The Discovery of Biomarkers in Cancer Immunotherapy

Natural killer cell-mediated immune surveillance in cancer: Role of tumor microenvironment

In the immunological surveillance against cancer, natural killer (NK) cells are essential effectors that help eradicate altered cells. The complex interactions that occur between NK cells and the tumor microenvironment (TME) are thoroughly examined in this review. The review examines how cytokine stimulation affects NK cell activation, focusing on the dynamic modulation of NK cell function within the TME. It looks at NK cell-related biomarkers such as PD-1/PD-L1, methylation HOXA9 (Homeobox A9), Stroma AReactive Invasion Front Areas (SARIFA), and NKG2A/HLA-E, providing critical information about prognosis and treatment outcomes. The changing landscape of immunotherapies-including checkpoint inhibitors, CAR-NK cells, and cytokine-based interventions-is examined in the context of enhancing NK cell activity. The review highlights the potential pathways for precision medicine going forward, focusing on customized immunotherapies based on unique biomarker profiles and investigating combination medicines to produce more robust anti-tumor responses.

Advances in Genomic Data and Biomarkers: Revolutionizing NSCLC Diagnosis and Treatment

Non-small cell lung cancer (NSCLC) is a significant public health concern with high mortality rates. Recent advancements in genomic data, bioinformatics tools, and the utilization of biomarkers have improved the possibilities for early diagnosis, effective treatment, and follow-up in NSCLC. Biomarkers play a crucial role in precision medicine by providing measurable indicators of disease characteristics, enabling tailored treatment strategies. The integration of big data and artificial intelligence (AI) further enhances the potential for personalized medicine through advanced biomarker analysis. However, challenges remain in the impact of new biomarkers on mortality and treatment efficacy due to limited evidence. Data analysis, interpretation, and the adoption of precision medicine approaches in clinical practice pose additional challenges and emphasize the integration of biomarkers with advanced technologies such as genomic data analysis and artificial intelligence (AI), which enhance the potential of precision medicine in NSCLC. Despite these obstacles, the integration of biomarkers into precision medicine has shown promising results in NSCLC, improving patient outcomes and enabling targeted therapies. Continued research and advancements in biomarker discovery, utilization, and evidence generation are necessary to overcome these challenges and further enhance the efficacy of precision medicine. Addressing these obstacles will contribute to the continued improvement of patient outcomes in non-small cell lung cancer.

Development and validation of a necroptosis-related gene prognostic score to predict prognosis and efficiency of immunotherapy in gastric cancer

Necroptosis is a novel type of regulated cell death that is intimately associated with a variety of tumors. However, how necroptosis affects the identification of gastric cancer (GC) remains unclear. Here we seek to find new potential necroptosis-related biomarkers to predict GC prognosis and immunotherapy effect. We used Cox analysis to obtain shared prognostic markers related to necroptosis from five datasets (TCGA and four GEO datasets). Then, a necroptosis-related gene prognostic score (NRGPS) system was constructed using LASSO Cox regression, NRGPS consisting of three necroptosis-related mRNAs (AXL, RAI14, and NOX4) was identified, 31 pairs of GC and adjacent normal tissues from the Second Hospital of Harbin Medical University were collected and Real-Time Quantitative PCR (RT-qPCR) was used to detect the relative expression levels of the three necroptosis-related mRNAs, and external validation was performed on four GEO datasets (GSE84437, GSE26901, GSE62254 and GSE15459). In this study, Overall survival (OS) in the high-NRGPS group was significantly lower than in the low-NRGPS group. Cox regression analyses showed that NRGPS was an independent prognostic variable. Tumor-mutation-burden (TMB), tumor microenvironment (TME), microsatellite instability (MSI), and Tumor Immune Dysfunction and Exclusion (TIDE) scoring were used as predictors of the immunotherapy response. A cancer-friendly immune microenvironment, a high TIDE score, a low TMB, and a low MSI were all characteristics of the high-NRGPS group, and they all consistently showed that the issues seen there are related to immune escape in GC. The combination of three candidate genes may be an effective method for diagnostic assessment of GC prognosis and immunotherapy efficacy.

