The role of pembrolizumab in the treatment of advanced non-small cell lung cancer

CT-based deep learning radiomics biomarker for programmed cell death ligand 1 expression in non-small cell lung cancer

BACKGROUND

Programmed cell death ligand 1 (PD-L1), as a reliable predictive biomarker, plays an important role in guiding immunotherapy of lung cancer. To investigate the value of CT-based deep learning radiomics signature to predict PD-L1 expression in non-small cell lung cancers(NSCLCs).

METHODS

259 consecutive patients with pathological confirmed NSCLCs were retrospectively collected and divided into the training cohort and validation cohort according to the chronological order. The univariate and multivariate analyses were used to build the clinical model. Radiomics and deep learning features were extracted from preoperative non-contrast CT images. After feature selection, Radiomics score (Rad-score) and deep learning radiomics score (DLR-score) were calculated through a linear combination of the selected features and their coefficients. Predictive performance for PD-L1 expression was evaluated via the area under the curve (AUC) of receiver operating characteristic, the calibration curves, and the decision curve analysis.

RESULTS

The clinical model based on Cytokeratin 19 fragment and lobulated shape obtained an AUC of 0.767(95% CI: 0.673-0.860) in the training cohort and 0.604 (95% CI:0.477-0.731) in the validation cohort. 11 radiomics features and 15 deep learning features were selected by LASSO regression. AUCs of the Rad-score were 0.849 (95%CI: 0.783-0.914) and 0.717 (95%CI: 0.607-0.826) in the training cohort and validation cohort, respectively. AUCs of DLR-score were 0.938 (95%CI: 0.899-0.977) and 0.818(95%CI:0.727-0.910) in the training cohort and validation cohort, respectively. AUCs of the DLR-score were significantly higher than those of the Rad-score and the clinical model.

CONCLUSION

The CT-based deep learning radiomics signature could achieve clinically acceptable predictive performance for PD-L1 expression, which showed potential to be a surrogate imaging biomarker or a complement of immunohistochemistry assessment.

CT-based radiomic phenotypes of lung adenocarcinoma: a preliminary comparative analysis with targeted next-generation sequencing

Objectives

This study aimed to explore the relationship between computed tomography (CT)-based radiomic phenotypes and genomic profiles, including expression of programmed cell death-ligand 1 (PD-L1) and the 10 major genes, such as epidermal growth factor receptor (EGFR), tumor protein 53 (TP53), and Kirsten rat sarcoma viral oncogene (KRAS), in patients with lung adenocarcinoma (LUAD).

Methods

In total, 288 consecutive patients with pathologically confirmed LUAD were enrolled in this retrospective study. Radiomic features were extracted from preoperative CT images, and targeted genomic data were profiled through next-generation sequencing. PD-L1 expression was assessed by immunohistochemistry staining (chi-square test or Fisher's exact test for categorical data and the Kruskal-Wallis test for continuous data). A total of 1,013 radiomic features were obtained from each patient's CT images. Consensus clustering was used to cluster patients on the basis of radiomic features.

Results

The 288 patients were classified according to consensus clustering into four radiomic phenotypes: Cluster 1 (n = 11) involving mainly large solid masses with a maximum diameter of 5.1 ± 2.0 cm; Clusters 2 and 3 involving mainly part-solid and solid masses with maximum diameters of 2.1 ± 1.4 cm and 2.1 ± 0.9 cm, respectively; and Cluster 4 involving mostly small ground-glass opacity lesions with a maximum diameter of 1.0 ± 0.9 cm. Differences in maximum diameter, PD-L1 expression, and TP53, EGFR, BRAF, ROS1, and ERBB2 mutations among the four clusters were statistically significant. Regarding targeted therapy and immunotherapy, EGFR mutations were highest in Cluster 2 (73.1%); PD-L1 expression was highest in Cluster 1 (45.5%).

Conclusion

Our findings provide evidence that CT-based radiomic phenotypes could non-invasively identify LUADs with different molecular characteristics, showing the potential to provide personalized treatment decision-making support for LUAD patients.

Expression of PD-L1, PD-L2, and IDO1 on tumor cells and density of CD8-positive tumor-infiltrating lymphocytes in early-stage lung adenocarcinoma according to histological subtype

PURPOSE

This study examined the expression of programmed cell death-ligand 1 (PD-L1), programmed cell death-ligand 2 (PD-L2), and indoleamine 2,3-dioxygenase-1 (IDO1) in tumor cells and cluster of differentiation 8 (CD8)-positive tumor-infiltrating lymphocytes (TILs) in early-stage lung adenocarcinoma according to histological subtypes.

