A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies

Utilizing Clinicopathological and Radiomic Features for Risk Stratification of Lung Cancer Recurrence

RATIONALE AND OBJECTIVES

To predict recurrence risk in patients with surgically resected non-small cell lung cancer (NSCLC) using radiomic analysis and clinicopathological factors.

MATERIALS AND METHODS

293 patients with surgically resected stage IA-IIIA NSCLC were analyzed. Patients were randomly stratified into development and test cohorts. The development cohort was further divided into training and validation subsets for feature selection and model building, then applied to the test cohort. Pre-treatment computed tomography were segmented and 107 pyRadiomics features were extracted from intratumoral and peritumoral regions. Feature selection was performed using the maximum relevance minimum redundancy algorithm and Lasso regression. Clinical covariates were selected using univariable Cox regression. Radiomic, clinical, and radiomic-clinical models were constructed using a logistic regression classifier and evaluated using area under the curve (AUC). Kaplan-Meier curves for 3-year recurrence-free survival were compared between high-risk and low-risk groups using the log-rank test.

RESULTS

20 percent of patients experienced recurrence within 3 years. The radiomic-clinical model (AUC 0.77) outperformed the radiomic, clinical, and TNM stage models (AUC 0.76, 0.71, and 0.70, respectively) on the test set. Recurrence risk was five times higher in the high-risk group than the low-risk group (p<0.01) after stratification with the radiomic-clinical model. The most important features were regional lymph node metastases, the "GLDM Large Dependence Emphasis" texture, and the "Elongation" shape feature.

CONCLUSION

Radiomics analysis can be used in combination with clinicopathological features for effective recurrence risk stratification in patients with surgically resected NSCLC.

The correlation of tertiary lymphoid structures with tumor spread through air spaces and prognosis in lung adenocarcinoma: focusing on pathological spatial features

Lung adenocarcinoma (LADC) exhibits high spatial heterogeneity, with distinct spatial variations in pathological features. The distribution of tertiary lymphoid structures (TLS) in LADC is uneven, and different TLS characteristics play unique roles. To investigate the correlation between TLS features and other pathological characteristics, particularly tumor spread through air spaces (STAS), we analyzed TLS and other pathological features on whole-slide images stained with HE and CD20/CD23. Additionally, the 14-Gene assay was used to assess prognostic risk. Among 388 enrolled LADC patients, 226 (58.2%) were TLS-positive. TLS showed a negative correlation with various adverse pathological features, with boundary-area TLS demonstrating the strongest correlation with STAS quantity (r= -0.324, P < 0.001). Multivariate Cox analysis identified boundary-area TLS as an independent prognostic factor for recurrence-free survival (HR = 0.856, 95% CI = 0.759-0.966, P = 0.026), while mature TLS was an independent factor for overall survival (HR = 0.841, 95% CI = 0.717-0.988, P = 0.035). High-density TLS at the tumor boundary was associated with low-risk stratification by the 14-Gene assay (P = 0.013). This study highlights the negative correlation between TLS and STAS, especially in boundary areas, and emphasizes the impact of tumor microenvironment spatial characteristics on clinical outcomes. Assessment of spatial heterogeneity in LADC facilitates precise risk stratification for patients.

Molecular subtyping of stage I lung adenocarcinoma via molecular alterations in pre-invasive lesion progression

BACKGROUND

Patients with adenocarcinoma in situ (AIS) and minimally invasive (MIA) lung adenocarcinoma (LUAD) are curable by surgery, whereas 20% stage I patients die within five years after surgery. We hypothesize that poor-prognosis stage I patients may exhibit key molecular characteristics deviating from AIS/MIA. Therefore, we tried to reveal molecularly and prognostically distinct subtypes of stage I LUAD by applying key molecular alterations from AIS/MIA to invasive LUAD progression.

METHODS

The RNA and whole-exome sequencing data of 197 tumor-normal matched samples from patients with AIS, MIA, and invasive LUAD were analyzed. ddPCR quantified 202 samples from 182 patients at the absolute expression level. Immunohistochemical quantified the protein expression levels of ACTA2. RNA-seq data from 954 LUAD patients, including 541 stage I patients, along with 12 published datasets comprising 1,331 stage I LUAD patients, were used to validate our findings.

RESULTS

Focal adhesion (FA) was identified as the only pathway significantly perturbed at both genomic and transcriptomic levels by comparing 98 AIS/MIA and 99 LUAD. Then, two FA genes (COL11A1 and THBS2) were found strongly upregulated from AIS/MIA to stage I while steadily expressed from normal to AIS/MIA. Furthermore, unsupervised clustering separated stage I patients into two molecularly and prognostically distinct subtypes (S1 and S2) based on COL11A1 and THBS2 expressions (FA2). Subtype S1 resembled AIS/MIA, whereas S2 exhibited more somatic alterations and activated cancer-associated fibroblast. Immunohistochemistry on 73 samples also observed that CAF was more active in S2 compared to S1 and AIS/MIA. The prognostic value of these two genes identified from our knowledge-driven process was confirmed by 541 stage I patients in a prospective dataset, ddPCR and 12 published datasets.

CONCLUSIONS

We successfully revealed two molecularly and prognostically distinct subtypes of stage I LUAD by applying key molecular alterations from AIS/MIA to invasive LUAD progression. Our model may help reliably identify high-risk stage I patients for more intensive post-surgery treatment.

The evolving landscape of adjuvant therapy in T1-T2N0 resected non-small cell lung cancer: a narrative review

Background and Objective

Lung cancer recurrence after complete surgical resection of early-stage T1-T2N0 non-small cell lung cancer (NSCLC) remains a problem due to unrecognized micrometastatic disease. The objective of this review is to present and summarize data from major randomized trials in which have studied the survival benefit of adjuvant therapy for early-stage NSCLC.

Methods

Information used to write this paper was collected from PubMed and the National Clinical Trial registry from the National Library of Medicine.

Key Content and findings

Clinical trials that explored the use of adjuvant platinum-based chemotherapy historically have failed to show a benefit to giving adjuvant therapy in this early-stage patient population. Specifically, no survival benefit has been shown in stage IA (T1N0) tumors and stage IB tumors (T2aN0), less than 4 cm in size. As a result, adjuvant chemotherapy is currently recommended for only stage IB (pT2aN0) and IIA (pT2bN0) which are greater than 4 cm in size or have high-risk pathologic features. Newer and more effective treatments including targeted therapy against tumors with epidermal growth factor receptor (EGFR) driver mutants, tumors with anaplastic lymphoma kinase (ALK) rearrangements and immunotherapy have renewed interest in exploring the role of adjuvant therapy among early-stage patients. Three years of adjuvant osimertinib with or without adjuvant chemotherapy has been shown to improve overall survival (OS) in a trial population of IB-IIIA NSCLC patients and is approved for adjuvant use in EGFR mutant early-stage NSCLC.

Conclusions

In the future, appropriate patient selection for adjuvant therapy, driven by molecular high-risk features, circulating tumor DNA, or blood-based biomarkers will be important as the majority of early-stage patients are cured with surgical resection alone.

Correlations between 14-gene RNA-level assay and clinical and molecular features in resectable non-squamous non-small cell lung cancer: a cross-sectional study

Background

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide. Accurate risk stratification is essential for optimizing treatment strategies. A 14-gene RNA-level assay of lung cancer, which involves quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis of formalin-fixed paraffin-embedded (FFPE) tissue samples, offers a promising approach. The aim of our study was to assess the relationships between risk stratification, as determined by a 14-gene RNA-level assay, and various clinical and molecular characteristics.

Methods

We retrospectively collected the preoperative clinical information and molecular testing information from 102 resectable non-squamous NSCLC patients. The 14-gene RNA-level assay was performed by extracting RNA from FFPE samples, followed by reverse transcription and quantification via quantitative polymerase chain reaction (qPCR) to assess the expression levels of 11 cancer-associated genes and three housekeeping genes. These gene expression levels were used to calculate a risk score, enabling patient stratification into distinct risk groups. Based on the 14-gene risk stratification, we analyzed the correlations between the clinical and molecular characteristics across the high-, medium-, and low-risk groups.

Results

A total of 102 patients were included in the study. The mean age was 55.19 years, 67 (65.7%) patients were female, and 18 (17.6%) had a smoking history. The 14-gene risk stratification classified patients into low-risk (n=63), intermediate-risk (n=25), and high-risk (n=14) groups. No significant differences were observed in baseline demographics between the three risk groups. High-risk patients had significantly higher mean computed tomography (CT) value (P=0.01) and enhanced CT value (P=0.02) compared to low-risk patients. Genomic profiling of 89 patients revealed specific mutations that were significantly associated with the higher-risk groups. Tumor mutational burden (TMB) was higher in higher-risk groups (P=0.007). In clinically low-risk patients (n=85) as recognized by the NCCN guidelines, the 14-gene risk stratification model reclassified 30 out from the 85 clinically low-risk patients, with 19 placed in the medium-risk group and 11 in the high-risk group, while the remaining samples were still classified as low-risk. Additionally, we found that three patients who were not recommended for adjuvant therapy by the Multiple-gene INdex to Evaluate the Relative benefit of Various Adjuvant therapies (MINERVA) model were classified as high risk and 13 as intermediate risk.

