Interpretable systems biomarkers predict response to immune-checkpoint inhibitors.
Ontology highlight
ABSTRACT:
SUBMITTER: Lapuente-Santana O
PROVIDER: S-EPMC8369166 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Nivolumab is a fully human immunoglobulin (Ig) G4 antibody that selectively inhibits the programmed death 1 (PD-1) immune checkpoint molecule, and has recently been launched for the treatment of renal cell cancer (RCC) in Japan. Based on its promising anti-tumor efficacy and manageable safety profile demonstrated in the phase III Checkmate 025 trial, nivolumab therapy is rapidly being introduced in metastatic RCC clinical practice. The phase Ia study of atezolizumab, which is a humanized anti-PD-ligand 1 (PD-L1) monoclonal IgG1 antibody, also demonstrated excellent treatment results. The identification of biomarkers to predict the response and side-effects of checkpoint inhibitor therapy is thus urgently needed. In this review, we introduce the current candidate biomarkers of immune checkpoint inhibitor therapy. Based on the mechanism of efficacy, the number of neoantigens and expression of major histocompatibility complex molecules are strong candidate biomarkers. Despite the various interference factors, PD-L1 expression can be considered a potential biomarker. In terms of clinical factors, serum clinical factors and severity of adverse events are examined. Although further implementation in prospective studies is necessary, if validated, these biomarkers can be utilized to measure therapeutic response and design treatment strategies for metastatic RCC.
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Project description:Immune checkpoint inhibitors (ICPIs) have revolutionized the treatment paradigm of a wide range of malignancies with durable responses seen in even advanced, refractory cancers. Unfortunately, only a small proportion of patients with cancer derive meaningful benefit to ICPI therapy, and its use is also limited by significant immune and financial toxicities. Thus, there is a critical need for the development of biomarkers to reliably predict response to ICPI therapy. Only a few biomarkers are validated and approved for use with currently Food and Drug administration (FDA)-approved ICPIs. The development and broad application of biomarkers is limited by the lack of complete understanding of the complex interactions of tumor-host environment, the effect of immunotherapies on these already complex interactions, a lack of standardization and interpretation of biomarker assays across tumor types. Despite these challenges, the field of identifying predictive biomarkers is evolving at an unprecedented pace leaving the clinician responsible for identifying the patients that may derive optimal benefit from ICPIs. In this review, we provide clinicians with a current and practical update on the key, clinically relevant biomarkers of response to ICPIs. We categorize the current and emerging biomarkers of response to ICPIs in four major categories that govern anticancer response-the inflamed tumor, tumor antigens, immune suppression, and overall host environment.
| S-EPMC8267267 | biostudies-literature
Project description:Gastrointestinal (GI) cancers account for a large proportion of cancer deaths worldwide and pose a major public health challenge. Immunotherapy is considered to be one of the prominent and successful approaches in cancer treatment in recent years. Among them, immune checkpoint inhibitor (ICI) therapy, has received widespread attention, and many clinical findings support the feasibility of ICIs, with sustained responses and significantly prolonged lifespan observed in a wide range of tumors. However, patients treated with ICIs have not fully benefited, and therefore, the identification and development of biomarkers for predicting ICI treatment response have received further attention and exploration. From tumor genome to molecular interactions in the tumor microenvironment, and further expanding to circulating biomarkers and patient characteristics, the exploration of biomarkers is evolving with high-throughput sequencing as well as bioinformatics. More large-scale prospective and specific studies are needed to explore biomarkers in GI cancers. In this review, we summarize the known biomarkers used in ICI therapy for GI tumors. In addition, some ICI biomarkers applied to other tumors are included to provide insights and further validation for GI tumors. Moreover, we present single-cell analysis and machine learning approaches that have emerged in recent years. Although there are no clear applications yet, it can be expected that these techniques will play an important role in the application of biomarker prediction.
| S-EPMC8790411 | biostudies-literature
Project description:Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, only some patients respond to ICIs, and current biomarkers for ICI efficacy have limited performance. Here, we devised an interpretable machine learning (ML) model trained using patient-specific cell-cell communication networks (CCNs) decoded from the patient's bulk tumor transcriptome. The model could (i) predict ICI efficacy for patients across four cancer types (median AUROC: 0.79) and (ii) identify key communication pathways with crucial players responsible for patient response or resistance to ICIs by analyzing more than 700 ICI-treated patient samples from 11 cohorts. The model prioritized chemotaxis communication of immune-related cells and growth factor communication of structural cells as the key biological processes underlying response and resistance to ICIs, respectively. We confirmed the key communication pathways and players at the single-cell level in patients with melanoma. Our network-based ML approach can be used to expand ICIs' clinical benefits in cancer patients.