METHODS

We evaluated PD-L1, PD-L2, and IDO1 expression in tumor cells and CD8-positive TILs in surgically resected specimens from 196 stage 0 or I lung adenocarcinoma patients by immunohistochemical staining. We also examined the relationships between the expression of PD-L1, PD-L2, and IDO1 in tumor cells and the density of CD8-positive TILs and clinical factors. Patients were divided into three groups: A, adenocarcinoma in situ and minimally invasive adenocarcinoma (N = 32); B, lepidic predominant invasive adenocarcinoma (IAD; LPA; N = 66); and C, IAD except for LPA (N = 98).

RESULTS

PD-L1 was expressed only in Group C, but not in Groups A or B. The positive ratio of PD-L2 was significantly higher in Group C (63.3%), and that of IDO1 was also significantly higher in Group C (65.3%). The density of CD8-positive TILs was significantly higher in Group C (45 ± 2.4). There was no significant difference between the positive ratios of PD-L2 and IDO1 and the density of CD8-positive TILs in Group A (50.0%, 21.9%, and 36 ± 4.1, respectively) or Group B (60.6%, 25.8%, and 44 ± 3.0, respectively).

CONCLUSIONS

No cases in Groups A and B expressed PD-L1. The expression of immune-related factors, especially PD-L1 and IDO1, was significantly associated with Group C. This is the first report of the detailed examination of PD-L1, PD-L2, IDO1, and CD8 expression in lung adenocarcinoma subtypes with lepidic predominant components. Our results could help identify patients who would benefit from perioperative immunotherapy.

[First-line Combination Immunotherapy in Advanced Non-small Cell Lung Cancer]

Programmed death 1/programmed death ligand 1 (PD-1/PD-L1) inhibitor has become one of the important treatment options for patients with advanced non-small cell lung cancer (NSCLC). However, only a small subset of patients with NSCLC can currently receive single-agent PD-1 inhibitors as first-line therapy, for the limitations of population selection exclude most patients from immuno-oncology (IO) monotherapy. In order to expand the candidate population for IO first-line treatment and make more newly diagnosed patients benefit from IO treatment, a series of studies are focusing on the combination of IO and other drugs in NSCLC. We reviewed the latest clinical data of IO first-line combination therapy in recent years, suggesting that on the basis of PD-1/PD-L1 inhibitors, combined with other IO, chemotherapy, anti-angiogenic drugs, targeted therapy or radiotherapy may produce synergistic anti-tumor effects. It is expected to benefit more newly diagnosed patients.
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Epidermal growth factor induces STAT1 expression to exacerbate the IFNr-mediated PD-L1 axis in epidermal growth factor receptor-positive cancers

The epidermal growth factor (EGF) receptor (EGFR) overexpressed in many cancers, including lung and head and neck cancers, and is involved in cancer cell progression and survival. PD-L1, increases in tumor cells to evade and inhibit CD8+ T cells, is a clinical immunotherapeutic target. This study investigated the molecular mechanism of EGF on regulating PD-L1 in EGFR-positive cancers and determined potential agents to reduce PD-L1 expression. RNA sequencing (RNAseq) and bioinformatics analysis were performed to determine potential driver genes that regulate PD-L1 in tumor cells-derived tumorspheres which mimicking cancer stem cells. Then, the specific inhibitors targeting EGFR were applied to reduce the expression of PD-L1 in vitro and in vivo. We validated that EGF could induce PD-L1 expression in the selected EGFR-positive cancers. RNAseq results revealed that STAT1 increased as a driver gene in KOSC-3-derived tumorspheres; these data were analyzed using PANTHER followed by NetworkAnalyst. The blockade of EGFR by afatinib resulted in decreased STAT1 and IRF-1 levels, both are transcriptional factors of PD-L1, and disabled the IFNr-STAT1-mediated PD-L1 axis in vitro and in vivo. Moreover, STAT1 knockdown significantly reduced EGF-mediated PD-L1 expression, and ruxolitinib, a JAK1/JAK2 inhibitor, significantly inhibited STAT1 phosphorylation to reduce the IFNr-mediated PD-L1 axis. These results indicate that EGF exacerbates PD-L1 by increasing the protein levels of STAT1 to enforce the IFNr-JAK1/2-mediated signaling axis in selected EGFR-positive cancers. The inhibition of EGFR by afatinib significantly reduced PD-L1 and may be a potential strategy for enhancing immunotherapeutic efficacy.

