A 17-molecule set as a predictor of complete response to neoadjuvant chemotherapy with docetaxel, cisplatin, and 5-fluorouracil in esophageal cancer

Risk model based on genes regulating the response of tumor cells to T-cell-mediated killing in esophageal squamous cell carcinoma

Immune checkpoint inhibitors (ICIs) represent a promising therapeutic approach for esophageal squamous cell carcinoma (ESCC). However, the subpopulations of ESCC patients expected to benefit from ICIs have not been clearly defined. The anti-tumor cytotoxic activity of T cells is an important pharmacological mechanism of ICIs. In this study, the prognostic value of the genes regulating tumor cells to T cell-mediated killing (referred to as GRTTKs) in ESCC was explored by using a comprehensive bioinformatics approach. Training and validation datasets were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. A prognostic risk scoring model was developed by integrating prognostic GRTTKs from TCGA and GEO datasets using a ridge regression algorithm. Patients with ESCC were divided into high- and low-risk groups based on eight GRTTKs (EIF4H, CDK2, TCEA1, SPTLC2, TMEM209, RGP1, EIF3D, and CAPZA3) to predict overall survival in the TCGA cohort. Using Kaplan-Meier curves, receiver operating characteristic curves, and C-index analysis, the high reliability of the prognostic risk-scoring model was certified. The model scores served as independent prognostic factors, and combining clinical staging with risk scoring improved the predictive value. Patients in the high-risk group exhibited abundant immune cell infiltration, including immune checkpoint expression, antigen presentation capability, immune cycle gene expression, and high tumor inflammation signature scores. The high-risk group exhibited a greater response to immunotherapy and neoadjuvant chemotherapy than the low-risk group. Drug sensitivity analysis demonstrated lower IC50 for AZD6244 and PD.0332991 in high-risk groups and lower IC50 for cisplatin, ATRA, QS11, and vinorelbine in the low-risk group. Furthermore, the differential expression of GRTTK-related signatures including CDK2, TCEA1, and TMEM209 were verified in ESCC tissues and paracancerous tissues. Overall, the novel GRTTK-based prognostic model can serve as indicators to predict the survival status and immunotherapy response of patients with ESCC, thereby providing guidance for the development of personalized treatment strategies.

Association of immune-related expression profile with sensitivity to chemotherapy in esophageal squamous cell carcinoma

Neoadjuvant chemotherapy (NAC) followed by surgery is one of the standard therapeutic approaches in Japan for patients with locally advanced esophageal carcinoma. Recently, the JCOG1109 study revealed that NAC with docetaxel, cisplatin and 5-fluorouracil (5-FU) (DCF-NAC) is superior to NAC with cisplatin and 5-FU, and has now become the standard preoperative chemotherapy. Using a microarray system, we have previously investigated the expression profiles of endoscopic biopsy samples from patients with esophageal squamous cell carcinoma (ESCC) before DCF-NAC (preNAC) and identified 17 molecules as biomarkers predictive of a pathologically complete response to DCF-NAC. Here, we re-grouped our previous dataset based on the histopathological response grade with the addition of several microarray profiles and conducted a re-analysis using bioinformatic web tools including DAVID, GSEA, UALCAN, and CIBERSORTx. We identified 204 genes that were differentially expressed between the highly resistant and sensitive groups. Some of these differentially expressed genes (DEGs) were related to the immune response and showed higher expression in the sensitive group. UALCAN showed that high expression of 28 of the top 50 DEGs was associated with a favorable prognosis (p < 0.25), and that this reached a significant (p < 0.05) level for 18 of them, suggesting that patients with high expression of these genes might have benefited from chemotherapy and thus had a better outcome. In preNAC biopsy tissues from a DCF-sensitive case, we demonstrated the presence of cells expressing mRNA for CXCL9, one of the prognosis-related DEGs. Our results highlight the association of immune-related expression profile in preNAC ESCC with the DCF-NAC efficacy.

