Evolutionary characterization of lung adenocarcinoma morphology in TRACERx

Depletion-dependent activity-based protein profiling using SWATH/DIA-MS detects serine hydrolase lipid remodeling in lung adenocarcinoma progression

Systematic inference of enzyme activity in human tumors is key to understanding cancer progression and resistance to therapy. However, standard protein or transcript abundances are blind to the activity status of the measured enzymes, regulated, for example, by active-site amino acid mutations or post-translational protein modifications. Current methods for activity-based proteome profiling (ABPP), which combine mass spectrometry (MS) with chemical probes, quantify the fraction of enzymes that are catalytically active. Here, we describe depletion-dependent ABPP (dd-ABPP) combined with automated SWATH/DIA-MS, which simultaneously determines three molecular layers of studied enzymes: i) catalytically active enzyme fractions, ii) enzyme and background protein abundances, and iii) context-dependent enzyme-protein interactions. We demonstrate the utility of the method in advanced lung adenocarcinoma (LUAD) by monitoring nearly 4000 protein groups and 200 serine hydrolases (SHs) in tumor and adjacent tissue sections routinely collected for patient histopathology. The activity profiles of 23 SHs and the abundance of 59 proteins associated with these enzymes retrospectively classified aggressive LUAD. The molecular signature revealed accelerated lipoprotein depalmitoylation via palmitoyl(protein)hydrolase activities, further confirmed by excess palmitate and its metabolites. The approach is universal and applicable to other enzyme families with available chemical probes, providing clinicians with a biochemical rationale for tumor sample classification.

Characteristics of folic acid metabolism-related genes unveil prognosis and treatment strategy in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Folic acid metabolism-related genes (FAMGs) have received increased attention because of their distinct role in DNA synthesis and repair. Nevertheless, the function of FAMGs in LUAD remains ambiguous.

METHODS

LUAD transcriptome data from GEO and TCGA were analyzed. Patients were classified into two clusters based on gene expression levels, revealing distinct overall survival (OS) outcomes. Common differentially expressed genes (DEGs) were identified between LUAD and normal tissues, as well as between the two clusters. A prognostic risk model was established using Cox regression analysis to predict outcomes of LUAD patients and was validated with Kaplan-Meier and ROC curve analysis. Clinical correlations and enrichment analyses were carried out to explore the functions of DEGs and their associations with clinical characteristics of LUAD patients. The tumor microenvironment and drug sensitivity were evaluated between two risk subgroups. Moreover, expression levels of prognostic genes were validated across datasets using the Wilcoxon-test.

RESULTS

The study identified seventy-seven common DEGs and nine prognostic genes (ANLN, PLK1, DLGAP5, PRC1, CYP4B1, MKI67, KIF23, BIRC5, TK1). The risk model could effectively predict the prognosis of LUAD patients. Clinical correlation analysis revealed that age, pathologic-T, pathologic-N, and tumor stage were significantly correlated with the risk score. Enrichment analysis showed that DEGs between the two risk subgroups were predominantly enriched in cell cycle and cellular senescence pathways. Differences in immune cell infiltration and immunotherapy markers were markedly noted between the two risk subgroups. Drug sensitivity analysis disclosed significantly diverse responses to sixty-eight drugs between the two risk subgroups. Consistent expression tendencies of prognostic genes were observed across datasets.

CONCLUSION

The prognostic model based on FAMGs demonstrates considerable potential for guiding diagnosis and clinical management of LUAD patients.

Multi-omic profiling in breast cancer: utility for advancing diagnostics and clinical care

INTRODUCTION

Breast cancer remains a major global health challenge. While advances in precision oncology have contributed to improvements in patient outcomes and provided a deeper understanding of the biological mechanisms that drive the disease, historically, research and patients' allocation to treatment have heavily relied on single-omic approaches, analyzing individual molecular dimensions such as genomics, transcriptomics, or proteomics. While these have provided deep insights into breast cancer biology, they often fail to offer a complete understanding of the disease's complex molecular landscape.

AREAS COVERED

In this review, the authors explore the recent advancements in multi-omic research in the realm of breast cancer and use clinical data to show how multi-omic integration can offer a more holistic understanding of the molecular alterations and their functional consequences underlying breast cancer.

EXPERT OPINION

The overall developments in multi-omic research and AI are expected to complement precision diagnostics through potentially refining prognostic models, and treatment selection. Overcoming challenges such as cost, data complexity, and lack of standardization is crucial for unlocking the full potential of multi-omics and AI in breast cancer patient care to enable the advancement of personalized treatments and improve patient outcomes.

Genomic profiling of aggressive pathologic features in lung adenocarcinoma

INTRODUCTION

Pathologic features involving LVI (lympho-vascular invasion), PNI (perineural invasion), STAS (spread through air spaces), and Grade 3 pattern (from the International Association for the Study of Lung Cancer grading system) are related to having an aggressive phenotype and linked to poor prognosis. However, few studies have conducted in-depth analyses of these features simultaneously with genomic profiling.

METHODS

A total of 1559 sequencing of adenocarcinoma samples were included in the common driver mutations analysis, 1306 samples were brought into genomic mapping analysis. OncoSG's East Asian ancestry dataset was implemented for Tumor-Node-Metastasis-Biomarker (TNMB) classification and prognostic assessment.

RESULTS

EGFR was more significantly prevalent in LVI negativity (P = 0.021), STAS negativity (P = 0.002), and moderate grade (P < 0.001). ALK was significantly interrelated with LVI (P = 0.028), STAS (P < 0.001), and poor grade (P < 0.001); ROS1 and STAS positivity (P = 0.031), poor grade (P = 0.016) were significantly related. KRAS (P = 0.003) and BRAF-V600E (P = 0.002) were only significantly intertwined with poor grade. Apart from common driver mutations, TP53, CHEK2, KEAP1, PTEN, RB1, NF1 were significantly enriched in LVI samples (P < 0.05). TP53, PTEN, CTNNB1, HGF, NF1 were more prominent in STAS (P < 0.01). TP53, LRP1B, NF1 were significantly more prevalent in Grade 3 pattern (P < 0.001). The mixture of STK11, PTEN, and TOP2A generated by exclusive mutations may be a potential predictor of TNMB categorization towards survival. The HR of stage II compared I of TNMB was 2.28 (95 % CI 1.36-3.86, P < 0.001), while stage III compared II was 1.95 (95 % CI 1.04-3.21, P = 0.031).

CONCLUSIONS

This analysis demonstrated the correlation of pathologic features with common driver mutations, key mutations and canonical oncogenic signaling pathways. The data highlighted the similarities and differences among these features horizontally, and provide new insights in TNMB classification and prognostic assessment.

