Plasma cell-free DNA methylome-based liquid biopsy for accurate gastric cancer detection.

Early detection plays a critical role in mitigating mortality rates linked to gastric cancer. However, current clinical screening methods exhibit suboptimal efficacy. Methylation alterations identified from cell-free DNA (cfDNA) present a promising biomarker for early cancer detection. Our study focused on identifying gastric cancer-specific markers from cfDNA methylation to facilitate early detection. We enrolled 150 gastric cancer patients and 100 healthy controls in this study, and undertook genome-wide methylation profiling of cfDNA using cell-free methylated DNA immunoprecipitation and high-throughput sequencing. We identified 21 differentially methylated regions (DMRs) between the gastric tumor and nontumor groups using multiple algorithms. Subsequently, using the 21 DMRs, we developed a gastric cancer detection model by random forest algorithm in the discovery set, and validated the model in an independent set. The model was able to accurately discriminate gastric cancer with a sensitivity and specificity of 93.90% and 95.15% in the discovery set, respectively, and 88.38% and 94.23% in the validation set, respectively. These results underscore the efficacy and accuracy of cfDNA-derived methylation markers in distinguishing early stage gastric cancer. This study highlighted the significance of cfDNA methylation alterations in early gastric cancer detection.

Heterochromatin loosening by the Oct4 linker region facilitates Klf4 binding and iPSC reprogramming.

The success of Yamanaka factor reprogramming of somatic cells into induced pluripotent stem cells suggests that some factor(s) must remodel the nuclei from a condensed state to a relaxed state. How factor-dependent chromatin opening occurs remains unclear. Using FRAP and ATAC-seq, we found that Oct4 acts as a pioneer factor that loosens heterochromatin and facilitates the binding of Klf4 and the expression of epithelial genes in early reprogramming, leading to enhanced mesenchymal-to-epithelial transition. A mutation in the Oct4 linker, L80A, which shows impaired interaction with the BAF complex component Brg1, is inactive in heterochromatin loosening. Oct4-L80A also blocks the binding of Klf4 and retards MET. Finally, vitamin C or Gadd45a could rescue the reprogramming deficiency of Oct4-L80A by enhancing chromatin opening and Klf4 binding. These studies reveal a cooperation between Oct4 and Klf4 at the chromatin level that facilitates MET at the cellular level and shed light into the research of multiple factors in cell fate determination.

CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer.

BACKGROUND: Tumor mutational burden (TMB) is one of the most significant predictive biomarkers of immunotherapy efficacy in non-small cell lung cancer (NSCLC). Radiomics allows high-throughput extraction and analysis of advanced and quantitative medical imaging features. This study develops and validates a radiomic model for predicting TMB level and the response to immunotherapy based on CT features in NSCLC. METHOD: Pre-operative chest CT images of 127 patients with NSCLC were retrospectively studied. The 3D-Slicer software was used to outline the region of interest and extract features from the CT images. Radiomics prediction model was constructed by LASSO and multiple logistic regression in a training dataset. The model was validated by receiver operating characteristic (ROC) curves and calibration curves using external datasets. Decision curve analysis was used to assess the value of the model for clinical application. RESULTS: A total of 1037 radiomic features were extracted from the CT images of NSCLC patients from TCGA. LASSO regression selected three radiomics features (Flatness, Autocorrelation and Minimum), which were associated with TMB level in NSCLC. A TMB prediction model consisting of 3 radiomic features was constructed by multiple logistic regression. The area under the curve (AUC) value in the TCGA training dataset was 0.816 (95% CI: 0.7109-0.9203) for predicting TMB level in NSCLC. The AUC value in external validation dataset I was 0.775 (95% CI: 0.5528-0.9972) for predicting TMB level in NSCLC, and the AUC value in external validation dataset II was 0.762 (95% CI: 0.5669-0.9569) for predicting the efficacy of immunotherapy in NSCLC. CONCLUSION: The model based on CT radiomic features helps to achieve cost effective improvement in TMB classification and precise immunotherapy treatment of NSCLC patients.

CpGtools: a python package for DNA methylation analysis.

