Early-stage Lung Cancer Research Articles

Impact of ground-glass component on prognosis in early-stage lung cancer treated with stereotactic body radiotherapy via Helical Tomotherapy

PurposeThis study aims to investigate the prognostic impact of ground-glass opacity (GGO)-component in early-stage lung cancer patients treated with stereotactic body radiotherapy (SBRT).MethodsFrom January 2013 to December 2022, 239 early-stage lung cancer patients (T1-2N0M0) underwent SBRT. They were categorized into two groups based on the presence of GGO-component: 65 patients in the subsolid group with a consolidation tumor ratio (CTR) between 0.25 and 1 and 174 patients in the solid group with a CTR of 1. Lung cancer-specific survival (LCSS) and progression-free survival (PFS) were analyzed using Cox regression models for both univariate and multivariate analyses to identify prognostic factors. Stabilized inverse probability of treatment weighting (IPTW) was employed for adjusting confounding factors. Recurrence incidence was assessed using competing risk analysis and compared using Gray’s test.ResultsIn the multivariate analysis, female, peripheral location, and subsolid nodules were favorable prognostic factors for LCSS; peripheral location, subsolid nodules, and adjuvant therapy were favorable prognostic factors for PFS. Between the subsolid (n = 65) and solid groups (n = 174), the median LCSS were not reached (p = 0.003), with 3-, 5-, and 9-year LCSS rates of 94.7% versus 80.3%, 90.9% versus 64.1%, 82.7% versus 53.5%, respectively. The median PFS were 72.5 months and 50.5 months (p = 0.030), with 3-, 5-, and 9-year PFS rates of 75.4% versus 61.2%, 56.6% versus 44.9%, 48.6% versus 23.3%, respectively. After stabilized IPTW (n = 240), the median LCSS were not reached (p = 0.024), with 3-, 5-, and 9-year LCSS rates of 94.0% versus 82.4%, 92.2% versus 67.7%, 85.3% versus 58.2%, respectively. The median PFS were 60.2 months and 50.5 months (p = 0.096), with 3-, 5-, and 9-year PFS rates of 73.8% versus 61.0%, 53.5% versus 46.2%, 46.8% versus 22.4%, respectively. The subsolid group had lower rates of locoregional recurrence (LRR) (10.4% vs. 25.9%, p = 0.035) and distant metastasis (DM) (17.1% vs. 37.9%, p = 0.064) compared to the solid group.ConclusionsThe presence of GGO-component in the lesion is an independent prognostic factor for LCSS and PFS. Subsolid nodules treated with SBRT demonstrated better prognosis, with significantly lower rates of local-regional recurrence. We should highlight GGO-component as a practical indicator for risk stratification of SBRT patients to guide treatment decisions.

Association of DNA methylation of RASSF1A and SHOX2 with lung cancer risk: A systematic review and meta-analysis.

This study estimates the research upon the potential worth of Ras association domain family member 1 A (RASSF1A) and short stature homeobox 2 (SHOX2) DNA methylation in lung cancer (LC) diagnosis. Open-published research was searched through PubMed, EMBASE, the Cochrane Library, Web of Science, China National Knowledge Infrastructure, and Chinese Biology Medicine Literature Database. Data on true positives, false positives, false negatives, and true negatives were extracted. This meta-analysis included 22 studies encompassing 4109 subjects (2427 LC patients and 1682 controls). The combined sensitivity, specificity, and area under the curve for RASSF1A and SHOX2 DNA methylation were 0.77 (95% CI: 0.71-0.81), 0.90 (95% CI: 0.87-0.92), and 0.92 (95% CI: 0.87-0.92), respectively. The pooled positive likelihood ratio and negative likelihood ratio were 7.5 (5.9-9.7) and 0.26 (0.21-0.32). The combined diagnostic odds ratio was 29 (95% CI: 20-41). RASSF1A and SHOX2 DNA methylation may emerge as potential diagnostic biomarkers for early-stage LC.

A latent profile analysis of spiritual well-being and their relation to perceived social support and hope in patients with early-stage lung cancer.

