A gene expression assay for simultaneous measurement of microsatellite instability and anti-tumor immune activity

Characterizing m6A modification factors and their interactions in colorectal cancer: implications for tumor subtypes and clinical outcomes

BACKGROUND

The study aims to comprehensively combine colorectal cancer data cohorts in order to analyze the effects of various DNA methylation-coding genes on colorectal cancer patients. The annual incidence and mortality of colorectal cancer are very high, and there are no effective treatments for advanced colorectal cancer. DNA methylation is a method widely used to regulate epigenetics in the molecular mechanism study of tumors.

METHOD

Three single-cell cohorts GSE166555, GSE146771, and EMTAB8107, and five transcriptome cohorts GSE17536, GSE39582, GSE72970, and TCGA-CRC (TCGA-COAD and TCGA-READ) were applied in this study. 2 erasers (ALKBH5 and FTO), There are 7 writers (METTL3, METTL14, WTAP, VIRMA, RBM15, RBM15B, and ZC3H13) and 11 readers (YTHDC1, IGF2BP1, IGF2BP2, IGF2BP3, YTHDF1, YTHDF3, YTHDC2, and HNRNPA2B1, YTHDF2, HNRNPC and RBMX), a total of 20 M6A regulators, were used as the basis of the dataset in this study and were applied to the construction of molecular typing and prognostic models. Drugs that are differentially sensitive in methylation-regulated gene-related prognostic models were identified using the ConsensusClusterPlus package, which was also used to identify distinct methylation regulatory expression patterns in colorectal cancer and to model the relationship between tissue gene expression profiles and drug IC50 values. Finally, TISCH2 assessed which immune cells were significantly expressed with M6A scores. The immunosuppression of M6A methylation is spatially explained.

RESULTS

This study used data from 583 CRC patients in the TCGA-CRC cohort. Firstly, the mutation frequency and CNV variation frequency of 20 m6A modification-related factors were analyzed, and the corresponding histogram and heat map were drawn. The study next analyzed the expression variations between mutant and wild forms of the VIRMA gene and explored differences in the expression of these variables in tumor and normal tissues. In addition, the samples were divided into different subgroups by molecular clustering method based on m6A modification, and each subgroup's expression and clinicopathological characteristics were analyzed. Finally, we compared prognostic differences, tumor microenvironment (TME) characteristics, immune cell infiltration, and gene function enrichment among different subpopulations. We also developed a colorectal cancer m6A-associated gene signature and validated its prognostic effects across multiple cohorts. Finally, using single-cell RNA sequencing data, we confirmed that tumor cells show elevated expression of m6A-related gene signatures.

DISCUSSION

This study explored the mutation frequency, expression differences, interactions, molecular clustering, prognostic effect, and association with tumor characteristics of m6A modification-related factors in CRC and validated them at the single-cell level. These results clarify the association between m6A alteration and colorectal cancer (CRC) and offer important insights into the molecular recognition and management of cancer.

Detecting microsatellite instability by length comparison of microsatellites in the 3' untranslated region with RNA-seq

Microsatellite instability (MSI), a phenomenon caused by deoxyribonucleic acid (DNA) mismatch repair system deficiencies, is an important biomarker in cancer research and clinical diagnostics. MSI detection often involves next-generation sequencing data, with many studies focusing on DNA. Here, we introduce a novel approach by measuring microsatellite lengths directly from ribonucleic acid sequencing (RNA-seq) data and comparing its distribution to detect MSI. Our findings reveal distinct instability patterns between MSI-high (MSI-H) and microsatellite stable samples, indicating the efficacy of RNA-based MSI detection. Additionally, microsatellites in the 3'-untranslated regions showed the greatest predictive value for MSI detection. Notably, this efficacy extends to detecting MSI-H samples even in tumors not commonly associated with MSI. Our approach highlights the utility of RNA-seq data in MSI detection, facilitating more precise diagnostics through the integration of various biological data.

