TMB in NSCLC: A Broken Dream?

Predictive value of ZFHX4 mutation for the efficacy of immune checkpoint inhibitors in non-small cell lung cancer and melanoma

Studies have shown that the Zinc finger homeobox 4 (ZFHX4) might be a factor in the prognosis of malignancies. However, little is known about the association between the ZFHX4 mutation and the effectiveness of immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) and melanoma. Three public ICIs-treated NSCLC cohorts were divided into discovery cohort (n=75) and validation cohort (n=62), which were used to evaluate the relationship between ZFHX4 mutation and ICIs effectiveness in NSCLC. Seven ICIs-treated melanoma cohorts (n = 418) were used to analyze the relationship between ZFHX4 mutation and immunotherapy efficacy in melanoma. NSCLC and skin cutaneous melanoma (SKCM) cohorts from The Cancer Genome Atlas (TCGA) were used to investigate underlying mechanism. Patients with ZFHX4 mutant-type (ZFHX4-Mut) showed a superior objective response rate (ORR) (P < 0.01) and longer progression-free survival (PFS) (P < 0.05) than patients with ZFHX4 wild-type (ZFHX4-WT) in NSCLC cohorts. In the melanoma cohorts, patients carrying ZFHX4-Mut had a higher ORR (P = 0.042) and longer overall survival (OS) (P = 0.011). Besides, patients with NSCLC and melanoma harboring ZFHX4-Mut had a higher tumor mutation burden (TMB) (P<0.001) and tumor neoantigen burden (TNB) (P<0.001) than those harboring ZFHX4-WT. ZFHX4 mutation was associated with higher levels of plasma B cells, activated CD4+ memory T cells, and CD8+ T cells. Seven DNA damage repair pathways were significantly enriched in the ZFHX4-Mut group. ZFHX4 mutation could serve as a predicter for the efficacy of ICIs therapy in NSCLC and melanoma.

PRRX1-OLR1 axis supports CAFs-mediated lung cancer progression and immune suppression

OBJECTIVE

To investigate the mechanism by which cancer-associated fibroblasts (CAFs) affect the growth and immune evasion of lung cancer cells.

METHODS

Initially, datasets comparing CAFs with normal fibroblasts were downloaded from the GEO dataset GSE48397. Genes with the most significant differential expression were selected and validated using clinical data. Subsequently, CAFs were isolated, and the selected genes were knocked down in CAFs. Co-culture experiments were conducted with H1299 or A549 cells to analyze changes in lung cancer cell growth, migration, and immune evasion in vitro and in vivo. To further elucidate the upstream regulatory mechanism, relevant ChIP-seq data were downloaded from the GEO database, and the regulatory relationships were validated through ChIP-qPCR and luciferase reporter assays.

RESULTS

OLR1 was significantly overexpressed in CAFs and strongly correlated with adverse prognosis in lung cancer patients. Knockdown of OLR1 markedly inhibited CAFs' support for the growth and immune evasion of lung cancer cells in vitro and in vivo. ChIP-seq results demonstrated that PRRX1 can promote OLR1 expression by recruiting H3K27ac and H3K4me3, thereby activating CAFs. Knockdown of PRRX1 significantly inhibited CAFs' function, while further overexpression of OLR1 restored CAFs' support for lung cancer cell growth, migration, and immune evasion.

CONCLUSION

PRRX1 promotes OLR1 expression by recruiting H3K27ac and H3K4me3, activating CAFs, and thereby promoting the growth, migration, and immune evasion of lung cancer cells.

Non-Small Cell Lung Cancer Metastatic Without Oncogenic Alterations

This overview provides a thorough review of current treatment approaches for first-line management of nononcogenic addicted non-small cell lung cancer. We also address pertinent clinical decision-making queries encountered in everyday practice, such as the optimal treatment strategy for PD-L1-high patients, predictive factors for response to immune checkpoint inhibitors (ICI) both in terms of patient and cancer characteristics, the potential benefits of dual checkpoint blockade, and the unresolved issue of safe discontinuation strategies for long-term responders. Around one in five patients falls into this latter category while the majority develop either primary or acquired resistance to ICI-based first-line therapy, necessitating effective subsequent lines of treatment. Docetaxel, with or without combination of antiangiogenic agents, serves as the backbone of treatment, although evidence in the post-ICI setting is limited. Given that an inflamed tumor microenvironment (TME) is crucial for ICI responses, targeting the TME in cases of acquired resistance alongside continued ICI administration appears rational, although clinical trials so far have failed to confirm this hypothesis. Antibody-drug conjugates have emerged as a promising treatment modality, offering the potential for reduced toxicity and improved efficacy by targeting specific cancer antigens. Moreover, several chemotherapy-free approaches are currently under investigation for treatment-naïve patients, including alternative ICI and drugs targeting epitopes on both cancer and immune cells.

Identification roles of NFE2L3 in digestive system cancers

BACKGROUND

Morbidity and mortality rates of Digestive System Cancers (DSC) continue to pose human lives and health. Nuclear factor erythroid 2-like protein 3 (NFE2L3) is aberrantly expressed in DSC. This study aimed to explore the clinical value and underlying mechanisms of NFE2L3 as a novel biomarker in DSC.

METHODS

We utilized data from databases and clinical gastric cancer specimens to validate the aberrant expression level of NFE2L3 and further assessed the clinical value of NFE2L3. To investigate the potential molecular mechanism of NFE2L3, we analyzed the correlation of NFE2L3 with immune molecular mechanisms, constructed PPI network, performed GO analysis and KEGG analysis, and finally explored the biological function of NFE2L3 in gastric cancer cells.

