EPHA mutation as a predictor of immunotherapeutic efficacy in lung adenocarcinoma

Antithetical impacts of deleterious LRP1B mutations in non-squamous and squamous NSCLCs on predicting benefits from immune checkpoint inhibitor alone or with chemotherapy over chemotherapy alone: retrospective analyses of the POPLAR/OAK and CHOICE-01 trials

In non-small cell lung cancers, the non-squamous and squamous subtypes (nsqNSCLC and sqNSCLC) exhibit disparities in pathophysiology, tumor immunology, and potential genomic correlates affecting responses to immune checkpoint inhibitor (ICI)-based treatments. In our in-house training cohort (n=85), the presence of the LRP1B deleterious mutation (LRP1B-del) was associated with longer and shorter progression-free survival (PFS) on ICIs alone in nsqNSCLCs and sqNSCLCs, respectively (Pinteraction=0.008). These results were validated using a larger public ICI cohort (n=208, Pinteraction<0.001). Multiplex immunofluorescence staining revealed an association between LRP1B-del and increased and decreased numbers of tumor-infiltrating CD8+ T cells in nsqNSCLCs (P=0.040) and sqNSCLCs (P=0.014), respectively. In the POPLAR/OAK cohort, nsqNSCLCs with LRP1B-del demonstrated improved PFS benefits from atezolizumab over docetaxel (hazard ratio (HR) =0.70, P=0.046), whereas this benefit was negligible in those without LRP1B-del (HR=1.05, P=0.64). Conversely, sqNSCLCs without LRP1B-del benefited more from atezolizumab (HR=0.60, P=0.002) than those with LRP1B-del (HR=1.30, P=0.31). Consistent results were observed in the in-house CHOICE-01 cohort, in which nsqNSCLCs with LRP1B-del and sqNSCLCs without LRP1B-del benefited more from toripalimab plus chemotherapy than from chemotherapy alone (Pinteraction=0.008). This multi-cohort study delineates the antithetical impacts of LRP1B-del in nsqNSCLCs and sqNSCLCs on predicting the benefits from ICI alone or with chemotherapy over chemotherapy alone. Our findings highlight the distinct clinical utility of LRP1B-del in guiding treatment choices for nsqNSCLCs and sqNSCLCs, emphasizing the necessity for a detailed analysis based on pathological subtypes when investigating biomarkers for cancer therapeutics.

Identification of nomogram associated with durable clinical benefit gene for advanced non-small cell lung cancer with sensitivity to responsive to immunotherapy

Background

Immunotherapy has become the standard treatment for advanced non-small cell lung cancer (NSCLC). However, a subset of the most advanced NSCLC patients fails to respond adequately to Immune checkpoint inhibitors (ICIs). Developing new nomograms and integrating prognostic factors are crucial for improving the clinical predictability of NSCLC patients undergoing ICIs.

Methods

Clinical information and genomic data of NSCLC patients undergoing ICIs were retrieved from cBioPortal. Gene alterations associated with durable clinical benefit (DCB) were compared to those linked to no durable benefit (NDB). The Kaplan-Meier plot method was employed for survival analysis, and a novel nomogram was formulated by selecting pertinent clinical variables.

Results

For the NSCLC patients receiving immunotherapy, three subgroups were identified based on the treatment regimen, including anti-PD-1 monotherapy, anti-PD-1 combination with anti-CTLA-4, and first-line treatment. The mutation status of TP53, PGR, PTPRT, RELN, MUC19, LRP1B, and FAT3 was found to be associated with progression-free survival (PFS). Using the clinicopathological parameters and genomic data of the patients, we developed three novel nomograms to predict the prognosis of ICI treatment in different subgroups.

Conclusion

Our study revealed that PGR, PTPRD, RELN, MUC19, LRP1B, and FAT3 mutation could serve as predictive biomarkers. Our systematic nomograms demonstrate significant potential in predicting the prognosis for NSCLC patients with sensitivity to different ICI treatment strategies.

Identification of immune-related gene signature for non-small cell lung cancer patients with immune checkpoint inhibitors

Background

The utilization of immune checkpoint inhibitors (ICIs) has become the established protocol for treating advanced non-small cell lung cancer (NSCLC). This work aimed to identify the immune-related gene signature that can predict the prognosis of NSCLC patients receiving ICI treatment.

