Quantification of T- and B-cell immune receptor distribution diversity characterizes immune cell infiltration and lymphocyte heterogeneity in clear cell renal cell carcinoma

A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies

Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.

Toolkit for mapping the clonal landscape of tumor-infiltrating B cells

Our current understanding of whether B cell involvement in the tumor microenvironment benefits the patient or the tumor - in distinct cancers, subcohorts and individual patients - is quite limited. Both statements are probably true in most cases: certain clonal B cell populations contribute to the antitumor response, while others steer the immune response away from the desired mechanics. To step up to a new level of understanding and managing B cell behaviors in the tumor microenvironment, we need to rationally discern these roles, which are cumulatively defined by B cell clonal functional programs, specificities of their B cell receptors, specificities and isotypes of the antibodies they produce, and their spatial interactions within the tumor environment. Comprehensive analysis of these characteristics of clonal B cell populations is now becoming feasible with the development of a whole arsenal of advanced technical approaches, which include (1) methods of single-cell and spatial transcriptomics, genomics, and proteomics; (2) methods of massive identification of B cell specificities; (3) methods of deep error-free profiling of B cell receptor repertoires. Here we overview existing techniques, summarize their current application for B cells studies and propose promising future directions in advancing B cells exploration.

Chemical Complementarity of Tumor Resident, Adaptive Immune Receptor CDR3s and Previously Defined Hepatitis C Virus Epitopes Correlates with Improved Outcomes in Hepatocellular Carcinoma

To better understand how adaptive immune receptors (IRs) in hepatocellular carcinoma (HCC) microenvironments are related to disease outcomes, we employed a chemical complementarity scoring algorithm to quantify electrostatic complementarity between HCC tumor TRB or IGH complementarity-determining region 3 (CDR3) amino acid (AA) sequences and previously characterized hepatitis C virus (HCV) epitopes. High electrostatic complementarity between HCC-resident CDR3s and 12 HCV epitopes was associated with greater survival probabilities, as indicated by two distinct HCC IR CDR3 datasets. Two of the HCV epitopes, HCV*71871 (TRB) and HCV*13458 (IGH), were also determined to represent significantly larger electrostatic CDR3-HCV epitope complementarity in HCV-positive HCC cases, compared with HCV-negative HCC cases, with the CDR3s representing yet a third, independent HCC dataset. Overall, the results indicated the utility of CDR3 AA sequences as biomarkers for HCC patient stratification and as potential guides for the development of therapeutic reagents.

A multi-model based on radiogenomics and deep learning techniques associated with histological grade and survival in clear cell renal cell carcinoma

OBJECTIVES

This study aims to evaluate the efficacy of multi-model incorporated by radiomics, deep learning, and transcriptomics features for predicting pathological grade and survival in patients with clear cell renal cell carcinoma (ccRCC).

METHODS

In this study, data were collected from 177 ccRCC patients, including radiomics features, deep learning (DL) features, and RNA sequencing data. Diagnostic models were then created using these data through least absolute shrinkage and selection operator (LASSO) analysis. Additionally, a multi-model was developed by combining radiomics, DL, and transcriptomics features. The prognostic performance of the multi-model was evaluated based on progression-free survival (PFS) and overall survival (OS) outcomes, assessed using Harrell's concordance index (C-index). Furthermore, we conducted an analysis to investigate the relationship between the multi-model and immune cell infiltration.

RESULTS

The multi-model demonstrated favorable performance in discriminating pathological grade, with area under the ROC curve (AUC) values of 0.946 (95% CI: 0.912-0.980) and 0.864 (95% CI: 0.734-0.994) in the training and testing cohorts, respectively. Additionally, it exhibited statistically significant prognostic performance for predicting PFS and OS. Furthermore, the high-grade group displayed a higher abundance of immune cells compared to the low-grade group.

CONCLUSIONS

The multi-model incorporated radiomics, DL, and transcriptomics features demonstrated promising performance in predicting pathological grade and prognosis in patients with ccRCC.

CRITICAL RELEVANCE STATEMENT

We developed a multi-model to predict the grade and survival in clear cell renal cell carcinoma and explored the molecular biological significance of the multi-model of different histological grades.

KEY POINTS

1. The multi-model achieved an AUC of 0.864 for assessing pathological grade. 2. The multi-model exhibited an association with survival in ccRCC patients. 3. The high-grade group demonstrated a greater abundance of immune cells.