GDPLichi: a DNA Damage Repair-Related Gene Classifier for Predicting Lung Adenocarcinoma Immune Checkpoint Inhibitors Response

Lung cancer is one of the most common and mortal malignancies, usually with a poor prognosis in its advanced or recurrent stages. Recently, immune checkpoint inhibitors (ICIs) immunotherapy has revolutionized the treatment of human cancers including lung adenocarcinoma (LUAD), and significantly improved patients' prognoses. However, the prognostic and predictive outcomes differ because of tumor heterogeneity. Here, we present an effective method, GDPLichi (Genes of DNA damage repair to predict LUAD immune checkpoint inhibitors response), as the signature to predict the LUAD patient's response to the ICIs. GDPLichi utilized only 7 maker genes from 8 DDR pathways to construct the predictive model and classified LUAD patients into two subgroups: low- and high-risk groups. The high-risk group was featured by worse prognosis and decreased B cells, CD8+ T cells, CD8+ central memory T cells, hematopoietic stem cells (HSC), myeloid dendritic cells (MDC), and immune scores as compared to the low-risk group. However, our research also suggests that the high-risk group was more sensitive to ICIs, which might be explained by increased TMB, neoantigen, immune checkpoint molecules, and immune suppression genes' expression, but lower TIDE score as compared to the low-risk group. This conclusion was verified in three other LUAD cohort datasets (GSE30219, GSE31210, GSE50081).

USH2A Mutation is Associated With Tumor Mutation Burden and Antitumor Immunity in Patients With Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is one of the diseases with the highest morbidity and mortality in the world. At present, immunotherapy has become a valuable method for the treatment of COAD. Tumor mutational burden (TMB) is considered to be the most common biomarker for predicting immunotherapy. According to reports, the mutation rate of COAD ranks third. However, whether these gene mutations are related to TMB and immune response is still unknown. Here, COAD somatic mutation data were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Bioinformatics methods were used to study the relationships among gene mutations, COAD survival prognosis, and tumor immune response. A total of 22 of the top 40 mutations in TCGA and ICGC databases were the same. Among them, the USH2A mutation was associated with high TMB and poor clinical prognosis. According to Gene Set Enrichment Analysis (GSEA) and the CIBERSORT algorithm, we determined that the USH2A mutation upregulates signaling pathways involved in the immune system and the antitumor immune response. In cases with a USH2A mutation, the immune score and MSI score of TCGA samples increased, the expression of immune checkpoint genes decreased significantly, and the TIDE score decreased significantly. Dependent on the presence or absence of a USH2A mutation, TCGA COAD samples were analyzed for differentially expressed genes, 522 of which were identified. Using a univariate Cox analysis and LASSO COX analysis of these differential genes, a prediction model was established, which established significant differences in the infiltration of immune cells, immune checkpoint gene expression, immune score, MSI score, TMB, and TIDE in patients in high- and low-risk groups. In conclusion, mutation of USH2A is frequent in COAD and is related to an increase in TMB and the antitumor immunity. The differential genes screened by USH2A mutation allowed the construction of a risk model for predicting the survival and prognosis of cancer patients, in addition to providing new ideas for COAD immunotherapy.

A topography of immunotherapies against gastrointestinal malignancies

Gastrointestinal (GI) cancers are one of the leading causes of death worldwide. Although various approaches are implemented to improve the health condition of GI patients, none of the treatment protocols promise for eradicating cancer. However, a treatment mechanism against any kind of disease condition is already existing executing inside the human body. The 'immune system' is highly efficient to detect and destroy the unfavourable events of the body including tumor cells. The immune system can restrict the growth and proliferation of cancer. Cancer cells behave much smarter and adopt new mechanisms for hiding from the immune cells. Thus, cancer immunotherapy might play a decisive role to train the immune system against cancer. In this review, we have discussed the immunotherapy permitted for the treatment of GI cancers. We have discussed various methods and mechanisms, periodic development of cancer immunotherapies, approved biologicals, completed and ongoing clinical trials, role of various biopharmaceuticals, and epigenetic factors involved in GI cancer immunotherapies (graphical abstract Figure 1).

CD8+ TILs in NSCLC differentiate into TEMRA via a bifurcated trajectory: deciphering immunogenicity of tumor antigens

Background

CD8⁺ tumor-infiltrating lymphocytes (TILs) comprise phenotypically and functionally heterogeneous subpopulations. Of these, effector memory CD45RA re-expressing CD8⁺ T cells (Temra) have been discovered and characterized as the most terminally differentiated subset. However, their exact ontogeny and physiological importance in association with tumor progression remain poorly understood.