Methods

The ImmPort database was queried to obtain a list of immune-related genes (IRGs). Differentially expressed IRGs in NSCLC patients were identified using the TCGA database. RNA-seq data and clinical information from NSCLC patients receiving immunotherapy were obtained from the GEO database (GSE93157 and ////). A gene signature was generated through multivariate Cox and LASSO regression analyses. The prognostic value and function of this gene signature were thoroughly investigated using comprehensive bioinformatics analyses.

Results

A total of 6 prognostic-related genes were identified from 617 differentially expressed genes, and two prognostic-related differentially expressed genes (CAMP and IL17A) were determined to construct gene signature. Our gene signature demonstrated superior performance compared to other clinicopathological parameters in predicting the prognosis of NSCLC patients receiving immunotherapy, with an area under the ROC curve (AUC) of 0.812. Furthermore, immune infiltration analysis indicated that the high-risk group was enriched with resting CD4 T cell memory, while the low-risk group showed a "hot" tumor microenvironment that promotes anti-tumor immunity in NSCLC patients.

Conclusion

Gene signatures based on immune-related genes exhibited excellent indicator performance of prognosis and immune infiltration, which has the potential to be an effective biomarker for NSCLC with ICI treatment.

To be, or not to be: the dilemma of immunotherapy for non-small cell lung cancer harboring various driver mutations

INTRODUCTION

Lung cancer is one of primary cancer type with high incidence and mortality, non-small cell lung cancer (NSCLC) is the most common type of lung cncer. For advanced lung cancer, traditional chemotherapy and targeted therapy become difficult to solve the dilemma of further progress. In recent years, with the clinical application of immunotherapy, the therapeutic strategy of lung cancer has changed dramatically. At present, immunotherapy has shown conspicuous efficacy in NSCLC patients with high expression of programmed death-ligand 1 (PD-L1) and high tumor mutational burden (TMB). The discovery of driver mutations brings delightful hope for targeted cancer therapy. However, it remains controversial whether immunotherapy can be used in NSCLC patients with these specific driver mutations.

METHOD

This article summarized the latest research progresses of immunotherapy in advanced NSCLC. We paid close attention to the relevance of various driver mutations and immunotherapy in NSCLC patients, and summarized the predictive effects of several driver mutations and immunotherapy.

RESULTS

The mutations of KRAS, KRAS+TP53, EPHA (especially EPHA5), ZFHX3, ZFHX3+TP53, NOTCH, BRAF and LRP1B+FAT3 have potential to be used as biomarkers to predict the positive effectiveness of immunotherapy. ZFHX3, ZFHX3+TP53, STKII/LKB1+KEAP1+SMARCA4+PBRM1 mutations in LUAD patients get more positive effect in immunotherapy. While the mutations of EGFR, KEAP1, STKII/LKB1+KRAS, EML4-ALK, MET exon 14 skipping mutation, PBRM1, STKII/LKB1+KEAP1+SMARCA4+PBRM1, ERBB2, PIK3CA and RET often indicate poor benefit from immunotherapy.

CONCLUSION

Many gene mutations have been shown to be associated with immunotherapy efficacy. Gene mutations should be combined with PD-L1, TMB, etc. to predict the effect of immunotherapy.

PTPRD mutation is a prognostic biomarker for sensitivity to ICIs treatment in advanced non-small cell lung cancer

BACKGROUND

Immune checkpoint inhibitors (ICIs) have become the standard treatment for advanced non-small cell lung cancer (NSCLC). ICIs can provide durable responses and prolong survival for some patients. With the increasing routine of next-generation sequencing (NGS) in clinical practice, it is essential to integrate prognostic factors to establish novel nomograms to improve clinical prediction ability in NSCLC with ICIs treatment.

METHODS

Clinical information, response data, and genome data of advanced NSCLC treated ICIs were obtained from cBioPortal. The top 20 gene alterations in durable clinical benefit (DCB) were compared with those genes in no durable benefit (NDB). Survival analyses were performed using the Kaplan-Meier plot method and selected clinical variables to develop a novel nomogram.

RESULTS

The mutation of PTPRD was significantly related to progression free survival (PFS) and overall survival (OS) in advanced NSCLC with ICIs treatment (PFS: p = 0.0441, OS: p = 0.0086). The PTPRD mutation was closely related to tumor mutational burden (TMB) and tumor-infiltrating immune cells (TIICs). Two novel nomograms were built to predict the PFS and OS of advanced NSCLC patients with ICIs treatment.