RESULTS

NFE2L3 expression is up-regulated in DSC and has both prognostic and diagnostic value. NFE2L3 correlates with various immune mechanisms, PPI network suggests proteins interacting with NFE2L3, GSEA analysis suggests potential molecular mechanisms for NFE2L3 to play a role in cancer promotion, and in vitro cellular experiments also confirmed the effect of NFE2L3 on the biological function of gastric cancer cells.

CONCLUSION

Our study confirms the aberrant expression and molecular mechanisms of NFE2L3 in DSC, indicating that NFE2L3 could serve as a novel biomarker for diagnosis and prognosis of DSC.

Biological insights from plasma proteomics of non-small cell lung cancer patients treated with immunotherapy

Introduction

Immune checkpoint inhibitors have made a paradigm shift in the treatment of non-small cell lung cancer (NSCLC). However, clinical response varies widely and robust predictive biomarkers for patient stratification are lacking. Here, we characterize early on-treatment proteomic changes in blood plasma to gain a better understanding of treatment response and resistance.

Methods

Pre-treatment (T0) and on-treatment (T1) plasma samples were collected from 225 NSCLC patients receiving PD-1/PD-L1 inhibitor-based regimens. Plasma was profiled using aptamer-based technology to quantify approximately 7000 plasma proteins per sample. Proteins displaying significant fold changes (T1:T0) were analyzed further to identify associations with clinical outcomes using clinical benefit and overall survival as endpoints. Bioinformatic analyses of upregulated proteins were performed to determine potential cell origins and enriched biological processes.

Results

The levels of 142 proteins were significantly increased in the plasma of NSCLC patients following ICI-based treatments. Soluble PD-1 exhibited the highest increase, with a positive correlation to tumor PD-L1 status, and, in the ICI monotherapy dataset, an association with improved overall survival. Bioinformatic analysis of the ICI monotherapy dataset revealed a set of 30 upregulated proteins that formed a single, highly interconnected network, including CD8A connected to ten other proteins, suggestive of T cell activation during ICI treatment. Notably, the T cell-related network was detected regardless of clinical benefit. Lastly, circulating proteins of alveolar origin were identified as potential biomarkers of limited clinical benefit, possibly due to a link with cellular stress and lung damage.

Conclusions

Our study provides insights into the biological processes activated during ICI-based therapy, highlighting the potential of plasma proteomics to identify mechanisms of therapy resistance and biomarkers for outcome.

Identification and validation of a gap junction protein related signature for predicting the prognosis of renal clear cell carcinoma

Background

Gap junction proteins (GJPs) are a class of channel proteins that are closely related to cell communication and tumor development. The objective of this study was to screen out GJPs related prognostic signatures (GRPS) associated with clear cell renal cell carcinoma (ccRCC).

Materials and Methods

GJPs microarray data for ccRCC patients were obtained from The Gene Expression Omnibus (GEO) database, along with RNA sequencing data for tumor and paired normal tissues from The Cancer Genome Atlas (TCGA) database. In the TCGA database, least absolute shrinkage and selection Operator (LASSO) and Cox regression models were used to identify GJPs with independent prognostic effects as GRPS in ccRCC patients. According to the GRPS expression and regression coefficient from the multivariate Cox regression model, the risk score (RS) of each ccRCC patient was calculated, to construct the RS prognostic model to predict survival. Overall survival (OS) and progression-free survival (PFS) analyses; gene pan-cancer analysis; single gene survival analysis; gene joint effect analysis; functional enrichment analysis; tumor microenvironment (TME) analysis; tumor mutational burden (TMB) analysis; and drug sensitivity analysis were used to explore the biological function, mechanism of action and clinical significance of GRPS in ccRCC. Further verification of the genetic signature was performed with data from the GEO database. Finally, the cytofunctional experiments were used to verify the biological significance of GRPS associated GJPs in ccRCC cell lines.

Results

GJA5 and GJB1, which are GRPS markers of ccRCC patients, were identified through LASSO and Cox regression models. Low expression of GJA5 and GJB1 is associated with poor patient prognosis. Patients with high-RS had significantly shorter OS and PFS than patients with low-RS (p< 0.001). The risk of death for individuals with high-RS was 1.695 times greater than that for those with low-RS (HR = 1.695, 95%CI= 1.439-1.996, p< 0.001). Receiver Operating Characteristic (ROC) curve showed the great predictive power of the RS prognostic model for the survival rate of patients. The area under curve (AUC) values for predicting 1-year, 3-year and 5-year survival rates were 0.740, 0.781 and 0.771, respectively. The clinical column chart was also reliable for predicting the survival rate of patients, with AUC values of 0.859, 0.846 and 0.796 for predicting 1-year, 3-year and 5-year survival, respectively. The GRPS was associated with immune cell infiltration, the TME, the TMB, and sensitivity to chemotherapy drugs. Further in vitro experiments showed that knockdown of GJA5 or GJB1 could promote the proliferation, migration and epithelial-mesenchymal transition (EMT) and inhibit apoptosis of ccRCC cells.

Conclusion

GJA5 and GJB1 could be potential biological markers for predicting survival in patients with ccRCC.