Methods

We analyzed primary tumors and peripheral blood samples from 26 patients with non-small cell lung cancer and analyzed their phenotypes and functional characteristics using flow cytometry, RNA-sequencing, and bioinformatics.

Results

We found that tumor-infiltrating Temra (tilTemra) cells largely differ from peripheral blood Temra (pTemra), with distinct transcriptomes and functional properties. Notably, although majority of the pTemra was CD27⁻CD28⁻ double-negative (DN), a large fraction of tilTemra population was CD27⁺CD28⁺ double-positive (DP), a characteristic of early-stage, less differentiated effector cells. Trajectory analysis revealed that CD8⁺ TILs undergo a divergent sequence of events for differentiation into either DP or DN tilTemra. Such a differentiation toward DP tilTemra relied on persistent expression of CD27 and CD28 and was associated with weak T cell receptor engagement. Thus, a higher proportion of DP Temra was correlated with lower immunogenicity of tumor antigens and consequently lower accumulation of CD8⁺ TILs.

Conclusions

These data suggest a complex interplay between CD8⁺ T cells and tumors and define DP Temra as a unique subset of tumor-specific CD8⁺ TILs that are produced in patients with relatively low immunogenic cancer types, predicting immunogenicity of tumor antigens and CD8⁺ TIL counts, a reliable biomarker for successful cancer immunotherapy.

Insights into an Immunotherapeutic Approach to Combat Multidrug Resistance in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) has emerged as one of the most lethal cancers worldwide because of its high refractoriness and multi-drug resistance to existing chemotherapies, which leads to poor patient survival. Novel pharmacological strategies to tackle HCC are based on oral multi-kinase inhibitors like sorafenib; however, the clinical use of the drug is restricted due to the limited survival rate and significant side effects, suggesting the existence of a primary or/and acquired drug-resistance mechanism. Because of this hurdle, HCC patients are forced through incomplete therapy. Although multiple approaches have been employed in parallel to overcome multidrug resistance (MDR), the results are varying with insignificant outcomes. In the past decade, cancer immunotherapy has emerged as a breakthrough approach and has played a critical role in HCC treatment. The liver is the main immune organ of the lymphatic system. Researchers utilize immunotherapy because immune evasion is considered a major reason for rapid HCC progression. Moreover, the immune response can be augmented and sustained, thus preventing cancer relapse over the post-treatment period. In this review, we provide detailed insights into the immunotherapeutic approaches to combat MDR by focusing on HCC, together with challenges in clinical translation.

A Novel Prognostic Signature Based on Metabolism-Related Genes to Predict Survival and Guide Personalized Treatment for Head and Neck Squamous Carcinoma

Metabolic reprogramming contributes to patient prognosis. Here, we aimed to reveal the comprehensive landscape in metabolism of head and neck squamous carcinoma (HNSCC), and establish a novel metabolism-related prognostic model to explore the clinical potential and predictive value on therapeutic response. We screened 4752 metabolism-related genes (MRGs) and then identified differentially expressed MRGs in HNSCC. A novel 10-MRGs risk model for prognosis was established by the univariate Cox regression analysis and the least absolute shrinkage and selection operator (Lasso) regression analysis, and then verified in both internal and external validation cohort. Kaplan-Meier analysis was employed to explore its prognostic power on the response of conventional therapy. The immune cell infiltration was also evaluated and we used tumor immune dysfunction and exclusion (TIDE) algorithm to estimate potential response of immunotherapy in different risk groups. Nomogram model was constructed to further predict patients’ prognoses. We found the MRGs-related prognostic model showed good prediction performance. Survival analysis indicated that patients suffered obviously poorer survival outcomes in high-risk group (p < 0.001). The metabolism-related signature was further confirmed to be the independent prognostic value of HNSCC (HR = 6.387, 95% CI = 3.281-12.432, p < 0.001), the efficacy of predictive model was also verified by internal and external validation cohorts. We observed that HNSCC patients would benefit from the application of chemotherapy in the low-risk group (p = 0.029). Immunotherapy may be effective for HNSCC patients with high risk score (p < 0.01). Furthermore, we established a predictive nomogram model for clinical application with high performance. Our study constructed and validated a promising 10-MRGs signature for monitoring outcome, which may provide potential indicators for metabolic therapy and therapeutic response prediction in HNSCC.