| S-EPMC10830106 | biostudies-literature
Project description:The mechanisms underlying the resistance to immune checkpoint inhibitors (ICIs) therapy in metastatic urothelial carcinoma (mUC) patients are not clear. It is of great significance to discern mUC patients who could benefit from ICI therapy in clinical practice. In this study, we performed machine learning method and selected 10 prognostic genes for constructing the immunotherapy response nomogram for mUC patients. The calibration plot suggested that the nomogram had an optimal agreement with actual observations when predicting the 1- and 1.5-year survival probabilities. The prognostic nomogram had a favorable discrimination of overall survival of mUC patients, with area under the curve values of 0.815, 0.752, and 0.805 for ICI response (ICIR) prediction in the training cohort, testing cohort, and combined cohort, respectively. A further decision curve analysis showed that the prognostic nomogram was superior to either mutation burden or neoantigen burden for overall survival prediction when the threshold probability was >0.35. The immune infiltrate analysis indicated that the low ICIR-Score values in mUC patients were significantly related to CD8+ T cell infiltration and immune checkpoint-associated signatures. We also identified differentially mutated genes, which could act as driver genes and regulate the response to ICI therapy. In conclusion, we developed and validated an immunotherapy-responsive nomogram for mUC patients, which could be conveniently used for the estimate of ICI response and the prediction of overall survival probability for mUC patients.
| S-EPMC7477044 | biostudies-literature
Project description:Anti-programmed cell death protein 1 (PD-1) agents have become the standard of care for platinum-refractory recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) and are currently being evaluated in various disease settings. However, despite the gain in overall survival seen in some of the clinical trials, the majority of patients display primary resistance and do not benefit from these agents. Taking into consideration the potentially severe immune-related toxicities and their high cost, the search for predictive biomarkers of response is crucial. Besides Programmed death ligand-1 (PD-L1) expression, other biomarkers such as immune infiltration, tumor mutational burden or immune-gene expression profiling have been explored, but none of them has been validated in this disease. Among these, the microbiota has recently garnered tremendous interest since it has proven to influence the efficacy of PD-1 blockade in some tumor types. With the accumulating evidence on the effect of the microbiota in HNSCC tumorigenesis and progression, the study of its potential role as a predictive immune biomarker is warranted. This review examines the available evidence on emerging immune predictive biomarkers of response to anti-PD-1/PD-L1 therapy in HNSCC, introducing the microbiota and its potential use as a predictive immune biomarker in this disease.
| S-EPMC6336003 | biostudies-literature
Project description:BACKGROUND:Immune checkpoint inhibitors (ICIs), targeting CTLA-4 or PD-1 molecules, have shown impressive therapeutic results. However, only 20-40% of advanced melanoma patients have durable responses to ICI, and these positive effects must be balanced against severe off-target immune toxicity and high costs. This urges the development of predictive biomarkers for ICI response to select patients with likely clinical benefit from treatment. Although many candidate biomarkers exist, a systematic overview of biomarkers and their usefulness is lacking. OBJECTIVES:Here, we systematically review the current literature of clinical data of ICI treatment to provide an overview of candidate predictive biomarkers for ICI in melanoma patients. METHODS:To identify studies on biomarkers for clinical response or survival to ICI therapy in melanoma patients, we performed a systematic search in OVID MEDLINE and retrieved 429 publications, of which 67 met the eligibility criteria. RESULTS:Blood and genomic biomarkers were mainly studied for CTLA-4 ICI, while tumor tissue markers were analyzed for both CTLA-4 and PD-1 ICI. Blood cytology and soluble factors correlated more frequently to overall survival (OS) than to response, indicating their prognostic rather than predictive nature. Systemic T-cell response and regulation markers correlated to response, but progression-free survival or OS were not analyzed. Tumor tissue analyses revealed response correlations with mutational load, neoantigen load, immune-related gene expression, and CD8+ T-cell infiltration at the invasive margin. The predictive value of PD-L1 varied, possibly due to the influence of T-cell infiltration on tumor PD-L1 expression. Genomic biomarker studies addressed CTLA-4 and other immune-related genes. CONCLUSION:This review outlines all published biomarkers for ICI therapy and highlights potential candidate markers for future research. To date, PD-L1 is the best studied biomarker for PD-1 ICI response. The most promising candidate predictive biomarkers for ICI response have not yet been identified. Variations in outcome parameters, statistical power, and analyses hampered summary of the results. Further investigation of biomarkers in larger patient cohorts using standardized objectives and outcome measures is recommended.
