstacle for chemotherapy treatment of metastatic colorectal cancers. Improvement of the therapeutic response requires a thorough understanding of the mechanisms of resistance as well as informative biomarkers. In the REVEAL study, we have systematically compared the mutational patterns and expression profiles of primary tumor specimens before and after first-line chemotherapy treatment in the metastatic situation. In addition, we analyzed liquid biopsies pre, during, and after treatment. Alterations in gene expression appeared as the major drivers of chemotherapy resistance. We identified a gene expression signature differentiating primary tumors and metastases and validated this signature in two independent patient cohorts. Moreover, we evaluated the expression of two signature genes, SFRP2 and SPP1, as prognostic and potentially druggable biomarkers.

Abstract

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.

Real World Evaluation of the Prosigna/PAM50 Test in a Node-Negative Postmenopausal Swedish Population: A Multicenter Study

Simple Summary

Gene expression signatures can provide important information on the risk of recurrence in patients with hormone receptor positive early breast cancer, and they can guide postoperative treatment. We have investigated how the implementation of gene-expression-based risk signatures with the Prosigna^® test impacted patient management in Sweden. The two major conclusions of this study are that prognostic factors derived from routine pathology were poor predictors of the intrinsic subtype and the risk of recurrence score, and that gene-expression-based risk combined with clinicopathological biomarkers (tumor size, Ki67, tumor grade) spared patients from adjuvant chemotherapy, but also identified patients who would potentially benefit from this treatment.

Abstract

Molecular signatures to guide decisions for adjuvant chemotherapy are recommended in early ER-positive, HER2-negative breast cancer. The objective of this study was to assess what impact gene-expression-based risk testing has had following its recommendation by Swedish national guidelines. Postmenopausal women with ER-positive, HER2-negative and node negative breast cancer at intermediate clinical risk and eligible for chemotherapy were identified retrospectively from five Swedish hospitals. Tumor characteristics, results from Prosigna^® test and final treatment decision were available for all patients. Treatment recommendations were compared with the last version of regional guidelines before the introduction of routine risk signature testing. Among the 360 included patients, 41% (n = 148) had a change in decision for adjuvant treatment based on Prosigna^® test result. Out of the patients with clinical indication for adjuvant chemotherapy, 52% (n = 118) could avoid treatment based on results from Prosigna^® test. On the contrary, 23% (n = 30) of the patients with no indication were escalated to receive adjuvant chemotherapy after testing. Ki67 could not distinguish between the Prosigna^® risk groups or intrinsic subtypes and did not significantly differ between patients in which decision for adjuvant therapy was changed based on the test results. In conclusion, we report the first real-world data from implementation of gene-expression-based risk assessment in a Swedish context, which may facilitate the optimization of future versions of the national guidelines.

Novel Diagnostic Value of Driver Gene Transcription Signatures to Characterise Clear Cell Renal Cell Carcinoma, ccRCC

Routine molecular tumour diagnostics are augmented by DNA-based qualitative and quantitative molecular techniques detecting mutations of DNA. However, in the past decade, it has been unravelled that the phenotype of cancer, as it’s an extremely complex disease, cannot be fully described and explained by single or multiple genetic variants affecting only the coding regions of the genes. Moreover, studying the manifestation of these somatic mutations and the altered transcription programming—driven by genomic rearrangements, dysregulation of DNA methylation and epigenetic landscape—standing behind the tumorigenesis and detecting these changes could provide a more detailed characterisation of the tumour phenotype. Consequently, novel comparative cancer diagnostic pipelines, including DNA- and RNA-based approaches, are needed for a global assessment of cancer patients. Here we report, that by monitoring the expression patterns of key tumour driver genes by qPCR, the normal and the tumorous samples can be separated into distinct categories. Furthermore, we also prove that by examining the transcription signatures of frequently affected genes at 3p25, 3p21 and 9p21.3 genomic regions, the ccRCC (clear cell renal cell carcinoma) and non-tumorous kidney tissues can be distinguished based on the mRNA level of the selected genes. Our results open new diagnostics possibilities where the mRNA signatures of tumour drivers can supplement the DNA-based approaches providing a more precise diagnostics opportunity leading to determine more precise therapeutic protocols.

Gene Expression in Barrett's Esophagus Cell Lines Resemble Esophageal Squamous Cell Carcinoma Instead of Esophageal Adenocarcinoma

Esophageal adenocarcinoma (EAC) is strongly associated with Barrett's esophagus (BE), a pre-malignant condition resulting from gastric reflux. Esophageal squamous cell carcinoma (ESCC), the other major subtype of esophageal cancer, shows strong association with smoking and alcohol intake and no association with gastric reflux. In this study, we constructed and validated gene expression signatures of EAC vs. ESCC tumors using publicly available datasets, and subsequently assessed the enrichment levels of these signatures in commonly used EAC and ESCC cell lines, normal esophageal tissues and normal esophageal cell lines, and primary BE tissues and BE cell lines. We found that unlike ESCC cell lines which were quite similar to primary ESCC tumors, EAC cell lines were considerably different from primary EAC tumors but still more similar to EAC tumors than ESCC tumors, as the genes up in EAC vs. ESCC (EAChi) had considerably lower expression in EAC cell lines than EAC tumors. However, more surprisingly, unlike various normal cell lines (EPC2, Het-1A) which were very similar to various tissues from normal esophagus, BE cell lines (BAR-T, CP-A) were extremely different from primary BE tissues, as BE cell lines had substantially lower levels of EAChi and substantially higher levels of ESCChi gene expression. This ESCC-like profile of the BAR-T remained unaltered even after prolonged exposure to an acidic bile mixture in vitro resulting in malignant transformation of this cell line. However, primary BE tissues had EAC-like gene expression profiles as expected. Only one EAC case from the Cancer Genome Atlas resembled BE cell lines, and while it had the clinical profile and some mutational features of EAC, it had some mutational features, the copy number alteration profile, and the gene expression profile of ESCC instead. These incomprehensible changes in gene expression patterns may result in ambiguous changes in the phenotype and warrants careful evaluation to inform selection of appropriate in vitro tools for future studies on esophageal adenocarcinoma.

