RNA Sequencing of the Tumor Microenvironment in Precision Cancer Immunotherapy

Tissue biomarkers of immune checkpoint inhibitor therapy

Cancer immunotherapy has been rejuvenated by the growing understanding of the immune system's role in tumor activity over the past two decades. During cancer initiation and progression, tumor cells employ various mechanisms that resemble peripheral immune tolerance to evade the antitumor responses of the immune system. Immune checkpoint molecules are the major mechanism of immune resistance that are exploited by tumor cells to inhibit T-cell activation and suppress immune responses. The targeting of immune checkpoint pathways has led to substantial improvements in survival rates in a number of solid cancers. However, a lack of understanding of the heterogeneity of the tumor microenvironment (TME) has resulted in inefficient therapy responses. A greater understanding of the TME is needed to identify patients likely to respond, and those that will have resistance to immune checkpoint inhibitors (ICIs). Advancement in spatial single-cell technologies has allowed deeper insight into the phenotypic and functional diversities of cells in the TME. In this review, we provide an overview of ICI biomarkers and highlight how high-dimensional spatially resolved, single-cell approaches provide deep molecular insights into the TME and allow for the discovery of biomarkers of clinical benefit.

Monophosphoryl lipid A-assembled nanovaccines enhance tumor immunotherapy

Therapeutic cancer nanovaccines can induce strong antitumor immunity and establish long-term immune memory and have shown potential for curing tumors in some clinical trials. However, weak immunogenicity and safety concerns of nanocarriers limit the clinical translation of some therapeutic nanovaccines. Here, we developed minimal-component cancer nanovaccines, monophosphoryl lipid A (MPLA)-assembled nanovaccines (MANs), that could facilitate the clinical application of nanovaccines. The MANs were formed by protein antigens extracted from chemotherapy-induced tumor cell cultures and the amphiphilic immune adjuvant MPLA. Compared with free chemotherapy-induced antigens, MANs can activate the Toll-like receptor 4 (TLR4)-mediated signalling pathway and promote adaptive immunity against tumor antigens. Mechanistic analysis indicated that MANs induced antigen capture of DCs and promoted the activation of DCs and T cells, thereby optimizing the ratio of CD8+ T/Tregs in tumors and facilitating the transformation of the tumor immune microenvironment (TIME) from "cold" to "hot". In a CT26 colorectal cancer model, MANs+αPD-1 significantly improved the efficacy of αPD-1 treatment. Our work offers a strategy for designing minimal-component cancer nanovaccines with potential clinical benefits. STATEMENT OF SIGNIFICANCE: To address the weak immunogenicity of cancer vaccines and the safety concerns of nanocarriers, we prepared MPLA-assembled nanovaccines (MANs) using chemotherapy induced antigens and the immune adjuvant MPLA to promote cancer vaccines to clinical practice. MANs effectively internalized tumor antigens and induced DC maturation, indicating that the initial anti-tumor response had been activated. MANs+αPD-1 induced APCs, CD8+ T cells and memory T cells with positive anti-tumor effects to migrate to tumor tissue, thus leading to the transformation of the tumor immune microenvironment from "cold" to "hot". At the animal level, the combination of MANs and αPD-1 exerted synergistic effects and significantly enhanced tumor immunotherapy. Therefore, the treatment regimen of MANs+αPD-1 has potential clinical benefits.

High purity and integrity RNA from human cell lines stored in liquid nitrogen for over 20 years

Biobanks are a key resource for obtaining human cell lines for biomedical research, including for drug development projects. Such projects often include comparative RNA-sequencing of large panels of human cell lines from individuals affected by certain disorders and healthy controls, or from individuals with different drug response phenotypes. RNA extractions are typically done from growing cell cultures, a process that may take several weeks. However, maintaining large numbers of cell lines in parallel increases the project workload. Here, we show that extracting RNAs directly from frozen vials of human cell lines stored for over 20 years in a liquid nitrogen freezer yields RNAs with the high purity and integrity parameters that conform to those required for optimal RNA-sequencing and are closely similar to those obtained for RNAs extracted from growing human cell lines.