NSCLC: from tumorigenesis, immune checkpoint misuse to current and future targeted therapy

Non-small cell lung cancer (NSCLC) is largely promoted by a multistep tumorigenesis process involving various genetic and epigenetic alterations, which essentially contribute to the high incidence of mortality among patients with NSCLC. Clinical observations revealed that NSCLC also co-opts a multifaceted immune checkpoint dysregulation as an important driving factor in NSCLC progression and development. For example, a deregulated PI3K/AKT/mTOR pathway has been noticed in 50-70% of NSCLC cases, primarily modulated by mutations in key oncogenes such as ALK, EGFR, KRAS, and others. Additionally, genetic association studies containing patient-specific factors and local reimbursement criteria expose/reveal mutations in EGFR/ALK/ROS/BRAF/KRAS/PD-L1 proteins to determine the suitability of available immunotherapy or tyrosine kinase inhibitor therapy. Thus, the expression of such checkpoints on tumors and immune cells is pivotal in understanding the therapeutic efficacy and has been extensively studied for NSCLC treatments. Therefore, this review summarizes current knowledge in NSCLC tumorigenesis, focusing on its genetic and epigenetic intricacies, immune checkpoint dysregulation, and the evolving landscape of targeted therapies. In the context of current and future therapies, we emphasize the significance of antibodies targeting PD-1/PD-L1 and CTLA-4 interactions as the primary therapeutic strategy for immune system reactivation in NSCLC. Other approaches involving the promising potential of nanobodies, probodies, affibodies, and DARPINs targeting immune checkpoints are also described; these are under active research or clinical trials to mediate immune regulation and reduce cancer progression. This comprehensive review underscores the multifaceted nature, current state and future directions of NSCLC research and treatment.

CD57-positive CD8 + T cells define the response to anti-programmed cell death protein-1 immunotherapy in patients with advanced non-small cell lung cancer

Immune checkpoint inhibitors have transformed the treatment landscape of non-small cell lung cancer (NSCLC). However, accurately identifying patients who will benefit from immunotherapy remains a challenge. This study aimed to discover potential biomarkers for predicting immunotherapy response in NSCLC patients. Single-cell mass cytometry (CyTOF) was utilized to analyze immune cell subsets in peripheral blood mononuclear cells (PBMCs) obtained from NSCLC patients before and 12 weeks after single-agent immunotherapy. The CyTOF findings were subsequently validated using flow cytometry and multiplex immunohistochemistry/immunofluorescence in PBMCs and tumor tissues, respectively. RNA sequencing (RNA-seq) was performed to elucidate the underlying mechanisms. In the CyTOF cohort (n = 20), a high frequency of CD57+CD8+ T cells in PBMCs was associated with durable clinical benefit from immunotherapy in NSCLC patients (p = 0.034). This association was further confirmed in an independent cohort using flow cytometry (n = 27; p < 0.001), with a determined cutoff value of 12.85%. The cutoff value was subsequently validated in another independent cohort (AUC = 0.733). We also confirmed the CyTOF findings in pre-treatment formalin-fixed and paraffin-embedded tissues (n = 90; p < 0.001). RNA-seq analysis revealed 475 differentially expressed genes (DEGs) between CD57+CD8+ T cells and CD57-CD8+ T cells, with functional analysis identifying DEGs significantly enriched in immune-related signaling pathways. This study highlights CD57+CD8+ T cells as a promising biomarker for predicting immunotherapy success in NSCLC patients.

Roles of tissue-resident immune cells in immunotherapy of non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the most common and lethal type of lung cancer, with limited treatment options and poor prognosis. Immunotherapy offers hope for improving the survival and quality of life of NSCLC patients, but its efficacy depends on the tumor immune microenvironment (TME). Tissue-resident immune cells are a subset of immune cells that reside in various tissues and organs, and play an important role in fighting tumors. In NSCLC, tissue-resident immune cells are heterogeneous in their distribution, phenotype, and function, and can either promote or inhibit tumor progression and response to immunotherapy. In this review, we summarize the current understanding on the characteristics, interactions, and roles of tissue-resident immune cells in NSCLC. We also discuss the potential applications of tissue-resident immune cells in NSCLC immunotherapy, including immune checkpoint inhibitors (ICIs), other immunomodulatory agents, and personalized cell-based therapies. We highlight the challenges and opportunities for developing targeted therapies for tissue-resident immune cells and optimizing existing immunotherapeutic approaches for NSCLC patients. We propose that tissue-resident immune cells are a key determinant of NSCLC outcome and immunotherapy response, and warrant further investigation in future research.

The m6A reader IGF2BP1 manipulates BUB1B expression to affect malignant behaviors, stem cell properties, and immune resistance of non-small-cell lung cancer stem cells

N6-methyladenosine (m6A) modification is the most common internal modification in eukaryotic mRNA and an important mechanism for post-transcriptional regulation of genes. This study focuses on the role of the m6A reader insulin-like growth factor 2 mRNA binding protein 1 (IGF2BP1) in the malignant behaviors of non-small-cell lung cancer (NSCLC) cells and especially the cancer stem cells (CSCs). We obtained IGF2BP1 as an aberrantly upregulated gene linking to poor survival of patients with NSCLC by bioinformatics, and then confirmed increased IGF2BP1 expression in NSCLC tissues and cells, especially in the enriched CSCs. Knockdown of IGF2BP1 suppressed proliferation, mobility and epithelial-mesenchymal transition activity of NSCLC cells and CSCs, and it reduced stemness, self-renewal ability, xenograft tumorigenesis and immune resistance of the CSCs. IGF2BP1 was predicted to have a positive correlation with BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), and it upregulated BUB1B expression through m6A modification. Further overexpression of BUB1B in CSCs counteracted the effects of IGF2BP1 silencing and restored the malignant phenotype, self-renewal, and immune resistance of CSCs in vitro and in vivo. Taken together, this work demonstrates that IGF2BP1 manipulates BUB1B expression to affect malignant behaviors, stem cell properties and immune resistance of NSCLC stem cells.