The Evolution of Cancer Immunotherapy

Immunotherapy has changed the environment of cancer treatment by providing new and efficacious therapy options for many solid and hematologic malignancies. Although not a new field of oncology, immunotherapy has quickly developed into one of the most flourishing fields in medicine. In this review article, we explore key discoveries which helped to shape our current understanding of the immune system’s role in neoplasms. Many landmark developments include the advancements in checkpoint inhibitors, monoclonal antibodies, CAR-T cells and anti-cancer vaccines. We also explore the drawbacks and efficacy of various categories of immunotherapy. Ongoing investigations within immunotherapy, such as the gut microbiome, combining checkpoint inhibitors and gene sequencing, continue to personalize treatments for cancer patients, providing exciting and endless possibilities for the future.

Novel Pharmacological Options in the Treatment of Cholangiocarcinoma: Mechanisms of Resistance

Simple Summary

Cholangiocarcinoma, a tumor derived from epithelial cells of the biliary tree, is characterized by a dismal prognosis. Its late diagnosis, which makes surgical resection not an option for most patients, and its marked refractoriness to standard chemotherapy, justify its high position in the rank of the most lethal cancers. Identifying specific druggable genetic alterations constitutes a promising alternative for the use of personalized targeted anticancer agents, and immunotherapy, or drugs able to interact with proteins involved in the crosstalk between cancer and immune cells, could also be an option in the future. However, it has also been observed that some patients fail to respond to these new therapies or after an initial response, the disease progresses. Therefore, understanding the mechanisms of pharmacoresistance is of utmost importance to design more effective treatments.

Abstract

Despite the crucial advances in understanding the biology of cholangiocarcinoma (CCA) achieved during the last decade, very little of this knowledge has been translated into clinical practice. Thus, CCA prognosis is among the most dismal of solid tumors. The reason is the frequent late diagnosis of this form of cancer, which makes surgical removal of the tumor impossible, together with the poor response to standard chemotherapy and targeted therapy with inhibitors of tyrosine kinase receptors. The discovery of genetic alterations with an impact on the malignant characteristics of CCA, such as proliferation, invasiveness, and the ability to generate metastases, has led to envisage to treat these patients with selective inhibitors of mutated proteins. Moreover, the hope of developing new tools to improve the dismal outcome of patients with advanced CCA also includes the use of small molecules and antibodies able to interact with proteins involved in the crosstalk between cancer and immune cells with the aim of enhancing the immune system’s attack against the tumor. The lack of effect of these new therapies in some patients with CCA is associated with the ability of tumor cells to continuously adapt to the pharmacological pressure by developing different mechanisms of resistance. However, the available information about these mechanisms for the new drugs and how they evolve is still limited.

Computational Tumor Infiltration Phenotypes Enable the Spatial and Genomic Analysis of Immune Infiltration in Colorectal Cancer

Cancer immunotherapy has led to significant therapeutic progress in the treatment of metastatic and formerly untreatable tumors. However, drug response rates are variable and often only a subgroup of patients will show durable response to a treatment. Biomarkers that help to select those patients that will benefit the most from immunotherapy are thus of crucial importance. Here, we aim to identify such biomarkers by investigating the tumor microenvironment, i.e., the interplay between different cell types like immune cells, stromal cells and malignant cells within the tumor and developed a computational method that determines spatial tumor infiltration phenotypes. Our method is based on spatial point pattern analysis of immunohistochemically stained colorectal cancer tumor tissue and accounts for the intra-tumor heterogeneity of immune infiltration. We show that, compared to base-line models, tumor infiltration phenotypes provide significant additional support for the prediction of established biomarkers in a colorectal cancer patient cohort (n = 80). Integration of tumor infiltration phenotypes with genetic and genomic data from the same patients furthermore revealed significant associations between spatial infiltration patterns and common mutations in colorectal cancer and gene expression signatures. Based on these associations, we computed novel gene signatures that allow one to predict spatial tumor infiltration patterns from gene expression data only and validated this approach in a separate dataset from the Cancer Genome Atlas.