MOTIVATION: DNA methylation can be measured at the single CpG level using sodium bisulfite conversion of genomic DNA followed by sequencing or array hybridization. Many analytic tools have been developed, yet there is still a high demand for a comprehensive and multifaceted tool suite to analyze, annotate, QC and visualize the DNA methylation data. RESULTS: We developed the CpGtools package to analyze DNA methylation data generated from bisulfite sequencing or Illumina methylation arrays. The CpGtools package consists of three types of modules: (i) 'CpG position modules' focus on analyzing the genomic positions of CpGs, including associating other genomic and epigenomic features to a given list of CpGs and generating the DNA motif logo enriched in the genomic contexts of a given list of CpGs; (ii) 'CpG signal modules' are designed to analyze DNA methylation values, such as performing the PCA or t-SNE analyses, using Bayesian Gaussian mixture modeling to classify CpG sites into fully methylated, partially methylated and unmethylated groups, profiling the average DNA methylation level over user-specified genomics regions and generating the bean/violin plots and (iii) 'differential CpG analysis modules' focus on identifying differentially methylated CpGs between groups using different statistical methods including Fisher's Exact Test, Student's t-test, ANOVA, non-parametric tests, linear regression, logistic regression, beta-binomial regression and Bayesian estimation. AVAILABILITY AND IMPLEMENTATION: CpGtools is written in Python under the open-source GPL license. The source code and documentation are freely available at https://github.com/liguowang/cpgtools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction model for malignant pulmonary nodules based on cfMeDIP-seq and machine learning.

Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) is a new bisulfite-free technique, which can detect the whole-genome methylation of blood cell-free DNA (cfDNA). Using this technique, we identified differentially methylated regions (DMR) of cfDNA between lung tumors and normal controls. Based on the top 300 DMR, we built a random forest prediction model, which was able to distinguish malignant lung tumors from normal controls with high sensitivity and specificity of 91.0% and 93.3% (AUROC curve of 0.963). In summary, we reported a non-invasive prediction model that had good ability to distinguish malignant pulmonary nodules.

Enrichment and detection of circulating tumor cells by immunomagnetic beads and flow cytometry.

OBJECTIVE: The purpose of the article is to establish a quick enrichment and detection method using immunomagnetic beads and flow cytometry to analyze circulating tumor cells (CTCs) in the peripheral blood. RESULTS: After incubation with CD326-PE and CD45-APC antibodies, more than 60% MCF7 cells in M-Buffer could be detected while less than 10% of the same cells could be detected by flow cytometry (FCM) if spiked into blood. However, in combination with CD326 and CD45 immunomagnetic beads, detection rate of MCF7 cells in blood reached 57%. For circulating tumor cells, enrichment by CD326 and CD45 immunomagnetic beads improve the detection rate from nearly undetectable to more than 24.14%. CONCLUSIONS: Live CTCs in peripheral blood can be effectively and sensitively detected by using a combination of immunomagnetic beads (CD45 and CD326) and flow cytometry.

Vitamin C-dependent lysine demethylase 6 (KDM6)-mediated demethylation promotes a chromatin state that supports the endothelial-to-hematopoietic transition.

Hematopoietic stem cells (HSCs)/progenitor cells (HPCs) are generated from hemogenic endothelial cells (HECs) during the endothelial-to-hematopoietic transition (EHT); however, the underlying mechanism remains poorly understood. Here, using an array of approaches, including CRSPR/Cas9 gene knockouts, RNA-Seq, ChIP-Seq, ATAC-Seq etc., we report that vitamin C (Vc) is essential in HPC generation during human pluripotent stem cell (hPSC) differentiation in defined culture conditions. Mechanistically, we found that the endothelial cells generated in the absence of Vc fail to undergo the EHT because of an apparent failure in opening up genomic loci essential for hematopoiesis. Under Vc deficiency, these loci exhibited abnormal accumulation of histone H3 trimethylation at Lys-27 (H3K27me3), a repressive histone modification that arose because of lower activities of demethylases that target H3K27me3. Consistently, deletion of the two H3K27me3 demethylases, Jumonji domain-containing 3 (JMJD3 or KDM6B) and histone demethylase UTX (UTX or KDM6A), impaired HPC generation even in the presence of Vc. Furthermore, we noted that Vc and jmjd3 are also important for HSC generation during zebrafish development. Together, our findings reveal an essential role for Vc in the EHT for hematopoiesis, and identify KDM6-mediated chromatin demethylation as an important regulatory mechanism in hematopoietic cell differentiation.

PuHox52-mediated hierarchical multilayered gene regulatory network promotes adventitious root formation in Populus ussuriensis.

