ImmunoScore Signature: A Prognostic and Predictive Tool in Gastric Cancer. Ann Surg. 2018;267:504-513. [PMID: 28002059 DOI: 10.1097/sla.0000000000002116]

A computed tomography‑based radio‑clinical model for the prediction of microvascular invasion in gastric cancer

The objective of the present study was to build and validate a radio-clinical model integrating radiological features and clinical characteristics based on information available before surgery for prediction of microvascular invasion (MI) in gastric cancer. The retrospective study included a cohort of 534 patients (n=374 for the training set and n=160 for the test set) who were diagnosed with gastric cancer. All patients underwent contrast-enhanced computed tomography within one month before surgery. The focal area was mapped by ITK-SNAP. Radiomics features were extracted from portal venous phase CT images. Principal component analysis was used to reduce dimensionality, maximum relevance minimum redundancy, and least absolute shrinkage and selection operator to screen features most associated with MI. The radiomics signature was subsequently computed based on the coefficient weight assigned to it. The independent risk factors for MI of gastric cancer were determined using univariate analysis and multivariate logistic regression analysis. Univariate logistic regression analysis was used to assess the association between clinical characteristics and MI status. A radio-clinical model was constructed by employing multi-variable logistic regression analysis, incorporating radiomic features with clinical characteristics. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) and calibration curves were employed for the analysis and evaluation of the model's performance. The radiomics signature model had moderate recognition ability, with an area under ROC curve (AUC) of 0.77 for the training set and 0.73 for the test set. The radio-clinical model, consisting of rad-score and clinical features, could well discriminate the training set and test set (AUC=0.88 and 0.80, respectively). The calibration curves and DCA further validated the favorable fit and clinical applicability of the radio-clinical model. In conclusion, the radio-clinical model combining the radiomics signature and clinical characteristics may be used to individually predict MI in gastric cancer to aid in the development of a clinical treatment strategy.

Deep learning-based diagnosis and survival prediction of patients with renal cell carcinoma from primary whole slide images

There is an urgent clinical demand to explore novel diagnostic and prognostic biomarkers for renal cell carcinoma (RCC). We proposed deep learning-based artificial intelligence strategies. The study included 1752 whole slide images from multiple centres. Based on the pixel-level of RCC segmentation, the diagnosis diagnostic model achieved an area under the receiver operating characteristic curve (AUC) of 0.977 (95% CI 0.969-0.984) in the external validation cohort. In addition, our diagnostic model exhibited excellent performance in the differential diagnosis of RCC from renal oncocytoma, which achieved an AUC of 0.951 (0.922-0.972). The graderisk for the recognition of high-grade tumour achieved AUCs of 0.840 (0.805-0.871) in the Cancer Genome Atlas (TCGA) cohort, 0.857 (0.813-0.894) in the Shanghai General Hospital (General) cohort, and 0.894 (0.842-0.933) in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohort, for the recognition of high-grade tumour. The OSrisk for predicting 5-year survival status achieved an AUC of 0.784 (0.746-0.819) in the TCGA cohort, which was further verified in the independent general cohort and the CPTAC cohort, with AUCs of 0.774 (0.723-0.820) and 0.702 (0.632-0.765), respectively. Moreover, the competing-risk nomogram (CRN) showed its potential to be a prognostic indicator, with a hazard ratio (HR) of 5.664 (3.893-8.239, p<0.0001), outperforming other traditional clinical prognostic indicators. Kaplan-Meier survival analysis further illustrated that our CRN could significantly distinguish patients with high survival risk. Deep learning-based artificial intelligence could be a useful tool for clinicians to diagnose and predict the prognosis of RCC patients, thus improving the process of individualised treatment.

A LASSO Cox Regression Predictive Model for Patients Undergoing Surgery for Pancreatic Body and Tail Adenocarcinoma Patients: Comparative Long-Term Survival Analysis of Radical Antegrade Modular Pancreatosplenectomy (RAMPS) and Standard Retrograde Pancreatosplenectomy (SPRS)

PURPOSE

This study was designed to assess the advantages of radical antegrade modular pancreatosplenectomy (RAMPS) over standard retrograde pancreatosplenectomy (SPRS) in terms of disease-free survival (DFS) by comparing clinical outcomes.

METHODS

Clinical data from 154 patients who underwent distal pancreatectomy at Tianjin Medical University Cancer Institute and Hospital between January 2015 and August 2018 were collected. We compared the preoperative conditions, postoperative complications, and survival outcomes of patients who underwent two different surgical procedures. By creating a LASSO-Cox model, we determined the parameters affecting DFS and the risk ratios of the two surgical procedures on DFS.

RESULTS

The R0 resection rate (85.23% vs. 68.18%, P = 0.003), negative posterior margin rate (96.59% vs. 75.76%, P < 0.001), and tumor bed recurrence rate (15.29% vs. 40.00%, P = 0.001) significantly differed between the RAMPS and SPRS groups. The 1-, 3-, and 5-year survival and DFS rates of the RAMPS group were significantly better than those of the SPRS group (P < 0.05). Disease-free survival analysis based on Kaplan-Meier curves revealed that RAMPS was superior to SPRS (P < 0.001).

CONCLUSIONS

We recommend RAMPS as the preferred procedure for treating ductal adenocarcinoma of the pancreatic body and tail due to its enhanced lymph node repair capacity and visualization of posterior pancreatic sections, which can increase DFS in patients.

Mucosal microbiota characterization in gastric cancer identifies immune-activated-related transcripts relevant gastric microbiome signatures

Tumor microenvironment (TME) immune cells and gastric mucosal microbiome constitute two vital elements of tumor tissue. Increasing evidence has elucidated their clinicopathological significance in predicting outcomes and therapeutic efficacy. However, comprehensive characterization of immune cell-associated microbiome signatures in the TME is still in the early stages of development. Here, we characterized the gastric mucosa microbiome and its associations with immune-activated related transcripts (IATs) in 170 GC tumor tissues and matched non-tumor tissues using 16s rRNA gene sequencing and quantitative reverse transcription-PCR. Microbial diversity and richness were significantly higher in GC tumor tissues than in non-tumor tissues. Differences in microbial composition between the groups were evident, with Firmicutes, Proteobacteria, Bacteroidota, Campilobacterota, Actinobacteria, Fusobacteriota, Verrucomicrobiota, Acidobacteriota, and Cyanobacteria being the dominant phyla in the gastric mucosal microbiota. Microbial interaction network analysis revealed distinctive centralities of oral bacteria (such as Fusobacterium, Porphyromonas, Prevotella, etc.) in both tumor and normal mucosae networks, suggesting their significant influence on GC microbial ecology. Furthermore, we analyzed the expression of IATs (CXCL9, CXCL10, GZMA, GZMB, PRF1, CD8A, IFNG, TBX2, and TNF) and characterized IAT-relevant gastric microbiome signatures in GC patients. Our results showed that the expression of CXCL9, CXCL10, GZMA, GZMB, PRF1 and IFNG was significantly higher in tumor tissues than in adjacent normal tissues in GC patients. Notably, high expression of IATs in tumor tissues was associated with improved survival in GC patients and could serve as a powerful predictor for disease-free survival. Additionally, analysis of IAT levels and mucosal microbiota diversity revealed a correlation between higher IAT expression and increased microbiota richness and evenness in the IATs high group, suggesting potential interactions between mucosal microbiota and tumor immunopathology. Spearman correlation analysis showed positive associations between IAT expression and specific mucosal bacterial species. Notably, Akkermansia muciniphila demonstrated potential involvement in modulating GZMB expression in the GC mucosal microenvironment. These findings underscore the importance of mucosal microbiota alterations in GC and suggest potential therapeutic targets focusing on modulating the tumor microbiota for improved clinical outcomes. The detailed characterization of these elements has profound implications for both treatment and survival prediction in GC. We observed that microbial diversity and richness were significantly higher in GC tumor tissues compared to non-tumor tissues. These differences highlight the unique microbial landscape of GC tumors and suggest that the microbiome could influence tumor development and progression. Importantly, our study demonstrated that high expression levels of IATs in GC tumor tissues were associated with improved patient survival. This suggests that IATs not only reflect immune activation but also serve as valuable biomarkers for predicting disease-free survival. The potential of IATs as predictive markers underscores their utility in guiding therapeutic strategies and personalizing treatment approaches. Moreover, the correlation between higher IAT expression and increased microbiota richness and evenness suggests that a diverse and balanced microbiome may enhance immune responses and contribute to better clinical outcomes. These findings highlight the critical need to consider mucosal microbiota alterations in GC management. Targeting the tumor microbiota could emerge as a promising therapeutic strategy, potentially leading to more effective treatments and improved patient outcomes. Understanding and modulating the microbiome's role in GC opens new avenues for innovative therapeutic interventions and personalized medicine.

Overall survival prediction of gastric cancer using the gene signature of CT-detected extramural venous invasion combined with M2 macrophages infiltration

BACKGROUND

CT-detected Extramural venous invasion (EMVI) is known as an independent risk factor for distant metastasis in patients with advanced gastric cancer (GC). However, the molecular basis is not clear. In colorectal cancer, M2 macrophages plays a vital role in determining EMVI. This study aimed to investigate the relationship between CT-detected EMVI and the M2 macrophages as well as prognosis predictionusing a radiogenomic approach.

METHOD

We utilized EMVI-related genes (from mRNA sequencing of 13 GC samples correlated with EMVI score by spearman analysis, P < 0.01) to overlap the co-expression genes of WGCNA module and M2 macrophages related genes (from mRNA data of 371 GC patients in TCGA database), generating a total of 136 genes. An EMVI-M2-prognosis-related hub gene signature was constructed by COX and least absolute shrinkage and selection operator (LASSO) analysis from a training cohort TCGA database (n = 371) and validated it in a validation cohort from GEO database (n = 357). High- and low-risk groups were divided by hub gene (EGFLAM and GNG11) signature-derived risk scores. We assessed its predictive ability through Kaplan-Meier (K-M) curve and COX analysis. Furthermore, we utilized ESTIMATE to detect tumor mutation burden (TMB) and evaluate sensitivity to immune checkpoint inhibitors (ICIs). Expression of hub genes was tested using western blotting and immunohistochemistry (IHC) analysis.

RESULTS

The overall survival (OS) was significantly reduced in the high-risk group (Training/Validation: AUC = 0.701/0.620; P < 0.001/0.003). Furthermore, the risk score was identified as an independent predictor of OS in multivariate COX regression analyses (Training/Validation: HR = 1.909/1.928; 95% CI: 1.225-2.974/1.308-2.844). The low-risk group exhibited significantly higher TMB levels (P = 1.6e- 07) and greater sensitivity to ICIs. Significant higher expression of hub-genes was identified on multiple GC cell lines and original samples. Hub-genes knockdown in gastric cancer cell lines inhibited their proliferation, metastatic and invasive capacity to varying degrees. In vivo experiments indicate that EGFLAM, as one of the hub genes, its high expression can serve as a biomarker for low response to immunotherapy.

CONCLUSION

Our study demonstrated EMVI-M2 gene signature could effectively predict the prognosis of GC tissue, reflecting the relationship between EMVI and M2 macrophages.