Biomarker-focused multi-drug combination therapy and repurposing trial in mdx mice

Duchenne muscular dystrophy is initiated by dystrophin deficiency, but downstream pathophysiological pathways such as membrane instability, NFĸB activation, mitochondrial dysfunction, and induction of TGFβ fibrosis pathways are thought to drive the disability. Dystrophin replacement strategies are hopeful for addressing upstream dystrophin deficiency; however, all methods to date use semi-functional dystrophin proteins that are likely to trigger downstream pathways. Thus, combination therapies that can target multiple downstream pathways are important in treating DMD, even for dystrophin-replacement strategies. We sought to define blood pharmacodynamic biomarkers of drug response in the mdx mouse model of Duchenne muscular dystrophy using a series of repurposed drugs. Four-week-old mdx mice were treated for four weeks with four different drugs singly and in combination: vehicle, prednisolone, vamorolone, rituximab, β-aminoisobutyric acid (BAIBA) (11 treatment groups; n = 6/group). Blood was collected via cardiac puncture at study termination, and proteomic profiling was carried out using SOMAscan aptamer panels (1,310 proteins assayed). Prednisolone was tested alone and in combination with other drugs. It was found to have a good concordance of prednisolone-responsive biomarkers (56 increased by prednisolone, 39 decreased) focused on NFκB and TGFβ cascades. Vamorolone shared 45 (80%) of increased biomarkers and 13 (33%) of decreased biomarkers with prednisolone. Comparison of published human corticosteroid-responsive biomarkers to our mdx data showed 14% (3/22) concordance between mouse and human. Rituximab showed fewer drug-associated biomarkers, with the most significant being human IgG. On the other hand, BAIBA treatment (high and low dose) showed a drug-associated increase in 40 serum proteins and decreased 5 serum proteins. Our results suggest that a biomarker approach could be employed for assessing drug combinations in both mouse and human studies.

Reconnaissance of tumor immune microenvironment spatial heterogeneity in metastatic renal cell carcinoma and correlation with immunotherapy response

A clearer understanding of the tumor immune microenvironment (TIME) in metastatic clear cell renal cell carcinoma (ccRCC) may help to inform precision treatment strategies. We sought to identify clinically meaningful TIME signatures in ccRCC. We studied tumors from 39 patients with metastatic ccRCC using quantitative multiplexed immunofluorescence and relevant immune marker panels. Cell densities were analyzed in three regions of interest (ROIs): tumor core, tumor-stroma interface and stroma. Patients were stratified into low- and high-marker density groups using median values as thresholds. Log-rank and Cox regression analyses while controlling for clinical variables were used to compare survival outcomes to patterns of immune cell distributions. There were significant associations with increased macrophage (CD68⁺ CD163⁺ CD206⁺ ) density and poor outcomes across multiple ROIs in primary and metastatic tumors. In primary tumors, T-bet⁺ T helper type 1 (Th1) cell density was highest at the tumor-stromal interface (P = 0·0021), and increased co-expression of CD3 and T-bet was associated with improved overall survival (P = 0·015) and survival after immunotherapy (P = 0·014). In metastatic tumor samples, decreased forkhead box protein 3 (FoxP3)⁺ T regulatory cell density correlated with improved survival after immunotherapy (P = 0·016). Increased macrophage markers and decreased Th1 T cell markers within the TIME correlated with poor overall survival and treatment outcomes. Immune markers such as FoxP3 showed consistent levels across the TIME, whereas others, such as T-bet, demonstrated significant variance across the distinct ROIs. These findings suggest that TIME profiling outside the tumor core may identify clinically relevant associations for patients with metastatic ccRCC.

Designing and Immunomodulating Multiresponsive Nanomaterial for Cancer Theranostics

Cancer has been widely investigated yet limited in its manifestation. Cancer treatment holds innovative and futuristic strategies considering high disease heterogeneity. Chemotherapy, radiotherapy and surgery are the most explored pillars; however optimal therapeutic window and patient compliance recruit constraints. Recently evolved immunotherapy demonstrates a vital role of the host immune system to prevent metastasis recurrence, still undesirable clinical response and autoimmune adverse effects remain unresolved. Overcoming these challenges, tunable biomaterials could effectively control the co-delivery of anticancer drugs and immunomodulators. Current status demands a potentially new approach for minimally invasive, synergistic, and combinatorial nano-biomaterial assisted targeted immune-based treatment including therapeutics, diagnosis and imaging. This review discusses the latest findings of engineering biomaterial with immunomodulating properties and implementing novel developments in designing versatile nanosystems for cancer theranostics. We explore the functionalization of nanoparticle for delivering antitumor therapeutic and diagnostic agents promoting immune response. Through understanding the efficacy of delivery system, we have enlightened the applicability of nanomaterials as immunomodulatory nanomedicine further advancing to preclinical and clinical trials. Future and present ongoing improvements in engineering biomaterial could result in generating better insight to deal with cancer through easily accessible immunological interventions.