Miller-Payne Grading and 70-Gene Signature Are Associated With Prognosis of Hormone Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Early-Stage Breast Cancer After Neoadjuvant Chemotherapy

Introduction

HR+/HER2- breast cancer (BC) has a much lower pathological complete response (pCR) rate to neoadjuvant chemotherapy (NAC). Therefore, to better stratify the relapse risk for HR+/HER2- non-pCR populations, it is essential to accurate identification new prognostic markers.

Materials and Methods

The study retrospectively analyzed 105 stage II-III patients who were diagnosed with HR+/HER2- BC and received NAC followed by breast and axilla surgery between 2013 and 2019 in Sun Yat-Sen University Cancer Center. The Miller-Payne (MP) grading system was used to evaluate pathological responses to NAC. The 70-gene signature was used to classify the prognosis signatures.

Results

Among the 105 patients, the study demonstrated that larger tumor size and lower progesterone receptor level at baseline and larger tumor size postoperative were statistically significantly associated with worse disease-free survival (DFS) (p = 0.004, p = 0.021, and p = 0.001, respectively). Among 54 patients who underwent the 70-gene assays, 26 (48.1%) had a low-risk signature; 28 (51.9%) patients had a high-risk signature. Patients with poor response (MP grades 1-2) were more likely to with a high-risk 70-gene signature than those with good response (MP grades 4-5). The final analysis showed that DFS was longer in the low-risk group than in the high-risk group [52.4 vs. 36.1 months of the median DFS, hazard ratio (HR) for recurrence, 0.29; 95% confidence interval (CI), 0.10-0.80; p = 0.018]. DFS was longer in the good response (MP grades 3-4) group than in the poor response (MP grades 1-2) group (94.7% vs. 60% of the patients free from recurrence; HR, 0.16; 95% CI, 0.05-0.47; p = 0.037). When stratified by MP grades combined with the 70-gene signature, subgroup analyses showed the good-response low-risk group with the best DFS, whereas the poor-response high-risk group showed the worst DFS (p = 0.048). Due to the short median follow-up time of 34.5 months (5.9-75.1 months), MP grades and the 70-gene signature did not show significant prognostic value for overall survival.

Conclusion

The study showed that analysis of MP grades combined with the 70-gene signature with residual NAC-resistant breast samples has a significant correlation with DFS.

Using Breast Cancer Gene Expression Signatures in Clinical Practice: Unsolved Issues, Ongoing Trials and Future Perspectives

The development of gene expression signatures since the early 2000's has offered standardized assays to evaluate the prognosis of early breast cancer. Five signatures are currently commercially available and recommended by several international guidelines to individualize adjuvant chemotherapy decisions in hormone receptors-positive/HER2-negative early breast cancer. However, many questions remain unanswered about their predictive ability, reproducibility and external validity in specific populations. They also represent a new hope to tailor (neo)adjuvant systemic treatment, adjuvant radiation therapy, hormone therapy duration and to identify a subset of patients who might benefit from CDK4/6 inhibitor adjuvant treatment. This review will highlight these particular issues, address the remaining questions and discuss the ongoing and future trials.

Validation of Gene Expression-Based Predictive Biomarkers for Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

BACKGROUND

Neoadjuvant chemoradiotherapy (nCRT) followed by surgery is widely used for patients with locally advanced rectal cancer. However, response to nCRT varies substantially among patients, highlighting the need for predictive biomarkers that can distinguish non-responsive from responsive patients before nCRT. This study aimed to build novel multi-gene assays for predicting nCRT response, and to validate our signature and previously-reported signatures in multiple independent cohorts.

METHODS

Three microarray datasets of pre-therapeutic biopsies containing a total of 61 non-responders and 53 responders were used as the discovery cohorts to screen for genes that were consistently associated with nCRT response. The predictive values of signatures were tested in a meta-analysis using six independent datasets as the validation cohorts, consisted of a total of 176 non-responders and 99 responders.

RESULTS

We identified four genes, including BRCA1, GPR110, TNIK, and WDR4 in the discovery cohorts. Although our 4-gene signature and nine published signatures were evaluated, they were unable to predict nCRT response in the validation cohorts.

CONCLUSIONS

Although this is one of the largest studies addressing the validity of gene expression-based classifiers using pre-treatment biopsies from patients with rectal cancer, our findings do not support their clinically meaningful values to be predictive of nCRT response.

Interferon-related gene expression in response to TNF inhibitor treatment in ankylosing spondylitis patients: a pilot study

OBJECTIVE

Ankylosing spondylitis (AS) is a chronic inflammatory arthritis primarily affecting the spine and sacroiliac joints. TNF inhibitor (TNFi) drugs are recommended for patients not responding to NSAIDs; however, there is a significant need for biomarkers of response. IFN-regulated genes (IRGs) and other cytokines/chemokines are linked to autoimmune diseases and have been associated with treatment response. Our objective was to explore whether IRGs and cytokines/chemokines can be associated with response to TNFiagents in AS.

METHODS

Peripheral blood mononuclear cells were obtained from 26 AS patients who were to receive a TNFi (I, n = 15) or placebo (P, n = 11) at week 0 and week 22. Response (R)/non-response (NR) was defined as reduction in ASDAS ≥ 1.2 points or reduction in sacroiliac/vertebral MRI lesions. The expression of 96 genes was quantified using TaqMan assays. Finally, ELISA was used to measure IL-6 in serum samples from another 38 AS patients.

RESULTS

Analysis of gene expression in 26 baseline samples segregated patients into four groups defined by a signature of 15 genes (mainly IRGs). ASDAS response was associated with one group independently of treatment received. We then analysed response to the TNFi (n = 15) and identified a 12-gene signature associated with MRI response. A third IRG signature was also associated with a reduction in IRGs expression post-TNFi samples (n = 10 pairs). Finally, decreased circulating IL-6 was associated with BASDAI-R.

CONCLUSION

This pilot study suggests an association between IRG expression and response to TNFi in AS. These findings require validation in a larger cohort in order to construct predictive algorithms for patient stratification.