Prognostic value and immune landscapes of immunogenic cell death-associated lncRNAs in lung adenocarcinoma

Immunogenic cell death (ICD) has been demonstrated to activate T cells to kill tumor cells, which is closely related to tumor development, and long noncoding RNAs (lncRNAs) are also involved. However, it is not known whether ICD-related lncRNAs are associated with the development of lung adenocarcinoma (LUAD). We downloaded ICD-related genes from GeneCards and the transcriptome statistics of LUAD patients from The Cancer Genome Atlas (TCGA) and subsequently developed and verified a predictive model. A successful model was used together with other clinical features to construct a nomogram for predicting patient survival. To further study the mechanism of tumor action and to guide therapy, we performed enrichment analysis, tumor microenvironment analysis, somatic mutation analysis, drug sensitivity analysis and real-time quantitative polymerase chain reaction (RT-qPCR) analysis. Nine ICD-related lncRNAs with significant prognostic relevance were selected for model construction. Survival analysis demonstrated that overall survival was substantially shorter in the high-risk group than in the low-risk group (P < 0.001). This model was predictive of prognosis across all clinical subgroups. Cox regression analysis further supported the independent prediction ability of the model. Ultimately, a nomogram depending on stage and risk score was created and showed a better predictive performance than the nomogram without the risk score. Through enrichment analysis, the enriched pathways in the high-risk group were found to be primarily associated with metabolism and DNA replication. Tumor microenvironment analysis suggested that the immune cell concentration was lower in the high-risk group. Somatic mutation analysis revealed that the high-risk group contained more tumor mutations (P = 0.00018). Tumor immune dysfunction and exclusion scores exhibited greater sensitivity to immunotherapy in the high-risk group (P < 0.001). Drug sensitivity analysis suggested that the predictive model can also be applied to the choice of chemotherapy drugs. RT-qPCR analysis also validated the accuracy of the constructed model based on nine ICD-related lncRNAs. The prognostic model constructed based on the nine ICD-related lncRNAs showed good application value in assessing prognosis and guiding clinical therapy.

Prognostic Factors in Advanced Non-Small Cell Lung Cancer Patients Treated with Immunotherapy

Taking into account the huge epidemiologic impact of lung cancer (in 2020, lung cancer accounted for 2,206,771 of the cases and for 1,796,144 of the cancer-related deaths, representing the second most common cancer in female patients, the most common cancer in male patients, and the second most common cancer in male and female patients) and the current lack of recommendations in terms of prognostic factors for patients selection and management, this article aims to provide an overview of the current landscape in terms of currently available immunotherapy treatments and the most promising assessed prognostic biomarkers, highlighting the current state-of-the-art and hinting at future challenges.

Liquid Biopsy in NSCLC: An Investigation with Multiple Clinical Implications

Tissue biopsy is essential for NSCLC diagnosis and treatment management. Over the past decades, liquid biopsy has proven to be a powerful tool in clinical oncology, isolating tumor-derived entities from the blood. Liquid biopsy permits several advantages over tissue biopsy: it is non-invasive, and it should provide a better view of tumor heterogeneity, gene alterations, and clonal evolution. Consequentially, liquid biopsy has gained attention as a cancer biomarker tool, with growing clinical applications in NSCLC. In the era of precision medicine based on molecular typing, non-invasive genotyping methods became increasingly important due to the great number of oncogene drivers and the small tissue specimen often available. In our work, we comprehensively reviewed established and emerging applications of liquid biopsy in NSCLC. We made an excursus on laboratory analysis methods and the applications of liquid biopsy either in early or metastatic NSCLC disease settings. We deeply reviewed current data and future perspectives regarding screening, minimal residual disease, micrometastasis detection, and their implication in adjuvant and neoadjuvant therapy management. Moreover, we reviewed liquid biopsy diagnostic utility in the absence of tissue biopsy and its role in monitoring treatment response and emerging resistance in metastatic NSCLC treated with target therapy and immuno-therapy.

Therapies Targeting Immune Cells in Tumor Microenvironment for Non-Small Cell Lung Cancer

The tumor microenvironment (TME) plays critical roles in immune modulation and tumor malignancies in the process of cancer development. Immune cells constitute a significant component of the TME and influence the migration and metastasis of tumor cells. Recently, a number of therapeutic approaches targeting immune cells have proven promising and have already been used to treat different types of cancer. In particular, PD-1 and PD-L1 inhibitors have been used in the first-line setting in non-small cell lung cancer (NSCLC) with PD-L1 expression ≥1%, as approved by the FDA. In this review, we provide an introduction to the immune cells in the TME and their efficacies, and then we discuss current immunotherapies in NSCLC and scientific research progress in this field.

Advances in efficacy prediction and monitoring of neoadjuvant immunotherapy for non-small cell lung cancer

The use of immune checkpoint inhibitors (ICIs) has become mainstream in the treatment of non-small cell lung cancer (NSCLC). The idea of harnessing the immune system to fight cancer is fast developing. Neoadjuvant treatment in NSCLC is undergoing unprecedented change. Chemo-immunotherapy combinations not only seem to achieve population-wide treating coverage irrespective of PD-L1 expression but also enable achieving a pathological complete response (pCR). Despite these recent advancements in neoadjuvant chemo-immunotherapy, not all patients respond favorably to treatment with ICIs plus chemo and may even suffer from severe immune-related adverse effects (irAEs). Similar to selection for target therapy, identifying patients most likely to benefit from chemo-immunotherapy may be valuable. Recently, several prognostic and predictive factors associated with the efficacy of neoadjuvant immunotherapy in NSCLC, such as tumor-intrinsic biomarkers, tumor microenvironment biomarkers, liquid biopsies, microbiota, metabolic profiles, and clinical characteristics, have been described. However, a specific and sensitive biomarker remains to be identified. Recently, the construction of prediction models for ICI therapy using novel tools, such as multi-omics factors, proteomic tests, host immune classifiers, and machine learning algorithms, has gained attention. In this review, we provide a comprehensive overview of the different positive prognostic and predictive factors in treating preoperative patients with ICIs, highlight the recent advances made in the efficacy prediction of neoadjuvant immunotherapy, and provide an outlook for joint predictors.