CONCLUSIONS

Our study suggested that PTPRD mutations could serve as a predictive biomarker for the sensitivity to ICIs treatment and PFS and OS in advanced NSCLC with ICIs. Our systematic nomograms showed great potential value in clinical application to predict the PFS and OS for advanced NSCLC patients with ICIs.

POTEE mutation as a potential predictive biomarker for immune checkpoint inhibitors in lung adenocarcinoma

Precise selection of patients who could benefit from immune checkpoint inhibitors (ICIs) is an important challenge for immunotherapy in lung cancer. POTEE (POTE Ankyrin Domain Family Member E) is a member of one primate-specific gene family which have been identified as cancer-related antigens and potential target for immunotherapy of cancer. Here, we investigated the correlation between POTEE mutation and the clinical outcome of ICIs treatment in non-small cell lung cancer (NSCLC). We merged three NSCLC cohorts (n = 165) to assess predictive value of POTEE mutation of immunotherapy efficacy in NSCLC. The prognostic analysis and the potential molecular mechanism exploration were conducted based on the data from The Cancer Genome Atlas (TCGA) database. In the merged cohort, patients with POTEE-mutation (POTEE-Mut) had a significantly higher objective response rate (ORR) (100% vs 27.7%; P < 0.001) and longer progression-free survival (PFS) (P = 0.001; HR 0.08; 95% CI 0.01 - 0.54) compared to patients with POTEE wild-type (POTEE-WT) in NSCLC. Also, patients with POTEE-Mut showed higher ORR (100% vs 27.2%; P < 0.001) and longer PFS (P = 0.001; HR 0.07; 95% CI 0.01 - 0.52) in lung adenocarcinoma (LUAD). POTEE mutation was significantly associated with higher tumor mutational burden (TMB) and higher neoantigen load (NAL), but not with PD-L1 expression in LUAD. Gene set enrichment analyses (GSEA) analysis revealed prominent enrichment of signatures related to DNA repair in POTEE-Mut group (P < 0.001) in LUAD. Our results indicate that POTEE mutation could serve as a potential predictive biomarker for ICIs in LUAD. However, prospective cohort studies are still needed for further validation.

BAP1-related signature predicts benefits from immunotherapy over VEGFR/mTOR inhibitors in ccRCC: a retrospective analysis of JAVELIN Renal 101 and checkmate-009/010/025 trials

BACKGROUND

In patients with advanced clear cell renal cell carcinoma, despite the undoubted benefits from immune checkpoint inhibitor (ICI)-based therapies over monotherapies of angiogenic/mTOR inhibitors in the intention-to-treat population, approximately a quarter of the patients can scarcely gain advantage from ICIs, prompting the search for predictive biomarkers for patient selection.

METHODS

Clinical and multi-omic data of 2428 ccRCC patients were obtained from The Cancer Genome Atlas (TCGA, n = 537), JAVELIN Renal 101 (avelumab plus axitinib vs. sunitinib, n = 885), and CheckMate-009/010/025 (nivolumab vs. everolimus, n = 1006).

RESULTS

BAP1 mutations were associated with large progression-free survival (PFS) benefits from ICI-based immunotherapies over sunitinib/everolimus (pooled estimate of interaction HR = 0.71, 95% CI 0.51-0.99, P = 0.045). Using the top 20 BAP1 mutation-associated differentially expressed genes (DEGs) generated from the TCGA cohort, we developed the BAP1-score, negatively correlated with angiogenesis and positively correlated with multiple immune-related signatures concerning immune cell infiltration, antigen presentation, B/T cell receptor, interleukin, programmed death-1, and interferon. A high BAP1-score indicated remarkable PFS benefits from ICI-based immunotherapies over angiogenic/mTOR inhibitors (avelumab plus axitinib vs. sunitinib: HR = 0.55, 95% CI 0.43-0.70, P < 0.001; nivolumab vs. everolimus: HR = 0.72, 95% CI 0.52-1.00, P = 0.045), while these benefits were negligible in the low BAP1-score subgroup (HR = 1.16 and 1.02, respectively).