MSIMEP: Predicting microsatellite instability from microarray DNA methylation tumor profiles

Deficiency in DNA MMR activity results in tumors with a hypermutator phenotype, termed microsatellite instability (MSI). Beyond its utility in Lynch syndrome screening algorithms, today MSI has gained importance as predictive biomarker for various anti-PD-1 therapies across many different tumor types. Over the past years, many computational methods have emerged to infer MSI using either DNA- or RNA-based approaches. Considering this together with the fact that MSI-high tumors frequently exhibit a hypermethylated phenotype, herein we developed and validated MSIMEP, a computational tool for predicting MSI status from microarray DNA methylation tumor profiles of colorectal cancer samples. We demonstrated that MSIMEP optimized and reduced models have high performance in predicting MSI in different colorectal cancer cohorts. Moreover, we tested its consistency in other tumor types with high prevalence of MSI such as gastric and endometrial cancers. Finally, we demonstrated better performance of both MSIMEP models vis-à-vis a MLH1 promoter methylation-based one in colorectal cancer.

Immune-related gene-based prognostic index for predicting survival and immunotherapy outcomes in colorectal carcinoma

Introduction

Colorectal cancer shows high incidence and mortality rates. Immune checkpoint blockade can be used to treat colorectal carcinoma (CRC); however, it shows limited effectiveness in most patients.

Methods

To identify patients who may benefit from immunotherapy using immune checkpoint inhibitors, we constructed an immune-related gene prognostic index (IRGPI) for predicting the efficacy of immunotherapy in patients with CRC. Transcriptome datasets and clinical information of patients with CRC were used to identify differential immune-related genes between tumor and para-carcinoma tissue. Using weighted correlation network analysis and Cox regression analysis, the IRGPI was constructed, and Kaplan-Meier analysis was used to evaluate its predictive ability. We also analyzed the molecular and immune characteristics between IRGPI high-and low-risk subgroups, performed sensitivity analysis of ICI treatment, and constructed overall survival-related receiver operating characteristic curves to validate the IRGPI. Finally, IRGPI genes and tumor immune cell infiltration in CRC model mice with orthotopic metastases were analyzed to verify the results.

Results

The IRGPI was constructed based on the following 11 hub genes: ADIPOQ, CD36, CCL24, INHBE, UCN, IL1RL2, TRIM58, RBCK1, MC1R, PPARGC1A, and LGALS2. Patients with CRC in the high-risk subgroup showed longer overall survival than those in the low-risk subgroup, which was confirmed by GEO database. Clinicopathological features associated with cancer progression significantly differed between the high- and low-risk subgroups. Furthermore, Kaplan-Meier analysis of immune infiltration showed that the increased infiltration of naïve B cells, macrophages M1, and regulatory T cells and reduced infiltration of resting dendritic cells and mast cells led to a worse overall survival in patients with CRC. The ORC curves revealed that IRGPI predicted patient survival more sensitive than the published tumor immune dysfunction and rejection and tumor inflammatory signature.

Discussion

Thus, the low-risk subgroup is more likely to benefit from ICIs than the high-risk subgroup. CRC model mice showed higher proportions of Tregs, M1 macrophages, M2 macrophages and lower proportions of B cells, memory B cell immune cell infiltration, which is consistent with the IRGPI results. The IRGPI can predict the prognosis of patients with CRC, reflect the CRC immune microenvironment, and distinguish patients who are likely to benefit from ICI therapy.