Immuno-Oncology: Emerging Targets and Combination Therapies

Host immunity recognizes and eliminates most early tumor cells, yet immunological checkpoints, exemplified by CTLA-4, PD-1, and PD-L1, pose a significant obstacle to effective antitumor immune responses. T-lymphocyte co-inhibitory pathways influence intensity, inflammation and duration of antitumor immunity. However, tumors and their immunosuppressive microenvironments exploit them to evade immune destruction. Recent PD-1 checkpoint inhibitors yielded unprecedented efficacies and durable responses across advanced-stage melanoma, showcasing potential to replace conventional radiotherapy regimens. Neverthless, many clinical problems remain in terms of efficacy, patient-to-patient variability, and undesirable outcomes and side effects. In this review, we evaluate recent advances in the immuno-oncology field and discuss ways forward. First, we give an overview of current immunotherapy modalities, involving mainy single agents, including inhibitor monoclonal antibodies (mAbs) targeting T-cell checkpoints of PD-1 and CTLA-4. However, neoantigen recognition alone cannot eliminate tumors effectively in vivo given their inherent complex micro-environment, heterogeneous nature and stemness. Then, based mainly upon CTLA-4 and PD-1 checkpoint inhibitors as a "backbone," we cover a range of emerging ("second-generation") therapies incorporating other immunotherapies or non-immune based strategies in synergistic combination. These include targeted therapies such as tyrosine kinase inhibitors, co-stimulatory mAbs, bifunctional agents, epigenetic modulators (such as inhibitors of histone deacetylases or DNA methyltransferase), vaccines, adoptive-T-cell therapy, nanoparticles, oncolytic viruses, and even synthetic "gene circuits." A number of novel immunotherapy co-targets in pre-clinical development are also introduced. The latter include metabolic components, exosomes and ion channels. We discuss in some detail of the personalization of immunotherapy essential for ultimate maximization of clinical outcomes. Finally, we outline possible future technical and conceptual developments including realistic in vitro and in vivo models and inputs from physics, engineering, and artificial intelligence. We conclude that the breadth and quality of immunotherapeutic approaches and the types of cancers that can be treated will increase significantly in the foreseeable future.

Immune checkpoint inhibitors in advanced non-small cell lung cancer

The emergence of immune checkpoint inhibitors for the treatment of cancer has led to major changes to the therapeutic landscape of lung cancer. Improvements in overall survival relative to standard chemotherapy have been observed in the first-line and second-line therapy settings for patients with advanced non-small cell lung cancer (NSCLC) who are treated with immune checkpoint inhibitors. Consequently, every patient with advanced-stage NSCLC is now a candidate for immune checkpoint inhibitor therapy. However, it is clear that the benefit from therapy is not universal, and identification of biomarkers to select therapy has assumed importance. In addition to programmed cell death receptor ligand 1 expression, both tissue-based and blood-based markers are under evaluation to select patients. In an era of increasing costs of care and potential for toxicities related to immune checkpoint inhibition, proper patient selection is critical to the optimal use of this new class of agents. In addition, development of novel combination approaches has also emerged as an important way to improve the efficacy of immune checkpoint inhibition. Studies in earlier stages of NSCLC are already underway with the hope of improving the cure rate. In this article, the authors review the current landscape of immune checkpoint inhibitors in the treatment of advanced NSCLC. Cancer 2018;124:248-61. © 2017 American Cancer Society.

CEACAM1 as a multi-purpose target for cancer immunotherapy

CEACAM1 is an extensively studied cell surface molecule with established functions in multiple cancer types, as well as in various compartments of the immune system. Due to its multi-faceted role as a recently appreciated immune checkpoint inhibitor and tumor marker, CEACAM1 is an attractive target for cancer immunotherapy. Herein, we highlight CEACAM1's function in various immune compartments and cancer types, including in the context of metastatic disease. This review outlines CEACAM1's role as a therapeutic target for cancer treatment in light of these properties.

[Progress in Immunotherapy for Squamous Non-small Cell Lung Cancer]

近几年来，肺鳞癌在化疗及靶向治疗上的进展不够显著，但免疫治疗却在肺鳞癌的治疗上取得了突破性的进展。免疫治疗通过免疫系统来清除肿瘤细胞，主要分为免疫检查点抑制剂及治疗性疫苗。免疫检查点抑制剂，包括抗细胞毒性T淋巴细胞抗原4（cytotoxic T-lymphocyte associated antigen 4, CTLA-4）抗体与抗程序性死亡受体-1（programmed death receptor 1, PD-1）抗体等多种药物已进行了肺鳞癌的Ⅱ期、Ⅲ期临床试验，并取得了一定成果。免疫治疗将成为肺鳞癌治疗的一种重要手段。