Dataset Information

KRAS-mutant non-small cell lung cancer: Converging small molecules and immune checkpoint inhibition.

ABSTRACT: KRAS is the most frequent oncogene in non-small cell lung cancer (NSCLC), a molecular subset characterized by historical disappointments in targeted treatment approaches such as farnesyl transferase inhibition, downstream MEK inhibition, and synthetic lethality screens. Unlike other important mutational subtypes of NSCLC, preclinical work supports the hypothesis that KRAS mutations may be vulnerable to immunotherapy approaches, an efficacy associated in particular with TP53 co-mutation. In this review we detail reasons for previous failures in KRAS-mutant NSCLC, evidence to suggest that KRAS mutation is a genetic marker of benefit from immune checkpoint inhibition, and emerging direct inhibitors of K-Ras which will soon be combined with immunotherapy during clinical development. With signs of real progress in this subgroup of unmet need, we anticipate that KRAS mutant NSCLC will be the most important molecular subset of cancer to evaluate the combination of small molecules and immune checkpoint inhibitors (CPI).

SUBMITTER: Adderley H

PROVIDER: S-EPMC6444074 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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KRAS-mutant non-small cell lung cancer: Converging small molecules and immune checkpoint inhibition.

Adderley Helen H Blackhall Fiona H FH Lindsay Colin R CR

EBioMedicine 20190307

KRAS is the most frequent oncogene in non-small cell lung cancer (NSCLC), a molecular subset characterized by historical disappointments in targeted treatment approaches such as farnesyl transferase inhibition, downstream MEK inhibition, and synthetic lethality screens. Unlike other important mutational subtypes of NSCLC, preclinical work supports the hypothesis that KRAS mutations may be vulnerable to immunotherapy approaches, an efficacy associated in particular with TP53 co-mutation. In this ...[more]

PMID: 30852159

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Project description:BackgroundImmune checkpoint blockade (ICB) therapy has brought remarkable clinical benefits to patients with advanced non-small cell lung carcinoma (NSCLC). However, the prognosis remains largely variable.MethodsThe profiles of immune-related genes for patients with NSCLC were extracted from TCGA database, ImmPort dataset, and IMGT/GENE-DB database. Coexpression modules were constructed using WGCNA and 4 modules were identified. The hub genes of the module with the highest correlations with tumor samples were identified. Then integrative bioinformatics analyses were performed to unveil the hub genes participating in tumor progression and cancer-associated immunology of NSCLC. Cox regression and Lasso regression analyses were conducted to screen prognostic signature and to develop a risk model.ResultsFunctional analysis showed that immune-related hub genes were involved in the migration, activation, response, and cytokine-cytokine receptor interaction of immune cells. Most of the hub genes had a high frequency of gene amplifications. MASP1 and SEMA5A presented the highest mutation rate. The ratio of M2 macrophages and naïve B cells revealed a strong negative association while the ratio of CD8 T cells and activated CD4 memory T cells showed a strong positive association. Resting mast cells predicted superior overall survival. Interactions including protein-protein, lncRNA and transcription factor interactions were analyzed and 9 genes were selected by LASSO regression analysis to construct and verify a prognostic signature. Unsupervised hub genes clustering resulted in 2 distinct NSCLC subgroups. The TIDE score and the drug sensitivity of gemcitabine, cisplatin, docetaxel, erlotinib and paclitaxel were significantly different between the 2 immune-related hub gene subgroups.ConclusionsThese findings suggested that our immune-related genes can provide clinical guidance for the diagnosis and prognosis of different immunophenotypes and facilitate the management of immunotherapy in NSCLC.

Project description:BackgroundThe development of immune checkpoint inhibitors (ICIs) is a revolutionary milestone in the field of immune-oncology. However, the low response rate is the major problem of ICI treatment. The recent studies showed that response rate to single-agent programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibition in unselected non-small cell lung cancer (NSCLC) patients is 25% so that researchers defined several biomarkers to predict the response of immunotherapy in ICIs treatment. Common biomarkers like tumor mutational burden (TMB) and PD-L1 expression have several limitations, such as low accuracy and inadequately validated cutoff value.MethodsTwo published and an unpublished ICIs treatment NSCLC cohorts with 129 patients were collected and divided into a training cohort (n = 53), a validation cohort (n = 22), and two independent test cohorts (n = 34 and n = 20). We identified six immune-related pathways whose mutational status was significantly associated with overall survival after ICIs treatment. Then these pathways mutational status combined with TMB, PD-L1 expression and intratumor heterogeneity were incorporated to build a Bayesian-regularization neural networks (BRNN) model to predict the ICIs treatment response.ResultsWe firstly proved that TMB, PD-L1, and mutant-allele tumor heterogeneity (MATH) were independent biomarkers. The survival analysis of six immune-related pathways revealed the mutational status could distinguish overall survival after ICIs treatment. When predicting immunotherapy efficacy, the overall accuracy of area under curve (AUC) in validation cohort reaches 0.85, outperforming previous predictors in either sensitivity or specificity. And the AUC in two independent test cohorts reach 0.74 and 0.80.ConclusionWe developed a pathway-model that could predict the efficacy of ICIs in NSCLC patients. Our study made a significant contribution to solving the low prediction accuracy of immunotherapy of single biomarker. With the accumulation of larger data sets, further studies are warranted to refine the predictive performance of the approach.

Dataset Information

KRAS-mutant non-small cell lung cancer: Converging small molecules and immune checkpoint inhibition.

Publications

KRAS-mutant non-small cell lung cancer: Converging small molecules and immune checkpoint inhibition.

Similar Datasets

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