This study aimed to investigate the subtypes of spiritual well-being among patients with early-stage lung cancer, examine the characteristics of each subtype, and determine the relationship between spiritual well-being and perceived social support as well as hope within these subtypes. A sample of 418 patients with early-stage lung cancer from three hospitals in Hubei Province completed the general sociodemographic questionnaire, the Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being, the Perceived Social Support Scale, and the Herth Hope Index. Latent profile analysis (LPA) was used to identify the patterns of spiritual well-being in patients with early-stage lung cancer, and influencing factors of different patterns were identified by univariate analysis and multinomial logistic regression analysis. Three latent classes were identified: the "low spiritual well-being" (C1, 33.97%), the "moderate spiritual well-being" (C2, 38.28%), and the "high spiritual well-being" (C3, 27.75%). Factors such as nationality, religion, annual household disposable income, perceived social support scores, and hope scores influenced the potential categories of spiritual well-being in patients with early-stage lung cancer. The spiritual well-being of patients with early-stage lung cancer varies, necessitating customized spiritual care to meet the diverse needs of patients. Additionally, providing comprehensive social support and helping patients achieve positive states of spiritual well-being can promote better cancer treatment outcomes.

Non-invasive detection of early-stage lung cancer through exhaled breath volatile organic compound analysis

Non-invasive detection of early-stage lung cancer through exhaled breath volatile organic compound analysis

Conditional Survival of Patients With Early-Stage Non-Small Cell Lung Cancer Who Undergo Lobectomy, Segmentectomy, or Wedge Resection Using the NCDB.

Randomized clinical trials have shown that sub-lobar resection for clinical stage (cStage) IA non-small cell lung cancer (NSCLC) is non-inferior to lobar resection. We evaluated traditional and conditional survival differences between lobectomy, wedge resection, and segmentectomy in patients with cStage IA NSCLC. The National Cancer Database (2004-2019) was queried for patients with cStage IA (≤ 2 cm) NSCLC who underwent upfront lobectomy, segmentectomy, or wedge resection. Patients were stratified by extent of resection. Propensity-matched traditional (TSA) and conditional (CSA) survival analyses were performed. Propensity score included age, gender, histology, tumor grade, and Charlson-Deyo score. Number of lymph nodes (LN) harvested was also compared between groups. There were 46 395 patients who met the inclusion criteria, of whom 32 599 (70.3%) received lobectomy, 11 181 (24.1%) wedge resection, and 2615 (5.6%) segmentectomy. Following propensity matching, all groups contained 2615 patients. In the TSA, segmentectomy (hazard ratio [HR]: 1.19, 95% confidence interval [CI]: 1.08-1.32) and wedge resection (HR: 1.41, CI: 1.28-1.56) were associated with worse 5-year survival. This remained significant in CSA at 3- and 5-years post-resection in patients who underwent segmentectomy (HR: 1.24, CI: 1.08-1.43 and HR: 1.23, CI: 1.02-1.49, respectively) and wedge resection (HR: 1.42, CI: 1.24-1.63 and HR: 1.33, CI: 1.11-1.59, respectively). Wedge resection and segmentectomy were associated with a lower number of harvested LN (median = 4 and 6, respectively) compared to lobectomy (8, p < 0.001). Analysis of real-world data suggests that lobectomy is associated with improved traditional and conditional 5-year survival as well as LN harvest.

Interfacial cfDNA Enrichment and Amplification with On-Chip Thermoplasmonics for Highly Sensitive Cancerous Liquid Biopsy.

Tumor-derived cell-free DNA (cfDNA) has been exploited as an effective liquid biopsy biomarker for early cancer diagnosis. However, the fragmented and low-abundance nature in circulating blood pose challenges for highly sensitive cfDNA quantification. Herein, a multifunctional plasmonic biosensor termed Interfacial cfDNA Enrichment, Amplification and Sensing with on-chip Thermoplasmonics (INEAST) is developed for cfDNA-based liquid biopsy and lung cancer diagnosis. The INEAST biosensor achieved in situ thermoregulation and label-free cfDNA biosensing by simultaneously harnessing interfacial thermoplasmonics and localized surface plasmon resonance. Typical cfDNA biomarkers, including epidermal growth factor receptor (EGFR), tumor protein 53 (TP53), phosphatase and tensin homologue deleted on chromosome 10 (PTEN), and cyclin-dependent kinase inhibitor (CDKN2A), are quantified with detection limits down to femtomolar-level. Through further validation using blood samples from lung cancer patients, the proposed INEAST bioassays demonstrated superior reliability for lung cancer screening, particularly when combined with clinically available tumor-protein metrics. This study demonstrated that the INEAST biosensor enables rapid and sensitive cfDNA quantification, yielding a promising and compatible liquid biopsy for early-stage lung cancer diagnosis.