Enhanced phosphorylation of c-Jun by cisplatin treatment as a potential predictive biomarker for cisplatin response in combination with patient-derived tumor organoids

Despite recent advances in sequencing technology and large-scale drug screenings employing hundreds of cell lines, the predictive accuracy of mutation-based biomarkers is still insufficient as a guide for cancer therapy. Therefore, novel types of diagnostic methods using alternative biomarkers would be highly desirable. We have hypothesized that sensitivity-specific changes in the phosphorylation of signaling molecules could be useful in this respect. Here, with the aim of developing a method for predicting the response of cancers to cisplatin using a combination of specific biomarker(s) and patient-derived tumor organoids (PDOs), we found that cisplatin-sensitive cell lines or PDOs showed enhanced phosphorylation of c-Jun (p-c-Jun) within 24 h after cisplatin treatment. We also compared the responses of 6 PDOs to cisplatin with the therapeutic effect of neoadjuvant chemotherapy (docetaxel/cisplatin/5-fluorouracil) in 6 matched patients. Mechanistically, the c-Jun induction was partly related to TNF signaling induced by cisplatin. Our data suggest that enhanced phosphorylation of c-Jun in response to cisplatin treatment could be a predictive biomarker for the efficacy of cisplatin in selected cancer patients.

Next-generation proteomics of serum extracellular vesicles combined with single-cell RNA sequencing identifies MACROH2A1 associated with refractory COVID-19

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic is widespread; however, accurate predictors of refractory cases have not yet been established. Circulating extracellular vesicles, involved in many pathological processes, are ideal resources for biomarker exploration.

METHODS

To identify potential serum biomarkers and examine the proteins associated with the pathogenesis of refractory COVID-19, we conducted high-coverage proteomics on serum extracellular vesicles collected from 12 patients with COVID-19 at different disease severity levels and 4 healthy controls. Furthermore, single-cell RNA sequencing of peripheral blood mononuclear cells collected from 10 patients with COVID-19 and 5 healthy controls was performed.

RESULTS

Among the 3046 extracellular vesicle proteins that were identified, expression of MACROH2A1 was significantly elevated in refractory cases compared to non-refractory cases; moreover, its expression was increased according to disease severity. In single-cell RNA sequencing of peripheral blood mononuclear cells, the expression of MACROH2A1 was localized to monocytes and elevated in critical cases. Consistently, single-nucleus RNA sequencing of lung tissues revealed that MACROH2A1 was highly expressed in monocytes and macrophages and was significantly elevated in fatal COVID-19. Moreover, molecular network analysis showed that pathways such as "estrogen signaling pathway," "p160 steroid receptor coactivator (SRC) signaling pathway," and "transcriptional regulation by STAT" were enriched in the transcriptome of monocytes in the peripheral blood mononuclear cells and lungs, and they were also commonly enriched in extracellular vesicle proteomics.

CONCLUSIONS

Our findings highlight that MACROH2A1 in extracellular vesicles is a potential biomarker of refractory COVID-19 and may reflect the pathogenesis of COVID-19 in monocytes.

A Transcriptomic Liquid Biopsy Assay for Predicting Resistance to Neoadjuvant Therapy in Esophageal Squamous Cell Carcinoma

OBJECTIVE

The aim of this study was to establish a liquid-biopsy assay to predict response to neoadjuvant therapy (NAT) in esophageal squamous cell carcinoma (ESCC) patients.

SUMMARY BACKGROUND DATA

Pretreatment prediction of resistance to NAT is of great significance for the selection of treatment options in ESCC patients. In this study, we comprehensively translated tissue-based microRNA (miRNA) and messenger RNA (mRNA) expression biomarkers into a liquid biopsy assay.

METHODS

We analyzed 186 clinical ESCC samples, which included 128 formalin-fixed paraffin-embedded and a matched subset of 58 serum samples, from 2 independent institutions. We performed quantitative reverse-transcription polymerase chain reaction, and developed a resistance-prediction model using the logistic regression analyses.