CONCLUSION

In advanced ccRCCs, the BAP1-score is a biologically and clinically significant predictor of immune microenvironment and the clinical benefits from ICI-based immunotherapies over angiogenic/mTOR inhibitors, demonstrating its potential utility in optimizing the personalized therapeutic strategies in patients with advanced ccRCC.

May EPH/Ephrin Targeting Revolutionize Lung Cancer Treatment?

Lung cancer (LC) is the leading cause of cancer death in the United States. Erythropoietin-producing hepatocellular receptors (EPHs) comprise the largest receptor tyrosine kinases (RTKs) family in mammals. EPHs along with their ligands, EPH-family receptor-interacting proteins (ephrins), have been found to be either up- or downregulated in LC cells, hence exhibiting a defining role in LC carcinogenesis and tumor progression. In their capacity as membrane-bound molecules, EPHs/ephrins may represent feasible targets in the context of precision cancer treatment. In order to investigate available therapeutics targeting the EPH/ephrin system in LC, a literature review was conducted, using the MEDLINE, LIVIVO, and Google Scholar databases. EPHA2 is the most well-studied EPH/ephrin target in LC treatment. The targeting of EPHA2, EPHA3, EPHA5, EPHA7, EPHB4, EPHB6, ephrin-A1, ephrin-A2, ephrin-B2, and ephrin-B3 in LC cells or xenograft models not only directly correlates with a profound LC suppression but also enriches the effects of well-established therapeutic regimens. However, the sole clinical trial incorporating a NSCLC patient could not describe objective anti-cancer effects after anti-EPHA2 antibody administration. Collectively, EPHs/ephrins seem to represent promising treatment targets in LC. However, large clinical trials still need to be performed, with a view to examining the effects of EPH/ephrin targeting in the clinical setting.

PAPPA2 mutation as a novel indicator stratifying beneficiaries of immune checkpoint inhibitors in skin cutaneous melanoma and non-small cell lung cancer

BACKGROUND

Pappalysin 2 (PAPPA2) mutation, occurring most frequently in skin cutaneous melanoma (SKCM) and non-small cell lung cancer (NSCLC), is found to be related to anti-tumour immune response. However, the association between PAPPA2 and the efficacy of immune checkpoint inhibitors (ICIs) therapy remains unknown.

METHODS

To analyse the performance of PAPPA2 mutation as an indicator stratifying beneficiaries of ICIs, seven public cohorts with whole-exome sequencing (WES) data were divided into the NSCLC set (n = 165) and the SKCM set (n = 210). For further validation, 41 NSCLC patients receiving anti-PD-(L)1 treatment were enrolled in China cohort (n = 41). The mechanism was explored based on The Cancer Genome Atlas database (n = 1467).

RESULTS

In the NSCLC set, patients with PAPPA2 mutation (PAPPA2-Mut) demonstrated a significantly superior progress free survival (PFS, hazard ratio [HR], 0.28 [95% CI, 0.14-0.53]; p < 0.001) and objective response rate (ORR, 77.8% vs. 23.2%; p < 0.001) compared to those with wide-type PAPPA2 (PAPPA2-WT), consistent in the SKCM set (overall survival, HR, 0.49 [95% CI: 0.31-0.78], p < 0.001; ORR, 34.1% vs. 16.9%, p = 0.039) and China cohort. Similar results were observed in multivariable models. Accordingly, PAPPA2 mutation exhibited superior performance in predicting ICIs efficacy compared with other published ICIs-related gene mutations, such as EPHA family, MUC16, LRP1B and TTN, etc. In addition, combined utilization of PAPPA2 mutation and tumour mutational burden (TMB) could expand the identification of potential responders to ICIs therapy in both NSCLC set (HR, 0.36 [95% CI: 0.23-0.57], p < 0.001) and SKCM set (HR, 0.51 [95% CI: 0.34-0.76], p < 0.001). Moreover, PAPPA2 mutation was correlated with enhanced anti-tumour immunity including higher activated CD4 memory T cells level, lower Treg cells level, and upregulated DNA damage repair pathways.

CONCLUSIONS

Our findings indicated that PAPPA2 mutation could serve as a novel indicator to stratify beneficiaries from ICIs therapy in NSCLC and SKCM, warranting further prospective studies.