Adaptive immune receptor features related to breast cancer tissue in Kenyan patients: high immunoglobulin gene expression and high levels of gamma-delta T-cells

BACKGROUND

Characterization of the breast cancer (BC) immune response may provide information for a point of intervention, such as application of immunotherapeutic treatments. In this study, we sought to recover and characterize the adaptive immune receptor (IR) recombination reads from genomics files representing Kenyan patients, to better understand the immune response specifically related to those patients.

METHODS

We used a previously applied algorithm and software to obtain productive IR recombination reads from cancer and adjacent normal tissue samples representing 22 Kenyan BC patients.

RESULTS

From both the RNAseq and exome files, there were significantly more T-cell receptor (TCR) recombination reads recovered from tumor samples compared to marginal tissue samples. Also, the immunoglobulin (IG) genes were expressed at a much higher level than the TCR genes (p-value = 0.0183) in the tumor samples. And, the tumor IG CDR3s consistently represented more positively charged amino acid R-groups, in comparison to the marginal tissue, IG CDR3s.

CONCLUSION

For Kenyan patients, a high level of IG expression, representing specific CDR3 chemistries, was associated with BC. These results lay the foundation for studies that could support specific immunotherapeutic interventions for Kenyan BC patients.

TRB CDR3-cancer testis antigen chemical complementarity scoring for identifying productive immune responses in renal cell carcinoma

BACKGROUND

Immunogenomics approaches to the characterization of renal cell carcinoma (RCC) have helped to better our understanding of the features of RCC immune dysfunction. However, much is still unknown with regard to specific immune interactions and their impact in the tumor microenvironment.

OBJECTIVE

This study applied chemical complementarity scoring for the TRB complementarity determining region-3 (CDR3) amino acid sequences and cancer testis antigens (CTAs) to determine whether such complementarity correlated with survival and the expression of immune marker genes.

METHODS

TRB recombination reads from RCC tumor samples from RNAseq files obtained from two separate databases, Moffitt Cancer Center and The Cancer Genome Atlas (TCGA), were evaluated. Chemical complementarity scores (CSs) were calculated for TRB CDR3-CTA pairs and survival assessments based on those CSs were performed.

RESULTS

Moffitt Cancer Center and TCGA cases representing the upper 50th percentile of chemical CSs for TRB CDR3 amino acid sequences and the CTA POTEA were found to be associated with a better overall survival (OS) Also, greater tumor RNA expression of multiple immune signature genes, including granzyme A, granzyme B, and interferon-gamma were correlated with the higher chemical CSs.

CONCLUSIONS

These results indicate that TRB CDR3-CTA chemical complementarity scoring may be useful in distinguishing RCC cases with a productive, anti-tumor immune response from cases where basic immune parameter assessments are inconsistent with a productive immune response.

Dry and wet experiments reveal the significant role of FUT11 in clear cell renal cell carcinoma

BACKGROUND

Renal cancer is one of the most common urogenital tumors worldwide. Although numerous traditional and relatively new therapeutic strategies have been adopted for clear cell renal cell carcinoma (ccRCC) patients, their effects are not satisfactory enough for the improvement of patients. The pathogenesis and progression of ccRCC requires further investigations.

METHODS

Using a series of bioinformatic analyses, the expression levels, clinical relevance, and prognostic potential of FUT11 as well as its correlations with immune cells in ccRCC were investigated. The mRNA and protein expression levels of FUT11 in renal cancer cell lines and human tissues were determined using quantitative real-time-polymerase chain reaction (RT-PCR) and Western blot analyses. MTT, colony formation, Edu, and wound healing assays were performed to explore the function of FUT11 in renal cancer cell lines. The immunohistochemical staining of human and mouse tissues was performed to reveal the correlations between the expression levels of FUT11 and the infiltration level of immune cell subtypes. Using mouse xenograft models, the role of FUT11 was further investigated in-vivo.

RESULTS

The data mining and corresponding analyses indicated that the expression levels of FUT11 were elevated in renal cancer and independently correlated with the prognosis of ccRCC patients. The cibersort and ssGSEA algorithms revealed differential infiltration levels of immune cells between the patients with distinct expression levels of FUT11; these results were verified by the consequent human renal cancer tissues and animal models. The MTT, colony formation, EdU, and wound healing assays showed that the decreased expression level of FUT11 could promote the proliferation and migration of renal cancer cell lines. The animal models-based analysis showed similar results.

CONCLUSIONS

In conclusion, this study identified a novel important molecule correlated with the prognosis of ccRCC patients and revealed its immune-related role and its function in the proliferation and migration of renal cancer cells. This study might provide a novel basis for the treatment of renal cancer.