RESULTS

We first evaluated the potential of 4-miRNAs and 3-mRNAs panel, which robustly predicted resistance to NAT [area under the curve (AUC): 0.85]. Moreover, addition of tumor size to this panel increased predictive potential to establish a combination signature (AUC: 0.92). We successfully validated this signature performance in independent cohort, and our model was more accurate when the signature was combined with clinical predictors (AUC: 0.81) to establish a NAT resistance risk (NATRR) model. Finally, we successfully translated our NATRR model into a liquid biopsy assay (AUC: 0.78), and a multivariate regression analysis revealed this model as an independent predictor for response to NAT (odds ratio: 6.10; P < 0.01).

CONCLUSIONS

We successfully developed a liquid biopsy-based assay that allows robust prediction of response to NAT in ESCC patients, and our assay provides fundamentals of developing precision-medicine.

A single-nucleotide-polymorphism in the 5′-flanking region of MSX1 gene as a predictive marker candidate for platinum-based therapy of esophageal carcinoma

Background:

Platinum derivatives are important treatment options for patients with esophageal carcinoma (EC), and a predictive marker for platinum-based therapy is needed for precision medicine.

Patients and methods:

This study contained two cohorts consisting of EC patients treated using platinum-based chemoradiation therapy (CRT) as the first-line and another external cohort of nationwide clinicogenomic data from the BioBank Japan (BBJ).

Results:

Genome-wide association study (GWAS) of therapeutic outcomes, refractory disease or not, following platinum-based CRT as first-line in 94 patients in the first cohort suggested the association of 89 SNPs using p < 0.0001. The top 10 SNPs selected from each chromosomal region by odds ratio were evaluated for progression-free survival (PFS) and overall survival (OS) hazard ratios in the first cohort, resulting in four candidates (p < 0.0025). The four selected candidates were re-evaluated in another cohort of 24 EC patients, which included patients prospectively enrolled in this study to fulfill the sample size statistically suggested by the results of the first cohort, and of the four, only rs3815544 was replicated (p < 0.0125). Furthermore, this candidate genotype of rs3815544 proceeded to the re-evaluation study in an external cohort consisting of EC patients treated with platinum derivatives and/or by radiation therapy as the first-line treatment in BBJ, which confirmed that the alternative allele (G) of rs3815544 was statistically associated with non-response (SD or PD) to platinum-based therapy in EC patients (odds ratio = 1.801, p = 0.048). The methylation QTL database as well as online clinicogenomic databases suggested that the region including rs3815544 may regulate MSX1 expression through CpG methylation, and this down-regulation was statistically associated with poor prognosis after platinum-based therapies for EC.

Conclusion:

rs3815544 is a novel candidate predictive marker for platinum-based EC therapy.

Identification of Molecular Biomarkers and Key Pathways for Esophageal Carcinoma (EsC): A Bioinformatics Approach

Esophageal carcinoma (EsC) is a member of the cancer group that occurs in the esophagus; globally, it is known as one of the fatal malignancies. In this study, we used gene expression analysis to identify molecular biomarkers to propose therapeutic targets for the development of novel drugs. We consider EsC associated four different microarray datasets from the gene expression omnibus database. Statistical analysis is performed using R language and identified a total of 1083 differentially expressed genes (DEGs) in which 380 are overexpressed and 703 are underexpressed. The functional study is performed with the identified DEGs to screen significant Gene Ontology (GO) terms and associated pathways using the Database for Annotation, Visualization, and Integrated Discovery repository (DAVID). The analysis revealed that the overexpressed DEGs are principally connected with the protein export, axon guidance pathway, and the downexpressed DEGs are principally connected with the L13a-mediated translational silencing of ceruloplasmin expression, formation of a pool of free 40S subunits pathway. The STRING database used to collect protein-protein interaction (PPI) network information and visualize it with the Cytoscape software. We found 10 hub genes from the PPI network considering three methods in which the interleukin 6 (IL6) gene is the top in all methods. From the PPI, we found that identified clusters are associated with the complex I biogenesis, ubiquitination and proteasome degradation, signaling by interleukins, and Notch-HLH transcription pathway. The identified biomarkers and pathways may play an important role in the future for developing drugs for the EsC.

Positron emission tomography complete metabolic response as a favorable prognostic predictor in esophageal cancer following neoadjuvant chemotherapy with docetaxel/cisplatin/5-fluorouracil

BACKGROUND

¹⁸F-fluorodeoxyglucose-positron emission tomography (PET)/computed tomography is useful in diagnosing lymph node and distant metastases of esophageal cancer. However, its value for predicting survival is controversial.

