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Deep neural network classification based on somatic mutations potentially predicts clinical benefit of immune checkpoint blockade in lung adenocarcinoma.

ABSTRACT: Although several biomarkers have been proposed to predict the response of patients with lung adenocarcinoma (LUAD) to immune checkpoint blockade (ICB) therapy, existing challenges such as test platform uniformity, cutoff value definition, and low frequencies restrict their effective clinical application. Here, we attempted to use deep neural networks (DNNs) based on somatic mutations to predict the clinical benefit of ICB to LUAD patients undergoing immunotherapy. We used DNNs to train and validate the predictive model in three cohorts. Kaplan-Meier estimates determined the overall survival (OS) and progression-free survival (PFS) between specific subgroups. Then, we performed a relevant analysis on the multiple-dimension data types including immune cell infiltration, programmed death receptor 1 ligand (PD-L1) expression, and tumor mutational burden (TMB) from cohorts of LUAD public database and immunotherapeutic patients. Two classification groups (C1 and C2) in the training and two validation sets were identified for the efficacy of ICB via the DNN algorithm. Patients in C1 showed remarkably long OS and PFS to programmed death 1 (PD-1) inhibitors. The C1 group was significantly associated with increased expression of immune cell infiltration, immune checkpoints, activated T-effectors, and interferon gamma signature. C1 group also exhibited significantly higher TMB, neoantigens, transversion, or transition than the C2 group. This work provides novel insights that classification of DNNs using somatic mutations in LUAD could serve as a potentially predictive approach in developing a strategy for anti-PD-1/PD-L1 immunotherapy.

SUBMITTER: Peng J

PROVIDER: S-EPMC7051190 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: In lung adenocarcinoma (LUAD), the predictive role of immune-related subgroup classification in immune checkpoint blockade (ICB) therapy remains largely incomplete. Methods: Transcriptomics analysis was performed to evaluate the association between immune landscape and ICB therapy in lung adenocarcinoma and the associated underlying mechanism. First, the least absolute shrinkage and selection operator (LASSO) algorithm and K-means algorithm were used to identify immune related subgroups for LUAD cohort from the Cancer Genome Atlas (TCGA) database (n = 572). Second, the immune associated signatures of the identified subgroups were characterized by evaluating the status of immune checkpoint associated genes and the immune cell infiltration. Then, potential responses to ICB therapy based on the aforementioned immune related subgroup classification were evaluated via tumor immune dysfunction and exclusion (TIDE) algorithm analysis, and survival analysis and further Cox proportional hazards regression analysis were also performed for LUAD. In the end, gene set enrichment analysis (GSEA) was performed to explore the metabolic mechanism potentially responsible for immune related subgroup clustering. Additionally, two LUAD cohorts from the Gene Expression Omnibus (GEO) database were used as validation cohort. Results: A total of three immune related subgroups with different immune-associated signatures were identified for LUAD. Among them, subgroup 1 with higher infiltration scores for effector immune cells and immune checkpoint associated genes exhibited a potential response to IBC therapy and a better survival, whereas subgroup 3 with lower scores for immune checkpoint associated genes but higher infiltration scores for suppressive immune cells tended to be insensitive to ICB therapy and have an unfavorable prognosis. GSEA revealed that the status of glucometabolic reprogramming in LUAD was potentially responsible for the immune-related subgroup classification. Conclusion: In summary, immune related subgroup clustering based on distinct immune associated signatures will enable us to screen potentially responsive LUAD patients for ICB therapy before treatment, and the discovery of metabolism associated mechanism is beneficial to comprehensive therapeutic strategies making involving ICB therapy in combination with metabolism intervention for LUAD.

Project description:IntroductionImmunotherapy has improved outcomes for patients with non-small cell lung cancer (NSCLC), yet durable clinical benefit (DCB) is experienced in only a fraction of patients. Here, we test the hypothesis that radiomics features from baseline pretreatment 18F-FDG PET/CT scans can predict clinical outcomes of NSCLC patients treated with checkpoint blockade immunotherapy.MethodsThis study included 194 patients with histologically confirmed stage IIIB-IV NSCLC with pretreatment PET/CT images. Radiomics features were extracted from PET, CT, and PET+CT fusion images based on minimum Kullback-Leibler divergence (KLD) criteria. The radiomics features from 99 retrospective patients were used to train a multiparametric radiomics signature (mpRS) to predict DCB using an improved least absolute shrinkage and selection operator (LASSO) method, which was subsequently validated in both retrospective (N = 47) and prospective test cohorts (N = 48). Using these cohorts, the mpRS was also used to predict progression-free survival (PFS) and overall survival (OS) by training nomogram models using multivariable Cox regression analyses with additional clinical characteristics incorporated.ResultsThe mpRS could predict patients who will receive DCB, with areas under receiver operating characteristic curves (AUCs) of 0.86 (95%CI 0.79-0.94), 0.83 (95%CI 0.71-0.94), and 0.81 (95%CI 0.68-0.92) in the training, retrospective test, and prospective test cohorts, respectively. In the same three cohorts, respectively, nomogram models achieved C-indices of 0.74 (95%CI 0.68-0.80), 0.74 (95%CI 0.66-0.82), and 0.77 (95%CI 0.69-0.84) to predict PFS and C-indices of 0.83 (95%CI 0.77-0.88), 0.83 (95%CI 0.71-0.94), and 0.80 (95%CI 0.69-0.91) to predict OS.ConclusionPET/CT-based signature can be used prior to initiation of immunotherapy to identify NSCLC patients most likely to benefit from immunotherapy. As such, these data may be leveraged to improve more precise and individualized decision support in the treatment of patients with advanced NSCLC.