Plasma-Methylated SEPT9 for the Noninvasive Diagnosis of Gastric Cancer

Background. Gastric cancer (GC) is one of the most prevalent cancers globally. This study was designed to evaluate the potential performance of plasma SEPT9 methylation (mSEPT9) as a noninvasive biomarker for the diagnosis of GC. Methods. A total of 182 participants, i.e., 60 patients with GC, 39 with chronic superficial gastritis (CSG), 27 with chronic atrophic gastritis (CAG), 30 with gastric ulcer (GU), and 26 with gastric polys (GP), were recruited. The mSEPT9 level was measured using real-time polymerase chain reaction. Results. As a diagnostic target, mSEPT9 (1/3 algorithm) had a sensitivity of 48.33 (95% confidence interval (CI): 35.40–61.48%) and a specificity of 86.89% (95% CI: 79.28–92.09%), and mSEPT9 (2/3 algorithm) had a sensitivity of 33.33 (95% CI: 22.02–46.79%) and a specificity of 98.36% (95% CI: 93.61–99.72%). The area under the receiver operating characteristic curve (ROC) curve of mSEPT9 was 0.698 (95% CI: 0.609–0.787) for the differentiation of GC from benign gastric diseases. The effectiveness of mSEPT9 (1/3 algorithm) was superior to that of CEA, CA19-9, and CA72-4. mSEPT9 was positively correlated with T, N, M, and the clinical stage of GC. Conclusions. Plasma mSEPT9 might serve as a useful and noninvasive biomarker for the diagnosis of GC.

The Multi-Dimensional Biomarker Landscape in Cancer Immunotherapy

The field of immuno-oncology is now at the forefront of cancer care and is rapidly evolving. The immune checkpoint blockade has been demonstrated to restore antitumor responses in several cancer types. However, durable responses can be observed only in a subset of patients, highlighting the importance of investigating the tumor microenvironment (TME) and cellular heterogeneity to define the phenotypes that contribute to resistance as opposed to those that confer susceptibility to immune surveillance and immunotherapy. In this review, we summarize how some of the most widely used conventional technologies and biomarkers may be useful for the purpose of predicting immunotherapy outcomes in patients, and discuss their shortcomings. We also provide an overview of how emerging single-cell spatial omics may be applied to further advance our understanding of the interactions within the TME, and how these technologies help to deliver important new insights into biomarker discovery to improve the prediction of patient response.

Integrated in silico analysis of LRP2 mutations to immunotherapy efficacy in pan-cancer cohort

PURPOSE

Immunotherapy has emerged as a novel therapy, while many patients are refractory. Although, several biomarkers have been identified as predictive biomarkers for immunotherapy, such as tumor specific genes, PD-1/PD-L1, tumor mutation burn (TMB), and microsatellite instability (MSI), results remain unsatisfactory. The aim of this study is to evaluate the value of LRP2 mutations in predicating cancer immunotherapy.

METHODS

We investigated the characteristics of low-density lipoprotein receptor-related protein 2 (LRP2) mutation in the cancer genome atlas (TCGA) and explored the potential association of LRP2 mutations with immunotherapy. Characteristics of LRP2 mutations in 33 cancer types were analyzed using large-scale public data. The association of LRP2 mutations with immune cell infiltration and immunotherapy efficacy was evaluated. Finally, a LPR2 mutation signature (LMS) was developed and validated by TCGA-UCEC and pan-cancer cohorts. Furthermore, we demonstrated the predictive power of LMS score in independent immunotherapy cohorts by performing a meta-analysis.

RESULTS

Our results revealed that patients with LRP2 mutant had higher TMB and MSI compared with patients without LRP2 mutations. LRP2 mutations were associated with high levels of immune cells infiltration, immune-related genes expression and enrichment of immune related signaling pathways. Importantly, LRP2-mutated patients had a long overall survival (OS) after immunotherapy. In the endometrial cancer (EC) cohort, we found that patients with LRP2 mutations belonged to the POLE and MSI-H type and had a better prognosis. Finally, we developed a LRP2 mutations signature (LMS), that was significantly associated with prognosis in patients receiving immunotherapy.

CONCLUSION

These results indicated that LRP2 mutations can serve as a biomarker for personalized tumor immunotherapy. Importantly, LMS is a potential predictor of patients' prognosis after immunotherapy.