Multimodal radiopathological integration for prognosis and prediction of adjuvant chemotherapy benefit in resectable lung adenocarcinoma: A multicentre study

Lung adenocarcinoma (LUAD) has a heterogeneous prognosis and controversial postoperative treatment protocols. We aim to develop and validate a multimodal analysis framework that integrates CT images with H&E-stained whole-slide images (WSIs) to enhance risk stratification and predict adjuvant chemotherapy benefit in LUAD patients. We retrospectively collected data from 1039 resectable LUAD patients (stage I-III) across four centres, forming a training dataset (n = 303), two testing datasets (n = 197 and n = 228) for survival analysis, and a feature testing dataset (n = 311) for interpretability analysis. We extracted 487 tumour/peritumour radiomics features from CT images and 783 multiscale pathomics features from WSIs, characterising the shape of tumour (CT) and cancer nuclei (WSIs), as well as the intensity and texture of tumour/peritumour regions (CT) and tumour regions/epithelium/stroma (WSIs). A survival support vector machine (SVM) was employed to establish a radiopathomics signature using the optimal set of multimodal features, including 2 tumour radiomics features, 3 peritumour radiomics features, and 4 nuclei heterogeneity pathomics features. The radiopathomics signature outperformed both radiomics and pathomics signatures in predicting disease-free survival (DFS) (C-index: training dataset, 0.744 vs. 0.734 and 0.692; testing dataset 1, 0.719 vs. 0.701 and 0.638; testing dataset 2, 0.711 vs. 0.689 and 0.684), demonstrating greater robustness compared to the state-of-the-art deep learning integration approaches. It provided additional prognostic information beyond clinical risk factors (C-index of clinical plus radiopathomics vs. clinical models: training dataset, 0.763 vs. 0.676; testing dataset 1, 0.739 vs. 0.676; testing dataset 2, 0.711 vs. 0.699, p < 0.001). Compared to low-risk patients categorised by the radiopathomics signature, high-risk patients achieved comparable DFS when receiving adjuvant chemotherapy (training dataset, HR = 1.53, 95 % CI 0.85-2.73, p = 0.153; testing dataset 1 and 2, HR = 1.62, 95 % CI 0.92-2.85, p = 0.096), but had significantly worse DFS when only observed after surgery (training dataset, HR = 4.46, 95 % CI 2.82-7.05, p < 0.001; testing datasets 1 and 2, HR = 3.52, 95 % CI 2.26-5.49, p < 0.001), indicating the predictive value of the radiopathomics signature for adjuvant chemotherapy benefit (interaction p < 0.05). Further interpretability analysis revealed that the radiopathomics signature was associated with various prognostic/treatment-related biomarkers, including differentiation, immune phenotypes, and EGFR status. The multimodal integration framework offered a cost-effective approach for LUAD characterisation by leveraging complementary information from radiological and histopathological imaging. The radiopathomics signature demonstrated robust prognostic capabilities, providing valuable insights for postoperative treatment decisions.

Clinical Impact of Somatic Genomic Variants of Oncogenes and Tumor Suppressor Genes in Previously Treated Advanced Non-Small Cell Lung Cancer

PURPOSE

Next-generation sequencing in non-small cell lung cancer (NSCLC) identifies somatic genomic variants (SGVs) in cancer susceptibility genes (CSGs). We hypothesized that SGVs would be associated with poorer overall survival (OS) but greater benefit with immune checkpoint inhibitors over chemotherapy. We investigated the prevalence and predictive value of SGVs, using data from OAK and POPLAR trials comparing atezolizumab with docetaxel.

METHODS

We curated a list of SGVs (excluding TP53, EGFR, ALK, and ROS1) on the basis of CSGs associated with tumorigenesis. We classified participants as SGV mutant or wild-type using baseline plasma analyzed by the FoundationOne Liquid CDx assay. Cox regression analyses and interaction tests between SGV status and treatment were performed.

RESULTS

Of 762 participants, 29% harbored an SGV. The SGV mutant group had worse OS (hazard ratio [HR], 1.28, 95% CI, 1.06 to 1.54), and within each treatment arm (docetaxel: HR, 1.31; atezolizumab: HR, 1.27). In the atezolizumab arm, the SGV mutant group compared with wild-type had worse OS in the PD-L1 high (HR, 1.31 [95% CI, 0.59 to 2.91]) and low (HR, 1.38 [95% CI, 0.98 to 1.93]) subgroups. SGV with missense, splice, and nonsense mutations had significantly worse OS than wild-type in the docetaxel arm (log-rank P = .01) but not in the atezolizumab arm (log-rank P = .33). SGV status did not predict greater OS benefit with atezolizumab over docetaxel (interaction P = .67).

CONCLUSION

In advanced NSCLC after chemotherapy progression, plasma-detected SGVs are common, and associated with inferior OS. Plasma SGV status should be considered as a stratification factor in future trials.

Prognostic implications, genomic and immune characteristics of lung adenocarcinoma with lepidic growth pattern

AIMS

Conflicting data were provided regarding the prognostic impact and genomic features of lung adenocarcinoma (LUAD) with lepidic growth pattern (LP+A). Delineation of the genomic and immune characteristics of LP+A could provide deeper insights into its prognostic implications and treatment determination.

METHODS

We conducted a search of articles in PubMed, EMBASE and the Cochrane Library from inception to January 2024. A domestic cohort consisting of 52 LUAD samples was subjected to whole-exome sequencing as internal validation. Data from The Cancer Genomic Atlas and the Gene Expression Omnibus datasets were obtained to characterise the genomic and immune profiles of LP+A. Pooled HRs and rates were calculated.

RESULTS

The pooled results indicated that lepidic growth pattern was either predominant (0.35, 95% CI 0.22 to 0.56, p<0.01) or minor (HR 0.50, 95% CI 0.36 to 0.70, p<0.01) histological subtype was associated with favourable disease-free survival. Pooled gene mutation rates suggested higher EGFR mutation (0.55, 95% CI 0.46 to 0.64, p<0.01) and lower KRAS mutation (0.14, 95% CI 0.02 to 0.25, p=0.02) in lepidic-predominant LUAD. Lepidic-predominant LUAD had lower tumour mutation burden and pooled positive rate of PD-L1 expression compared with other subtypes. LP+A was characterised by abundance in resting CD4+memory T cells, monocytes and γδ T cells, as well as scarcity of cancer-associated fibroblasts.

CONCLUSIONS

LP+A was a unique histological subtype with a higher EGFR mutation rate, lower tumour mutation burden and immune checkpoint expression levels. Our findings suggested potential benefits from targeted therapy over immunotherapy in LP+A.

Excessive MYC Orchestrates Macrophages induced Chromatin Remodeling to Sustain Micropapillary-Patterned Malignancy in Lung Adenocarcinoma

Current understanding of micropapillary (MP)-subtype lung adenocarcinoma (LUAD) remains confined to biological activities and genomic landscapes. Unraveling the major regulatory programs underlying MP patterned malignancy offers opportunities to identify more feasible therapeutic targets for patients with MP LUAD. This study shows that patients with MP subtype LUAD have aberrant activation of the MYC pathway compared to patients with other subtypes. In vitro and xenograft mouse model studies reveal that MP pattern in malignancy cannot be solely due to aberrant MYC expression but requires the involvement of M2-like macrophages. Excessively expressed MYC leads to the accumulation of M2-like macrophages from the bone marrow, which secretes TGFβ, to induce the expression of FOSL2 in tumor cells, thereby remodeling chromatin accessibility at promoter regions of MP-pattern genes to promote the MYC-mediated de novo transcriptional regulation of these genes. Additionally, the MP-pattern in malignancy can be effectively alleviated by disrupting the TGFβ-FOSL2 axis. These findings reveal new functions for the M2-like macrophage-TGFβ-FOSL2 axis in MYC-overexpressing MP-subtype LUAD, identifying targetable vulnerabilities in this pathway.

