Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: a multicohort study

Genomic correlates of the response to first-line PD-1 blockade plus chemotherapy in patients with advanced non-small-cell lung cancer

BACKGROUND

Programmed death 1 (PD-1) blockade plus chemotherapy has become the new first-line standard of care for patients with advanced non-small-cell lung cancer (NSCLC). Yet not all NSCLC patients benefit from this regimen. This study aimed to investigate the predictors of PD-1 blockade plus chemotherapy in untreated advanced NSCLC.

METHODS

We integrated clinical, genomic, and survival data from 287 patients with untreated advanced NSCLC who were enrolled in one of five registered phase 3 trials and received PD-1 blockade plus chemotherapy or chemotherapy alone. We randomly assigned these patients into a discovery cohort (n = 125), a validation cohort (n = 82), and a control cohort (n = 80). The candidate genes that could predict the response to PD-1 blockade plus chemotherapy were identified using data from the discovery cohort and their predictive values were then evaluated in the three cohorts. Immune deconvolution was conducted using transcriptome data of 1014 NSCLC patients from The Cancer Genome Atlas dataset.

RESULTS

A genomic variation signature, in which one or more of the 15 candidate genes were altered, was correlated with significantly inferior response rates and survival outcomes in patients treated with first-line PD-1 blockade plus chemotherapy in both discovery and validation cohorts. Its predictive value held in multivariate analyses when adjusted for baseline parameters, programmed cell death ligand 1 (PD-L1) expression level, and tumor mutation burden. Moreover, applying both the 15-gene panel and PD-L1 expression level produced better performance than either alone in predicting benefit from this treatment combination. Immune landscape analyses revealed that tumors with one or more variation in the 15-gene panel were associated with few immune infiltrates, indicating an immune-desert tumor microenvironment.

CONCLUSION

These findings indicate that a 15-gene panel can serve as a negative prediction biomarker for first-line PD-1 blockade plus chemotherapy in patients with advanced NSCLC.

Multiple omics integrative analysis identifies GARS1 as a novel prognostic and immunological biomarker: from pan-cancer to bladder cancer

Glycyl-tRNA synthetase (GARS1) is differentially expressed across cancers. In this study, the value of GARS1 in the diagnosis and prognosis of various cancers was comprehensively evaluated by multiple omics integrative pan-cancer analysis and experimental verification. Through Kaplan-Meier, ROC and multiple databases, we explored GARS1 expression and prognostic and diagnostic patterns across cancers. The GARS1 relative reaction network was identified in PPI, GO, KEGG, methylation models and the genetic mutation atlas. Further research on the GARS1 value in bladder urothelial carcinoma (BLCA) was conducted by regression and nomogram models. We further analyzed the correlation between GARS1 and immune markers and cells in BLCA. Finally, in vitro experiments were used to validate GARS1 the oncogenic function of GARS1 in BLCA. We found that GARS1 was highly expressed across cancers, especially in BLCA. GARS1 expression was correlated with poor survival and had high diagnostic value in most tumor types. GARS1 is significantly associated with tRNA-related pathways whose mutation sites are mainly located on tRNA synthetase. In addition, Upregulation of GARS1 was connected with immune cell infiltration and five key MMR genes. M2 macrophages, TAMs, Th1 and T-cell exhaustion, and marker sets associated with GARS1 expression indicated specific immune infiltration in BLCA. Finally, in vitro experiments validated that GARS1 expression promotes BLCA cell proliferation and metastasis and inhibits apoptosis. Overall, GARS1 can be a novel prognostic and immunological biomarker through multiple omics integrative pan-cancer analysis. The expression of GARS1 in BLCA was positively correlated with specific immune infiltration, indicating that GARS1 might be related to the tumor immune microenvironment.

The modification role and tumor association with a methyltransferase: KMT2C

Histone methylation can affect chromosome structure and binding to other proteins, depending on the type of amino acid being modified and the number of methyl groups added, this modification may promote transcription of genes (H3K4me2, H3K4me3, and H3K79me3) or reduce transcription of genes (H3K9me2, H3K9me3, H3K27me2, H3K27me3, and H4K20me3). In addition, advances in tumor immunotherapy have shown that histone methylation as a type of protein post-translational modification is also involved in the proliferation, activation and metabolic reprogramming of immune cells in the tumor microenvironment. These post-translational modifications of proteins play a crucial role in regulating immune escape from tumors and immunotherapy. Lysine methyltransferases are important components of the post-translational histone methylation modification pathway. Lysine methyltransferase 2C (KMT2C), also known as MLL3, is a member of the lysine methyltransferase family, which mediates the methylation modification of histone 3 lysine 4 (H3K4), participates in the methylation of many histone proteins, and regulates a number of signaling pathways such as EMT, p53, Myc, DNA damage repair and other pathways. Studies of KMT2C have found that it is aberrantly expressed in many diseases, mainly tumors and hematological disorders. It can also inhibit the onset and progression of these diseases. Therefore, KMT2C may serve as a promising target for tumor immunotherapy for certain diseases. Here, we provide an overview of the structure of KMT2C, disease mechanisms, and diseases associated with KMT2C, and discuss related challenges.

A novel artificial intelligence network to assess the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features

Background

Immune checkpoint inhibitors (ICIs) have revolutionized gastrointestinal cancer treatment, yet the absence of reliable biomarkers hampers precise patient response prediction.

Methods

We developed and validated a genomic mutation signature (GMS) employing a novel artificial intelligence network to forecast the prognosis of gastrointestinal cancer patients undergoing ICIs therapy. Subsequently, we explored the underlying immune landscapes across different subtypes using multiomics data. Finally, UMI-77 was pinpointed through the analysis of drug sensitization data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. The sensitivity of UMI-77 to the AGS and MKN45 cell lines was evaluated using the cell counting kit-8 (CCK8) assay and the plate clone formation assay.

Results

Using the artificial intelligence network, we developed the GMS that independently predicts the prognosis of gastrointestinal cancer patients. The GMS demonstrated consistent performance across three public cohorts and exhibited high sensitivity and specificity for 6, 12, and 24-month overall survival (OS) in receiver operating characteristic (ROC) curve analysis. It outperformed conventional clinical and molecular features. Low-risk samples showed a higher presence of cytolytic immune cells and enhanced immunogenic potential compared to high-risk samples. Additionally, we identified the small molecule compound UMI-77. The half-maximal inhibitory concentration (IC50) of UMI-77 was inversely related to the GMS. Notably, the AGS cell line, classified as high-risk, displayed greater sensitivity to UMI-77, whereas the MKN45 cell line, classified as low-risk, showed less sensitivity.

Conclusion

The GMS developed here can reliably predict survival benefit for gastrointestinal cancer patients on ICIs therapy.

