Non-Small Cell Lung Cancer as a Precision Oncology Paradigm: Emerging Targets and Tumor Mutational Burden (TMB)

How can we deliver on the promise of precision medicine in oncology and beyond? A practical roadmap for action

Background

Precision medicine (PM) is a form of personalized medicine that recognizes that individuals with the same condition may have different underlying factors and uses molecular information to provide tailored treatments. This approach can improve treatment outcomes and transform lives through favorable risk/benefit ratios, avoidance of ineffective interventions, and possible cost savings, as evidenced in the field of lung cancer and other oncology/therapeutic settings, including cardiac disease, diabetes, and rare diseases. However, the potential benefits of PM have yet to be fully realized.

Discussion

There are many barriers to the implementation of PM in clinical practice, including fragmentation of the PM landscape, siloed approaches to address shared challenges, unwarranted variation in availability and access to PM, lack of standardization, and limited understanding of patients' experience and needs throughout the PM pathway. We believe that a diverse, intersectoral multistakeholder collaboration, with three main pillars of activity: generation of data to demonstrate the benefit of PM, education to support informed decision-making, and addressing barriers across the patient pathway, is necessary to reach the shared goal of making PM an accessible and sustainable reality. Besides healthcare providers, researchers, policymakers/regulators/payers, and industry representatives, patients in particular must be equal partners and should be central to the PM approach-from early research through to clinical trials and approval of new treatments-to ensure it represents their entire experience and identifies barriers, solutions, and opportunities at the point of delivery.

Conclusion

We propose a practical and iterative roadmap to advance PM and call for all stakeholders across the healthcare system to employ a collaborative, cocreated, patient-centered methodology to close gaps and fully realize the potential of PM.

Correlation of tumor mutational burden with prognosis and immune infiltration in lung adenocarcinoma

Background

Tumor mutational burden (TMB) plays an important role in the evaluation of immunotherapy efficacy in lung adenocarcinoma (LUAD).

Objective

To improve the clinical management of LUAD by investigating the prognostic value of TMB and the relationship between TMB and immune infiltration.

Methods

TMB scores were calculated from the mutation data of 587 LUAD samples from The Cancer Genome Atlas (TCGA), and patients were divided into low-TMB and high-TMB groups based on the quartiles of the TMB score. Differentially expressed genes (DEGs), immune cell infiltration and survival analysis were compared between the low-TMB and high-TMB groups. We queried the expression of genes in lung cancer tissues through the GEPIA online database and performed experimental validation of the function of aberrant genes expressed in lung cancer tissues.

Results

We obtained sample information from TCGA for 587 LUAD patients, and the results of survival analysis for the high- and low- TMB groups suggested that patients in the high-TMB group had lower survival rates than those in the low-TMB group. A total of 756 DEGs were identified in the study, and gene set enrichment analysis (GSEA) showed that DEGs in the low-TMB group were enriched in immune-related pathways. Among the differentially expressed genes obtained, 15 immune-related key genes were screened with the help of ImmPort database, including 5 prognosis-related genes (CD274, PDCD1, CTLA4, LAG3, TIGIT). No difference in the expression of PDCD1, CTLA4, LAG3, TIGIT in lung cancer tissues and differential expression of CD274 in lung cancer tissues.

Conclusions

The survival rate of LUAD patients with low TMB was better than that of LUAD patients with high TMB. CD274 expression was down regulated in human LUAD cell lines H1299, PC-9, A549 and SPC-A1, which inhibited malignant progression of A549 cells.