Dynamic change of polarity in spread through air spaces of pulmonary malignancies

Spread through air spaces (STAS) is a histological finding of lung tumours where tumour cells exist within the air space of the lung parenchyma beyond the margin of the main tumour. Although STAS is an important prognostic factor, the pathobiology of STAS remains unclear. Here, we investigated the mechanism of STAS by analysing the relationship between STAS and polarity switching in vivo and in vitro. Histopathological analysis revealed that apical membranes were observed outside the STAS lesions around colorectal cancer (CRC) lung metastases and lung adenocarcinomas. When apical-out CRC organoids were administered intratracheally to mice, the organoids had greater metastatic potential than did single cells. To investigate the pathobiology of STAS, we established an in vitro model of STAS in which CRC or lung cancer organoids were co-cultured with 2D-cultured mouse airway epithelial organoids (2D-MAOs). Adhesion of cancer organoids to 2D-MAOs was much less than to type I collagen or endothelial cells, suggesting a protective role of the airway epithelium against adhesion. Loss of the apical membrane of CRC organoids at the contact surface with 2D-MAOs after adhesion was responsible for establishing adhesion. When airway epithelium was stimulated by transforming growth factor beta 1 (TGF-β1), adhesion of CRC organoids was enhanced. Among TGF-β1-induced genes in airway epithelium, follistatin-like protein 1 (FSTL1) increased CRC organoid adhesion by promoting loss of the apical membrane. These results suggested that TGF-β1-induced FSTL1 may promote metastatic progression of STAS by altering the polarity status. Elucidating the mechanism of STAS could contribute to the improvement of survival in patients with pulmonary malignancies associated with STAS. © 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Impact of histopathological subtypes on invasive lung adenocarcinoma: from epidemiology to tumour microenvironment to therapeutic strategies

Lung adenocarcinoma is the most prevalent type of lung cancer, with invasive lung adenocarcinoma being the most common subtype. Screening and early treatment of high-risk individuals have improved survival; however, significant differences in prognosis still exist among patients at the same stage, especially in the early stages. Invasive lung adenocarcinoma has different histological morphologies and biological characteristics that can distinguish its prognosis. Notably, several studies have found that the pathological subtypes of invasive lung adenocarcinoma are closely associated with clinical treatment. This review summarised the distribution of various pathological subtypes of invasive lung adenocarcinoma in the population and their relationship with sex, smoking, imaging features, and other histological characteristics. We comprehensively analysed the genetic characteristics and biomarkers of the different pathological subtypes of invasive lung adenocarcinoma. Understanding the interaction between the pathological subtypes of invasive lung adenocarcinoma and the tumour microenvironment helps to reveal new therapeutic targets for lung adenocarcinoma. We also extensively reviewed the prognosis of various pathological subtypes and their effects on selecting surgical methods and adjuvant therapy and explored future treatment strategies.

Clinicopathological Predictors of the Presence of Blood Circulating Tumor DNA in Early-Stage Non-Small Cell Lung Cancers

The implementation of lung cancer screening programs across the world has drawn considerable attention to improving early-stage lung cancer detection and prognostication. Several blood-based assays detecting circulating tumor DNA (ctDNA) recently emerged as noninvasive methods to detect malignancies. However, their limited sensitivity and predictive value remain a hurdle to their clinical use. We aimed to evaluate the association between clinicopathological parameters and presurgical ctDNA detection in clinical stage I non-small cell lung cancer patients to further understand ctDNA shedding biology. The cohort included 180 adenocarcinomas (LUAD) and 80 squamous cell carcinomas (LUSC) stage I patients who underwent lung cancer resection. Patients' clinical and pathological features were collected. A multicancer early-detection test (GRAIL LLC) was used to detect ctDNA using targeted methylation patterns. The association between the cell-free DNA tumor methylated fraction (TMeF) and the clinicopathological predictors was evaluated using univariate and multivariate modeling. LUSC was associated with a higher TMeF than LUAD. Pathological stage, tumor grade, and tumor volume were key determinants of ctDNA detection in both LUSC and LUAD. In LUAD, ctDNA detection also correlated with histologic pattern composition, necrosis, acute inflammation, and, to a lesser degree, spread through alveolar spaces and lymphovascular invasion. Based on our results, we propose classification methods for both LUAD (using histologic pattern composition) and LUSC (using tumor grade and pathological stage) to identify patients likely to have high ctDNA levels. These results confirm previous findings and suggest that previously unidentified factors, including histologic pattern composition and acute inflammation, influence ctDNA levels. These results will help in understanding the ctDNA shedding process and may allow identification of patients eligible for ctDNA detection-based follow-up.

A novel classification method for LUAD that guides personalized immunotherapy on the basis of the cross-talk of coagulation- and macrophage-related genes

Purpose

The coagulation process and infiltration of macrophages affect the progression and prognosis of lung adenocarcinoma (LUAD) patients. This study was designed to explore novel classification methods that better guide the precise treatment of LUAD patients on the basis of coagulation and macrophages.

Methods

Weighted gene coexpression network analysis (WGCNA) was applied to identify M2 macrophage-related genes, and TAM marker genes were acquired through the analysis of scRNA-seq data. The MSigDB and KEGG databases were used to obtain coagulation-associated genes. The intersecting genes were defined as coagulation and macrophage-related (COMAR) genes. Unsupervised clustering analysis was used to evaluate distinct COMAR patterns for LUAD patients on the basis of the COMAR genes. The R package "limma" was used to identify differentially expressed genes (DEGs) between COMAR patterns. A prognostic risk score model, which was validated through external data cohorts and clinical samples, was constructed on the basis of the COMAR DEGs.

Results

In total, 33 COMAR genes were obtained, and three COMAR LUAD subtypes were identified on the basis of the 33 COMAR genes. There were 341 DEGs identified between the three COMAR subtypes, and 60 prognostic genes were selected for constructing the COMAR risk score model. Finally, 15 prognosis-associated genes (CORO1A, EPHA4, FOXM1, HLF, IFIH1, KYNU, LY6D, MUC16, PPARG, S100A8, SPINK1, SPINK5, SPP1, VSIG4, and XIST) were included in the model, which was efficient and robust in predicting LUAD patient prognosis and clinical outcomes in patients receiving anti-PD-1/PD-L1 immunotherapy.

Conclusions

LUAD can be classified into three subtypes according to COMAR genes, which may provide guidance for precise treatment.

Multi-omics analyses reveal biological and clinical insights in recurrent stage I non-small cell lung cancer

Post-operative recurrence rates of stage I non-small cell lung cancer (NSCLC) range from 20% to 40%. Nonetheless, the molecular mechanisms underlying recurrence hitherto remain largely elusive. Here, we generate genomic, epigenomic and transcriptomic profiles of paired tumors and adjacent tissues from 122 stage I NSCLC patients, among which 57 patients develop recurrence after surgery during follow-up. Integrated analyses illustrate that the presence of predominantly solid or micropapillary histological subtypes, increased genomic instability, and APOBEC-related signature are associated with recurrence. Furthermore, TP53 missense mutation in DNA-binding domain could contribute to shorter time to recurrence. DNA hypomethylation is pronounced in recurrent NSCLC, and PRAME is the significantly hypomethylated and overexpressed gene in recurrent lung adenocarcinoma (LUAD). Mechanistically, hypomethylation at TEAD1 binding site facilitates the transcriptional activation of PRAME. Inhibition of PRAME restrains the tumor metastasis via downregulation of epithelial-mesenchymal transition-related genes. We also identify that enrichment of AT2 cells with higher copy number variation burden, exhausted CD8 + T cells and Macro_SPP1, along with the reduced interaction between AT2 and immune cells, is essential for the formation of ecosystem in recurrent LUAD. Finally, multi-omics clustering could stratify the NSCLC patients into 4 subclusters with varying recurrence risk and subcluster-specific therapeutic vulnerabilities. Collectively, this study constitutes a promising resource enabling insights into the biological mechanisms and clinical management for post-operative recurrence of stage I NSCLC.