Conclusions

Our results indicate that 14-gene RNA-level assay is correlated with specific genetic mutations, including TP53, KRAS, and LRP1B. These insights provide a stronger foundation for integrating molecular risk assessment with clinical and imaging data, offering more comprehensive information to guide more targeted and effective adjuvant therapy strategies in the future management of lung cancer.

Multiregional transcriptomic profiling provides improved prognostic insight in localized non-small cell lung cancer

Lung Cancer remains the leading cause of cancer deaths in the USA and worldwide. Non-small cell lung cancer (NSCLC) harbors high transcriptomic intratumor heterogeneity (RNA-ITH) that limits the reproducibility of expression-based prognostic models. In this study, we used multiregional RNA-seq data (880 tumor samples from 350 individuals) from both public (TRACERx) and internal (MDAMPLC) cohorts to investigate the effect of RNA-ITH on prognosis in localized NSCLC at the gene, signature, and tumor microenvironment levels. At the gene level, the maximal expression of hazardous genes (expression negatively associated with survival) but the minimal expression of protective genes (expression positively associated with survival) across different regions within a tumor were more prognostic than the average expression. Following that, we examined whether multiregional expression profiling can improve the performance of prognostic signatures. We investigated 11 gene signatures collected from previous publications and one signature developed in this study. For all of them, the prognostic prediction accuracy can be significantly improved by converting the regional expression of signature genes into sample-specific expression with a simple function-taking the maximal expression of hazardous genes and the minimal expression of protective genes. In the tumor microenvironment, we found a similar rule also seems applicable to immune ITH. We calculated the infiltration levels of major immune cell types in each region of a sample based on expression deconvolution. Prognostic analysis indicated that the region with the lowest infiltration level of protective or highest infiltration level of hazardous immune cells determined the prognosis of NSCLC patients. Our study highlighted the impact of RNA-ITH on the prognostication of NSCLC, which should be taken into consideration to optimize the design and application of expression-based prognostic biomarkers and models. Multiregional assays have the great potential to significantly improve their applications to prognostic stratification.

Soluble PD-L1 shows no association to relapse and overall survival in early stage non-small cell lung cancer (NSCLC)

BACKGROUND

Cancer immune evasion is critical in non-small cell lung cancer (NSCLC) and has been targeted by immunotherapy. High soluble (s)PD-L1 is associated with reduced survival and treatment failure in advanced stages. Here we evaluated the effects of sPD-L1 on T cells, relapse free survival, and overall survival in early stage NSCLC.

METHODS

In vitro T cell stimulation was performed in the presence of sPD-L1 to evaluate its immunomodulatory activity. Data from The Cancer Genome Atlas (TCGA) were investigated for PD-L1 splice variants and enzymes involved in proteolytic cleavage (i.e. ADAM10). Plasma from 74 NSCLC (stage IA-IIIB), as well as an additional 73 (control cohort) patients was collected prior to curative surgery. Thereafter sPD-L1 levels from an immunosorbent assay were correlated with patient outcome.

RESULTS

In vitro sPD-L1 inhibited IFN-γ production and proliferation of T cells and induced a terminal effector CD4 T cell subtype expressing CD27. Data from the TCGA demonstrated that elevated mRNA levels of ADAM10 is a negative predictor of outcome in NSCLC patients. To investigate the clinical relevance of these in vitro and TCGA findings, we quantified sPD-L1 in the plasma of early-stage NSCLC patients. In the first cohort we found significantly higher sPD-L1 levels in relapsing NSCLC patients, with a multivariate analysis revealing high sPD-L1 (>1000 pg/mL) as an independent predictor of survival. However, these findings could not be validated in two independent control cohorts.

DISCUSSION

Although in vitro and TCGA data support the suppressive effect of sPD-L1 we were unable to translate this in our clinical setting. These results may be due to the small patient number and their heterogeneity as well as the lack of a standardized sPD-L1 ELISA. Our inconclusive results regarding the value of sPD-L1 in early stage NSCLC warrant assay validation and further investigation in larger (neo-)adjuvant trials.

Histopathologic pattern and molecular risk stratification are associated with prognosis in patients with stage IB lung adenocarcinoma

Background

The benefit of adjuvant therapy remains controversial in completely resected (R0) stage IB non-small cell lung cancer (NCLSC) patients. In this study, we aimed to explore potential prognostic factors in stage IB NSCLC patients.

Methods

This study included 215 patients with R0 stage IB lung adenocarcinoma (LUAD) (tumor size: 3-4 cm). DNA sequencing was performed with surgical samples of 126 patients using a panel of 9 driver genes. The molecular risk stratification was assessed by a 14-gene quantitative polymerase chain reaction assay.

Results

Among the 215 patients, 67.9% had micropapillary/solid (MIP/SOL)-predominant tumors. Epidermal growth factor receptor (EGFR) mutations were detected in 75 of 126 patients (59.5%). MIP/SOL tumors harbored less common EGFR mutations than the other histologic patterns (50.6% vs. 79.5%, P=0.003). Molecular risk stratification was successfully assessed in 99 patients, of whom 37.4%, 26.3%, and 36.4% were high, intermediate, and low risk, respectively. The MIP/SOL pattern was associated with shorter disease-free survival (DFS) [hazard ratio (HR) =2.16, 95% confidence interval: 1.28-3.67; P=0.01]. The molecular high-risk patients had shorter DFS than the low- (HR =2.93, P=0.01) and intermediate-risk patients (HR =2.35, P=0.06). The prognostic value of molecular risk stratification was also significant in the MIP/SOL subset (median DFS high-risk: 45 months, low and intermediate risk: not reached; P=0.03).

Conclusions

Our study showed that both the MIP/SOL pattern and molecular high-risk category were adverse prognostic factors in stage IB NSCLC patients. Our results suggest that combining histologic classification and molecular risk stratification may help to identify the subset of patients with poor prognosis.

The Overlooked Cornerstone in Precise Medicine: Personalized Postoperative Surveillance Plan for NSCLC

Non-small cell lung cancer recurrence after curative-intent surgery remains a challenge despite advancements in treatment. We review postoperative surveillance strategies and their impact on overall survival, highlighting recommendations from clinical guidelines and controversies. Studies suggest no clear benefit from more intensive imaging, whereas computed tomography scans reveal promise in detecting recurrence. For early-stage disease, including ground-glass opacities and adenocarcinoma in situ or minimally invasive adenocarcinoma, less frequent surveillance may suffice owing to favorable prognosis. Liquid biopsy, especially circulating tumor deoxyribonucleic acid, holds potential for detecting minimal residual disease. Clinicopathologic factors and genomic profiles can also provide information about site-specific metastases. Machine learning may enable personalized surveillance plans on the basis of multi-omics data. Although precision medicine transforms non-small cell lung cancer treatment, optimizing surveillance strategies remains essential. Tailored surveillance strategies and emerging technologies may enhance early detection and improve patients' survival, necessitating further research for evidence-based protocols.

Molecular and immune characterization of Chinese early-stage non-squamous non-small cell lung cancer: a multi-omics cohort study

Background

Albeit considered with superior survival, around 30% of the early-stage non-squamous non-small cell lung cancer (Ns-NSCLC) patients relapse within 5 years, suggesting unique biology. However, the biological characteristics of early-stage Ns-NSCLC, especially in the Chinese population, are still unclear.

Methods

Multi-omics interrogation of early-stage Ns-NSCLC (stage I-III), paired blood samples and normal lung tissues (n=76) by whole-exome sequencing (WES), RNA sequencing, and T-cell receptor (TCR) sequencing were conducted.

Results

An average of 128 exonic mutations were identified, and the most frequently mutant gene was EGFR (55%), followed by TP53 (37%) and TTN (26%). Mutations in MUC17, ABCA2, PDE4DIP, and MYO18B predicted significantly unfavorable disease-free survival (DFS). Moreover, cytobands amplifications in 8q24.3, 14q13.1, 14q11.2, and deletion in 3p21.1 were highlighted in recurrent cases. Higher incidence of human leukocyte antigen loss of heterozygosity (HLA-LOH), higher tumor mutational burden (TMB) and tumor neoantigen burden (TNB) were identified in ever-smokers than never-smokers. HLA-LOH also correlated with higher TMB, TNB, intratumoral heterogeneity (ITH), and whole chromosomal instability (wCIN) scores. Interestingly, higher ITH was an independent predictor of better DFS in early-stage Ns-NSCLC. Up-regulation of immune-related genes, including CRABP2, ULBP2, IL31RA, and IL1A, independently portended a dismal prognosis. Enhanced TCR diversity of peripheral blood mononuclear cells (PBMCs) predicted better prognosis, indicative of a noninvasive method for relapse surveillance. Eventually, seven machine-learning (ML) algorithms were employed to evaluate the predictive accuracy of clinical, genomic, transcriptomic, and TCR repertoire data on DFS, showing that clinical and RNA features combination in the random forest (RF) algorithm, with area under the curve (AUC) of 97.5% and 83.3% in the training and testing cohort, respectively, significantly outperformed other methods.