Molecular and Clinicopathological Characteristics of Lung Cancer Concomitant Chronic Obstructive Pulmonary Disease (COPD)

Introduction

Chronic obstructive pulmonary disease (COPD) and lung cancer often coexist, but its pathophysiology and genomics features are still unclear.

Methods

In this study, we retrospectively collected lung cancer concomitant COPD (COPD-LC) and non-COPD lung cancer (non-COPD-LC) patients, who performed next generation sequencing (NGS) and had clinicopathological information simultaneously. The COPD-LC data from the TCGA cohort were collected to conduct further analysis.

Results

A total of 51 COPD-LC patients and 88 non-COPD-LC patients were included in the study. Clinicopathological analysis showed that proportion of male gender, older age, and smoking patients were all substantially higher in COPD-LC group than in non-COPD-LC group (all P<0.01). Comparing the genomic data of the two groups in our cohort, COPD-LC had higher mutation frequency of LRP1B (43% vs 9%, P = 0.001), EPHA5 (24% vs 1%, P = 0.002), PRKDC (14% vs 1%, P = 0.039), PREX2 (14% vs 0%, P = 0.012), and FAT1 (14% vs 0%, P = 0.012), which had a relationship with improved tumor immunity. Immunotherapy biomarker of PD-L1 positive expression (62.5% vs 52.0%, P = 0.397) and tumor mutation burden (TMB, median TMB: 7.09 vs 2.94, P = 0.004) also were higher in COPD-LC. In addition, RNA data from TCGA further indicated tumor immunity increased in COPD-LC. Whereas, COPD-LC had lower frequency of EGFR mutation (19% vs 50%, P = 0.013) and EGFR mutant COPD-LC treated with EGFR-TKI had worse progression-free survival (PFS) (HR = 3.52, 95% CI: 1.27–9.80, P = 0.01).

Conclusion

In this retrospective study, we first explored molecular features of COPD-LC in a Chinese population. Although COPD-LC had lower EGFR mutant frequency and worse PFS with target treatment, high PD-L1 expression and TMB indicated these patients may benefit from immunotherapy.

Comprehensive Analysis of Programmed Cell Death Signature in the Prognosis, Tumor Microenvironment and Drug Sensitivity in Lung Adenocarcinoma

Programmed cell death (PCD) is a process that regulates the homeostasis of cells in the body, and it plays an important role in tumor immunity. However, the expression profile and clinical characteristics of PCD-related genes remain unclear. In this study, we comprehensively analysed the PCD genes with the tumor microenvironment (TME), drug sensitivity, immunothearapy response, and evaluated their prognostic value through systematic bioinformatics methods.We identified 125 PCD-related regulatory factors, which were expressed differently in lung adenocarcinoma (LUAD) and normal lung tissues. 32 PCD related prognostic genes associated with LUAD were identified by univariate Cox analysis. 23 PCD-related gene signature was constructed, and all LUAD patients in the Cancer Genome Atlas (TCGA) dataset were stratified as low-risk or high-risk groups according to the risk score. This signature had a powerful prognostic value, which was validated in three independent data sets and clinical subtypes. Additionally, it has unique properties in TME. Further analysis showed that different risk groups have different immune cell infiltration, immune inflammation profile, immune pathways, and immune subtypes. In addition, the low-risk group had a better immunotherapy response with higher levels of multiple immune checkpoints and lower Tumor immune dysfunction and exclusion (TIDE) score, while the high-risk group was sensitive to multiple chemotherapeutic drugs because of its lower IC50. In short, this is the first model to predict the prognosis and immunological status of LUAD patients based on PCD-related genes. It may be used as a predictor of immunotherapy response to achieve customized treatment of LUAD.

DNA-methylome-assisted classification of patients with poor prognostic subventricular zone associated IDH-wildtype glioblastoma