EXO1 is a key gene for lung-resident memory T cells and has diagnostic and predictive values for lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a very common and lethal kind of lung malignancy. An increasing number of studies indicated that tissue-resident memory T (TRM) cells played significant roles in anti-cancer immunity. In our previous study, EXO1 was found to be a core gene for TRM cells in the prognosis of LUAD. However, the roles of EXO1 in the tumor microenvironment, and its application in the diagnosis and prognosis prediction of LUAD are still inadequately explored. In this study, the RNA expression, DNA methylation, CNV, somatic mutation data of EXO1, and the corresponding patients' clinical information from publicly available databases were analyzed using bioinformatic methods. The results were validated through immunohistochemical staining of EXO1 in LUAD samples. The results showed EXO1 was aberrantly highly expressed in LUAD tissues. High expression of EXO1 was a risky factor for LUAD patients. The expression level of EXO1 was associated with many clinical features such as TNM stages. It can also distinguish normal tissues and LUAD tumor tissues accurately. EXO1 expression was correlated with the infiltration of immune cells, and high expression of EXO1 was an adverse effect on LUAD patients receiving anti-PD-1/PD-L1 immunotherapy. Moreover, patients with EXO1 mutation had worse DSS, DFI and PFI.

Prediction of prognosis and immune response in lung adenocarcinoma based on mitophagy and lactate-related gene signatures

BACKGROUND

Lung adenocarcinoma (LUAD) causes leading death worldwide. Mitophagy and lactate metabolism accumulation are distinctive features of LUAD. We aimed to identify lactate-related genes (LRGs) signatures based on mitophagy for predicting prognosis and immune response in LUAD.

METHODS

The gene expression and clinical data were downloaded from TCGA and GEO database. First, the subtype analysis was analyzed based on 29 mitophagy genes. Survival, immune, and function differences between the different subtypes were analyzed. Then, based on mitophagy genes and 14 LRGs, the best LRGs were screened to construct a risk score model and combined with clinical factors to establish a nomogram for predicting patient survival. Finally, the expression level and molecular function of the key candidate gene OGDH were verified by in vitro experiments.

RESULTS

All the LUAD samples were divided into 2 subtypes: sub1 and sub2. The sub2 possessed worse survival. Immune score, immune checkpoint genes, and human leucocyte antigen genes in sub1 were higher than in sub2. Six optimal mitophagy-related LRGs were used to construct a risk score model. A high-risk score indicates poorer survival, higher tumor mutation burden, and higher drug sensitivity. The nomogram was robust in predicting LUAD survival. The experiments in vitro showed that knockdown of OGDH inhibited the proliferation, migration and invasion in LUAD cells.

CONCLUSIONS

A nomogram based on the construction of the mitophagy-related lactate genes predicts prognosis and immune response in LUAD. These results could help with risk stratification and targeted therapy for LUAD.

The hype around ctDNA guiding an informed perioperative therapeutic strategy in early-stage non-small cell lung cancer

Non-small cell lung cancer (NSCLC) remains a dire disease being the first cause of cancer death among both genders. Early-stage NSCLC often has better treatment outcomes despite it being a highly heterogeneous disease. So far, the neo-adjuvant chemotherapy strategies have led to a small benefit with an improvement of 5% in overall survival as an absolute benefit. Recently, the introduction of immune checkpoint inhibitors combined with chemotherapy has shown robust efficacy in terms of event-free survival and overall survival. Thus, these combinations are today considered a new standard of care in early-stage NSCLC. The application of these strategies to all-comer population lead to confounding definitive results regarding the efficacy and predictive biomarkers are urgently needed balancing the promise of healing than toxicities. At present, the clinical staging TNM system guides the clinical choice, however it is not entirely sufficient. Circulant tumoral DNA (ctDNA) emerged as a promising prognostic and predictive biomarker that may guide the future perioperative strategy and pave the way to personalized medicine also in this exciting field. This narrative review aims to put in the context the employment of ctDNA, give some perspective and suggestions weighing the pros and cons of this technique for our tomorrow clinical practice.

Ultrasensitive ctDNA detection for preoperative disease stratification in early-stage lung adenocarcinoma

Circulating tumor DNA (ctDNA) detection can predict clinical risk in early-stage tumors. However, clinical applications are constrained by the sensitivity of clinically validated ctDNA detection approaches. NeXT Personal is a whole-genome-based, tumor-informed platform that has been analytically validated for ultrasensitive ctDNA detection at 1-3 ppm of ctDNA with 99.9% specificity. Through an analysis of 171 patients with early-stage lung cancer from the TRACERx study, we detected ctDNA pre-operatively within 81% of patients with lung adenocarcinoma (LUAD), including 53% of those with pathological TNM (pTNM) stage I disease. ctDNA predicted worse clinical outcome, and patients with LUAD with <80 ppm preoperative ctDNA levels (the 95% limit of detection of a ctDNA detection approach previously published in TRACERx) experienced reduced overall survival compared with ctDNA-negative patients with LUAD. Although prospective studies are needed to confirm the clinical utility of the assay, these data show that our approach has the potential to improve disease stratification in early-stage LUADs.

A reproducibility study on invasion in small pulmonary adenocarcinoma according to the WHO and a modified classification, supported by biomarkers

OBJECTIVES

Evaluating invasion in non-mucinous adenocarcinoma (NMA) of the lung is crucial for accurate pT-staging. This study compares the World Health Organization (WHO) with a recently modified NMA classification.

MATERIALS AND METHODS

A retrospective case-control study was conducted on small NMA pT1N0M0 cases with a 5-year follow-up. Seventy cases were reviewed by 42 pulmonary pathologists first according to the WHO classification and after tutorial according to a modified classification. A third round was conducted based on feedback from 41 peers of previous rounds. Additionally, orthogonal biomarker analysis was performed.

RESULTS

In the first two rounds, 42 pathologists from 13 countries assessed all 70 cases, while 36 pathologists evaluated 41 non-unanimous cases in the third round. Kappa values for invasiveness increased in rounds 1, 2, and 3 to 0.27, 0.45 and 0.62, respectively. In contrast to low variation in total tumor size measurements (6 %), a marked increase in invasive tumor size variation was observed (42 %), which was associated with high uncertainty. In the third round 10 cases were non-invasive, all without recurrence. The modified classification showed in the 3rd round marked reduction of the variation in pT staging compared to the current WHO classification. Proliferation rate, tumor mutational burden, and transcriptomic profiles supported the distinction between invasive cases and non-invasive cases of the modified classification.

CONCLUSION

The modified classification demonstrates essentially higher reproducibility compared to the current WHO classification in NMA. The modified classification proves valuable in identifying low-risk lesions that are entirely non-invasive, and is supported by biomarker analysis.

Enhanced CT-Based Intratumoral and Peritumoral Radiomics Nomograms Predict High-Grade Patterns of Invasive Lung Adenocarcinoma

RATIONALE AND OBJECTIVES

Extraction of intratumoral and peritumoral radiomics features combined with clinical factors to establish nomograms to predict high-grade patterns (micropapillary and solid) of invasive adenocarcinoma of the lung (IAC).

MATERIALS AND METHODS

A retrospective study was conducted on 463 patients with pathologically confirmed IAC. Patients were randomized in a 7:3 ratio into a training cohort (n = 324) and a testing cohort (n = 139). A total of 2154 CT-based radiomic features were extracted from each of the four regions: gross tumor volume (GTV) and gross peritumoral tumor volume (GPTV3, GPTV6, GPTV9) containing peri-tumor regions of 3 mm, 6 mm, and 9 mm. A radiomics nomogram was constructed based on the optimal radiomics model and clinically independent predictors.