The State of Cancer Care in the United Arab Emirates in 2022

Cancer is the third-leading cause of death in the United Arab Emirates (UAE); cancer care in the UAE has evolved dramatically over the last 40 years, from a single center in Al Ain in 1981 to more than 30 cancer centers and clinics across the UAE, with at least four comprehensive cancer centers in the UAE nowadays. Despite the significant progress in medical care, cancer quality control across the UAE is still lacking, with significant variations in cancer care across the cancer centers. Access to clinical trials is still hampered by a lack of expertise and research infrastructure and a small population, which renders patient accrual for trials a major challenge. Education and training are other areas for improvement that require immediate attention, and, in this review, we try to address these critical aspects for stakeholders to consider better cancer care in the UAE. Early cancer detection and screening are still evolving in the UAE, and a national screening program is lacking. There is also a need to address barriers to screening and to consider less invasive screening methods such as approved blood-based screening, which is likely to be more acceptable to the UAE population. In this review, we also address new topics that have not been addressed previously, including oncology medical tourism, psycho-oncology, onco-fertility, precision oncology, survivorship, oncology nursing, cancer support programs, and the oncology sector's response to the COVID-19 pandemic, all in the context of the UAE cancer landscape. Finally, we provide recommendations for policymakers, regulators, payers, patient advocacy groups, and the UAE oncology community regarding the delivery and future planning of high-quality cancer care. These recommendations are aligned with the UAE government's vision to reduce cancer mortality and provide high-quality healthcare for its citizens and residents.

Computational Analyses of YY1 and Its Target RKIP Reveal Their Diagnostic and Prognostic Roles in Lung Cancer

Simple Summary

Lung cancer (LC) is the tumor with the highest global mortality rate. Novel personalized therapies are currently being tested (e.g., targeted inhibitors, the immune-checkpoint inhibitors), but they cannot yet prevent the very frequent relapse and generalized metastases observed in a large population of LC patients. Currently, there is an urgent need for novel reliable biomarkers for the prognosis and diagnosis of LC. Through the systematic analysis of multiple deposited expression datasets, this report aims to explore the role of the Yin-Yang 1 (YY1) transcription factor and its target the Raf Kinase Inhibitory Protein (RKIP) in LC. The computational analysis suggested the predictive diagnostic and prognostic roles for both YY1 and RKIP stimulating further studies for proving their implication as novel biomarkers, as well as therapeutically druggable targets in LC.

Abstract

Lung cancer (LC) represents a global threat, being the tumor with the highest mortality rate. Despite the introduction of novel therapies (e.g., targeted inhibitors, immune-checkpoint inhibitors), relapses are still very frequent. Accordingly, there is an urgent need for reliable predictive biomarkers and therapeutically druggable targets. Yin-Yang 1 (YY1) is a transcription factor that may work either as an oncogene or a tumor suppressor, depending on the genotype and the phenotype of the tumor. The Raf Kinase Inhibitory Protein (RKIP), is a tumor suppressor and immune enhancer often found downregulated in the majority of the examined cancers. In the present report, the role of both YY1 and RKIP in LC is thoroughly explored through the analysis of several deposited RNA and protein expression datasets. The computational analyses revealed that YY1 negatively regulates RKIP expression in LC, as corroborated by the deposited YY1-ChIP-Seq experiments and validated by their robust negative correlation. Additionally, YY1 expression is significantly higher in LC samples compared to normal matching ones, whereas RKIP expression is lower in LC and high in normal matching tissues. These observed differences, unlike many current biomarkers, bear a diagnostic significance, as proven by the ROC analyses. Finally, the survival data support the notion that both YY1 and RKIP might represent strong prognostic biomarkers. Overall, the reported findings indicate that YY1 and RKIP expression levels may play a role in LC as potential biomarkers and therapeutic targets. However, further studies will be necessary to validate the in silico results.

Integrated genomic analysis of proteasome alterations across 11,057 patients with 33 cancer types: clinically relevant outcomes in framework of 3P medicine

Relevance

Proteasome, a cylindrical complex containing 19S regulatory particle lid, 19S regulatory particle base, and 20S core particle, acted as a major mechanism to regulate the levels of intracellular proteins and degrade misfolded proteins, which involved in many cellular processes, and played important roles in cancer biological processes. Elucidation of proteasome alterations across multiple cancer types will directly contribute to cancer medical services in the context of predictive, preventive, and personalized medicine (PPPM / 3P medicine).

Purpose

This study aimed to investigate proteasome gene alterations across 33 cancer types for discovery of effective biomarkers and therapeutic targets in the framework of PPPM practice in cancers.