Preoperative fractional exhaled nitric oxide is a risk and predictive factor of postoperative cough for early-stage non-small cell lung cancer patients: a longitudinal study

BackgroundTo determine whether preoperative fractional exhaled nitric oxide (FENO) level is a risk and predictive factor of postoperative cough by using the Leicester Cough Questionnaire in Mandarin-Chinese (LCQ-MC).Methods292 early-stage non-small cell lung cancer (NSCLC) patients without preoperative cough were enrolled. 138 patients (47.2%) developed postoperative cough, univariate and multivariate logistic regression analysis were performed to identify the independent risk factors of postoperative cough. For an exploratory analysis, patients with cough were divided into low and high- FENO [≥ 31 parts per billion (ppb)] groups. The LCQ-CM was used to evaluate changes and recovery trajectory of postoperative cough over time between the two groups for 12 months after surgery.ResultsThe independent factors of postoperative cough included preoperative FENO level [odds ratio (OR) 1.106, 95% confidence interval (CI): 1.076–1.137, p < 0.001] and duration of anesthesia (OR 1.008, 95% CI: 1.002–1.013, p = 0.004). The low-FENO group reported significantly higher LCQ-MC scores at 1 month after surgery and returned to preoperative physical (28 vs. 91 days), psychological (28 vs. 60 days), social (28 vs. 80 days) and total (28 vs. 91 days) scores faster than the high-FENO group (all p < 0.05).ConclusionHigher preoperative FENO level and longer duration of anesthesia were independent risk factors related to postoperative cough. Additionally, patients with high preoperative FENO level had worse cough-related quality of life and slower recovery from postoperative cough.Trial RegistrationThis study was approved by the Chinese Clinical Trial Registry (Clinicaltrials.gov number: ChiCTR1900023419) on 26 May 2019 and the first patient was enrolled after pre-registration.

Image fusion for intraoperative evaluation of microwave ablation in non-small cell lung cancer: a retrospective cohort study.

Patients with early-stage non-small cell lung cancer (NSCLC) who are intolerant to surgery have a poor prognosis. Microwave ablation is an effective treatment method. However, the density of the lesion may occasionally be similar to that of the ablation zone, thus rendering it difficult to identify the relative position of the lesion and ablation zone during ablation. This study aimed to use image fusion technology to help confirm the relative position of lesions and ablation zones and to facilitate immediate assessment of the residual lesions. This retrospective study was conducted in Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, between August 2019 and August 2021, and consecutively included patients who had undergone computed tomography (CT)-guided percutaneous microwave ablation for NSCLC. A single radiologist manually fused the intraoperative CT images alignment of the lesions. Two other clinicians examined the postoperative CT images and the fused CT images to determine whether a lesion remained and to measure the minimum ablative margin (MAM). One of the clinicians assessed the complete ablation rate on CT images 6 months after ablation. The Chi-squared test was used to calculate the kappa coefficients between the two investigators. Binary logistic regression was used to analyze the correlation between each characteristic parameter of the lesion and image quality. The clinicians involved in this study all had over 10 years of work experience. Among the 29 participants, comprising 32 lesions retrospectively included (median age 65 years; 41.4% male), the image fusion success rate was 96.88%, and the median fusion time was10 minutes. The rate of accurate recognition for the relative position of pulmonary nodules and ablation zone on original CT images of the two physicians was 7.52% and 12.90%, respectively, while that achieved from the fused CT images was 87.1% and 86.0%, respectively; in agreement testing, the kappa coefficient between the physicians was 0.859 (P<0.001). The MAM on fused images was 3.05±1.35 mm (range, 1.1-6.7 mm). The quality of fused images was positively correlated with the CT values of focus (P=0.009). The complete ablation rate (93.5%) indicated by immediate image fusion post ablation was consistent with that determined by CT images after 6 months after ablation, with 100% sensitivity and specificity. Image fusion is a valuable strategy for evaluating whether intraoperative ground glass nodules are completely ablated by microwave ablation.

Stereotactic ablative radiotherapy (SABR) for patients with lung tumor and severe pulmonary function impairment.