RESULTS

The GPTV3 radiomics model showed better predictive performance in the testing group compared to the GTV (0.840), GPTV6 (0.843), and GPTV9 (0.734) models, with an AUC value of 0.889 in the testing group. In the clinical model, tumor density and the presence of a spiculation sign were identified as independent predictors. The nomogram, which combined these independent predictors with the GPTV3-Radscore, proved to be clinically useful.

CONCLUSION

The GPTV3 radiomics model was superior to the GTV, GPTV6, and GPTV9 radiomics models in predicting high-grade patterns (HGP) of IAC. In addition, nomograms based on GPTV3 radiomics features and clinically independent predictors can further improve the prediction efficiency.

Prospective validation of ORACLE, a clonal expression biomarker associated with survival of patients with lung adenocarcinoma

Human tumors are diverse in their natural history and response to treatment, which in part results from genetic and transcriptomic heterogeneity. In clinical practice, single-site needle biopsies are used to sample this diversity, but cancer biomarkers may be confounded by spatiogenomic heterogeneity within individual tumors. Here we investigate clonally expressed genes as a solution to the sampling bias problem by analyzing multiregion whole-exome and RNA sequencing data for 450 tumor regions from 184 patients with lung adenocarcinoma in the TRACERx study. We prospectively validate the survival association of a clonal expression biomarker, Outcome Risk Associated Clonal Lung Expression (ORACLE), in combination with clinicopathological risk factors, and in stage I disease. We expand our mechanistic understanding, discovering that clonal transcriptional signals are detectable before tissue invasion, act as a molecular fingerprint for lethal metastatic clones and predict chemotherapy sensitivity. Lastly, we find that ORACLE summarizes the prognostic information encoded by genetic evolutionary measures, including chromosomal instability, as a concise 23-transcript assay.

Applications of single-cell analysis in immunotherapy for lung cancer: Current progress, new challenges and expectations

BACKGROUND

Lung cancer is a prevalent form of cancer worldwide, presenting a substantial risk to human well-being. Lung cancer is classified into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). The advancement of tumor immunotherapy, specifically immune checkpoint inhibitors and adaptive T-cell therapy, has encountered substantial obstacles due to the rapid progression of SCLC and the metastasis, recurrence, and drug resistance of NSCLC. These challenges are believed to stem from the tumor heterogeneity of lung cancer within the tumor microenvironment.

AIM OF REVIEW

This review aims to comprehensively explore recent strides in single-cell analysis, a robust sequencing technology, concerning its application in the realm of tumor immunotherapy for lung cancer. It has been effectively integrated with transcriptomics, epigenomics, genomics, and proteomics for various applications. Specifically, these techniques have proven valuable in mapping the transcriptional activity of tumor-infiltrating lymphocytes in patients with NSCLC, identifying circulating tumor cells, and elucidating the heterogeneity of the tumor microenvironment.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review emphasizes the paramount significance of single-cell analysis in mapping the immune cells within NSCLC patients, unveiling circulating tumor cells, and elucidating the tumor microenvironment heterogeneity. Notably, these advancements highlight the potential of single-cell analysis to revolutionize lung cancer immunotherapy by characterizing immune cell fates, improving therapeutic strategies, and identifying promising targets or prognostic biomarkers. It is potential to unravel the complexities within the tumor microenvironment and enhance treatment strategies marks a significant step towards more effective therapies and improved patient outcomes.

Second Primary Lung Cancer - An Emerging Issue in Lung Cancer Survivors

As a result of an increased focus on early detection including lung cancer screening, combined with more curative treatment options, the 5-year survival rates for lung cancer are improving. Welcome though this is, it brings new, hitherto unseen challenges. As more patients are cured and survive longer, they are at risk of developing second primary cancers, particularly lung cancer. In this review, we examine the challenges that surveillance, diagnosis, and management of second primary lung cancer (SPLC) bring and how these can be addressed. Recent data from prospective follow-up studies suggests that the incidence of SPLC may be higher than previously appreciated, partly due to an increase in multi-focal adenocarcinoma spectrum disease. Over 5 years, up to 1 in 6 long-term lung cancer survivors may develop a SPLC. Although not routinely used in clinical practice at present, genomic approaches for differentiating SPLC from intrapulmonary metastases of the first primary are emerging, and we highlight how this could be used to help differentiate lesions. An accurate distinction between SPLC and the recurrence of the first primary is of paramount importance due to the very different management strategies that may be required. Wrongly classifying an SPLC as a recurrence of the first primary may have significant consequences for patient management and overall survival. Updated approaches to the classification of SPLC combining clinical history, histopathological assessment, and genomic profiling are needed. Finally, we review the potential role of early detection biomarkers in the identification of SPLC, focusing in particular on blood-based biomarkers that are being examined in a multi-center prospective study recruiting lung cancer survivors.

Graph Attention-Based Fusion of Pathology Images and Gene Expression for Prediction of Cancer Survival

Multimodal machine learning models are being developed to analyze pathology images and other modalities, such as gene expression, to gain clinical and biological insights. However, most frameworks for multimodal data fusion do not fully account for the interactions between different modalities. Here, we present an attention-based fusion architecture that integrates a graph representation of pathology images with gene expression data and concomitantly learns from the fused information to predict patient-specific survival. In our approach, pathology images are represented as undirected graphs, and their embeddings are combined with embeddings of gene expression signatures using an attention mechanism to stratify tumors by patient survival. We show that our framework improves the survival prediction of human non-small cell lung cancers, outperforming existing state-of-the-art approaches that leverage multimodal data. Our framework can facilitate spatial molecular profiling to identify tumor heterogeneity using pathology images and gene expression data, complementing results obtained from more expensive spatial transcriptomic and proteomic technologies.

The International Association for the Study of Lung Cancer (IASLC) Staging Project for Lung Cancer: Recommendation to Introduce Spread Through Air Spaces as a Histologic Descriptor in the Ninth Edition of the TNM Classification of Lung Cancer. Analysis of 4061 Pathologic Stage I NSCLC

INTRODUCTION

Spread through air spaces (STAS) consists of lung cancer tumor cells that are identified beyond the edge of the main tumor in the surrounding alveolar parenchyma. It has been reported by meta-analyses to be an independent prognostic factor in the major histologic types of lung cancer, but its role in lung cancer staging is not established.

METHODS

To assess the clinical importance of STAS in lung cancer staging, we evaluated 4061 surgically resected pathologic stage I R0 NSCLC collected from around the world in the International Association for the Study of Lung Cancer database. We focused on whether STAS could be a useful additional histologic descriptor to supplement the existing ones of visceral pleural invasion (VPI) and lymphovascular invasion (LVI).

RESULTS

STAS was found in 930 of 4061 of the pathologic stage I NSCLC (22.9%). Patients with tumors exhibiting STAS had a significantly worse recurrence-free and overall survival in both univariate and multivariable analyses involving cohorts consisting of all NSCLC, specific histologic types (adenocarcinoma and other NSCLC), and extent of resection (lobar and sublobar). Interestingly, STAS was independent of VPI in all of these analyses.

CONCLUSIONS

These data support our recommendation to include STAS as a histologic descriptor for the Ninth Edition of the TNM Classification of Lung Cancer. Hopefully, gathering these data in the coming years will facilitate a thorough analysis to better understand the relative impact of STAS, LVI, and VPI on lung cancer staging for the Tenth Edition TNM Stage Classification.