Implication of PD‑L1 polymorphisms rs2297136 on clinical outcomes of patients with advanced NSCLC who received PD‑1 blockades: A retrospective exploratory study

Clinically, programmed death-1 (PD-1) blockades have demonstrated promising therapeutic outcomes for patients with advanced non-small cell lung cancer (NSCLC). The present study aimed to examine the impact of programmed death-ligand 1 (PD-L1) polymorphism on clinical outcomes of patients with advanced NSCLC who were treated with PD-1 blockades therapy. The present study was designed as a retrospective analysis, where a consecutive screening of 89 patients with advanced NSCLC who received PD-1 blockades monotherapy were screened. Biological specimens were collected to determine the presence of polymorphism and PD-L1 mRNA expression through genotyping. The analysis focused on examining the relationship between the genotype status of PD-L1 polymorphism and clinical outcomes. Among the 89 patients with advanced NSCLC, the use of PD-1 blockades monotherapy resulted in objective response rate (ORR) of 22.5%, a median progression-free survival (PFS) of 3.4 months [95% Confidence Interval (CI): 1.80-5.00) and a median overall survival (OS) of 11.3 months (95% CI: 7.93-14.67). The analysis of polymorphism indicated that only rs2297136 had clinical significance. Among the 89 patients with NSCLC, the prevalence of rs2297136 was as follows: A total of 58 cases (65.2%) had the AA genotype, 28 cases (31.5%) had the AG genotype and 3 cases (3.4%) had the GG genotype. This resulted in a minor allele frequency of 0.19, which was in consistent with Hardy-Weinberg Equilibrium (P=0.865). The correlation analysis between genotype status of rs2297136 and clinical outcomes indicated that patients with the AA genotype had an ORR of 19.0%, while those with the AG/GG genotype had an ORR of 29.0% (P=0.278). Additionally, the median PFS for the AA genotype was 2.95 months, compared with 5.30 months for the AG/GG genotype (P=0.038). Accordingly, median OS of the AA and AG/GG genotypes was 8.8 and 18.4 months, respectively (P=0.011). The mRNA expression of PD-L1 was significantly higher in patients with AG/GG genotype compared with those with AA genotype (P<0.001). In clinical practice, PD-1 blockades demonstrated promising effectiveness in treating patients with advanced NSCLC. The presence of the rs2297136 variant in PD-L1 gene could potentially be used as a biomarker to predict the clinical outcomes of PD-1 blockades.

Identifying survival of pan-cancer patients under immunotherapy using genomic mutation signature with large sample cohorts

Although immune checkpoint inhibitors have led to durable clinical response in multiple cancers, only a small proportion of patients respond to this treatment. Therefore, we aim to develop a predictive model that utilizes gene mutation profiles to accurately identify the survival of pan-cancer patients with immunotherapy. Here, we develop and evaluate three different nomograms using two cohorts containing 1,594 cancer patients whose mutation profiles are obtained by MSK-IMPACT sequencing and 230 cancer patients receiving whole-exome sequencing, respectively. Using eighteen genes (SETD2, BRAF, NCOA3, LATS1, IL7R, CREBBP, TET1, EPHA7, KDM5C, MET, KMT2D, RET, PAK7, CSF1R, JAK2, FAT1, ASXL1 and SPEN), the first nomogram stratifies patients from both cohorts into High-Risk and Low-Risk groups. Pan-cancer patients in the High-Risk group exhibit significantly shorter overall survival and progression-free survival than patients in the Low-Risk group in both cohorts. Meanwhile, the first nomogram also accurately identifies the survival of patients with melanoma or lung cancer undergoing immunotherapy, or pan-cancer patients treated with anti-PD-1/PD-L1 inhibitor or anti-CTLA-4 inhibitor. The model proposed is not a prognostic model for the survival of pan-cancer patients without immunotherapy, but a simple, effective and robust predictive model for pan-cancer patients' survival under immunotherapy, and could provide valuable assistance for clinical practice.

Determining the prognosis of Lung cancer from mutated genes using a deep learning survival model: a large multi-center study

BACKGROUND

Gene status has become the focus of prognosis prediction. Furthermore, deep learning has frequently been implemented in medical imaging to diagnose, prognosticate, and evaluate treatment responses in patients with cancer. However, few deep learning survival (DLS) models based on mutational genes that are directly associated with patient prognosis in terms of progression-free survival (PFS) or overall survival (OS) have been reported. Additionally, DLS models have not been applied to determine IO-related prognosis based on mutational genes. Herein, we developed a deep learning method to predict the prognosis of patients with lung cancer treated with or without immunotherapy (IO).

METHODS

Samples from 6542 patients from different centers were subjected to genome sequencing. A DLS model based on multi-panels of somatic mutations was trained and validated to predict OS in patients treated without IO and PFS in patients treated with IO.

RESULTS

In patients treated without IO, the DLS model (low vs. high DLS) was trained using the training MSK-MET cohort (HR = 0.241 [0.213-0.273], P < 0.001) and tested in the inter-validation MSK-MET cohort (HR = 0.175 [0.148-0.206], P < 0.001). The DLS model was then validated with the OncoSG, MSK-CSC, and TCGA-LUAD cohorts (HR = 0.420 [0.272-0.649], P < 0.001; HR = 0.550 [0.424-0.714], P < 0.001; HR = 0.215 [0.159-0.291], P < 0.001, respectively). Subsequently, it was fine-tuned and retrained in patients treated with IO. The DLS model (low vs. high DLS) could predict PFS and OS in the MIND, MSKCC, and POPLAR/OAK cohorts (P < 0.001, respectively). Compared with tumor-node-metastasis staging, the COX model, tumor mutational burden, and programmed death-ligand 1 expression, the DLS model had the highest C-index in patients treated with or without IO.

CONCLUSIONS

The DLS model based on mutational genes can robustly predict the prognosis of patients with lung cancer treated with or without IO.

Exploration of the radiosensitivity-related prognostic risk signature in patients with glioma: evidence from microarray data

BACKGROUND

Gene expression signatures can be used as prognostic biomarkers in various types of cancers. We aim to develop a gene signature for predicting the response to radiotherapy in glioma patients.

METHODS

Radio-sensitive and radio-resistant glioma cell lines (M059J and M059K) were subjected to microarray analysis to screen for differentially expressed mRNAs. Additionally, we obtained 169 glioblastomas (GBM) samples and 5 normal samples from The Cancer Genome Atlas (TCGA) database, as well as 80 GBM samples and 4 normal samples from the GSE7696 set. The "DESeq2" R package was employed to identify differentially expressed genes (DEGs) between the normal brain samples and GBM samples. Combining the prognostic-related molecules identified from the TCGA, we developed a radiosensitivity-related prognostic risk signature (RRPRS) in the training set, which includes 152 patients with glioblastoma. Subsequently, we validated the reliability of the RRPRS in a validation set containing 616 patients with glioma from the TCGA database, as well as an internal validation set consisting of 31 glioblastoma patients from the Nanfang Hospital, Southern Medical University.

RESULTS

Based on the microarray and LASSO COX regression analysis, we developed a nine-gene radiosensitivity-related prognostic risk signature. Patients with glioma were divided into high- or low-risk groups based on the median risk score. The Kaplan-Meier survival analysis showed that the progression-free survival (PFS) of the high-risk group was significantly shorter. The signature accurately predicted PFS as assessed by time-dependent receiver operating characteristic curve (ROC) analyses. Stratified analysis demonstrated that the signature is specific to predict the outcome of patients who were treated using radiotherapy. Univariate and multivariate Cox regression analysis revealed that the predictor was an independent predictor for the prognosis of patients with glioma. The prognostic nomograms accompanied by calibration curves displayed the 1-, 2-, and 3-year PFS and OS in patients with glioma.

