Project description:Immunotherapy has improved the prognosis of patients with advanced non-small cell lung
cancer (NSCLC), but only a small subset of patients achieved clinical benefit. The purpose of our study was to integrate multidimensional data using a machine learning method to predict the therapeutic efficacy of immune checkpoint inhibitors (ICIs) monotherapy in patients with advanced NSCLC.The authors retrospectively enrolled 112 patients with stage IIIB-IV NSCLC receiving ICIs monotherapy. The random forest (RF) algorithm was used to establish efficacy prediction models based on five different input datasets, including precontrast computed tomography (CT) radiomic data, postcontrast CT radiomic data, combination of the two CT radiomic data, clinical data, and a combination of radiomic and clinical data. The 5-fold cross-validation was used to train and test the random forest classifier. The performance of the models was assessed according to the area under the curve (AUC) in the receiver operating characteristic (ROC) curve. Among these models(RF MLP LR XGBoost), our reproduced onnx models have better performance, especially for random forest. The response variable with a value (1/0) indicates the (efficacy/inefficacy) of PD-1/PD-L1 monotherapy in patients with advanced NSCLC
Project description:Inactivating mutations in LKB1/STK11 are present in ~20% of non-small cell lung cancers (NSCLC) and portend poor response to anti-PD-1 immunotherapy in patients and genetically engineered mouse model (GEMMs). Here, we sought to uncover the basis for immunotherapy resistance of these tumors and to define strategies that overcome this barrier. Whereas high tumor mutational burden (TMB) often correlates with response to anti-PD1 treatment, we found that LKB1-deficient NSCLCs from non-smokers and GEMMs exhibited striking elevations in nonsynonymous mutations compared to LKB1 wildtype tumors. Correspondingly, LKB1 mutant NSCLC cell lines showed defects in both replication dependent and independent double-strand DNA break (DSB) repair, which were reversed upon LKB1 re-expression.
Project description:Inactivating mutations in LKB1/STK11 are present in ~20% of non-small cell lung cancers (NSCLC) and portend poor response to anti-PD-1 immunotherapy in patients and genetically engineered mouse model (GEMMs). Here, we sought to uncover the basis for immunotherapy resistance of these tumors and to define strategies that overcome this barrier. Whereas high tumor mutational burden (TMB) often correlates with response to anti-PD1 treatment, we found that LKB1-deficient NSCLCs from non-smokers and GEMMs exhibited striking elevations in nonsynonymous mutations compared to LKB1 wildtype tumors. Correspondingly, LKB1 mutant NSCLC cell lines showed defects in both replication dependent and independent double-strand DNA break (DSB) repair, which were reversed upon LKB1 re-expression.
2021-03-17 | GSE137244 | GEO
Project description:Gut resistome of NSCLC patients treated with immunotherapy
Project description:The tumor microenvironment (TME) profoundly influences responses to immune checkpoint blockade (ICB) therapies, however characterizing its complexity has been challenging. While the immune-inflamed, immune-excluded, and immune-desert T cell immunophenotypes are commonly used to classify the TME, their association with clinical outcomes to ICBs remains inconsistent. Here we demonstrated that integrating T cell immunophenotypes and tumor mutational burden (TMB) enables a more precise stratification of tumors into five distinct subtypes: immune-inflamed phenotype with high TMB (TMB-H), immune-inflamed phenotype with low TMB (TMB-L), TMB-H excluded phenotype, TMB-L excluded phenotype, and desert phenotype. Subsequently, we revealed the underlying mechanisms of tumor resistance to ICBs of each phenotype within the novel classification and elucidate several combination treatment approaches aiming at overcoming these inherent resistance mechanisms. Our study suggested that tailored combination therapy regimens addressing distinct patterns of immune resistance in different TME subtypes hold promise for enhancing immunotherapy efficacy
Project description:Non-small cell lung cancers (NSCLCs) harboring KEAP1 mutations are characterized by worse overall outcomes and resistance to immunotherapy. Here, we identified a molecular mechanism by which KEAP1 targets EMSY for ubiquitin-mediated degradation to regulate homologous recombination repair (HRR) and anti-tumor immunity. Loss of KEAP1 in NSCLC induces stabilization of EMSY producing a BRCAness phenotype characterized by HRR defects and sensitivity to PARP inhibitors (PARPis). Defective HRR increases genomic instability leading to high tumor mutational burden (TMB), which prompts an innate immune response. Notably, EMSY accumulation also suppresses the type I interferon response and impairs innate immune signaling, fostering cancer immune evasion. Activation of the type I interferon response in the tumor microenvironment using a STING agonist results in the engagement of innate and adaptive immune signaling, impairing the growth of KEAP1-mutant tumors. Our results suggest that targeting the PARP and STING pathways, individually or in combination, represent a novel therapeutic strategy in NSCLC patients harboring alterations in KEAP1.
Project description:<p>The effectiveness of immunotherapy varies among patients with advanced non-small cell lung cancer (NSCLC). Here we investigated the baseline immune status in stage IV NSCLC patients treated with anti-PD-1 plus chemotherapy to understand the immune mechanisms and unveil systemic markers associated with treatment response. Responders had elevated frequencies of circulating T cells expressing CD69, TCF-1 and CXCR-3. In contrast, non-responders presented increased frequencies of CTLA-4, CD161 and IL-10 expressing CD4+ and CD8+ T cells. These systemic T cell immune profiles were mirrored in the tumor microenvironment of an independent cohort. Concurrent CTLA-4 and PD-1 blockade was able to reactivate an anti-tumor profile in T cells from non-responder patients, emphasizing the pivotal role of CTLA-4 in contributing to an immunosuppressive environment that hinders effective treatment in NSCLC. This work supports the implementation of personalized immunotherapies based on systemic immune biomarkers, offering a promising approach to enhance treatment outcomes in advanced NSCLC.</p>