A Genomic-Pathologic Annotated Risk Model to Predict Recurrence in Early-Stage Lung Adenocarcinoma

SMARCA4-deficient central nervous system metastases: A case series and systematic review

SMARCA4 alterations can be encountered in a variety of human neoplasms, and metastases to the central nervous system (CNS) are rare, offering a challenge to neuropathologists despite not representing a distinct diagnostic entity. To better understand the clinical and histologic presentation of such neoplasms, we report an observational case series and systematic review of 178 unique articles that yielded 15 published cases and 7 cases from institutional files. In the systematic review, the median age was 58 years, the male-to-female ratio was 2:1, and the most common diagnosis was lung adenocarcinoma; all CNS metastases were discovered within 1 year of presentation. In the case series, the median age was 58 years, the male-to-female ratio was 6:1, and all known metastases originated from the lung. Most patients had a smoking history and died of disease. GATA-3 positivity was seen in most case series examples. Concurrent TP53 mutations (83.3%) and a high tumor mutation rate (60%) were common. To our knowledge, this is the only case series and systematic review in the English literature aimed at assessing SMARCA4-altered metastases in the CNS and vertebral column. We highlight the challenges of neuropathologic evaluation of such tumors and provide observational evidence of early metastases, histologic appearances, and immunohistochemical findings, including previously unreported GATA-3 positivity.

Targeting chromosomal instability in patients with cancer

Chromosomal instability (CIN) is a hallmark of cancer and a driver of metastatic dissemination, therapeutic resistance, and immune evasion. CIN is present in 60-80% of human cancers and poses a formidable therapeutic challenge as evidenced by the lack of clinically approved drugs that directly target CIN. This limitation in part reflects a lack of well-defined druggable targets as well as a dearth of tractable biomarkers enabling direct assessment and quantification of CIN in patients with cancer. Over the past decade, however, our understanding of the cellular mechanisms and consequences of CIN has greatly expanded, revealing novel therapeutic strategies for the treatment of chromosomally unstable tumours as well as new methods of assessing the dynamic nature of chromosome segregation errors that define CIN. In this Review, we describe advances that have shaped our understanding of CIN from a translational perspective, highlighting both challenges and opportunities in the development of therapeutic interventions for patients with chromosomally unstable cancers.

High-risk features associated with recurrence in stage I lung adenocarcinoma

OBJECTIVE

There is a lack of knowledge regarding the use of prognostic features in stage I lung adenocarcinoma (LUAD). Thus, we investigated clinicopathologic features associated with recurrence after complete resection for stage I LUAD.

METHODS

We performed a retrospective analysis of patients with pathologic stage I LUAD who underwent R0 resection from 2010 to 2020. Exclusion criteria included history of lung cancer, induction or adjuvant therapy, noninvasive or mucinous LUAD, and death within 90 days of surgery. Fine and Gray competing-risk regression assessed associations between clinicopathologic features and disease recurrence.

RESULTS

In total, 1912 patients met inclusion criteria. Most patients (1565 [82%]) had stage IA LUAD, and 250 developed recurrence: 141 (56%) distant and 109 (44%) locoregional only. The 5-year cumulative incidence of recurrence was 12% (95% CI, 11%-14%). Higher maximum standardized uptake value of the primary tumor (hazard ratio [HR], 1.04), sublobar resection (HR, 2.04), higher International Association for the Study of Lung Cancer grade (HR, 5.32 [grade 2]; HR, 7.93 [grade 3]), lymphovascular invasion (HR, 1.70), visceral pleural invasion (HR, 1.54), and tumor size (HR, 1.30) were independently associated with a hazard of recurrence. Tumors with 3 to 4 high-risk features had a higher cumulative incidence of recurrence at 5 years than tumors without these features (30% vs 4%; P < .001).

CONCLUSIONS

Recurrence after resection for stage I LUAD remains an issue for select patients. Commonly reported clinicopathologic features can be used to define patients at high risk of recurrence and should be considered when assessing the prognosis of patients with stage I disease.

Artificial Intelligence in Surgical Research: Accomplishments and Future Directions

MINI-ABSTRACT

The study introduces various methods of performing conventional ML and their implementation in surgical areas, and the need to move beyond these traditional approaches given the advent of big data.

OBJECTIVE

Investigate current understanding and future directions of machine learning applications, such as risk stratification, clinical data analytics, and decision support, in surgical practice.

SUMMARY BACKGROUND DATA

The advent of the electronic health record, near unlimited computing, and open-source computational packages have created an environment for applying artificial intelligence, machine learning, and predictive analytic techniques to healthcare. The "hype" phase has passed, and algorithmic approaches are being developed for surgery patients through all stages of care, involving preoperative, intraoperative, and postoperative components. Surgeons must understand and critically evaluate the strengths and weaknesses of these methodologies.

METHODS

The current body of AI literature was reviewed, emphasizing on contemporary approaches important in the surgical realm.

RESULTS AND CONCLUSIONS

The unrealized impacts of AI on clinical surgery and its subspecialties are immense. As this technology continues to pervade surgical literature and clinical applications, knowledge of its inner workings and shortcomings is paramount in determining its appropriate implementation.

[Mutational Signatures Analysis of Micropapillary Components and Exploration of ZNF469 Gene in Early-stage Lung Adenocarcinoma with Ground-glass Opacities]

BACKGROUND

In China, lung cancer remains the cancer with the highest incidence and mortality rate. Among early-stage lung adenocarcinomas (LUAD), the micropapillary (MPP) component is prevalent and typically exhibits high aggressiveness, significantly correlating with early metastasis, lymphatic infiltration, and reduced five-year survival rates. Therefore, the study is to explore the similarities and differences between MPP and non-micropapillary (non-MPP) components in malignant pulmonary nodules characterized by GGOs in early-stage LUAD, identify unique mutational features of the MPP component and analyze the relationship between the ZNF469 gene, a member of the zinc-finger protein family, and the prognosis of early-stage LUAD, as well as its correlation with immune infiltration.

METHODS

A total of 31 malignant pulmonary nodules of LUAD were collected and dissected into paired MPP and non-MPP components using microdissection. Whole-exome sequencing (WES) was performed on the components of early-stage malignant pulmonary nodules. Mutational signatures analysis was conducted using R packages such as maftools, Nonnegative Matrix Factorization (NMF), and Sigminer to unveil the genomic mutational characteristics unique to MPP components in invasive LUAD compared to other tumor tissues. Furthermore, we explored the expression of the ZNF469 gene in LUAD using The Cancer Genome Atlas (TCGA) database to investigate its potential association with the prognosis. We also investigated gene interaction networks and signaling pathways related to ZNF469 in LUAD using the GeneMANIA database and conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Lastly, we analyzed the correlation between ZNF469 gene expression and levels of immune cell infiltration in LUAD using the TIMER and TISIDB databases.

RESULTS

MPP components exhibited a higher number of genomic variations, particularly the 13th COSMIC (Catalogue of Somatic Mutations in Cancer) mutational signature characterized by the activity of the cytidine deaminase APOBEC family, which was unique to MPP components compared to non-MPP components in tumor tissues. This suggests the potential involvement of APOBEC in the progression of MPP components in early-stage LUAD. Additionally, MPP samples with high similarity to APOBEC signature displayed a higher tumor mutational burden (TMB), indicating that these patients may be more likely to benefit from immunotherapy. The expression of ZNF469 was significantly upregulated in LUAD compared to normal tissue, and was associated with poor prognosis in LUAD patients (P<0.05). Gene interaction network analysis and GO/KEGG enrichment analysis revealed that COL6A1, COL1A1, COL1A2, TGFB2, MMP2, COL8A2 and C2CD4C interacted with ZNF469 and were mainly involved in encoding collagen proteins and participating in the constitution of extracellular matrix. ZNF469 expression was positively correlated with immune cell infiltration in LUAD (P<0.05).

CONCLUSIONS

The study has unveiled distinctive mutational signatures in the MPP components of early-stage invasive LUAD in the Asian population. Furthermore, we have identified that the elevated expression of mutated ZNF469 impacts the prognosis and immune infiltration in LUAD, suggesting its potential as a diagnostic and prognostic biomarker in LUAD.

Software Application Profile: dynamicLM-a tool for performing dynamic risk prediction using a landmark supermodel for survival data under competing risks

MOTIVATION

Providing a dynamic assessment of prognosis is essential for improved personalized medicine. The landmark model for survival data provides a potentially powerful solution to the dynamic prediction of disease progression. However, a general framework and a flexible implementation of the model that incorporates various outcomes, such as competing events, have been lacking. We present an R package, dynamicLM, a user-friendly tool for the landmark model for the dynamic prediction of survival data under competing risks, which includes various functions for data preparation, model development, prediction and evaluation of predictive performance.

IMPLEMENTATION

dynamicLM as an R package.