CONCLUSION

Our study established a new nine-gene radiosensitivity-related prognostic risk signature that can predict the prognosis of patients with glioma who received radiotherapy. The nomogram showed great potential to predict the prognosis of patients with glioma treated using radiotherapy.

RAIDS atlas of significant genetic and protein biomarkers in cervical cancer

Loss of function in epigenetic acting genes together with driver alterations in the PIK3CA pathway have been shown significantly associated with poor outcome in cervical squamous cell cancer. More recently, a CoxBoost analysis identified 16 gene alterations and 30 high level activated proteins to be of high interest, due to their association with either good or bad outcome, in the context of treatment received by chemoradiation. The objectives here were to review and confirm the significance of these molecular alterations as suggested by literature reports and to pinpoint alternate treatments options for poor-responders to chemoradiation.

Network meta-analysis of first-line immune checkpoint inhibitor therapy in advanced non-squamous non-small cell lung cancer patients with PD-L1 expression ≥ 50

INTRODUCTION

The optimal first-line immunotherapy regimen for advanced non-squamous non-small cell lung cancer (NS-NSCLC) patients with programmed cell death ligand 1 (PD-L1) expression ≥ 50% remains unclear. Our aim is to determine the most effective treatment regimen through a network meta-analysis (NMA) comparing these treatments.

METHODS

A systematic search was performed in PubMed, Cochrane Library, Web of Science, and Embase databases, and a Bayesian network meta-analysis was conducted. To ensure transparency, the study was registered in the International Prospective Register of Systematic Reviews (CRD42022349712).

RESULTS

The analysis included 11 randomized controlled trials (RCTs) with 2037 patients and 12 immunotherapy combinations. ICI-ICI, ICI alone, and chemotherapy-ICI showed significant advantages over chemotherapy in terms of overall survival (OS) and progression-free survival (PFS). Pembrolizumab plus chemotherapy showed the best OS results compared to chemotherapy. Tislelizumab plus chemotherapy and sintilimab plus chemotherapy provided the best PFS results.

CONCLUSIONS

For NS-NSCLC patients with PD-L1 ≥ 50%, pembrolizumab plus chemotherapy, tislelizumab plus chemotherapy, and sintilimab plus chemotherapy are recommended as good treatment options based on the results of this Network meta-analysis (NMA).

Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations

Only 30-40% of advanced melanoma patients respond effectively to immunotherapy in clinical practice, so it is necessary to accurately identify the response of patients to immunotherapy pre-clinically. Here, we develop KP-NET, a deep learning model that is sparse on KEGG pathways, and combine it with transfer- learning to accurately predict the response of advanced melanomas to immunotherapy using KEGG pathway-level information enriched from gene mutation and copy number variation data. The KP-NET demonstrates best performance with AUROC of 0.886 on testing set and 0.803 on an unseen evaluation set when predicting responders (CR/PR/SD with PFS ≥6 mo) versus non-responders (PD/SD with PFS <6 mo) in anti-CTLA-4 treated melanoma patients. The model also achieves an AUROC of 0.917 and 0.833 in predicting CR/PR versus PD, respectively. Meanwhile, the AUROC is 0.913 when predicting responders versus non-responders in anti-PD-1/PD-L1 melanomas. Moreover, the KP-NET reveals some genes and pathways associated with response to anti-CTLA-4 treatment, such as genes PIK3CA, AOX1 and CBLB, and ErbB signaling pathway, T cell receptor signaling pathway, et al. In conclusion, the KP-NET can accurately predict the response of melanomas to immunotherapy and screen related biomarkers pre-clinically, which can contribute to precision medicine of melanoma.

Comprehensive bioinformatic analysis constructs a CXCL model for predicting survival and immunotherapy effectiveness in ovarian cancer

Background: Immunotherapy has limited effectiveness in ovarian cancer (OC) patients, highlighting the need for reliable biomarkers to predict the effectiveness of these treatments. The C-X-C motif chemokine ligands (CXCLs) have been shown to be associated with survival outcomes and immunotherapy efficacy in cancer patients. In this study, we aimed to evaluate the predictive value of 16 CXCLs in OC patients.

Methods: We analyzed RNA-seq data from The Cancer Genome Atlas, Gene Expression Omnibus, and UCSC Xena database and conducted survival analysis. Consensus cluster analysis was used to group patients into distinct clusters based on their expression patterns. Biological pathway alterations and immune infiltration patterns were examined across these clusters using gene set variation analysis and single-sample gene set enrichment analysis. We also developed a CXCL scoring model using principal component analysis and evaluated its effectiveness in predicting immunotherapy response by assessing tumor microenvironment cell infiltration, tumor mutational burden estimation, PD-L1/CTLA4 expression, and immunophenoscore analysis (IPS).

Results: Most CXCL family genes were overexpressed in OC tissues compared to normal ovarian tissues. Patients were grouped into three distinct CXCL clusters based on their CXCL expression pattern. Additionally, using differentially expressed genes among the CXCL clusters, patients could also be grouped into three gene clusters. The CXCL and gene subtypes effectively predicted survival and immune cell infiltration levels for OC patients. Furthermore, patients with high CXCL scores had significantly better survival outcomes, higher levels of immune cell infiltration, higher IPS, and higher expression of PD-L1/CTLA4 than those with low CXCL scores.

Conclusion: The CXCL score has the potential to be a promising biomarker to guide immunotherapy in individual OC patients and predict their clinical outcomes and immunotherapy responses.

A new ferroptosis-related genetic mutation risk model predicts the prognosis of skin cutaneous melanoma

Background: Ferroptosis is an iron-dependent cell death mode and closely linked to various cancers, including skin cutaneous melanoma (SKCM). Although attempts have been made to construct ferroptosis-related gene (FRG) signatures for predicting the prognosis of SKCM, the prognostic impact of ferroptosis-related genetic mutations in SKCM remains lacking. This study aims to develop a prediction model to explain the relationship between ferroptosis-related genetic mutations and clinical outcomes of SKCM patients and to explore the potential value of ferroptosis in SKCM treatment. Methods: FRGs which significantly correlated with the prognosis of SKCM were firstly screened based on their single-nucleotide variant (SNV) status by univariate Cox regression analysis. Subsequently, the least absolute shrinkage and selection operator (LASSO) and Cox regressions were performed to construct a new ferroptosis-related genetic mutation risk (FerrGR) model for predicting the prognosis of SKCM. We then illustrate the survival and receiver operating characteristic (ROC) curves to evaluate the predictive power of the FerrGR model. Moreover, independent prognostic factors, genomic and clinical characteristics, immunotherapy, immune infiltration, and sensitive drugs were compared between high-and low-FerrGR groups. Results: The FerrGR model was developed with a good performance on survival and ROC analysis. It was a robust independent prognostic indicator and followed a nomogram constructed to predict prognostic outcomes for SKCM patients. Besides, FerrGR combined with tumor mutational burden (TMB) or MSI (microsatellite instability) was considered as a combined biomarker for immunotherapy response. The high FerrGR group patients were associated with an inhibitory immune microenvironment. Furthermore, potential drugs target to high FerrGR samples were predicted. Conclusion: The FerrGR model is valuable to predict prognosis and immunotherapy in SKCM patients. It offers a novel therapeutic option for SKCM.