The NSCLC immunotherapy response predicted by tumor-infiltrating T cells via a non-invasive radiomic approach

Introductions

Identifying patients with non-small cell lung cancer (NSCLC) who are optimal candidates for immunotherapy is a cornerstone in clinical decision-making. The tumor immune microenvironment (TIME) is intricately linked with both the prognosis of the malignancy and the efficacy of immunotherapeutic interventions. CD8+ T cells, and more specifically, tissue-resident memory CD8+ T cells [CD8+ tissue-resident memory T (TRM) cells] are postulated to be pivotal in orchestrating the immune system's assault on tumor cells. Nevertheless, the accurate quantification of immune cell infiltration-and by extension, the prediction of immunotherapeutic efficacy-remains a significant scientific frontier.

Methods

In this study, we introduce a cutting-edge non-invasive radiomic model, grounded in TIME markers (CD3+ T, CD8+ T, and CD8+ TRM cells), to infer the levels of immune cell infiltration in NSCLC patients receiving immune checkpoint inhibitors and ultimately predict their response to immunotherapy. Data from patients who had surgical resections (cohort 1) were employed to construct a radiomic model capable of predicting the TIME. This model was then applied to forecast the TIME for patients under immunotherapy (cohort 2). Conclusively, the study delved into the association between the predicted TIME from the radiomic model and the immunotherapeutic outcomes of the patients.

Result

For the immune cell infiltration radiomic prediction models in cohort 1, the AUC values achieved 0.765, 0.763, and 0.675 in the test set of CD3+ T, CD8+ T, and CD8+ TRM, respectively. While the AUC values for the TIME-immunotherapy predictive value were 0.651, 0.763, and 0.829 in the CD3-immunotherapy response model, CD8-immunotherapy response model, and CD8+ TRM-immunotherapy response model in cohort 2, respectively. The CD8+ TRM-immunotherapy model exhibited the highest predictive value and was significantly better than the CD3-immunotherapy model in predicting the immunotherapy response. The progression-free survival (PFS) analysis based on the predicted levels of CD3+ T, CD8+ T, and CD8+ TRM immune cell infiltration showed that the CD8+ T cell infiltration level was an independent factor (P=0.014, HR=0.218) with an AUC value of 0.938.

Discussion

Our empirical evidence reveals that patients with substantial CD8+ T cell infiltration experience a markedly improved PFS compared with those with minimal infiltration, asserting the status of the CD8+ T cell as an independent prognosticator of PFS in the context of immunotherapy. Although CD8+ TRM cells demonstrated the greatest predictive accuracy for immunotherapy response, their predictive strength for PFS was marginally surpassed by that of CD8+ T cells. These insights advocate for the application of the proposed non-invasive radiomic model, which utilizes TIME analysis, as a reliable predictor for immunotherapy outcomes and PFS in NSCLC patients.

Virtual multiplexed immunofluorescence staining from non-antibody-stained fluorescence imaging for gastric cancer prognosis

BACKGROUND

Multiplexed immunofluorescence (mIF) staining, such as CODEX and MIBI, holds significant clinical value for various fields, such as disease diagnosis, biological research, and drug development. However, these techniques are often hindered by high time and cost requirements.

METHODS

Here we present a Multimodal-Attention-based virtual mIF Staining (MAS) system that utilises a deep learning model to extract potential antibody-related features from dual-modal non-antibody-stained fluorescence imaging, specifically autofluorescence (AF) and DAPI imaging. The MAS system simultaneously generates predictions of mIF with multiple survival-associated biomarkers in gastric cancer using self- and multi-attention learning mechanisms.

FINDINGS

Experimental results with 180 pathological slides from 94 patients with gastric cancer demonstrate the efficiency and consistent performance of the MAS system in both cancer and noncancer gastric tissues. Furthermore, we showcase the prognostic accuracy of the virtual mIF images of seven gastric cancer related biomarkers, including CD3, CD20, FOXP3, PD1, CD8, CD163, and PD-L1, which is comparable to those obtained from the standard mIF staining.

INTERPRETATION

The MAS system rapidly generates reliable multiplexed staining, greatly reducing the cost of mIF and improving clinical workflow.

FUNDING

Stanford 2022 HAI Seed Grant; National Institutes of Health 1R01CA256890.

The game-changing impact of POLE mutations in oncology-a review from a gynecologic oncology perspective

Somatic mutations within the exonuclease proofreading domain (EDM) of the DNA polymerase Pol ϵ (POLE) gene are increasingly being discovered in ovarian, colorectal, urological, and, especially, endometrial carcinoma (EC), where these are found in up to 10% of the cases. In EC, there are five confirmed pathogenic somatic POLE-EDM mutations that are located at codons 286, 411, 297, 456, and 459, and these are called "hotspot" mutations. POLE mutant tumors are ultramutated entities with a frequency of base substitution mutations that is among the highest in human tumors. Interestingly, these mutations are associated with excellent clinical outcome in EC. An additional six "non-hotspot" POLE-EDM EC mutations are also considered pathogenic, and they also confer a favorable prognosis. Currently, de-escalation of adjuvant treatment is recommended for patients with EC with stage I-II tumors involving any of these 11 EDM mutations, even in patients with other clinicopathological risk factors. The high tumor mutational burden and the consequent increased infiltration of immune cells due to the overexpression of different neoantigens are probably responsible for the improved prognosis. Ongoing studies are examining POLE hotspot mutations among many non-gynecologic tumors, although the impact of such mutations on clinical outcomes is still a topic of debate. Therapeutic modalities for these hypermutated tumors are also an important consideration, including the need for or de-escalation of adjuvant treatments and the response to immune therapy. This review addresses the critical role of POLE mutations in gynecologic oncology and oncology in general, focusing on definitions, variants, underlying pathogenic mechanisms, upcoming developments in the field, and the clinic behavior associated with such mutations.

Lymphocyte Infiltration Score and Spatial Characteristics Refined the Prognosis and Denosumab Treatment Responsiveness Indicators for Giant Cell Tumor of Bone

PURPOSE

The prognostic value of lymphocyte infiltration score (LIS) and its nearest neighbor distance to tumor cells (NNDTC) in giant cell tumor of bone (GCTB) is currently not well established. This study aims to characterize LIS and NNDTC and examine their correlation with denosumab treatment responsiveness, clinicopathologic features, and patient prognosis.

METHODS

Using multiplexed quantitative immunofluorescence, LIS was evaluated in 253 tumor specimens, whereas NNDTC was computed using HALO software. Subsequently, we analyzed the association of these parameters with patient outcomes (progression-free survival [PFS] and overall survival [OS]), clinicopathologic features, and denosumab treatment responsiveness.

RESULTS

Low LIS was indicative of both poor PFS and OS (both P < .001). In addition, LIS was significantly associated with sex (P = .046), Enneking staging (P < .001), Ki-67 expression (P = .007), and denosumab treatment responsiveness (P = .005). Lower CD8+ (tumor interior [TI]) NNDTC, and CD3+ (TI) NNDTC were associated with worse PFS (P = .003 and .038, respectively), whereas lower CD8+ (TI) NNDTC was associated with worse OS (P = .001), but CD8+ (tumor infiltrating margin) NNDTC had the opposite effect (P = .002). Moreover, NNDTC showed a correlation with several clinicopathologic features. Importantly, LIS outperformed Enneking and Campanacci staging systems in predicting the clinical outcomes of GCTB.

CONCLUSION

These findings suggest that LIS is a reliable predictive tool for clinically relevant outcomes and response to denosumab therapy in patients with GCTB. These parameters may prove to be useful in guiding prognostic risk stratification and therapeutic optimization for patients.

CircRHBDD1 promotes immune escape via IGF2BP2/PD-L1 signaling and acts as a nanotherapeutic target in gastric cancer

BACKGROUND

Circular RNAs (circRNAs) have been implicated in the development and progression of gastric cancer (GC). However, it remains unclear whether dysregulated circRNA affects immune escape and the efficacy of immunotherapy in GC. Our aim is to investigate the molecular mechanism of circRNA affecting GC immunotherapy and identify effective molecular therapeutic targets.

METHODS

The differential expression profile of circRNAs was established through circRNA sequencing, comparing three paired GC tissues with their adjacent non-cancerous gastric tissues. The expression level of circRHBDD1 in GC tissues was then assessed using quantitative reverse transcription polymerase chain reaction (qRT-PCR). The biological characteristics of circRHBDD1 were verified through a series of experiments, including agarose gel electrophoresis assays, RNase R treatment, and actinomycin D experiments. The prognostic value of circRHBDD1 in GC was evaluated by conducting both univariate and multivariate survival analyses. Furthermore, loss- and gain-of-function approaches were utilized to investigate the impact of circRHBDD1 on GC immune escape. RNA-sequencing, immunoprecipitation, flow cytometry, and methylated RNA immunoprecipitation (meRIP) analysis were performed to elucidate the underlying molecular mechanisms.

RESULTS

We discovered that circRHBDD1 exhibited remarkably high expression levels in GC tissues and cell lines. Notably, the high expression of circRHBDD1 was significantly correlated with poor overall survival and disease-free survival among GC patients. Both in vitro and in vivo experiments revealed that circRHBDD1 upregulated the expression of PD-L1 and impeded the infiltration of CD8+ T cells. Further, we found that circRHBDD1 binds to IGF2BP2, disrupting the interaction between E3 ligase TRIM25 and IGF2BP2, and ultimately inhibiting IGF2BP2 ubiquitination and degradation. Intriguingly, IGF2BP2 enhances PD-L1 mRNA stability through m6A modification. Additionally, we developed Poly (lactide-co-glycolic acid) (PLGA)-Polyethylene glycol (PEG)-based nanoparticles loaded with circRHBDD1 siRNA. In vivo experiments validated that the combination of PLGA-PEG(si-circRHBDD1) and anti-PD-1 offers a safe and efficacious nano-drug regimen for cancer immunotherapy.

CONCLUSION

Our results demonstrated that circRHBDD1 promoted GC immune escape by upregulating the expression of PD-L1 and reprogramming T cell-mediated immune response. Inhibition of circRHBDD1 expression could potentially enhance the response of GC patients to immunotherapy, thus improving treatment outcomes. Additionally, the development of a nanodrug delivery system provides a feasible approach for future clinical applications.

Identifying an immunosenescence-associated gene signature in gastric cancer by integrating bulk and single-cell sequencing data

It has been believed that immunosenescence plays a crucial role in tumorigenesis and cancer therapy. Nevertheless, there is still a lack of understanding regarding its role in determining clinical outcomes and therapy selection for gastric cancer patients, due to the lack of a feasible immunosenescence signature. Therefore, this research aims to develop a gene signature based on immunosenescence, which is used for stratification of gastric cancer. By integrative analysis of bulk transcriptome and single-cell data, we uncovered immunosenescence features in gastric cancer. Random forest algorithm was used to select hub genes and multivariate Cox algorithm was applied to construct a scoring system to evaluate the prognosis and the response to immunotherapy and chemotherapy. The Cancer Genome Atlas of Stomach Adenocarcinoma (TCGA-STAD) cohort was implemented as the training cohort and two independent cohorts from the Gene Expression Omnibus (GEO) database were used for validation. The model was further tested by our Fudan cohort. In this study, immunosenescence was identified as a hallmark of gastric cancer that is linked with transcriptomic features, genomic variations, and distinctive tumor microenvironment (TME). Four immunosenescence genes, including APOD, ADIPOR2, BRAF, and C3, were screened out to construct a gene signature for risk stratification. Higher risk scores indicated strong predictive power for poorer overall survival. Notably, the risk score signature could reliably predict response to chemotherapy and immunotherapy, with patients with high scores benefiting from immunotherapy and patients with low scores responding to chemotherapy. We report immunosenescence as a hitherto unheralded hallmark of gastric cancer that affects prognosis and treatment efficiency.