Specific subsets of urothelial bladder carcinoma infiltrating T cells associated with poor prognosis

Comprehensive investigation of tumor-infiltrating lymphocytes in cancer is crucial to explore the effective immunotherapies, but the composition of infiltrating T cells in urothelial bladder carcinoma (UBC) remains elusive. Here, single-cell RNA sequencing (scRNA-seq) were performed on total 30,905 T cells derived from peripheral blood, adjacent normal and tumor tissues from two UBC patients. We identified 18 distinct T cell subsets based on molecular profiles and functional properties. Specifically, exhausted T (TEx) cells, exhausted NKT (NKTEx) cells, Ki67+ T cells and B cell-like T (B-T) cells were exclusively enriched in UBC. Additionally, the gene signatures of TEx, NKTEx, Ki67+ T and B-T cells were significantly associated with poor survival in patients with BC and various tumor types. Finally, IKZF3 and TRGC2 are the potential biomarkers of TEx cells. Overall, our study demonstrated an exhausted context of T cells in UBC, which layed a theoretical foundation for the development of effective tumor immunotherapies.

Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction

Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.

Disruption of SLFN11 Deficiency-Induced CCL2 Signaling and Macrophage M2 Polarization Potentiates Anti-PD-1 Therapy Efficacy in Hepatocellular Carcinoma

BACKGROUND & AIMS

The therapeutic effect of immune checkpoint inhibitors (ICIs) is poor in hepatocellular carcinoma (HCC) and varies greatly among individuals. Schlafen (SLFN) family members have important functions in immunity and oncology, but their roles in cancer immunobiology remain unclear. We aimed to investigate the role of the SLFN family in immune responses against HCC.

METHODS

Transcriptome analysis was performed in human HCC tissues with or without response to ICIs. A humanized orthotopic HCC mouse model and a co-culture system were constructed, and cytometry by time-of-flight technology was used to explore the function and mechanism of SLFN11 in the immune context of HCC.

RESULTS

SLFN11 was significantly up-regulated in tumors that responded to ICIs. Tumor-specific SLFN11 deficiency increased the infiltration of immunosuppressive macrophages and aggravated HCC progression. HCC cells with SLFN11 knockdown promoted macrophage migration and M2-like polarization in a C-C motif chemokine ligand 2-dependent manner, which in turn elevated their own PD-L1 expression by activating the nuclear factor-κB pathway. Mechanistically, SLFN11 suppressed the Notch pathway and C-C motif chemokine ligand 2 transcription by binding competitively with tripartite motif containing 21 to the RNA recognition motif 2 domain of RBM10, thereby inhibiting tripartite motif containing 21-mediated RBM10 degradation to stabilize RBM10 and promote NUMB exon 9 skipping. Pharmacologic antagonism of C-C motif chemokine receptor 2 potentiated the antitumor effect of anti-PD-1 in humanized mice bearing SLFN11 knockdown tumors. ICIs were more effective in patients with HCC with high serum SLFN11 levels.

CONCLUSIONS

SLFN11 serves as a critical regulator of microenvironmental immune properties and an effective predictive biomarker of ICIs response in HCC. Blockade of C-C motif chemokine ligand 2/C-C motif chemokine receptor 2 signaling sensitized SLFN11^low HCC patients to ICI treatment.

Comparison of the tumor immune microenvironment and checkpoint blockade biomarkers between stage III and IV non-small cell lung cancer

BACKGROUND

Adjuvant immune checkpoint blockade (ICB) following chemoradiotherapy and adding ICB to chemotherapy have been key advances for stages III-IV non-small cell lung cancer (NSCLC) treatment. However, known biomarkers like PD-L1 are not consistently indicative of ICB response. Other markers within the tumor immune microenvironment (TIME) may better reflect ICB response and/or resistance mechanisms, but an understanding of how TIMEs differ between stage III and IV NSCLC has not been explored.

METHODS

Real-world data from unresectable, stage III-IV, non-squamous, pretreatment NSCLCs (stage III n = 106, stage IV n = 285) were retrospectively analyzed. PD-L1 immunohistochemistry (IHC) was compared to CD274 gene expression. Then, differential gene expression levels, pathway enrichment, and immune infiltrate between stages were calculated from whole-transcriptome RNA-seq. Analyses were stratified by EGFR status.

RESULTS

PD-L1 IHC and CD274 expression in tumor cells were highly correlated (n = 295, P < 2.2e-16, ⍴ = 0.74). CTLA4 expression was significantly increased in stage III tumors (P = 1.32e-04), while no differences were observed for other ICB-related genes. Metabolic pathway activity was significantly enriched in stage IV tumors (P = 0.004), whereas several immune-related KEGG pathways were enriched in stage III. Stage IV tumors had significantly increased macrophage infiltration (P = 0.0214), and stage III tumors had a significantly higher proportion of CD4 + T cells (P = 0.017). CD4 + T cells were also relatively more abundant in EGFR-mutant tumors vs. wild-type (P = 0.0081).