Unique profile on the progress free survival and overall survival in patients with advanced non-small cell lung cancer in the Qujing area, Southwest China

Background

China's southwestern region, Qujing, harbors a high incidence of non-small cell lung cancer (NSCLC) and related mortality. This study was designed to reveal the impact of an immune-related prognostic signature (IRPS) on advanced NSCLC in the Qujing.

Methods

Tissue specimens from an independent cohort of 37 patients with advanced NSCLC were retrospectively evaluated to determine the relationship between the IRPS estimated by next-generation sequencing (NGS) and clinical outcome. To compare the IRPS in tissue and the clinical outcomes between Qujing and non-Qujing populations, we analyzed datasets of 23 patients with advanced NSCLC from The Cancer Genome Atlas (TCGA) database. In addition, an independent cohort (n=111) of blood specimens was retrospectively analyzed to determine the relationship between the IRPS and clinical outcome. Finally, we evaluated the utility of the blood IRPS in classifying 24 patients with advanced NSCLC who might benefit from immunotherapy.

Results

In cohort 1, the Qujing population with tTMB-H (≥ 10 mutations/Mb) or KRAS mutations had shorter progression-free survival (PFS) (hazard ratio [HR] 0.37, 0.14 to 0.97, P = 0.04; HR 0.23, 0.08 to 0.66, P < 0.01) and overall survival (OS) (HR 0.05, 0.01 to 0.35, P < 0.01; HR 0.22, 0.07 to 0.66, P < 0.01). In cohort 2 of the Qujing population, bTMB-H (≥ 6 mutations per Mb) and KRAS mutations were related to PFS (HR 0.59, 0.36 to 0.99, P = 0.04; HR 0.50, 0.26 to 0.98, P = 0.04) and OS (HR 0.58, 0.35 to 0.96, P = 0.03; HR 0.48, 0.25 to 0.93, P = 0.03). Notably, the Qujing population with bTMB-H had superior PFS (HR 0.32, 0.09 to 1.09, P = 0.01), OS (HR 0.33, 0.10 to 1.13, P < 0.01) and objective response rates (ORRs) (83.3% vs. 14.3% vs. 20.0%, P <0.01) to immunotherapy than other populations.

Conclusions

These findings show that tTMB, bTMB and KRAS mutations appear to be independent validated IRPSs that predict the clinical outcomes of Qujing populations with advanced NSCLC and that bTMB may be used as a reliable IRPS to predict the clinical benefit from anti-PD-1 therapies among populations from Qujing with advanced NSCLC.

Systematic pan-cancer analysis of the potential tumor diagnosis and prognosis biomarker P4HA3

Purpose: Prolyl 4-hydroxylase subunit alpha 3 (P4HA3) is implicated in several cancers' development. However, P4HA3 has not been reported in other cancers, and the exact mechanism of action is currently unknown. Materials and methods: First, the expression profile of P4HA3 was analyzed using a combination of the University of California Santa Cruz (UCSC) database, Cancer Cell Line Encyclopedia (CCLE) database, and Genotype-Tissue Expression (GTEx) database. UniCox and Kaplan-Meier were used to analyze the predictive value of P4HA3. The expression of P4HA3 was analyzed in clinical staging, immune subtypes, and Molecular subtypes. Secondly, the correlation of P4HA3 with immunomodulatory genes, immune checkpoint genes, RNA modification genes, immune cell infiltration, cancer-related functional status, tumor stemness index, DNA mismatch repair (MMR) genes and DNA Methyltransferase was examined. The role of P4HA3 in DNA methylation, copy number variation (CNV), mutational status, tumor mutational burden (TMB), and microsatellite instability (MSI) was also analyzed. In addition, gene set enrichment analysis (GSEA) was used to explore the potential functional mechanisms of P4HA3 in pan-cancer. Finally, P4HA3-related drugs were searched in CellMiner, Genomics of Drug Sensitivity in Cancer (GDSC), and Cancer Therapeutics Response Portal (CTRP) databases. Results: P4HA3 is significantly overexpressed in most cancers and is associated with poor prognosis. P4HA3 is strongly associated with clinical cancer stage, immune subtypes, molecular subtypes, immune regulatory genes, immune checkpoint genes, RNA modifier genes, immune cell infiltration, cancer-related functional status, tumor stemness index, MMR Gene, DNA Methyltransferase, DNA methylation, CNV, mutational status, TMB, and MSI are closely related. Available enrichment analysis revealed that P4HA3 is associated with the epithelial-mesenchymal transition and immune-related pathways. There are currently 20 drugs associated with P4HA3. Conclusion: In human pan-cancer, P4HA3 is associated with poor patient prognosis and multiple immune cells and may be a novel immunotherapeutic target. It may act on tumor progression through the epithelial-mesenchymal transition (EMT) pathway.

Identification of comutation in signaling pathways to predict the clinical outcomes of immunotherapy

BACKGROUND

Immune checkpoint blockades (ICBs) have emerged as a promising treatment for cancer. Recently, tumour mutational burden (TMB) and neoantigen load (NAL) have been proposed to be potential biomarkers to predict the efficacy of ICB; however, they were limited by difficulties in defining the cut-off values and inconsistent detection platforms. Therefore, it is critical to identify more effective predictive biomarkers for screening patients who will potentially benefit from immunotherapy. In this study, we aimed to identify comutated signaling pathways to predict the clinical outcomes of immunotherapy.

METHODS

Here, we comprehensively analysed the signaling pathway mutation status of 9763 samples across 33 different cancer types from The Cancer Genome Atlas (TCGA) by mapping the somatic mutations to the pathways. We then explored the comutated pathways that were associated with increased TMB and NAL by using receiver operating characteristic (ROC) curve analysis and multiple linear regressions.

RESULTS

Our results revealed that comutation of the Spliceosome (Sp) pathway and Hedgehog (He) signaling pathway (defined as SpHe-comut⁺) could be used as a predictor of increased TMB and NAL and was associated with increased levels of immune-related signatures. In seven independent immunotherapy cohorts, we validated that SpHe-comut⁺ patients exhibited a longer overall survival (OS) or progression-free survival (PFS) and a higher objective response rate (ORR) than SpHe-comut^- patients. Moreover, a combination of SpHe-comut status with PD-L1 expression further improved the predictive value for ICB therapy.

CONCLUSION

Overall, SpHe-comut⁺ was demonstrated to be an effective predictor of immunotherapeutic benefit in seven independent immunotherapy cohorts and may serve as a potential and convenient biomarker for the clinical application of ICB therapy.

Identification and validation of a genomic mutation signature as a predictor for immunotherapy in NSCLC

Currently, the benefits of immune checkpoint inhibitor (ICI) therapy prediction via emerging biomarkers have been identified, and the association between genomic mutation signatures (GMS) and immunotherapy benefits has been widely recognized as well. However, the evidence about non-small cell lung cancer (NSCLC) remains limited. We analyzed 310 immunotherapy patients with NSCLC from the Memorial Sloan Kettering Cancer Center (MSKCC) cohort. Lasso Cox regression was used to construct a GMS, and the prognostic value of GMS could be able to verify in the Rizvi cohort (N=240) and Hellmann cohort (N=75). We further conducted immunotherapy-related characteristics analysis in The Cancer Genome Atlas (TCGA) cohort (N=1052). A total of seven genes (ZFHX3, NTRK3, EPHA7, MGA, STK11, EPHA5, TP53) were identified for GMS model construction. Compared with GMS-high patients, patients with GMS-low had longer overall survival (OS; P<0.001) in the MSKCC cohort and progression-free survival (PFS; P<0.001) in the validation cohort. Multivariate Cox analysis revealed that GMS was an independent predictive factor for NSCLC patients in both the MSKCC and validation cohort. Meanwhile, we found that GMS-low patients reflected enhanced antitumor immunity in TCGA cohort. The results indicated that GMS had not only potential predictive value for the benefit of immunotherapy but also may serve as a potential biomarker to guide clinical ICI treatment decisions for NSCLC.