Enhanced PSO feature selection with Runge-Kutta and Gaussian sampling for precise gastric cancer recurrence prediction

Gastric cancer (GC), characterized by its inconspicuous initial symptoms and rapid invasiveness, presents a formidable challenge. Overlooking postoperative intervention opportunities may result in the dissemination of tumors to adjacent areas and distant organs, thereby substantially diminishing prospects for patient survival. Consequently, the prompt recognition and management of GC postoperative recurrence emerge as a matter of paramount urgency to mitigate the deleterious implications of the ailment. This study proposes an enhanced feature selection model, bRSPSO-FKNN, integrating boosted particle swarm optimization (RSPSO) with fuzzy k-nearest neighbor (FKNN), for predicting GC. It incorporates the Runge-Kutta search, for improved model accuracy, and Gaussian sampling, enhancing the search performance and helping to avoid locally optimal solutions. It outperforms the sophisticated variants of particle swarm optimization when evaluated in the CEC 2014 test suite. Furthermore, the bRSPSO-FKNN feature selection model was introduced for GC recurrence prediction analysis, achieving up to 82.082 % and 86.185 % accuracy and specificity, respectively. In summation, this model attains a notable level of precision, poised to ameliorate the early warning system for GC recurrence and, in turn, advance therapeutic options for afflicted patients.

The ratio of intratumoral CD15+ neutrophils to CD8+ lymphocytes predicts recurrence in patients with gastric cancer after curative resection

BACKGROUND

An elevated neutrophil-to-lymphocyte ratio (NLR) in peripheral blood is an independent prognostic indicator of various cancers.

AIMS

In this study, we aimed to investigate the prognostic relevance of the intratumoral immune cell balance in gastric cancer.

METHODS AND RESULTS

The study included 82 patients who underwent curative resection for gastric cancer. The intratumoral cluster of differentiation (CD) 15- and CD8-positive cells were evaluated using immunohistochemical staining. Additionally, clinicopathological factors and prognoses were analyzed. Patients with high intratumoral CD15/CD8 ratios had significantly lower overall survival (OS) and relapse-free survival (RFS) compared to those with low CD15/CD8 ratios (p = .0026 and p < .0001, respectively). Additionally, a high CD15/CD8 ratio was associated with lymph node metastasis (p = .019). Patients with high NLR had a significantly lower RFS than those with low NLR (p = .0050). Multivariate analysis revealed that the intratumoral CD15/CD8 ratio, NLR, and venous invasion were independent prognostic indicators of RFS (CD15/CD8 ratio: p < .001, hazard ratio (HR) = 14.7, 95% confidence interval (CI) = 3.8-56.8; NLR: p = .010, HR = 5.4, 95% CI = 1.5-19.6; venous invasion: p = .005, HR = 7.4, 95% CI = 1.8-29.7).

CONCLUSION

In summary, we found that the intratumoral CD15/CD8 ratio is an independent prognostic factor following gastric cancer resection and its increase is associated with lymph node metastasis and microscopic lymph vessel invasion. Immunological evaluation with additional aspects of innate immunity may be useful in predicting cancer prognosis.

Pathomics Signature for Prognosis and Chemotherapy Benefits in Stage III Colon Cancer

Importance

The current TNM staging system may not provide adequate information for prognostic purposes and to assess the potential benefits of chemotherapy for patients with stage III colon cancer.

Objective

To develop and validate a pathomics signature to estimate prognosis and benefit from chemotherapy using hematoxylin-eosin (H-E)-stained slides.

Design, Setting, and Participants

This retrospective prognostic study used data from consecutive patients with histologically confirmed stage III colon cancer at 2 medical centers between January 2012 and December 2015. A total of 114 pathomics features were extracted from digital H-E-stained images from Nanfang Hospital of Southern Medical University, Guangzhou, China, and a pathomics signature was constructed using a least absolute shrinkage and selection operator Cox regression model in the training cohort. The associations of the pathomics signature with disease-free survival (DFS) and overall survival (OS) were evaluated. Patients at the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, formed the validation cohort. Data analysis was conducted from September 2022 to March 2023.

Main Outcomes and Measures

The prognostic accuracy of the pathomics signature as well as its association with chemotherapy response were evaluated.

Results

This study included 785 patients (mean [SD] age, 62.7 [11.1] years; 437 [55.7%] male). A pathomics signature was constructed based on 4 features. Multivariable analysis revealed that the pathomics signature was an independent factor associated with DFS (hazard ratio [HR], 2.46 [95% CI, 2.89-4.13]; P < .001) and OS (HR, 2.78 [95% CI, 2.34-3.31]; P < .001) in the training cohort. Incorporating the pathomics signature into pathomics nomograms resulted in better performance for the estimation of prognosis than the traditional model in a concordance index comparison in the training cohort (DFS: HR, 0.88 [95% CI, 0.86-0.89] vs HR, 0.73 [95% CI, 0.71-0.75]; P < .001; OS: HR, 0.85 [95% CI, 0.84-0.86] vs HR, 0.74 [95% CI, 0.72-0.76]; P < .001) and validation cohort (DFS: HR, 0.83 [95% CI, 0.82-0.85] vs HR, 0.70 [95% CI, 0.67-0.72]; P < .001; OS: HR, 0.80 [95% CI, 0.78-0.82] vs HR, 0.69 [0.67-0.72]; P < .001). Further analysis revealed that patients with a low pathomics signature were more likely to benefit from chemotherapy (eg, combined cohort: DFS: HR, 0.44 [95% CI, 0.28-0.69]; P = .001; OS: HR, 0.43 [95% CI, 0.29-0.64]; P < .001).

Conclusions and Relevance

These findings suggest that a pathomics signature could help identify patients most likely to benefit from chemotherapy in stage III colon cancer.

Gastric cancer immune microenvironment score predicts neoadjuvant chemotherapy efficacy and prognosis

The efficacy of neoadjuvant chemotherapy (NACT) in patients with advanced gastric cancer (GC) varies greatly. Thus, we aimed to verify the predictive value of tumor-infiltrating immune cells (TIICs) on the treatment response to NACT and the prognosis of patients with advanced GC, and to explore the impact of NACT on the tumor immune microenvironment (TIME). Paired tumor tissues (pre- and post-NACT) from patients with advanced GC were collected for this study. TIICs were assessed using immunohistochemistry staining and analyzed using logistic regression to establish an immune microenvironment score for GC (ISGC score) and predict NACT efficacy. Kaplan-Meier curves were used to evaluate the survival outcome of patients. The results showed that TIME was dramatically heterogeneous between NACT response and nonresponse patients. In the validation cohort, the ISGC score demonstrated good predictive performance for treatment response to NACT. Moreover, high ISGC indicated better long-term survival in patients with advanced GC. Furthermore, tumor-infiltrated T cells (CD3+ and CD8+) and CD11c+ macrophages were significantly increased in the response group, while CD163+ macrophages and FOXP3+ Treg cells were decreased after NACT. However, opposite results were exhibited in the nonresponse group. Finally, we found that the percentage of programmed cell death ligand 1 (PD-L1)-positive tumors was 31% (32/104) pre-NACT and 49% (51/104) post-NACT, and almost all patients with elevated PD-L1 were in the NACT response group. The ISGC model accurately predicted NACT efficacy and classified patients with GC into different survival groups. NACT regulates the TIME in GC, which may provide strategies for personalized immunotherapy.

Radiomics signature for dynamic changes of tumor-infiltrating CD8+ T cells and macrophages in cervical cancer during chemoradiotherapy

BACKGROUND

Our previous study suggests that tumor CD8+ T cells and macrophages (defined as CD68+ cells) infiltration underwent dynamic and heterogeneous changes during concurrent chemoradiotherapy (CCRT) in cervical cancer patients, which correlated with their short-term tumor response. This study aims to develop a CT image-based radiomics signature for such dynamic changes.

METHODS

Thirty cervical squamous cell carcinoma patients, who were treated with CCRT followed by brachytherapy, were included in this study. Pre-therapeutic CT images were acquired. And tumor biopsies with immunohistochemistry at primary sites were performed at baseline (0 fraction (F)) and immediately after 10F. Radiomics features were extracted from the region of interest (ROI) of CT images using Matlab. The LASSO regression model with ten-fold cross-validation was utilized to select features and construct an immunomarker classifier and a radiomics signature. Their performance was evaluated by the area under the curve (AUC).

RESULTS

The changes of tumor-infiltrating CD8+T cells and macrophages after 10F radiotherapy as compared to those at baseline were used to generate the immunomarker classifier (AUC= 0.842, 95% CI:0.680-1.000). Additionally, a radiomics signature was developed using 4 key radiomics features to predict the immunomarker classifier (AUC=0.875, 95% CI:0.753-0.997). The patients stratified based on this signature exhibited significant differences in treatment response (p = 0.004).

CONCLUSION

The radiomics signature could be used as a potential predictor for the CCRT-induced dynamic alterations of CD8+ T cells and macrophages, which may provide a less invasive approach to appraise tumor immune status during CCRT in cervical cancer compared to tissue biopsy.

Integrative analysis revealed a correlation of PIAS family genes expression with prognosis, immunomodulation and chemotherapy

BACKGROUND

Protein inhibitor of activated STATs (PIAS) has pleiotropic biological effects, such as protein post-translational modification, transcriptional coregulation and gene editing. It is reported that PIAS family genes are also correlated with immune cells infiltration in cancers that highlights their unnoticed biological role in tumor progression. However, the relationship of their expression with prognosis, immune cell infiltration, tumor microenvironment, and immunotherapy in pan-cancer has been rarely reported.

METHODS

The multi-omics data were used to investigate the expression level of PIAS family members in pan-cancer, and the prognostic value of their expression in different tumors was analyzed by univariate Cox regression and Kaplan-Meier. Correlation analysis was used to investigate the relationship of PIAS gene expression with tumor microenvironment, immune infiltrating subtypes, stemness score and drug sensitivity. In addition, we also used wound healing and transwell assays to verify the biological effects of PIAS family gene expression on invasion and metastasis of HCC cells.

RESULTS

We found that PIAS family genes expression is significantly heterogeneous in tumors by multi-genomic analysis, and associated with poor prognosis in patients with multiple types of cancer. Furthermore, we also found that genetic alterations of PIAS family genes were not only common in different types of human tumors, but were also significantly associated with disease-free survival (DFS) across pan-cancer. Single-cell analysis revealed that PIAS family genes were mainly distributed in monocytes/macrophages. Additionally, we also found that their expression was associated with tumor microenvironment (including stromal cells and immune cells) and stemness score (DNAss and RNAss). Drug sensitivity analysis showed that PIAS family genes were able to predict the response to chemotherapy and immunotherapy. PIAS family genes expression is closely related to tumor metastasis, especially PIAS3. High PIAS3 expression significantly promotes the migration and invasion of liver cancer cell lines (HCC-LM3 and MHCC97-H).