GENERAL FEATURES

The package includes options for incorporating time-varying covariates, capturing time-dependent effects of predictors and fitting a cause-specific landmark model for time-to-event data with or without competing risks. Tools for evaluating the prediction performance include time-dependent area under the ROC curve, Brier Score and calibration.

AVAILABILITY

Available on GitHub [https://github.com/thehanlab/dynamicLM].

Construction of 11 metabolic-related lncRNAs to predict the prognosis in lung adenocarcinoma

OBJECTIVE

To explore the metabolism-related lncRNAs in the tumorigenesis of lung adenocarcinoma.

METHODS

The transcriptome data and clinical information about lung adenocarcinoma patients were acquired in TCGA (The Cancer Genome Atlas). Metabolism-related genes were from the GSEA (Gene Set Enrichment Analysis) database. Through differential expression analysis and Pearson correlation analysis, lncRNAs about lung adenocarcinoma metabolism were identified. The samples were separated into the training and validation sets in the proportion of 2:1. The prognostic lncRNAs were determined by univariate Cox regression analysis and LASSO (Least absolute shrinkage and selection operator) regression. A risk model was built using Multivariate Cox regression analysis, evaluated by the internal validation data. The model prediction ability was assessed by subgroup analysis. The Nomogram was constructed by combining clinical indicators with independent prognostic significance and risk scores. C-index, calibration curve, DCA (Decision Curve Analysis) clinical decision and ROC (Receiver Operating Characteristic Curve) curves were obtained to assess the prediction ability of the model. Based on the CIBERSORT analysis, the correlation between lncRNAs and tumor infiltrating lymphocytes was obtained.

RESULTS

From 497 lung adenocarcinoma and 54 paracancerous samples, 233 metabolic-related and 11 prognostic-related lncRNAs were further screened. According to the findings of the survival study, the low-risk group had a greater OS (Overall survival) than the high-risk group. ROC analysis indicated AUC (Area Under Curve) value was 0.726. Then, a nomogram with T, N stage and risk ratings was developed according to COX regression analysis. The C-index was 0.743, and the AUC values of 3- and 5-year survival were 0.741 and 0.775, respectively. The above results suggested the nomogram had a good prediction ability. The results based on the CIBERSORT algorithm demonstrated the lncRNAs used to construct the model had a strong correlation with the polarization of immune cells.

CONCLUSIONS

The study identified 11 metabolic-related lncRNAs for lung adenocarcinoma prognosis, on which basis a prognostic risk scoring model was created. This model may have a good predictive potential for lung adenocarcinoma.

Artificial intelligence-based recurrence prediction outperforms classical histopathological methods in pulmonary adenocarcinoma biopsies

INTRODUCTION

Between 10 and 50% of early-stage lung adenocarcinoma patients experience local or distant recurrence. Histological parameters such as a solid or micropapillary growth pattern are well-described risk factors for recurrence. However, not every patient presenting with such a pattern will develop recurrence. Designing a model which can more accurately predict recurrence on small biopsy samples can aid the stratification of patients for surgery, (neo-)adjuvant therapy, and follow-up.

MATERIAL AND METHODS

In this study, a statistical model on biopsies fed with histological data from early and advanced-stage lung adenocarcinomas was developed to predict recurrence after surgical resection. Additionally, a convolutional neural network (CNN)-based artificial intelligence (AI) classification model, named AI-based Lung Adenocarcinoma Recurrence Predictor (AILARP), was trained to predict recurrence, with an ImageNet pre-trained EfficientNet that was fine-tuned on lung adenocarcinoma biopsies using transfer learning. Both models were validated using the same biopsy dataset to ensure that an accurate comparison was demonstrated.

RESULTS

The statistical model had an accuracy of 0.49 for all patients when using histology data only. The AI classification model yielded a test accuracy of 0.70 and 0.82 and an area under the curve (AUC) of 0.74 and 0.87 on patch-wise and patient-wise hematoxylin and eosin (H&E) stained whole slide images (WSIs), respectively.

CONCLUSION

AI classification outperformed the traditional clinical approach for recurrence prediction on biopsies by a fair margin. The AI classifier may stratify patients according to their recurrence risk, based only on small biopsies. This model warrants validation in a larger lung biopsy cohort.

Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma

Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up. Through histologic, mutational, and transcriptomic profiling of tumor and adjacent-normal tissue, we identified an inflammatory gene signature in tumor-adjacent tissue as the strongest clinical predictor of disease progression. Single-cell transcriptomic analysis demonstrated the progression-associated inflammatory signature was expressed in both immune and non-immune cells, and cell type-specific profiling in monocytes further improved outcome predictions. Additional analyses of tumor-adjacent transcriptomic data from The Cancer Genome Atlas validated the association of the inflammatory signature with worse outcomes across cancers. Collectively, our study suggests that molecular profiling of tumor-adjacent tissue can identify patients at high risk for disease progression.

Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma

Introduction

Lung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy.

Methods

This study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites.

Results

Finally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models.

Conclusion

To summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.

Persistent increase and improved survival of stage I lung cancer based on a large-scale real-world sample of 26,226 cases

BACKGROUND

Lung cancer prevails and induces high mortality around the world. This study provided real-world information on the evolution of clinicopathological profiles and survival outcomes of lung cancer, and provided survival information within stage I subtypes.

METHODS

Patients pathologically confirmed with lung cancer between January 2009 and December 2018 were identified with complete clinicopathological information, molecular testing results, and follow-up data. Shifts in clinical characteristics were evaluated using χ2 tests. Overall survival (OS) was calculated through the Kaplan-Meier method.

RESULTS

A total of 26,226 eligible lung cancer patients were included, among whom 62.55% were male and 52.89% were smokers. Non-smokers and elderly patients took increasingly larger proportions in the whole patient population. The proportion of adenocarcinoma increased from 51.63% to 71.80%, while that of squamous carcinoma decreased from 28.43% to 17.60%. Gene mutations including EGFR (52.14%), KRAS (12.14%), and ALK (8.12%) were observed. Female, younger, non-smoking, adenocarcinoma patients and those with mutated EGFR had better survival prognoses. Importantly, this study validated that early detection of early-stage lung cancer patients had contributed to pronounced survival benefits during the decade. Patients with stage I lung cancer, accounted for an increasingly considerable proportion, increasing from 15.28% to 40.25%, coinciding with the surgery rate increasing from 38.14% to 54.25%. Overall, period survival analyses found that 42.69% of patients survived 5 years, and stage I patients had a 5-year OS of 84.20%. Compared with that in 2009-2013, the prognosis of stage I patients in 2014-2018 was dramatically better, with 5-year OS increasing from 73.26% to 87.68%. Regarding the specific survival benefits among stage I patients, the 5-year survival rates were 95.28%, 93.25%, 82.08%, and 74.50% for stage IA1, IA2, IA3, and IB, respectively, far more promising than previous reports.

CONCLUSIONS

Crucial clinical and pathological changes have been observed in the past decade. Notably, the increased incidence of stage I lung cancer coincided with an improved prognosis, indicating actual benefits of early detection and management of lung cancer.

Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer

OBJECTIVE

Lung cancer exhibits unpredictable recurrence in low-stage tumors and variable responses to different therapeutic interventions. Predicting relapse in early-stage lung cancer can facilitate precision medicine and improve patient survivability. While existing machine learning models rely on clinical data, incorporating genomic information could enhance their efficiency. This study aims to impute and integrate specific types of genomic data with clinical data to improve the accuracy of machine learning models for predicting relapse in early-stage, non-small cell lung cancer patients.

METHODS

The study utilized a publicly available TCGA lung cancer cohort and imputed genetic pathway scores into the Spanish Lung Cancer Group (SLCG) data, specifically in 1348 early-stage patients. Initially, tumor recurrence was predicted without imputed pathway scores. Subsequently, the SLCG data were augmented with pathway scores imputed from TCGA. The integrative approach aimed to enhance relapse risk prediction performance.

RESULTS

The integrative approach achieved improved relapse risk prediction with the following evaluation metrics: an area under the precision-recall curve (PR-AUC) score of 0.75, an area under the ROC (ROC-AUC) score of 0.80, an F1 score of 0.61, and a Precision of 0.80. The prediction explanation model SHAP (SHapley Additive exPlanations) was employed to explain the machine learning model's predictions.

CONCLUSION

We conclude that our explainable predictive model is a promising tool for oncologists that addresses an unmet clinical need of post-treatment patient stratification based on the relapse risk while also improving the predictive power by incorporating proxy genomic data not available for specific patients.

Individualized dynamic methylation-based analysis of cell-free DNA in postoperative monitoring of lung cancer

BACKGROUND

The feasibility of DNA methylation-based assays in detecting minimal residual disease (MRD) and postoperative monitoring remains unestablished. We aim to investigate the dynamic characteristics of cancer-related methylation signals and the feasibility of methylation-based MRD detection in surgical lung cancer patients.