To evaluate clinical outcomes after SABR in a cohort of early-stage non-small cell lung cancer (NSCLC) or pulmonary metastases in chronic obstructive pulmonary disease (COPD) patients with forced expiratory volume in the first second predicted (FEV1) ≤ 50%. Retrospective single-center study was performed to analyze clinical outcomes and toxicities in COPD patients with severe lung dysfunction treated with SABR from 1st June 2015 to 31st October 2022. Thirty four patients (forty locations) were enrolled for analysis. Median follow-up was 2.9years. Median age was 73.5years (range, 65.6-80.1). FEV1 was 38% (range, 28.2-50.0) prior to radiotherapy. Median overall survival (OS) was 41.1months (95% CI 38.9-not reached). OS rates at 2-, 3-, and 5- years were 79%, 71%, and 36%, respectively. Cancer-specific survival rates at 2-, 3-, and 5- years were 96%, 96%, and 68%, respectively. Local control rates at 2-, 3-, and 5- years were 88%, 83%, and 83%, respectively. No grade 4 or 5 toxicity was observed. The most common acute toxicity was pneumonitis (38.2%), of which only 1 patient (2.9%) reported grade 3 acute toxicity. Lung SABR in patients with poor pulmonary function may be effective with acceptable toxicity.

Accurate diagnosis of early-stage lung cancer by targeting lipid droplets with a two-photon near-infrared fluorescence probe

Clinically, solid nodules with adenocarcinoma (SN–ADC) exhibit faster growth and greater invasiveness than ground-glass nodules with adenocarcinoma (GGN-ADC). Metabolic reprogramming is a critical feature of tumor cells and may facilitate the rapid progression of early-stage lung cancer. Herein, we used laser microdissection to precisely isolate malignant regions from five SN–ADC and six GGN–ADC samples for proteomic analysis. Bioinformatic analysis, western blotting, and electron microscopy conducted in this study revealed a significant upregulation of lipid droplet-associated protein (PLIN2) and marked increase in lipid droplet (LD) accumulation in SN–ADC tissues. Consequently, a novel two-photon near-infrared fluorescence probe was developed to monitor the dynamic changes in LDs in lung cancer cells. Further, experimental validation confirmed the effectiveness of the LD probe (LD-probe) in monitoring the changes in LDs in tumor cells and distinguishing LD differences between tumor and normal tissues. In particular, the probe successfully detected significant differences in LD behavior between SN–ADC and GGN–ADC in early-stage lung cancer. Thus, this study developed a rapid and effective tool for the diagnosis of early-stage lung cancer. The proposed method offers deeper insight into the monitoring of the behavior of LDs in tumor cells, with potential clinical applications.

Non-Invasive Detection of Lung Cancer: Integrating Genomic Data with Ensemble Learning for Improved Early Diagnosis

The early and accurate diagnosis of lung cancer, a significant contributor to global cancer-related mortality, remains a paramount challenge in healthcare. Conventional diagnostic methods often lack the sensitivity required for early-stage detection, prompting the exploration of non-invasive alternatives. Leveraging advancements in genomics and bioinformatics, this study investigates the potential of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) analysis for early-stage lung cancer diagnosis. Two distinct datasets are utilized: GSE4115, comprising gene expression data from bronchial airway epithelial cells of smokers, and GSE33356, focusing on genomic alterations in Taiwanese female non-smoking lung cancer patients. The research employs comprehensive data pre-processing and feature reduction techniques, including normalization and Kernel Principal Component Analysis (KPCA). Subsequently, an ensemble of diverse learners, including Random Forests, AdaBoost, Bagging, Support Vector Machines (SVMs), and Neural Networks, is trained on the original datasets. A novel ensemble stacking approach is proposed, wherein initial predictions from the base learners are combined through logistic regression - the meta-learner to enhance predictive performance. The study aims to contribute to advancements in lung cancer detection by providing a more precise and non-invasive diagnostic method. By integrating DNA and RNA analysis with ensemble learning techniques, the research endeavours to enhance medical outcomes and potentially save lives through early-stage lung cancer detection.