CONCLUSION

Directly comparing the TIMEs of stage III and IV NSCLC, these results carry implications for further studies of ICB response in non-resectable stage III NSCLC and guide further research of prognostic biomarkers and therapeutic targets.

The Sesquiterpene Lactone-Rich Fraction of Inula helenium L. Enhances the Antitumor Effect of Anti-PD-1 Antibody in Colorectal Cancer: Integrative Phytochemical, Transcriptomic, and Experimental Analyses

Treatment strategies combining immune checkpoint inhibitors with sesquiterpene lactones have attracted much attention as a promising approach for cancer treatment. We systemically analyzed gene expression profiles of cells in response to two major sesquiterpene lactones, alantolactone and isoalantolactone, and determined whether the sesquiterpene lactone-rich fraction of Inula helenium L. (SFIH) enhances the antitumor effect of anti-PD-1 antibody in MC38 colorectal cancer-bearing mice. Gene expression and pathway analysis using RNA sequencing data were used to identify the SFIH-driven combined activity with anti-PD-1 antibody. The results showed that SFIH significantly enhanced the antitumor effect of anti-PD-1 antibody by reducing tumor growth and increasing the survival time of mice. Specifically, SFIH exhibited antitumor activity when combined with anti-PD-1 antibody, and the effects were further enhanced compared with monotherapy. An analysis of immune cells indicated that combination treatment with SFIH and anti-PD-1 antibody significantly increased the proportion of CD8⁺ T cells. Moreover, combination treatment enhanced antitumor immunity by decreasing the population of myeloid-derived suppressor cells and increasing the number of M1-like macrophages. Pathway enrichment analysis revealed that combination therapy activated immune-related pathways to a greater extent than monotherapy. In conclusion, our integrative analysis demonstrates that SFIH enhances the response of murine tumors to anti-PD-1 antibody. These findings provide insight into developing integrative therapeutics and molecular data for the use of natural products as an adjunct treatment for colorectal cancer.

Tumor Immune Microenvironment and Immunotherapy in Non-Small Cell Lung Cancer: Update and New Challenges

Non-small cell lung cancer (NSCLC) is a serious threat to the health of older adults. Despite the significant progress in immunotherapy, effective treatments for NSCLC remain limited. The development of tumors indicates failure in immune surveillance and the successful immune escape of tumor cells. Research on the tumor immune microenvironment (TIME) revealed these opposing immune processes and contributed to the discovery of new methods to suppress the immune escape and restore the immune surveillance functions. This paper aimed to provide updates on the current findings regarding the relevance of TIME in NSCLC treatment. It also aimed to introduce the TIME, immune editing, cancer immunotherapy, and new challenges. Based on the clinical data, the combination of neoadjuvant chemotherapy and immune checkpoint inhibitor (ICI) therapy is suitable for patients with NSCLC who are not eligible to undergo surgery. Combined ICI therapy after epidermal growth factor receptor (EGFR)/tyrosine kinase inhibitor (TKI) therapy should be considered in patients with EGFR mutations. Chemoradiotherapy may increase the density of CD8⁺ lymphocytes, which is significantly associated with better prognosis. For older patients and those with advanced-stage disease, regional tumor treatments, such as stereotactic radiation therapy and percutaneous cryoablation, may be more suitable, but further studies are needed to confirm this. In conclusion, restoring immune surveillance is as important as removing cancerous tissues; further studies that include the use of combined treatment methods, individualized treatment plans, and immunonutrition are warranted.

Prognostic value of tumor immune biomarkers in biopsies from patients with refractory solid cancers

PD-L1 and tumor-infiltrating lymphocytes play a key role in the immune escape of cancer, although their prognostic value remains unknown in patients with refractory solid cancer compared to other known prognostic estimation methods. In this ancillary study, we assessed the prognostic value of previously-defined prognostic scores (such as the Royal Marsden Hospital (RMH) score) and of PD-L1, CD3, CD8 and FOXP3 expressions based on immunohistochemistry (IHC) and RNA sequencing (RNAseq) of tumor samples from patients included in the personalized-medicine MOSCATO-02 trial. We collected biopsies with successful IHC analysis from 266 patients treated between April 2016 and September 2017, among whom 170 (63.9%) also had a matched RNAseq. We used a Random Forest model to identify the best prognostic factor, and a Lasso-penalized Cox model to validate the findings. We found that the RMH score was the strongest prognostic factor, with high scores associated with a higher risk of death (Hazard Ratio (HR)=1.29; CI95%[1.19-1.21]). The PD-L1 expression score obtained from IHC analyses was the second-best performing predictor, with the 1+ score (low expression) linked to a lower risk of death (HR=0.564; CI95%[0.539-0.580]). Other tested variables, including primary tumor type and subsequent treatments received following biopsy, were not found significantly linked to prognosis. We found modest correlation between IHC and RNAseq expressions of immune genes, but RNAseq related better to prognosis. Overall, our study supports the use of the RMH score and the assessment of PD-L1 expression in IHC to estimate prognosis in patients with advanced cancer.