METHODS

Matched tumor, tumor-adjacent tissues, and longitudinal blood samples from a cohort (MEDAL) were analyzed by ultra-deep targeted sequencing and bisulfite sequencing. A tumor-informed methylation-based MRD (timMRD) was employed to evaluate the methylation status of each blood sample. Survival analysis was performed in the MEDAL cohort (n = 195) and validated in an independent cohort (DYNAMIC, n = 36).

RESULTS

Tumor-informed methylation status enabled an accurate recurrence risk assessment better than the tumor-naïve methylation approach. Baseline timMRD-scores were positively correlated with tumor burden, invasiveness, and the existence and abundance of somatic mutations. Patients with higher timMRD-scores at postoperative time-points demonstrated significantly shorter disease-free survival in the MEDAL cohort (HR: 3.08, 95% CI: 1.48-6.42; P = 0.002) and the independent DYNAMIC cohort (HR: 2.80, 95% CI: 0.96-8.20; P = 0.041). Multivariable regression analysis identified postoperative timMRD-score as an independent prognostic factor for lung cancer. Compared to tumor-informed somatic mutation status, timMRD-scores yielded better performance in identifying the relapsed patients during postoperative follow-up, including subgroups with lower tumor burden like stage I, and was more accurate among relapsed patients with baseline ctDNA-negative status. Comparing to the average lead time of ctDNA mutation, timMRD-score yielded a negative predictive value of 97.2% at 120 days prior to relapse.

CONCLUSIONS

The dynamic methylation-based analysis of peripheral blood provides a promising strategy for postoperative cancer surveillance.

TRIAL REGISTRATION

This study (MEDAL, MEthylation based Dynamic Analysis for Lung cancer) was registered on ClinicalTrials.gov on 08/05/2018 (NCT03634826). https://clinicaltrials.gov/ct2/show/NCT03634826 .

A Graph-Based Approach to Identify Factors Contributing to Postoperative Lung Cancer Recurrence among Patients with Non-Small-Cell Lung Cancer

The accurate identification of the preoperative factors impacting postoperative cancer recurrence is crucial for optimizing neoadjuvant and adjuvant therapies and guiding follow-up treatment plans. We modeled the causal relationship between radiographical features derived from CT scans and the clinicopathologic factors associated with postoperative lung cancer recurrence and recurrence-free survival. A retrospective cohort of 363 non-small-cell lung cancer (NSCLC) patients who underwent lung resections with a minimum 5-year follow-up was analyzed. Body composition tissues and tumor features were quantified based on preoperative whole-body CT scans (acquired as a component of PET-CT scans) and chest CT scans, respectively. A novel causal graphical model was used to visualize the causal relationship between these factors. Variables were assessed using the intervention do-calculus adjustment (IDA) score. Direct predictors for recurrence-free survival included smoking history, T-stage, height, and intramuscular fat mass. Subcutaneous fat mass, visceral fat volume, and bone mass exerted the greatest influence on the model. For recurrence, the most significant variables were visceral fat volume, subcutaneous fat volume, and bone mass. Pathologic variables contributed to the recurrence model, with bone mass, TNM stage, and weight being the most important. Body composition, particularly adipose tissue distribution, significantly and causally impacted both recurrence and recurrence-free survival through interconnected relationships with other variables.

Feasibility of shape-sensing robotic-assisted bronchoscopy for biomarker identification in patients with thoracic malignancies

OBJECTIVE

Molecular diagnostic assays require samples with high nucleic acid content to generate reliable data. Similarly, programmed death-ligand 1 (PD-L1) immunohistochemistry (IHC) requires samples with adequate tumor content. We investigated whether shape-sensing robotic-assisted bronchoscopy (ssRAB) provides adequate samples for molecular and predictive testing.

METHODS

We retrospectively identified diagnostic samples from a prospectively collected database. Pathologic reports were reviewed to assess adequacy of samples for molecular testing and feasibility of PD-L1 IHC. Tumor cellularity was quantified by an independent pathologist using paraffin-embedded sections. Univariable and multivariable linear regression models were constructed to assess associations between lesion- and procedure-related variables and tumor cellularity.

RESULTS

In total, 128 samples were analyzed: 104 primary lung cancers and 24 metastatic lesions. On initial pathologic assessment, ssRAB samples were deemed to be adequate for molecular testing in 84% of cases; on independent review of cellular blocks, median tumor cellularity was 60% (interquartile range, 25%-80%). Hybrid capture-based next-generation sequencing was successful for 25 of 26 samples (96%), polymerase chain reaction-based molecular testing (Idylla; Biocartis) was successful for 49 of 52 samples (94%), and PD-L1 IHC was successful for 61 of 67 samples (91%). Carcinoid and small cell carcinoma histologic subtype and adequacy on rapid on-site evaluation were associated with higher tumor cellularity.

CONCLUSIONS

The ssRAB platform provided adequate tissue for next-generation sequencing, polymerase chain reaction-based molecular testing, and PD-L1 IHC in >80% of cases. Tumor histology and adequacy on intraoperative cytologic assessment might be associated with sample quality and suitability for downstream assays.

Prognostic significance of SH2D5 expression in lung adenocarcinoma and its relation to immune cell infiltration

Objective

Through analyzing the SH2D5 expression profiles, clinical features, and immune infiltration in lung adenocarcinoma (LUAD), the study was intended to discuss the correlations of SH2D5 with prognosis and immune infiltration in LUAD.

Methods

We downloaded transcriptome and clinical data of LUAD patients from TCGA, GEO, and CCLE databases. Sangerbox, R language, GEPIA, UALCAN, and Kaplan-Meier Plotter were adopted to analyze the SH2D5 expression patterns, prognosis, and clinical features. Spearman correlation analysis was performed to determine the association between SH2D5 expression and immune cell infiltration and immune checkpoint genes. The miRNA-SH2D5 relations were predicted by miRDB and starbase. Lastly, quantitative PCR, IHC and Western blot were implemented for validation.

Results

A prominent up-regulation of SH2D5 was noted in the LUAD group relative to the normal group, which was validated by quantitative PCR, IHC and Western blot. SH2D5 expression was inversely related to overall survival (OS) of LUAD patients as well as B cell immune infiltration. Additionally, SH2D5 expression was negatively correlated with dendritic cells resting (p < 0.001), plasma cells (p < 0.001), mast cells resting (p = 0.031) and T cells CD4 memory resting (p = 0.036) in LUAD patients with abundant SH2D5 expression correlated with poor prognosis. Furthermore, enrichment analysis suggested that SH2D5 was associated with lung cancer and immunity. Lastly, we investigated the relationship between the expression of SH2D5 and the use of antitumor drugs.

Conclusion

High SH2D5 expression shares an association with unfavorable prognosis in LUAD, and SH2D5 may also provide new ideas for immunotherapy as a potential therapeutic target.

Genomic mapping of metastatic organotropism in lung adenocarcinoma

We analyzed 2,532 lung adenocarcinomas (LUAD) to identify the clinicopathological and genomic features associated with metastasis, metastatic burden, organotropism, and metastasis-free survival. Patients who develop metastasis are younger and male, with primary tumors enriched in micropapillary or solid histological subtypes and with a higher mutational burden, chromosomal instability, and fraction of genome doublings. Inactivation of TP53, SMARCA4, and CDKN2A are correlated with a site-specific shorter time to metastasis. The APOBEC mutational signature is more prevalent among metastases, particularly liver lesions. Analyses of matched specimens show that oncogenic and actionable alterations are frequently shared between primary tumors and metastases, whereas copy number alterations of unknown significance are more often private to metastases. Only 4% of metastases harbor therapeutically actionable alterations undetected in their matched primaries. Key clinicopathological and genomic alterations in our cohort were externally validated. In summary, our analysis highlights the complexity of clinicopathological features and tumor genomics in LUAD organotropism.

CT-derived body composition associated with lung cancer recurrence after surgery

OBJECTIVES

To evaluate the impact of body composition derived from computed tomography (CT) scans on postoperative lung cancer recurrence.

METHODS

We created a retrospective cohort of 363 lung cancer patients who underwent lung resections and had verified recurrence, death, or at least 5-year follow-up without either event. Five key body tissues and ten tumor features were automatically segmented and quantified based on preoperative whole-body CT scans (acquired as part of a PET-CT scan) and chest CT scans, respectively. Time-to-event analysis accounting for the competing event of death was performed to analyze the impact of body composition, tumor features, clinical information, and pathological features on lung cancer recurrence after surgery. The hazard ratio (HR) of normalized factors was used to assess individual significance univariately and in the combined models. The 5-fold cross-validated time-dependent receiver operating characteristics analysis, with an emphasis on the area under the 3-year ROC curve (AUC), was used to characterize the ability to predict lung cancer recurrence.

RESULTS

Body tissues that showed a standalone potential to predict lung cancer recurrence include visceral adipose tissue (VAT) volume (HR = 0.88, p = 0.047), subcutaneous adipose tissue (SAT) density (HR = 1.14, p = 0.034), inter-muscle adipose tissue (IMAT) volume (HR = 0.83, p = 0.002), muscle density (HR = 1.27, p < 0.001), and total fat volume (HR = 0.89, p = 0.050). The CT-derived muscular and tumor features significantly contributed to a model including clinicopathological factors, resulting in an AUC of 0.78 (95% CI: 0.75-0.83) to predict recurrence at 3 years.