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma

Aging is an inevitable time-dependent process associated with a gradual decline in many physiological functions. Importantly, some studies have supported that aging may be involved in the development of lung adenocarcinoma (LUAD). However, no studies have described an aging-related gene (ARG)-based prognosis signature for LUAD. Accordingly, in this study, we analyzed ARG expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). After LASSO and Cox regression analyses, a six ARG-based signature (APOC3, EPOR, H2AFX, MXD1, PLCG2, and YWHAZ) was constructed using TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of overall survival (OS). Cox regression analysis indicated that the ARG signature was an independent prognostic factor in LUAD. A nomogram based on the ARG signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Moreover, to visualize the prediction results, we established a web-based calculator yurong.shinyapps.io/ARGs_LUAD/. Calibration plots showed good consistency between the prediction of the nomogram and actual observations. Receiver operating characteristic curve and decision curve analyses indicated that the ARG nomogram had better OS prediction and clinical net benefit than the staging system. Taken together, these results established a genetic signature for LUAD based on ARGs, which may promote individualized treatment and provide promising novel molecular markers for immunotherapy.

Elastic Net Models Based on DNA Copy Number Variations Predicts Clinical Features, Expression Signatures, and Mutations in Lung Adenocarcinoma

In the precision medicine of lung adenocarcinoma, the identification and prediction of tumor phenotypes for specific biomolecular events are still not studied in depth. Various earlier researches sheds light on the close correlation between genetic expression signatures and DNA copy number variations (CNVs), for which analysis of CNVs provides valuable information about molecular and phenotypic changes in tumorigenesis. In this study, we propose a comprehensive analysis combining genome-wide association analysis and an Elastic Net Regression predictive model, focus on predicting the levels of many gene expression signatures in lung adenocarcinoma, based upon DNA copy number features alone. Additionally, we predicted many other key phenotypes, including clinical features (pathological stage), gene mutations, and protein expressions. These Elastic Net prediction methods can also be applied to other gene sets, thereby facilitating their use as biomarkers in monitoring therapy.

An extracellular vesicle-related gene expression signature identifies high-risk patients in medulloblastoma

BACKGROUND

Medulloblastoma (MB) is a malignant brain tumor in childhood. It comprises 4 subgroups with different clinical behaviors. The aim of this study was to characterize the transcriptomic landscape of MB, both at the level of individual tumors as well as in large patient cohorts.

METHODS

We used a combination of single-cell transcriptomics, cell culture models and biophysical methods such as nanoparticle tracking analysis and electron microscopy to investigate intercellular communication in the MB tumor niche.

RESULTS

Tumor cells of the sonic hedgehog (SHH)-MB subgroup show a differentiation blockade. These cells undergo extensive metabolic reprogramming. The gene expression profiles of individual tumor cells show a partial convergence with those of tumor-associated glial and immune cells. One possible cause is the transfer of extracellular vesicles (EVs) between cells in the tumor niche. We were able to detect EVs in co-culture models of MB tumor cells and oligodendrocytes. We also identified a gene expression signature, EVS, which shows overlap with the proteome profile of large oncosomes from prostate cancer cells. This signature is also present in MB patient samples. A high EVS expression is one common characteristic of tumors that occur in high-risk patients from different MB subgroups or subtypes.

CONCLUSIONS

With EVS, our study uncovered a novel gene expression signature that has a high prognostic significance across MB subgroups.

A Gene Expression Signature to Predict Nucleotide Excision Repair Defects and Novel Therapeutic Approaches

Nucleotide excision repair (NER) resolves DNA adducts, such as those caused by ultraviolet light. Deficient NER (dNER) results in a higher mutation rate that can predispose to cancer development and premature ageing phenotypes. Here, we used isogenic dNER model cell lines to establish a gene expression signature that can accurately predict functional NER capacity in both cell lines and patient samples. Critically, none of the identified NER deficient cell lines harbored mutations in any NER genes, suggesting that the prevalence of NER defects may currently be underestimated. Identification of compounds that induce the dNER gene expression signature led to the discovery that NER can be functionally impaired by GSK3 inhibition, leading to synergy when combined with cisplatin treatment. Furthermore, we predicted and validated multiple novel drugs that are synthetically lethal with NER defects using the dNER gene signature as a drug discovery platform. Taken together, our work provides a dynamic predictor of NER function that may be applied for therapeutic stratification as well as development of novel biological insights in human tumors.

Prognostic Cancer Gene Expression Signatures: Current Status and Challenges

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.

Autophagy is associated with a robust specific transcriptional signature in breast cancer subtypes

Previous works have described that autophagy could be associated to both pro- and anti-cancer properties according to numerous factors, such as the gene considered, the step of autophagy involved or the cancer model used. These data might be explained by the fact that some autophagy-related genes may be involved in other cellular processes and therefore differently regulated according to the type or the grade of the tumor. Indeed, using different approaches of transcriptome analysis in breast cancers, and further confirmation using digital PCR, we identified a specific signature of autophagy gene expression associated to Luminal A or Triple Negative Breast Cancers (TNBC). Moreover, we confirmed that ATG5, an autophagy gene specifically expressed in TNBC, favored cell migration, whereas BECN1, an autophagy gene specifically associated with ER-positive breast cancers, induced opposite effects. We also showed that overall inhibition of autophagy promoted cell migration suggesting that the role of individual ATG genes in cancer phenotypes was not strictly dependent of their function during autophagy. Finally, our work led to the identification of TXNIP1 as a potential biomarker associated to autophagy induction in breast cancers. This gene could become an essential tool to quantify autophagy levels in fixed biopsies, sort tumors according to their autophagy levels and determine the best therapeutic treatment.

An Integrated In Silico and In Vivo Approach to Identify Protective Effects of Palonosetron in Cisplatin-Induced Nephrotoxicity

Cisplatin is widely used to treat various types of cancers, but it is often limited by nephrotoxicity. Here, we employed an integrated in silico and in vivo approach to identify potential treatments for cisplatin-induced nephrotoxicity (CIN). Using publicly available mouse kidney and human kidney organoid transcriptome datasets, we first identified a 208-gene expression signature for CIN and then used the bioinformatics database Cmap and Lincs Unified Environment (CLUE) to identify drugs expected to counter the expression signature for CIN. We also searched the adverse event database, Food and Drug Administration. Adverse Event Reporting System (FAERS), to identify drugs that reduce the reporting odds ratio of developing cisplatin-induced acute kidney injury. Palonosetron, a serotonin type 3 receptor (5-hydroxytryptamine receptor 3 (5-HT3R)) antagonist, was identified by both CLUE and FAERS analyses. Notably, clinical data from 103 patients treated with cisplatin for head and neck cancer revealed that palonosetron was superior to ramosetron in suppressing cisplatin-induced increases in serum creatinine and blood urea nitrogen levels. Moreover, palonosetron significantly increased the survival rate of zebrafish exposed to cisplatin but not to other 5-HT3R antagonists. These results not only suggest that palonosetron can suppress CIN but also support the use of in silico and in vivo approaches in drug repositioning studies.