Tumor microenvironment-aware, single-transcriptome prediction of microsatellite instability in colorectal cancer using meta-analysis

Detecting microsatellite instability (MSI) in colorectal cancers (CRCs) is essential because it is the determinant of treatment strategies, including immunotherapy and chemotherapy. Yet, no attempt has been made to exploit transcriptomic profile and tumor microenvironment (TME) of it to unveil MSI status in CRC. Hence, we developed a novel TME-aware, single-transcriptome predictor of MSI for CRC, called MAP (Microsatellite instability Absolute single sample Predictor). MAP was developed utilizing recursive feature elimination-random forest with 466 CRC samples from The Cancer Genome Atlas, and its performance was validated in independent cohorts, including 1118 samples. MAP showed robustness and predictive power in predicting MSI status in CRC. Additional advantages for MAP were demonstrated through comparative analysis with existing MSI classifier and other cancer types. Our novel approach will provide access to untouched vast amounts of publicly available transcriptomic data and widen the door for MSI CRC research and be useful for gaining insights to help with translational medicine.

Experimental and Meta-Analytic Validation of RNA Sequencing Signatures for Predicting Status of Microsatellite Instability

Microsatellite instability (MSI) is an important diagnostic and prognostic cancer biomarker. In colorectal, cervical, ovarian, and gastric cancers, it can guide the prescription of chemotherapy and immunotherapy. In laboratory diagnostics of susceptible tumors, MSI is routinely detected by the size of marker polymerase chain reaction products encompassing frequent microsatellite expansion regions. Alternatively, MSI status is screened indirectly by immunohistochemical interrogation of microsatellite binding proteins. RNA sequencing (RNAseq) profiling is an emerging source of data for a wide spectrum of cancer biomarkers. Recently, three RNAseq-based gene signatures were deduced for establishing MSI status in tumor samples. They had 25, 15, and 14 gene products with only one common gene. However, they were developed and tested on the incomplete literature of The Cancer Genome Atlas (TCGA) sampling and never validated experimentally on independent RNAseq samples. In this study, we, for the first time, systematically validated these three RNAseq MSI signatures on the literature colorectal cancer (CRC) (n = 619), endometrial carcinoma (n = 533), gastric cancer (n = 380), uterine carcinosarcoma (n = 55), and esophageal cancer (n = 83) samples and on the set of experimental CRC RNAseq samples (n = 23) for tumors with known MSI status. We found that all three signatures performed well with area under the curve (AUC) ranges of 0.94-1 for the experimental CRCs and 0.94-1 for the TCGA CRC, esophageal cancer, and uterine carcinosarcoma samples. However, for the TCGA endometrial carcinoma and gastric cancer samples, only two signatures were effective with AUC 0.91-0.97, whereas the third signature showed a significantly lower AUC of 0.69-0.88. Software for calculating these MSI signatures using RNAseq data is included.

RNA Sequencing for Personalized Treatment of Metastatic Leiomyosarcoma: Case Report

Uterine leiomyosarcoma (UL) is a rare malignant tumor that develops from the uterine smooth muscle tissue. Due to the low frequency and lack of sufficient data from clinical trials there is currently no effective treatment that is routinely accepted for UL. Here we report a case of a 65-years-old female patient with metastatic UL, who progressed on ifosfamide and doxorubicin therapy and developed severe hypertensive crisis after administration of second line pazopanib, which lead to treatment termination. Rapid progression of the tumor stressed the need for the alternative treatment options. We performed RNA sequencing and whole exome sequencing profiling of the patient's biopsy and applied Oncobox bioinformatic algorithm to prioritize targeted therapeutics. No clinically relevant mutations associated with drug efficiencies were found, but the Oncobox transcriptome analysis predicted regorafenib as the most effective targeted treatment option. Regorafenib administration resulted in a complete metabolic response which lasted for 10 months. In addition, RNA sequencing analysis revealed a novel cancer fusion transcript of YWHAE gene with fusion partner JAZF1. Several chimeric transcripts for YWHAE and JAZF1 genes were previously found in uterine neoplasms and some of them were associated with tumor prognosis. However, their combination was detected in this study for the first time. Taken together, these findings evidence that RNA sequencing may complement analysis of clinically relevant mutations and enhance management of oncological patients by suggesting putative treatment options.