Methods

Proteasome gene data, including gene expression RNAseq, somatic mutation, tumor mutation burden (TMB), copy number variant (CNV), microsatellite instability (MSI) score, clinical characteristics, immune phenotype, 22 immune cells, cancer stemness index, drug sensitivity, and related pathways, were systematically analyzed with publically available database and bioinformatics across 11,057 patients with 33 cancer types.

Results

Differentially expressed proteasome genes were extensively found between tumor and control tissues. PSMB4 occurred the top mutation event among proteasome genes, and those proteasome genes were significantly associated with TMB and MSI score. Most of proteasome genes were positively related to CNV among single deletion, control copy number, and single gain. Kaplan-Meier curves and COX regression survival analysis showed proteasome genes were significantly associated with patient survival rate across 33 cancer types. Furthermore, the expressions of proteasome genes were significantly different among different clinical stages and immune subtypes. The expressions of proteasome genes were correlated with immune-related scores (ImmuneScore, StromalScore, and ESTIMATEScore), 22 immune cells, and cancer stemness. The sensitivities of multiple drugs were closely related to proteasome gene expressions. The identified proteasome and proteasome-interacted proteins were significantly enriched in various cancer-related pathways.

Conclusions

This study provided the first landscape of proteasome alterations across 11,057 patients with 33 cancer types and revealed that proteasome played a significant and wide functional role in cancer biological processes. These findings are the precious scientific data to reveal the common and specific alterations of proteasome genes among 33 cancer types, which benefits the research and practice of PPPM in cancers.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-021-00256-z.

Identification of Immune-Related Gene Signatures in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Accumulating evidence indicates that immunotherapy helped to improve the survival and quality-of-life of patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC) besides chemotherapy and gene targeting treatment. This study aimed to develop immune-related gene signatures in LUAD and LUSC subtypes, respectively. LUAD and LUSC samples were divided into high- and low-abundance groups of immune cell infiltration (Immunity_H and Immunity_L) based on the abundance of immune cell infiltrations. The distribution of immune cells was significantly different between the high- and low-immunity subtypes in LUAD and LUSC samples. The differentially expressed genes (DEGs) between those two groups in LUAD and LUSC contain some key immune-related genes, such as PDL1, PD1, CTLA-4, and HLA families. The DEGs were enriched in multiple immune-related pathways. Furthermore, the seven-immune-related-gene-signature (CD1B, CHRNA6, CLEC12B, CLEC17A, CLNK, INHA, and SLC14A2) prognostic model-based high- and low-risk groups were significantly associated with LUAD overall survival and clinical characteristics. The eight-immune-related-gene-signature (C4BPB, FCAMR, GRAPL, MAP1LC3C, MGC2889, TRIM55, UGT1A1, and VIPR2) prognostic model-based high- and low-risk groups were significantly associated with LUSC overall survival and clinical characteristics. The prognostic models were tested as good ones by receiver operating characteristic, principal component analysis, univariate and multivariate analysis, and nomogram. The verifications of these two immune-related-gene-signature prognostic models showed consistency in the train and test cohorts of LUAD and LUSC. In addition, patients with LUAD in the low-risk group responded better to immunotherapy than those in the high-risk group. This study revealed two reliable immune-related-gene-signature models that were significantly associated with prognosis and tumor microenvironment cell infiltration in LUAD and LUSC, respectively. Evaluation of the integrated characterization of multiple immune-related genes and pathways could help to predict the response to immunotherapy and monitor immunotherapy strategies.

Efficacy of immune checkpoint inhibitor therapy in patients with RET fusion-positive non-small-cell lung cancer

Aim: To describe outcomes of patients with rearraned during transfection (RET) fusion-positive non-small-cell lung cancer (NSCLC) who received immune checkpoint inhibitor (ICI)-based treatments in the US. Patients & methods: Using de-identified Flatiron Health-Foundation Medicine NSCLC Clinico-Genomic and Guardant Health databases, treatment patterns and outcomes of 69 patients with advanced/metastatic RET fusion-positive NSCLC who received ICI-based treatment were described. Results: Median real-world progression-free survival and overall survival months were 4.2 (95% CI: 1.4-8.4) and 19.1 (6.9-not reached), respectively, among patients in Clinico-Genomic database (n = 17) receiving first-line ICI-based therapy. In the Guardant Health database, progression-free survival was unavailable, and the median overall survival was not reached (n = 29). Conclusion: Outcomes associated with ICI-based treatments in the first-line setting among patients with RET fusion-positive NSCLC are consistent with unselected populations reported in literature.