CONCLUSIONS

Taking together, these findings contribute to determine whether the PIAS family genes are a potential oncogenic target gene, which have important contribution for the development of cancer immunotherapy.

Global analysis of T-cell groups reveals immunological features and common antigen targets of digestive tract tumors

BACKGROUND

T cells are key players in the tumor immune microenvironment (TIME), as they can recognize and eliminate cancer cells that express neoantigens derived from somatic mutations. However, the diversity and specificity of T-cell receptors (TCRs) that recognize neoantigens are largely unknown, due to the high variability of TCR sequences among individuals.

METHODS

To address this challenge, we applied GLIPH2, a novel algorithm that groups TCRs based on their predicted antigen specificity and HLA restriction, to cluster the TCR repertoire of 1,702 patients with digestive tract cancer. The patients were divided into five groups based on whether they carried tumor-infiltrating or clonal-expanded TCRs and calculated their TCR diversity. The prognosis, tumor subtype, gene mutation, gene expression, and immune microenvironment of these groups were compared. Viral specificity inference and immunotherapy relevance analysis performed for the TCR groups.

RESULTS

This approach reduced the complexity of TCR sequences to 249 clonally expanded and 150 tumor-infiltrating TCR groups, which revealed distinct patterns of TRBV usage, HLA association, and TCR diversity. In gastric adenocarcinoma (STAD), patients with tumor-infiltrating TCRs (Patients-TI) had significantly worse prognosis than other patients (Patients-nonTI). Patients-TI had richer CD8+ T cells in the immune microenvironment, and their gene expression features were positively correlated with immunotherapy response. We also found that tumor-infiltrating TCR groups were associated with four distinct tumor subtypes, 26 common gene mutations, and 39 gene expression signatures. We discovered that tumor-infiltrating TCRs had cross-reactivity with viral antigens, indicating a possible link between viral infections and tumor immunity.

CONCLUSION

By applying GLIPH2 to TCR sequences from digestive tract tumors, we uncovered novel insights into the tumor immune landscape and identified potential candidates for shared TCRs and neoantigens.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels the molecular feature of M2 macrophages of head and neck squamous cell carcinoma

The connection between head and neck squamous cell carcinoma (HNSC) and M2 tumour-associated macrophages is not yet fully understood. We gathered gene expression profiles and clinical data from HNSC patients in the TCGA database. Using Consensus Clustering, we categorized these patients into M2 macrophage-related clusters. We developed a M2 macrophage-related signature (MRS) through statistical analyses. Additionally, we assessed gene expression in HNSC cells using single-cell sequencing data (GSE139324). We identified three distinct M2 macrophage-related clusters in HNSC, each with different prognostic outcomes and immune characteristics. Patients with different MRS profiles exhibited variations in immune infiltration, genetic mutations and prognosis. FCGR2A may play a role in creating an immunosuppressive tumour microenvironment and could potentially serve as a therapeutic target for HNSC. Our study demonstrated that M2 macrophage-related genes significantly impact the development and progression of HNSC. The M2 macrophage-related model offered a more comprehensive assessment of HNSC patient prognosis, genetic mutations and immune features. FCGR2A was implicated in immunosuppressive microenvironments and may hold promise for the development of novel immunotherapeutic strategies for HNSC.

Laparoscopic cytoreductive surgery and hyperthermic intraperitoneal chemotherapy for gastric cancer with intraoperative detection of limited peritoneal metastasis: a Phase II study of CLASS-05 trial

Background

Systemic chemotherapy for gastric cancer with peritoneal metastasis has limited clinical benefit; for those with intraoperative detection of occult peritoneal metastasis, cytoreductive surgery followed by hyperthermic intraperitoneal chemotherapy (HIPEC) is an alternative treatment. However, the feasibility and effects of this modality and criteria for selecting suitable groups remain unclear. This study aimed to explore the safety and efficacy of laparoscopic cytoreductive surgery (L-CRS) followed by HIPEC in gastric cancer with limited peritoneal metastasis, and this study also aimed to determine the optimized cut-off of the peritoneal cancer index.

Methods

Between March 2017 and November 2019, patients diagnosed with gastric cancer peritoneal metastases by using laparoscopy and the Sugarbaker peritoneal cancer index of ≤12 were eligible for inclusion. All patients received L-CRS (including gastrectomy with D2 lymph node dissection) and resection of visible peritoneal metastasis, followed by post-operative HIPEC, and systemic chemotherapy. The primary end points were median progression-free survival and median survival time, and the secondary outcomes were morbidity and mortality within 30 days after surgery.

Results

Thirty patients were eligible for analysis, of whom 19 (63.3%) were female, and the overall mean age was 53.0 years. The post-operative morbidity was 20% and the severe complication rate was 10%. The median survival time was 27.0 months with a 2-year overall survival rate of 52.3% and median progression-free survival was 14.0 months with a 2-year progression-free survival of 30.4%.

Conclusions

L-CRS followed by HIPEC can be safely performed for gastric cancer with limited peritoneal metastasis and potential survival benefits.

DeepRisk network: an AI-based tool for digital pathology signature and treatment responsiveness of gastric cancer using whole-slide images

BACKGROUND

Digital histopathology provides valuable information for clinical decision-making. We hypothesized that a deep risk network (DeepRisk) based on digital pathology signature (DPS) derived from whole-slide images could improve the prognostic value of the tumor, node, and metastasis (TNM) staging system and offer chemotherapeutic benefits for gastric cancer (GC).

METHODS

DeepRisk is a multi-scale, attention-based learning model developed on 1120 GCs in the Zhongshan dataset and validated with two external datasets. Then, we assessed its association with prognosis and treatment response. The multi-omics analysis and multiplex Immunohistochemistry were conducted to evaluate the potential pathogenesis and spatial immune contexture underlying DPS.

RESULTS

Multivariate analysis indicated that the DPS was an independent prognosticator with a better C-index (0.84 for overall survival and 0.71 for disease-free survival). Patients with low-DPS after neoadjuvant chemotherapy responded favorably to treatment. Spatial analysis indicated that exhausted immune clusters and increased infiltration of CD11b+CD11c+ immune cells were present at the invasive margin of high-DPS group. Multi-omics data from the Cancer Genome Atlas-Stomach adenocarcinoma (TCGA-STAD) hint at the relevance of DPS to myeloid derived suppressor cells infiltration and immune suppression.

CONCLUSION

DeepRisk network is a reliable tool that enhances prognostic value of TNM staging and aid in precise treatment, providing insights into the underlying pathogenic mechanisms.

Identification of a lipid homeostasis-related gene signature for predicting prognosis, immunity, and chemotherapeutic effect in patients with gastric cancer

Gastric cancer (GC) is one of the most common and deadliest cancers worldwide. Lipid homeostasis is essential for tumour development because lipid metabolism is one of the most important metabolic reprogramming pathways within tumours. Elucidating the mechanism of lipid homeostasis in GC might significantly improve treatment strategies and patient prognosis. GSE62254 was applied to construct a lipid homeostasis-related gene signature score (HGSscore) by multiple bioinformatic algorithms including weighted gene coexpression network analysis (WGCNA) and LASSO-Cox regression. A nomogram based on HGSscore and relevant clinical characteristics was constructed to predict the survival of patients with GC. TIMER and xCell were used to evaluate immune and stromal cell infiltration in the tumour microenvironment. Correlations between lipid homeostasis-related genes and chemotherapeutic efficacy were analysed in GSCAlite. RT‒qPCR and cell viability assays were applied to verify the findings in this study. HGSscore was constructed based on eighteen lipid homeostasis-related genes that were selected by WGCNA and LASSO-Cox regression. HGSscore was strongly associated with advanced TNM stage and showed satisfactory value in predicting GC prognosis in three independent cohorts. Furthermore, we found that HGSscore was associated with the tumour mutation burden (TMB) and immune/stromal cell infiltration, which are related to GC prognosis, indicating that lipid homeostasis impacts the formation of the tumour microenvironment (TME). With respect to the GSCAlite platform, PLOD2 and TGFB2 were shown to be positively related to chemotherapeutic resistance, while SLC10A7 was a favourable factor for chemotherapy efficacy. Cell viability assays showed that disrupted lipid homeostasis could attenuate GC cell viability. Moreover, RT‒qPCR revealed that lipid homeostasis could influence expression of specific genes. We identified a lipid homeostasis-related gene signature that correlated with survival, clinical characteristics, the TME, and chemotherapeutic efficacy in GC patients. This research provides a new perspective for improving prognosis and guiding individualized chemotherapy for patients with GC.

An analysis of the relationship of triglyceride glucose index with gastric cancer prognosis: A retrospective study

AIMS/INTRODUCTION

Gastric cancer, one of the most common malignant tumors worldwide, is affected by insulin resistance. The triglyceride glucose (TYG) index is considered a surrogate indicator of insulin resistance; however, its prognostic value in patients with gastric cancer remains obscure. This study aimed to determine whether the TYG index could predict the long-term prognosis of patients with gastric cancer after radical resection gastrectomy.

MATERIALS AND METHODS

We retrospectively analyzed patients with gastric cancer who underwent radical resection gastrectomy. The preoperative TYG index was calculated using the patients' laboratory data. Patients were divided into two groups based on a high or low TYG index. We observed overall survival and evaluated the clinical application value of the index using Cox proportional hazards regression to calculate independent parameters. A prediction model was also established.

RESULTS

In total, 822 patients with gastric cancer were included. The high and low TYG index groups comprised 353 and 469 patients, respectively. The overall survival time was significantly longer in the high-index group than in the low-index group. In the multivariate analysis, TYG index, preoperative age, surgical procedure, tumor node metastasis (TNM) stage, N stage, and postoperative complications (all p < 0.01) were considered independent prognostic predictors. Based on the multivariate analysis, the riglyceride glucose (TYG) index hazard ratio was 0.70 (95% confidence interval, 0.54-0.89, p = 0.004).

CONCLUSIONS

We established a model with a high clinical application value and clinical practice relevance to predict the prognosis of gastric cancer. In this model, TYG was an independent protective factor for gastric cancer prognosis.

Comprehensive assessment of immune context and immunotherapy response via noninvasive imaging in gastric cancer

BACKGROUNDThe tumor immune microenvironment can provide prognostic and therapeutic information. We aimed to develop noninvasive imaging biomarkers from computed tomography (CT) for comprehensive evaluation of immune context and investigate their associations with prognosis and immunotherapy response in gastric cancer (GC).METHODSThis study involved 2,600 patients with GC from 9 independent cohorts. We developed and validated 2 CT imaging biomarkers (lymphoid radiomics score [LRS] and myeloid radiomics score [MRS]) for evaluating the IHC-derived lymphoid and myeloid immune context respectively, and integrated them into a combined imaging biomarker [LRS/MRS: low(-) or high(+)] with 4 radiomics immune subtypes: 1 (-/-), 2 (+/-), 3 (-/+), and 4 (+/+). We further evaluated the imaging biomarkers' predictive values on prognosis and immunotherapy response.RESULTSThe developed imaging biomarkers (LRS and MRS) had a high accuracy in predicting lymphoid (AUC range: 0.765-0.773) and myeloid (AUC range: 0.736-0.750) immune context. Further, similar to the IHC-derived immune context, 2 imaging biomarkers (HR range: 0.240-0.761 for LRS; 1.301-4.012 for MRS) and the combined biomarker were independent predictors for disease-free and overall survival in the training and all validation cohorts (all P < 0.05). Additionally, patients with high LRS or low MRS may benefit more from immunotherapy (P < 0.001). Further, a highly heterogeneous outcome on objective response ​rate was observed in 4 imaging subtypes: 1 (-/-) with 27.3%, 2 (+/-) with 53.3%, 3 (-/+) with 10.2%, and 4 (+/+) with 30.0% (P < 0.0001).CONCLUSIONThe noninvasive imaging biomarkers could accurately evaluate the immune context and provide information regarding prognosis and immunotherapy for GC.