Project description:Accurately detecting and examining lung nodules early is key in diagnosing lung cancers and thus one of the best ways to prevent lung cancer deaths. Radiologists spend countless hours detecting small spherical-shaped nodules in computed tomography (CT) images. In addition, even after detecting nodule candidates, a considerable amount of effort and time is required for them to determine whether they are real nodules. The aim of this paper is to introduce a high performance nodule classification method that uses three dimensional deep convolutional neural networks (DCNNs) and an ensemble method to distinguish nodules between non-nodules. In this paper, we use a three dimensional deep convolutional neural network (3D DCNN) with shortcut connections and a 3D DCNN with dense connections for lung nodule classification. The shortcut connections and dense connections successfully alleviate the gradient vanishing problem by allowing the gradient to pass quickly and directly. Connections help deep structured networks to obtain general as well as distinctive features of lung nodules. Moreover, we increased the dimension of DCNNs from two to three to capture 3D features. Compared with shallow 3D CNNs used in previous studies, deep 3D CNNs more effectively capture the features of spherical-shaped nodules. In addition, we use an alternative ensemble method called the checkpoint ensemble method to boost performance. The performance of our nodule classification method is compared with that of the state-of-the-art methods which were used in the LUng Nodule Analysis 2016 Challenge. Our method achieves higher competition performance metric (CPM) scores than the state-of-the-art methods using deep learning. In the experimental setup ESB-ALL, the 3D DCNN with shortcut connections and the 3D DCNN with dense connections using the checkpoint ensemble method achieved the highest CPM score of 0.910. The result demonstrates that our method of using a 3D DCNN with shortcut connections, a 3D DCNN with dense connections, and the checkpoint ensemble method is effective for capturing 3D features of nodules and distinguishing nodules between non-nodules.

Project description:PurposeDexamethasone, a uniquely potent corticosteroid, is frequently administered to patients with brain tumors to decrease tumor-associated edema, but limited data exist describing how dexamethasone affects the immune system systemically and intratumorally in patients with glioblastoma (GBM), particularly in the context of immunotherapy.Experimental designWe evaluated the dose-dependent effects of dexamethasone when administered with programmed cell death 1 (PD-1) blockade and/or radiotherapy in immunocompetent C57BL/6 mice with syngeneic GL261 and CT-2A GBM tumors. Clinically, the effect of dexamethasone on survival was evaluated in 181 patients with isocitrate dehydrogenase (IDH) wild-type GBM treated with PD-(L)1 blockade, with adjustment for relevant prognostic factors.ResultsDespite the inherent responsiveness of GL261 to immune checkpoint blockade, concurrent dexamethasone administration with anti-PD-1 therapy reduced survival in a dose-dependent manner. Concurrent dexamethasone also abrogated survival following anti-PD-1 therapy with or without radiotherapy in immune-resistant CT-2A models. Dexamethasone decreased T-lymphocyte numbers by increasing apoptosis, in addition to decreasing lymphocyte functional capacity. Myeloid and natural killer cell populations were also generally reduced by dexamethasone. Thus, dexamethasone appears to negatively affect both adaptive and innate immune responses. As a clinical correlate, a retrospective analysis of 181 consecutive patients with IDH wild-type GBM treated with PD-(L)1 blockade revealed poorer survival among those on baseline dexamethasone. Upon multivariable adjustment with relevant prognostic factors, baseline dexamethasone administration was the strongest predictor of poor survival [reference, no dexamethasone; <2 mg HR, 2.16; 95% confidence interval (CI), 1.30-3.68; P = 0.003 and ≥2 mg HR, 1.97; 95% CI, 1.23-3.16; P = 0.005].ConclusionsOur preclinical and clinical data indicate that concurrent dexamethasone therapy may be detrimental to immunotherapeutic approaches for patients with GBM.

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Deep neural network classification based on somatic mutations potentially predicts clinical benefit of immune checkpoint blockade in lung adenocarcinoma.

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