AIM

To evaluate the value of PET complete metabolic response (CMR) as a prognostic predictor for esophageal cancer.

METHODS

Between June 2013 and December 2017, 58 patients with squamous cell esophageal cancer who underwent neoadjuvant chemotherapy (NAC) in Oita University were enrolled in this retrospective cohort study. Tumors were clinically staged using fluorodeoxyglucose-PET/computed tomography before and after NAC. After NAC, maximal standardized uptake value ≤ 2.5 was defined as PET-CMR, and maximal standardized uptake value > 2.5 was defined as non-PET-CMR. We compared short-term outcomes between the PET-CMR group and non-PET-CMR group and evaluated prognostic factors by univariate and multivariate analyses.

RESULTS

The PET-CMR group included 22 patients, and the non-PET-CMR group included 36 patients. There were no significant differences in intraoperative and postoperative complications between the two groups. Five-year relapse-free survival and overall survival in the PET-CMR group were significantly more favorable than those in the non-PET-CMR group (38.6 mo vs 20.8 mo, P = 0.021; 42.8 mo vs 25.1 mo, P = 0.011, respectively). PET-CMR was a significant prognostic factor in terms of relapse-free survival by univariate analysis (hazard ratio: 2.523; 95% confidence interval: 1.034–7.063; P < 0.041). Particularly, PET-computed tomography negative N was an independent prognostic factor of relapse-free survival and overall survival by multivariate analysis.

CONCLUSION

PET-CMR after NAC is considered a favorable prognostic factor for esophageal cancer. Evaluation by PET-computed tomography could be useful in clinical decision making for esophageal cancer.

Transcriptomic biomarkers for predicting response to neoadjuvant treatment in oesophageal cancer

Oesophageal cancer is a devastating disease with poor outcomes and is the sixth leading cause of cancer death worldwide. In the setting of resectable disease, there is clear evidence that neoadjuvant chemotherapy and chemoradiotherapy result in improved survival. Disappointingly, only 15%-30% of patients obtain a histopathological response to neoadjuvant therapy, often at the expense of significant toxicity. There are no predictive biomarkers in routine clinical use in this setting and the ability to stratify patients for treatment could dramatically improve outcomes. In this review, we aim to outline current progress in evaluating predictive transcriptomic biomarkers for neoadjuvant therapy in oesophageal cancer and discuss the challenges facing biomarker development in this setting. We place these issues in the wider context of recommendations for biomarker development and reporting. The majority of studies focus on messenger RNA (mRNA) and microRNA (miRNA) biomarkers. These studies report a range of different genes involved in a wide variety of pathways and biological processes, and this is explained to a large extent by the different platforms and analysis methods used. Many studies are also vastly underpowered so are not suitable for identifying a candidate biomarker. Multiple molecular subtypes of oesophageal cancer have been proposed, although little is known about how these relate to clinical outcomes. We anticipate that the accumulating wealth of genomic and transcriptomic data and clinical trial collaborations in the coming years will provide unique opportunities to stratify patients in this poor-prognosis disease and recommend that future biomarker development incorporates well-designed retrospective and prospective analyses.

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review)

The new era of artificial intelligence (AI) has introduced revolutionary data-driven analysis paradigms that have led to significant advancements in information processing techniques in the context of clinical decision-support systems. These advances have created unprecedented momentum in computational medical imaging applications and have given rise to new precision medicine research areas. Radiogenomics is a novel research field focusing on establishing associations between radiological features and genomic or molecular expression in order to shed light on the underlying disease mechanisms and enhance diagnostic procedures towards personalized medicine. The aim of the current review was to elucidate recent advances in radiogenomics research, focusing on deep learning with emphasis on radiology and oncology applications. The main deep learning radiogenomics architectures, together with the clinical questions addressed, and the achieved genetic or molecular correlations are presented, while a performance comparison of the proposed methodologies is conducted. Finally, current limitations, potentially understudied topics and future research directions are discussed.