| S-EPMC5625582 | biostudies-literature
Project description:Cancer growth and progression are associated with immune suppression. Cancer cells have the ability to activate different immune checkpoint pathways that harbor immunosuppressive functions. Monoclonal antibodies that target immune checkpoints provided an immense breakthrough in cancer therapeutics. Among the immune checkpoint inhibitors, PD-1/PD-L1 and CTLA-4 inhibitors showed promising therapeutic outcomes, and some have been approved for certain cancer treatments, while others are under clinical trials. Recent reports have shown that patients with various malignancies benefit from immune checkpoint inhibitor treatment. However, mainstream initiation of immune checkpoint therapy to treat cancers is obstructed by the low response rate and immune-related adverse events in some cancer patients. This has given rise to the need for developing sets of biomarkers that predict the response to immune checkpoint blockade and immune-related adverse events. In this review, we discuss different predictive biomarkers for anti-PD-1/PD-L1 and anti-CTLA-4 inhibitors, including immune cells, PD-L1 overexpression, neoantigens, and genetic and epigenetic signatures. Potential approaches for further developing highly reliable predictive biomarkers should facilitate patient selection for and decision-making related to immune checkpoint inhibitor-based therapies.
| S-EPMC6292890 | biostudies-literature
Project description:PurposeAppropriate preclinical mouse models are needed to evaluate the response to immunotherapeutic agents. Immunocompetent mouse models have rarely been reported for gastric cancer. Thus, we investigated immunophenotypes and responses to immune checkpoint inhibitor (ICI) in immunocompetent mouse models using various murine gastric cancer cell lines.Materials and methodsWe constructed subcutaneous syngeneic tumors with murine gastric cancer cell lines, YTN3 and YTN16, in C57BL/6J mice. Mice were intraperitoneally treated with IgG isotype control or an anti-programmed death-ligand 1 (PD-L1) neutralizing antibody. We used immunohistochemistry to evaluate the tumor-infiltrating immune cells of formalin-fixed paraffin-embedded mouse tumor tissues. We compared the protein and RNA expression between YTN3 and YTN16 cell lines using a mouse cytokine array and RNA sequencing.ResultsThe mouse tumors revealed distinct histological and molecular characteristics. YTN16 cells showed upregulation of genes and proteins related to immunosuppression, such as Ccl2 (CCL2) and Csf1 (M-CSF). Macrophages and exhausted T cells were more enriched in YTN16 tumors than in YTN3 tumors. Several YTN3 tumors were completely regressed by the PD-L1 inhibitor, whereas YTN16 tumors were unaffected. Although treatment with a PD-L1 inhibitor increased infiltration of T cells in both the tumors, the proportion of exhausted immune cells did not decrease in the non-responder group.ConclusionWe confirmed the histological and molecular features of cancer cells with various responses to ICI. Our models can be used in preclinical research on ICI resistance mechanisms to enhance clinical efficacy.
| S-EPMC9873335 | biostudies-literature
Project description:Immune checkpoint inhibitors (ICIs) have revolutionized the treatment landscape of non-small cell lung cancer (NSCLC), either used in monotherapy or in combination with chemotherapy. While some patients achieve durable responses, some will not get benefit from this treatment. Early identification of non- responder patients could avoid unnecessary treatment, potentially serious immune-related adverse events and reduce treatment costs. PD-L1 expression using immunohistochemistry is the only approved biomarker for the selection of patients that can benefit from immunotherapy. However, application of PD-L1 as a biomarker of treatment efficacy shows many deficiencies probably due to the complexity of the tumor microenvironment and the technical limitations of the samples. Thus, there is an urgent need to find other biomarkers, ideally blood biomarkers to help us to identify different subgroups of patients in a minimal invasive way. In this review, we summarize the emerging blood-based markers that could help to predict the response to ICIs in NSCLC.
| S-EPMC9181575 | biostudies-literature