Conclusions

This study comprehensively profiled the genomic, transcriptomic, and TCR repertoire spectrums of Chinese early-stage Ns-NSCLC, shedding light on biological underpinnings and candidate biomarkers for prognosis development.

Beneficial implications of adjuvant chemotherapy for stage IB lung adenocarcinoma exhibiting elevated SUVmax in FDG-PET/CT: a retrospective study from a single center

Background

Controversy surrounds the efficacy of adjuvant chemotherapy (ACT) in the treatment of stage I lung adenocarcinoma (LUAD). The objective of this study was to examine the impact of the maximum standardized uptake value (SUVmax) as measured by 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) on the efficacy of ACT in patients diagnosed with stage I LUAD.

Methods

We scrutinized the medical records of 928 consecutive patients who underwent complete surgical resection for pathological stage I LUAD at our institution. The ideal cut-off value for primary tumor SUVmax in terms of disease-free survival (DFS) and overall survival (OS) was determined using the X-tile software. The Kaplan-Meier method and Cox regression analysis were used for survival analysis.

Results

Based on the SUVmax algorithm, the ideal cutoff values were determined to be 4.9 for DFS and 5.0 for OS. We selected 5.0 as the threshold because OS is the more widely accepted predictive endpoint. In a multivariate Cox regression analysis, SUVmax ≥ 5.0, problematic IB stage, and sublobectomy were identified as independent risk factors for poor DFS and OS. It is noteworthy that patients who were administered ACT had significantly longer DFS and OS than what was observed in the subgroup of patients with pathological stage IB LUAD and SUVmax ≥ 5.0 (p < 0.035 and p ≤ 0.046, respectively). However, there was no observed survival advantage for patients in stages IA or IB who had an SUVmax < 5.0.

Conclusion

The preoperative SUVmax of tumors served as an indicator of the impact of ACT in the context of completely resected pathological stage I LUAD. Notably, patients within the Stage IB category exhibiting elevated SUVmax levels emerged as a subgroup experiencing substantial benefits from postoperative ACT.

Genomic profiling for non-small cell lung cancer: Clinical relevance in staging and prognosis

Lung cancer is one of the most common cancers prevalent and around 80% of all cases are non-small cell lung cancer (NSCLC). Due to high recurrence rates, the mortality of NSCLC is high. Conventional staging systems allowed risk classification of patients in order to simplify the patient selection for adjuvant chemotherapy. Gene expression analysis has been shown to possess advantage over conventional staging systems in NSCLC in terms of patients risk classification. This article reviews the evidences on the genomic profiling of NSCLC patients into high and low-risk groups based on the expression of genes involved in various proliferative pathways.

The impact of adjuvant EGFR-TKIs and 14-gene molecular assay on stage I non-small cell lung cancer with sensitive EGFR mutations

Background

Currently, the role of EGFR-TKIs as adjuvant therapy for stage I, especially IA NSCLC, after surgical resection remains unclear. We aimed to compare the effect of adjuvant EGFR-TKIs with observation in such patients by incorporating an established 14-gene molecular assay for risk stratification.

Methods

This retrospective cohort study was conducted at the First Affiliated Hospital of Guangzhou Medical University (Study ID: ChNCRCRD-2022-GZ01). From March 2013 to February 2019, completely resected stage I NSCLC (8th TNM staging) patients with sensitive EGFR mutation were included. Patients with eligible samples for molecular risk stratification were subjected to the 14-gene prognostic assay. Inverse probability of treatment weighting (IPTW) was employed to minimize imbalances in baseline characteristics.

Findings

A total of 227 stage I NSCLC patients were enrolled, with 55 in EGFR-TKI group and 172 in the observation group. The median duration of follow-up was 78.4 months. After IPTW, the 5-year DFS (HR = 0.30, 95% CI, 0.14-0.67; P = 0.003) and OS (HR = 0.26, 95% CI, 0.07-0.96; P = 0.044) of the EGFR-TKI group were significantly better than the observation group. For subgroup analyses, adjuvant EGFR-TKIs were associated with favorable 5-year DFS rates in both IA (100.0% vs. 84.5%; P = 0.007), and IB group (98.8% vs. 75.3%; P = 0.008). The 14-gene assay was performed in 180 patients. Among intermediate-high-risk patients, EGFR-TKIs were associated with a significant improvement in 5-year DFS rates compared to observation (96.0% vs. 70.5%; P = 0.012), while no difference was found in low-risk patients (100.0% vs. 94.9%; P = 0.360).

Interpretation

Our study suggested that adjuvant EGFR-TKI might improve DFS and OS of stage IA and IB EGFR-mutated NSCLC, and the 14-gene molecular assay could help patients that would benefit the most from treatment.

Funding

This work was supported by China National Science Foundation (82022048, 82373121).

Accuracy of minimal residual disease detection by circulating tumor DNA profiling in lung cancer: a meta-analysis

BACKGROUND

The sensitivity and specificity of minimal residual disease detected by circulating tumor DNA profiling (ctDNA MRD) in lung cancer, with particular attention to the distinction between landmark strategy and surveillance strategy, for predicting relapse in lung cancer patients after definitive therapy has yet to be determined.

METHODS

The prognostic value of ctDNA MRD by landmark strategy and surveillance strategy was evaluated in a large cohort of patients with lung cancer who received definitive therapy using a systemic literature review and meta-analysis. Recurrence status stratified by ctDNA MRD result (positive or negative) was extracted as the clinical endpoint. We calculated the area under the summary receiver operating characteristic curves, and pooled sensitivities and specificities. Subgroup analyses were conducted based on histological type and stage of lung cancer, types of definitive therapy, and ctDNA MRD detection methods (detection technology and strategy such as tumor-informed or tumor-agnostic).

RESULTS

This systematic review and meta-analysis of 16 unique studies includes 1251 patients with lung cancer treated with definitive therapy. The specificity of ctDNA MRD in predicting recurrence is high (0.86-0.95) with moderate sensitivity (0.41-0.76), whether shortly after treatment or during the surveillance. The landmark strategy appears to be more specific but less sensitive than the surveillance strategy.

CONCLUSIONS

Our study suggests that ctDNA MRD is a relatively promising biomarker for relapse prediction among lung cancer patients after definitive therapy, with a high specificity but suboptimal sensitivity, whether in landmark strategy or surveillance strategy. Although surveillance ctDNA MRD analysis decreases specificity compared with the landmark strategy, the decrease is minimal compared to the increase in sensitivity for relapse prediction of lung cancer.

The Evolving Role for Systemic Therapy in Resectable Non-small Cell Lung Cancer

During the last 2 decades, the understanding of non-small cell lung cancer (NSCLC) has evolved from a purely histologic classification system to a more complex model synthesizing clinical, histologic, and molecular data. Biomarker-driven targeted therapies have been approved by the United States Food and Drug Administration for patients with metastatic NSCLC harboring specific driver alterations in EGFR, HER2, KRAS, BRAF, MET, ALK, ROS1, RET, and NTRK. Novel immuno-oncology agents have contributed to improvements in NSCLC-related survival at the population-level. However, only in recent years has this nuanced understanding of NSCLC permeated into the systemic management of patients with resectable tumors.

A narrative review of genetic biomarkers in non-small cell lung cancer: an update and future perspectives

Background and Objective

Lung cancer has long been the leading cause of cancer deaths in the United States. Lung cancer has a poor prognosis, and our understanding of who will maximally benefit from different therapies is incomplete. This article discusses genetic biomarkers that may help in this regard.

Methods

From origin until February 25, 2022, PubMed database was searched for terms "non-small cell lung cancer", "genomics" and "biomarker", with special attention paid to literature published within the past 10 years. Search was language restricted to English. Additional literature was identified through hand searches of the references of retrieved literature.