The Rheumatoid Arthritis Gene Expression Signature Among Women Who Improve or Worsen During Pregnancy: A Pilot Study

OBJECTIVE

To assess whether gene expression signatures associated with rheumatoid arthritis (RA) before pregnancy differ between women who improve or worsen during pregnancy, and to determine whether these expression signatures are altered during pregnancy when RA improves or worsens.

METHODS

Clinical data and blood samples were collected before pregnancy (T0) and at the third trimester (T3) from 11 women with RA and 5 healthy women. RA disease activity was assessed using the Clinical Disease Activity Index (CDAI). At each timepoint, RA-associated gene expression signatures were identified using differential expression analysis of RNA sequencing profiles between women with RA and healthy women.

RESULTS

Of the women with RA, 6 improved by T3 (RA_improved), 3 worsened (RA_worsened),and 2 were excluded. At T0, mean CDAI scores were similar in both groups (RA_improved 11.2 ± 9.8; RA_worsened 13.8 ± 6.7; Wilcoxon rank-sum test: P = 0.6). In the RA_improved group, 89 genes were differentially expressed at T0 (q < 0.05 and fold change ≥ 2) compared to healthy women. When RA improved at T3, 65 of 89 (73%) of these genes no longer displayed RA-associated expression. In the RA_worsened group, a largely different RA gene expression signature (429 genes) was identified at T0. When RA disease activity worsened at T3, 207 of 429 (48%) genes lost their differential expression, while an additional 151 genes became newly differentially expressed.

CONCLUSION

In our pilot dataset, pre-pregnancy RA expression signatures differed between women who subsequently improved or worsened during pregnancy, suggesting that inherent genomic differences may influence how pregnancy affects disease activity. Further, these RA signatures were altered during pregnancy as disease activity changed.

The use of a gene expression signature and connectivity map to repurpose drugs for bipolar disorder

To create a gene expression signature (GES) to represent the biological effects of a combination of known drugs for bipolar disorder (BD) on cultured human neuronal cells (NT2-N) and rat brains, which also has evidence of differential expression in individuals with BD. To use the GES to identify new drugs for BD using Connectivity Map (CMap).Methods: NT2-N (n = 20) cells and rats (n = 8) were treated with a BD drug combination (lithium, valproate, quetiapine and lamotrigine) or vehicle for 24 and 6 h, respectively. Following next-generation sequencing, the differential expression of genes was assessed using edgeR in R. The derived GES was compared to differentially expressed genes in post-mortem brains of individuals with BD. The GES was then used in CMap analysis to identify similarly acting drugs.Results: A total of 88 genes showed evidence of differential expression in response to the drug combination in both models, and therefore comprised the GES. Six of these genes showed evidence of differential expression in post-mortem brains of individuals with BD. CMap analysis identified 10 compounds (camptothecin, chlorambucil, flupenthixol, valdecoxib, rescinnamine, GW-8510, cinnarizine, lomustine, mifepristone and nimesulide) acting similarly to the BD drug combination.Conclusions: This study shows that GES and CMap can be used as tools to repurpose drugs for BD.

Characterization of tumors with ultralow tumor mutational burden in Japanese cancer patients

Tumor mutational burden analysis using whole‐exome sequencing highlights features of tumors with various mutations or known driver alterations. Cancers with few changes in the exon regions have unclear characteristics, even though low‐mutated tumors are often detected in pan‐cancer analysis. In the present study, we analyzed tumors with low tumor mutational burden listed in the Japanese version of The Cancer Genome Atlas, a data set of 5020 primary solid tumors. Our analysis revealed that detection rates of known driver mutations and copy number variation were decreased in samples with tumor mutational burden below 1.0 (ultralow tumor), compared with those in samples with low tumor mutational burden (≤5 mutations/Mb). This trend was also observed in The Cancer Genome Atlas data set. In the ultralow tumor mutational burden tumors, expression analysis showed decreased TP53 inactivation and chromosomal instability. TP53 inactivation frequently correlated with PI3K/mTOR‐related gene expression, implying suppression of the PI3K/mTOR pathway in ultralow tumor mutational burden tumors. In common with mutational burden, the T cell‐inflamed gene expression profiling signature was a potential marker for prediction of an immune checkpoint inhibitor response, and some ultralow tumor mutational burden tumor populations highly expressed this signature. Our analysis focused on how these tumors could provide insight into tumors with low somatic alteration that are difficult to detect solely using whole‐exome sequencing.

The Performance of Gene Expression Signature-Guided Drug-Disease Association in Different Categories of Drugs and Diseases

A gene expression signature (GES) is a group of genes that shows a unique expression profile as a result of perturbations by drugs, genetic modification or diseases on the transcriptional machinery. The comparisons between GES profiles have been used to investigate the relationships between drugs, their targets and diseases with quite a few successful cases reported. Especially in the study of GES-guided drugs–disease associations, researchers believe that if a GES induced by a drug is opposite to a GES induced by a disease, the drug may have potential as a treatment of that disease. In this study, we data-mined the crowd extracted expression of differential signatures (CREEDS) database to evaluate the similarity between GES profiles from drugs and their indicated diseases. Our study aims to explore the application domains of GES-guided drug–disease associations through the analysis of the similarity of GES profiles on known pairs of drug–disease associations, thereby identifying subgroups of drugs/diseases that are suitable for GES-guided drug repositioning approaches. Our results supported our hypothesis that the GES-guided drug–disease association method is better suited for some subgroups or pathways such as drugs and diseases associated with the immune system, diseases of the nervous system, non-chemotherapy drugs or the mTOR signaling pathway.