A novel pathway mutation perturbation score predicts the clinical outcomes of immunotherapy

The link between tumor genetic variations and immunotherapy benefits has been widely recognized. Recent studies suggested that the key biological pathways activated by accumulated genetic mutations may act as an effective biomarker for predicting the efficacy of immune checkpoint inhibitor (ICI) therapy. Here, we developed a novel individual Pathway Mutation Perturbation (iPMP) method that measures the pathway mutation perturbation level by combining evidence of the cumulative effect of mutated genes with the position of mutated genes in the pathways. In iPMP, somatic mutations on a single sample were first mapped to genes in a single pathway to infer the pathway mutation perturbation score (PMPscore), and then, an integrated PMPscore profile was produced, which can be used in place of the original mutation dataset to identify associations with clinical outcomes. To illustrate the effect of iPMP, we applied it to a melanoma cohort treated with ICIs and identified seven significant perturbation pathways, which jointly constructed a pathway-based signature. With the signature, patients were classified into two subgroups with significant distinctive overall survival and objective response rate to immunotherapy. Moreover, the pathway-based signature was consistently validated in two independent melanoma cohorts. We further applied iPMP to two non-small cell lung cancer cohorts and also obtained good performance. Altogether, the iPMP method could be used to identify the significant mutation perturbation pathways for constructing the pathway-based biomarker to predict the clinical outcomes of immunotherapy. The iPMP method has been implemented as a freely available R-based package (https://CRAN.R-project.org/package=PMAPscore).

Pancancer analysis of a potential gene mutation model in the prediction of immunotherapy outcomes

Background: Immune checkpoint blockade (ICB) represents a promising treatment for cancer, but predictive biomarkers are needed. We aimed to develop a cost-effective signature to predict immunotherapy benefits across cancers.

Methods: We proposed a study framework to construct the signature. Specifically, we built a multivariate Cox proportional hazards regression model with LASSO using 80% of an ICB-treated cohort (n = 1661) from MSKCC. The desired signature named SIGP was the risk score of the model and was validated in the remaining 20% of patients and an external ICB-treated cohort (n = 249) from DFCI.

Results: SIGP was based on 18 candidate genes (NOTCH3, CREBBP, RNF43, PTPRD, FAM46C, SETD2, PTPRT, TERT, TET1, ROS1, NTRK3, PAK7, BRAF, LATS1, IL7R, VHL, TP53, and STK11), and we classified patients into SIGP high (SIGP-H), SIGP low (SIGP-L) and SIGP wild type (SIGP-WT) groups according to the SIGP score. A multicohort validation demonstrated that patients in SIGP-L had significantly longer overall survival (OS) in the context of ICB therapy than those in SIGP-WT and SIGP-H (44.00 months versus 13.00 months and 14.00 months, p &lt; 0.001 in the test set). The survival of patients grouped by SIGP in non-ICB-treated cohorts was different, and SIGP-WT performed better than the other groups. In addition, SIGP-L + TMB-L (approximately 15% of patients) had similar survivals to TMB-H, and patients with both SIGP-L and TMB-H had better survival. Further analysis on tumor-infiltrating lymphocytes demonstrated that the SIGP-L group had significantly increased abundances of CD8+ T cells.

Conclusion: Our proposed model of the SIGP signature based on 18-gene mutations has good predictive value for the clinical benefit of ICB in pancancer patients. Additional patients without TMB-H were identified by SIGP as potential candidates for ICB, and the combination of both signatures showed better performance than the single signature.

PD-L1 expression, tumor mutational burden, and immune cell infiltration in non-small cell lung cancer patients with epithelial growth factor receptor mutations

Background

Immunotherapy using programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) inhibitors seems less effective in non-small cell lung cancer (NSCLC) patients with epithelial growth factor receptor (EGFR) mutations. Varied responses to PD-1/PD-L1 inhibitors have recently been observed in NSCLC patients harboring different types of EGFR mutations. Some EGFR-mutated NSCLC patients may benefit from PD-1/PD-L1 inhibitors. At present, PD-L1 expression, tumor mutational burden (TMB), and tumor immune microenvironment (TIME) are biomarkers for predicting the efficacy of PD-1/PD-L1 inhibitors in NSCLC patients. We retrospectively evaluated PD-L1 expression, TMB, and immune cell infiltration in NSCLC patients with EGFR mutation subtypes.

Methods

PD-L1 expression, TMB, and the abundance of immune cell infiltration in NSCLC patients were evaluated in public databases and clinical samples. TMB was detected using the NGS technique, PD-L1 was detected using immunohistochemistry, and the abundance of immune cell infiltration in NSCLC samples was detected using multiple immunohistochemistry.

Results

PD-L1 expression and TMB were lower in EGFR-mutated NSCLCs than in wild-type patients. Differences in the abundance of immune cell infiltration were also observed between EGFR-mutated and wild-type NSCLC. The expression of PD-L1, TMB, and abundance of immune cell infiltration were different in patients harboring different subtypes of EGFR mutations. Patients with uncommon EGFR mutations, especially the G719X mutation, showed higher TMB and expressions of PD-L1 than classical EGFR mutations. M1 macrophages were higher in uncommon EGFR mutations than classical EGFR mutations.

Conclusions

The expression of PD-L1 and TMB in uncommon EGFR-mutated NSCLCs, especially the G719X mutation, were higher than those for classical EGFR-mutated NSCLCs and similar to EGFR wild-type. The abundance of immune cell infiltration in uncommon EGFR-mutated NSCLCs was similar to that in EGFR wild-type. Our findings suggest that uncommon EGFR-mutated NSCLCs may benefit from PD-1/PD-L1 inhibitors.

An immune gene signature to predict prognosis and immunotherapeutic response in lung adenocarcinoma

Lung adenocarcinoma is one of the most common malignant tumors worldwide. The purpose of this study was to construct a stable immune gene signature for prediction of prognosis (IGSPP) and response to immune checkpoint inhibitors (ICIs) therapy in LUAD patients. Five genes were screened by weighted gene coexpression network analysis, Cox regression and LASSO regression analyses and were used to construct the IGSPP. The survival rate of the IGSPP low-risk group was higher than that of the IGSPP high-risk group. Multivariate Cox regression analysis showed that IGSPP could be used as an independent prognostic factor for the overall survival of LUAD patients. IGSPP genes were enriched in cell cycle pathways. IGSPP gene mutation rates were higher in the high-risk group. CD4 memory-activated T cells, M0 and M1 macrophages had higher infiltration abundance in the high-risk group, which was associated with poor overall survival. In contrast, the abundance of resting CD4 memory T cells, monocytes, resting dendritic cells and resting mast cells associated with a better prognosis was higher in the low-risk group. TIDE scores and the expressions of different immune checkpoints showed that patients in the high-risk IGSPP group benefited more from ICIs treatment. In short, an IGSPP of LUAD was constructed and characterized. It could be used to predict the prognosis and benefits of ICIs treatment in LUAD patients.