Integration of tumor extrinsic and intrinsic features associates with immunotherapy response in non-small cell lung cancer

The efficacy of immune checkpoint blockade (ICB) varies greatly among metastatic non-small cell lung cancer (NSCLC) patients. Loss of heterozygosity at the HLA-I locus (HLA-LOH) has been identified as an important immune escape mechanism. However, despite HLA-I disruptions in their tumor, many patients have durable ICB responses. Here we seek to identify HLA-I-independent features associated with ICB response in NSCLC. We use single-cell profiling to identify tumor-infiltrating, clonally expanded CD4⁺ T cells that express a canonical cytotoxic gene program and NSCLC cells with elevated HLA-II expression. We postulate cytotoxic CD4⁺ T cells mediate anti-tumor activity via HLA-II on tumor cells and augment HLA-I-dependent cytotoxic CD8⁺ T cell interactions to drive ICB response in NSCLC. We show that integrating tumor extrinsic cytotoxic gene expression with tumor mutational burden is associated with longer time to progression in a real-world cohort of 123 NSCLC patients treated with ICB regimens, including those with HLA-LOH.

Some cancer patients with impaired HLA-I still respond to immunotherapy. Here the authors combine a cytotoxic gene signature from CD4+ and CD8+ T cells with tumor mutational burden to predict immunotherapy response in NSCLC patients, including those with HLA-LOH.

Construction of a Prognostic Immune-Related LncRNA Risk Model for Gastric Cancer

Gastric cancer (GC) is one of the most common malignancies, and novel prognostic biomarkers for it are urgently required. This study is aimed at screening a group of immune-related lncRNAs (IRLs) in predicting the prognosis of GC patients. Genetic and clinical information from the 360 GC patients was included in this study. Eight IRLs in lncRNA-miRNA-mRNA network were screened out according to differential expression analysis. A novel risk score model with three IRLs (MIR4435-1HG, UCA1, and RP11-617F23.1) were identified, and patients were assigned to a high-risk group and a low-risk group. Patients in the low-risk group had a better prognosis. In addition, two nomograms were developed to predict the prognosis of GC. We evaluated the correlation between IRLs and the immune infiltration level of GC using TIMER. Furthermore, we verified that RP11-617F23.1 was significantly upregulated in human GC tissues compared with their adjacent tissues. And, patients with high RP11-617F23.1 expression in tumor tissues had poorer survival. In conclusion, we established a novel risk model based on IRLs for predicting the prognosis of GC. Meanwhile, a novel IRL, RP11-617F23.1, could serve as a predictor of prognosis for patients with GC.

Tumor Microenvironment Evaluation for Gastrointestinal Cancer in the Era of Immunotherapy and Machine Learning

A dynamic and mutualistic interplay between tumor cells and the surrounding tumor microenvironment (TME) triggered the initiation, progression, metastasis, and therapy response of solid tumors. Recent clinical breakthroughs in immunotherapy for gastrointestinal cancer conferred considerable attention to the estimation of TME, and the maturity of next-generation sequencing (NGS)-based technology contributed to the availability of increasing datasets and computational toolbox for deciphering TME compartments. In the current review, we demonstrated the components of TME, multiple methodologies involved in TME detection, and prognostic and predictive TME signatures derived from corresponding methods for gastrointestinal cancer. The TME evaluation comprises traditional, radiomics, and NGS-based high-throughput methodologies, and the computational algorithms are comprehensively discussed. Moreover, we systemically elucidated the existing TME-relevant signatures in the prognostic, chemotherapeutic, and immunotherapeutic settings. Collectively, we highlighted the clinical and technological advances in TME estimation for clinical translation and anticipated that TME-associated biomarkers may be promising in optimizing the future precision treatment for gastrointestinal cancer.