Identifying Predictive Gene Expression and Signature Related to Temozolomide Sensitivity of Glioblastomas

Temozolomide (TMZ) is considered a standard chemotherapeutic agent for glioblastoma (GBM). Characterizing the biological molecules and signaling pathways involved in TMZ sensitivity would be helpful for selecting therapeutic schemes and evaluating prognosis for GBM. Thus, in the present study, we selected 34 glioma cell lines paired with specific IC₅₀ values of TMZ obtained from CancerRxGene and RNA-seq data downloaded from the Cancer Cell Line Encyclopedia to identify genes related to TMZ sensitivity. The results showed that 1,373 genes were related to the response of GBM cells to TMZ. Biological function analysis indicated that epithelial–mesenchymal transition, Wnt signaling, and immune response were the most significantly activated functions in TMZ-resistant cell lines. Additionally, negative regulation of telomere maintenance via telomerase was enriched in TMZ-sensitive glioma cell lines. We also preliminarily observed a synergistic effect of combination treatment comprising TMZ and a telomerase inhibitor in vitro. We identified six genes (MROH8, BET1, PTPRN2, STC1, NKX3-1, and ARMC10) using the random survival forests variable hunting algorithm based on the minimum error rate of the gene combination and constructed a gene expression signature. The signature was strongly related to GBM clinical characteristics and exhibited good prognosis accuracy for both The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets. Patients in the high score group had a shorter survival time than those in the low score group (11.2 vs. 22.2 months, hazard ratio = 7.31, p = 4.59e−11) of the TCGA dataset. The CGGA dataset was selected as a validation group with 40 patients in the high score set and 43 patients in the low score set (12.5 vs. 28.8 months, hazard ratio = 3.42, p = 8.61e−5). Moreover, the signature showed a better prognostic value than MGMT promoter methylation in both datasets. We also developed a nomogram for clinical use that integrated the TMZ response signature and four other risk factors to individually predict patient survival after TMZ chemotherapy. Overall, our study provides promising therapeutic targets and potential guidance for adjuvant therapy of GBM.

A twelve-gene signature for survival prediction in malignant melanoma patients

Background

Melanoma is defined as a highly mutational heterogeneous disease containing numerous alternations of the molecule. However, due to the phenotypically and genetically heterogeneity of malignant melanoma, conventional clinical characteristics remain restricted or limited in the ability to accurately predict individual outcomes and survival. This study aimed to establish an accurate gene expression signature to predict melanoma prognosis.

Methods

In this study, we established an RNA sequencing-based 12-gene signature data of melanoma patients obtained from 2 independent databases: the Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. We evaluated the quality of each gene to predict survival conditions in each database by employing univariate and multivariate regression models. A prognostic risk score based on a prognostic signature was determined. This prognostic gene signature further classified patients into low-risk and high-risk groups.

Results

Based on a prognostic signature, a prognostic risk score was determined. This prognostic gene signature further divided the patients into low-risk and high-risk groups. In the chemotherapy and radiotherapy groups of the TCGA cohort and V-raf murine sarcoma viral oncogene homolog B1 (BRAF) expression group in the GEO cohort, patients in the low-risk group had a longer survival duration compared to patients in the high-risk group. Nevertheless, the immunotherapy group in the TCGA database and neuroblastoma RAS viral oncogene homolog (NRAS) expression group in the GEO database had no significant differences in statistics. Moreover, this gene signature was associated with patient prognosis regardless of whether the Breslow depth was greater than or less than 3.75 mm. Stratified gene set enrichment analysis (GSEA) revealed that certain immune-related pathways, such as the T-cell signaling pathway, chemokine signaling pathway, and primary immunodeficiency, were significantly enriched in the low-risk group of both TCGA and GEO cohorts. This information implied the immune-related properties of the 12-gene signature.

Conclusions

Our study emphasizes the significance of the gene expression signature in that it may be an indispensable prognostic predictor in melanoma and has great potential for application in personalized treatment.

A single-cell atlas of the developing Drosophila ovary identifies follicle stem cell progenitors

Addressing the complexity of organogenesis at a system-wide level requires a complete understanding of adult cell types, their origin, and precursor relationships. The Drosophila ovary has been a model to study how coordinated stem cell units, germline, and somatic follicle stem cells maintain and renew an organ. However, lack of cell type-specific tools have limited our ability to study the origin of individual cell types and stem cell units. Here, we used a single-cell RNA sequencing approach to uncover all known cell types of the developing ovary, reveal transcriptional signatures, and identify cell type-specific markers for lineage tracing. Our study identifies a novel cell type corresponding to the elusive follicle stem cell precursors and predicts subtypes of known cell types. Altogether, we reveal a previously unanticipated complexity of the developing ovary and provide a comprehensive resource for the systematic analysis of ovary morphogenesis.

PAC-5 Gene Expression Signature for Predicting Prognosis of Patients with Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAC) is one of the most aggressive malignancies. Intratumoural molecular heterogeneity impedes improvement of the overall survival rate. Current pathological staging system is not sufficient to accurately predict prognostic outcomes. Thus, accurate prognostic model for patient survival and treatment decision is demanded. Using differentially expressed gene analysis between normal pancreas and PAC tissues, the cancer-specific genes were identified. A prognostic gene expression model was computed by LASSO regression analysis. The PAC-5 signature (LAMA3, E2F7, IFI44, SLC12A2, and LRIG1) that had significant prognostic value in the overall dataset was established, independently of the pathological stage. We provided evidence that the PAC-5 signature further refined the selection of the PAC patients who might benefit from postoperative therapies. SLC12A2 and LRIG1 interacted with the proteins that were implicated in resistance of EGFR kinase inhibitor. DNA methylation was significantly involved in the gene regulations of the PAC-5 signature. The PAC-5 signature provides new possibilities for improving the personalised therapeutic strategies. We suggest that the PAC-5 genes might be potential drug targets for PAC.

Coagulation FXIII-A Protein Expression Defines Three Novel Sub-populations in Pediatric B-Cell Progenitor Acute Lymphoblastic Leukemia Characterized by Distinct Gene Expression Signatures

Background: Leukemic B-cell precursor (BCP) lymphoblasts were identified as a novel expression site for coagulation factor XIII subunit A (FXIII-A). Flow cytometry (FC) revealed three distinct expression patterns, i.e., FXIII-A negative, FXIII-A dim, and FXIII-A bright subgroups. The FXIII-A negative subgroup was significantly associated with the “B-other” genetic category and had an unfavorable disease outcome.

