The Multi-Dimensional Biomarker Landscape in Cancer Immunotherapy

Exosomal long non-coding RNAs in cancer: Interplay, modulation, and therapeutic avenues

In the intricate field of cancer biology, researchers are increasingly intrigued by the emerging role of exosomal long non-coding RNAs (lncRNAs) due to their multifaceted interactions, complex modulation mechanisms, and potential therapeutic applications. These exosomal lncRNAs, carried within extracellular vesicles, play a vital partin tumorigenesis and disease progression by facilitating communication networks between tumor cells and their local microenvironment, making them an ideal candidates for use in a liquid biopsy approach. However, exosomal lncRNAs remain an understudied area, especially in cancer biology. Therefore this review aims to comprehensively explore the dynamic interplay between exosomal lncRNAs and various cellular components, including interactions with tumor-stroma, immune modulation, and drug resistance mechanisms. Understanding the regulatory functions of exosomal lncRNAs in these processes can potentially unveil novel diagnostic markers and therapeutic targets for cancer. Additionally, the emergence of RNA-based therapeutics presents exciting opportunities for targeting exosomal lncRNAs, offering innovative strategies to combat cancer progression and improve treatment outcomes. Thus, this review provides insights into the current understanding of exosomal lncRNAs in cancer biology, highlighting their crucial roles, regulatory mechanisms, and the evolving landscape of therapeutic interventions. Furthermore, we have also discussed the advantage of exosomes as therapeutic carriers of lncRNAs for the development of personalized targeted therapy for cancer patients.

Molecular and immune pathobiology of human angiosarcoma

Angiosarcoma is a rare endothelial-derived malignancy that is extremely diverse in anatomy, aetiology, molecular and immune characteristics. While novel therapeutic approaches incorporating targeted agents and immunotherapy have yielded significant improvements in patient outcomes across several cancers, their impact on angiosarcoma remains modest. Contributed by its heterogeneous nature, there is currently a lack of novel drug targets in this disease entity and no reliable biomarkers that predict response to conventional treatment. This review aims to examine the molecular and immune landscape of angiosarcoma in association with its aetiology, anatomical sites, prognosis and therapeutic options. We summarise current efforts to characterise angiosarcoma subtypes based on molecular and immune profiling. Finally, we highlight promising technologies such as single-cell spatial "omics" that may further our understanding of angiosarcoma and propose strategies that can be similarly applied for the study of other rare cancers.

Tertiary lymphoid structures: new immunotherapy biomarker

Immunotherapy shows substantial advancement in cancer and is becoming widely used in clinical practice. A variety of biomarkers have been proposed to predict the efficacy of immunotherapy, but most of them have low predictive ability. Tertiary lymphoid structures (TLSs), the aggregation of multiple lymphocytes, have been found to exist in various tumor tissues. TLSs have been shown to correlate with patient prognosis and immunotherapy response. This review summarizes the characteristics of TLSs and the inducing factors of TLS formation, presents available evidence on the role of TLSs in predicting immunotherapy response in different cancers, and lastly emphasizes their predictive potential for neoadjuvant immunotherapy efficacy.

Special Issue "Cancer Immunotherapy: Tumor Microenvironment, Biomarker Discovery and Immune Resistance"

Since the launch of this Special Issue entitled "Cancer Immunotherapy: Tumor Microenvironment, Biomarker Discovery and Immune Resistance", the field of cancer immunotherapy has continued to witness rapid growth in the development of novel agents, improvements in our understanding of mechanisms of response and resistance, and the maturation of emerging technologies such as artificial intelligence, machine learning, single-cell sequencing and spatial profiling [...].

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

Genetic interrogation for sequence and copy number variants in systemic lupus erythematosus

Early-onset systemic lupus erythematosus presents with a more severe disease and is associated with a greater genetic burden, especially in patients from Black, Asian or Hispanic ancestries. Next-generation sequencing techniques, notably whole exome sequencing, have been extensively used in genomic interrogation studies to identify causal disease variants that are increasingly implicated in the development of autoimmunity. This Review discusses the known casual variants of polygenic and monogenic systemic lupus erythematosus and its implications under certain genetic disparities while suggesting an age-based sequencing strategy to aid in clinical diagnostics and patient management for improved patient care.