Key Content and Findings

The most robustly described biomarkers for non-small cell lung cancer (NSCLC) are assessment of specific gene mutations. These are currently used in clinical practice for both prediction and prognostication. Abnormal mutation status of STK11/LKB1 and KEAP1-NFE2L2 are associated with poor response to radiotherapy (RT), and STK11/LKB1 is further associated with resistance to PD-L1 immunotherapy. Abnormal TP53 is associated with decreased benefit from cisplatin in squamous cell carcinoma (SCC). In terms of prognostication, RB1 mutations are associated with decreased overall survival (OS) in NSCLC and KEAP1-NFE2L2 mutations are associated with increased local recurrence (LR).Additional work has focused on gene expression levels, as well as analysis of genetic factors and signaling molecules affecting the tumor microenvironment (TME). High levels of Rad51c and NFE2L2 are associated with resistance to chemotherapy, and high Rad51c levels are further associated with resistance to RT. High nuclear expression of β-catenin has additionally been associated with poor RT response. Further, there is increasing evidence that some long non-coding RNAs (lncRNAs) may play a crucial role in regulation of tumor radiosensitivity. Much of this work has had promising early results but will require further validation before routine clinical use. Finally, there is evidence that quantification of some signaling molecules and microRNAs (miRNAs) may have clinical utility in predicting adverse outcomes in RT.

Conclusions

An improved understanding of tumor genetics in NSCLC has led to the development of targeted therapies and improved prognostication. As more work is done in this field, more and more genetic biomarkers will become candidates for clinical use. Much work will be required to validate these findings in the clinical setting.

Prevalence, clinical and molecular characteristics of early stage EGFR-mutated lung cancer in a real-life West-European cohort: Implications for adjuvant therapy

OBJECTIVES

The landmark ADAURA study recently demonstrated a significant disease-free survival benefit of adjuvant osimertinib in patients with resected EGFR-mutated lung adenocarcinoma. However, data on prevalence rates and stage distribution of EGFR mutations in non-small cell lung cancer in Western populations are limited since upfront EGFR testing in early stage lung adenocarcinoma is not common practice. Here, we present a unique, real-world, unselected cohort of lung adenocarcinoma to aid in providing a rationale for routine testing of early stage lung cancers for EGFR mutations in the West-European population.

MATERIAL AND METHODS

We performed routine unbiased testing of all cases, regardless of TNM stage, with targeted next-generation sequencing on 486 lung adenocarcinoma cases between 01- January 2014 and 01 February 2020. Clinical and pathological data, including co-mutations and morphology, were collected. EGFR-mutated cases were compared to KRAS-mutated cases to investigate EGFR-specific characteristics.

RESULTS

In total, 53 of 486 lung adenocarcinomas (11%) harboured an EGFR mutation. In early stages (stage 0-IIIA), the prevalence was 13%, versus 9% in stage IIIB-IV. Nine out of 130 (7%) stage IB-IIIA patients fit the ADAURA criteria. Early stage cases harboured more L858R mutations (p = 0.02), fewer exon 20 insertions (p = 0.048), fewer TP53 co-mutations (p = 0.007), and were more frequently never smokers (p = 0.04) compared to late stage cases with EGFR mutations. The KRAS-mutated cases were distributed more evenly across TNM stages compared to the EGFR-mutated cases.

CONCLUSION

As (neo-)adjuvant targeted therapy regimes enter the field of lung cancer treatment, molecular analysis of early stage non-small cell lung cancer becomes relevant. Testing for EGFR mutations in early stage lung adenocarcinoma holds a substantial yield in our population, as our number needed to test ratio for adjuvant osimertinib was 14.4. The observed differences between early and late stage disease warrant further analysis to work towards better prognostic stratification and more personalised treatment.

The association of postoperative radiotherapy with survival in resected N2 non-small cell lung cancer

Background

The current staging system for completely resected pathologic N2 non-small cell lung cancer (NSCLC) treated with chemotherapy is not suitable for distinguishing those patients most likely to benefit from postoperative radiotherapy (PORT). This study aimed to construct a survival prediction model that will enable individualized prediction of the net survival benefit of PORT in patients with completely resected N2 NSCLC treated with chemotherapy.

Methods

A total of 3,094 cases from between 2002 and 2014 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patient characteristics were included as covariates, and their association with overall survival (OS) with and without PORT was assessed. Data from 602 patients from China were included for external validation.

Results

Age, sex, the number of examined/positive lymph nodes, tumor size, the extent of surgery, and visceral pleural invasion (VPI) were significantly associated with OS (P<0.05). Two nomograms were developed based on clinical variables to estimate individuals' net survival difference attributable to PORT. The calibration curve showed excellent agreement between the OS predicted by the prediction model and that actually observed. In the training cohort, the C-index for OS was 0.619 [95% confidence interval (CI): 0.598-0.641] in the PORT group and 0.627 (95% CI: 0.605-0.648) in the non-PORT group. Results showed that PORT could improve OS [hazard ratio (HR): 0.861; P=0.044] for patients with a positive PORT net survival difference.

Conclusions

Our practical survival prediction model can be used to make an individualized estimate of the net survival benefit of PORT for patients with completely resected N2 NSCLC who have been treated with chemotherapy.

Emerging precision neoadjuvant systemic therapy for patients with resectable non-small cell lung cancer: current status and perspectives

Over the past decade, targeted therapy for oncogene-driven NSCLC and immune checkpoint inhibitors for non-oncogene-driven NSCLC, respectively, have greatly improved the survival and quality of life for patients with unresectable NSCLC. Increasingly, these biomarker-guided systemic therapies given before or after surgery have been used in patients with early-stage NSCLC. In March 2022, the US FDA granted the approval of neoadjuvant nivolumab and chemotherapy for patients with stage IB-IIIA NSCLC. Several phase II/III trials are evaluating the clinical efficacy of various neoadjuvant immune checkpoint inhibitor combinations for non-oncogene-driven NSCLC and neoadjuvant molecular targeted therapies for oncogene-driven NSCLC, respectively. However, clinical application of precision neoadjuvant treatment requires a paradigm shift in the biomarker testing and multidisciplinary collaboration at the diagnosis of early-stage NSCLC. In this comprehensive review, we summarize the current diagnosis and treatment landscape, recent advances, new challenges in biomarker testing and endpoint selections, practical considerations for a timely multidisciplinary collaboration at diagnosis, and perspectives in emerging neoadjuvant precision systemic therapy for patients with resectable, early-stage NSCLC. These biomarker-guided neoadjuvant therapies hold the promise to improve surgical and pathological outcomes, reduce systemic recurrences, guide postoperative therapy, and improve cure rates in patients with resectable NSCLC.

Five-gene signature for the prediction of response to immune checkpoint inhibitors in patients with gastric and urothelial carcinomas

BACKGROUND

Ample evidence supports the potential of programmed death-ligand 1 (PD-L1) expression, detected by immunohistochemistry, as a predictive biomarker for immunotherapy in patients with advanced cancers. To predict the response to immune checkpoint inhibitors in patients with gastric and urothelial carcinomas, we aimed to replace PD-L1 combined positive score (CPS) with CD274 mRNA in the original four-gene signature and PD-L1 CPS model developed by us.

METHOD

We used quantitative real-time polymerase chain reaction (qRT-PCR) to measure the expression levels of five target genes in a cohort of 49 patients (33 with gastric cancer and 16 with urothelial carcinoma) who had received immunotherapy and whose therapeutic responses were available. The predictive performance was evaluated using R package maxstat.

RESULTS

Cutoff values of mRNA expression level were measured using the log-rank statistics for progression-free survival (PFS). Based on these cutoffs, immunotherapy responses were predicted and sorted into responder (n = 12, 24.5%) and non-responder (n = 37, 75.5%) groups. The median PFS values of predicted responders and non-responders were 14.8 months (95% confidence interval [CI]: 0-34.7) and 4.7 months (95% CI: 1.0-8.4, p = 0.02), respectively. Among the 12 predicted responders, 10 had microsatellite-stable tumors with a low tumor mutational burden. The actual clinical responses (complete and partial) were higher in the responder group than those in the non-responder group: 83.3% and 16.2%, respectively.

CONCLUSION

We modified a predictive biomarker for CD274 mRNA expression to predict the response to immunotherapy in patients with gastric or urothelial carcinomas.

Development and validation of a nomogram for predicting survival time and making treatment decisions for clinical stage IA NSCLC based on the SEER database

Background

The aim of this study was to establish and validate a nomogram model for accurate prediction of patients' survival with T1aN0M0 none small cell lung cancer (NSCLC).

Methods

The patients, diagnosed with the stage IA NSCLC from 2004-2015, were identified from the Surveillance, Epidemiology and End Results (SEER) database. The variables with a P-value < 0.05 in a multivariate Cox regression were selected to establish the nomogram. The discriminative ability of the model was evaluated by the concordance index (C-index). The proximity of the nomogram prediction to the actual risk was depicted by a calibration plot. The clinical usefulness was estimated by the decision curve analysis (DCA). Survival curves were made with Kaplan-Meier method and compared by Log-Rank test.

Results

Eight variables, including treatment, age, sex, race, marriage, tumor size, histology, and grade were selected to develop the nomogram model by univariate and multivariate cox regression. The C-index was 0.704 (95% CI, 0.694-0.714) in the training set and 0.713 (95% CI, 0.697-0.728) in the test set, which performed significantly better than 8th edition AJCC TNM stage system (0.550, 95% CI, 0.408-0.683, P < 0.001). The calibration curve showed that the prediction ability of 3-years and 5-years survival rate demonstrated a high degree of agreement between the nomogram model and the actual observation. The DCA curves also proved that the nomogram-assisted decisions could improve patient outcomes.