Methods: RNA was extracted from bone marrow lymphoblasts of 42 pediatric patients with BCP-acute lymphoblastic leukemia (ALL). FXIII-A expression was determined by multiparameter FC. Genetic diagnosis was based on conventional cytogenetic method and fluorescence in situ hybridization. Affymetrix GeneChip Human Primeview array was used to analyze global expression pattern of 28,869 well-annotated genes. Microarray data were analyzed by Genespring GX14.9.1 software. Gene Ontology analysis was performed using Cytoscape 3.4.0 software with ClueGO application. Selected differentially expressed genes were validated by RT-Q-PCR.

Results: We demonstrated, for the first time, the general expression of F13A1 gene in pediatric BCP-ALL samples. The intensity of F13A1 expression corresponded to the FXIII-A protein expression subgroups which defined three characteristic and distinct gene expression signatures detected by Affymetrix oligonucleotide microarrays. Relative gene expression intensity of ANGPTL2, EHMT1 FOXO1, HAP1, NUCKS1, NUP43, PIK3CG, RAPGEF5, SEMA6A, SPIN1, TRH, and WASF2 followed the pattern of change in the intensity of the expression of the F13A1 gene. Common enhancer elements of these genes revealed by in silico analysis suggest that common transcription factors may regulate the expression of these genes in a similar fashion. PLAC8 was downregulated in the FXIII-A bright subgroup. Gene expression signature of the FXIII-A negative subgroup showed an overlap with the signature of “B-other” samples. DFFA, GIGYF1, GIGYF2, and INTS3 were upregulated and CD3G was downregulated in the “B-other” subgroup. Validated genes proved biologically and clinically relevant. We described differential expression of genes not shown previously to be associated with pediatric BCP-ALL.

Conclusions: Gene expression signature according to FXIII-A protein expression status defined three novel subgroups of pediatric BCP-ALL. Multiparameter FC appears to be an easy-to-use and affordable method to help in selecting FXIII-A negative patients who require a more elaborate and expensive molecular genetic investigation to design precision treatment.

A Tyrosine Kinase Expression Signature Predicts the Post-Operative Clinical Outcome in Triple Negative Breast Cancers

Triple negative breast cancer (TNBC) represent 15% of breast cancers. Histoclinical features and marketed prognostic gene expression signatures (GES) failed to identify good- and poor-prognosis patients. Tyrosine kinases (TK) represent potential prognostic and/or therapeutic targets for TNBC. We sought to define a prognostic TK GES in a large series of TNBC. mRNA expression and histoclinical data of 6379 early BCs were collected from 16 datasets. We searched for a TK-based GES associated with disease-free survival (DFS) and tested its robustness in an independent validation set. A total of 1226 samples were TNBC. In the learning set of samples (N = 825), we identified a 13-TK GES associated with DFS. This GES was associated with cell proliferation and immune response. In multivariate analysis, it outperformed the previously published GESs and classical prognostic factors in the validation set (N = 401), in which the patients classified as "low-risk" had a 73% 5-year DFS versus 53% for "high-risk" patients (p = 1.85 × 10-3). The generation of 100,000 random 13-gene signatures by a resampling scheme showed the non-random nature of our classifier, which was also prognostic for overall survival in multivariate analysis. We identified a robust and non-random 13-TK GES that separated TNBC into subgroups of different prognosis. Clinical and functional validations are warranted.

Impact of Gene Expression Associated with Glucocorticoid-Induced Transcript 1 (GLCCI1) on Severe Asthma and Future Exacerbation

Genetic variations in glucocorticoid-induced transcript 1 (GLCCI1) have been associated with the response to corticosteroid treatment. However, the associations of GLCCI1 polymorphisms or gene expression with the prognosis of asthma and pathophysiological factors related to steroid insensitivity remain unclear. We sought to investigate the associations of GLCCI1, nuclear factor (erythroid-derived 2)-like 2 (Nrf2), and histone deacetylase 2 (HDAC2) mRNA expression levels and the GLCCI1 rs37973 polymorphism with asthma severity and future exacerbation in patients with asthma. Subjects included 25 patients with severe asthma and 127 patients with nonsevere asthma. mRNA expression levels in peripheral blood mononuclear cells were measured and evaluated as predictors of severe asthma using receiver operating characteristic (ROC) analysis. The hazard ratios of the mRNA expression levels for time to first exacerbation in the 1-year follow-up period were calculated. GLCCI1, Nrf2, and HDAC2 mRNA expression levels were significantly lower in patients with severe asthma than in patients with nonsevere asthma and could predict severe asthma with an area under the ROC curve of 0.68, 0.71, and 0.65, respectively. In contrast, no relationship was found between the GLCCI1 rs37973 polymorphism and severe asthma. The hazard ratios for asthma exacerbation in patients with low GLCCI1, Nrf2, and HDAC2 mRNA expression levels were 3.24 (95% confidence interval, 1.42-7.40), 3.13 (1.37-7.16), and 2.98 (1.22-7.25), respectively. Patients with severe asthma could be distinguished by lower GLCCI1, Nrf2, and HDAC2 mRNA levels in peripheral blood cells, and all of these gene signatures could predict future asthma exacerbations.

Avoiding over- and undertreatment in patients with resected node-positive breast cancer with the use of gene expression signatures: are we there yet?

Prediction of benefit from adjuvant chemotherapy following resection of early breast cancer and, as a result, proper selection of candidates remains an elusive goal since the relative magnitude of benefit is the same regardless of the presence of clinicopathologic factors. Multiple studies, including randomized trials, establish the role of certain gene expression signatures in node-negative disease since they predict the risk of breast cancer relapse being so low that adjuvant chemotherapy can be omitted. In contrast, more limited data are available in higher risk, node-positive breast cancer patients, making the exclusion of adjuvant chemotherapy potentially hazardous. 'Prospective-retrospective' studies and limited prospective data show that several signatures, namely Oncotype Dx, MammaPrint, Prosigna, EndoPredict and Breast Cancer Index, select with different levels of success node-positive patients at very low risk for distant recurrence despite not receiving chemotherapy, although the long-term follow-up is still awaited. Pending, however the publication of the results from ongoing randomized studies which enroll patients with node-positive disease, major caution is warranted. Improper use and misinterpretation of these transcriptomic profiles can lead to undertreatment and exposure of patients to unnecessary risks resulting in increased breast cancer mortality for patients with axillary node-positive disease. With this review we critically discuss the available data on gene expression signatures that are used in clinical practice and offer practical recommendations regarding the management of patients with ER-positive, human epidermal growth factor receptor 2 (HER2)-negative, node-positive breast cancer.