CONCLUSIONS

Body composition features (e.g., muscle density, or muscle and inter-muscle adipose tissue volumes) can improve the prediction of recurrence when combined with clinicopathological factors.

See Lung Cancer with an AI

A lot has happened in the field of lung cancer screening in recent months. The ongoing discussion and documentation published by the scientific community and policymakers are of great importance to the entire European community and perhaps beyond. Lung cancer is the main worldwide killer. Low-dose computed tomography-based screening, together with smoking cessation, is the only tool to fight lung cancer, as it has already been proven in the United States of America but also European randomized controlled trials. Screening requires a lot of well-organized specialized work, but it can be supported by artificial intelligence (AI). Here we discuss whether and how to use AI for patients, radiologists, pulmonologists, thoracic surgeons, and all hospital staff supporting screening process benefits.

Comprehensively Analyze the Prognosis Significance and Immune Implication of PTPRO in Lung Adenocarcinoma

Immunotherapy for lung adenocarcinoma (LUAD) is considered to be a promising treatment option, but only a minority of patients benefit from it. Therefore, it is essential to clarify the regulation mechanism of the tumor immune microenvironment (TIM) of the LUAD. Receptor-type protein tyrosine phosphatase (PTPRO) has been shown to be a tumor suppressor in a variety of tumor; however, its role in LUAD has never been reported. In this study, we first found that PTPRO was lowly expressed in LUAD and positively correlated with patient prognosis. Next, we investigated the relationship between PTPRO and clinical characteristics, and the results showed that gender, age, T, and stage were closely related to the expression level of PTPRO. Moreover, we performed univariate and multivariate analyses, and the results revealed that PTPRO was a protective factor for LUAD. By constructing a nomogram based on the expression level of PTPRO and various clinical characteristics, it was proved that the nomogram has a good predictive capacity. Furthermore, we analyzed the coexpression network of PTPRO through multiple databases and performed GO and KEGG enrichment analyses. The results demonstrated that PTPRO was involved in the regulation of multiple immune pathways. In addition, we analyzed whether PTPRO expression of LUAD regulate immune cell infiltration and the results demonstrated that PTPRO was closely related to the infiltration of various immune cells. Finally, we predicted LUAD sensitivity to chemotherapeutics and response to immunotherapy by PTPRO expression levels. The results showed that PTPRO expression level affect the sensitivity of various chemotherapeutic drugs and may be involved in the efficacy of immunotherapy. These results we obtained suggested that PTPRO is closely related to the prognosis and TIM of LUAD, which may be a potential immunotherapeutic target for LUAD.

SMARCA4: Current status and future perspectives in non-small-cell lung cancer

SMARCA4, also known as transcription activator, is an ATP-dependent catalytic subunit of SWI/SNF (SWItch/Sucrose NonFermentable) chromatin-remodeling complexes that participates in the regulation of chromatin structure and gene expression by supplying energy. As a tumor suppressor that has aberrant expression in ∼10% of non-small-cell lung cancers (NSCLCs), SMARCA4 possesses many biological functions, including regulating gene expression, differentiation and transcription. Furthermore, NSCLC patients with SMARCA4 alterations have a weak response to conventional chemotherapy and poor prognosis. Therefore, the mechanisms of SMARCA4 in NSCLC development urgently need to be explored to identify novel biomarkers and precise therapeutic strategies for this subtype. This review systematically describes the biological functions of SMARCA4 and its role in NSCLC development, metastasis, functional epigenetics and potential therapeutic approaches for NSCLCs with SMARCA4 alterations. Additionally, this paper explores the relationship and regulatory mechanisms shared by SMARCA4 and its mutually exclusive catalytic subunit SMARCA2. We aim to provide innovative treatment strategies and improve clinical outcomes for NSCLC patients with SMARCA4 alterations.

Analysis of Tumor Mutational Burden, Progression-Free Survival, and Local-Regional Control in Patents with Locally Advanced Non-Small Cell Lung Cancer Treated With Chemoradiation and Durvalumab

IMPORTANCE

The addition of consolidative durvalumab to chemoradiation has improved disease control and survival in locally advanced non-small cell lung cancer (NSCLC). However, there remains a need to identify biomarkers for response to this therapy to allow for risk adaptation and personalization.

OBJECTIVES

To evaluate whether TMB or other variants associated with radiation response are also associated with outcomes following definitive chemoradiation and adjuvant durvalumab among patients with locally advanced unresectable NSCLC.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included consecutive patients with unresectable locally advanced NSCLC treated with chemoradiation and adjuvant durvalumab between November 2013 and March 2020 who had prospective comprehensive genomic profiling. This study was completed at a multisite tertiary cancer center. The median (IQR) follow-up time was 26 (21-36) months. Statistical analysis was conducted from April to October 2022.

EXPOSURES

Patients were grouped into TMB-high (≥10 mutations/megabase [mt/Mb]) and TMB-low (<10 mt/Mb) groups and were additionally evaluated by the presence of somatic alterations associated with radiation resistance (KEAP1/NFE2L2) or radiation sensitivity (DNA damage repair pathway).

MAIN OUTCOMES AND MEASURES

The primary outcomes were 24-month local-regional failure (LRF) and progression-free survival (PFS).

RESULTS

In this cohort study of 81 patients (46 [57%] male patients; median [range] age, 67 [45-85] years), 36 patients (44%) had TMB-high tumors (≥10 mt/Mb). Patients with TMB-high vs TMB-low tumors had markedly lower 24-month LRF (9% [95% CI, 0%-46%] vs 51% [95% CI, 36%-71%]; P = .001) and improved 24-month PFS (66% [95% CI, 54%-84%] vs 27% [95% CI, 13%-40%]; P = .003). The 24-month LRF was 52% (95% CI, 25%-84%) among patients with KEAP1/NFE2L2-altered tumors compared with 27% (95% CI, 17%-42%) among patients with KEAP1/NFE2L2-wildtype tumors (P = .05). On Cox analysis, only TMB status was associated with LRF (hazard ratio [HR], 0.17; 95% CI, 0.03-0.64; P = .02) and PFS (HR, 0.45; 95% CI, 0.21-0.90; P = .03). Histology, disease stage, Eastern Cooperative Oncology Group status, programmed cell death ligand 1 expression, and pathogenic KEAP1/NFE2L2, KRAS, and DNA damage repair pathway alterations were not significantly associated with LRF or PFS.

CONCLUSIONS AND RELEVANCE

In this cohort study, TMB-high status was associated with improved local-regional control and PFS after definitive chemoradiation and adjuvant durvalumab. TMB status may facilitate risk-adaptive radiation strategies in unresectable locally advanced NSCLC.

Post-operative ctDNA monitoring in stage I colon cancer: A case report

Circulating tumor DNA (ctDNA) level monitoring after surgery for colon cancer has been studied in stage II and III colon cancer to risk-stratify patients for adjuvant therapy. However, there is less data regarding the role of this diagnostic tool in the management of stage I disease, where current recommended surveillance is limited to screening colonoscopy at one year. In this report, we describe the case of a 57-year-old man with stage I colon cancer who underwent complete resection with adequate lymph node surgical sampling, normal preoperative CEA and no evidence of metastatic disease on initial imaging. The patient elected to undergo serial ctDNA monitoring after surgery. Rising ctDNA levels, five months after resection, prompted cross-sectional imaging which demonstrated metastatic disease to the liver. The patient subsequently received five cycles of leucovorin, 5-fluorouracil, oxaliplatin, and irinotecan with bevacizumab (FOLFOXIRI-Bev) and definitive microwave ablation to the liver metastases, with resulting undetectable ctDNA levels. The patient's imaging and colonoscopy one-year post-operatively showed no evidence of disease, with ctDNA levels remaining undetectable. This report highlights the value of ctDNA monitoring in patients with early-stage colon cancer and suggests that further, large-scale studies may be warranted to determine its appropriate clinical use.

Identification of mutational signature for lung adenocarcinoma prognosis and immunotherapy prediction

There is no robust genomic signature to predict the prognosis of patients with early-stage lung adenocarcinoma (LUAD). It was known that clonal heterogeneity was closely associated to tumour progression and prognosis prediction. Herein, using stage I patients from The Cancer Genome Atlas, we identified the clonal/subclonal events of each gene and preselected a set of genes with prognosis-specific mutation patterns based on a robust published transcriptomic prognostic signature. Subsequently, we constructed a mutational prognostic signature (MPS), whose prognostic performance was independently validated in two datasets of stage I samples. The predicted high-risk patients had significantly higher immune cell infiltration, along with higher expression of cytotoxic and immune checkpoint genes, and an integrated dataset with 88 samples confirmed that high-risk patients could benefit from immunotherapy. The developed MPS can identify the high-risk patients with stage I LUAD and improve individualised treatment planning of high-risk patients who might benefit from immunotherapy. KEY MESSAGES: We creatively developed a prognostic signature (57-MPS) based on clonal diversity. The high-risk samples displayed an underlying immunosuppressive mechanism. 57-MPS improved the predictive performance of PD-L1 for immunotherapy.