A genomic signature for accurate classification and prediction of clinical outcomes in cancer patients treated with immune checkpoint blockade immunotherapy

Tumor mutational burden (TMB) is associated with clinical response to immunotherapy, but application has been limited to a subset of cancer patients. We hypothesized that advanced machine-learning and proper modeling could identify mutations that classify patients most likely to derive clinical benefits. Training data: Two sets of public whole-exome sequencing (WES) data for metastatic melanoma. Validation data: One set of public non-small cell lung cancer (NSCLC) data. Least Absolute Shrinkage and Selection Operator (LASSO) machine-learning and proper modeling were used to identify a set of mutations (biomarker) with maximum predictive accuracy (measured by AUROC). Kaplan-Meier and log-rank methods were used to test prediction of overall survival. The initial model considered 2139 mutations. After pruning, 161 mutations (11%) were retained. An optimal threshold of 0.41 divided patients into high-weight (HW) or low-weight (LW) TMB groups. Classification for HW-TMB was 100% (AUROC = 1.0) on melanoma learning/testing data; HW-TMB was a prognostic marker for longer overall survival. In validation data, HW-TMB was associated with survival (p = 0.0057) and predicted 6-month clinical benefit (AUROC = 0.83) in NSCLC. In conclusion, we developed and validated a 161-mutation genomic signature with "outstanding" 100% accuracy to classify melanoma patients by likelihood of response to immunotherapy. This biomarker can be adapted for clinical practice to improve cancer treatment and care.

A pragmatic adaptive enrichment design for selecting the right target population for cancer immunotherapies

One of the challenges in the design of confirmatory trials is to deal with uncertainties regarding the optimal target population for a novel drug. Adaptive enrichment designs (AED) which allow for a data-driven selection of one or more prespecified biomarker subpopulations at an interim analysis have been proposed in this setting but practical case studies of AEDs are still relatively rare. We present the design of an AED with a binary endpoint in the highly dynamic setting of cancer immunotherapy. The trial was initiated as a conventional trial in early triple-negative breast cancer but amended to an AED based on emerging data external to the trial suggesting that PD-L1 status could be a predictive biomarker. Operating characteristics are discussed including the concept of a minimal detectable difference, that is, the smallest observed treatment effect that would lead to a statistically significant result in at least one of the target populations at the interim or the final analysis, respectively, in the setting of AED.

Immunotherapies and Combination Strategies for Immuno-Oncology

The advent of novel immunotherapies in the treatment of cancers has dramatically changed the landscape of the oncology field. Recent developments in checkpoint inhibition therapies, tumor-infiltrating lymphocyte therapies, chimeric antigen receptor T cell therapies, and cancer vaccines have shown immense promise for significant advancements in cancer treatments. Immunotherapies act on distinct steps of immune response to augment the body's natural ability to recognize, target, and destroy cancerous cells. Combination treatments with immunotherapies and other modalities intend to activate immune response, decrease immunosuppression, and target signaling and resistance pathways to offer a more durable, long-lasting treatment compared to traditional therapies and immunotherapies as monotherapies for cancers. This review aims to briefly describe the rationale, mechanisms of action, and clinical efficacy of common immunotherapies and highlight promising combination strategies currently approved or under clinical development. Additionally, we will discuss the benefits and limitations of these immunotherapy approaches as monotherapies as well as in combination with other treatments.