The Combiome Hypothesis: Selecting Optimal Treatment for Cancer Patients

Existing approaches for cancer diagnosis are inefficient in the use of diagnostic tissue, and decision-making is often sequential, typically resulting in delayed treatment initiation. Future diagnostic testing needs to be faster and optimize increasingly complex treatment decisions. We envision a future where comprehensive testing is routine. Our approach, termed the "combiome," combines holistic information from the tumor, and the patient's immune system. The combiome model proposed here advocates synchronized up-front testing with a panel of sensitive assays, revealing a more complete understanding of the patient phenotype and improved targeting and sequencing of treatments. Development and eventual adoption of the combiome model for diagnostic testing may provide better outcomes for all cancer patients, but will require significant changes in workflows, technology, regulations, and administration. In this review, we discuss the current and future testing landscape, targeting of personalized treatments, and technological and regulatory advances necessary to achieve the combiome.

Current status and future perspective of immune checkpoint inhibitors in colorectal cancer

Immune checkpoint inhibitors (ICIs), as a subverter of immunotherapy in oncology, are changing all aspects of therapy for malignant tumors, especially their remarkable effects on melanoma and non-small cell lung cancer (NSCLC). For colorectal cancer (CRC), only a small number of patients with high immunogenicity (microsatellite instability-high/mismatch-repair deficient (MSI-H/dMMR)) benefit greatly from ICIs treatment, and most CRC patients with low immunogenicity (microsatellite instability-low/mismatch-repair proficient (MSI-L/pMMR)) do not. Currently, numerous clinical trials are ongoing to improve CRC patients' response to ICIs immunotherapy through better patient selection and novel combination strategies. Thus, this review discusses the current status and latest progress of ICIs treatment in CRC. We expect that these studies can change the pattern of CRC immunotherapy in the future.

Cytolytic activity score as a biomarker for antitumor immunity and clinical outcome in patients with gastric cancer

BACKGROUND

A simple measure of immune cytolytic activity (CYT) base on mRNA expression levels of two genes, GZMA and PRF1, was recently reported. Here, we aimed to evaluate the CYT score's potential as a measure of antitumor immunity and predictor of clinical outcome in gastric cancer (GC) patients.

MATERIALS AND METHODS

We evaluated the correlations between tumor-infiltrating immune cells and the CYT score in 238 GC samples from The Cancer Genome Atlas (TCGA). Next, we investigated CYT score associations with molecular subtypes, somatic mutation load, and immune checkpoint molecules in GC samples from TCGA and Asian Cancer Research Group (ACRG). Moreover, we evaluated the clinical significance of the CYT score calculated by reverse transcription (RT)-quantitative PCR (qPCR) data in 123 GC samples and the association of the CYT score with the response to anti-PD-1 therapy in 7 GC samples from Kyushu University Hospital.

RESULTS

The CYT score positively correlated with the proportions of tumor-infiltrating CD8+ T cells and macrophages and negatively correlated with the proportion of regulatory T cells in GC tissues. A high CYT score was associated with common immune checkpoint molecules, a high mutation, the Epstein-Barr virus subtype, and the microsatellite instability subtype in GC. Moreover, a low CYT score was a poor prognosis factor in patients with GC. Finally, the CYT score was higher in a responder to anti-PD-1 therapy compared to nonresponders.

CONCLUSION

The CYT score reflects antitumor immunity and predicts clinical outcome in GC patients.

Efficacy and safety of immune checkpoint inhibitors in gastric cancer: a network meta-analysis of well-designed randomized controlled trials

Background

Immune checkpoint inhibitors (ICIs) that inhibit the programmed death 1 (PD-1)/programmed death-ligand 1 (PD-L1) and cytotoxic T-lymphocyte antigen 4 (CTLA-4) interactions have shown promising prospects as treatment options for advanced gastric cancer (AGC). This manuscript analyzed well designed clinical trials to evaluate the efficacy and safety of immunotherapy in AGC.