Costs of stereotactic ablative radiotherapy compared to conventional radiotherapy in the treatment of non-small cell lung cancer – a micro-costing study using Time-Driven Activity Based Costing (TDABC)

BackgroundLung cancer is one of the leading causes of morbidity and mortality in Brazil. Radiotherapy is an important therapeutic option, but the techniques used remain subjects of discussion. In this study, we compared the costs of conventional radiotherapy (CRT) and stereotactic ablative radiotherapy (SABR) in the treatment of early-stage non-small cell lung cancer (NSCLC).MethodsThis cost analysis study adopted a micro-costing approach, following the TDABC (Time-Driven Activity-Based Costing) methodology. The study was conducted at a specialized public cancer hospital in São Paulo, Brazil. The analysis involved seven macro-processes related to radiotherapy treatment, identifying resources, costs, and time estimates for each step.ResultsThe cost analysis revealed that SABR treatment for NSCLC is significantly cheaper than CRT. The direct costs of SABR treatment ranged from $2,777.25 to $3,797.49, while CRT ranged from $5,562.65 to $6,052.94. The cost related to CRT treatment constituted more than 80% of the total costs, whereas in SABR, it ranges from 59 to 68%. Planning represented 9% to 10% of the cost in CRT, increasing to 22% to 30% in SABR.ConclusionsThe results highlight that SABR treatment is a cheaper option for early-stage NSCLC patients when compared to CRT. Furthermore, the increased time required for CRT treatment limits the number of patients who can be treated. These results may influence healthcare policies and the financing of the healthcare system, directly benefiting patients and promoting the efficient allocation of resources.

Social determinants of health and variability in treatment for patients with early-stage Non-Small Cell Lung Cancer.

In non-small cell lung cancer (NSCLC), social determinants of health (SDOHs) influence treatment, but SDOHs with geographic precision are infrequently used in real-world research due to privacy considerations. This research aims to characterize the influence of census-tract level SDOHs on treatment for stage I and IIa NSCLC. Patients diagnosed between 1/1/17 and 9/30/22 with stages I and IIa NSCLC in the Syapse Learning Health Network had their addresses geocoded and linked to five census tract-level indicators of SDOH (social vulnerability index (SVI), percent (%) housing burden, % broadband internet access, primary care shortage area, and rurality). Clinical and demographic characteristics were ascertained from medical records. Nested multinomial logistic regression models estimated associations between SDOHs and initial treatment using two-sided Wald tests. The collective statistical significance of SDOHs was assessed with a likelihood ratio test (LRT) comparing nested models. Descriptive statistics described time-to-treatment-initiation. Among 3595 patients, 58% were initially treated with surgery, 29% with radiation, and 12% with "other." Two SDOH variables were associated with increased relative risk ratios (RRR) for radiation therapy compared to surgery: living in primary care shortage areas (RRR 1.61, 95% CI: (1.23-2.10)) and living in non-metropolitan areas (RRR 1.45 (1.02-2.07)). The LRT suggested that the five SDOH variables collectively improved the treatment model. Further, patients in areas with high SVI, low internet access, and high housing-burden initiated treatment later. When using precise estimates of geospatial SDOHs, these measures were associated with treatment, and should be considered in analyses of cancer outcomes.

Multiplex digital profiling of DNA methylation heterogeneity for sensitive and cost-effective cancer detection in low-volume liquid biopsies.

Molecular alterations in cancerous tissues exhibit intercellular genetic and epigenetic heterogeneity, complicating the performance of diagnostic assays, particularly for early cancer detection. Conventional liquid biopsy methods have limited sensitivity and/or ability to assess epigenetic heterogeneity of rare epiallelic variants cost-effectively. We report an approach, named REM-DREAMing (Ratiometric-Encoded Multiplex Discrimination of Rare EpiAlleles by Melt), which leverages a digital microfluidic platform that incorporates a ratiometric fluorescence multiplex detection scheme and precise digital high-resolution melt analysis to enable low-cost, parallelized analysis of heterogeneous methylation patterns on a molecule-by-molecule basis for the detection of cancer in liquid biopsies. We applied the platform to simultaneously assess intermolecular epigenetic heterogeneity in five methylation biomarkers for improved, blood-based screening for early-stage non-small cell lung cancer. In a cohort of 48 low-volume liquid biopsy specimens from patients with indeterminant pulmonary nodules, we show that assessment of intermolecular methylation density distributions can notably improve the performance of multigene methylation biomarker panels for the early detection of cancer.

Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer

Liquid biopsies have the potential to revolutionize cancer care through non-invasive early detection of tumors. Developing a robust liquid biopsy test requires collecting high-dimensional data from a large number of blood samples across heterogeneous groups of patients. We propose that the generative capability of variational auto-encoders enables learning a robust and generalizable signature of blood-based biomarkers. In this study, we analyze orphan non-coding RNAs (oncRNAs) from serum samples of 1050 individuals diagnosed with non-small cell lung cancer (NSCLC) at various stages, as well as sex-, age-, and BMI-matched controls. We demonstrate that our multi-task generative AI model, Orion, surpasses commonly used methods in both overall performance and generalizability to held-out datasets. Orion achieves an overall sensitivity of 94% (95% CI: 87%–98%) at 87% (95% CI: 81%–93%) specificity for cancer detection across all stages, outperforming the sensitivity of other methods on held-out validation datasets by more than ~ 30%.

Drainless Uniportal VATS Wedge Resection for Early Non-Small Cell Lung Cancer: Propensity Analysis of the Effect of Polyglycolic Acid Sheet (NeoveilTM).

Objectives: Absorbable biomaterials as adjuvant therapy after thoracoscopy are sometimes used in clinical scenarios. With the prevalence of enhanced rapid recovery in thoracic surgery, drainless video-assisted thoracoscopy surgery (VATS) is often adopted by thoracic surgeons. Here, we discuss utilizing an absorbable biomaterial, NeoveilTM (Polyglycolic Acid sheet), for drainless VATS to treat early lung cancer. Methods: This single-center retrospective study was conducted from January 2018 to December 2022 at the National Taiwan University Hospital. We included patients who underwent drainless VATS for early-stage non-small cell lung cancer (NSCLC) in our institute. Propensity analysis was used to minimize selection bias. Outcome measurements were in-hospital stay, operation time, rate of thoracocentesis or chest drain re-insertion, complication rate, and perioperative course. Results: During the study period, 158 lung cancer patients were performed with drainless VATS wedge resection. Among them, Neoveil for stapling line coverage was done in 72 patients, while 86 patients did not receive Neoveil. After propensity analysis, we had 58 patients using Neoveil after drainless thoracoscopic lung resection, compared fairly with 58 patients without Neoveil after the same procedure. The basic characteristics are comparable regarding age, gender, BMI, operation methods, and lung cancer stage after propensity matching. The in-hospital stay (3.2 days in the Neoveil group and 5.6 days in the non-Neoveil group) and operation time (95.7 min in the Neoveil group and 59.3 min in the non-Neoveil group) are significantly different (p = 0.0001). One versus four patients was noted for postoperative conversion chest drainage insertion in each group (p = 0.17). Neither late complications nor recurrence/metastasis occurred in both groups during the following. Conclusions: Based on our 5-year retrospective study, which is balanced with propensity analysis, drainless thoracoscopic surgery treating early lung cancer can be enhanced by Neoveil with faster recovery by reducing the hospital stay, though with longer operation time.

ASO Author Reflections: Surgical Procedures for Early-Stage Lung Cancer Patients with Low Muscle Mass.

ASO Author Reflections: Surgical Procedures for Early-Stage Lung Cancer Patients with Low Muscle Mass.

Abstract IA021: Plasma-only ultrasensitive liquid biopsy to monitor tumor burden and clonal evolution

Abstract Individual bulk tumor biopsies are invasive and may fail to capture clonal heterogeneity within primary tumors and metastatic sites of disease. Further, analyses of subclonal variants are often hindered by background error rates related to tissue preservation methods (e.g. FFPE) and sequencing artifact. Use of plasma-only ultrasensitive liquid biopsy approaches may allow for noninvasive inference of clonality and dynamically track changes in tumor burden. We previously developed MRD-EDGE, a tumor-informed machine learning classifier for ultrasensitive liquid biopsy monitoring of single nucleotide variants (SNVs) and copy number variants (CNVs) using standard plasma whole genome sequencing. For SNVs, MRD- EDGE uses a novel machine learning framework to classify individual cell free DNA (cfDNA) fragments as circulating tumor DNA or cfDNA sequencing artifact. We demonstrated the potential of plasma-only MRD-EDGE to identify SNVs in advanced melanoma and monitor tumor burden throughout treatment with immune checkpoint inhibiton. In tumor-informed settings, MRD-EDGE was used to identify minimal residual disease at tumor fractions (TFs) of 1:100,000 and below. Accurate SNV classification is a function of classifier performance and background error rate. To reduce error rates, we combined MRD-EDGE SNV with paired plus- minus sequencing (ppmSeq) from Ultima Genomics. ppmSeq is a PCR-free approach that leverages the hydrogen bonds of complementary DNA to carry matched native strands directly into sequencing, enabling scalable duplex sequencing. Use of MRD-EDGE SNV together with ppmSeq enables ultrasensitive tracking of early-stage non-small cell lung cancer throughout neoadjuvant immunotherapy. Plasma-only MRD-EDGE is therefore poised to enable dynamic monitoring of tumor burden and clonal composition outside the scope of tumor-informed approaches, including the comparison of SNV composition in cancer recurrence with preoperative disease. Citation Format: Adam Widman, Alexandre P. Cheng, John Zinno, Srinivas Rajagopalan, Ashish Saxena, Nasser Altorki, Dan A. Landau. Plasma-only ultrasensitive liquid biopsy to monitor tumor burden and clonal evolution [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr IA021.