Liquid Biopsies in Lung Cancer

As lung cancer has the highest cancer-specific mortality rates worldwide, there is an urgent need for new therapeutic and diagnostic approaches to detect early-stage tumors and to monitor their response to the therapy. In addition to the well-established tissue biopsy analysis, liquid-biopsy-based assays may evolve as an important diagnostic tool. The analysis of circulating tumor DNA (ctDNA) is the most established method, followed by other methods such as the analysis of circulating tumor cells (CTCs), microRNAs (miRNAs), and extracellular vesicles (EVs). Both PCR- and NGS-based assays are used for the mutational assessment of lung cancer, including the most frequent driver mutations. However, ctDNA analysis might also play a role in monitoring the efficacy of immunotherapy and its recent accomplishments in the landscape of state-of-the-art lung cancer therapy. Despite the promising aspects of liquid-biopsy-based assays, there are some limitations regarding their sensitivity (risk of false-negative results) and specificity (interpretation of false-positive results). Hence, further studies are needed to evaluate the usefulness of liquid biopsies for lung cancer. Liquid-biopsy-based assays might be integrated into the diagnostic guidelines for lung cancer as a tool to complement conventional tissue sampling.

Therapeutic strategies for non-small cell lung cancer: Experimental models and emerging biomarkers to monitor drug efficacies

While new targeted therapies have considerably changed the treatment and prognosis of non-small cell lung cancer (NSCLC), they are frequently unsuccessful due to primary or acquired resistances. Chemoresistance is a complex process that combines cancer cell intrinsic mechanisms including molecular and genetic abnormalities, aberrant interactions within the tumor microenvironment, and the pharmacokinetic characteristics of each molecule. From a pharmacological point of view, two levers could improve the response to treatment: (i) developing tools to predict the response to chemo- and targeted therapies and (ii) gaining a better understanding of the influence of the tumor microenvironment. Both personalized medicine approaches require the identification of relevant experimental models and biomarkers to understand and fight against chemoresistance mechanisms. After describing the main therapies in NSCLC, the scope of this review will be to identify and to discuss relevant in vitro and ex vivo experimental models that are able to mimic tumors. In addition, the interests of these models in the predictive responses to proposed therapies will be discussed. Finally, this review will evaluate the involvement of novel secreted biomarkers such as tumor DNA or micro RNA in predicting responses to anti-tumor therapies.

Prognostic markers of ferroptosis-related long non-coding RNA in lung adenocarcinomas

Ferroptosis is a recently established type of iron-dependent programmed cell death. Growing studies have focused on the function of ferroptosis in cancers, including lung adenocarcinoma (LUAD). However, the factors involved in the regulation of ferroptosis-related genes are not fully understood. In this study, we collected data from lung adenocarcinoma datasets of the Cancer Genome Atlas (TCGA-LUAD). The expression profiles of 60 ferroptosis-related genes were screened, and two differentially expressed ferroptosis subtypes were identified. We found the two ferroptosis subtypes can predict clinical outcomes and therapeutic responses in LUAD patients. Furthermore, key long non-coding RNAs (lncRNAs) were screened by single factor Cox and least absolute shrinkage and selection operator (LASSO) based on which co-expressed with the 60 ferroptosis-related genes. We then established a risk score model which included 13 LUAD ferroptosis-related lncRNAs with a multi-factor Cox regression. The risk score model showed a good performance in evaluating the outcome of LUAD. What's more, we divided TCGA-LUAD tumor samples into two groups with high- and low-risk scores and further explored the differences in clinical characteristics, tumor mutation burden, and tumor immune cell infiltration among different LUAD tumor risk score groups and evaluate the predictive ability of risk score for immunotherapy benefit. Our findings provide good support for immunotherapy in LUAD in the future.

Establishing a whole blood CD4+ T cell immunity measurement to predict response to anti-PD-1

BACKGROUND

Biomarkers that can accurately predict the efficacy of immune checkpoint inhibitors (ICIs) against programmed death 1 (PD-1) ligand in cancer immunotherapy are urgently needed. We have previously reported a novel formula that predicts the response to treatment with second-line nivolumab with high sensitivity and specificity in patients with non-small cell lung cancer (NSCLC) previously treated with chemotherapy. The formula was based on the percentages of CD62L^lowCD4⁺ T cells (effector T cells; %Teff) and CD4⁺CD25⁺FOXP3⁺ T cells (regulatory T cells; %Treg) in the peripheral blood before treatment estimated using the peripheral blood mononuclear cell (PBMC) method. Here, we investigated the applicability of the formula (K-index) to predict the response to treatment with another ICI to expand its clinical applicability. Furthermore, we developed a simpler assay method based on whole blood (WB) samples to overcome the limitations of the PBMC method, such as technical difficulties, in obtaining the K-index.