Establishment and characterization of novel high mucus-producing lung tumoroids derived from a patient with pulmonary solid adenocarcinoma

Among mucus-producing lung cancers, invasive mucinous adenocarcinoma of the lung is a rare and unique subtype of pulmonary adenocarcinoma. Notably, mucus production may also be observed in the five subtypes of adenocarcinoma grouped under the higher-level diagnosis of Invasive Non-mucinous Adenocarcinomas (NMA). Overlapping pathologic features in mucus-producing tumors can cause diagnostic confusion with significant clinical consequences. In this study, we established lung tumoroids, PDT-LUAD#99, from a patient with NMA and mucus production. The tumoroids were derived from the malignant pleural effusion of a patient with lung cancer and have been successfully developed for long-term culture (> 11 months). Karyotyping by fluorescence in situ hybridization using an alpha-satellite probe showed that tumoroids harbored aneuploid karyotypes. Subcutaneous inoculation of PDT-LUAD#99 lung tumoroids into immunodeficient mice resulted in tumor formation, suggesting that the tumoroids were derived from cancer. Xenografts from PDT-LUAD#99 lung tumoroids reproduced the solid adenocarcinoma with mucin production that was observed in the patient's metastatic lymph nodes. Immunoblot analysis showed MUC5AC secretion into the culture supernatant of PDT-LUAD#99 lung tumoroids, which in contradistinction was barely detected in the culture supernatants of NCI-A549 and NCI-H2122 pulmonary adenocarcinoma cells known for their mucin-producing abilities. Here, we established a novel high-mucus-producing lung tumoroids from a solid adenocarcinoma. This preclinical model may be useful for elucidating the pathogenesis of mucus-producing lung cancer.

Spatial Architecture of Myeloid and T Cells Orchestrates Immune Evasion and Clinical Outcome in Lung Cancer

Understanding the role of the tumor microenvironment (TME) in lung cancer is critical to improving patient outcomes. We identified four histology-independent archetype TMEs in treatment-naïve early-stage lung cancer using imaging mass cytometry in the TRACERx study (n = 81 patients/198 samples/2.3 million cells). In immune-hot adenocarcinomas, spatial niches of T cells and macrophages increased with clonal neoantigen burden, whereas such an increase was observed for niches of plasma and B cells in immune-excluded squamous cell carcinomas (LUSC). Immune-low TMEs were associated with fibroblast barriers to immune infiltration. The fourth archetype, characterized by sparse lymphocytes and high tumor-associated neutrophil (TAN) infiltration, had tumor cells spatially separated from vasculature and exhibited low spatial intratumor heterogeneity. TAN-high LUSC had frequent PIK3CA mutations. TAN-high tumors harbored recently expanded and metastasis-seeding subclones and had a shorter disease-free survival independent of stage. These findings delineate genomic, immune, and physical barriers to immune surveillance and implicate neutrophil-rich TMEs in metastasis.

SIGNIFICANCE

This study provides novel insights into the spatial organization of the lung cancer TME in the context of tumor immunogenicity, tumor heterogeneity, and cancer evolution. Pairing the tumor evolutionary history with the spatially resolved TME suggests mechanistic hypotheses for tumor progression and metastasis with implications for patient outcome and treatment. This article is featured in Selected Articles from This Issue, p. 897.

A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies

Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.

Pathologic Assessment and Staging of Multiple Non-Small Cell Lung Carcinomas: A Paradigm Shift with the Emerging Role of Molecular Methods

Non-small cell lung carcinomas (NSCLCs) commonly present as 2 or more separate tumors. Biologically, this encompasses 2 distinct processes: separate primary lung carcinomas (SPLCs), representing independently arising tumors, and intrapulmonary metastases (IPMs), representing intrapulmonary spread of a single tumor. The advent of computed tomography imaging has substantially increased the detection of multifocal NSCLCs. The strategies and approaches for distinguishing between SPLCs and IPMs have evolved significantly over the years. Recently, genomic sequencing of somatic mutations has been widely adopted to identify targetable alterations in NSCLC. These molecular techniques have enabled pathologists to reliably discern clonal relationships among multiple NSCLCs in clinical practice. However, a standardized approach to evaluating and staging multiple NSCLCs using molecular methods is still lacking. Here, we reviewed the historical context and provided an update on the growing applications of genomic testing as a clinically relevant benchmark for determining clonal relationships in multiple NSCLCs, a practice we have designated "comparative molecular profiling." We examined the strengths and limitations of the morphology-based distinction of SPLCs vs IPMs and highlighted pivotal clinical and pathologic insights that have emerged from studying multiple NSCLCs using genomic approaches as a gold standard. Lastly, we suggest a practical approach for evaluating multiple NSCLCs in the clinical setting, considering the varying availability of molecular techniques.

18 F-FDG PET/CT characteristics of IASLC grade 3 invasive adenocarcinoma and the value of 18 F-FDG PET/CT for preoperative prediction: a new prognostication model

OBJECTIVE

This study is performed to investigate the imaging characteristics of the International Association for the Study of Lung Cancer grade 3 invasive adenocarcinoma (IAC) on PET/CT and the value of PET/CT for preoperative predicting this tumor.

MATERIALS AND METHODS

We retrospectively enrolled patients with IAC from August 2015 to September 2022. The clinical characteristics, serum tumor markers, and PET/CT features were analyzed. T test, Mann-Whitney U test, χ 2 test, Logistic regression analysis, and receiver operating characteristic analysis were used to predict grade 3 tumor and evaluate the prediction effectiveness.

RESULTS

Grade 3 tumors had a significantly higher maximum standardized uptake value (SUV max ) and consolidation-tumor-ratio (CTR) ( P  < 0.001), while Grade 1 - 2 tumors were prone to present with air bronchogram sign or vacuole sign ( P  < 0.001). A stepwise logistic regression analysis revealed that smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR were useful predictors for Grade 3 tumors. The established prediction model based on the above 5 parameters generated a high AUC (0.869) and negative predictive value (0.919), respectively.

CONCLUSION

Our study demonstrates that grade 3 IAC has a unique PET/CT imaging feature. The prognostication model established with smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR can effectively predict grade 3 tumors before the operation.

Are we getting closer to a successful neoantigen cancer vaccine?

Although significant advances in immunotherapy have revolutionized the treatment of many cancer types over the past decade, the field of vaccine therapy, an important component of cancer immunotherapy, despite decades-long intense efforts, is still transmitting signals of promises and awaiting strong data on efficacy to proceed with regulatory approval. The field of cancer vaccines faces standard challenges, such as tumor-induced immunosuppression, immune response in inhibitory tumor microenvironment (TME), intratumor heterogeneity (ITH), permanently evolving cancer mutational landscape leading to neoantigens, and less known obstacles: neoantigen gain/loss upon immunotherapy, the timing and speed of appearance of neoantigens and responding T cell clonotypes and possible involvement of immune interference/heterologous immunity, in the complex interplay between evolving tumor epitopes and the immune system. In this review, we discuss some key issues related to challenges hampering the development of cancer vaccines, along with the current approaches focusing on neoantigens. We summarize currently well-known ideas/rationales, thus revealing the need for alternative vaccine approaches. Such a discussion should stimulate vaccine researchers to apply out-of-box, unconventional thinking in search of new avenues to deal with critical, often yet unaddressed challenges on the road to a new generation of therapeutics and vaccines.