Glioblastoma (GBM) derived from the “stem cell” rich subventricular zone (SVZ) may constitute a therapy-refractory subgroup of tumors associated with poor prognosis. Risk stratification for these cases is necessary but is curtailed by error prone imaging-based evaluation. Therefore, we aimed to establish a robust DNA methylome-based classification of SVZ GBM and subsequently decipher underlying molecular characteristics. MRI assessment of SVZ association was performed in a retrospective training set of IDH-wildtype GBM patients (n = 54) uniformly treated with postoperative chemoradiotherapy. DNA isolated from FFPE samples was subject to methylome and copy number variation (CNV) analysis using Illumina Platform and cnAnalysis450k package. Deep next-generation sequencing (NGS) of a panel of 130 GBM-related genes was conducted (Agilent SureSelect/Illumina). Methylome, transcriptome, CNV, MRI, and mutational profiles of SVZ GBM were further evaluated in a confirmatory cohort of 132 patients (TCGA/TCIA). A 15 CpG SVZ methylation signature (SVZM) was discovered based on clustering and random forest analysis. One third of CpG in the SVZM were associated with MAB21L2/LRBA. There was a 14.8% (n = 8) discordance between SVZM vs. MRI classification. Re-analysis of these patients favored SVZM classification with a hazard ratio (HR) for OS of 2.48 [95% CI 1.35–4.58], p = 0.004 vs. 1.83 [1.0–3.35], p = 0.049 for MRI classification. In the validation cohort, consensus MRI based assignment was achieved in 62% of patients with an intraclass correlation (ICC) of 0.51 and non-significant HR for OS (2.03 [0.81–5.09], p = 0.133). In contrast, SVZM identified two prognostically distinct subgroups (HR 3.08 [1.24–7.66], p = 0.016). CNV alterations revealed loss of chromosome 10 in SVZM– and gains on chromosome 19 in SVZM– tumors. SVZM– tumors were also enriched for differentially mutated genes (p < 0.001). In summary, SVZM classification provides a novel means for stratifying GBM patients with poor prognosis and deciphering molecular mechanisms governing aggressive tumor phenotypes.

Identification of Immune Subtypes of Lung Squamous Cell Carcinoma by Integrative Genome-Scale Analysis

Background

Characterization of the tumor microenvironment is helpful to understand the tumor immune environment of lung cancer and help predict the prognosis.

Methods

First, immune subtypes were identified by consensus subtype among lung squamous carcinoma (LUSC) patients. Immune cell infiltration was evaluated by CIBERSORT and ESTIMATE analyses. Then, based on differentially expressed genes (DEGs) identified, a risk score model was constructed. Finally, gene FPR1 was validated by using YTMLC-90.

Findings

LUSC samples were divided into four heterogeneous immune subtypes, with significantly different prognoses with subtype 4 having the poorest overall survival (OS). The immune infiltration score showed that subtype 4 was characterized as immune enriched and fibrotic, while subtype 3 was tumor enriched. DEG analysis showed that upregulated genes in subtype 4 were enriched of neutrophil and exhausted T cell-related biological processes. Based on a univariate Cox regression model, prognostic 7 immune-related genes were combined to construct a risk score model and able to predict OS rates in the validation datasets. Wound healing and transwell assay were conducted to evaluate the invasion property after activating the gene FPR1.

Interpretation

The analysis of tumor immune microenvironments among LUSC subtypes may provide new insights into the strategy of immunotherapy.

Predictive Value of Max's Giant Associated Protein Mutation in Outcomes of Lung Adenocarcinoma Patients Treated With Immune Checkpoint Inhibitors

Treatment with immune checkpoint inhibitors (ICIs) has considerably improved prognosis in multiple cancers. However, regardless of PD-L1 expression and TMB, better predictive biomarkers are required to identify ICI-responsive patients. We analyzed a pan-cancer cohort as the discovery cohort to identify the role of Max’s giant associated protein (MGA) mutation in the outcome of ICI treatment in different types of cancers. A pooled lung adenocarcinoma (LUAD) cohort was considered as the validation cohort. Another two LUAD cohorts who received conventional treatment were included for prognostic analysis and mechanism exploration. In the discovery cohort, MGA mutation was a favorable survival biomarker for patients with LUAD than in those with other types of cancers. MGA mutation was positively correlated with the TMB score. The results of the validation cohort were consistent with those of the discovery cohort. Patients with MGA mutation in the TMB-low subgroup had longer survival. Two LUAD cohorts who received standard treatment showed that the MGA mutation was not a prognostic biomarker for standard treatment. Mechanically, we found that the co-mutant genes did not affect the prognostic role of MGA mutation. Gene-set enrichment analysis revealed that genes belonging to the immunodeficiency pathway were enriched in the MGA wild-type group in LUAD. Moreover, activated NK cells were more enriched in the MGA mutant LUAD group. In conclusion, our results demonstrated that MGA mutation was an independent predictive biomarker for ICI therapy. These results may provide a novel insight into identifying potential patients with LUAD for ICI therapy.