Clinical characteristics and treatment patterns in early-stage resected non-small cell lung cancer patients: a descriptive retrospective study

Clinical characteristics and treatment patterns in early-stage resected non-small cell lung cancer patients: a descriptive retrospective study

Abstract B058: Novel machine learning lung cancer classifier shows significant correlation with cancer-specific fragmentomics features

Abstract Lung cancer is the second most prevalent cancer, and the leading cause of cancer-related deaths, in the US. Since the survival rate of patients diagnosed with lung cancer at earlier stages is dramatically higher than those diagnosed at later stages, technologies that shift lung cancer diagnosis to an earlier stage would be expected to improve survival rates. Fragmentomics, the study of short circulating cell-free DNA (cfDNA) fragments in the blood, is an emerging field in cancer diagnostics that has the potential to catalyze such a shift. Studies have shown that fragmentomics features, such as fragment sizes and end motifs, can be used to discriminate normal cfDNA from circulating tumor DNA (ctDNA) generated through tumor necrosis and apoptosis. Using this knowledge, machine learning tools, trained to identify ctDNA-specific features, are being leveraged in novel liquid biopsy assays to improve early-stage lung cancer detection. To create a machine learning classifier that detects discrete signals of ctDNA in individuals with lung cancer, Genece Health utilized low pass (∼3x coverage) whole genome sequencing (Illumina) of plasma extracted cfDNA from &amp;gt;400 lung cancer samples and &amp;gt;1,000 negative controls. This data was used to generate our novel FEMS (Fragment End Motifs and Sizes) dataframe, containing &amp;gt;30,000 features, by combining fragment lengths with their corresponding 5’ 4bp end motifs. The FEMS dataframe was then used to train a neural network machine learning classifier. To evaluate the performance of our machine learning classifier, we investigated whether the classifier scores correlated with well-known hallmarks of ctDNA. First, consistent with published ctDNA characteristics, we observed significant (p&amp;lt;0.01) enrichment of fragment sizes ≤167bp in lung cancer samples. Concordantly, our classifier scores are significantly correlated with this fragment size enrichment. Also, both fragment size enrichment and our classifier scores are correlated with lung cancer stage, with higher enrichment and higher scores observed in later stages. Next, we show that our classifier scores are correlated with specific end motif enrichments or depletions in lung cancer samples. For example, in lung cancer samples, CCCA end motifs were significantly enriched (p&amp;lt;0.01) in fragment sizes 120-140bp (max at 128bp), while significantly depleted in fragment sizes 190-210bp (min at 204bp). Once again, our classifier scores were significantly correlated with both the enrichment of this end motif in short fragments and its depletion in long fragments. Last, we assessed the classifier’s performance in an independent cohort (46 lung cancer and 132 non-cancer samples) and show 89.2% sensitivity with 90.4% specificity (auROC=0.957). The correlation observed between our classifier scores and published ctDNA characteristics, combined with the strong performance in an independent cohort, suggests that Genece’s novel machine learning classifier is effectively leveraging ctDNA-specific fragmentomics features for the identification of lung cancer in patient samples. Citation Format: Carlos Guzman, Michael L Salmans, Mengchi Wang, Byung In Lee, Andrew R Carson. Novel machine learning lung cancer classifier shows significant correlation with cancer-specific fragmentomics features [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B058.