Multi-omics consensus portfolio to refine the classification of lung adenocarcinoma with prognostic stratification, tumor microenvironment, and unique sensitivity to first-line therapies

BACKGROUND

Molecular classification of lung adenocarcinoma (LUAD) based on transcriptomic features has been widely studied. The complementarity of data obtained from multilayer molecular biology could help the LUAD classification via combining multi-omics information.

METHODS

We successfully divided samples from the The Cancer Genome Atlas (TCGA) (n=437) into four subtypes (CS1, CS2, CS3 and CS4) by 10 comprehensive multi-omics clustering methods in the "movics" R package. Meanwhile, external validation sets from different sequencing technologies proved the robustness of the grouping model. The relationship between subtypes, prognosis, molecular features, tumor microenvironment and response to first-line therapy was further analyzed. Next we used univariate Cox regression analysis and Lasso regression analysis to explore the application of biomarkers in clinical prognosis and constructed a prognostic model.

RESULTS

CS1 showed the worst overall survival (OS) among all four clusters, possibly related to its poor immune infiltration, higher tumor mutation and worse chromosomal stability. Patients in different subtypes differed significantly in cancer stem cell characteristics, activation of cancer-related pathways, sensitivity to chemotherapy and immunotherapy. The prognostic model showed good predictive performance. The 1-, 2- and 3-year areas under the curve of risk score were 0.779, 0.742 and 0.678, respectively. Seven genes (DKK1, TSPAN7, ID1, DLGAP5, HHIPL2, CD40 and SEMA3C) used to build the model may be potential therapeutic targets for LUAD.

CONCLUSIONS

Four LUAD subtypes with different molecular characteristics and clinical implications were identified successfully through bioinformatic analysis. Our results may contribute to precision medicine and inform the development of rational clinical strategies for targeted and immune therapies.

A germline SNP in BRMS1 predisposes patients with lung adenocarcinoma to metastasis and can be ameliorated by targeting c-fos

About 50% of patients with early-stage, surgically resected lung cancer will develop distant metastasis. There remains an unmet need to identify patients likely to develop recurrence and to design innovative therapies to decrease this risk. Two primary isoforms of BRMS1, v1 and v2, are present in humans. Using next-generation sequencing of BRMS1 on matched human noncancerous lung tissue and non-small cell lung cancer (NSCLC) specimens, we identified single-nucleotide polymorphism (SNP) rs1052566 that results in an A273V mutation of BRMS1v2. This SNP is homozygous (BRMS1v2^A273V/A273V) in 8% of the population and correlates with aggressive biology in lung adenocarcinoma (LUAD). Mechanistically, we show that BRMS1v2 A273V abolishes the metastasis suppressor function of BRMS1v2 and promotes robust cell invasion and metastases by activation of c-fos-mediated gene-specific transcriptional regulation. BRMS1v2 A273V increases cell invasion in vitro and increases metastases in both tail-vein injection xenografts and LUAD patient-derived organoid (PDO) intracardiac injection metastasis in vivo models. Moreover, we show that BRMS1v2 A273V fails to interact with nuclear Src, thereby activating intratumoral c-fos in vitro. Higher c-fos results in up-regulation of CEACAM6, which drives metastases in vitro and in vivo. Using both xenograft and PDO metastasis models, we repurposed T5224 for treatment, a c-fos pharmacologic inhibitor investigated in clinical trials for arthritis, and observed suppression of metastases in BRMS1v2^A273V/A273V LUAD in mice. Collectively, we elucidate the mechanism of BRMS1v2^A273V/A273V-induced metastases and offer a putative therapeutic strategy for patients with LUAD who have this germline alteration.

Enhancer-associated regulatory network and gene signature based on transcriptome and methylation data to predict the survival of patients with lung adenocarcinoma

Accumulating evidence has proved that aberrant methylation of enhancers plays regulatory roles in gene expression for various cancers including lung adenocarcinoma (LUAD). In this study, the transcriptome and methylation data of The Cancer Genome Atlas (TCGA)-LUAD cohort were comprehensively analyzed with a five-step Enhancer Linking by Methylation/Expression Relationships (ELMER) process. Step 1: 131,371 distal (2 kb upstream from the transcription start site) probes were obtained. Step 2: 10,665 distal hypomethylated probes were identified in an unsupervised mode with the get.diff.meth function. Step 3: 699 probe-gene pairs with negative correlations were screened using the get.pair function in an unsupervised mode. Step 4: After mapping with probes, 768 motifs were obtained and 24 of them were enriched. Step 5: 127 transcription factors (TFs) with differential expressions and negative correlations with methylation levels were screened, which were corresponding to 21 motifs. After the ELMER process, a prognostic “TFs-motifs-genes” regulatory network was constructed. The Least absolute shrinkage and selection operator (LASSO) and Stepwise regression analyses were further applied to identify variables in the TCGA-LUAD cohort and an eight-gene signature was constructed for calculating the risk score. The risk score was verified in two independent validation cohorts. The area under curve values of receiver operating characteristic curves predicting 1-, 3-, and 5-years survival ranged from 0.633 to 0.764. With the increase of the risk scores, both the survival statuses and clinical traits showed a worse tendency. There were significant differences in the degrees of immune cell infiltration, TMB values, and TIDE scores between the high-risk and low-risk groups. Finally, a better-performing prognostic nomogram was integrated with the risk score and other clinical traits. In short, this multi-omics analysis demonstrated the application of ELMER in analyzing enhancer-associated regulatory network in LUAD, which provided promising strategies for epigenetic therapy and prognostic biomarkers.

Whole-Exome Sequencing Reveals the Genomic Features of the Micropapillary Component in Ground-Glass Opacities

Background: Micropapillary components are observed in a considerable proportion of ground-glass opacities (GGOs) and contribute to the poor prognosis of patients with invasive lung adenocarcinoma (LUAD). However, the underlying mutational processes related to the presence of micropapillary components remain obscure, limiting the development of clinical interventions. Methods: We collected 31 GGOs, which were separated into paired micropapillary and non-micropapillary components using microdissection. Whole-exome sequencing (WES) was performed on the GGO components, and bioinformatics analysis was conducted to reveal the genomic features of the micropapillary component in invasive LUAD. Results: The micropapillary component had more genomic variations, including tumor mutation burden, intratumoral heterogeneity, and copy number variation. We also observed the enrichment of AID/APOBEC mutation signatures and an increased activation of the RTK/Ras, Notch, and Wnt oncogenic pathways within the micropapillary component. A phylogenetic analysis further suggested that ERBB2/3/4, NCOR1/2, TP53, and ZNF469 contributed to the micropapillary component’s progression during the early invasion of LUAD, a finding that was validated in the TCGA cohort. Conclusions: Our results revealed specific mutational characteristics of the micropapillary component of invasive LUAD in an Asian population. These characteristics were associated with the formation of high-grade invasive patterns. These preliminary findings demonstrated the potential of targeting the micropapillary component in patients with early-stage LUAD.

Impact of High-Grade Patterns in Early-Stage Lung Adenocarcinoma: A Multicentric Analysis

Objective

The presence of micropapillary and solid adenocarcinoma patterns leads to a worse survival and a significantly higher tendency to recur. This study aims to assess the impact of pT descriptor combined with the presence of high-grade components on long-term outcomes in early-stage lung adenocarcinomas.

Methods

We retrospectively collected data of consecutive resected pT1-T3N0 lung adenocarcinoma from nine European Thoracic Centers. All patients who underwent a radical resection with lymph-node dissection between 2014 and 2017 were included. Differences in Overall Survival (OS) and Disease-Free Survival (DFS) and possible prognostic factors associated with outcomes were evaluated also after performing a propensity score matching to compare tumors containing non-high-grade and high-grade patterns.

Results

Among 607 patients, the majority were male and received a lobectomy. At least one high-grade histological pattern was seen in 230 cases (37.9%), of which 169 solid and 75 micropapillary. T1a-b-c without high-grade pattern had a significant better prognosis compared to T1a-b-c with high-grade pattern (p = 0.020), but the latter had similar OS compared to T2a (p = 0.277). Concurrently, T1a-b-c without micropapillary or solid patterns had a significantly better DFS compared to those with high-grade patterns (p = 0.034), and it was similar to T2a (p = 0.839). Multivariable analysis confirms the role of T descriptor according to high-grade pattern both for OS (p = 0.024; HR 1.285 95% CI 1.033–1.599) and DFS (p = 0.003; HR 1.196, 95% CI 1.054–1.344, respectively). These results were confirmed after the propensity score matching analysis.