Pan-Cancer Study of SHC-Adaptor Protein 1 (SHC1) as a Diagnostic, Prognostic and Immunological Biomarker in Human Cancer

Background: Recent studies highlight the carcinogenesis role of SHC-adaptor protein 1 (SHC1) in cancer initiation, development, and progression. However, its aberrant expression, diagnostic and prognostic value remain unknown in a variety of tumors.

Methods: The SHC1 expression profiles were analyzed using GTEx database, TCGA database, Oncomine and CPTAC database. The survival analysis was conducted using GEPIA2, Kaplan-Meier Plotter, UALCAN, and PrognoScan. The diagnostic values of SHC1 were calculated with the “pROC” package in R software. The genetic alteration of SHC1 and mutations were analyzed using cBioPortal. TIMER2 was employed to estimate the correlations between SHC1 expression and tumor-infiltrating immune cells in the TCGA cohort. Enrichment analysis of SHC1 was conducted using the R package “clusterProfiler.”

Results: SHC1 was ubiquitously highly expressed and closely associated with worse prognosis of multiple major cancer types (all p < 0.05). Further, SHC1 gene mutations were strongly linked to poor OS and DFS in SKCM (all p < 0.05). An enhanced phosphorylation level of SHC1 at the S139 site was observed in clear cell RCC. Additionally, the results revealed SHC1 expression was strongly linked to TMB, MMRs, MSI, TAMs, DNA methylation, m6A RNA methylation, tumor-associated immune infiltration, and immune checkpoints in multiple cancers (all p < 0.05). In addition, the results of the ROC analysis indicated the SHC1 exhibited strong diagnostic capability for KICH (AUC = 0.92), LIHC (AUC = 0.95), and PAAD (AUC = 0.95). Finally, enrichment analysis indicated that SHC1 may potentially involve in the regulation of numerous signaling pathways in cancer metabolism and protein phosphorylation-related functions.

Conclusions: These findings highlight that SHC1 plays an important role in the tumor immune microenvironment, and SHC1 has been identified to have prognostic and diagnostic value in multiple cancers. Thus, SHC1 is a potential target for cancer immunotherapy and effective prognostic and diagnostic biomarker.

[Pan-cancer analysis of the expression pattern of long non-coding RNA MIR22HG]

OBJECTIVE

To conduct a pan-cancer analysis of the expression of long non-coding RNA (lncRNA) MIR22HG and explore its association with clinical characteristics.

METHODS

We analyzed the expression of MIR22HG in different tumors and its association with clinical staging, lymph node metastasis, tumor mutation burden (TMB) and microsatellite instability (MSI) using R package based on the Cancer Genome Atlas (TCGA) datasets. The relationship between MIR22HG expression and infiltrating immune cells was analyzed using TIMER algorithm. The association of MIR22HG gene alteration frequency with the clinical outcomes was examined using cBioPortal online software. Data form Genomics of Drug Sensitivity in Cancer (GDSC) were used to analyze the relationship between MIR22HG and the sensitivity of chemotherapy drugs. We specifically analyzed MIR22HG expression in hepatocellular carcinoma (HCC) and its correlation with sorafenib treatment using GEO database and verified the results in 12 pairs of HCC specimens. Kaplan-Meier analysis was performed to analyze the correlation of MIR22HG with the outcomes of sorafenib treatment. We also tested the effects of MIR22HG overexpression and knockdown on IC₅₀ of sorafenib in HCC cells.

RESULTS

MIR22HG was downregulated in most tumors (P < 0.05), where its deletion mutations were frequent, and associated with a poor prognosis (P < 0.05). In many tumors, MIR22HG expression level was correlated with clinical stage, lymph node metastasis, TMB, MSI, immune cell infiltration, immune checkpoint-related genes, and sensitivity to common chemotherapeutic drugs (P < 0.05). Among the 6 common infiltrating immune cells in cancers, neutrophil infiltration had the strongest correlation with MIR22HG expression level, especially in breast cancer, rectal cancer and kidney renal papillary cell carcinoma (P < 0.05). MIR22HG was downregulated in HCC in association with HCC progression (P < 0.05). In HCC patients, a low MIR22HG expression was associated with a favorable outcome after sorafenib treatment (HR=2.94, P=0.075) and was capable of predicting the response to sorafenib treatment (AUC=0.8095). Compared with the negative control, MIR22HG overexpression obviously reduced sorafenib sensitivity (with IC₅₀ of 7.731 vs 15.61) while MIR22HG knockdown increased sorafenib sensitivity of HCC cells (with IC₅₀ of 7.986 vs 5.085).

CONCLUSION

MIR22HG expression level is correlated with clinical stage, lymph node metastasis, TMB, MSI, immune cell infiltration, and chemosensitivity in most cancer, suggesting its potential as an immunotherapeutic target and also a prognostic biomarker for tumors.

Cancer mutation profiles predict ICIs efficacy in patients with non-small cell lung cancer

Although immune checkpoint inhibitors (ICIs) have produced remarkable responses in non-small cell lung cancer (NSCLC) patients, receivers still have a relatively low response rate. Initial response assessment by conventional imaging and evaluation criteria is often unable to identify whether patients can achieve durable clinical benefit from ICIs. Overall, there are sparse effective biomarkers identified to screen NSCLC patients responding to this therapy. A lot of studies have reported that patients with specific gene mutations may benefit from or resist to immunotherapy. However, the single gene mutation may be not effective enough to predict the benefit from immunotherapy for patients. With the advancement in sequencing technology, further studies indicate that many mutations often co-occur and suggest a drastic transformation of tumour microenvironment phenotype. Moreover, co-mutation events have been reported to synergise to activate or suppress signalling pathways of anti-tumour immune response, which also indicates a potential target for combining intervention. Thus, the different mutation profile (especially co-mutation) of patients may be an important concern for predicting or promoting the efficacy of ICIs. However, there is a lack of comprehensive knowledge of this field until now. Therefore, in this study, we reviewed and elaborated the value of cancer mutation profile in predicting the efficacy of immunotherapy and analysed the underlying mechanisms, to provide an alternative way for screening dominant groups, and thereby, optimising individualised therapy for NSCLC patients.

Harnessing Liquid Biopsies to Guide Immune Checkpoint Inhibitor Therapy

Immunotherapy (IO), involving the use of immune checkpoint inhibition, achieves improved response-rates and significant disease-free survival for some cancer patients. Despite these beneficial effects, there is poor predictability of response and substantial rates of innate or acquired resistance, resulting in heterogeneous responses among patients. In addition, patients can develop life-threatening adverse events, and while these generally occur in patients that also show a tumor response, these outcomes are not always congruent. Therefore, predicting a response to IO is of paramount importance. Traditionally, tumor tissue analysis has been used for this purpose. However, minimally invasive liquid biopsies that monitor changes in blood or other bodily fluid markers are emerging as a promising cost-effective alternative. Traditional biomarkers have limitations mainly due to difficulty in repeatedly obtaining tumor tissue confounded also by the spatial and temporal heterogeneity of tumours. Liquid biopsy has the potential to circumvent tumor heterogeneity and to help identifying patients who may respond to IO, to monitor the treatment dynamically, as well as to unravel the mechanisms of relapse. We present here a review of the current status of molecular markers for the prediction and monitoring of IO response, focusing on the detection of these markers in liquid biopsies. With the emerging improvements in the field of liquid biopsy, this approach has the capacity to identify IO-eligible patients and provide clinically relevant information to assist with their ongoing disease management.