Immunotherapy versus standard chemotherapy for treatment of extensive-stage small-cell lung cancer: a systematic review

Aim: We conducted a systematic review and network meta-analysis to evaluate the efficacy of immunotherapy versus chemotherapy to treat extensive-stage small-cell lung cancer. Methods: We analyzed several eligible clinical trials using fixed or random-effects models to evaluate relative treatment effects depending on heterogeneity. Results: In the experimental group, immunotherapy showed significant improvement in overall survival (hazard ratio [HR]: 0.82; 95% CI: 0.74-0.89; I² = 31.4%; p < 0.001) and progression-free survival (HR: 0.77; 95% CI: 0.80-0.83; I² = 22.7%; p < 0.001). Conclusion: Immunotherapy is likely to significantly improve extensive-stage small-cell lung cancer patients' overall survival and progression-free survival compared with standard chemotherapy. Anti-PD L1 exhibited superior overall survival compared with anti-PD 1 and anti-CTLA4.

Molecular biomarker testing for non-small cell lung cancer: consensus statement of the Korean Cardiopulmonary Pathology Study Group

Molecular biomarker testing is the standard of care for non–small cell lung cancer (NSCLC) patients. In 2017, the Korean Cardiopulmonary Pathology Study Group and the Korean Molecular Pathology Study Group co-published a molecular testing guideline which contained almost all known genetic changes that aid in treatment decisions or predict prognosis in patients with NSCLC. Since then there have been significant changes in targeted therapies as well as molecular testing including newly approved targeted drugs and liquid biopsy. In order to reflect these changes, the Korean Cardiopulmonary Pathology Study Group developed a consensus statement on molecular biomarker testing. This consensus statement was crafted to provide guidance on what genes should be tested, as well as methodology, samples, patient selection, reporting and quality control.

Tumor mutation burden (TMB)-associated signature constructed to predict survival of lung squamous cell carcinoma patients

Lung squamous cell carcinoma (LUSC) is a common type of lung cancer with high incidence and mortality rate. Tumor mutational burden (TMB) is an emerging biomarker for selecting patients with non-small cell lung cancer (NSCLC) for immunotherapy. This study aimed to reveal TMB involved in the mechanisms of LUSC and develop a model to predict the overall survival of LUSC patients. The information of patients with LUSC were obtained from the cancer genome atlas database (TCGA). Differentially expressed genes (DEGs) between low- and the high-TMB groups were identified and taken as nodes for the protein-protein interaction (PPI) network construction. Gene oncology (GO) enrichment analysis and gene set enrichment analysis (GSEA) were used to investigate the potential molecular mechanism. Then, we identified the factors affecting the prognosis of LUSC through cox analysis, and developed a risk score signature. Kaplan-Meier method was conducted to analyze the difference in survival between the high- and low-risk groups. We constructed a nomogram based on the risk score model and clinical characteristics to predict the overall survival of patients with LUSC. Finally, the signature and nomogram were further validated by using the gene expression data downloaded from the Gene Expression Omnibus (GEO) database. 30 DEGs between high- and low-TMB groups were identified. PPI analysis identified CD22, TLR10, PIGR and SELE as the hub genes. Cox analysis indicated that FAM107A, IGLL1, SELE and T stage were independent prognostic factors of LUSC. Low-risk scores group lived longer than that of patients with high-risk scores in LUSC. Finally, we built a nomogram that integrated the clinical characteristics (TMN stage, age, gender) with the three-gene signature to predict the survival probability of LUSC patients. Further verification in the GEO dataset. TMB might contribute to the pathogenesis of LUSC. TMB-associated genes can be used to develope a model to predict the OS of lung squamous cell carcinoma patients.