Prediction of putative small molecules for manipulation of enriched signalling pathways in hESC-derived early cardiovascular progenitors by bioinformatics analysis

Human pluripotent stem cell-derived cardiovascular progenitor cells (CPCs) are considered as powerful tools for cardiac regenerative medicine and developmental study. Mesoderm posterior1⁺ (MESP1⁺ ) cells are identified as the earliest CPCs from which almost all cardiac cell types are generated. Molecular insights to the transcriptional regulatory factors of early CPCs are required to control cell fate decisions. Herein, the microarray data set of human embryonic stem cells (hESCs)-derived MESP1⁺ cells was analysed and differentially expressed genes (DEGs) were identified in comparison to undifferentiated hESCs and MESP1-negative cells. Then, gene ontology and pathway enrichment analysis of DEGs were carried out with the subsequent prediction of putative regulatory small molecules for modulation of CPC fate. Some key signalling cascades of cardiogenesis including Hippo, Wnt, transforming growth factor-β, and PI3K/Akt were highlighted in MESP1⁺ cells. The transcriptional regulatory network of MESP1⁺ cells were visualised through interaction networks of DEGs. Additionally, 35 promising chemicals were predicted based on correlations with gene expression signature of MESP1⁺ cells for effective in vitro CPC manipulation. Studying the transcriptional profile of MESP1⁺ cells resulted into the identification of important signalling pathways and chemicals which could be introduced as powerful tools to manage proliferation and differentiation of hESC-derived CPCs more efficiently.

A Pathway-Based Strategy to Identify Biomarkers for Lung Cancer Diagnosis and Prognosis

Current research has identified several potential biomarkers for lung cancer diagnosis or prognosis. However, most of these biomarkers are derived from a relatively small number of samples using algorithms at the gene level. Hence, gene expression signatures discovered in these studies have little overlaps. In this study, we proposed a new strategy to identify biomarkers from multiple datasets at the pathway level. We integrated the genome-wide expression data of lung cancer tissues from 13 published studies and applied our strategy to identify lung cancer diagnostic and prognostic biomarkers. We identified a 32-gene signature that differentiates lung adenocarcinomas from other lung cancer subtypes. We also discovered a 43-gene signature that can predict the outcome of human lung cancers. We tested their performance in several independent cohorts, which confirmed their robust prognostic and diagnostic power. Furthermore, we showed that the proposed gene expression signatures were independent of several traditional clinical indicators in lung cancer management. Our results suggest that the pathway-based strategy is useful to identify transcriptomic biomarkers from large-scale gene expression datasets that were collected from multiple sources.

High GPR56 surface expression correlates with a leukemic stem cell gene signature in CD34-positive AML

Acute myeloid leukemia (AML) is driven by a minor fraction of leukemic stem cells (LSCs) whose persistence is considered being the primary cause of disease relapse. A detailed characterization of the surface immunophenotype of LSCs to discriminate them from bulk leukemic blasts may enable successful targeting of this population thereby improving patient outcomes in AML. To identify surface markers, which may reflect LSC activity at diagnosis, we performed a detailed analysis of 16 putative LSC markers in CD34/38 leukemic subcompartments of 150 diagnostic AML samples using multicolor flow cytometry. The most promising markers were then selected to determine a possible correlation of their expression with a recently published LSC gene signature. We found GPR56 and CLL-1 to be the most prominently differently expressed surface markers in AML subcompartments. While GPR56 was highest expressed within the LSC-enriched CD34⁺ 38^- subcompartment as compared to CD34⁺ 38⁺ and CD34^- leukemic bulk cells, CLL-1 expression was lowest in CD34⁺ 38^- leukemic cells and increased in CD34⁺ 38⁺ and CD34^- blasts. Furthermore, high GPR56 surface expression in CD34⁺ 38^- leukemic cells correlated with a recently published LSC gene expression signature and was associated with decreased overall survival in patients receiving intensive chemotherapy. In contrast, CLL-1 expression correlated inversely with the LSC gene signature and was not informative on outcome. Our data strongly support GPR56 as a promising clinically relevant marker for identifying leukemic cells with LSC activity at diagnosis in CD34-positive AML.

Assessment of the performance of the TGx-DDI biomarker to detect DNA damage-inducing agents using quantitative RT-PCR in TK6 cells

Gene expression biomarkers are now available for application in the identification of genotoxic hazards. The TGx-DDI transcriptomic biomarker can accurately distinguish DNA damage-inducing (DDI) from non-DDI exposures based on changes in the expression of 64 biomarker genes. The 64 genes were previously derived from whole transcriptome DNA microarray profiles of 28 reference agents (14 DDI and 14 non-DDI) after 4 h treatments of TK6 human lymphoblastoid cells. To broaden the applicability of TGx-DDI, we tested the biomarker using quantitative RT-PCR (qPCR), which is accessible to most molecular biology laboratories. First, we selectively profiled the expression of the 64 biomarker genes using TaqMan qPCR assays in 96-well arrays after exposing TK6 cells to the 28 reference agents for 4 h. To evaluate the classification capability of the qPCR profiles, we used the reference qPCR signature to classify 24 external validation chemicals using two different methods-a combination of three statistical analyses and an alternative, the Running Fisher test. The qPCR results for the reference set were comparable to the original microarray biomarker; 27 of the 28 reference agents (96%) were accurately classified. Moreover, the two classification approaches supported the conservation of TGx-DDI classification capability using qPCR; the combination of the two approaches accurately classified 21 of the 24 external validation chemicals, demonstrating 100% sensitivity, 81% specificity, and 91% balanced accuracy. This study demonstrates that qPCR can be used when applying the TGx-DDI biomarker and will improve the accessibility of TGx-DDI for genotoxicity screening. Environ. Mol. Mutagen. 60: 122-133, 2019. © 2018 Her Majesty the Queen in Right of Canada Environmental and Molecular Mutagenesis.