Surgical approach does not influence changes in circulating immune cell populations following lung cancer resection

INTRODUCTION

The multimodal management of operable non-small cell lung cancer (NSCLC) continues to evolve rapidly. The immune milieu allowing for immunotherapeutic benefit can be affected by multiple parameters including clinicopathologic and genetic. Surgery induced physiological changes has received attention for modulating and affecting post-operative oncotaxis and immunosuppression. Here, we sought to investigate how surgical stress influences phenotype of peripheral blood mononuclear cells (PBMCs) in patients with NSCLC who underwent lobectomy.

METHODS

Blood was prospectively collected from patients with Stage IA-IIIA NSCLC undergoing lung resection between 2016 and 2018. Samples were obtained pre-operatively, 24 h and 4 weeks after surgery. PBMCs were isolated and subject to high-dimensional flow cytometry, analyzing a total of 115 cell populations with a focus on myeloid cells, T cell activation, and T cell trafficking. We further evaluated how surgical approach influenced post-operative PBMC changes, whether the operation was conducted in an open fashion with thoracotomy, or with minimally invasive Video Assisted Thoracoscopic Surgery (VATS).

RESULTS

A total of 76 patients met the inclusion criteria (Open n = 55, VATS n = 21). Surgical resection coincided with a decrease in T lymphocyte populations, including total CD3+ T cells, CD8+ T cells, and T effector memory cells, as well as an increase in monocytic myeloid-derived suppressor cells (mMDSC). Post-operative changes in PBMC populations were resolved after 4 weeks. Surgical-induced changes in immune populations were equivalent in patients undergoing open thoracotomy and VATS.

DISCUSSION

Surgical stress resulted in transient reduction in T cells and T effector memory cells, and increase of mMDSC following resection in NSCLC patients. The immune profile modulation was similar regardless of surgical approach. These findings suggest that surgical approach does not seem to affect mononuclear cell lines obtained from peripheral blood. Thus, the decision regarding surgical approach should be patient centered, rather than based on post-operative treatment response optimization.

Recent advances in lung cancer genomics: Application in targeted therapy

Genomic characterization of lung cancer has not only improved our understanding of disease biology and carcinogenesis but also revealed several therapeutic opportunities. Targeting tumor dependencies on specific genomic alterations (oncogene addiction) has accelerated the therapeutic developments and significantly improved the outcomes even in advanced stage of disease. Identification of genomic alterations predicting response to specific targeted treatment is the key to success for this "personalized treatment" approach. Availability of multiple choices of therapeutic options for specific genomic alterations highlight the importance of optimum sequencing of drugs. Multiplex gene testing has become mandatory in view of constantly increasing number of therapeutic targets and effective treatment options. Influence of genomic characteristics on response to immunotherapy further makes comprehensive genomic profiling necessary before therapeutic decision making. A comprehensive elucidation of resistance mechanisms and directed treatments have made the continuum of care possible and transformed this deadly disease into a chronic condition. Liquid biopsy-based approach has made the dynamic monitoring of disease possible and enabled treatment optimizations accordingly. Current lung cancer management is the perfect example of "precision-medicine" in clinical oncology.

Clinicopathological features and genomic profiles of hepatoid adenocarcinoma of the lung: Report of four cases

OBJECTIVE

Pathological features of hepatoid adenocarcinoma of the lung (HAL) are similar to those of hepatocellular carcinoma (HCC) and HAL has a poor prognosis. In this study, we aimed to elucidate clinicopathologic and molecular features of HAL.

METHODS

Four cases of HAL patients with one lobe of the lung resected were enrolled into the study. Next generation sequencing (NGS) of a 425-gene panel was performed on tumor tissue samples.

RESULTS

The most frequently mutated gene was TP53 in three cases of primary HAL and one case of metastatic HAL, with a mutation rate of 100%. Also, CDK8, CDKN2A, EPHA5, SMARCA4, and STK11 were detected as high-frequency mutations, with a mutation rate of 50%. The types of TP53 mutation included two missense variants and two frameshift ones. The TP53 mutation was related to the occurrence of HAL.

CONCLUSION

HAL could be caused by genetic mutations and is closely related to TP53 mutation.

De Novo Mutation in Non-Tyrosine Kinase Domain of ROS1 as a Potential Predictor of Immune Checkpoint Inhibitors in Melanoma

Purpose

Despite the success of targeted therapy in c-ros oncogene 1 (ROS1)-rearranged cancers, especially non-small cell lung cancer (NSCLC), the clinical significance of ROS1 de novo mutation has not yet been understood. We sought to elucidate the predictive effect of ROS1 mutation for immune checkpoint inhibitor (ICI) therapy in melanoma.

Methods

The Cancer Genome Atlas [TCGA (n = 10967)] and Memorial Sloan Kettering Cancer Center [MSK (n = 10,945)] datasets, as well as two clinical cohorts of melanoma received ICI [CA209-038 (n = 73) and MEL-IPI (n = 110)], were included to explore the prevalence, prognostic effect, and immunotherapeutic predictive effect of ROS1 mutation in melanoma. Overall survival (OS) was defined as the primary outcome.

Results

Overall, melanoma accounted for the highest proportion of ROS1 mutation (~20%) which made up the majority (~95%) of the ROS1-alterated cases. Remarkably, ROS1 mutation yielded longer OS from ICI than the wild-type counterpart in the MSK melanoma population [hazard ratio (HR) 0.47, 95% confidence interval (CI) 0.30-0.74], and two external melanoma cohorts (CA209-038: HR 0.42, 95% CI 0.20-0.89; MEL-IPI: HR 0.55, 95% CI 0.34-0.91), without affecting the prognosis of patients. Elevated tumor mutational burden and enrichment of DNA damage repair was observed in ROS1 mutated patients, providing an explanation for the favorable responses to ICI therapy. Precisely, ROS1 mutation in non-protein tyrosine kinase (PTK) domain but not PTK mutation was responsible for the immunotherapy-specific responses of the ROS1 mutated patients in melanoma.

Conclusions

Collectively, ROS1 mutation, specifically the non-PTK mutation, is a potential predictor of ICI therapy in melanoma, which is distinct from the well-established role of ROS1 rearrangement for targeted therapy in NSCLC.

