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OBJECTIVES: To differentiate invasive lepidic predominant adenocarcinoma (iLPA) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) of lung utilizing visual semantic and computer-aided detection (CAD)-based texture features on subjects initially diagnosed as AIS or MIA with CT-guided biopsy. MATERIALS AND METHODS: From 2011 to 2017, all patients with CT-guided biopsy results of AIS or MIA who subsequently underwent resection were identified. CT scan before the biopsy was used to assess visual semantic and CAD texture features, totaling 23 semantic and 95 CAD-based quantitative texture variables. The least absolute shrinkage and selection operator (LASSO) method or forward selection was used to select the most predictive feature and combination of semantic and texture features for detection of invasive lung adenocarcinoma. RESULTS: Among the 33 core needle-biopsied patients with AIS/MIA pathology, 24 (72.7%) had invasive LPA and 9 (27.3%) had AIS/MIA on resection. On CT, visual semantic features included 21 (63.6%) part-solid, 5 (15.2%) pure ground glass, and 7 (21.2%) solid nodules. LASSO selected seven variables for the model, but all were not statistically significant. Volume was found to be statistically significant when assessing the correlation between independent variables using the backward selection technique. The LASSO selected tumor_Perc95, nodule surround, small cyst-like spaces, and volume when assessing the correlation between independent variables. CONCLUSIONS: Lung biopsy results showing noninvasive LPA underestimate invasiveness. Although statistically non-significant, some semantic features showed potential for predicting invasiveness, with septal stretching absent in all noninvasive cases, and solid consistency present in a significant portion of invasive cases.

Accurate diagnosis of lung cancer is important for treatment decision-making. Tumor biopsy and histologic examination are the standard for determining histologic lung cancer subtypes. Liquid biopsy, particularly cell-free DNA (cfDNA), has recently shown promising results in cancer detection and classification. In this study, we investigate the potential of cfDNA methylome for the noninvasive classification of lung cancer histologic subtypes. We focused on the two most prevalent lung cancer subtypes, lung adenocarcinoma and lung squamous cell carcinoma. Using a fragment-based marker discovery approach, we identified robust subtype-specific methylation markers from tumor samples. These markers were successfully validated in independent cohorts and associated with subtype-specific transcriptional activity. Leveraging these markers, we constructed a subtype classification model using cfDNA methylation profiles, achieving an AUC of 0.808 in cross-validation and an AUC of 0.747 in the independent validation. Tumor copy-number alterations inferred from cfDNA methylome analysis revealed potential for treatment selection. In summary, our study demonstrates the potential of cfDNA methylome analysis for noninvasive lung cancer subtyping, offering insights for cancer monitoring and early detection.SignificanceThis study explores the use of cfDNA methylomes for the classification of lung cancer subtypes, vital for effective treatment. By identifying specific methylation markers in tumor tissues, we developed a robust classification model achieving high accuracy for noninvasive subtype detection. This cfDNA methylome approach offers promising avenues for early detection and monitoring.

There is an unmet clinical need to develop novel strategies to overcome resistance to tyrosine kinase inhibitors (TKI) in patients with oncogene-driven lung adenocarcinoma (LUAD). The objective of this study was to determine whether simvastatin could overcome TKI resistance using the in vitro and in vivo LUAD models. Human LUAD cell lines, tumor cells, and patient-derived xenograft (PDX) models from TKI-resistant LUAD were treated with simvastatin, either alone or in combination with a matched TKI. Tumor growth inhibition was measured by the 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay and expression of molecular targets was assessed by immunoblots. Tumors were assessed by histopathology, IHC stain, immunoblots, and RNA sequencing. We found that simvastatin had a potent antitumor effect in tested LUAD cell lines and PDX tumors, regardless of tumor genotypes. Simvastatin and TKI combination did not have antagonistic cytotoxicity in these LUAD models. In an osimertinib-resistant LUAD PDX model, simvastatin and osimertinib combination resulted in a greater reduction in tumor volume than simvastatin alone (P < 0.001). Immunoblots and IHC stain also confirmed that simvastatin inhibited TKI targets. In addition to inhibiting 3-hydroxy-3-methyl-glutaryl-CoA (HMG-CoA) reductase, RNA sequencing and Western blots identified the proliferation, migration, and invasion-related genes (such as PI3K/Akt/mTOR, YAP/TAZ, focal adhesion, extracellular matrix receptor), proteasome-related genes, and integrin (α3β1, αvβ3) signaling pathways as the significantly downregulated targets in these PDX tumors treated with simvastatin and a TKI. The addition of simvastatin is a safe approach to overcome acquired resistance to TKIs in several oncogene-driven LUAD models, which deserve further investigation.

BackgroundRNA-seq has brought forth significant discoveries regarding aberrations in RNA processing, implicating these RNA variants in a variety of diseases. Aberrant splicing and single nucleotide variants (SNVs) in RNA have been demonstrated to alter transcript stability, localization, and function. In particular, the upregulation of ADAR, an enzyme that mediates adenosine-to-inosine editing, has been previously linked to an increase in the invasiveness of lung adenocarcinoma cells and associated with splicing regulation. Despite the functional importance of studying splicing and SNVs, the use of short-read RNA-seq has limited the community's ability to interrogate both forms of RNA variation simultaneously.ResultsWe employ long-read sequencing technology to obtain full-length transcript sequences, elucidating cis-effects of variants on splicing changes at a single molecule level. We develop a computational workflow that augments FLAIR, a tool that calls isoform models expressed in long-read data, to integrate RNA variant calls with the associated isoforms that bear them. We generate nanopore data with high sequence accuracy from H1975 lung adenocarcinoma cells with and without knockdown of ADAR. We apply our workflow to identify key inosine isoform associations to help clarify the prominence of ADAR in tumorigenesis.ConclusionsUltimately, we find that a long-read approach provides valuable insight toward characterizing the relationship between RNA variants and splicing patterns.