Molecular heterogeneity in histomorphologic subtypes of lung adeno carcinoma represents a challenge for treatment decision

Lung cancer is the leading cause in cancer related death, with non-small cell lung cancer (NSCLC) being the most frequent subtype. The importance of NSCLC is reflected by the various targeted therapy options especially for NSCLC adenocarcinomas (lung adeno carcinoma (LUAD)) as well as a set of options for immune therapies. However, despite these therapy advances, the majority of patients do not show a long-term response to either targeted therapy or immune checkpoint inhibition. One reason for treatment failure appears to be the NSCLC tumor heterogeneity. NSCLC heterogeneity might lead to an insufficient molecular characterization of a given sample due to the limited tumor material used for pathological assessment as the majority of analyses is performed on small biopsies. To get a more detailed insight into the tumor heterogeneity of NSCLC LUAD, especially in the light of its different histomorphological growth patterns, we analysed isolated NSCLC growth pattern areas and the corresponding entire tumor samples of a cohort of 31 NSLCS LUAD patients and compared their mutational landscape and their expression profiles. While significant differences of complex biomarkers, like tumor mutational burden (TMB) or microsatellite instability (MSI), were not detected between the five growth patterns -lepidic, papillary, micropapillary, acinar, and solid- we observed various subclonal mutations and copy number variants. Moreover, RNASeq analysis revealed growth pattern specific expression profiles affecting cellular processes like apoptosis, metastasis and proliferation. Taken together, our data provide novel insights into the tumor heterogeneity of LUAD required to overcome tumor heterogeneity related therapy resistance.

The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma

The introduction of the International Association for the Study of Lung Cancer grading system has furthered interest in histopathological grading for risk stratification in lung adenocarcinoma. Complex morphology and high intratumoral heterogeneity present challenges to pathologists, prompting the development of artificial intelligence (AI) methods. Here we developed ANORAK (pyrAmid pooliNg crOss stReam Attention networK), encoding multiresolution inputs with an attention mechanism, to delineate growth patterns from hematoxylin and eosin-stained slides. In 1,372 lung adenocarcinomas across four independent cohorts, AI-based grading was prognostic of disease-free survival, and further assisted pathologists by consistently improving prognostication in stage I tumors. Tumors with discrepant patterns between AI and pathologists had notably higher intratumoral heterogeneity. Furthermore, ANORAK facilitates the morphological and spatial assessment of the acinar pattern, capturing acinus variations with pattern transition. Collectively, our AI method enabled the precision quantification and morphology investigation of growth patterns, reflecting intratumoral histological transitions in lung adenocarcinoma.

Spatial transcriptomics reveals heterogeneity of histological subtypes between lepidic and acinar lung adenocarcinoma

BACKGROUND

Patients who possess various histological subtypes of early-stage lung adenocarcinoma (LUAD) have considerably diverse prognoses. The simultaneous existence of several histological subtypes reduces the clinical accuracy of the diagnosis and prognosis of early-stage LUAD due to intratumour intricacy.

METHODS

We included 11 postoperative LUAD patients pathologically confirmed to be stage IA. Single-cell RNA sequencing (scRNA-seq) was carried out on matched tumour and normal tissue. Three formalin-fixed and paraffin-embedded cases were randomly selected for 10× Genomics Visium analysis, one of which was analysed by digital spatial profiler (DSP).

RESULTS

Using DSP and 10× Genomics Visium analysis, signature gene profiles for lepidic and acinar histological subtypes were acquired. The percentage of histological subtypes predicted for the patients from samples of 11 LUAD fresh tissues by scRNA-seq showed a degree of concordance with the clinicopathologic findings assessed by visual examination. DSP proteomics and 10× Genomics Visium transcriptomics analyses revealed that a negative correlation (Spearman correlation analysis: r = -.886; p = .033) between the expression levels of CD8 and the expression trend of programmed cell death 1(PD-L1) on tumour endothelial cells. The percentage of CD8+ T cells in the acinar region was lower than in the lepidic region.

CONCLUSIONS

These findings illustrate that assessing patient histological subtypes at the single-cell level is feasible. Additionally, tumour endothelial cells that express PD-L1 in stage IA LUAD suppress immune-responsive CD8+ T cells.

Sublobar resection is associated with less lymph nodes examined and lower delivery of adjuvant therapy in patients with 1.5- to 2.0-cm clinical IA2 non-small-cell lung cancer: a retrospective cohort study

OBJECTIVES

CALGB140503, in which nodal sampling was mandated, reported non-inferior disease-free survival for patients undergoing sublobar resection (SLR) compared to lobectomy (L). Outside of trial settings, the adequacy of lymphadenectomy during SLR has been questioned. We sought to evaluate whether SLR is associated with suboptimal lymphadenectomy, differences in pathologic upstaging and survival in patients with 1.5- to 2.0-cm tumours using real-world data.

MATERIALS AND METHODS

Using the National Cancer Database(2018-2019), we evaluated patients with 1.5- to 2.0-cm non-small-cell lung cancer who underwent resection (sublobar versus lobectomy). We studied factors associated with nodal upstaging (logistic regression) and survival (Cox regression and Kaplan-Meier method) after propensity matching to adjust for differences among groups.

RESULTS

Among 3196 patients included, SLR was performed in 839 (26.3%) (of which 588 were wedge resections) and L was performed in 2357 (73.7%) patients. More patients undergoing SLR (21.7%) compared to L (2.1%) had no lymph nodes sampled (P < 0.001). Those undergoing SLR had fewer total lymph nodes examined (4 vs 11, P < 0.001) and were less likely to have pathologic nodal metastases (4.7% vs 9%, P < 0.001) compared to L. Multivariable analysis identified L [adjusted odds ratio (aOR) 2.21, 95% confidence interval, 1.47-3.35] to be independently associated with pathologic N+ disease. Overall survival was not associated with the type of procedure but was significantly decreased in those with N+ disease.

CONCLUSIONS

Despite comparable overall survival to L, SLR is associated with suboptimal lymphadenectomy in patients with 1.5-2.0 cm non-small-cell lung cancer. Surgeons should be careful to perform adequate lymphadenectomy when performing SLR to mitigate nodal under-staging and to identify appropriate patients for systemic therapy.

[Hypothesis of Genetic Diversity Selection in the Occurrence and Development of Lung Cancer: Molecular Evolution and Clinical Significance]

So far, the monoclonal hypothesis of tumor occurrence and development cannot be justified. The genetic diversity selection hypothesis for the occurrence and development of lung cancer links Mendelian genetics with Darwin's theory of evolution, suggesting that the genetic diversity of tumor cell populations with polyclonal origins-monoclonal selection-subclonal expansion is the result of selection pressure. Normal cells acquire mutations in oncogenic driver genes and have a selective advantage over other cells, becoming tumor initiating cells; In the interaction with the tumor microenvironment (TME), the vast majority of initiating cells are recognized and killed by the human immune system. If immune escape occurs, the incidence of malignant tumors will greatly increase, and subclonal expansion, intratumour heterogeneity, etc. will occur. This article proposed the hypothesis of genetic diversity selection and analyzed its clinical significance.
.

Clinicopathological significances of cribriform pattern in lung adenocarcinoma

The present study aimed to investigate the clinicopathological and prognostic implications of the cribriform pattern in lung adenocarcinoma through a meta-analysis. The estimated rates of cribriform pattern in lung adenocarcinomas were investigated. The correlations between cribriform pattern and clinicopathological characteristics, including genetic alterations and prognosis were evaluated. The estimated rate of cribriform pattern was 0.150 (95% confidence interval [CI], 0.101-0.218) in lung adenocarcinoma. The estimated rates of cribriform pattern in the 5% and 10% criteria were 0.230 (95% CI 0.125-0.386) and 0.130 (95% CI 0.062-0.252), respectively. The presence of cribriform pattern was significantly correlated with larger tumor size (> 30 mm), spread through air spaces, and lymph node metastasis (P < 0.001, P < 0.001, and P = 0.007, respectively, in the meta-regression test). There were no significant differences between cribriform pattern, smoking history, and vascular and lymphatic invasion. In lung adenocarcinoma with cribriform pattern, the estimated rates of ALK rearrangement, KRAS, and EGFR mutations were 0.407 (95% CI 0.165-0.704), 0.330 (95% CI 0.117-0.646), and 0.249 (95% CI 0.125-0.437), respectively. ALK rearrangement was significantly more frequent in lung adenocarcinomas with cribriform pattern than in those without. The overall survival rate was significantly worse in lung adenocarcinomas with a cribriform pattern than in those without (hazard ratio 2.051, 95% CI 1.369-3.075). In conclusion, the presence of a cribriform pattern can be a useful predictor of the clinicopathological characteristics and prognosis of patients with lung adenocarcinoma.