Adventitious root (AR) formation is critically important in vegetative propagation through cuttings in some plants, especially woody species. However, the underlying molecular mechanisms remain elusive. Here, we report the identification of a poplar homeobox gene, PuHox52, which was induced rapidly (within 15 min) at the basal ends of stems upon cutting and played a key regulatory role in adventitious rooting. We demonstrated that overexpression of PuHox52 significantly increased the number of ARs while suppression of PuHox52 had the opposite effect. A multilayered hierarchical gene regulatory network (ML-hGRN) mediated by PuHox52 was reverse-engineered and demonstrated to govern AR formation. PuHox52 regulated AR formation through upregulation of nine hub regulators, including a jasmonate signaling pathway gene, PuMYC2, and an auxin signaling pathway gene, PuAGL12. We also identified coherent type 4 feed-forward loops within this ML-hGRN; PuHox52 repressed PuHDA9, which encodes a histone deacetylase, and led to an increase in acetylation and presumably expression of three hub regulators, PuWRKY51, PuLBD21 and PuIAA7. Our results indicate that the ML-hGRN mediated by PuHox52 governs AR formation at the basal ends of stem cuttings from poplar trees.

Identification of an autophagy-related gene signature that can improve prognosis of hepatocellular carcinoma patients.

BACKGROUND: Autophagy is a programmed cell degradation mechanism that has been associated with several physiological and pathophysiological processes, including malignancy. Improper induction of autophagy has been proposed to play a pivotal role in the progression of hepatocellular carcinoma (HCC). METHODS: Univariate Cox regression analysis of overall survival (OS) was performed to identify risk-associated autophagy-related genes (ARGs) in HCC data set from The Cancer Genome Atlas (TCGA). Multivariate cox regression was then performed to develop a risk prediction model for the prognosis of 370 HCC patients. The multi-target receiver operating characteristic (ROC) curve was used to determine the model's accuracy. Besides, the relationship between drug sensitivity and ARGs expression was also examined. RESULTS: A total of 62 differentially expressed ARGs were identified in HCC patients. Univariate and multivariate regression identified five risk-associated ARGs (HDAC1, RHEB, ATIC, SPNS1 and SQSTM1) that were correlated with OS in HCC patients. Of importance, the risk-associated ARGs were independent risk factors in the multivariate risk model including clinical parameters such as malignant stage (HR = 1.433, 95% CI = 1.293-1.589, P < 0.001). In addition, the area under curve for the prognostic risk model was 0.747, which indicates the high accuracy of the model in prediction of HCC outcomes. Interestingly, the risk-associated ARGs were also correlated with drug sensitivity in HCC cell lines. CONCLUSION: We developed a novel prognostic risk model by integrating the molecular signature and clinical parameters of HCC, which can effectively predict the outcomes of HCC patients.

Germline mutation and aberrant transcripts of WWOX in a syndrome with multiple primary tumors.

Multiple primary tumors are defined by the presence of two or more independent primary tumors in the same or different organs of an individual patient. However, the underlying genetic cause for the development of multiple primary tumors is largely unknown. In the study, we report a rare case with four synchronous distinct histological cancer types in a 26 years old Chinese female. In the patient, whole-exome sequencing identified a homozygous germline insertion mutation in WWOX which encodes the DNA repair-related enzyme, WW domain containing oxidoreductase. The mutation was found in a heterozygous state in her parents and brother without any cancer phenotype thus far. Surprisingly, we found multiple novel aberrant WWOX transcripts in the patient's normal colon tissue. The patient's colon metastasis from clear cell adenocarcinoma of the ovary showed a nonhypermutated profile enriched for C-T transition, and harbored somatic pathogenic mutations of HRAS, BRCA2, SMAD4, CHEK2, and AKT1 genes. To our knowledge, this is the first study reporting WWOX gene aberrations in a young patient with the early occurrence of multiple primary tumors. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Measure transcript integrity using RNA-seq data.

BACKGROUND: Stored biological samples with pathology information and medical records are invaluable resources for translational medical research. However, RNAs extracted from the archived clinical tissues are often substantially degraded. RNA degradation distorts the RNA-seq read coverage in a gene-specific manner, and has profound influences on whole-genome gene expression profiling. RESULT: We developed the transcript integrity number (TIN) to measure RNA degradation. When applied to 3 independent RNA-seq datasets, we demonstrated TIN is a reliable and sensitive measure of the RNA degradation at both transcript and sample level. Through comparing 10 prostate cancer clinical samples with lower RNA integrity to 10 samples with higher RNA quality, we demonstrated that calibrating gene expression counts with TIN scores could effectively neutralize RNA degradation effects by reducing false positives and recovering biologically meaningful pathways. When further evaluating the performance of TIN correction using spike-in transcripts in RNA-seq data generated from the Sequencing Quality Control consortium, we found TIN adjustment had better control of false positives and false negatives (sensitivity = 0.89, specificity = 0.91, accuracy = 0.90), as compared to gene expression analysis results without TIN correction (sensitivity = 0.98, specificity = 0.50, accuracy = 0.86). CONCLUSION: TIN is a reliable measurement of RNA integrity and a valuable approach used to neutralize in vitro RNA degradation effect and improve differential gene expression analysis.