Conclusion

We established and validated a prognostic nomogram to predict 3-years and 5-years overall survival in stage IA NSCLC.

Prognostic value of Beclin 1, EGFR and ALK in non-squamous non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is one of the most malignant tumors. The study was carried out to investigate the prognostic value of Beclin 1, EGFR and ALK for this cancer. Patients diagnosed with non-squamous NSCLC and admitted to our hospital from January 2011 to September 2016 were analyzed. Expression of Beclin 1 and mutation of EGFR and ALK were assessed using polymerase chain reaction (PCR) and fluorescent in situ hybridization (FISH) and analyzed for their relationship with demographic and clinical characteristics of the patients. Multivariate Cox regression models were applied to analyze the risk factors associated with survival and receiver response curves (ROC) were plotted to determine the prognostic value of Beclin 1, EGFR and ALK for patients with non-squamous NSCLC. Compared with adjacent normal tissue, Beclin 1 expression was elevated in the cancer tissue significantly; assessments of EGFR and ALK mutations showed that out of the 480 patients, 233 (48.5%) and 75 (12.6%) patients had EGFR and ALK mutations. Univariate analysis revealed that Beclin 1 level, EGFR and ALK mutations were associated with lymph node metastasis, TNM stage, tumor differentiation and prognosis, but not with gender, age and smoking status. The Kaplan-Meier survival analysis indicated that low Beclin 1 expression and positive EGFR and ALK rearrangements were associated with higher survival rate and longer progress-free survival (PFS). Multivariate Cox regression analysis showed that Beclin 1, EGFR, ALK mutations, tumor differentiation grade, TNM stage and lymph node metastasis were independently associated with PFS. ROC analysis showed that Beclin 1, EGFR and ALK were significant predictors for PFS; the areas under curve (AUC) for Beclin 1, EGFR and ALK were 0.812 (P = 0.018, cut-off value: 1.2), 0.781 (P = 0.011, cut-off value: 15%) and 0.722 (P = 0.010, cut-off value: 11%), respectively, suggesting that they have significant prognostic value for lung cancer patients. Our data indicate that Beclin 1, EGFR and ALK genes are associated with the prognosis of patients with non-squamous NSCLC. High Beclin 1 expression and negative EGFR and ALK mutations predict a poor prognosis with PFS.

Discovery of pathway-independent protein signatures associated with clinical outcome in human cancer cohorts

Proteomic data provide a direct readout of protein function, thus constituting an information-rich resource for prognostic and predictive modeling. However, protein array data may not fully capture pathway activity due to the limited number of molecules and incomplete pathway coverage compared to other high-throughput technologies. For the present study, our aim was to improve clinical outcome prediction compared to published pathway-dependent prognostic signatures for The Cancer Genome Atlas (TCGA) cohorts using the least absolute shrinkage and selection operator (LASSO). RPPA data is particularly well-suited to the LASSO due to the relatively low number of predictors compared to larger genomic data matrices. Our approach selected predictors regardless of their pathway membership and optimally combined their RPPA measurements into a weighted risk score. Performance was assessed and compared to that of the published signatures using two unbiased approaches: 1) 10 iterations of threefold cross-validation for unbiased estimation of hazard ratio and difference in 5-year survival (by Kaplan-Meier method) between predictor-defined high and low risk groups; and 2) a permutation test to evaluate the statistical significance of the cross-validated log-rank statistic. Here, we demonstrate strong stratification of 445 renal clear cell carcinoma tumors from The Cancer Genome Atlas (TCGA) into high and low risk groups using LASSO regression on RPPA data. Median cross-validated difference in 5-year overall survival was 32.8%, compared to 25.2% using a published receptor tyrosine kinase (RTK) prognostic signature (median hazard ratios of 3.3 and 2.4, respectively). Applicability and performance of our approach was demonstrated in three additional TCGA cohorts: ovarian serous cystadenocarcinoma (OVCA), sarcoma (SARC), and cutaneous melanoma (SKCM). The data-driven LASSO-based approach is versatile and well-suited for discovery of new protein/disease associations.

Big data in basic and translational cancer research

Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of 'big data' in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment.

[Consensus on Postoperative Recurrence Prediction of Non-small Cell Lung Cancer Based on Molecular Markers]

Significant progress has been made in lung cancer screening, surgery, chemoradiation, targeted therapy, and immunotherapy recently. Surgical resection is the most important treatment for localized non-small cell lung cancer (NSCLC) so far, but there are still many patients who develop local recurrence or distant metastases within 5 years of surgery. Currently, the risk factors of recurrence in patients with NSCLC are mainly based on clinical and pathological features, which hardly identify patients at high risk of recurrence accurately. With the development of new detection technologies, a number of molecular markers that may have a predictive risk of recurrence in NSCLC have been discovered over the years. In order to summarize the molecular markers related to postoperative recurrence in NSCLC patients, we have formulated a consensus on the prediction of postoperative recurrence of NSCLC based on molecular markers. This consensus mainly focuses on the early stage NSCLC patients, discusses and summarizes the risk factors of disease recurrence from the molecular level. It is hoped that more and more valuable information can be provided for the management of patients, so as to provide more guidance for the perioperative management of the patients with early stage NSCLC in the future.
.

Interleukin-11: A Potential Biomarker and Molecular Therapeutic Target in Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer and is a fast progressive disease when left untreated. Identification of potential biomarkers in NSCLC is an ongoing area of research that aims to detect, diagnose, and prognosticate patients early to optimize treatment. We review the role of interleukin-11 (IL11), a stromal-cell derived pleiotropic cytokine with profibrotic and cellular remodeling properties, as a potential biomarker in NSCLC. This review identifies the need for biomarkers in NSCLC, the potential sources of IL11, and summarizes the available information leveraging upon published literature, publicly available datasets, and online tools. We identify accumulating evidence suggesting IL11 to be a potential biomarker in NSCLC patients. Further in-depth studies into the pathophysiological effects of IL11 on stromal-tumor interaction in NSCLC are warranted and current available literature highlights the potential value of IL11 detection as a diagnostic and prognostic biomarker in NSCLC.

DNA Damage Response Gene-Based Subtypes Associated With Clinical Outcomes in Early-Stage Lung Adenocarcinoma

DNA damage response (DDR) pathways play a crucial role in lung cancer. In this retrospective analysis, we aimed to develop a prognostic model and molecular subtype based on the expression profiles of DDR-related genes in early-stage lung adenocarcinoma (LUAD). A total of 1,785 lung adenocarcinoma samples from one RNA-seq dataset of The Cancer Genome Atlas (TCGA) and six microarray datasets of Gene Expression Omnibus (GEO) were included in the analysis. In the TCGA dataset, a DNA damage response gene (DRG)–based signature consisting of 16 genes was constructed to predict the clinical outcomes of LUAD patients. Patients in the low-DRG score group had better outcomes and lower genomic instability. Then, the same 16 genes were used to develop DRG-based molecular subtypes in the TCGA dataset to stratify early-stage LUAD into two subtypes (DRG1 and DRG2) which had significant differences in clinical outcomes. The Kappa test showed good consistency between molecular subtype and DRG (K = 0.61, p < 0.001). The DRG subtypes were significantly associated with prognosis in the six GEO datasets (pooled estimates of hazard ratio, OS: 0.48 (0.41–0.57), p < 0.01; DFS: 0.50 (0.41–0.62), p < 0.01). Furthermore, patients in the DRG2 group benefited more from adjuvant therapy than standard-of-care, which was not observed in the DRG1 group. In summary, we constructed a DRG-based molecular subtype that had the potential to predict the prognosis of early-stage LUAD and guide the selection of adjuvant therapy for early-stage LUAD patients.

Updated Prognostic Factors in Localized NSCLC

Lung cancer is the most common cause of cancer mortality worldwide, and non-small cell lung cancer (NSCLC) represents 80% of lung cancer subtypes. Patients with localized non-small cell lung cancer may be considered for upfront surgical treatment. However, the overall 5-year survival rate is 59%. To improve survival, adjuvant chemotherapy (ACT) was largely explored and showed an overall benefit of survival at 5 years < 7%. The evaluation of recurrence risk and subsequent need for ACT is only based on tumor stage (TNM classification); however, more than 25% of patients with stage IA/B tumors will relapse. Recently, adjuvant targeted therapy has been approved for EGFR-mutated resected NSCLC and trials are evaluating other targeted therapies and immunotherapies in adjuvant settings. Costs, treatment duration, emergence of resistant clones and side effects stress the need for a better selection of patients. The identification and validation of prognostic and theranostic markers to better stratify patients who could benefit from adjuvant therapies are needed. In this review, we report current validated clinical, pathological and molecular prognosis biomarkers that influence outcome in resected NSCLC, and we also describe molecular biomarkers under evaluation that could be available in daily practice to drive ACT in resected NSCLC.