Conclusions

pT1 lung adenocarcinomas with a high-grade component have similar prognosis of pT2a tumors.

Genomic Landscape, Clinical Features and Outcomes of Non-Small Cell Lung Cancer Patients Harboring BRAF Alterations of Distinct Functional Classes

Simple Summary

Non-small cell lung cancer (NSCLC) patients harboring BRAF non-V600 alterations constitute a heterogeneous and poorly studied population orphan of targeted therapies. We conducted a systematic review to detect all BRAF alterations of defined functional class across different cancer types. Then, we searched for NSCLC patients harboring these alterations in the cancer bioportal and in POPLAR and OAK trials using patient-level data, to investigate clinical and genomic differences associated with each BRAF functional class and the prognostic impact of BRAF non-V600 mutations. We found that NSCLC patients harboring distinct classes of BRAF alterations have different clinical characteristics, clinical features and genomic landscape. Moreover, BRAF non-V600 alterations were associated with a poor prognostic impact, apparently regardless of the treatment received. These peculiar features may suggest the use of tailored treatments according to each class of BRAF alteration.

Abstract

Background: In non-small cell lung cancer (NSCLC), BRAF class 1 alterations are effectively targeted by BRAF inhibitors. Conversely, targeted therapies have very low or absent activity in patients carrying class 2 and 3 alterations. The spectrum of BRAF alterations in NSCLC patients, and their accompanying clinical features, genomic landscape and treatment outcomes have been poorly reported. Patients and methods: We identified BRAF alterations of defined functional class across different tumors through a systematic review. Then, we selected NSCLC patients carrying BRAF alterations, according to the systematic review, in the cBioPortal (cBioPortal cohort) to collect and analyze clinical, biomolecular and survival data. Finally, we identified NSCLC patients carrying BRAF non-V600 mutations enrolled in POPLAR and OAK trials (POPLAR/OAK cohort), extracting clinical and survival data for survival analyses. Results: 100 different BRAF non-V600 alterations were identified through the systematic review. In the cBioPortal cohort (n = 139), patients harboring class 2 and 3 alterations were more frequently smokers and had higher tumor mutational burden compared to those carrying class 1 alterations. The spectrum of most frequently co-altered genes was significantly different between BRAF alterations classes, including SETD2, STK11, POM121L12, MUC16, KEAP1, TERT, TP53 and other genes. In the POPLAR/OAK cohort, patients carrying non-V600 BRAF alterations were characterized by poor prognosis compared to BRAF wild-type patients. Conclusions: Different classes of BRAF alterations confer distinctive clinical features, biomolecular signature and disease behavior to NSCLC patients. Non-V600 alterations are characterized by poor prognosis, but key gene co-alterations involved in cancer cell survival and immune pathways may suggest their potential sensitivity to tailored treatments.

Development of a prognostic signature based on immune-related genes and the correlation with immune microenvironment in breast cancer

Breast cancer (BC) is an inflammatory tumor caused by a variety of pathological factors, and is still the most common malignant tumor in women. Immune-related genes (IRGs) play a prominent role in the oncogenesis and progression of BC, and are of tumor-specific expression patterns that would benefit the prognosis evaluation. However, there were no systematic studies concerning the possibilities of IRGs in BC prognosis. In this study, the Cancer Genome Atlas (TCGA) database was used to integrate the expression profiles of IRG with the overall survival (OS) rate of 1039 breast cancer patients. The Cox regression analysis was used to predict the survival-related IRGs in BC. Then, we successfully screened a total of 6 IRGs, including PSME2, ULBP2, IGHE, SCG2, SDC1, and SSTR1, and accordingly constructed a prognosis prediction model of BC. Based on the IRG-related model, the BC patients were divided into high- and low-risk groups, and the association between the prognostic model and tumor immune microenvironment (TME) was further explored. The prognostic model reflected the infiltration of various immune cells. Moreover, the low-risk group was found to be with higher immunophenoscore and distinct mutation signatures compared with the high-risk group. The histological validation showed that SDC1, as well as M2 macrophage biomarker CD206, were both of higher abundance in BC samples of high-risk patients, compared with those of low-risk patients. Our results identify the clinically significant IRGs and demonstrate the importance of the IRG-based immune prognostic model in BC monitoring, prognosis prediction, and therapy.

Dysregulated Immune and Metabolic Microenvironment Is Associated with the Post-Operative Relapse in Stage I Non-Small Cell Lung Cancer

Simple Summary

The underlying mechanism of post-operative relapse of non-small cell lung cancer (NSCLC) remained poorly understood. This study highlights that both tumors and adjacent tissues from stage I NSCLC with relapse show a dysregulated immune and metabolic environment. Immune response shifts from an active state in primary tumors to a suppressive state in recurrent tumors. A model based on the enriched biological features in the primary tumors with relapse could effectively predict recurrence for stage I NSCLC. These results provide insights into the underpinning of the post-operative relapse and suggest that identifying NSCLC patients with a high risk of relapse could help the clinical decision of applying appropriate therapeutic interventions.

Abstract

The underlying mechanism of post-operative relapse of non-small cell lung cancer (NSCLC) remains poorly understood. We enrolled 57 stage I NSCLC patients with or without relapse and performed whole-exome sequencing (WES) and RNA sequencing (RNA-seq) on available primary and recurrent tumors, as well as on matched tumor-adjacent tissues (TATs). The WES analysis revealed that primary tumors from patients with relapse were enriched with USH2A mutation and 2q31.1 amplification. RNA-seq data showed that the relapse risk was associated with aberrant immune response and metabolism in the microenvironment of primary lesions. TATs from the patients with relapse showed an immunosuppression state. Moreover, recurrent lesions exhibited downregulated immune response compared with their paired primary tumors. Genomic and transcriptomic features were further subjected to build a prediction model classifying patients into groups with different relapse risks. We show that the recurrence risk of stage I NSCLC could be ascribed to the altered immune and metabolic microenvironment. TATs might be affected by cancer cells and facilitate the invasion of tumors. The immune microenvironment in the recurrent lesions is suppressed. Patients with a high risk of relapse need active post-operative intervention.

Effects of Tumor Mutational Burden and Gene Alterations Associated with Radiation Response on Outcomes of Postoperative Radiation Therapy in Non-Small Cell Lung Cancer

PURPOSE

Postoperative radiation therapy (PORT) in resected non-small cell lung cancer (NSCLC) improves locoregional outcomes, but recent randomized data do not support its unselected use. We assessed if tumor mutational burden (TMB) and mutations in genes associated with radiation sensitivity can select patients for PORT.

METHODS AND MATERIALS

Patients with resected NSCLC treated with and without PORT who underwent tumor genomic profiling were examined. The incidence of locoregional failures (LRFs) in patients with deleterious mutations in DNA damage response and repair (DDR) genes and genes associated with radiation resistance (KEAP1/NFE2L2/STK11/PIK3CA) were investigated. Cox modeling and receiver operating characteristic curve (ROC) analysis assessed the relationship between TMB and locoregional control (LRC).

RESULTS

Eighty-nine patients with NSCLC treated with PORT were analyzed, with a 2-year LRF rate of 19% (95% confidence interval, 10%-27%). Among patients treated with PORT, those with mutations in radiation resistance genes (n = 16 [18%]) had significantly more LRFs than patients without mutations (2-year LRF rate: 60% vs 11%; P < .001). On multivariate analysis, radiation-resistance mutations were associated with LRF after PORT (hazard ratio, 7.42; P < .001). Patients with mutations identified in DDR genes (n = 15 [17%]) had significantly improved LRC (P = .048) and no LRF events after PORT. On multivariate analysis, a higher TMB was associated with improved LRC after PORT (hazard ratio, 0.86; P = .01), and TMB was associated with PORT outcomes (area under ROC curve, 0.67-0.77). These genomic markers were not similarly associated with LRF in patients not treated with PORT.

CONCLUSIONS

The data suggest that patients with radiation-resistance gene alterations may derive minimal benefit from PORT, whereas patients with high TMB and/or alterations in DDR genes may benefit from PORT and be suited for future precision-RT strategies. Prospective studies are necessary to validate these findings.

Computational methods and translational applications for targeted next-generation sequencing platforms

During the past decade, next-generation sequencing (NGS) technologies have become widely adopted in cancer research and clinical care. Common applications within the clinical setting include patient stratification into relevant molecular subtypes, identification of biomarkers of response and resistance to targeted and systemic therapies, assessment of heritable cancer risk based on known pathogenic variants, and longitudinal monitoring of treatment response. The need for efficient downstream processing and reliable interpretation of sequencing data has led to the development of novel algorithms and computational pipelines, as well as structured knowledge bases that link genomic alterations to currently available drugs and ongoing clinical trials. Cancer centers around the world use different types of targeted solid-tissue and blood based NGS assays to analyze the genomic and transcriptomic profile of patients as part of their routine clinical care. Recently, cross-institutional collaborations have led to the creation of large pooled datasets that can offer valuable insights into the genomics of rare cancers.