Identification and validation of tissue or ctDNA PTPRD phosphatase domain deleterious mutations as prognostic and predictive biomarkers for immune checkpoint inhibitors in non-squamous NSCLC

BACKGROUND

With the revolutionary progress of immune checkpoint inhibitors (ICIs) achieved in non-small cell lung cancers (NSCLC), identifying patients benefiting from ICIs becomes critical and urgent. The associations of genomic alterations in protein tyrosine phosphatase receptor-type (PTPRs) and ICIs responses are unknown.

METHODS

Whole-exome sequencing (WES) of 73 advanced NSCLC tumors sampled before anti-PD-(L)1 therapy was carried out with corresponding clinical data collected as a discovery cohort to find the associations of PTPR mutations and ICI responses. Three validation cohorts consolidated by 7 public cohorts of 1920 NSCLC patients with WES or target sequencing data of tumor tissue-derived DNA or circulating tumor DNA (ctDNA) and relevant clinical data were applied as validation cohorts. The lung adenocarcinoma (LUAD) cohort (n=586) in The Cancer Genome Atlas (TCGA) database was used for analyzing the potential anti-tumor immunologic mechanisms.

RESULTS

With the highest mutation frequency among all PTPRs, PTPRD mutations in non-squamous NSCLC (ns-NSCLC) were linked to longer progression-free survivals (PFS, 324 vs 63 days, hazard ratio (HR)=0.36, p= 0.0152) and higher objective response rate (ORR, p=0.0099). In validation cohort 1 (n=377), ns-NSCLC patients with tissue PTPRD mutations had favorable PFS (9.10 vs 4.33 months, HR=0.62, p=0.0184) and ORR (p=0.013). In validation cohort 2 (n=406), ns-NSCLC patients with tissue PTPRD mutations had favorable overall survivals (OS, over 40 vs 11.94 months, HR=0.57, p=0.011). In validation cohort 3 (n=1137), ns-NSCLC patients with ctDNA PTPRD mutations had longer PFS (6.97 vs 2.73 months, HR=0.63, p=0.028) and higher ORR (p=0.047). Moreover, it was deleterious mutations in phosphatase domains (phosphatase-mut), rather than other mutations (other-mut), that were responsible of PTPRD's prediction efficiency. In addition, in validation cohort 3, ctDNA phosphatase-mut also functioned as a predictive biomarker helping identify patients benefiting more from ICIs than chemotherapy (interaction P for PFS=0.0506, for OS=0.04). Univariate and multivariate regression analysis revealed that phosphatase-mut was independent on PD-L1 expression and tumor mutation burden (TMB) to predict. In silico analysis based on TCGA LUAD cohort discovered enhanced anti-tumor immunity in phosphatase-mut patients.

CONCLUSIONS

Tissue or ctDNA PTPRD phosphatase domain deleterious mutations might function as a both prognostic and predictive biomarker predicting clinical outcomes of ICIs in ns-NSCLC patients, independent on TMB or PD-L1 expression.

TGFBR2 mutation predicts resistance to immune checkpoint inhibitors in patients with non-small cell lung cancer

Background:

Resistance or even hyper-progression to immune checkpoint inhibitors (ICIs) manifesting as accelerated disease progression or death has impeded the clinical use of ICIs. The transforming growth factor beta (TGFβ) receptor pathway has been identified in contributing to immune dysfunction, which might be associated with resistance to ICIs. We aimed to explore the role of TGFβ in the resistance to ICIs in non-small cell lung cancer (NSCLC) in this study.

Methods:

Public cohorts with patients treated with ICIs or chemotherapy including POPLAR/OAK (n = 853), MSKCC (n = 1662) and Van Allen (n = 57) and TCGA (n = 3210) cohorts were obtained and analyzed.