Cisplatin Nanoparticles Promote Intratumoral CD8+ T Cell Priming via Antigen Presentation and T Cell Receptor Crosstalk

Nanomedicines are highly promising for cancer therapy due to their minimal side effects. However, little is known regarding their host immune response, which may limit their clinical efficacy and applications. Here, we find that cisplatin (CDDP)-loaded poly(l-glutamic acid)-graft-methoxy poly(ethylene glycol) complex nanoparticles (CDDP-NPs) elicit a strong antitumor CD8⁺ T cell-mediated immune response in a tumor-bearing mouse model compared to free CDDP. Mechanistically, the sustained retention of CDDP-NPs results in persistent tumor MHC-I overexpression, which promotes the formation of MHC-I-antigen peptide complex (pMHC-I), enhances the interaction between pMHC-I and T cell receptor (TCR), and leads to the activation of TCR signaling pathway and CD8⁺ T cell-mediated immune response. Furthermore, CDDP-NPs upregulate the costimulatory OX40 on intratumoral CD8⁺ T cells, and synergize with the agonistic OX40 antibody (aOX40) to suppress tumor growth by 89.2%. Our study provides a basis for the efficacy advantage of CDDP-based nanomedicines and immunotherapy.

Ovarian cancer-specific dysregulated genes with prognostic significance: scRNA-Seq with bulk RNA-Seq data and experimental validation

A major cause of gynecological cancer -related deaths worldwide, ovarian cancer is characterized by heterogeneity in both tumor cells and the tumor microenvironment (TME). Our study aimed to characterize tumor cell heterogeneity and the infiltration of M2 tumor-associated macrophages (TAMs) in the ovarian cancer TME by single-cell RNA-Seq (scRNA-Seq) analysis combined with bulk RNA sequencing (bulk RNA-Seq). Several highly variable genes were identified in ovarian cancer tissues, and tumor cell heterogeneity and infiltrating immune tumor cell heterogeneity were characterized in ovarian cancer cells. M2 TAMs in the TME were the predominant phenotype of TAM. Further, M2 TAM infiltration in the TME was negatively correlated with poor prognosis of ovarian cancer patients. Four M2 TAM-associated genes (SLAMF7, GNAS, TBX2-AS1, and LYPD6) correlated with the prognostic survival of ovarian cancer patients. Knockdown of SLAMF7 or GNAS mRNA repressed malignancy and cisplatin resistance of ovarian cancer cells. ScRNA-Seq combined with bulk RNA-Seq identified the same four genes associated with M2 TAMs. The prognostic risk score model based on these four genes may hold favorable predictive value for the prognosis of ovarian cancer patients.

Spatial-context-aware RNA-sequence prediction from head and neck cancer histopathology images

Molecular profiling of the tumor in addition to the histological tumor analysis can provide robust information for targeted cancer therapies. Often such data are not available for analysis due to processing delays, cost or inaccessibility. In this paper, we proposed a deep learning-based method to predict RNA-sequence expression (RNA-seq) from Hematoxylin and Eosin whole-slide images (H&E WSI) in head and neck cancer patients. Conventional methods utilize a patch-by-patch prediction and aggregation strategy to predict RNA-seq at a whole-slide level. However, these methods lose spatial-contextual relationships between patches that comprise morphology interactions crucial for predicting RNA-seq. We proposed a novel framework that employs a neural image compressor to preserve the spatial relationships between patches and generate a compressed representation of the whole-slide image, and a customized deep-learning regressor to predict RNA-seq from the compressed representation by learning both global and local features. We tested our proposed method on publicly available TCGA-HNSC dataset comprising 43 test patients for 10 oncogenes. Our experiments showed that the proposed method achieves a 4.12% higher mean correlation and predicts 6 out of 10 genes with better correlation than a state-of-the-art baseline method. Furthermore, we provided interpretability using pathway analysis of the best-predicted genes, and activation maps to highlight the regions in an H&E image that are the most salient of the RNA-seq prediction.Clinical relevance-The proposed method has the potential to discover genetic biomarkers directly from the histopathology images which could be used to pre-screen the patients before actual genetic testing thereby saving cost and time.

Nanotechnology-Assisted RNA Delivery: From Nucleic Acid Therapeutics to COVID-19 Vaccines

In recent years, the main quest of science has been the pioneering of the groundbreaking biomedical strategies needed for achieving a personalized medicine. Ribonucleic acids (RNAs) are outstanding bioactive macromolecules identified as pivotal actors in regulating a wide range of biochemical pathways. The ability to intimately control the cell fate and tissue activities makes RNA-based drugs the most fascinating family of bioactive agents. However, achieving a widespread application of RNA therapeutics in humans is still a challenging feat, due to both the instability of naked RNA and the presence of biological barriers aimed at hindering the entrance of RNA into cells. Recently, material scientists' enormous efforts have led to the development of various classes of nanostructured carriers customized to overcome these limitations. This work systematically reviews the current advances in developing the next generation of drugs based on nanotechnology-assisted RNA delivery. The features of the most used RNA molecules are presented, together with the development strategies and properties of nanostructured vehicles. Also provided is an in-depth overview of various therapeutic applications of the presented systems, including coronavirus disease vaccines and the newest trends in the field. Lastly, emerging challenges and future perspectives for nanotechnology-mediated RNA therapies are discussed.