An integrated analysis of prognostic and immune infiltrates for hub genes as potential survival indicators in patients with lung adenocarcinoma

Objective

Lung adenocarcinoma (LUAD) is one of the major subtypes of lung cancer that is associated with poor prognosis. The aim of this study was to identify useful biomarkers to enhance the treatment and diagnosis of LUAD.

Methods

GEO2R was used to identify common up-regulated differentially expressed genes (DEGs) in the GSE32863, GSE40791, and GSE75037 datasets. The DEGs were submitted to Metascape for gene ontology and pathway enrichment analysis as well as construction of the protein-protein interaction (PPI) network, while the molecular complex detection (MCODE) plug-in was employed to filter important subnetworks. The expression levels of the hub genes and their prognostic values were evaluated using the UALCAN, GEPIA2, and Kaplan-Meier plotter databases. The timer algorithm was utilized to determine the correlation between immune cell infiltration and the expression levels of hub genes in LUAD tissues. In addition, the hub gene mutation landscape and the correlation analysis with tumor mutational burden (TMB) score were evaluated using maftools package and ggstatsplot package in R software, respectively.

Results

We identified 156 common up-regulated DEGs, with gene ontology and pathway enrichment analysis indicating that they were mostly enriched in mitotic cell cycle process and cell cycle pathway. DEGs in the subnetwork with the largest number of genes were AURKB, CCNB2, CDC20, CDCA5, CDCA8, CENPF, and KNTC1. The seven hub genes were highly expressed in LUAD tissues and were associated with poor prognosis. These hub genes were negatively correlated with most immune cells. The somatic mutation landscape showed that AURKB, CDC20, CENPF, and KNTC1 had mutations and were positively correlated with TMB scores.

Conclusions

Our findings demonstrate that increased expression of seven hub genes is associated with poor prognosis for LUAD patients. Additionally, the TMB score indicates that the high expression of hub gene increases immune cell infiltration in patients with lung adenocarcinoma which may significantly improve response to immunotherapy.

Application of Artificial Intelligence in Lung Cancer

Simple Summary

Lung cancer is the leading cause of malignancy-related mortality worldwide. AI has the potential to help to treat lung cancer from detection, diagnosis and decision making to prognosis prediction. AI could reduce the labor work of LDCT, CXR, and pathology slides reading. AI as a second reader in LDCT and CXR reading reduces the effort of radiologists and increases the accuracy of nodule detection. Introducing AI to WSI in digital pathology increases the Kappa value of the pathologist and help to predict molecular phenotypes with radiomics and H&E staining. By extracting radiomics from image data and WSI from the histopathology field, clinicians could use AI to predict tumor properties such as gene mutation and PD-L1 expression. Furthermore, AI could help clinicians in decision-making by predicting treatment response, side effects, and prognosis prediction in medical treatment, surgery, and radiotherapy. Integrating AI in the future clinical workflow would be promising.

Abstract

Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.

CT-based Radiogenomic Analysis of Clinical Stage I Lung Adenocarcinoma with Histopathologic Features and Oncologic Outcomes

Background A preoperative predictive model is needed that can be used to identify patients with lung adenocarcinoma (LUAD) who have a higher risk of recurrence or metastasis. Purpose To investigate associations between CT-based radiomic consensus clustering of stage I LUAD and clinical-pathologic features, genomic data, and patient outcomes. Materials and Methods Patients who underwent complete surgical resection for LUAD from April 2014 to December 2017 with preoperative CT and next-generation sequencing data were retrospectively identified. Comprehensive radiomic analysis was performed on preoperative CT images; tumors were classified as solid, ground glass, or mixed. Patients were clustered into groups based on their radiomics features using consensus clustering, and clusters were compared with tumor genomic alterations, histopathologic features, and recurrence-specific survival (Kruskal-Wallis test for continuous data, χ² or Fisher exact test for categorical data, and log-rank test for recurrence-specific survival). Cluster analysis was performed on the entire cohort and on the solid, ground-glass, and mixed lesion subgroups. Results In total, 219 patients were included in the study (median age, 68 years; interquartile range, 63-74 years; 150 [68%] women). Four radiomic clusters were identified. Cluster 1 was associated with lepidic, acinar, and papillary subtypes (76 of 90 [84%]); clusters 2 (13 of 50 [26%]) and 4 (13 of 45 [29%]) were associated with solid and micropapillary subtypes (P < .001). The EGFR alterations were highest in cluster 1 (38 of 90 [42%], P = .004). Clusters 2, 3, and 4 were associated with lymphovascular invasion (19 of 50 [38%], 14 of 34 [41%], and 28 of 45 [62%], respectively; P < .001) and tumor spread through air spaces (32 of 50 [64%], 21 of 34 [62%], and 31 of 45 [69%], respectively; P < .001). STK11 alterations (14 of 45 [31%]; P = .006), phosphoinositide 3-kinase pathway alterations (22 of 45 [49%], P < .001), and risk of recurrence (log-rank P < .001) were highest in cluster 4. Conclusion CT-based radiomic consensus clustering enabled identification of associations between radiomic features and clinicalpathologic and genomic features and outcomes in patients with clinical stage I lung adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Nishino in this issue.

Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients

Metastatic progression is the main cause of death in cancer patients, whereas the underlying genomic mechanisms driving metastasis remain largely unknown. Here, we assembled MSK-MET, a pan-cancer cohort of over 25,000 patients with metastatic diseases. By analyzing genomic and clinical data from this cohort, we identified associations between genomic alterations and patterns of metastatic dissemination across 50 tumor types. We found that chromosomal instability is strongly correlated with metastatic burden in some tumor types, including prostate adenocarcinoma, lung adenocarcinoma, and HR+/HER2+ breast ductal carcinoma, but not in others, including colorectal cancer and high-grade serous ovarian cancer, where copy-number alteration patterns may be established early in tumor development. We also identified somatic alterations associated with metastatic burden and specific target organs. Our data offer a valuable resource for the investigation of the biological basis for metastatic spread and highlight the complex role of chromosomal instability in cancer progression.

Molecular charcterization of lung adenocarcinoma combining whole exome sequencing, copy number analysis and gene expression profiling

INTRODUCTION

Lung cancer is the leading cause of cancer mortality worldwide; lung adenocarcinoma (LUAD) corresponds to about 40% of lung cancers. LUAD is a genetically heterogeneous disease and the definition of this heterogeneity is of fundamental importance for prognosis and treatment.

AREAS COVERED

Based on primary literature, this review provides an updated analysis of multiomics studies based on the study of mutation profiling, copy number alterations and gene expression allowing for definition of molecular subgroups, prognostic factors based on molecular biomarkers, and identification of therapeutic targets. The authors sum up by providing the reader with their expert opinion on the potentialities of multiomics analysis of LUADs.

EXPERT OPINION

A detailed and comprehensive study of the co-occurring genetic abnormalities characterizing different LUAD subsets represents a fundamental tool for a better understanding of the disease heterogeneity and for the identification of subgroups of patients responding or resistant to targeted treatments and for the discovery of new therapeutic targets. It is expected that a comprehensive characterization of LUADs may provide a fundamental contribution to improve the survival of LUAD patients.

Recurrence risk stratification based on a competing-risks nomogram to identify patients with esophageal cancer who may benefit from postoperative radiotherapy

BACKGROUND

A reliable model is needed to estimate the risk of postoperative recurrence and the benefits of postoperative radiotherapy (PORT) in patients with thoracic esophageal squamous cell cancer (TESCC).

METHODS

The study retrospectively reviewed 3652 TESCC patients in stage IB-IVA after radical esophagectomy, with or without PORT. In one institution as the training cohort (n = 1620), independent risk factors associated with locoregional recurrence (LRR), identified by the competing-risks regression, were used to establish a predicting nomogram, which was validated in an external cohort (n = 1048). Area under curve (AUC) values of receiver operating characteristic curves were calculated to evaluate discrimination. Risk stratification was conducted using a decision tree analysis based on the cumulative point score of the LRR nomogram. After balancing the baseline of characteristics between treatment groups by inverse probability of treatment weighting, the effect of PORT was evaluated in each risk group.

RESULTS

Sex, age, tumor location, tumor grade, and N category were identified as independent risk factors for LRR and added into the nomogram. The AUC values were 0.638 and 0.706 in the training and validation cohorts, respectively. Three risk groups were established. For patients in the intermediate- and high-risk groups, PORT significantly improved the 5-year overall survival by 10.2% and 9.4%, respectively (p < 0.05). Although PORT was significantly associated with reduced LRR in the low-risk group, overall survival was not improved.

CONCLUSION

The nomogram can effectively estimate the individual risk of LRR, and patients in the intermediate- and high-risk groups are highly recommended to undergo PORT.

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.