Association of the pathomics-collagen signature with lymph node metastasis in colorectal cancer: a retrospective multicenter study

BACKGROUND

Lymph node metastasis (LNM) is a prognostic biomarker and affects therapeutic selection in colorectal cancer (CRC). Current evaluation methods are not adequate for estimating LNM in CRC. H&E images contain much pathological information, and collagen also affects the biological behavior of tumor cells. Hence, the objective of the study is to investigate whether a fully quantitative pathomics-collagen signature (PCS) in the tumor microenvironment can be used to predict LNM.

METHODS

Patients with histologically confirmed stage I-III CRC who underwent radical surgery were included in the training cohort (n = 329), the internal validation cohort (n = 329), and the external validation cohort (n = 315). Fully quantitative pathomics features and collagen features were extracted from digital H&E images and multiphoton images of specimens, respectively. LASSO regression was utilized to develop the PCS. Then, a PCS-nomogram was constructed incorporating the PCS and clinicopathological predictors for estimating LNM in the training cohort. The performance of the PCS-nomogram was evaluated via calibration, discrimination, and clinical usefulness. Furthermore, the PCS-nomogram was tested in internal and external validation cohorts.

RESULTS

By LASSO regression, the PCS was developed based on 11 pathomics and 9 collagen features. A significant association was found between the PCS and LNM in the three cohorts (P < 0.001). Then, the PCS-nomogram based on PCS, preoperative CEA level, lymphadenectasis on CT, venous emboli and/or lymphatic invasion and/or perineural invasion (VELIPI), and pT stage achieved AUROCs of 0.939, 0.895, and 0.893 in the three cohorts. The calibration curves identified good agreement between the nomogram-predicted and actual outcomes. Decision curve analysis indicated that the PCS-nomogram was clinically useful. Moreover, the PCS was still an independent predictor of LNM at station Nos. 1, 2, and 3. The PCS nomogram displayed AUROCs of 0.849-0.939 for the training cohort, 0.837-0.902 for the internal validation cohort, and 0.851-0.895 for the external validation cohorts in the three nodal stations.

CONCLUSIONS

This study proposed that PCS integrating pathomics and collagen features was significantly associated with LNM, and the PCS-nomogram has the potential to be a useful tool for predicting individual LNM in CRC patients.

Pathological complete response to neoadjuvant chemotherapy may improve antitumor immune response via reduction of regulatory T cells in muscle-invasive bladder cancer

The prognosis for patients who achieve a pathologic complete response in bladder cancer is excellent, but the association between their prognosis and the tumor microenvironment is unclear. We investigated the tumor immune microenvironment of those with pathological complete response after platinum-based neoadjuvant chemotherapy for cT2-4aN0M0 bladder cancer using multiplex fluorescence immunohistochemistry. Our retrospective study included 12 patients with pathological complete response who underwent radical cystectomy following neoadjuvant chemotherapy for cT2-4aN0M0 muscle-invasive bladder cancer. We assessed the density of several immune cell types in pretreatment and posttreatment tissues via multiplex fluorescence immunohistochemical analysis. The median age was 67 years; 10 patients were male. Nine (75%) and 3 (25%) patients were cT2 and cT3, respectively. The 5-year progression-free and overall survivals were 90% and 100%, respectively. The densities of regulatory T cells (Treg; CD3+CD4+FoxP3+ cell) were significantly decreased and almost disappeared in the tumor microenvironment of posttreatment tissue compared with pretreatment tissue. Other immune cells, such as effector T cells or M2 macrophages, were not significantly changed between posttreatment and pretreatment tissues. In pathological complete response, Tregs in the tumor immune microenvironment were significantly decreased after platinum-based chemotherapy for muscle-invasive bladder cancer. The temporary arresting of immune response in the tumor microenvironment may reflect a favorable prognosis due to the decrease of Tregs with tumor shrinkage and improve the host tumor immune response.

Prediction of prognosis of patients with hepatocellular carcinoma based on immune-related score

BACKGROUND

Immune-related scores are currently used for prognostic evaluation and as an immunotherapy reference in various cancers. However, the relationship between immune-related score and hepatocellular carcinoma (HCC) prognosis has not yet been investigated. This study aimed to explore the clinical application value of immune-related score for predicting HCC prognosis-related indicators including disease-free survival (DFS) and overall survival (OS), and to construct a clinical nomogram prediction model related to verification.

METHODS

This study included 284 HCC patients who were selected from the Cancer Genome Atlas (TCGA) database and linked to the immune-related score downloaded from the public platform. A Cox proportional hazards regression model was used to estimate the adjusted risk ratio, and a nomogram was constructed based on multivariate analysis results and clinical significance. The model was internally verified by bootstrap. The performance of the prediction model was evaluated using the C-index and calibration curves.

RESULTS

Patients were divided into three subgroups according to the immune-related score level. Compared with patients in the low immune-related score group, the DFS of patients in the medium and high immune-related score groups was significantly prolonged (HR: 0.53, 95% CI: 0.32-0.87; HR: 0.37, 95% CI: 0.21-0.63, respectively). The OS of patients in the medium and high immune-related score groups was also significantly prolonged (HR: 0.43, 95% CI: 0.20-0.95, p = 0.038; HR: 0.29, 95% CI: 0.14-0.58, p < 0.001, respectively). The C-indexes for predicting DFS and OS were 0.687 (95% CI: 0.665-0.700) and 0.743 (95% CI: 0.709-0.776), respectively. The calibration curves of 3-year and 5-year DFS and OS showed that the results predicted by the nomogram were in good agreement with the actual observations.

CONCLUSIONS

Moderate/high-grade immune-related score was significantly associated with better DFS and OS in HCC patients. In addition, a nomogram for prognosis estimation can help clinicians predict the survival status of patients.

Early Immune Changes Support Signet Ring Cell Dormancy in CDH1-Driven Hereditary Diffuse Gastric Carcinogenesis

Stage IA gastric adenocarcinoma, characterized by foci of intramucosal signet ring cells (SRC), is found in nearly all asymptomatic patients with germline pathogenic CDH1 variants and hereditary diffuse gastric cancer syndrome (HDGC). The molecular steps involved in initiating malignant transformation and promoting SRC dormancy in HDGC are unknown. Here, whole-exome bulk RNA sequencing (RNA-seq) of SRCs and adjacent non-SRC epithelium (NEP) was performed on laser-capture microdissected (LCM) regions of interest found in risk-reducing total gastrectomy specimens from patients with HDGC (Clinicaltrials.gov ID: NCT03030404). In total, 20 patients (6 male, 14 female) with confirmed HDGC were identified. Analysis of differentially expressed genes (DEG) demonstrated upregulation of certain individual EMT and proliferation genes. However, no oncogenic pathways were found to be upregulated in SRCs. Rather, SRC regions had significant enrichment in pathways involved in T-cell signaling. CIBERSORTx predicted significant increases in the presence of regulatory T cells (Treg) specific to SRC regions. IHC confirmed an increase in FOXP3+ cells in SRC foci, as well as elevations in CD4+ T cells and HLA-DR staining. In summary, the tumor immune microenvironment is microscopically inseparable from stage IA gastric SRCs using a granular isolation technique. An elevation in CD4+ T cells within SRC regions correlates with clinically observed SRC dormancy, while Treg upregulation represents a potential immune escape mechanism.

IMPLICATIONS

Characterization of the tumor-immune microenvironment in HDGC underscores the potential for the immune system to shape the transcriptional profile of the earliest tumors, which suggests immune-directed therapy as a potential cancer interception strategy in diffuse-type gastric cancer.

Co-expression pattern of SLC transporter genes associated with the immune landscape and clinical outcomes in gastric cancer

Solute carrier (SLC) transporters play a dual role in the occurrence and progression of tumours by acting as both suppressors and promoters. However, the overall impact of SLC transcriptome signatures on the tumour microenvironment, biological behaviour and clinical stratification of gastric cancer has not been thoroughly investigated. Therefore, we comprehensively analysed the expression profiles of the SLC transporter family members to identify novel molecular subtypes in gastric cancer. We identified two distinct SLC subtypes, SLC-S1 and SLC-S2, using non-negative matrix factorization. These subtypes were markedly linked with the tumour microenvironment landscape, biological pathway activation and distinct clinical features of gastric cancer. Furthermore, a new scoring model, the SLC score, was developed to quantify the SLC subtypes. High SLC scores indicated a pattern of 'SLC-S2', characterized by stromal infiltration and activation, poor prognosis and insensitivity to chemotherapy and immunotherapy, but high sensitivity to imatinib. The SLC score could serve as a supplement to the Tumour Node Metastasis (TNM) staging system to guide personalized treatment strategies and predict prognosis for patients with gastric cancer.

A Noninvasive Approach to Evaluate Tumor Immune Microenvironment and Predict Outcomes in Hepatocellular Carcinoma

It is widely recognized that tumor immune microenvironment (TIME) plays a crucial role in tumor progression, metastasis, and therapeutic response. Despite several noninvasive strategies have emerged for cancer diagnosis and prognosis, there are still lack of effective radiomic-based model to evaluate TIME status, let alone predict clinical outcome and immune checkpoint inhibitor (ICIs) response for hepatocellular carcinoma (HCC). In this study, we developed a radiomic model to evaluate TIME status within the tumor and predict prognosis and immunotherapy response. A total of 301 patients who underwent magnetic resonance imaging (MRI) examinations were enrolled in our study. The intra-tumoral expression of 17 immune-related molecules were evaluated using co-detection by indexing (CODEX) technology, and we construct Immunoscore (IS) with the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression method to evaluate TIME. Of 6115 features extracted from MRI, five core features were filtered out, and the Radiomic Immunoscore (RIS) showed high accuracy in predicting TIME status in testing cohort (area under the curve = 0.753). More importantly, RIS model showed the capability of predicting therapeutic response to anti-programmed cell death 1 (PD-1) immunotherapy in an independent cohort with advanced HCC patients (area under the curve = 0.731). In comparison with previously radiomic-based models, our integrated RIS model exhibits not only higher accuracy in predicting prognosis but also the potential guiding significance to HCC immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00136-8.

Superiority of CRP-albumin-lymphocyte index as a prognostic biomarker for patients with gastric cancer

OBJECTIVES

The new C-reactive protein (CRP)-albumin-lymphocyte (CALLY) index is an immune nutrition scoring system based on serum CRP) serum albumin, and lymphocyte counts. The aim of this study was to verify the prognostic value of the CALLY index in patients with gastric cancer and to evaluate the superiority of this new system.