Impact of interaction networks of B cells with other cells on tumorigenesis, progression and response to immunotherapy of renal cell carcinoma: A review

Ample evidence indicates that the development and progression of renal cell carcinoma (RCC) are complex pathological processes involving interactions between tumor cells, immune cells and stromal components. Tumor infiltrated immune cells determine whether tumor advancement is promoted or inhibited. Among them, infiltrated B lymphocytes are present in all stages of RCC, playing a major role in determining tumor formation and advancement, as an essential part in the tumor microenvironment (TME). Although the advent of targeted and immune therapies has remarkably improved the survival of patients with advanced RCC, few cases can achieve complete response due to drug resistance. In this review article, we intend to summary the recent studies that outline the interaction networks of B cells with other cells, discuss the role of B cells in RCC development and progression, and assess their impact on RCC immunotherapy.

Prognostic Profiling of the EMT-Associated and Immunity-Related LncRNAs in Lung Squamous Cell Carcinomas

Lung squamous cell carcinoma (Lung SCC) is associated with metastatic disease, resulting in poor clinical prognosis and a low survival rate. The aberrant epithelial–mesenchymal transition (EMT) and long non-coding RNA (LncRNA) are critical attributors to tumor metastasis and invasiveness in Lung SCC. The present study divided lncRNAs into two subtypes, C1 and C2 (Cluster 1 and Cluster 2), according to the correlation of EMT activity within the public TCGA and GEO databases. Subsequently, the differential clinical characteristics, mutations, molecular pathways and immune cell deconvolution between C1 and C2 were evaluated. Lastly, we further identified three key lncRNAs (DNM3OS, MAGI2-AS3 and LINC01094) that were associated with EMT and, at the same time, prognostic for the clinical outcomes of Lung SCC patients. Our study may provide a new paradigm of metastasis-associated biomarkers for predicting the prognosis of Lung SCC.

TTN mutations predict a poor prognosis in patients with thyroid cancer

OBJECTIVE

We aimed to investigate the relationship between titin (TTN) gene mutations and thyroid cancer (THCA) and to explore the feasibility of the TTN gene as a potential prognostic indicator of THCA.

METHODS

From TCGA-THCA cohort, we performed a series of analyses to evaluate the prognostic value and potential mechanism of TTN in THCA. These patients were divided into the mutant-type (MUT) group and the wild-type (WT) group. Differentially expressed genes (DEGs) in the two groups were screened using the 'DESeq2' R package. Functional enrichment analysis was performed, and the protein-protein interaction (PPI) network, transcription factor (TF)-target interaction networks, and competitive endogenous RNA (ceRNA) regulatory networks were established for the DEGs. The TIMER database was applied for immune cell infiltration. Survival analysis and Cox regression analysis were used to analyze the potential prognostic value of the TTN gene.

RESULTS

Differential expression analysis showed that 409 genes were significantly up-regulated and 36 genes were down-regulated. Functional enrichment analysis revealed that TTN gene mutations played a potential role in the development of THCA. Analysis of the immune microenvironment indicated that TTN gene mutations were significantly associated with enrichment of M0 macrophages. Survival analysis showed that the MUT group predicted poorer prognosis than the WT group. Cox regression analysis demonstrated that TTN gene mutations were an independent risk factor for THCA. Nomograms also confirmed the prognostic values of the TTN gene in THCA. Conclusions In summary, our results demonstrated that TTN gene mutations predict poor prognosis in patients with THCA. This is the first study to research TTN gene mutations in THCA and to investigate their prognostic value in THCA.

Rules of Engagement: The Lymphocyte Receptor Ecosystem in Renal Cell Carcinoma

Immune receptor repertoires provide insight into the clonal distribution of tumor-infiltrating lymphocytes, yet the clinical implications of T-cell receptor (TCR) and B-cell receptor (BCR) repertoire diversity in cancer are unclear. In this issue of Cancer Research, Ferral-Fairbanks and colleagues reveal the interplay between repertoire diversity, tumor molecular features, and clinical outcome in renal cell carcinoma (RCC). The authors show that aggressive tumors harbor increasingly diverse TCR and BCR repertoires and that both repertoires are altered by common RCC driver mutations. Moreover, the authors demonstrate that high TCR diversity is associated with improved overall survival. This study highlights the contribution of lymphocyte receptor dynamics to the emerging complexity of RCC antitumor immune responses. See related article by Ferral-Fairbanks et al., p. 929.