Development and Validation of a Novel Gene Signature for Predicting the Prognosis by Identifying m5C Modification Subtypes of Cervical Cancer

Background: 5-Methylcytidine (m5C) is the most common RNA modification and plays an important role in multiple tumors including cervical cancer (CC). We aimed to develop a novel gene signature by identifying m5C modification subtypes of CC to better predict the prognosis of patients.

Methods: We obtained the expression of 13 m5C regulatory factors from The Cancer Genome Atlas (TCGA all set, 257 patients) to determine m5C modification subtypes by the “nonnegative matrix factorization” (NMF). Then the “limma” package was used to identify differentially expressed genes (DEGs) between different subtypes. According to these DEGs, we performed Cox regression and Kaplan-Meier (KM) survival analysis to establish a novel gene signature in TCGA training set (128 patients). We also verified the risk prediction effect of gene signature in TCGA test set (129 patients), TCGA all set (257 patients) and GSE44001 (300 patients). Furthermore, a nomogram including this gene signature and clinicopathological parameters was established to predict the individual survival rate. Finally, the expression and function of these signature genes were explored by qRT-PCR, immunohistochemistry (IHC) and proliferation, colony formation, migration and invasion assays.

Results: Based on consistent clustering of 13 m5C-modified genes, CC was divided into two subtypes (C1 and C2) and the C1 subtype had a worse prognosis. The 4-gene signature comprising FNDC3A, VEGFA, OPN3 and CPE was constructed. In TCGA training set and three validation sets, we found the prognosis of patients in the low-risk group was much better than that in the high-risk group. A nomogram incorporating the gene signature and T stage was constructed, and the calibration plot suggested that it could accurately predict the survival rate. The expression levels of FNDC3A, VEGFA, OPN3 and CPE were all high in cervical cancer tissues. Downregulation of FNDC3A, VEGFA or CPE expression suppressed the proliferation, migration and invasion of SiHa cells.

Conclusions: Two m5C modification subtypes of CC were identified and then a 4-gene signature was established, which provide new feasible methods for clinical risk assessment and targeted therapies for CC.

Signature identification of relapse-related overall survival of early lung adenocarcinoma after radical surgery

Background

The widespread use of low-dose chest CT screening has improved the detection of early lung adenocarcinoma. Radical surgery is the best treatment strategy for patients with early lung adenocarcinoma; however, some patients present with postoperative recurrence and poor prognosis. Through this study, we hope to establish a model that can identify patients that are prone to recurrence and have poor prognosis after surgery for early lung adenocarcinoma.

Materials and Methods

We screened prognostic and relapse-related genes using The Cancer Genome Atlas (TCGA) database and the GSE50081 dataset from the Gene Expression Omnibus (GEO) database. The GSE30219 dataset was used to further screen target genes and construct a risk prognosis signature. Time-dependent ROC analysis, calibration degree analysis, and DCA were used to evaluate the reliability of the model. We validated the TCGA dataset, GSE50081, and GSE30219 internally. External validation was conducted in the GSE31210 dataset.

Results

A novel four-gene signature (INPP5B, FOSL2, CDCA3, RASAL2) was established to predict relapse-related survival outcomes in patients with early lung adenocarcinoma after surgery. The discovery of these genes may reveal the molecular mechanism of recurrence and poor prognosis of early lung adenocarcinoma. In addition, ROC analysis, calibration analysis and DCA were used to verify the genetic signature internally and externally. Our results showed that our gene signature had a good predictive ability for recurrence and prognosis.

Conclusions

We established a four-gene signature and predictive model to predict the recurrence and corresponding survival rates in patients with early lung adenocarcinoma after surgery. These may be helpful for reforumulating post-operative consolidation treatment strategies.

Preoperative clinical and tumor genomic features associated with pathologic lymph node metastasis in clinical stage I and II lung adenocarcinoma

While next-generation sequencing (NGS) is used to guide therapy in patients with metastatic lung adenocarcinoma (LUAD), use of NGS to determine pathologic LN metastasis prior to surgery has not been assessed. To bridge this knowledge gap, we performed NGS using MSK-IMPACT in 426 treatment-naive patients with clinical N2-negative LUAD. A multivariable logistic regression model that considered preoperative clinical and genomic variables was constructed. Most patients had cN0 disease (85%) with pN0, pN1, and pN2 rates of 80%, 11%, and 9%, respectively. Genes altered at higher rates in pN-positive than in pN-negative tumors were STK11 (p = 0.024), SMARCA4 (p = 0.006), and SMAD4 (p = 0.011). Fraction of genome altered (p = 0.037), copy number amplifications (p = 0.001), and whole-genome doubling (p = 0.028) were higher in pN-positive tumors. Multivariable analysis revealed solid tumor morphology, tumor SUVmax, clinical stage, SMARCA4 and SMAD4 alterations were independently associated with pathologic LN metastasis. Incorporation of clinical and tumor genomic features can identify patients at risk of pathologic LN metastasis; this may guide therapy decisions before surgical resection.

The Emerging Importance of Tumor Genomics in Operable Non-Small Cell Lung Cancer

During the last two decades, next-generation sequencing (NGS) has played a key role in enhancing non-small cell lung cancer treatment paradigms through the application of "targeted therapy" in advanced and metastatic disease. The use of specific tyrosine kinase inhibitors in patients with oncogenic driver alterations, such as EGFR, ALK, ROS1, BRAF V600E, MET, and NTRK mutations, among others, has changed treatment approaches and improved outcomes in patients with late-stage disease. Although NGS technology has mostly been used in the setting of systemic therapy to identify targets, response to therapy, and mechanisms of resistance, it has multiple potential applications for patients with earlier-stage disease, as well. In this review, we discuss the emerging role of NGS technologies to better understand tumor biology in patients with non-small cell lung cancer who are undergoing surgery with curative intent. In this patient cohort, we examine tumor heterogeneity, the underlying tumor genomics associated with lung adenocarcinoma subtypes, the prediction of recurrence after complete surgical resection, the use of plasma circulating tumor DNA for detection of early cancers and monitoring for minimal residual disease, the differentiation of separate primaries from intrapulmonary metastases, and the use of NGS to guide induction and adjuvant therapies.

Targeting P2 receptors in purinergic signaling: a new strategy of active ingredients in traditional Chinese herbals for diseases treatment

Adenosine triphosphate (ATP) and its metabolites adenosine diphosphate, adenosine monophosphate, and adenosine in purinergic signaling pathway play important roles in many diseases. Activation of P2 receptors (P2R) channels and subsequent membrane depolarization can induce accumulation of extracellular ATP, and furtherly cause kinds of diseases, such as pain- and immune-related diseases, cardiac dysfunction, and tumorigenesis. Active ingredients of traditional Chinese herbals which exhibit superior pharmacological activities on diversified P2R channels have been considered as an alternative strategy of disease treatment. Experimental evidence of potential ingredients in Chinese herbs targeting P2R and their pharmacological activities were outlined in the study.

KRAS G12C Mutation Is Associated with Increased Risk of Recurrence in Surgically Resected Lung Adenocarcinoma

PURPOSE

KRAS ^G12C is the most common KRAS mutation in primary lung adenocarcinoma. Phase I clinical trials have demonstrated encouraging clinical activity of KRAS ^G12C inhibitors in the metastatic setting. We investigated disease-free survival (DFS) and tumor genomic features in patients with surgically resected KRAS ^G12C-mutant lung adenocarcinoma.

EXPERIMENTAL DESIGN

Patients who underwent resection of stage I-III lung adenocarcinoma and next-generation sequencing (NGS) were evaluated. Exclusion criteria were receipt of induction therapy, incomplete resection, and low-quality NGS. Mutations were classified as KRAS wild-type (KRAS ^wt), G12C (KRAS ^G12C), or non-G12C (KRAS ^other). DFS was compared between groups using the log-rank test; factors associated with DFS were assessed using Cox regression. Mutual exclusivity and cooccurrence, tumor clonality, and mutational signatures were assessed.

RESULTS

In total, 604 patients were included: 374 KRAS ^wt (62%), 95 KRAS ^G12C (16%), and 135 KRAS ^other (22%). Three-year DFS was not different between KRAS-mutant and KRAS ^wt tumors. However, 3-year DFS was worse in patients with KRAS ^G12C than KRAS ^other tumors (log-rank P = 0.029). KRAS ^G12C tumors had more lymphovascular invasion (51% vs. 37%; P = 0.032) and higher tumor mutation burden [median (interquartile range), 7.0 (5.3-10.8) vs. 6.1 (3.5-9.7); P = 0.021], compared with KRAS ^other tumors. KRAS ^G12C mutation was independently associated with worse DFS on multivariable analysis. Our DFS findings were externally validated in an independent The Cancer Genome Atlas cohort.

CONCLUSIONS

KRAS ^G12C mutations are associated with worse DFS after complete resection of stage I-III lung adenocarcinoma. These tumors harbor more aggressive clinicopathologic and genomic features than other KRAS-mutant tumors. We identified a high-risk group for whom KRAS ^G12C inhibitors may be investigated to improve survival.