METHODS

We retrospectively analyzed the data of patients with gastric cancer who were followed up from the INSCOC database between May 2013 and December 2018. Through simple random sampling, patients with gastric cancer were placed into one of two groups: the training group (n = 684) or the verification group (n = 290) in a ratio of 7:3. Correlation analysis, Kaplan-Meier method, and cubic spline function were used to analyze the relationship between the CALLY index and overall survival (OS) in these patients. Based on the results of Cox regression analysis of the training cohort, a nomogram model for predicting 1 -, 2 -, 3-, and 5-y OS was established and verified internally. The prediction accuracy and benefit of the nomogram in gastric cancer were evaluated by calibration and clinical decision curve and compared with the traditional TNM gastric cancer staging system.

RESULTS

The CALLY index was negatively correlated with the age of patients with gastric cancer (men, r = -0.1; women, r = -0.1), but positively correlated with body mass index (BMI; men, r = 0.063; women, r = 0.058), and the cutoff value of the CALLY index was determined as 1.12. The OS of patients with gastric cancer and a CALLY index >1.12 was significantly higher than that of patients with gastric cancer and a CALLY index ≤1.12 (P < 0.0001). There was an L-shaped dose-response relationship between the CALLY index and OS in patients with gastric cancer, and age, TNM stage, surgical treatment, chemotherapy, BMI, and the CALLY index were significantly correlated with the prognosis of patients with gastric cancer. Tumor TNM stage, BMI, and the CALLY index were independent risk factors affecting the prognosis of patients with gastric cancer. The CALLY index was a protective factor in the following patient factors: diagnosis of gastric cancer; <65 y of age; male; TNM 3 stage; BMI 18.5 to 23.9 kg/m2; smoker; consumer of alcohol; no radio- or chemotherapy; surgery; presence of diabetes, hypertension, or both; no family history of cancer; experienced a significant interaction with chemotherapy and surgery. A nomogram based on TNM staging, BMI, and the CALLY index has good predictive ability and clinical application value. Compared with traditional TNM staging systems, the nomogram has better resolution and accuracy in predicting 1 -, 2 -, 3-, and 5-year OS.

CONCLUSION

The CALLY index can be used as an independent prognostic factor for patients with gastric cancer, and constructs a nomogram prediction model combining TNM staging, BMI, and CALLY index, which yields better predictions than traditional TNM staging.

Development and validation of a novel radiomics nomogram for prediction of early recurrence in colorectal cancer

BACKGROUND

Early recurrence (ER) is a significant concern following curative resection of advanced colorectal cancer (CRC) and is linked to poor long-term survival. Reliable prediction of ER is challenging, necessitating the development of a novel radiomics-based nomogram for CRC patients.

METHODS

We enrolled 405 patients, with 298 in the training set and 107 in the external test set. Radiomic features were extracted from preoperative venous-phase computed tomography (CT) images. A radiomics signature was created using univariate logistic regression analyses and the least absolute shrinkage and selection operator algorithm. Clinical factors were integrated into the analyses to develop a comprehensive predictive tool in a multivariate logistic regression model, resulting in a radiomics nomogram. Subsequently, the calibration, discrimination, and clinical usefulness of the nomogram were evaluated.

RESULTS

The radiomics signature, consisting of four selected CT features, was significantly associated with ER in both the training and test datasets (P < 0.05). Independent predictors of ER included TNM stage, carcinoembryonic antigen level and differentiation grade were identified. The radiomics nomogram, incorporating all these predictors, exhibited good predictive ability in both the training set with an area under the curve (AUC) of 0.82 (95 % confidence interval (CI), 0.74-0.90) and the test set with an AUC of 0.85 (95 % CI, 0.72-0.99), surpassing the performance of any single candidate factor alone. Furthermore, additional analysis demonstrated that the nomogram was clinically useful.

CONCLUSIONS

We have developed a radiomics-based nomogram that effectively predicts early recurrence in CRC patients, enhancing the potential for timely intervention and improved outcomes.

Deciphering tumor-infiltrating dendritic cells in the single-cell era

Dendritic cells (DCs) serve as a pivotal link connecting innate and adaptive immunity by processing tumor-derived antigens and activating T cells. The advent of single-cell sequencing has revolutionized the categorization of DCs, enabling a high-resolution characterization of the previously unrecognized diversity of DC populations infiltrating the intricate tumor microenvironment (TME). The application of single-cell sequencing technologies has effectively elucidated the heterogeneity of DCs present in the tumor milieu, yielding invaluable insights into their subpopulation structures and functional diversity. This review provides a comprehensive summary of the current state of knowledge regarding DC subtypes in the TME, drawing from single-cell studies conducted across various human tumors. We focused on the categorization, functions, and interactions of distinct DC subsets, emphasizing their crucial roles in orchestrating tumor-related immune responses. Additionally, we delve into the potential implications of these findings for the identification of predictive biomarkers and therapeutic targets. Enhanced insight into the intricate interplay between DCs and the TME promises to advance our comprehension of tumor immunity and, in turn, pave the way for the development of more efficacious cancer immunotherapies.

The molecular mechanism of actions and clinical utilities of tumor infiltrating lymphocytes in gastrointestinal cancers: a comprehensive review and future prospects toward personalized medicine

Gastrointestinal (GI) cancers remain a significant global health burden, accounting for a substantial number of cases and deaths. Regrettably, the inadequacy of dependable biomarkers hinders the precise forecasting of patient prognosis and the selection of appropriate therapeutic sequencing for individuals with GI cancers, leading to suboptimal outcomes for numerous patients. The intricate interplay between tumor-infiltrating lymphocytes (TILs) and the tumor immune microenvironment (TIME) has been shown to be a pivotal determinant of response to anti-cancer therapy and consequential clinical outcomes across a multitude of cancer types. Therefore, the assessment of TILs has garnered global interest as a promising prognostic biomarker in oncology, with the potential to improve clinical decision-making substantially. Moreover, recent discoveries in immunotherapy have progressively changed the landscape of cancer treatment and significantly prolonged the survival of patients with advanced cancers. Nonetheless, the response rate remains constrained within solid tumor sufferers, even when TIL landscapes appear comparable, which calls for the development of our understanding of cellular and molecular cross-talk between TIME and tumor. Hence, this comprehensive review encapsulates the extant literature elucidating the TILs' underlying molecular pathogenesis, prognostic significance, and their relevance in the realm of immunotherapy for patients afflicted by GI tract cancers. Within this review, we demonstrate that the type, density, and spatial distribution of distinct TIL subpopulations carries pivotal implications for the prediction of anti-cancer treatment responses and patient survival. Furthermore, this review underscores the indispensable role of TILs in modulating therapeutic responses within distinct molecular subtypes, such as those characterized by microsatellite stability or programmed cell death ligand-1 expression in GI tract cancers. The review concludes by outlining future directions in TIL-based personalized medicine, including integrating TIL-based approaches into existing treatment regimens and developing novel therapeutic strategies that exploit the unique properties of TILs and their potential as a promising avenue for personalized cancer treatment.

Pre-treatment inflamed tumor immune microenvironment is associated with FOLFIRINOX response in pancreatic cancer

Introduction

Pancreatic adenocarcinoma (PDAC) is an aggressive tumor with limited response to both chemotherapy and immunotherapy. Pre-treatment tumor features within the tumor immune microenvironment (TiME) may influence treatment response. We hypothesized that the pre-treatment TiME composition differs between metastatic and primary lesions and would be associated with response to modified FOLFIRINOX (mFFX) or gemcitabine-based (Gem-based) therapy.

Methods

Using RNAseq data from a cohort of treatment-naïve, advanced PDAC patients in the COMPASS trial, differential gene expression analysis of key immunomodulatory genes in were analyzed based on multiple parameters including tumor site, response to mFFX, and response to Gem-based treatment. The relative proportions of immune cell infiltration were defined using CIBERSORTx and Dirichlet regression.

Results

145 samples were included in the analysis; 83 received mFFX, 62 received Gem-based therapy. Metastatic liver samples had both increased macrophage (1.2 times more, p < 0.05) and increased eosinophil infiltration (1.4 times more, p < 0.05) compared to primary lesion samples. Further analysis of the specific macrophage phenotypes revealed an increased M2 macrophage fraction in the liver samples. The pre-treatment CD8 T-cell, dendritic cell, and neutrophil infiltration of metastatic samples were associated with therapy response to mFFX (p < 0.05), while mast cell infiltration was associated with response to Gem-based therapy (p < 0.05). Multiple immunoinhibitory genes such as ADORA2A, CSF1R, KDR/VEGFR2, LAG3, PDCD1LG2, and TGFB1 and immunostimulatory genes including C10orf54, CXCL12, and TNFSF14/LIGHT were significantly associated with worse survival in patients who received mFFX (p = 0.01). There were no immunomodulatory genes associated with survival in the Gem-based cohort.

Discussion

Our evidence implies that essential differences in the PDAC TiME exist between primary and metastatic tumors and an inflamed pretreatment TiME is associated with mFFX response. Defining components of the PDAC TiME that influence therapy response will provide opportunities for targeted therapeutic strategies that may need to be accounted for in designing personalized therapy to improve outcomes.

IDH1R132H mutation increases radiotherapy efficacy and a 4-gene radiotherapy-related signature of WHO grade 4 gliomas

The prognosis for the WHO grade 4 IDH-mutant astrocytoma is better than IDH-wildtype glioblastoma (GBM) patients. The purpose of this study is to explore the potential mechanism of how IDH1 mutation can increase the efficacy of radiotherapy and to establish a risk-score model to predict the efficacy of radiotherapy in WHO grade 4 gliomas. First, we conducted experimental study on the effect of IDH1R132H mutation on glioma cells in vitro. Radiosensitivity of glioma cells was detected by γ-H2AX after 5 Gy radiation. Cell proliferation, migration and invasion were determined respectively by CCK-8, EDU, monolayer cell migration scratch assay and Transwell assay. Then we analyzed IDH1 gene status and the survival of WHO grade 4 glioma patients received radiotherapy in our center and verified our results by analyzing CGGA and TCGA database. For the risk-score model, we use CGGA data to find genetic differences between WHO grade 4 IDH-mutant astrocytoma and IDH-wildtype GBM patients, and determined a 4-gene radiotherapy-related signature through survival analysis by R software. Evaluation and verification through different glioma validation sets and different statistical methods. For in vitro experiments, we established glioma cells stably overexpressing IDH1 wild-type and IDH1-mutant proteins. γ-H2AX assay showed that IDH1-mutant glioma cells had higher radiosensitivity than wild-type. CCK-8 and EDU assay showed that proliferation capacity of IDH1-mutant glioma cells declined. Transwell assay and monolayer cell migration scratch assay also showed that IDH1-mutant glioma cells reduced migration and invasion capabilities. Among the 83 WHO grade 4 glioma patients who received radiotherapy in our center, WHO grade 4 IDH-mutant astrocytoma patients had longer OS and PFS versus IDH-wildtype GBM (P = 0.0336, P = 0.0324, respectively). TCGA and CGGA database analysis had the similar results. Through complex analysis of CGGA and TCGA databases, we established a risk-model that can predict the efficacy of radiotherapy for WHO grade 4 glioma patients. The 4-gene radiotherapy-related signature including ADD3, GRHPR, RHBDL1 and SLC9A9. Patients in the high-risk group had worse OS compared to low-risk group (P = 0.0001). High- and low-risk groups of patients receiving radiotherapy have significant survival differences, while patients who did not receive radiotherapy have no survival difference both in CGGA and TCGA databases. WHO grade 4 IDH-mutant astrocytoma is more radiosensitive than IDH-wildtype GBM patients. Our 4-gene radiotherapy-related signature can predict the radiation efficacy of WHO grade 4 glioma patients, and it may provide some reference for clinical treatment options.