Results:

The expression of immune-checkpoint related genes, including programmed death-ligand 1 (CD274), lymphocyte-activation gene 3 (LAG3), T cell immunoreceptor with Ig and ITIM domains (TIGIT), cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), programmed cell death ligand 1 (PDCD1), and programmed cell death 1 ligand 2 (PDCD1LG2) were significantly upregulated in transforming growth factor beta TGFβ receptor 2 (TGFβR2)-mutated patients than those with wild-type TGFBR2 (p < 0.05). In the POPLAR/OAK cohort, TGFBR2-mutated patients showed shorter progression-free survival (PFS) [ p = 0.004; hazard ratio (HR), 2.83; 95% confidence interval (CI), 1.34–6.00] and overall survival (OS) ( p = 0.0006; HR, 3.46; 95% CI, 1.63–7.35) than those with wild-type TGFBR2 when treated with ICIs but not chemotherapy. In the merged MSKCC and Van Allen cohorts, a similar result was observed that the OS was inferior in patients with mutated TGFBR2 compared with those with wild-type TGFBR2 (p = 0.007; HR, 2.53; 95% CI, 1.25–5.12). The association between TGBFR2 mutation and survival remained significant in multivariable cox regression in both POPLAR/OAK cohort (p = 0.02; HR, 2.53; 95% CI, 1.17–5.45) and merged cohort (p = 0.008; HR, 2.63; 95% CI, 1.29–5.35). We further evaluated the association between TGFBR2 mutations and OS in multiple types of tumors. The association between TGFBR2 mutations and OS remained significant in NSCLC (p = 0.02; HR, 2.47; 95% CI, 1.16–5.26), but not in other type of tumors.

Conclusions:

We identified that TGFBR2 mutation predicted the resistance to ICIs in NSCLCs. The clinical delivery of ICIs should be cautious in those patients.

RBM10 Deficiency Is Associated With Increased Immune Activity in Lung Adenocarcinoma

Introduction

RBM10 is one of the frequently mutated genes in lung adenocarcinoma (LUAD). Previous studies have confirmed that RBM10 could suppress the disease progression and cell proliferation in LUAD, but its loss-of-function mutations are more frequent in early-stage disease and decrease with the advancement of the clinical stage. This is contradictory to its role of tumor suppressor. Here, we conducted a systematic analysis to elucidate whether there was other potential biological significance of RBM10 deficiency during the progression of LUAD.

Materials and Methods

The whole exome sequencing data of 39 tumor samples from early-stage LUADs (GGN cohort) and genomic and transcriptome data of the Cancer Genome Atlas (TCGA) LUAD cohort (TCGA_LUAD cohort) and a Chinese LUAD cohort (CHOICE_ADC cohort) were first obtained. Systematic bioinformatic analyses were then conducted to determine gene expression signature, immune infiltration levels and predicted immunotherapy response. Immunohistochemistry (IHC) was also conducted to validate the result of immune infiltration.

Results

The mutation rate of RBM10 was significantly higher in the GGN cohort than that in the TCGA_LUAD and CHOICE_ADC cohorts. In both TCGA_LUAD and CHOICE_ADC cohorts, multiple immune related pathways were markedly enriched in RBM10 deficient group. Further analyses showed that tumors with RBM10 mutations displayed higher TMB, and LUADs with RBM10 deficiency also showed higher HLA expression levels, including many HLA class I and II molecules. Additionally, many immune cells, including myeloid dendritic cells, macrophages, neutrophils and CD8+T cells, showed higher infiltration levels in LUADs with RBM10 deficiency. Finally, some immune checkpoint molecules, such as PD-L1 and TIM-3, were highly expressed in RBM10 deficient population and the predicted immunotherapy response was calculated through TIDE algorithm, showing that IFNG expression, MSI score and CD8 expression were higher in RBM10 deficient group, while MDSC and M2 macrophage were lower in RBM10 deficient group.

Conclusion

Our study demonstrates that RBM10 deficient LUADs show higher HLA expression and immune cell infiltration, and some immune checkpoint molecules are also highly expressed. In brief, RBM10 deficiency could enhance anti-tumor immunity in LUAD.

Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer

Objective

The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC).

Methods

In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F] FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n = 74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalised linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence.

Results

Standardised uptake value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule predicting the outcome resulted in an area under the curve (AUC) of 0.87.

Conclusions

Radiogenomic data may provide clinically relevant information in NSCLC patients regarding the histotype, aggressiveness, and progression. Gene expression analysis showed potential new biomarkers and targets valuable for patient management and treatment. The application of ML allows to increase the efficacy of radiogenomic analysis and provides novel insights into cancer biology.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05371-7.