RNA toxicity and missplicing in the common eye disease fuchs endothelial corneal dystrophy.

Fuchs endothelial corneal dystrophy (FECD) is an inherited degenerative disease that affects the internal endothelial cell monolayer of the cornea and can result in corneal edema and vision loss in severe cases. FECD affects ∼5% of middle-aged Caucasians in the United States and accounts for >14,000 corneal transplantations annually. Among the several genes and loci associated with FECD, the strongest association is with an intronic (CTG·CAG)n trinucleotide repeat expansion in the TCF4 gene, which is found in the majority of affected patients. Corneal endothelial cells from FECD patients harbor a poly(CUG)n RNA that can be visualized as RNA foci containing this condensed RNA and associated proteins. Similar to myotonic dystrophy type 1, the poly(CUG)n RNA co-localizes with and sequesters the mRNA-splicing factor MBNL1, leading to missplicing of essential MBNL1-regulated mRNAs. Such foci and missplicing are not observed in similar cells from FECD patients who lack the repeat expansion. RNA-Seq splicing data from the corneal endothelia of FECD patients and controls reveal hundreds of differential alternative splicing events. These include events previously characterized in the context of myotonic dystrophy type 1 and epithelial-to-mesenchymal transition, as well as splicing changes in genes related to proposed mechanisms of FECD pathogenesis. We report the first instance of RNA toxicity and missplicing in a common non-neurological/neuromuscular disease associated with a repeat expansion. The FECD patient population with this (CTG·CAG)n trinucleotide repeat expansion exceeds that of the combined number of patients in all other microsatellite expansion disorders.

PVAAS: identify variants associated with aberrant splicing from RNA-seq.

MOTIVATION: RNA-seq has been widely used to study the transcriptome. Comparing to microarray, sequencing-based RNA-seq is able to identify splicing variants and single nucleotide variants in one experiment simultaneously. This provides unique opportunity to detect variants that associated with aberrant splicing. Despite the popularity of RNA-seq, no bioinformatics tool has been developed to leverage this advantage to identify variants associated with aberrant splicing. RESULTS: We have developed PVAAS, a tool to identify single nucleotide variants that associated with aberrant alternative splicing from RNA-seq data. PVAAS works in three steps: (i) identify aberrant splicings; (ii) use user-provided variants or perform variant calling; (iii) assess the significance of association between variants and aberrant splicing events.

MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing.

BACKGROUND: Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome. RESULTS: For optimization of tools and parameters, MAP-RSeq was validated using both simulated and real datasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of reads, gene expression assessment and exon read counting, identification of expressed single nucleotide variants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This workflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes. Several clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The results from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of diseases for better diagnosis and treatment of patients. CONCLUSIONS: Our software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants, mapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed on a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from http://bioinformaticstools.mayo.edu/research/maprseq/.

Cisplatin resistance-related transcriptome and methylome integration identifies PCDHB4 as a novel prognostic biomarker in small cell lung cancer.

Platinum-based chemo-resistance is the major issue for the treatment of small cell lung cancer (SCLC). The integrative analysis of multi-omics data is a reliable approach for discovering novel biomarkers associated with chemo-resistance. Here, multi-omics integrative analysis and Cox regression found that higher expression of PCDHB4 was associated with poorer survival of SCLC patients who received chemotherapy. PCDHB4 gene was hypomethylated and upregulated in SCLC, which was validated in the levels of promoter methylation, mRNA, and protein expression. Mechanistically, using bulk RNA-seq data, functional enrichment analysis indicated that higher PCDHB4 expression was associated with lower immune infiltration. The analysis of single-cell RNA-seq (scRNA-seq) found that SCLC cells with PCDHB4 expression exhibited the characteristics of stemness and EMT. In addition, the high expression and hypomethylation of PCDHB4 were also significantly associated with poor survival in lung squamous cell carcinoma. In summary, PCDHB4 is a potential prognostic biomarker of platinum-based chemotherapy in SCLC.