METHODS

The K-index was evaluated using the PBMC method in 59 patients with NSCLC who received first-line pembrolizumab treatment. We also assessed the K-index using the WB method and estimated the correlation between the measurements obtained using both methods in 76 patients with lung cancer.

RESULTS

This formula consistently predicted the response to first-line pembrolizumab therapy in patients with NSCLC. The WB method correlated well with the PBMC method to obtain %Teff, %Treg, and the formula value. The WB method showed high repeatability (coefficient of variation, < 10%). The data obtained using WB samples collected in tubes containing either heparin or EDTA-2K and stored at room temperature (18-24 °C) for one day after blood sampling did not differ. Additionally, the performance of the WB method was consistent in different flow cytometry instruments.

CONCLUSIONS

The K-index successfully predicted the response to first-line therapy with pembrolizumab, as reported earlier for the second-line therapy with nivolumab in patients with NSCLC. The WB method established in this study can replace the cumbersome PBMC method in obtaining the K-index. Overall, this study suggests that the K-index can predict the response to anti-PD-1 therapy in various cancers, including NSCLC.

Recent Advances in DNA Vaccines against Lung Cancer: A Mini Review

Lung cancer is regarded as the major causes of patient death around the world. Although the novel tumor immunotherapy has made great progress in the past decades, such as utilizing immune checkpoint inhibitors or oncolytic viruses, the overall 5-year survival of patients with lung cancers is still low. Thus, development of effective vaccines to treat lung cancer is urgently required. In this regard, DNA vaccines are now considered as a promising immunotherapy strategy to activate the host immune system against lung cancer. DNA vaccines are able to induce both effective humoral and cellular immune responses, and they possess several potential advantages such as greater stability, higher safety, and being easier to manufacture compared to conventional vaccination. In the present review, we provide a global overview of the mechanism of cancer DNA vaccines and summarize the innovative neoantigens, delivery platforms, and adjuvants in lung cancer that have been investigated or approved. Importantly, we highlight the recent advance of clinical studies in the field of lung cancer DNA vaccine, focusing on their safety and efficacy, which might accelerate the personalized design of DNA vaccine against lung cancer.

In Silico Identification and Validation of Cuproptosis-Related LncRNA Signature as a Novel Prognostic Model and Immune Function Analysis in Colon Adenocarcinoma

Background: Colon adenocarcinoma (COAD) is the most common subtype of colon cancer, and cuproptosis is a recently newly defined form of cell death that plays an important role in the development of several malignant cancers. However, studies of cuproptosis-related lncRNAs (CRLs) involved in regulating colon adenocarcinoma are limited. The purpose of this study is to develop a new prognostic CRLs signature of colon adenocarcinoma and explore its underlying biological mechanism. Methods: In this study, we downloaded RNA-seq profiles, clinical data and tumor mutational burden (TMB) data from the TCGA database, identified cuproptosis-associated lncRNAs using univariate Cox, lasso regression analysis and multivariate Cox analysis, and constructed a prognostic model with risk score based on these lncRNAs. COAD patients were divided into high- and low-risk subgroups based on the risk score. Cox regression was also used to test whether they were independent prognostic factors. The accuracy of this prognostic model was further validated by receiver operating characteristic curve (ROC), C-index and Nomogram. In addition, the lncRNA/miRNA/mRNA competing endogenous RNA (ceRNA) network and protein−protein interaction (PPI) network were constructed based on the weighted gene co-expression network analysis (WGCNA). Results: We constructed a prognostic model based on 15 cuproptosis-associated lncRNAs. The validation results showed that the risk score of the model (HR = 1.003, 95% CI = 1.001−1.004; p < 0.001) could serve as an independent prognostic factor with accurate and credible predictive power. The risk score had the highest AUC (0.793) among various factors such as risk score, stage, gender and age, also indicating that the model we constructed to predict patient survival was better than other clinical characteristics. Meanwhile, the possible biological mechanisms of colon adenocarcinoma were explored based on the lncRNA/miRNA/mRNA ceRNA network and PPI network constructed by WGCNA. Conclusion: The prognostic model based on 15 cuproptosis-related lncRNAs has accurate and reliable predictive power to effectively predict clinical outcomes in colon adenocarcinoma patients.

Identification and validation of a novel prognostic model of inflammation-related gene signature of lung adenocarcinoma

Previous literatures have suggested the importance of inflammatory response during lung adenocarcinoma (LUAD) development. This study aimed at exploring the inflammation-related genes and developing a prognostic signature for predicting the prognosis of LUAD. Survival‑associated inflammation-related genes were identified by univariate Cox regression analysis in the dataset of The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) penalized Cox regression model was used to derive a risk signature which is significantly negatively correlated with OS and divide samples into high-, medium- and low-risk group. Univariate and multivariate Cox analyses suggested that the level of risk group was an independent prognostic factor of the overall survival (OS). Time-dependent receiver operating characteristic (ROC) curve indicated the AUC of 1-, 3- and 5-years of the risk signature was 0.715, 0.719, 0.699 respectively. A prognostic nomogram was constructed by integrating risk group and clinical features. The independent dataset GSE30219 of Gene Expression Omnibus (GEO) was used for verification. We further explored the differences among risk groups in Gene set enrichment analysis (GSEA), tumor mutation and tumor microenvironment. Furthermore, Single Sample Gene Set Enrichment Analysis (ssGSEA) and the results of Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) suggested the status of immune cell infiltration was highly associated with risk groups. We demonstrated the prediction effect of CTLA-4 and PD-1/PD-L1 inhibitors in the low-risk group was better than that in the high-risk group using two methods of immune score include immunophenoscore from The Cancer Immunome Atlas (TCIA) and TIDE score from Tumor Immune Dysfunction and Exclusion (TIDE). In addition, partial targeted drugs and chemotherapy drugs for lung cancer had higher drug sensitivity in the high-risk group. Our findings provide a foundation for future research targeting inflammation-related genes to predictive prognosis and some reference significance for the selection of immunotherapy and drug regimen for lung adenocarcinoma.