Prediction and therapeutic targeting of the tumor microenvironment-associated gene CTSK in gastric cancer

BACKGROUND

Cathepsin-K (CTSK) is overexpressed in Gastric cancer (GC) and the mechanism of its overexpression in GC is still unclear. The present work found CTSK as a potential predictive biomarker and immunotherapeutic target for GC based on the tumor microenvironment (TME).

METHODS

From public databases, gene expression profiles and clinical data of GC were downloaded to analyze the distribution of stromal and immune cells and tumor abundance in TME. Differentially expressed genes (DEGs) associated with TME were obtained by differential analysis, followed by cross-screening to obtain CTSK as a gene associated with TME. Next, a series of methods and tools were employed to explore the relationships between clinicopathological features of GC and CTSK expression as well as prognosis, tumor immune microenvironment, immune checkpoints and drug sensitivity. And GSEA was used to investigate the potential role of CTSK in the tumor microenvironment of GC.

RESULTS

From the dataset, we obtained a total of 656 DEGs associated with TME and the stromal component of TME was found to be closely involved in GC prognosis. CTSK was cross-screened as the key gene associated with TME by the PPI network and univariate Cox regression analysis. Pan-cancer analysis revealed significant high expression of CTSK in a variety of cancers. Subsequently, we hypothesized that high-expressed CTSK was closely correlated with poor prognosis and lymph node metastasis of tumors, and that CTSK, a GC TME-related gene, was largely involved in a range of biological behaviors of tumors, with a significant correlation between several immune cells.

CONCLUSION

CTSK was validated as a potential prognostic biomarker related to TME of GC and could be a promising next-generation immunotherapeutic target for GC.

Integrated analysis of single-cell and bulk RNA-sequencing data reveals the prognostic value and molecular function of THSD7A in gastric cancer

The biological role and prognostic value of thrombospondin domain-containing 7A (THSD7A) in gastric cancer remain unclear. Our purpose was to determine the molecular mechanisms underlying the functioning of THSD7A and its prognostic value in gastric cancer. Gastric cancer-associated single cell and bulk RNA sequencing data obtained from two databases, were analyzed. We used bulk RNA sequencing to examine the differential expression of THSD7A in gastric cancer and normal gastric tissues and explored the relationship between THSD7A expression and clinicopathological characteristics. Kaplan-Meier survival and Cox analyses revealed the prognostic value of THSD7A. Gene set enrichment and immune infiltration analyses were used to determine the cancer-promoting mechanisms of THSD7A and its effect on the immune microenvironment. We explored the relationship between THSD7A expression and sensitivity of anti-tumor drugs and immune checkpoint levels. Biological functions of THSD7A were validated at single-cell and in vitro levels. THSD7A expression was significantly increased in gastric cancer samples. High THSD7A expression was associated with poor clinical phenotypes and prognoses. Cox analysis showed that THSD7A was an independent risk factor for patients with gastric cancer. Enrichment analysis suggested that epithelial-mesenchymal transition and inflammatory responses may be potential pro-cancer mechanisms of THSD7A. Upregulation of THSD7A promoted infiltration by M2 macrophages and regulatory T cells. High THSD7A expression suppressed the sensitivity of patients with gastric cancer to drugs, such as 5-fluorouracil, bleomycin, and cisplatin, and upregulated immune checkpoints, such as HAVCR2, PDCD1LG2, TIGIT, and CTLA4. At the single cell level, THSD7A was an endothelial cell-associated gene and endothelial cells overexpressing THSD7A showed unique pro-oncogenic effects. In vitro experiments confirmed that THSD7A was overexpressed in gastric cancer samples and cells, and that knocking out THSD7A significantly inhibited gastric cancer cell proliferation and invasion. THSD7A overexpression may be a unique prognostic marker and therapeutic target in gastric cancer. Therefore, our study provides a new perspective on the precise treatment of gastric cancer.

Immunobiological signatures and the emerging role of SPP1 in predicting tumor heterogeneity, malignancy, and clinical outcomes in stomach adenocarcinoma

BACKGROUND

Immunotherapy, as a form of immunobiological therapy, represents a promising approach for enhancing patients' immune responses. This work aims to present innovative ideas and insights for prognostic assessment and clinical treatment of stomach adenocarcinoma (STAD) by leveraging immunobiological signatures.

METHODS

We employed weighted gene co-expression network analysis (WGCNA) and unsupervised clustering analysis to identify hub genes. These hub genes were utilized to construct a prognostic risk model, and their impact on the tumor microenvironment (TME) and DNA variations was assessed using large-scale STAD patient cohorts. Additionally, we conducted transfection experiments with plasmids to investigate the influence of SPP1 on the malignancy of HGC27 and NCI-N87 cells.

RESULTS

Unsupervised clustering of 12 immune-related genes (IRGs) revealed three distinct alteration patterns with unique molecular phenotypes, clinicopathological characteristics, prognosis, and TME features. Using LASSO and multivariate Cox regression analyses, we identified three hub genes (MMP12, SPP1, PLAU) from the IRGs to establish a risk signature. This IRG-related risk model significantly stratified the prognosis risk among STAD patients in the training (n = 522), testing (n = 521), and validation (n = 300) cohorts. Notably, there were discernible differences in therapy responses and TME characteristics, such as tumor purity and lymphocyte infiltration, between the risk model groups. Subsequently, a nomogram that incorporates the IRG signature and clinicopathological factors demonstrated superior sensitivity and specificity in predicting outcomes for STAD patients. Furthermore, down-regulation of SPP1, as observed after siRNA transfection, significantly inhibited the proliferation and migration abilities of HGC27 and NCI-N87 cells.

CONCLUSIONS

In summary, this study highlights the critical role of immune-related signatures in STAD and offers novel insights into prognosis indicators and immunotherapeutic targets for this condition. SPP1 emerges as an independent prognostic factor for STAD and appears to regulate STAD progression by influencing the immune microenvironment.

Development of an immunogenic cell death prognostic signature for predicting clinical outcome and immune infiltration characterization in stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is a common gastric histological cancer type with a high mortality rate. Immunogenic cell death (ICD) plays a key factor during carcinogenesis progress, whereas the prognostic value and role of ICD-related genes (ICDRGs) in STAD remain unclear. The MSigDB database collecting ICDRGs were selected by univariate Cox regression analysis and LASSO algorithm to establish a novel risk model. The Kaplan-Meier survival analysis indicated a significant difference of OS rate of patients by risk score stratification. ESTIMATE, CIBERSORT, and single sample gene set enrichment analysis (ssGSEA) algorithms were conducted to estimate the immune infiltration landscape by risk stratification. Subgroup analysis and tumor mutation burden analysis were also analyzed to identify characteristics between groups. Differences in therapeutic responsiveness to chemotherapeutic drugs and targeted drugs were also analyzed between high-risk group and low-risk group. The impact of one ICDRG, GPX1, on the proliferation, migration and invasiveness of was confirmed by in vitro experiments in GC cells to test the reliability of bioinformatics results. This study gives evidence of the involvement of ICD process in STAD and provides a new perspective for further accurate assessment of prognosis and therapeutic efficacy in STAD patients. Stomach adenocarcinoma (STAD) is a common gastric histological cancer type with a high mortality rate. Immunogenic cell death (ICD) plays a key factor during carcinogenesis progress, whereas the prognostic value and role of ICD-related genes (ICDRGs) in STAD remains unclear. The MSigDB database collected ICDRGs were selected by univariate Cox regression analysis and LASSO algorithm to establish a novel risk model. The Kaplan-Meier survival analysis indicated a significant difference of OS rate of patients by risk score stratification. ESTIMATE, CIBERSORT, and single sample gene set enrichment analysis (ssGSEA) algorithms were conducted to estimate the immune infiltration landscape by risk stratification. Subgroup analysis and tumor mutation burden analysis were also analyzed to identify characteristics between groups. Differences in therapeutic responsiveness to chemotherapeutic drugs and targeted drugs were also analyzed between high-risk group and low-risk group. The impact of one ICDRG, GPX1, on the proliferation, migration and invasiveness of was confirmed by in vitro experiments in GC cells to test the reliability of bioinformatics results. This study gives evidence of the involvement of ICD process in STAD and provides a new perspective for further accurate assessment of prognosis and therapeutic efficacy in STAD patients.

Evaluation of dynamic recurrence risk for locally advanced gastric cancer in the clinical setting of adjuvant chemotherapy: a real-world study with IPTW-based conditional recurrence analysis

BACKGROUND

The long-term dynamic recurrence hazard of locally advanced gastric cancer (LAGC) in the clinical setting of adjuvant chemotherapy (ACT) remains unclear.

PURPOSE

This study aimed to investigate the dynamic recurrence risk of LAGC in patients who received ACT or not.

METHODS

The study assessed data from patients with LAGC who underwent radical gastrectomy between January, 2010 and October, 2015. Inverse probability of treatment weighting (IPTW) was performed to reduce selection bias between the ACT and observational (OBS) groups. Conditional recurrence-free survival (cRFS) and restricted mean survival time (RMST) were used to assess the survival differences.

RESULTS

In total, 1,661 LAGC patients were included (ACT group, n = 1,236 and OBS group, n = 425). The recurrence hazard gradually declined; in contrast, cRFS increased with RFS already accrued. Following IPTW adjustment, the cRFS rates were higher in the ACT group than those in the OBS group for patients at baseline or with accrued RFS of 1 and 2 years (p˂0.05). However, the cRFS rates of the ACT group were comparable with those of the OBS group for patients with accrued RFS of 3 or more years (p > 0.05). Likewise, the 5-year △RMST between the ACT and OBS groups demonstrated a similar trend. Moreover, the hematological metastasis rate of the ACT group was significantly lower than that of the OBS group for patients at baseline or with accrued RFS of 1 and 2 years, respectively (p˂0.05).

CONCLUSIONS

Although ACT could provide substantial benefits for patients with LAGC, the differences in recurrence hazard between the ACT and OBS groups may attenuate over time, which could help guide surveillance and alleviate patients' anxiety. Further prospective large-scale studies are warranted.

The T Cell Immunoscore as a Reference for Biomarker Development Utilizing Real-World Data from Patients with Advanced Malignancies Treated with Immune Checkpoint Inhibitors

BACKGROUND

We aimed to determine the prognostic value of an immunoscore reflecting CD3+ and CD8+ T cell density estimated from real-world transcriptomic data of a patient cohort with advanced malignancies treated with immune checkpoint inhibitors (ICIs) in an effort to validate a reference for future machine learning-based biomarker development.

METHODS

Transcriptomic data was collected under the Total Cancer Care Protocol (NCT03977402) Avatar® project. The real-world immunoscore for each patient was calculated based on the estimated densities of tumor CD3+ and CD8+ T cells utilizing CIBERSORTx and the LM22 gene signature matrix. Then, the immunoscore association with overall survival (OS) was estimated using Cox regression and analyzed using Kaplan-Meier curves. The OS predictions were assessed using Harrell's concordance index (C-index). The Youden index was used to identify the optimal cut-off point. Statistical significance was assessed using the log-rank test.