Clinicopathological significance of DAPK gene promoter hypermethylation in non-small cell lung cancer: A meta-analysis

BACKGROUND

Death-associated protein kinase (DAPK) has a strong function of tumor suppression involving apoptosis regulation, autophagy, and metastasis inhibition. Hypermethylation of CpG islands in DAPK gene promoter region is one of the important ways to inactivate this tumor suppressor gene, which might promote lung carcinogenesis. However, the clinicopathological significance of the DAPK promoter hypermethylation in lung cancer remains unclear. In this study, we performed a meta-analysis trying to estimate the clinicopathological significance of DAPK promoter hypermethylation in non-small cell lung cancer (NSCLC).

METHODS

A detailed literature search for publications relevant to DAPK gene promoter methylation and NSCLC was made in PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, CSTJ, Wanfang databases, and SinoMed (CBM). The random-effects model and fixed-effects model were utilized to pool the relative ratio based on the heterogeneity test in the meta-analysis.

RESULTS

A total of 41 studies with 3348 patients were included. The frequency of DAPK methylation was significantly higher in NSCLC than in non-malignant control (odds ratio (OR) = 6.88, 95% confidence interval (CI):  4.17-11.35, P < 0.00001). The pooled results also showed that DAPK gene promoter hypermethylation was significantly associated with poor prognosis for overall survival in patients with NSCLC (hazard ratio: 1.23, 95% CI:1.01-1.52, P = 0.04). Moreover, DAPK gene promoter hypermethylation was significantly associated with squamous cell carcinoma (OR: 1.25, 95% CI: 1.01-1.54, P = 0.04) and smoking behavior (OR: 1.42, 95% CI: 1.04-1.93, P = 0.03) but not with TNM stage, tumor differentiation, age, or gender.

CONCLUSION

DAPK promoter hypermethylation might be a candidate diagnostic and prognostic tumor marker for NSCLC.

Novel Genetic Prognostic Signature for Lung Adenocarcinoma Identified by Differences in Gene Expression Profiles of Low- and High-Grade Histological Subtypes

The 2021 WHO classification proposed a pattern-based grading system for early-stage invasive non-mucinous lung adenocarcinoma. Lung adenocarcinomas with high-grade patterns have poorer outcomes than those with lepidic-predominant patterns. This study aimed to establish genetic prognostic signatures by comparing differences in gene expression profiles between low- and high-grade adenocarcinomas. Twenty-six (9 low- and 17 high-grade adenocarcinomas) patients with histologically “near-pure” patterns (predominant pattern comprising >70% of tumor areas) were selected retrospectively. Using RNA sequencing, gene expression profiles between the low- and high-grade groups were analyzed, and genes with significantly different expression levels between these two groups were selected for genetic prognostic signatures. In total, 196 significant candidate genes (164 upregulated and 32 upregulated in the high- and low-grade groups, respectively) were identified. After intersection with The Cancer Genome Atlas–Lung Adenocarcinoma prognostic genes, three genes, exonuclease 1 (EXO1), family with sequence similarity 83, member A (FAM83A), and disks large-associated protein 5 (DLGAP5), were identified as prognostic gene signatures. Two independent cohorts were used for validation, and the areas under the time-dependent receiver operating characteristic were 0.784 and 0.703 in the GSE31210 and GSE30219 cohorts, respectively. Our result showed the feasibility and accuracy of this novel three-gene prognostic signature for predicting the clinical outcomes of lung adenocarcinoma.

Update on adjuvant therapy in completely resected NSCLC patients

In patients with completely resected non‐small cell lung cancer (NSCLC), postoperative adjuvant chemotherapy has been associated with improvement in survival by minimizing the risk of recurrence. For years, systemic chemotherapy including platinum based regimen has been a mainstay treatment modality of adjuvant treatment after complete resection. ADAURA study showed that among completely resected IB to IIIA NSCLC, disease‐free survival was significantly better in patients under adjuvant osimertinib than a placebo group. After the advent of a variety of new treatment regimens, such as third generation TKI and immunotherapy, the landscape of postoperative adjuvant treatment has been changing. In this review, we discuss some key issues regarding choice of adjuvant treatment after complete resection in NSCLC, and provide further updates on recent advances in treatment modalities.

Genetic and immunologic features of recurrent stage I lung adenocarcinoma

Although surgery for early-stage lung cancer offers the best chance of cure, recurrence still occurs between 30 and 50% of the time. Why patients frequently recur after complete resection of early-stage lung cancer remains unclear. Using a large cohort of stage I lung adenocarcinoma patients, distinct genetic, genomic, epigenetic, and immunologic profiles of recurrent tumors were analyzed using a novel recurrence classifier. To characterize the tumor immune microenvironment of recurrent stage I tumors, unique tumor-infiltrating immune population markers were identified using single cell RNA-seq on a separate cohort of patients undergoing stage I lung adenocarcinoma resection and applied to a large study cohort using digital cytometry. Recurrent stage I lung adenocarcinomas demonstrated higher mutation and lower methylation burden than non-recurrent tumors, as well as widespread activation of known cancer and cell cycle pathways. Simultaneously, recurrent tumors displayed downregulation of immune response pathways including antigen presentation and Th1/Th2 activation. Recurrent tumors were depleted in adaptive immune populations, and depletion of adaptive immune populations and low cytolytic activity were prognostic of stage I recurrence. Genomic instability and impaired adaptive immune responses are key features of stage I lung adenocarcinoma immunosurveillance escape and recurrence after surgery.

Improved outcomes and staging in non-small-cell lung cancer guided by a molecular assay

There remains a critical need for improved staging of non-small-cell lung cancer, as recurrence and mortality due to undetectable metastases at the time of surgery remain high even after complete resection of tumors currently categorized as 'early stage.' A 14-gene quantitative PCR-based expression profile has been extensively validated to better identify patients at high-risk of 5-year mortality after surgical resection than conventional staging - mortality that almost always results from previously undetectable metastases. Furthermore, prospective studies now suggest a predictive benefit in disease-free survival when the assay is used to guide adjuvant chemotherapy decisions in early-stage non-small-cell lung cancer patients.

A highly predictive autoantibody-based biomarker panel for prognosis in early-stage NSCLC with potential therapeutic implications

BACKGROUND

Lung cancer is the leading cause of cancer-related death worldwide. Surgical resection remains the definitive curative treatment for early-stage disease offering an overall 5-year survival rate of 62%. Despite careful case selection, a significant proportion of early-stage cancers relapse aggressively within the first year post-operatively. Identification of these patients is key to accurate prognostication and understanding the biology that drives early relapse might open up potential novel adjuvant therapies.

METHODS

We performed an unsupervised interrogation of >1600 serum-based autoantibody biomarkers using an iterative machine-learning algorithm.

RESULTS

We identified a 13 biomarker signature that was highly predictive for survivorship in post-operative early-stage lung cancer; this outperforms currently used autoantibody biomarkers in solid cancers. Our results demonstrate significantly poor survivorship in high expressers of this biomarker signature with an overall 5-year survival rate of 7.6%.

CONCLUSIONS

We anticipate that the data will lead to the development of an off-the-shelf prognostic panel and further that the oncogenic relevance of the proteins recognised in the panel may be a starting point for a new adjuvant therapy.

Prediction of prognosis and immunotherapy response with a robust immune-related lncRNA pair signature in lung adenocarcinoma

The tumor immune microenvironment plays essential roles in regulating inflammation, angiogenesis, immune modulation, and sensitivity to therapies. Here, we developed a powerful prognostic signature with immune-related lncRNAs (irlncRNAs) in lung adenocarcinoma (LUAD). We obtained differentially expressed irlncRNAs by intersecting the transcriptome dataset for The Cancer Genome Atlas (TCGA)-LUAD cohort and the ImmLnc database. A rank-based algorithm was applied to select top-ranking altered irlncRNA pairs for the model construction. We built a prognostic signature of 33 irlncRNA pairs comprising 40 unique irlncRNAs in the TCGA-LUAD cohort (training set). The immune signature significantly dichotomized LUAD patients into high- and low-risk groups regarding overall survival, which is likewise independently predictive of prognosis (hazard ratio = 3.580, 95% confidence interval = 2.451-5.229, P < 0.001). A nomogram with a C-index of 0.79 demonstrates the superior prognostic accuracy of the signature. The prognostic accuracy of the signature of 33 irlncRNA pairs was validated using the GSE31210 dataset (validation set) from the Gene Expression Omnibus database. Immune cell infiltration was calculated using ESTIMATE, CIBERSORT, and MCP-count methodologies. The low-risk group exhibited high immune cell infiltration, high mutation burden, high expression of CTLA4 and human leukocyte antigen genes, and low expression of mismatch repair genes, which predicted response to immunotherapy. Interestingly, pRRophetic analysis demonstrated that the high-risk group possessed reverse characteristics was sensitive to chemotherapy. The established immune signature shows marked clinical and translational potential for predicting prognosis, tumor immunogenicity, and therapeutic response in LUAD.