[Immunotherapy for non-small cell lung cancer (NSCLC)]

Within a few years the introduction of immune checkpoint inhibitors (ICI) fundamentally changed the treatment landscape of patients with metastatic non-small cell lung cancer (NSCLC) and improved survival for a relevant proportion of patients. Immune monotherapies are highly efficient in cancers showing a PD-L1 overexpression ≥ 50% of tumor cells, all others with a lower level and independent from the PD-L1 expression can be treated with various treatment combinations. In a curative setting all PD-L1 positive patients (≥ 1%) who underwent chemoradiotherapy to reduce disease relapse and subsequently to improve survival should undergo an ICI maintenance treatment. Furthermore, positive results from phase III studies are also available for adjuvant treatment of patients with resectable NSCLC, whereby an EMA approval is currently pending. The treatment with ICIs has given rise to a new class of immune-mediated adverse side effects, which occur in approximately one third of the patients and range from easily substituted endocrinopathies to life-threatening organ toxicity. An anticipatory monitoring as well as interdisciplinary treatment are therefore the keys to avoiding progression of higher grade potentially fatal toxicities.

Comprehensive Analysis and Reinforcement Learning of Hypoxic Genes Based on Four Machine Learning Algorithms for Estimating the Immune Landscape, Clinical Outcomes, and Therapeutic Implications in Patients With Lung Adenocarcinoma

Background

Patients with lung adenocarcinoma (LUAD) exhibit significant heterogeneity in therapeutic responses and overall survival (OS). In recent years, accumulating research has uncovered the critical roles of hypoxia in a variety of solid tumors, but its role in LUAD is not currently fully elucidated. This study aims to discover novel insights into the mechanistic and therapeutic implications of the hypoxia genes in LUAD cancers by exploring the potential association between hypoxia and LUAD.

Methods

Four machine learning approaches were implemented to screen out potential hypoxia-related genes for the prognosis of LUAD based on gene expression profile of LUAD samples obtained from The Cancer Genome Atlas (TCGA), then validated by six cohorts of validation datasets. The risk score derived from the hypoxia-related genes was proven to be an independent factor by using the univariate and multivariate Cox regression analyses and Kaplan-Meier survival analyses. Hypoxia-related mechanisms based on tumor mutational burden (TMB), the immune activity, and therapeutic value were also performed to adequately dig deeper into the clinical value of hypoxia-related genes. Finally, the expression level of hypoxia genes was validated at protein level and clinical samples from LUAD patients at transcript levels.

Results

All patients in TCGA and GEO-LUAD group were distinctly stratified into low- and high-risk groups based on the risk score. Survival analyses demonstrated that our risk score could serve as a powerful and independent risk factor for OS, and the nomogram also exhibited high accuracy. LUAD patients in high-risk group presented worse OS, lower TMB, and lower immune activity. We found that the model is highly sensitive to immune features. Moreover, we revealed that the hypoxia-related genes had potential therapeutic value for LUAD patients based on the drug sensitivity and chemotherapeutic response prediction. The protein and gene expression levels of 10 selected hypoxia gene also showed significant difference between LUAD tumors tissues and normal tissues. The validation experiment showed that the gene transcript levels of most of their genes were consistent with the levels of their translated proteins.

Conclusions

Our study might contribute to the optimization of risk stratification for survival and personalized management of LUAD patients by using the hypoxia genes, which will provide a valuable resource that will guide both mechanistic and therapeutic implications of the hypoxia genes in LUAD cancers.

PRLHR Immune Genes Associated With Tumor Mutation Burden can be Used as Prognostic Markers in Patients With Gliomas

Tumor mutation burden (TMB) is a useful biomarker for predicting the prognosis and efficacy of immune checkpoint inhibitor (ICIs). In this study, we aimed to explore the prognostic value of TMB and TMB-related PRLHR immune genes as prognostic markers in patients with gliomas. We downloaded MAF files, RNA-seq data, and clinical information from the Cancer Genome Atlas (TCGA) database. The TMB of glioma was calculated and its correlation with clinical features and prognosis was analyzed. We found that TMB was statistically correlated with the grade and age of patients with gliomas. Kaplan-Meier curve analysis showed that low TMB was associated with better clinical outcome in patients with gliomas. Additionally, a predictive model based on five HUB genes (FABP5, VEGFA, SAA1, ADM, and PRLHR) was constructed to predict OS in patients with gliomas. Receiver operating characteristic curve analysis shows that the model is reliable in predicting the risk of survival and prognosis. Immune microenvironment analysis revealed a correlation between TMB and infiltrating immune cells. The clinical-related immune gene, PRLHR, can be used as an independent prognostic factor for patients with brain glioma, and it is negatively correlated with the grade of glioma and age of patients with glioma. We found that the higher the tumor grade and the older the age, the lower the PRLHR expression, which was verified by CGGA database and independent experimental data. These results suggest that PRLHR may be a tumor suppressor for the progression of glioma and might provide a new therapeutic target for the treatment and improvement of survival rate in patients with glioma.