Construction of Ovarian Cancer Prognostic Model Based on the Investigation of Ferroptosis-Related lncRNA.

(1) Background: Ovarian cancer (OV) has the high mortality rate among gynecological cancers worldwide. Inefficient early diagnosis and prognostic prediction of OV leads to poor survival in most patients. OV is associated with ferroptosis, an iron-dependent form of cell death. Ferroptosis, believed to be regulated by long non-coding RNAs (lncRNAs), may have potential applications in anti-cancer treatments. In this study, we aimed to identify ferroptosis-related lncRNA signatures and develop a novel model for predicting OV prognosis. (2) Methods: We downloaded data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression, and Gene Expression Omnibus (GEO) databases. Prognostic lncRNAs were screened by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and a prognostic model was constructed. The model's predictive ability was evaluated by Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) curves. The expression levels of these lncRNAs included in the model were examined in normal and OV cell lines using quantitative reverse transcriptase polymerase chain reaction. (3) Results: We constructed an 18 lncRNA prognostic prediction model for OV based on ferroptosis-related lncRNAs from TCGA patient samples. This model was validated using TCGA and GEO patient samples. KM analysis showed that the prognostic model was able to significantly distinguish between high- and low-risk groups, corresponding to worse and better prognoses. Based on the ROC curves, our model shows stronger prediction precision compared with other traditional clinical factors. Immune cell infiltration, immune checkpoint expression levels, and Tumor Immune Dysfunction and Exclusion analyses are also insightful for OV immunotherapy. (4) Conclusions: The prognostic model constructed in this study has potential for improving our understanding of ferroptosis-related lncRNAs and providing a new tool for prognosis and immune response prediction in patients with OV.

The identification of a two-gene prognostic model based on cisplatin resistance-related ceRNA network in small cell lung cancer.

BACKGROUND: Small cell lung cancer (SCLC) is a very malignant tumor with rapid growth and early metastasis. Platinum-based chemo-resistance is the major issue for SCLC treatment failure. Identifying a new prognostic model will help to make an accurate treatment decision for SCLC patients. METHODS: Using the genomics of drug sensitivity in cancer (GDSC) database, we identified cisplatin resistance-related lncRNAs in SCLC cells. Based on the competing endogenous RNA (ceRNA) network, we identified the mRNAs correlated with the lncRNAs. Using Cox and LASSO regression analysis, a prognostic model was established. The survival prediction accuracy was evaluated by receiver operating characteristic (ROC) curve and Kaplan-Meier analysis. GSEA, GO, KEGG and CIBERSORT tools were used for functional enrichment and immune cells infiltration analysis. RESULTS: We first screened out 10 differentially expressed lncRNAs between cisplatin resistant and sensitive SCLC cells from GDSC database. Based on ceRNA network, 31 mRNAs were identified with a correlation with the 10 lncRNAs. Furthermore, two genes (LIMK2 and PI4K2B) were identified by Cox and LASSO regression analysis to construct a prognostic model. Kaplan-Meier analysis indicated that the high-risk group had a poor overall survival compared with the low-risk group. The predicted area under the ROC curve (AUC) was 0.853 in the training set, and the AUC was 0.671 in the validation set. In the meanwhile, the low expression of LIMK2 or the high expression of PI4K2B in SCLC tumors was also significantly associated with poor overall survival in both training and validation sets. Functional enrichment analysis showed that the low-risk group was enriched in the apoptosis pathway and high immune infiltration of T cells. Finally, an apoptosis-related gene Cathepsin D (CTSD) was identified to be up-regulated in the low-risk group, and its higher expression correlated with better overall survival in SCLC. CONCLUSION: We established a prognostic model and potential biomarkers (LIMK2, PI4K2B and CTSD), which could help to improve the risk stratification of SCLC patients.

Proteomics Analysis Revealed Smad3 as a Potential Target of the Synergistic Antitumor Activity of Disulfiram and Cisplatin in Ovarian Cancer.