A genomic mutation signature predicts the clinical outcomes of immunotherapy and characterizes immunophenotypes in gastrointestinal cancer

The association between genetic variations and immunotherapy benefit has been widely recognized, while such evidence in gastrointestinal cancer remains limited. We analyzed the genomic profile of 227 immunotherapeutic gastrointestinal cancer patients treated with immunotherapy, from the Memorial Sloan Kettering (MSK) Cancer Center cohort. A gastrointestinal immune prognostic signature (GIPS) was constructed using LASSO Cox regression. Based on this signature, patients were classified into two subgroups with distinctive prognoses (p < 0.001). The prognostic value of the GIPS was consistently validated in the Janjigian and Pender cohort (N = 54) and Peking University Cancer Hospital cohort (N = 92). Multivariate analysis revealed that the GIPS was an independent prognostic biomarker. Notably, the GIPS-high tumor was indicative of a T-cell-inflamed phenotype and immune activation. The findings demonstrated that GIPS was a powerful predictor of immunotherapeutic survival in gastrointestinal cancer and may serve as a potential biomarker guiding immunotherapy treatment decisions.

Next-Generation Sequencing with Liquid Biopsies from Treatment-Naïve Non-Small Cell Lung Carcinoma Patients

Recently, the liquid biopsy (LB), a non-invasive and easy to repeat approach, has started to compete with the tissue biopsy (TB) for detection of targets for administration of therapeutic strategies for patients with advanced stages of lung cancer at tumor progression. A LB at diagnosis of late stage non-small cell lung carcinoma (NSCLC) is also being performed. It may be asked if a LB can be complementary (according to the clinical presentation or systematics) or even an alternative to a TB for treatment-naïve advanced NSCLC patients. Nucleic acid analysis with a TB by next-generation sequencing (NGS) is gradually replacing targeted sequencing methods for assessment of genomic alterations in lung cancer patients with tumor progression, but also at baseline. However, LB is still not often used in daily practice for NGS. This review addresses different aspects relating to the use of LB for NGS at diagnosis in advanced NSCLC, including its advantages and limitations.

HILPDA Is a Prognostic Biomarker and Correlates With Macrophage Infiltration in Pan-Cancer

Background: The protein hypoxia-inducible lipid droplet-associated (HILPDA) is differentially expressed in various tumors. However, its role and correlation with immune cell infiltration in most tumors remain unclear. Methods: HILPDA expression was analyzed in pan-cancer data from The Cancer Genome Atlas (TCGA) database. The influence of HILPDA in clinical prognosis was evaluated using clinical survival data from TCGA. Enrichment analysis of HILPDA was conducted using the R package "clusterProfiler." We downloaded the immune cell infiltration score of TCGA samples from published articles and analyzed the correlation between the magnitude of immune cell infiltration and HILPDA expression. Results: HILPDA was highly expressed and associated with worse overall survival, disease-specific survival, and progression-free interval in most tumor types. In addition, HILPDA expression was significantly associated with the glycolysis pathway and infiltration of immune cells. Tumor-associated macrophage (TAM) infiltration increased in tissues with high HILPDA expression in most tumor types. Immunosuppressive genes, such as PD-L1, PD-1, TGFB1, and TGFBR1 were positively correlated with HILPDA. Conclusions: Our study suggests that HILPDA is a marker of poor prognosis. High HILPDA may contribute to TAM infiltration and be associated with tumor immunosuppression status.

The Importance of STK11/LKB1 Assessment in Non-Small Cell Lung Carcinomas

Despite the recent implementation of immunotherapy as a single treatment or in combination with chemotherapy for first-line treatment of advanced non-small cell lung cancer (NSCLC), many patients do not benefit from this regimen due to primary treatment resistance or toxicity. Consequently, there is an urgent need to develop efficient biomarkers that can select patients who will benefit from immunotherapy thereby providing the appropriate treatment and avoiding toxicity. One of the biomarkers recently described for the stratification of NSCLC patients undergoing immunotherapy are mutations in STK11/LKB1, which are often associated with a lack of response to immunotherapy in some patients. Therefore, the purpose of this review is to describe the different cellular mechanisms associated with STK11/LKB1 mutations, which may explain the lack of response to immunotherapy. Moreover the review addresses the co-occurrence of additional mutations that may influence the response to immunotherapy and the current clinical studies that have further explored STK11/LKB1 as a predictive biomarker. Additionally this work includes the opportunities and limitations to look for the STK11/LKB1 status in the therapeutic strategy for NSCLC patients.

A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside

Background

Accumulating evidence suggests that lymphocyte infiltration in the tumor microenvironment is positively correlated with tumorigenesis and development, while the role of Tregs (regulatory T cells) has been controversial. Therefore, we attempted to discover the possible value of Tregs for lung adenocarcinoma (LUAD).

Methods

The gene-sequencing data of LUAD were applied from three Gene Expression Omnibus (GEO) datasets—GSE10072, GSE32863 and GSE43458; the corresponding fractions of tumor-infiltrating immune cells were extracted from the CIBERSORTx portal. Weighted gene coexpression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were conducted to identify the significant module and candidate genes related to Tregs. The role of candidate genes in LUAD was further verified using data from The Cancer Genome Atlas (TCGA) database. Finally, we constructed a nomogram model to predict the prognosis of LUAD by plotting Kaplan-Meier (K-M), receiver operating characteristic (ROC) and calibration curves, which elucidated the performance of the nomogram.

Results

In total, 10,047 genes in 333 samples (196 tumor and 137 normal samples) from the GEO database were included. By WGCNA and PPI analysis, we identified a significant black module and 36 candidate genes related to Treg. Next, the candidate genes were verified using TCGA data by Cox regression analysis to screen 13 hub genes that stratified LUAD patients into low- or high-risk groups. Low-risk patients showed a significantly longer overall survival (OS) than high-risk patients (3-year OS: 70.2% vs. 35.2%; 5-year OS: 36.6% vs. 0; P=1.651E-09), and the areas under the ROC curves (AUCs) showed good (3-year AUC: 0.733; 5-year AUC: 0.777). Next, we constructed a survival nomogram combining the hub genes and clinical parameters; the low-risk patients still showed a favorable prognosis compared with that of the high-risk patients (P=7.073E-13), and the AUCs were better (3-year AUC: 0.763; 5-year AUC: 0.873).

Conclusions

We revealed the role of immune-infiltrating Treg-related genes in LUAD and constructed a prognostic nomogram, which may help clinicians make optimal therapeutic decisions and help patients obtain better outcomes.

Development of EGFR TKIs and Options to Manage Resistance of Third-Generation EGFR TKI Osimertinib: Conventional Ways and Immune Checkpoint Inhibitors

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs) have been first-line therapy in the treatment of non-small cell lung cancer (NSCLC) harboring EGFR sensitive mutations. Progression inevitably happens after 10–14 months of first- or second-generation EGFR TKIs treatment for acquired resistance. Owing to the successful identification of EGFR T790M, third-generation EGFR TKIs such as osimertinib were developed to target such resistance mutation. Nowadays, osimertinib has shown its efficacy both in first-line and second-line after resistance to previous generations of TKI treatment of EGFR-mutant NSCLC. However, drug resistance also emerges on third-generation EGFR TKIs. Multiple mechanisms of acquired resistance have been identified, and some novel strategies were reported to overcome third-generation TKI resistance. Immune checkpoint inhibitors (ICIs) have dramatically changed the prognosis of selected patients. For patients with EGFR-addicted metastatic NSCLC, ICIs have also revealed a potential role. In this review, we will take stock of mechanisms of acquired resistance to third-generation TKIs and discuss current challenges and future perspectives in clinical practice.