Machine Learning Predictor of Immune Checkpoint Blockade Response in Gastric Cancer.
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ABSTRACT:
SUBMITTER: Sung JY
PROVIDER: S-EPMC9265060 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Gastric cancer (GC) is one of the most important malignancies worldwide because of its high incidence and mortality. The very low survival rates are mainly related to late diagnosis and limited treatment options. GC is the final clinical outcome of a stepwise process that starts with a chronic and sustained inflammatory reaction mounted in response to Helicobacter pylori infection. The bacterium modulates innate and adaptive immunity presumably as part of the strategies to survive, which favors the creation of an immunosuppressive microenvironment that ultimately facilitates GC progression. T-cell exhaustion, which is characterized by elevated expression of immune checkpoint (IC) proteins, is one of the most salient manifestations of immunosuppressive microenvironments. It has been consistently demonstrated that the tumor-immune microenvironment(TIME)-exhausted phenotype can be reverted by blocking ICs with monoclonal antibodies. Although these therapies are associated with long-lasting response rates, only a subset of patients derive clinical benefit, which varies according to tumor site. The search for biomarkers to predict the response to IC inhibition is a matter of intense investigation as this may contribute to maximize disease control, reduce side effects, and minimize cost. The approval of pembrolizumab for its use in GC has rocketed immuno-oncology research in this cancer type. In this review, we summarize the current knowledge centered around the immune contexture and recent findings in connection with IC inhibition in GC.
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Project description:Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment1. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy2. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery3 were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.
| S-EPMC8791834 | biostudies-literature
Project description:: More than 1.6 million new cases of cancer will be diagnosed in the U.S. in 2016, resulting in more than 500,000 deaths. Although chemotherapy has been the mainstay of treatment in advanced cancers, immunotherapy development, particularly with PD-1 inhibitors, has changed the face of treatment for a number of tumor types. One example is the subset of tumors characterized by mismatch repair deficiency and microsatellite instability that are highly sensitive to PD-1 blockade. Hereditary forms of cancer have been noted for more than a century, but the molecular changes underlying mismatch repair-deficient tumors and subsequent microsatellite unstable tumors was not known until the early 1990s. In this review article, we discuss the history and pathophysiology of mismatch repair, the process of testing for mismatch repair deficiency and microsatellite instability, and the role of immunotherapy in this subset of cancers.Mismatch repair deficiency has contributed to our understanding of carcinogenesis for the past 2 decades and now identifies a subgroup of traditionally chemotherapy-insensitive solid tumors as sensitive to PD-1 blockade. This article seeks to educate oncologists regarding the nature of mismatch repair deficiency, its impact in multiple tumor types, and its implications for predicting the responsiveness of solid tumors to immune checkpoint blockade.
| S-EPMC5061538 | biostudies-other
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:An effective antitumor immune response requires interaction between cells of the adaptive and innate immune system. Three key elements are required: generation of activated tumor-directed T cells, infiltration of activated T cells into the tumor microenvironment, and killing of tumor cells by activated T cells. Tumor immune evasion can occur as a result of the disruption of each of these three key T cell activities, resulting in three distinct cancer-immune phenotypes. The immune inflamed phenotype, characterized by the presence of a robust tumor immune infiltrate, suggests impaired activated T cell killing of tumor cells related to the presence of inhibitory factors. Programmed death receptor-1 (PD-1) is an inhibitory transmembrane protein expressed on T cells, B cells, and NK cells. The interaction between PD-1 and its ligands (PD-L1/L2) functions as an immune checkpoint against unrestrained cytotoxic T effector cell activity-it promotes peripheral T effector cell exhaustion and conversion of T effector cells to immunosuppressive T regulatory (Treg) cells. Immune checkpoint inhibitors, which block the PD-1/PD-L1 axis and reactivate cytotoxic T effector cell function, are actively being investigated for the treatment of breast cancer.
| S-EPMC6061922 | biostudies-literature
Project description:Despite neoadjuvant/conversion chemotherapy, the prognosis of cT4a/bN+ gastric cancer is poor. Immune checkpoint inhibitors (ICIs) and antiangiogenic agents have shown activity in late-stage gastric cancer, but their efficacy in the neoadjuvant/conversion setting is unclear. In this single-armed, phase II, exploratory trial (NCT03878472), we evaluate the efficacy of a combination of ICI (camrelizumab), antiangiogenesis (apatinib), and chemotherapy (S-1 ± oxaliplatin) for neoadjuvant/conversion treatment of cT4a/bN+ gastric cancer. The primary endpoints are pathological responses and their potential biomarkers. Secondary endpoints include safety, objective response, progression-free survival, and overall survival. Complete and major pathological response rates are 15.8% and 26.3%. Pathological responses correlate significantly with microsatellite instability status, PD-L1 expression, and tumor mutational burden. In addition, multi-omics examination reveals several putative biomarkers for pathological responses, including RREB1 and SSPO mutation, immune-related signatures, and a peripheral T cell expansion score. Multi-omics also demonstrates dynamic changes in dominant tumor subclones, immune microenvironments, and T cell receptor repertoires during neoadjuvant immunotherapy. The toxicity and post-surgery complications are limited. These data support further validation of ICI- and antiangiogenesis-based neoadjuvant/conversion therapy in large randomized trials and provide candidate biomarkers.