INTRODUCTION: Among gynecological cancers, ovarian cancer has a high mortality rate. Cisplatin-based chemotherapy is commonly used for the treatment of ovarian cancer. However, the clinical efficacy of cisplatin in ovarian cancer is limited due to the development of chemo-resistance during treatment. OBJECTIVE: In the study, we aimed to investigate the synergistic anti-cancer activity and targets of the FDA-approved drug disulfiram combined with cisplatin in ovarian cancer. METHODS: The cell viability was determined by Celltier-Glo luminescent assay. The synergistic anti-cancer activity was assessed by combination index. Cell cycle and apoptosis were detected by flow cytometry. The in vivo anti-tumor activity and side effects were evaluated using a xenografted mice model. The synergistic anti-cancer targets were identified by a mass spectrometry-based proteomics analysis. RESULTS: In this study, we first found that disulfiram synergistically enhanced the anti-tumor activity of cisplatin in chemo-resistant ovarian cancer cells, which was accompanied by the enhanced induction of cellular apoptosis. Secondly, the in vivo study demonstrated that the combination treatment of disulfiram and cisplatin dramatically inhibited tumor growth and had no apparent side effects in ovarian cancer xenografted mice. Finally, proteomics analysis identified SMAD3 as a potential target of disulfiram-cisplatin combined treatment, and the down-regulation of SMAD3 could increase cisplatin-induced cell death in ovarian cancer. CONCLUSION: Combination treatment of disulfiram and cisplatin synergistically inhibited the growth of ovarian cancer through down-regulating SMAD3. As a repurposed drug, disulfiram could be quickly transformed into a clinic to overcome cisplatin resistance for the treatment of ovarian cancer.

Repurposing Disulfiram as a Chemo-Therapeutic Sensitizer: Molecular Targets and Mechanisms.

Currently, chemotherapy is still the main strategy for cancer treatment. However, chemotherapy resistance remains a challenge. Disulfiram (DSF) is an FDA-approved medicine for the treatment of alcoholism; however, it was later revealed to have anticancer properties. Importantly, numerous studies have shown that DSF can be employed as a chemotherapeutic sensitizer to enhance the anticancer efficacy of chemo-drugs in a variety of cancers. Furthermore, the combinations of DSF and chemo-drugs have been tested in clinical trials. In the review, we summarized the possible molecular targets and mechanisms of DSF to reverse chemo-resistance. We also further discussed the opportunities and challenges of DSF as a chemo-therapeutic sensitizer. In conclusion, DSF could be a potentially repurposed drug that sensitizes cancer cells to chemotherapy in the clinic.

Association of the tissue infiltrated and peripheral blood immune cell subsets with response to radiotherapy for rectal cancer.

BACKGROUND: Tumor microenvironment plays pivotal roles in carcinogenesis, cancer development and metastasis. Composition of cancer immune cell subsets can be inferred by deconvolution of gene expression profile accurately. Compositions of the cell types in cancer microenvironment including cancer infiltrating immune and stromal cells have been reported to be associated with the cancer outcomes markers for cancer prognosis. However, rare studies have been reported on their association with the response to preoperative radiotherapy for rectal cancer. METHODS: In this paper, we deconvoluted the immune/stromal cell composition from the gene expression profiles. We compared the composition of immune/stromal cell types in the RT responsive versus nonresponsive for rectal cancer. We also compared the peripheral blood immune cell subset composition in the stable diseases versus progressive diseases of rectal cancer patients with fluorescence-activated cell sorting from our institution. RESULTS: Compared with the non-responsive group, the responsive group showed higher proportions of CD4+ T cell (0.1378 ± 0.0368 vs. 0.1071 ± 0.0373, p = 0.0215), adipocytes, T cells CD4 memory resting, and lower proportions of CD8+ T cell (0.1798 ± 0.0217 vs. 0.2104 ± 0.0415, p = 0.0239), macrophages M2, and preadipocytes in their cancer tissue. The responsive patients showed a higher ratio of CD4+/CD8+ T cell proportions (mean 0.7869 vs. 0.5564, p = 0.0210). Consistently, the peripheral blood dataset showed higher proportion of CD4+ T cells and higher ratio of CD4+/CD8+ T cells, and lower proportion of CD8+ T cells for favorable prognosis. We validated these results with a pooled dataset of GSE3493 and GSE35452, and more peripheral blood data, respectively. Finally, we imported these eight cell features including eosinophils and macrophage M1 to Support Vector Machines and could predict the pre-radiotherapy responsive versus non-responsive with an accuracy of 76%, ROC AUC 0.77, 95% confidential interval of 0.632-0.857, better than the gene signatures. CONCLUSIONS: Our results showed that the proportions of tumor-infiltrating subsets and peripheral blood immune cell subsets can be important immune cell markers and treatment targets for outcomes of radiotherapy for rectal cancer.