Progress Toward Identifying Exact Proxies for Predicting Response to Immunotherapies

Clinical value and utility of checkpoint inhibitors, a drug class targeting adaptive immune suppression pathways (PD-1, PDL-1, and CTLA-4), is growing rapidly and maintains status of a landmark achievement in oncology. Their efficacy has transformed life expectancy in multiple deadly cancer types (melanoma, lung cancer, renal/urothelial carcinoma, certain colorectal cancers, lymphomas, etc.). Despite significant clinical development efforts, therapeutic indication of approved checkpoint inhibitors are not as wide as the oncology community and patients would like them to be, potentially bringing into question their universal efficacy across tumor histologies. With the main goal of expanding immunotherapy applications, identifying of biomarkers to accurately predict therapeutic response and treatment related side-effects are a paramount need in the field. Specificities surrounding checkpoint inhibitors in clinic, such as unexpected tumor response patterns (pseudo- and hyper-progression), late responders, as well as specific immune mediated toxicities, complicate the management of patients. They stem from the complexities and dynamics of the tumor/host immune interactions, as well as baseline tumor biology. Search for clinically effective biomarkers therefore calls for a holistic approach, rather than implementation of a single analyte. The goal is to achieve dynamic and comprehensive acquisition, analyses and interpretation of immunological and biologic information about the tumor and the immune system, and to compute these parameters into an actionable, maximally predictive value at the individual patient level. Limitation delaying swift incorporation of validated immuno-oncology biomarkers span from standardized biospecimens acquisition and processing, selection of proficient biomarker discovery and validation methods, to establishing multidisciplinary consortiums and data sharing platforms. Multi-disciplinary efforts have already yielded some approved (PDL-1 and MSI-status) and other advanced tests (TMB, neoantigen pattern, and TIL infiltration rate). Importantly, clinical trial taskforces now recognize the imperative of the biomarker-driven trial design and execution, to enable translating biomarker discoveries into the clinical setting. This will ensure we utilize the “conspiracy” between the peripheral and intra-tumoral dynamic markers in shaping responses to checkpoint blockade, for the ultimate patient benefit.

Analysis of immune-related signatures of lung adenocarcinoma identified two distinct subtypes: implications for immune checkpoint blockade therapy

Immune checkpoint blockade (ICB) therapies have revolutionized the treatment of human cancers including lung adenocarcinoma (LUAD). However, our understanding of the immune subtyping of LUAD and its association with clinical response of immune checkpoint inhibitor remains incomplete. Here we performed molecular subtyping and association analysis of LUAD from the Cancer Genome Atlas (TCGA) and validated findings from TCGA cohort in 9 independent validation cohorts. We conducted consensus molecular subtyping with nonnegative matrix factorization (NMF). Potential response of ICB therapy was estimated with Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. We identified 2 distinct subtypes of LUAD in TCGA cohort that were characterized by significantly different survival outcomes (i.e., high- and low-risk subtypes). The high-risk subtype was featured by lower TIDE score, upregulation of programmed death-ligand 1 (PD-L1) expression, and higher tumor mutation burden (TMB). The high-risk subtype also harbored significantly elevated cell cycle modulators CDK4/CDK6 and TP53 mutation. These observations were validated in 9 independent LUAD cohorts. Our findings suggest that immune checkpoint blockade therapy may be efficacious for high-risk subtype of LUAD patients.

Molecular markers related to immunosurveillance as predictive and monitoring tools in non-small cell lung cancer: recent accomplishments and future promises

Introduction: The landscape of systemic treatment options for lung cancer has rapidly evolved with the emergence of immunomodulatory agents such as neutralizing antibodies targeting the programmed cell death protein 1 (PD-1) and its ligand (PD-L1). Another major breakthrough was the introduction of biomarkers, such as PD-L1 expression and tumor mutational burden (TMB), predicting response to immunotherapy. However, markers for monitoring treatment response are still lacking.Areas covered: PD-L1 and TMB represent static pre-treatment evaluations. Dynamic biomarkers are required, along with static ones, to accurately predict and monitor immunotherapy response and to discriminate between responders and non-responders early in the course of treatment. The tumor immune contexture offers potential candidates that can be tested through the liquid biopsy approach, such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), exosomes, microRNAs (miRNAs), circular RNAs (circRNAs), RNA splice variants, and immune cell subsets.Expert opinion: A holistic approach combining information from tissue at the time of diagnosis and serial liquid biopsy data could lead to a novel combinatorial biomarker panel with enhanced treatment monitoring potential. Incorporating information from additional parts of the tumor-host ecosystem, such as metabolic markers and the microbiome is expected to provide added value to this strategy.