Methods

PubMed, Embase, the Cochrane Library, and Medline were searched for randomized controlled trials (RCTs) of AGC treatments that were published before April 2020. Progression-free survival (PFS), overall survival (OS), objective response rate (ORR), and treatment-related adverse events (TRAEs) were evaluated to determine the efficacy and safety of ICIs. Network meta-analysis was performed using a random-effects model under the Bayesian framework. The ability of each treatment was ranked using the surface under the cumulative ranking (SUCRA) curve.

Results

Our analysis included five studies having seven immunotherapy regimens and 1,730 patients. The network meta-analysis showed that nivolumab 1 mg/kg every 3 weeks plus ipilimumab 3 mg/kg every 3 weeks (88.369%) was the regimen most likely to improve PFS. Nivolumab 3 mg/kg every 3 weeks (84.563%) and nivolumab 1 mg/kg every 3 weeks plus ipilimumab 3 mg/kg every 3 weeks (84.556%) were similarly best for OS outcome with excellent tolerance. The regimen of avelumab 10 mg/kg every 2 weeks (91.167%) had the lowest TRAEs. All immunotherapies had similar response rates.

Conclusions

We recommend nivolumab 3 mg/kg every 2 weeks or nivolumab 1 mg/kg every 3 weeks plus ipilimumab 3 mg/kg every 3 weeks as the preferred regimen due to their high efficacies.

CD226hiCD8+ T Cells Are a Prerequisite for Anti-TIGIT Immunotherapy

Clinical trials are evaluating the efficacy of anti-TIGIT for use as single-agent therapy or in combination with programmed death 1 (PD-1)/programmed death-ligand 1 blockade. How and whether a TIGIT blockade will synergize with immunotherapies is not clear. Here, we show that CD226^loCD8⁺ T cells accumulate at the tumor site and have an exhausted phenotype with impaired functionality. In contrast, CD226^hiCD8⁺ tumor-infiltrating T cells possess greater self-renewal capacity and responsiveness. Anti-TIGIT treatment selectively affects CD226^hiCD8⁺ T cells by promoting CD226 phosphorylation at tyrosine 322. CD226 agonist antibody-mediated activation of CD226 augments the effect of TIGIT blockade on CD8⁺ T-cell responses. Finally, mFOLFIRINOX treatment, which increases CD226^hiCD8⁺ T cells in patients with pancreatic ductal adenocarcinoma, potentiates the effects of TIGIT or PD-1 blockade. Our results implicate CD226 as a predictive biomarker for cancer immunotherapy and suggest that increasing numbers of CD226^hiCD8⁺ T cells may improve responses to anti-TIGIT therapy.

PreMSIm: An R package for predicting microsatellite instability from the expression profiling of a gene panel in cancer

Microsatellite instability (MSI) is a genomic property of the cancers with defective DNA mismatch repair and is a useful marker for cancer diagnosis and treatment in diverse cancer types. In particular, MSI has been associated with the active immune checkpoint blockade therapy response in cancer. Most of computational methods for predicting MSI are based on DNA sequencing data and a few are based on mRNA expression data. Using the RNA-Seq pan-cancer datasets for three cancer cohorts (colon, gastric, and endometrial cancers) from The Cancer Genome Atlas (TCGA) program, we developed an algorithm (PreMSIm) for predicting MSI from the expression profiling of a 15-gene panel in cancer. We demonstrated that PreMSIm had high prediction performance in predicting MSI in most cases using both RNA-Seq and microarray gene expression datasets. Moreover, PreMSIm displayed superior or comparable performance versus other DNA or mRNA-based methods. We conclude that PreMSIm has the potential to provide an alternative approach for identifying MSI in cancer.