CONIPHER: a computational framework for scalable phylogenetic reconstruction with error correction

Intratumor heterogeneity provides the fuel for the evolution and selection of subclonal tumor cell populations. However, accurate inference of tumor subclonal architecture and reconstruction of tumor evolutionary histories from bulk DNA sequencing data remains challenging. Frequently, sequencing and alignment artifacts are not fully filtered out from cancer somatic mutations, and errors in the identification of copy number alterations or complex evolutionary events (e.g., mutation losses) affect the estimated cellular prevalence of mutations. Together, such errors propagate into the analysis of mutation clustering and phylogenetic reconstruction. In this Protocol, we present a new computational framework, CONIPHER (COrrecting Noise In PHylogenetic Evaluation and Reconstruction), that accurately infers subclonal structure and phylogenetic relationships from multisample tumor sequencing, accounting for both copy number alterations and mutation errors. CONIPHER has been used to reconstruct subclonal architecture and tumor phylogeny from multisample tumors with high-depth whole-exome sequencing from the TRACERx421 dataset, as well as matched primary-metastatic cases. CONIPHER outperforms similar methods on simulated datasets, and in particular scales to a large number of tumor samples and clones, while completing in under 1.5 h on average. CONIPHER enables automated phylogenetic analysis that can be effectively applied to large sequencing datasets generated with different technologies. CONIPHER can be run with a basic knowledge of bioinformatics and R and bash scripting languages.

Comprehensive analyses for the coagulation and macrophage-related genes to reveal their joint roles in the prognosis and immunotherapy of lung adenocarcinoma patients

Purpose

This study aims to explore novel biomarkers related to the coagulation process and tumor-associated macrophage (TAM) infiltration in lung adenocarcinoma (LUAD).

Methods

The macrophage M2-related genes were obtained by Weighted Gene Co-expression Network Analysis (WGCNA) in bulk RNA-seq data, while the TAM marker genes were identified by analyzing the scRNA-seq data, and the coagulation-associated genes were obtained from MSigDB and KEGG databases. Survival analysis was performed for the intersectional genes. A risk score model was subsequently constructed based on the survival-related genes for prognosis prediction and validated in external datasets.

Results

In total, 33 coagulation and macrophage-related (COMAR) genes were obtained, 19 of which were selected for the risk score model construction. Finally, 10 survival-associated genes (APOE, ARRB2, C1QB, F13A1, FCGR2A, FYN, ITGB2, MMP9, OLR1, and VSIG4) were involved in the COMAR risk score model. According to the risk score, patients were equally divided into low- and high-risk groups, and the prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. The ROC curve indicated that the risk score model had high sensitivity and specificity, which was validated in multiple external datasets. Moreover, the model also had high efficacy in predicting the clinical outcomes of LUAD patients who received anti-PD-1/PD-L1 immunotherapy.

Conclusion

The COMAR risk score model constructed in this study has excellent predictive value for the prognosis and immunotherapeutic clinical outcomes of patients with LUAD, which provides potential biomarkers for the treatment and prognostic prediction.

Exploring the Potential of Non-Coding RNAs as Liquid Biopsy Biomarkers for Lung Cancer Screening: A Literature Review

Lung cancer represent the leading cause of cancer mortality, so several efforts have been focused on the development of a screening program. To address the issue of high overdiagnosis and false positive rates associated to LDCT-based screening, there is a need for new diagnostic biomarkers, with liquid biopsy ncRNAs detection emerging as a promising approach. In this scenario, this work provides an updated summary of the literature evidence about the role of non-coding RNAs in lung cancer screening. A literature search on PubMed was performed including studies which investigated liquid biopsy non-coding RNAs biomarker lung cancer patients and a control cohort. Micro RNAs were the most widely studied biomarkers in this setting but some preliminary evidence was found also for other non-coding RNAs, suggesting that a multi-biomarker based liquid biopsy approach could enhance their efficacy in the screening context. However, further studies are needed in order to optimize detection techniques as well as diagnostic accuracy before introducing novel biomarkers in the early diagnosis setting.

Spatially Resolved Proteomic and Transcriptomic Profiling of Anaplastic Lymphoma Kinase-Rearranged Pulmonary Adenocarcinomas Reveals Key Players in Inter- and Intratumoral Heterogeneity

Pulmonary adenocarcinomas (pADCs) with an ALK rearrangement are a rare cancer subtype, necessitating comprehensive molecular investigations to unravel their heterogeneity and improve therapeutic strategies. In this pilot study, we employed spatial transcriptomic (NanoString GeoMx) and proteomic profiling to investigate seven treatment-naïve pADCs with an ALK rearrangement. On each FFPE tumor slide, 12 smaller and 2-6 larger histopathologically annotated regions were selected for transcriptomic and proteomic analysis, respectively. The correlation between proteomics and transcriptomics was modest (average Pearson's r = 0.43 at the gene level). Intertumoral heterogeneity was more pronounced than intratumoral heterogeneity, and normal adjacent tissue exhibited distinct molecular characteristics. We identified potential markers and dysregulated pathways associated with tumors, with a varying extent of immune infiltration, as well as with mucin and stroma content. Notably, some markers appeared to be specific to the ALK-driven subset of pADCs. Our data showed that within tumors, elements of the extracellular matrix, including FN1, exhibited substantial variability. Additionally, we mapped the co-localization patterns of tumor microenvironment elements. This study represents the first spatially resolved profiling of ALK-driven pADCs at both the gene and protein expression levels. Our findings may contribute to a better understanding of this cancer type prior to treatment with ALK inhibitors.

Genomic-transcriptomic evolution in lung cancer and metastasis

Intratumour heterogeneity (ITH) fuels lung cancer evolution, which leads to immune evasion and resistance to therapy1. Here, using paired whole-exome and RNA sequencing data, we investigate intratumour transcriptomic diversity in 354 non-small cell lung cancer tumours from 347 out of the first 421 patients prospectively recruited into the TRACERx study2,3. Analyses of 947 tumour regions, representing both primary and metastatic disease, alongside 96 tumour-adjacent normal tissue samples implicate the transcriptome as a major source of phenotypic variation. Gene expression levels and ITH relate to patterns of positive and negative selection during tumour evolution. We observe frequent copy number-independent allele-specific expression that is linked to epigenomic dysfunction. Allele-specific expression can also result in genomic-transcriptomic parallel evolution, which converges on cancer gene disruption. We extract signatures of RNA single-base substitutions and link their aetiology to the activity of the RNA-editing enzymes ADAR and APOBEC3A, thereby revealing otherwise undetected ongoing APOBEC activity in tumours. Characterizing the transcriptomes of primary-metastatic tumour pairs, we combine multiple machine-learning approaches that leverage genomic and transcriptomic variables to link metastasis-seeding potential to the evolutionary context of mutations and increased proliferation within primary tumour regions. These results highlight the interplay between the genome and transcriptome in influencing ITH, lung cancer evolution and metastasis.