Immunotherapy-related biomarkers: Confirmations and uncertainties

Immunotherapy profoundly changed oncology treatment, becoming one of the main therapeutical strategies. Remarkable improvement has been achieved in survival outcomes, but the percentage of patients who benefit from immunotherapy is still limited. Only one-third of patients receiving immune checkpoint inhibitors (ICIs) achieve long-term response. Several patients are not responsive to treatment or relapse after an initial response. To date, programmed death-ligand 1, microsatellite instability, and tumor mutational burden are the three biomarkers validated to predict the ICIs response, but a single variable seems still insufficient in the patient's selection. Considering the substantial and increasing use of these drugs, the identification of new predictive biomarkers of ICI response is of paramount importance. We summarize the state of the art and the clinical use of immune biomarkers in oncology, highlighting the strength and weaknesses of currently approved biomarkers, describing the emerging tissues and circulating biomarkers, and outlining future perspectives.

Evolving therapeutic landscape of diffuse large B-cell lymphoma: challenges and aspirations

Diffuse large B-cell lymphoma (DLBCL) represents the commonest subtype of non-Hodgkin lymphoma and encompasses a group of diverse disease entities, each harboring unique molecular and clinico-pathological features. The understanding of the molecular landscape of DLBCL has improved significantly over the past decade, highlighting unique genomic subtypes with implications on targeted therapy. At the same time, several new treatment modalities have been recently approved both in the frontline and relapsed settings, ending a dearth of negative clinical trials that plagued the past decade. Despite that, in the real-world setting, issues like drug accessibility, reimbursement policies, physician and patient preference, as well as questions regarding optimal sequencing of treatment options present difficulties and challenges in day-to-day oncology practice. Here, we review the recent advances in the therapeutic armamentarium of DLBCL and discuss implications on the practice landscape, with a particular emphasis on the context of the healthcare system in Singapore.

Single-Cell Transcriptomics for Unlocking Personalized Cancer Immunotherapy: Toward Targeting the Origin of Tumor Development Immunogenicity

Cancer immunotherapy is a promising approach for treating malignancies through the activation of anti-tumor immunity. However, the effectiveness and safety of immunotherapy can be limited by tumor complexity and heterogeneity, caused by the diverse molecular and cellular features of tumors and their microenvironments. Undifferentiated tumor cell niches, which we refer to as the "Origin of Tumor Development" (OTD) cellular population, are believed to be the source of these variations and cellular heterogeneity. From our perspective, the existence of distinct features within the OTD is expected to play a significant role in shaping the unique tumor characteristics observed in each patient. Single-cell transcriptomics is a high-resolution and high-throughput technique that provides insights into the genetic signatures of individual tumor cells, revealing mechanisms of tumor development, progression, and immune evasion. In this review, we explain how single-cell transcriptomics can be used to develop personalized cancer immunotherapy by identifying potential biomarkers and targets specific to each patient, such as immune checkpoint and tumor-infiltrating lymphocyte function, for targeting the OTD. Furthermore, in addition to offering a possible workflow, we discuss the future directions of, and perspectives on, single-cell transcriptomics, such as the development of powerful analytical tools and databases, that will aid in unlocking personalized cancer immunotherapy through the targeting of the patient's cellular OTD.

DNA Methylation Signatures of Response to Conventional Synthetic and Biologic Disease-Modifying Antirheumatic Drugs (DMARDs) in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a complex condition that displays heterogeneity in disease severity and response to standard treatments between patients. Failure rates for conventional, target synthetic, and biologic disease-modifying rheumatic drugs (DMARDs) are significant. Although there are models for predicting patient response, they have limited accuracy, require replication/validation, or for samples to be obtained through a synovial biopsy. Thus, currently, there are no prediction methods approved for routine clinical use. Previous research has shown that genetics and environmental factors alone cannot explain the differences in response between patients. Recent studies have demonstrated that deoxyribonucleic acid (DNA) methylation plays an important role in the pathogenesis and disease progression of RA. Importantly, specific DNA methylation profiles associated with response to conventional, target synthetic, and biologic DMARDs have been found in the blood of RA patients and could potentially function as predictive biomarkers. This review will summarize and evaluate the evidence for DNA methylation signatures in treatment response mainly in blood but also learn from the progress made in the diseased tissue in cancer in comparison to RA and autoimmune diseases. We will discuss the benefits and challenges of using DNA methylation signatures as predictive markers and the potential for future progress in this area.