Longitudinal plasma proteomic profiling of patients with non-small cell lung cancer undergoing immune checkpoint blockade

Background

Immune checkpoint inhibitors (ICIs) have revolutionized the cancer therapy landscape due to long-term benefits in patients with advanced metastatic disease. However, robust predictive biomarkers for response are still lacking and treatment resistance is not fully understood.

Methods

We profiled approximately 800 pre-treatment and on-treatment plasma proteins from 143 ICI-treated patients with non-small cell lung cancer (NSCLC) using ELISA-based arrays. Different clinical parameters were collected from the patients including specific mutations, smoking habits, and body mass index, among others. Machine learning algorithms were used to identify a predictive signature for response. Bioinformatics tools were used for the identification of patient subtypes and analysis of differentially expressed proteins and pathways in each response group.

Results

We identified a predictive signature for response to treatment comprizing two proteins (CXCL8 and CXCL10) and two clinical parameters (age and sex). Bioinformatic analysis of the proteomic profiles identified three distinct patient clusters that correlated with multiple parameters such as response, sex and TNM (tumors, nodes, and metastasis) staging. Patients who did not benefit from ICI therapy exhibited significantly higher plasma levels of several proteins on-treatment, and enrichment in neutrophil-related proteins.

Conclusions

Our study reveals potential biomarkers in blood plasma for predicting response to ICI therapy in patients with NSCLC and sheds light on mechanisms underlying therapy resistance.

Vaccine Therapy in Non-Small Cell Lung Cancer

Immunotherapy using immune checkpoint modulators has revolutionized the oncology field, emerging as a new standard of care for multiple indications, including non-small cell lung cancer (NSCLC). However, prognosis for patients with lung cancer is still poor. Although immunotherapy is highly effective in some cases, not all patients experience significant or durable responses, and further strategies are needed to improve outcomes. Therapeutic cancer vaccines are designed to exploit the body’s immune system to activate long-lasting memory against tumor cells that ensure tumor regression, with minimal toxicity. A unique feature of cancer vaccines lies in their complementary approach to boost antitumor immunity that could potentially act synergistically with immune checkpoint inhibitors (ICIs). However, single-line immunization against tumor epitopes with vaccine-based therapeutics has been disappointingly unsuccessful, to date, in lung cancer. The high level of success of several recent vaccines against SARS-CoV-2 has highlighted the evolving advances in science and technology in the vaccines field, raising hope that this strategy can be successfully applied to cancer treatments. In this review, we describe the biology behind the cancer vaccines, and discuss current evidence for the different types of therapeutic cancer vaccines in NSCLC, including their mechanisms of action, current clinical development, and future strategies.

Therapeutic Implications of Tumor Microenvironment in Lung Cancer: Focus on Immune Checkpoint Blockade

In the last decade, the treatment of non-small cell lung cancer (NSCLC) has been revolutionized by the introduction of immune checkpoint inhibitors (ICI) directed against programmed death protein 1 (PD-1) and its ligand (PD-L1), or cytotoxic T lymphocyte antigen 4 (CTLA-4). In spite of these improvements, some patients do not achieve any benefit from ICI, and inevitably develop resistance to therapy over time. Tumor microenvironment (TME) might influence response to immunotherapy due to its prominent role in the multiple interactions between neoplastic cells and the immune system. Studies investigating lung cancer from the perspective of TME pointed out a complex scenario where tumor angiogenesis, soluble factors, immune suppressive/regulatory elements and cells composing TME itself participate to tumor growth. In this review, we point out the current state of knowledge involving the relationship between tumor cells and the components of TME in NSCLC as well as their interactions with immunotherapy providing an update on novel predictors of benefit from currently employed ICI or new therapeutic targets of investigational agents. In first place, increasing evidence suggests that TME might represent a promising biomarker of sensitivity to ICI, based on the presence of immune-modulating cells, such as Treg, myeloid derived suppressor cells, and tumor associated macrophages, which are known to induce an immunosuppressive environment, poorly responsive to ICI. Consequently, multiple clinical studies have been designed to influence TME towards a pro-immunogenic state and subsequently improve the activity of ICI. Currently, the mostly employed approach relies on the association of "classic" ICI targeting PD-1/PD-L1 and novel agents directed on molecules, such as LAG-3 and TIM-3. To date, some trials have already shown promising results, while a multitude of prospective studies are ongoing, and their results might significantly influence the future approach to cancer immunotherapy.

Current Immunotherapeutic Strategies Targeting the PD-1/PD-L1 Axis in Non-Small Cell Lung Cancer with Oncogenic Driver Mutations

Treatment strategies targeting programed cell death 1 (PD-1) or its ligand, PD-L1, have been developed as immunotherapy against tumor progression for various cancer types including non-small cell lung cancer (NSCLC). The recent pivotal clinical trials of immune-checkpoint inhibiters (ICIs) combined with cytotoxic chemotherapy have reshaped therapeutic strategies and established various first-line standard treatments. The therapeutic effects of ICIs in these clinical trials were analyzed according to PD-L1 tumor proportion scores or tumor mutational burden; however, these indicators are insufficient to predict the clinical outcome. Consequently, molecular biological approaches, including multi-omics analyses, have addressed other mechanisms of cancer immune escape and have revealed an association of NSCLC containing specific driver mutations with distinct immune phenotypes. NSCLC has been characterized by driver mutation-defined molecular subsets and the effect of driver mutations on the regulatory mechanism of PD-L1 expression on the tumor itself. In this review, we summarize the results of recent clinical trials of ICIs in advanced NSCLC and the association between driver alterations and distinct immune phenotypes. We further discuss the current clinical issues with a future perspective for the role of precision medicine in NSCLC.