Comprehensive Analysis of TP53 and KEAP1 Mutations and Their Impact on Survival in Localized- and Advanced-Stage NSCLC

INTRODUCTION

TP53 and KEAP1 are frequently mutated in NSCLC, but their prognostic value is ambiguous, particularly in localized stage tumors.

METHODS

This retrospective cohort study included a total of 6297 patients with NSCLC who were diagnosed between November 1998 and February 2020. The primary end point was overall survival. Patients were diagnosed in a central pathology laboratory as part of the Network Genomic Medicine collaboration, encompassing more than 300 lung cancer-treating oncology centers in Germany. All patients underwent molecular testing, including targeted next-generation panel sequencing and in situ hybridization.

RESULTS

A total of 6297 patients with NSCLC were analyzed. In 1518 surgically treated patients (Union for International Cancer Control [UICC] I-IIIA), truncating TP53 mutations and KEAP1 mutations were independent negative prognostic markers in multivariable analysis (hazard ratio [HR]_{TP53truncating} = 1.43, 95% confidence interval [CI]: 1.07-1.91, p = 0.015; HR_KEAP1mut = 1.68, 95% CI:1.24-2.26, p = 0.001). Consistently, these mutations were associated with shorter disease-free survival. In 4779 patients with advanced-stage (UICC IIIB-IV) tumors, TP53 mutations did not predict outcome in univariable analysis. In contrast, KEAP1 mutations remained a negative prognostic factor (HR_KEAP1mut = 1.40, 95% CI: 1.23-1.61, p < 0.001) in patients with advanced-stage tumors. Furthermore, those with KEAP1-mutant tumors with co-occurring TP53 missense mutations had longer overall survival than those with KEAP1-mutant tumors with wild-type or truncating TP53 mutations.

CONCLUSIONS

This study found that TP53 and KEAP1 mutations were prognostic for localized and advanced-stage NSCLC. The increased relative hazard of harboring TP53 or KEAP1 mutations was comparable to an increase in one UICC stage. Our data suggest that molecular stratification on the basis of TP53 and KEAP1 mutation status should be implemented for localized and advanced-stage NSCLC to optimize and modify clinical decision-making.

Molecular Risk Stratification is Independent of EGFR Mutation Status in Identifying Early-Stage Non-Squamous Non-Small Cell Lung Cancer Patients at Risk for Recurrence and Likely to Benefit From Adjuvant Chemotherapy

BACKGROUND

A clinically-certified gene expression profile improved survival in a cohort of stage I-IIA NSCLC patients by identifying those likely to benefit from adjuvant intervention. EGFR mutation status has not provided this type of predictive risk discrimination in stage IA NSCLC, and overtreatment of low-risk stage IB patients may have limited the overall benefit seen recently in the adjuvant application of a third-generation TKI. We compared EGFR mutation data to molecular risk stratification in a prospective, early-stage cohort.

MATERIALS AND METHODS

Two hundred fifty eligible stage I-IIA non-squamous NSCLC patients underwent prospective molecular risk stratification by the 14-gene prognostic assay. Platinum doublet adjuvant chemotherapy (AC) was recommended for molecular high-risk (MHR). Differences in freedom from recurrence (FFR) and disease-free survival (DFS) were evaluated.

RESULTS

At 29 months, prospective molecular testing yielded an estimated FFR of 94.6% and 72.4% in low-risk and untreated MHR patients, respectively, and 97.0% among MHR patients receiving AC (P < .001). In contrast, there was no association between EGFR status and recurrence, while molecular risk predicted survival and response to AC within both the EGFR mutation(+) and mutation(-) populations. Sixty-seven percent of EGFR(+) and 49% of EGFR(-) patients were molecular low-risk.

CONCLUSION

This prospective study demonstrates the utility of the 14-gene assay independent of EGFR mutation. Basing adjuvant intervention in early-stage NSCLC on EGFR status alone may undertreat up to 51% of EGFR(-) patients likely to benefit from adjuvant intervention, and overtreat as many as 67% of EGFR(+) patients more likely to be free of residual disease.

Gene Expression Profiling as a Potential Tool for Precision Oncology in Non-Small Cell Lung Cancer

Recent technological advances and the application of high-throughput mutation and transcriptome analyses have improved our understanding of cancer diseases, including non-small cell lung cancer. For instance, genomic profiling has allowed the identification of mutational events which can be treated with specific agents. However, detection of DNA alterations does not fully recapitulate the complexity of the disease and it does not allow selection of patients that benefit from chemo- or immunotherapy. In this context, transcriptional profiling has emerged as a promising tool for patient stratification and treatment guidance. For instance, transcriptional profiling has proven to be especially useful in the context of acquired resistance to targeted therapies and patients lacking targetable genomic alterations. Moreover, the comprehensive characterization of the expression level of the different pathways and genes involved in tumor progression is likely to better predict clinical benefit from different treatments than single biomarkers such as PD-L1 or tumor mutational burden in the case of immunotherapy. However, intrinsic technical and analytical limitations have hindered the use of these expression signatures in the clinical setting. In this review, we will focus on the data reported on molecular classification of non-small cell lung cancer and discuss the potential of transcriptional profiling as a predictor of survival and as a patient stratification tool to further personalize treatments.

Identification of a Five-Gene Prognostic Signature Related to B Cells Infiltration in Pancreatic Adenocarcinoma

Background

Pancreatic adenocarcinoma (PAAD) is an extremely malignant cancer. Immunotherapy is a promising avenue to increase the survival time of patients with PAAD.

Methods

RNA sequencing and clinical data for PAAD were downloaded from the TCGA database. The ssGSEA method and weighted gene co-expression network analysis were used to calculate the relative abundance of tumor-infiltrating immune cells and identify the related modules. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were used to construct a prognostic model. MCPcounter and EPIC were also used to assess immune cell components using gene expression profiles.

Results

The B cells closely related module was identified, and five genes, including ARID5A, CLEC2B, MICAL1, MZB1, and RAPGEF1, were ultimately selected to establish a prognostic signature to calculate the risk scores of PAAD patients. Kaplan–Meier curves showed worse survival in the high-risk patients (p < 0.05), and the area under the receiver operating characteristic (ROC) curves of risk score for 1-year and 3-year survival were 0.78 and 0.80, respectively, based on the training set. Similar results were verified using the validated and combined sets. Interestingly, the low-risk group presented significantly elevated immune and stromal scores, proportion of B cells, and associations between these five genes and B cells were identified using multiple methods including ssGSEA, MCPcounter, and EPIC.

Conclusion

This is the first attempt to study a B cells-related prognostic signature, which is instrumental in the exploration of novel prognostic biomarkers in PAAD.

Identification of Prognostic Signature and Gliclazide as Candidate Drugs in Lung Adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer, with high incidence and mortality. To improve the curative effect and prolong the survival of patients, it is necessary to find new biomarkers to accurately predict the prognosis of patients and explore new strategy to treat high-risk LUAD.

METHODS

A comprehensive genome-wide profiling analysis was conducted using a retrospective pool of LUAD patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE18842, GSE19188, GSE40791 and GSE50081 and The Cancer Genome Atlas (TCGA). Differential gene analysis and Cox proportional hazard model were used to identify differentially expressed genes with survival significance as candidate prognostic genes. The Kaplan-Meier with log-rank test was used to assess survival difference. A risk score model was developed and validated using TCGA-LUAD and GSE50081. Additionally, The Connectivity Map (CMAP) was used to predict drugs for the treatment of LUAD. The anti-cancer effect and mechanism of its candidate drugs were studied in LUAD cell lines.

RESULTS

We identified a 5-gene signature (KIF20A, KLF4, KRT6A, LIFR and RGS13). Risk Score (RS) based on 5-gene signature was significantly associated with overall survival (OS). Nomogram combining RS with clinical pathology parameters could potently predict the prognosis of patients with LUAD. Moreover, gliclazide was identified as a candidate drug for the treatment of high-RS LUAD. Finally, gliclazide was shown to induce cell cycle arrest and apoptosis in LUAD cells possibly by targeting CCNB1, CCNB2, CDK1 and AURKA.

CONCLUSION

This study identified a 5-gene signature that can predict the prognosis of patients with LUAD, and Gliclazide as a potential therapeutic drug for LUAD. It provides a new direction for the prognosis and treatment of patients with LUAD.

Predictive markers based on transcriptome modules for vinorelbine-based adjuvant chemotherapy for lung adenocarcinoma patients

OBJECTIVES

Microtubule inhibitors (MTIs) are widely used as anti-cancer drugs for various types of tumors. Vinorelbine, an MTI, is utilized in postoperative adjuvant chemotherapy, especially for lung adenocarcinoma. However, no molecular markers are able to identify patients for whom MTIs would be effective. In this study, we attempted to identify practical markers to predict the efficacy of MTI-based adjuvant chemotherapy.