| S-EPMC9810618 | biostudies-literature
Project description:Cancer-associated fibroblasts (CAFs) are an integral component of the tumor microenvironment (TME). Most CAFs shape the TME toward an immunosuppressive milieu and attenuate the efficacy of immune checkpoint blockade (ICB) therapy. However, the detailed mechanism of how heterogeneous CAFs regulate tumor response to ICB therapy has not been defined. Here, we show that a recently defined CAF subset characterized by the expression of Meflin, a glycosylphosphatidylinositol-anchored protein marker of mesenchymal stromal/stem cells, is associated with survival and favorable therapeutic response to ICB monotherapy in patients with non-small cell lung cancer (NSCLC). The prevalence of Meflin-positive CAFs was positively correlated with CD4-positive T-cell infiltration and vascularization within non-small cell lung cancer tumors. Meflin deficiency and CAF-specific Meflin overexpression resulted in defective and enhanced ICB therapy responses in syngeneic tumors in mice, respectively. These findings suggest the presence of a CAF subset that promotes ICB therapy efficacy, which adds to our understanding of CAF functions and heterogeneity.
| S-EPMC8897596 | biostudies-literature
Project description:Despite impressive durable responses, immune checkpoint inhibitors do not provide a long-term benefit to the majority of patients with cancer. Understanding genomic correlates of response and resistance to checkpoint blockade may enhance benefits for patients with cancer by elucidating biomarkers for patient stratification and resistance mechanisms for therapeutic targeting. Here we review emerging genomic markers of checkpoint blockade response, including those related to neoantigens, antigen presentation, DNA repair, and oncogenic pathways. Compelling evidence also points to a role for T cell functionality, checkpoint regulators, chromatin modifiers, and copy-number alterations in mediating selective response to immune checkpoint blockade. Ultimately, efforts to contextualize genomic correlates of response into the larger understanding of tumor immune biology will build a foundation for the development of novel biomarkers and therapies to overcome resistance to checkpoint blockade.
| S-EPMC6599710 | biostudies-literature
Project description:BackgroundOropharyngeal cancer (OPC) exhibits diverse immunological properties; however, their implications for immunotherapy are unknown.MethodsWe analysed 37 surgically resected and nine recurrent or metastatic anti-programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1)-treated OPC tumours. OPCs were classified into immune-rich (IR), mesenchymal (MS) and xenobiotic (XB) subtypes based on RNA-sequencing data.ResultsAll IR type tumours were human papillomavirus (HPV) positive, most XB types were HPV negative, and MS types showed mixed HPV status. The IR type showed an enriched T cell exhaustion signature with PD-1+ CD8+ T cells and type I macrophages infiltrating the tumour nest on multiplex immunohistochemistry. The MS type showed an exclusion of CD8+ T cells from the tumour nest and high MS and tumour growth factor-β signatures. The XB type showed scant CD8+ T cell infiltration and focal CD73 expression. The IR type was associated with a favourable response signature during anti-PD-1/PD-L1 therapy and showed a high APOBEC mutation signature, whereas the MS and XB types showed resistance signature upregulation. Among anti-PD-1/PD-L1-treated OPC patients, the IR type showed a favourable clinical response (3/4 patients), whereas the XB type showed early progression (3/3 patients).ConclusionOur analysis classified OPCs into three subtypes with distinct immune microenvironments that are potentially related to the response to anti-PD-1/PD-L1 therapy.
| S-EPMC7251088 | biostudies-literature
Project description:BACKGROUND:Despite the great achievements made in immune-checkpoint-blockade (ICB) in cancer therapy, there are no effective predictive biomarkers in gastrointestinal (GI) cancer. METHODS:This study included 93 metastatic GI patients treated with ICBs. The first cohort comprising 73?GI cancer patients were randomly assigned into discovery (n=44) and validation (n=29) cohorts. Comprehensive genomic profiling was performed on all samples to determine tumor mutational burden (TMB) and copy-number alterations (CNAs). A subset of samples was collected for RNA immune oncology (IO) panel sequencing, microsatellite instability (MSI)/mismatch repair and program death ligand 1 (PD-L1) expression evaluation. In addition, 20 gastric cancer (GC) patients were recruited as the second validation cohort. RESULTS:In the first cohort of 73?GI cancer patients, a lower burden of CNA was observed in patients with durable clinical benefit (DCB). In both the discovery (n=44) and validation (n=29) subsets, lower burden of CNA was associated with an improved clinical benefit and better overall survival (OS). Efficacy also correlated with a higher TMB. Of note, a combinatorial biomarker of TMB and CNA may better stratify DCB patients from ICB treatment, which was further confirmed in the second validation cohort of 20 GC patients. Finally, patients with lower burden of CNA revealed increased immune signatures in our cohort and The Cancer Genome Atlas data sets as well. CONCLUSIONS:Our results suggest that the burden of CNA may have superior predictive value compared with other signatures, including PD-L1, MSI and TMB. The joint biomarker of CNA burden and TMB may better stratify DCB patients, thereby providing a rational choice for GI patients treated with ICBs.
| S-EPMC7430454 | biostudies-literature