High-dimensional immune-profiling in cancer: implications for immunotherapy

Immunotherapy is a rapidly growing field for cancer treatment. In contrast to conventional cancer therapies, immunotherapeutic strategies focus on reactivating the immune system to mount an antitumor response. Despite the encouraging outcome in clinical trials, a large proportion of patients still do not respond to treatment and many experience different degrees of immune-related adverse events. Furthermore, it is now increasingly appreciated that even many conventional cancer therapies such as radiotherapy could have a positive impact on the host immune system for better clinical response. Hence, there is a need to better understand tumor immunity in order to design immunotherapeutic strategies, especially evidence-based combination therapies, for improved clinical outcomes. With this aim, cancer research turned its attention to profiling the immune contexture of either the tumor microenvironment (TME) or peripheral blood to uncover mechanisms and biomarkers which might aid in precision immunotherapeutics. Conventional technologies used for this purpose were limited by the depth and dimensionality of the data. Advances in newer techniques have, however, greatly improved the breadth and depth, as well as the quantity and quality of data that can be obtained. The result of these advances is a wealth of new information and insights on how the TME could be affected by various immune cell-types, and how this might in turn impact the clinical outcome of cancer patients . We highlight herein some of the high-dimensional technologies currently employed in immune profiling in cancer and summarize the insights and potential benefits they could bring in designing better cancer immunotherapies.

The methods and advances of adaptive immune receptors repertoire sequencing

The adaptive immune response is a powerful tool, capable of recognizing, binding to, and neutralizing a vast number of internal and external threats via T or B lymphatic receptors with widespread sets of antigen specificities. The emergence of high-throughput sequencing technology and bioinformatics provides opportunities for research in the fields of life sciences and medicine. The analysis and annotation for immune repertoire data can reveal biologically meaningful information, including immune prediction, target antigens, and effective evaluation. Continuous improvements of the immunological repertoire sequencing methods and analysis tools will help to minimize the experimental and calculation errors and realize the immunological information to meet the clinical requirements. That said, the clinical application of adaptive immune repertoire sequencing requires appropriate experimental methods and standard analytical tools. At the population cell level, we can acquire the overview of cell groups, but the information about a single cell is not obtained accurately. The information that is ignored may be crucial for understanding the heterogeneity of each cell, gene expression and drug response. The combination of high-throughput sequencing and single-cell technology allows us to obtain single-cell information with low-cost and high-throughput. In this review, we summarized the current methods and progress in this area.

Applications of single-cell and bulk RNA sequencing in onco-immunology

The rising interest for precise characterization of the tumour immune contexture has recently brought forward the high potential of RNA sequencing (RNA-seq) in identifying molecular mechanisms engaged in the response to immunotherapy. In this review, we provide an overview of the major principles of single-cell and conventional (bulk) RNA-seq applied to onco-immunology. We describe standard preprocessing and statistical analyses of data obtained from such techniques and highlight some computational challenges relative to the sequencing of individual cells. We notably provide examples of gene expression analyses such as differential expression analysis, dimensionality reduction, clustering and enrichment analysis. Additionally, we used public data sets to exemplify how deconvolution algorithms can identify and quantify multiple immune subpopulations from either bulk or single-cell RNA-seq. We give examples of machine and deep learning models used to predict patient outcomes and treatment effect from high-dimensional data. Finally, we balance the strengths and weaknesses of single-cell and bulk RNA-seq regarding their applications in the clinic.

Current Achievements and Applications of Transcriptomics in Personalized Cancer Medicine

Over the last decades, transcriptome profiling emerged as one of the most powerful approaches in oncology, providing prognostic and predictive utility for cancer management. The development of novel technologies, such as revolutionary next-generation sequencing, enables the identification of cancer biomarkers, gene signatures, and their aberrant expression affecting oncogenesis, as well as the discovery of molecular targets for anticancer therapies. Transcriptomics contribute to a change in the holistic understanding of cancer, from histopathological and organic to molecular classifications, opening a more personalized perspective for tumor diagnostics and therapy. The further advancement on transcriptome profiling may allow standardization and cost reduction of its analysis, which will be the next step for transcriptomics to become a canon of contemporary cancer medicine.