Skeletal Muscle MicroRNAs as Key Players in the Pathogenesis of Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder, for which, to date, no effective treatment to ameliorate the clinical manifestations is available. The long-standing view of ALS as affecting only motor neurons has been challenged by the finding that the skeletal muscle plays an active role in the disease pathogenesis and can be a valuable target for therapeutic strategies. In recent years, non-coding RNAs, including microRNAs, have emerged as important molecules that play key roles in several cellular mechanisms involved in the pathogenic mechanisms underlying various human conditions. In this review, we summarize how the expression of some microRNAs is dysregulated in the skeletal muscle of ALS mouse models and patients. Shedding light on the mechanisms underlying microRNAs dysregulation in the skeletal muscle could clarify some of the processes involved in the pathogenesis of ALS and especially identify new promising therapeutic targets in patients.

Chemical genomics reveals inhibition of breast cancer lung metastasis by Ponatinib via c-Jun

Metastasis is the leading cause of human cancer deaths. Unfortunately, no approved drugs are available for anti-metastatic treatment. In our study, high-throughput sequencing-based high-throughput screening (HTS²) and a breast cancer lung metastasis (BCLM)-associated gene signature were combined to discover anti-metastatic drugs. After screening of thousands of compounds, we identified Ponatinib as a BCLM inhibitor. Ponatinib significantly inhibited the migration and mammosphere formation of breast cancer cells in vitro and blocked BCLM in multiple mouse models. Mechanistically, Ponatinib represses the expression of BCLM-associated genes mainly through the ERK/c-Jun signaling pathway by inhibiting the transcription of JUN and accelerating the degradation of c-Jun protein. Notably, JUN expression levels were positively correlated with BCLM-associated gene expression and lung metastases in breast cancer patients. Collectively, we established a novel approach for the discovery of anti-metastatic drugs, identified Ponatinib as a new drug to inhibit BCLM and revealed c-Jun as a crucial factor and potential drug target for BCLM. Our study may facilitate the therapeutic treatment of BCLM as well as other metastases.

A novel non-canonical Wnt signature for prostate cancer aggressiveness

Activation of the Canonical Wnt pathway (CWP) has been linked to advanced and metastatic prostate cancer, whereas the Wnt5a-induced non-canonical Wnt pathway (NCWP) has been associated with both good and poor prognosis. A newly discovered NCWP, Wnt5/Fzd2, has been shown to induce epithelial-to-mesenchymal transition (EMT) in cancers, but has not been investigated in prostate cancer. The aim of this study was to investigate if the CWP and NCWP, in combination with EMT, are associated with metabolic alterations, aggressive disease and biochemical recurrence in prostate cancer. An initial analysis was performed using integrated transcriptomics, ex vivo and in vivo metabolomics, and histopathology of prostatectomy samples (n=129), combined with at least five-year follow-up. This analysis detected increased activation of NCWP through Wnt5a/ Fzd2 as the most common mode of Wnt activation in prostate cancer. This activation was associated with increased expression of EMT markers and higher Gleason score. The transcriptional association between NCWP and EMT was confirmed in five other publicly available patient cohorts (1519 samples in total). A novel gene expression signature of concordant activation of NCWP and EMT (NCWP-EMT) was developed, and this signature was significantly associated with metastasis and shown to be a significant predictor of biochemical recurrence. The NCWP-EMT signature was also associated with decreased concentrations of the metabolites citrate and spermine, which have previously been linked to aggressive prostate cancer. Our results demonstrate the importance of NCWP and EMT in prostate cancer aggressiveness, suggest a novel gene signature for improved risk stratification, and give new molecular insight.

Bringing the next Generation of Immuno-Oncology Biomarkers to the Clinic

The recent successes in the use of immunotherapy to treat cancer have led to a multiplicity of new compounds in development. Novel clinical-grade biomarkers are needed to guide the choice of these agents to obtain the maximal likelihood of patient benefit. Predictive biomarkers for immunotherapy differ from the traditional biomarkers used for targeted therapies: the complexity of the immune response and tumour biology requires a more holistic approach than the use of a single analyte biomarker. This paper reviews novel biomarker approaches for the effective development of immune-oncology therapies, highlighting the promise of the advances in next-generation gene expression profiling that allow biologic information to be efficiently organized and interpreted for a maximum predictive value at the individual patient level.

Validation of a hypoxia related gene signature in multiple soft tissue sarcoma cohorts

PURPOSE

There is a need for adjuvant/neo-adjuvant treatment strategies to prevent metastatic relapse in soft tissue sarcoma (STS). Tumor hypoxia is associated with a high-risk of metastasis and is potentially targetable. This study aimed to derive and validate a hypoxia mRNA signature for STS for future biomarker-driven trials of hypoxia targeted therapy.

MATERIALS AND METHODS

RNA sequencing was used to identify seed genes induced by hypoxia in seven STS cell lines. Primary tumors in a training cohort (French training) were clustered into two phenotypes by seed gene expression and a de novo hypoxia signature derived. Prognostic significance of the de novo signature was evaluated in the training and two independent validation (French validation and The Cancer Genome Atlas) cohorts.

RESULTS

37 genes were up-regulated by hypoxia in all seven cell lines, and a 24-gene signature was derived. The high-hypoxia phenotype defined by the signature was enriched for well-established hypoxia genes reported in the literature. The signature was prognostic in univariable analysis, and in multivariable analysis in the training (n = 183, HR 2.16, P = 0.0054) and two independent validation (n = 127, HR 3.06, P = 0.0019; n = 258, HR 2.05, P = 0.0098) cohorts. Combining information from the de novo hypoxia signature and a genome instability signature significantly improved prognostication. Transcriptomic analyses showed high-hypoxia tumors had more genome instability and lower immune scores.

CONCLUSIONS

A 24-gene STS-specific hypoxia signature may be useful for prognostication and identifying patients for hypoxia-targeted therapy in clinical trials.