MATERIALS AND METHODS

We explored a novel combination of molecular marker candidates, based on gene expression network analysis constructed using an omics panel of 26 lung adenocarcinoma cell lines. We then applied the obtained classification method to predict the efficacy of MTI treatment in patients who received adjuvant chemotherapy. RNA sequencing (RNA-seq) analysis was conducted using surgical specimens from 24 Japanese lung adenocarcinoma patients treated postoperatively with vinorelbine.

RESULTS

We identified four modules within the network with module activities that were significantly associated with sensitivity to MTIs. Two modules were associated with high sensitivity to MTIs: genes with low differentiation or transdifferentiation of lung adenocarcinomas. On the other hand, MTI-low sensitivity modules were enriched in common epithelial genes and markers of well-differentiated lung adenocarcinomas. We also classified lung adenocarcinoma cases using the module activities associated with MTI efficacy and stratify the cases with MTI resistance.

CONCLUSION

We demonstrate that the constructed classification method is useful for identifying patients with MTI resistance which results in a high risk of cancer relapse.

A novel 12-gene signature as independent prognostic model in stage IA and IB lung squamous cell carcinoma patients

BACKGROUND

There is currently no formal consensus on the administration of adjuvant chemotherapy to stage I lung squamous cell carcinoma (LUSC) patients despite the poor prognosis. The side effects of adjuvant chemotherapy need to be balanced against the risk of tumour recurrence. Prognostic markers are thus needed to identify those at higher risks and recommend individualised treatment regimens.

METHODS

Clinical and sequencing data of stage I patients were retrieved from the Lung Squamous Cell Carcinoma project of the Cancer Genome Atlas (TCGA) and three tissue microarray datasets. In a novel K-resample gene selection algorithm, gene-wise Cox proportional hazard regressions were repeated for 50 iterations with random resamples from the TCGA training dataset. The top 200 genes with the best predictive power for survival were chosen to undergo an L1-penalised Cox regression for further gene selection.

RESULTS

A total of 602 samples of LUSC were included, of which 42.2% came from female patients, 45.3% were stage IA cancer. From an initial pool of 11,212 genes in the TCGA training dataset, a final set of 12 genes were selected to construct the multivariate Cox prognostic model. Among the 12 selected genes, 5 genes, STAU1, ADGRF1, ATF7IP2, MALL and KRT23, were adverse prognostic factors for patients, while seven genes, NDUFB1, CNPY2, ZNF394, PIN4, FZD8, NBPF26 and EPYC, were positive prognostic factors. An equation for risk score was thus constructed from the final multivariate Cox model. The model performance was tested in the sequestered TCGA testing dataset and validated in external tissue microarray datasets (GSE4573, GSE31210 and GSE50081), demonstrating its efficacy in stratifying patients into high- and low-risk groups with significant survival difference both in the whole set (including stage IA and IB) and in the stage IA only subgroup of each set. The prognostic power remains significant after adjusting for standard clinical factors. When benchmarked against other prominent gene-signature based prognostic models, the model outperformed the rest in the TCGA testing dataset and in predicting long-term risk at eight years in all three validation datasets.

CONCLUSION

The 12-gene prognostic model may serve as a useful complementary clinical risk-stratification tool for stage I and especially stage IA lung squamous cell carcinoma patients to guide clinical decision making.

Identification of molecular signatures associated with early relapse after complete resection of lung adenocarcinomas

The only potentially curative treatment for lung adenocarcinoma patients remains complete resection of early-stage tumors. However, many patients develop recurrence and die of their disease despite curative surgery. Underlying mechanisms leading to establishment of systemic disease after complete resection are mostly unknown. We therefore aimed at identifying molecular signatures of resected lung adenocarcinomas associated with the risk of an early relapse. The study comprised 89 patients with totally resected stage IA–IIIA lung adenocarcinomas. Patients suffering from an early relapse within two years after surgery were compared to patients without a relapse in two years. Patients were clinically and molecular pathologically characterized. Tumor tissues were immunohistochemically analyzed for the expression of Ki67, CD45, CD4, CD8, PD1, PD-L1, PD-L2 and CD34, by Nanostring nCounter PanCancer Immune Profiling Panel as well as a comprehensive methylome profiling using the Infinium MethylationEPIC BeadChip. We detected differential DNA methylation patterns as well as significantly differentially expressed genes associated with an early relapse after complete resection. Especially, CD1A was identified as a potential biomarker, whose reduced expression is associated with an early relapse. These findings might help to develop biomarkers improving risk assessment and patient selection for adjuvant therapy as well as establish novel targeted therapeutic strategies.

Molecular Analysis of ZNF71 KRAB in Non-Small-Cell Lung Cancer

Our previous study found that zinc finger protein 71 (ZNF71) mRNA expression was associated with chemosensitivity and its protein expression was prognostic of non-small-cell lung cancer (NSCLC). The Krüppel associated box (KRAB) transcriptional repression domain is commonly present in human zinc finger proteins, which are linked to imprinting, silencing of repetitive elements, proliferation, apoptosis, and cancer. This study revealed that ZNF71 KRAB had a significantly higher expression than the ZNF71 KRAB-less isoform in NSCLC tumors (n = 197) and cell lines (n = 117). Patients with higher ZNF71 KRAB expression had a significantly worse survival outcome than patients with lower ZNF71 KRAB expression (log-rank p = 0.04; hazard ratio (HR): 1.686 [1.026, 2.771]), whereas ZNF71 overall and KRAB-less expression levels were not prognostic in the same patient cohort. ZNF71 KRAB expression was associated with epithelial-to-mesenchymal transition (EMT) in both patient tumors and cell lines. ZNF71 KRAB was overexpressed in NSCLC cell lines resistant to docetaxel and paclitaxel treatment compared to chemo-sensitive cell lines, consistent with its association with poor prognosis in patients. Therefore, ZNF71 KRAB isoform is a more effective prognostic factor than ZNF71 overall and KRAB-less expression for NSCLC. Functional analysis using CRISPR-Cas9 and RNA interference (RNAi) screening data indicated that a knockdown/knockout of ZNF71 did not significantly affect NSCLC cell proliferation in vitro.

Preoperative systemic immune-inflammation index predicts prognosis and guides clinical treatment in patients with non-small cell lung cancer

Objectives: The purpose of the present study was to evaluate the prognostic value of a systemic immune-inflammation index (SII) and the relationship between SII and the effectiveness of postoperative treatment in patients with non-small cell lung cancer (NSCLC).

Methods: A total of 538 patients diagnosed with NSCLC who had undergone curative surgery were retrospectively enrolled in the study. Clinicopathologic and laboratory variables were collected. SII was defined as neutrophil × platelet/lymphocyte counts. Both univariate and multivariate analyses were performed to analyze the prognostic value of these factors.

Results: The preoperative SII level was associated with sex, smoking history, histological type, lesion type, resection type, pathological stage, neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), lymphocyte/monocyte ratio (LMR), fibrinogen and bone metastasis (P<0.05). The univariate and multivariate analyses revealed that SII was an independent prognostic factor for disease-free survival (DFS, P=0.033) and overall survival (OS, P=0.020). Furthermore, the prognostic value of SII was also verified regardless of the histological type and pathological stage. The subgroup analysis demonstrated that patients with a high SII may benefit from adjuvant therapy (P=0.024 for DFS and P=0.012 for OS).

Conclusion: An increased preoperative SII may independently predict the poor DFS and OS in patients with resectable NSCLC. SII may help select NSCLC patients who might benefit from adjuvant chemotherapy.

Molecular biomarkers in early stage lung cancer

Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival. Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs. Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians’ capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease’s mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.

Denoising Autoencoder, A Deep Learning Algorithm, Aids the Identification of A Novel Molecular Signature of Lung Adenocarcinoma

Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small number of samples with supervised approaches. Recent advances in unsupervised machine learning promise to leverage very large datasets for making better predictions of disease biomarkers. Denoising autoencoder (DA) is one of the unsupervised deep learning algorithms, which is a stochastic version of autoencoder techniques. The principle of DA is to force the hidden layer of autoencoder to capture more robust features by reconstructing a clean input from a corrupted one. Here, a DA model was applied to analyze integrated transcriptomic data from 13 published lung cancer studies, which consisted of 1916 human lung tissue samples. Using DA, we discovered a molecular signature composed of multiple genes for lung adenocarcinoma (ADC). In independent validation cohorts, the proposed molecular signature is proved to be an effective classifier for lung cancer histological subtypes. Also, this signature successfully predicts clinical outcome in lung ADC, which is independent of traditional prognostic factors. More importantly, this signature exhibits a superior prognostic power compared with the other published prognostic genes. Our study suggests that unsupervised learning is helpful for biomarker development in the era of precision medicine.