A pan-cancer analysis of the human tumor coagulome and its link to the tumor immune microenvironment

OBJECTIVE

Solid tumors often establish a procoagulable state that can lead to venous thromboembolism (VTE). Although some of the key genes involved in this process are known, no previous study has compared the "coagulome", i.e., the expression of coagulation/fibrinolysis genes, across different primary tumor types. It is also unclear whether the coagulome is associated with specific characteristics of the tumor microenvironment (TME). We aimed to address this question.

METHODS

We analyzed the expression of the genes F3, PLAU, PLAT, PLAUR, SERPINB2, and SERPINE1 in 32 cancer types using data from The Cancer Genome Atlas (TCGA) and other freely available resources.

RESULTS

We identified specific expression patterns of procoagulant and fibrinolytic genes. The expression of the Tissue Factor (F3) was found to be tumor type dependent, with the highest expression in glioblastoma (GBM), a highly procoagulable tumor type. Conversely, high expression of the fibrinolysis gene cluster PLAU, PLAUR, SERPINE1 was consistently linked to the characteristics of the TME (monocytic infiltration) and high expression of important checkpoints of the immune response, such as PD-L2 and CD276/B7-H3.

CONCLUSION

These tumor-specific patterns of expression might partially explain the differences in VTE risk among tumor types. We propose that biomarkers of coagulation fibrinolysis might provide valuable information about the TME in cancer patients.

Accelerating the Development of Personalized Cancer Immunotherapy by Integrating Molecular Patients' Profiles with Dynamic Mathematical Models

We review the evolution, achievements, and limitations of the current paradigm shift in medicine, from the "one-size-fits-all" model to "Precision Medicine." Precision, or personalized, medicine-tailoring the medical treatment to the personal characteristics of each patient-engages advanced statistical methods to evaluate the relationships between static patient profiling (e.g., genomic and proteomic), and a simple clinically motivated output (e.g., yes/no responder). Today, precision medicine technologies that have facilitated groundbreaking advances in oncology, notably in cancer immunotherapy, are approaching the limits of their potential, mainly due to the scarcity of methods for integrating genomic, proteomic and clinical patient information. A different approach to treatment personalization involves methodologies focusing on the dynamic interactions in the patient-disease-drug system, as portrayed in mathematical modeling. Achievements of this scientific approach, in the form of algorithms for predicting personal disease dynamics in individual patients under immunotherapeutic drugs, are reviewed as well. The contribution of the dynamic approaches to precision medicine is limited, at present, due to insufficient applicability and validation. Yet, the time is ripe for amalgamating together these two approaches, for maximizing their joint potential to personalize and improve cancer immunotherapy. We suggest the roadmap toward achieving this goal, technologically, and urge clinicians, pharmacologists, and computational biologists to join forces along the pharmaco-clinical track of this development.

Liquid biopsy and tumor heterogeneity in metastatic solid tumors: the potentiality of blood samples

In a large number of cancer types, treatment selection depends on the presence of specific tumor biomarkers. Due to the dynamic nature of cancer, very often these predictive biomarkers are not uniformly present in all cancer cells. Tumor heterogeneity represents indeed one of the main causes of therapeutic failure, and its decoding remains a major ongoing challenge in the field.

Liquid biopsy is the sampling and analysis of non-solid biological tissue often through rapid and non-invasive methods, which allows the assessment in real-time of the evolving landscape of cancer. Samples can be obtained from blood and most other bodily fluids. A blood-based liquid biopsy can capture circulating tumor cells and leukocytes, as well as circulating tumor-derived nucleic acids.

In this review, we discuss the current and possibly future applications of blood-based liquid biopsy in oncology, its advantages and its limitations in clinical practice. We specifically focused on its role as a tool to capture tumor heterogeneity in metastatic cancer patients.

Genomics and precision surgery for head and neck squamous cell carcinoma

The identification of the biological determinants that shape the response of tumors to medical therapies offers perspectives for better patient stratification and therapeutic targeting. Here, we discuss how genomics could help to improve the surgical treatment of head and neck squamous cell carcinoma (HNSCC). We examine the potential use of genomic analyses for: i) refining and standardizing the indications for surgery, ii) the choice of surgical procedure, and iii) the follow-up of patients with resected tumors. We highlight the studies that used genomics to explore the contribution of tumor biology to the outcome of surgery. We discuss the important developments that are challenging current surgical practice in HNSCC, such as neoadjuvant immunotherapy and the analysis of circulating DNA. Genomic analyses provide practical tools that could help improve the pathological diagnosis and staging of HNSCC, and increase the appreciation of the importance of tumor biology in the outcome of surgery. Identification of biomarkers will likely contribute to a move toward precision surgery of HNSCC, i.e. the personalization of surgical practice based on tumor biology.