RESULTS

Our study encompassed 522 patients with four cancer types. The median duration to death was 10.5 months for the 275 participants who encountered an event. For the entire cohort, the results demonstrated that transcriptomics-based immunoscore could significantly predict patients at risk of death (p-value < 0.001). Notably, patients with an intermediate-high immunoscore achieved better OS than those with a low immunoscore. In subgroup analysis, the prediction of OS was significant for melanoma and head and neck cancer patients but did not reach significance in the non-small cell lung cancer or renal cell carcinoma cohorts.

CONCLUSIONS

Calculating CD3+ and CD8+ T cell immunoscore using real-world transcriptomic data represents a promising signature for estimating OS with ICIs and can be used as a reference for future machine learning-based biomarker development.

Development of a tumor microenvironment-related prognostic signature in glioma to predict immune landscape and potential therapeutic drugs

The involvement of the tumor microenvironment (TME) in the biology of gliomas has expanded, while it is yet uncertain its potential of supporting diagnosis and therapy choices. According to immunological characteristics and overall survival, cohorts of glioma patients from public databases were separated into two TME-relevant clusters in this analysis. Based on differentially expressed genes between TME clusters and correlative regression analysis, a 21-gene molecular classifier of TME-related prognostic signature (TPS) was constructed. Afterward, the prognostic efficacy and effectiveness of TPS were assessed in the training and validation groups. The outcome demonstrated that TPS might be utilized alone or in conjunction with other clinical criteria to act as a superior prognostic predictor for glioma. Also, high-risk glioma patients classified by TPS were considered to associate with enhanced immune infiltration, greater tumor mutation, and worse general prognosis. Finally, possible treatment medicines specialized for different risk subgroups of TPS were evaluated in drug databases.

Establishment and validation of an immune infiltration predictive model for ovarian cancer

BACKGROUND

The most prevalent mutation in ovarian cancer is the TP53 mutation, which impacts the development and prognosis of the disease. We looked at how the TP53 mutation associates the immunophenotype of ovarian cancer and the prognosis of the disease.

METHODS

We investigated the state of TP53 mutations and expression profiles in culturally diverse groups and datasets and developed an immune infiltration predictive model relying on immune-associated genes differently expressed between TP53 WT and TP53 MUT ovarian cancer cases. We aimed to construct an immune infiltration predictive model (IPM) to enhance the prognosis of ovarian cancer and investigate the impact of the IPM on the immunological microenvironment.

RESULTS

TP53 mutagenesis affected the expression of seventy-seven immune response-associated genes. An IPM was implemented and evaluated on ovarian cancer patients to distinguish individuals with low- and high-IPM subgroups of poor survival. For diagnostic and therapeutic use, a nomogram is thus created. According to pathway enrichment analysis, the pathways of the human immune response and immune function abnormalities were the most associated functions and pathways with the IPM genes. Furthermore, patients in the high-risk group showed low proportions of macrophages M1, activated NK cells, CD8+ T cells, and higher CTLA-4, PD-1, PD-L1, and TIM-3 than patients in the low-risk group.

CONCLUSIONS

The IPM model may identify high-risk patients and integrate other clinical parameters to predict their overall survival, suggesting it is a potential methodology for optimizing ovarian cancer prognosis.

A nomogram based on collagen signature for predicting the immunoscore in colorectal cancer

Objectives

The Immunoscore can categorize patients into high- and low-risk groups for prognostication in colorectal cancer (CRC). Collagen plays an important role in immunomodulatory functions in the tumor microenvironment (TME). However, the correlation between collagen and the Immunoscore in the TME is unclear. This study aimed to construct a collagen signature to illuminate the relationship between collagen structure and Immunoscore.

Methods

A total of 327 consecutive patients with stage I-III stage CRC were included in a training cohort. The fully quantitative collagen features were extracted at the tumor center and invasive margin of the specimens using multiphoton imaging. LASSO regression was applied to construct the collagen signature. The association of the collagen signature with Immunoscore was assessed. A collagen nomogram was developed by incorporating the collagen signature and clinicopathological predictors after multivariable logistic regression. The performance of the collagen nomogram was evaluated via calibration, discrimination, and clinical usefulness and then tested in an independent validation cohort. The prognostic values of the collagen nomogram were assessed using Cox regression and the Kaplan-Meier method.

Results

The collagen signature was constructed based on 16 collagen features, which included 6 collagen features from the tumor center and 10 collagen features from the invasive margin. Patients with a high collagen signature were more likely to show a low Immunoscore (Lo IS) in both cohorts (P<0.001). A collagen nomogram integrating the collagen signature and clinicopathological predictors was developed. The collagen nomogram yielded satisfactory discrimination and calibration, with an AUC of 0.925 (95% CI: 0.895-0.956) in the training cohort and 0.911 (95% CI: 0.872-0.949) in the validation cohort. Decision curve analysis confirmed that the collagen nomogram was clinically useful. Furthermore, the collagen nomogram-predicted subgroup was significantly associated with prognosis. Moreover, patients with a low-probability Lo IS, rather than a high-probability Lo IS, could benefit from chemotherapy in high-risk stage II and stage III CRC patients.

Conclusions

The collagen signature is significantly associated with the Immunoscore in the TME, and the collagen nomogram has the potential to individualize the prediction of the Immunoscore and identify CRC patients who could benefit from adjuvant chemotherapy.

Prognostic Value of T-Cell Density in the Tumor Center and Outer Margins in Gastric Cancer

Tumor-infiltrating lymphocytes are associated with the survival of gastric cancer patients. T-cell densities in the tumor and its periphery were previously identified as prognostic T-cell markers for resectable gastric cancer. Immunohistochemistry for 5 T-cell markers, CD3, CD45RO, CD8, FOXP3, and granzyme B was performed on serial sections of N = 251 surgical resection specimens of patients treated with surgery only in the D1/D2 trial. Positive T cells were digitally quantified into tiles of 0.25 mm2 across 3 regions: the tumor center (TC), the inner invasive margin, and the outer invasive margin (OIM). A classification and regression tree model was employed to identify the optimal combination of median T-cell densities per region with cancer-specific survival (CSS) as the outcome. All statistical tests were 2-sided. CD8OIM was identified as the most dominant prognostic factor, followed by FOXP3TC, resulting in a decision tree containing 3 prognostically distinct subgroups with high (Hi) or low (Lo) density of the markers: CD8OIMHi, CD8OIMLo/FOXP3TCHi, and CD8OIMLo/FOXP3TCLo. In a multivariable Cox regression analysis, which included pathological T and N stages, Lauren histologic types, EBV status, microsatellite instability, and type of surgery, the immune subgroups were independent predictors for CSS. CSS was lower for CD8OIMLo/FOXP3TCHi (HR: 5.02; 95% CI: 2.03-12.42) and for CD8OIMLo/FOXP3TCLo (HR: 7.99; 95% CI: 3.22-19.86), compared with CD8OIMHi (P < .0001). The location and density of both CD8+ and FOXP3+ T cells in resectable gastric cancer are independently associated with survival. The combination of CD8OIM and FOXP3TC T-cell densities is a promising stratification factor that should be validated in independent studies.

Construction and validation of a signature for T cell-positive regulators related to tumor microenvironment and heterogeneity of gastric cancer

Background

Positive regulators of T cell function play a vital role in the proliferation and differentiation of T cells. However, their functions in gastric cancer have not been explored so far.

Methods

The TCGA-STAD dataset was utilized to perform consensus clustering in order to identify subtypes related to T cell-positive regulators. The prognostic differentially expressed genes of these subtypes were identified using the least absolute shrinkage and selection operator (LASSO) regression analysis. To validate the robustness of the identified signature, verification analyses were conducted across the TCGA-train, TCGA-test, and GEO datasets. Additionally, a nomogram was constructed to enhance the clinical efficacy of this predictive tool. Transwell migration, colony formation, and T cell co-culture assays were used to confirm the function of the signature gene in gastric cancer and its influence on T cell activation.

Results

Two distinct clusters of gastric cancer, related to T cell-positive regulation, were discovered through the analysis of gene expression. These clusters exhibited notable disparities in terms of survival rates (P = 0.028), immune cell infiltration (P< 0.05), and response to immunotherapy (P< 0.05). Furthermore, a 14-gene signature was developed to classify gastric cancer into low- and high-risk groups, revealing significant differences in survival rates, tumor microenvironment, tumor mutation burden, and drug sensitivity (P< 0.05). Lastly, a comprehensive nomogram model was constructed, incorporating risk factors and various clinical characteristics, to provide an optimal predictive tool. Additionally, an assessment was conducted on the purported molecular functionalities of low- and high-risk gastric cancers. Suppression of DNAAF3 has been observed to diminish the migratory and proliferative capabilities of gastric cancer, as well as attenuate the activation of T cells induced by gastric cancer within the tumor microenvironment.

Conclusion

We identified an ideal prognostic signature based on the positive regulators of T cell function in this study.

Biology-guided deep learning predicts prognosis and cancer immunotherapy response

Substantial progress has been made in using deep learning for cancer detection and diagnosis in medical images. Yet, there is limited success on prediction of treatment response and outcomes, which has important implications for personalized treatment strategies. A significant hurdle for clinical translation of current data-driven deep learning models is lack of interpretability, often attributable to a disconnect from the underlying pathobiology. Here, we present a biology-guided deep learning approach that enables simultaneous prediction of the tumor immune and stromal microenvironment status as well as treatment outcomes from medical images. We validate the model for predicting prognosis of gastric cancer and the benefit from adjuvant chemotherapy in a multi-center international study. Further, the model predicts response to immune checkpoint inhibitors and complements clinically approved biomarkers. Importantly, our model identifies a subset of mismatch repair-deficient tumors that are non-responsive to immunotherapy and may inform the selection of patients for combination treatments.

Neutrophil elastase as a potential biomarker related to the prognosis of gastric cancer and immune cell infiltration in the tumor immune microenvironment

Exploring biomarkers interrelated the tumor immune microenvironment (TIME) provides novel ideas for predicting the prognosis of gastric cancer (GC) and developing new treatment strategies. We analyzed the differential gene expression levels between the high and low StromalScore and ImmuneScore groups. Neutrophil elastase (ELANE) was evaluated as a potential biomarker by conducting intersection analysis of the protein-protein interaction network and univariate Cox regression analysis. The expression of ELANE was evaluated by immunohistochemistry. Its prognostic value was evaluated using Kaplan-Meier (K-M) survival curves and multivariate Cox regression analysis and its potential biological molecular mechanism was examined by gene set enrichment analysis (GSEA). We applied the CIBERSORT computing method to analyze the relationship between ELANE and tumor immune-infiltrating cells (TIICs). K-M survival curve showed that higher ELANE expression was closely related to shorter overall survival. The Cox regression analysis indicated that the high expression of ELANE was an independent prognostic risk factor in patients with GC. The GSEA revealed that genes in the ELANE high-expression group were involved in the signaling pathways regulating immune response; genes in the ELANE low-expression group were involved in the signaling pathways that regulate metabolism. ELANE might be participate in the change of TIME from immunodominant to metabolically dominant and its expression was closely related to tumor mutation burden and multiple TIICs. ELANE is a potential biomarker for predicting the GC patients' survival and prognosis. It influences the tumor immune cell infiltration in the TIME, and affects the TIME to maintain their immune status.