Spatial transcriptomics reveal topological immune landscapes of Asian head and neck angiosarcoma

Angiosarcomas are rare malignant tumors of the endothelium, arising commonly from the head and neck region (AS-HN) and recently associated with ultraviolet (UV) exposure and human herpesvirus-7 infection. We examined 81 cases of angiosarcomas, including 47 cases of AS-HN, integrating information from whole genome sequencing, gene expression profiling and spatial transcriptomics (10X Visium). In the AS-HN cohort, we observed recurrent somatic mutations in CSMD3 (18%), LRP1B (18%), MUC16 (18%), POT1 (16%) and TP53 (16%). UV-positive AS-HN harbored significantly higher tumor mutation burden than UV-negative cases (p = 0.0294). NanoString profiling identified three clusters with distinct tumor inflammation signature scores (p < 0.001). Spatial transcriptomics revealed topological profiles of the tumor microenvironment, identifying dominant but tumor-excluded inflammatory signals in immune-hot cases and immune foci even in otherwise immune-cold cases. In conclusion, spatial transcriptomics reveal the tumor immune landscape of angiosarcoma, and in combination with multi-omic information, may improve implementation of treatment strategies.

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.

Emerging predictive biomarkers for novel therapeutics in peripheral T-cell and natural killer/T-cell lymphoma

Peripheral T-cell lymphoma (PTCL) and natural killer/T-cell lymphoma (NKTCL) are rare subtypes of non-Hodgkin's lymphoma that are typically associated with poor treatment outcomes. Contemporary first-line treatment strategies generally involve the use of combination chemoimmunotherapy, radiation and/or stem cell transplant. Salvage options incorporate a number of novel agents including epigenetic therapies (e.g. HDAC inhibitors, DNMT inhibitors) as well as immune checkpoint inhibitors. However, validated biomarkers to select patients for individualized precision therapy are presently lacking, resulting in high treatment failure rates, unnecessary exposure to drug toxicities, and missed treatment opportunities. Recent advances in research on the tumor and microenvironmental factors of PTCL and NKTCL, including alterations in specific molecular features and immune signatures, have improved our understanding of these diseases, though several issues continue to impede progress in clinical translation. In this Review, we summarize the progress and development of the current predictive biomarker landscape, highlight potential knowledge gaps, and discuss the implications on novel therapeutics development in PTCL and NKTCL.

Decoding Roles of Exosomal lncRNAs in Tumor-Immune Regulation and Therapeutic Potential

Exosomes are nanovesicles secreted into biofluids by various cell types and have been implicated in different physiological and pathological processes. Interestingly, a plethora of studies emphasized the mediating role of exosomes in the bidirectional communication between donor and recipient cells. Among the various cargoes of exosomes, long non-coding RNAs (lncRNAs) have been identified as crucial regulators between cancer cells and immune cells in the tumor microenvironment (TME) that can interfere with innate and adaptive immune responses to affect the therapeutic efficiency. Recently, a few major studies have focused on the exosomal lncRNA-mediated interaction between cancer cells and immune cells infiltrated into TME. Nevertheless, a dearth of studies pertains to the immune regulating role of exosomal lncRNAs in cancer and is still in the early stages. Comprehensive mechanisms of exosomal lncRNAs in tumor immunity are not well understood. Herein, we provide an overview of the immunomodulatory function of exosomal lncRNAs in cancer and treatment resistance. In addition, we also summarize the potential therapeutic strategies toward exosomal lncRNAs in TME.

Rationale Efficacy and Safety Evidence of Lenvatinib and Pembrolizumab Association in Anaplastic Thyroid Carcinoma

Anaplastic thyroid carcinoma (ATC) are highly aggressive malignant tumors with poor overall prognosis despite multimodal therapy. As ATC are extremely rare, no randomized controlled study has been published for metastatic disease. Thyrosine kinase inhibitors, especially lenvatinib and immune checkpoint inhibitors such as pembrolizumab, are emerging drugs for ATC. Few studies have reported the efficacity of pembrolizumab and lenvatinib association, resulting in its frequent off-label use. In this review, we discuss rationale efficacy and safety evidence for the association of lenvatinib and pembrolizumab in ATC. First, we discuss preclinical rationale for pembrolizumab monotherapy, lenvatinib monotherapy and synergistic action of pembrolizumab and lenvatinib in the metastatic setting. We also discuss clinical evidence for immunotherapy and pembrolizumab in ATC through the analysis of studies evaluating immunotherapy, lenvatinib and pembrolizumab lenvatinib association in ATC. In addition, we discuss the safety of this association and potential predictive biomarkers of efficiency.