ERK1/2 signaling regulates the immune microenvironment and macrophage recruitment in glioblastoma

The tumor microenvironment is an important determinant of glioblastoma (GBM) progression and response to treatment. How oncogenic signaling in GBM cells modulates the composition of the tumor microenvironment and its activation is unclear. We aimed to explore the potential local immunoregulatory function of ERK1/2 signaling in GBM. Using proteomic and transcriptomic data (RNA seq) available for GBM tumors from The Cancer Genome Atlas (TCGA), we show that GBM with high levels of phosphorylated ERK1/2 have increased infiltration of tumor-associated macrophages (TAM) with a non-inflammatory M2 polarization. Using three human GBM cell lines in culture, we confirmed the existence of ERK1/2-dependent regulation of the production of the macrophage chemoattractant CCL2/MCP1. In contrast with this positive regulation of TAM recruitment, we found no evidence of a direct effect of ERK1/2 signaling on two other important aspects of TAM regulation by GBM cells: (1) the expression of the immune checkpoint ligands PD-L1 and PD-L2, expressed at high mRNA levels in GBM compared with other solid tumors; (2) the production of the tumor metabolite lactate recently reported to dampen tumor immunity by interacting with the receptor GPR65 present on the surface of TAM. Taken together, our observations suggest that ERK1/2 signaling regulates the recruitment of TAM in the GBM microenvironment. These findings highlight some potentially important particularities of the immune microenvironment in GBM and could provide an explanation for the recent observation that GBM with activated ERK1/2 signaling may respond better to anti-PD1 therapeutics.

Integrated genomic profiling expands clinical options for patients with cancer

Genomic analysis of paired tumor-normal samples and clinical data can be used to match patients to cancer therapies or clinical trials. We analyzed 500 patient samples across diverse tumor types using the Tempus xT platform by DNA-seq, RNA-seq and immunological biomarkers. The use of a tumor and germline dataset led to substantial improvements in mutation identification and a reduction in false-positive rates. RNA-seq enhanced gene fusion detection and cancer type classifications. With DNA-seq alone, 29.6% of patients matched to precision therapies supported by high levels of evidence or by well-powered studies. This proportion increased to 43.4% with the addition of RNA-seq and immunotherapy biomarker results. Combining these data with clinical criteria, 76.8% of patients were matched to at least one relevant clinical trial on the basis of biomarkers measured by the xT assay. These results indicate that extensive molecular profiling combined with clinical data identifies personalized therapies and clinical trials for a large proportion of patients with cancer and that paired tumor-normal plus transcriptome sequencing outperforms tumor-only DNA panel testing.

Defining housekeeping genes suitable for RNA-seq analysis of the human allograft kidney biopsy tissue

BACKGROUND

RNA-seq is poised to play a major role in the management of kidney transplant patients. Rigorous definition of housekeeping genes (HKG) is essential for further progress in this field. Using single genes or a limited set HKG is inherently problematic since their expression might be altered by specific diseases in the patients being studied.

METHODS

To generate a HKG set specific for kidney transplantation, we performed RNA-sequencing from renal allograft biopsies collected in a variety of clinical settings. Various normalization methods were applied to identify transcripts that had a coefficient of variation of expression that was below the 2nd percentile across all samples, and the corresponding genes were designated as housekeeping genes. Comparison with transcriptomic data from the Gene Expression Omnibus (GEO) database, pathway analysis and molecular biological functions were utilized to validate the housekeeping genes set.

RESULTS

We have developed a bioinformatics solution to this problem by using nine different normalization methods to derive large HKG gene sets from a RNA-seq data set of 47,611 transcripts derived from 30 biopsies. These biopsies were collected in a variety of clinical settings, including normal function, acute rejection, interstitial nephritis, interstitial fibrosis/tubular atrophy and polyomavirus nephropathy. Transcripts with coefficient of variation below the 2nd percentile were designated as HKG, and validated by showing their virtual absence in diseased allograft derived transcriptomic data sets available in the GEO. Pathway analysis indicated a role for these genes in maintenance of cell morphology, pyrimidine metabolism, and intracellular protein signaling.

CONCLUSIONS

Utilization of these objectively defined HKG data sets will guard against errors resulting from focusing on individual genes like 18S RNA, actin & tubulin, which do not maintain constant expression across the known spectrum of renal allograft pathology.