Lung Cancer Cohort Research Articles

A phase 1 dose escalation/expansion study of GSK5764227 (GSK’227), a B7-homolog 3 (B7-H3) protein targeted antibody-drug conjugate (ADC), in patients with advanced solid tumors, including gastrointestinal (GI) cancers.

TPS847 Background: B7-H3 is an immune checkpoint protein overexpressed in multiple solid tumors with limited expression in normal tissues. GSK’227 (HS-20093), a novel B7-H3-targeted ADC, is composed of a human anti–B7-H3 monoclonal antibody linked to a topoisomerase I inhibitor via a protease-cleavable linker, and has shown acceptable safety and promising antitumor activity in patients of Asian origin with advanced solid tumors (NCT05276609; NCT05830123). The current study will evaluate the safety, tolerability, efficacy, and pharmacokinetics (PK) of GSK’227 in patients with solid tumors, including GI cancers, in a global population. Methods: This two-part (dose-escalation [1a] and expansion [1b]) global, open-label, Phase I study (NCT06551142) will enroll ~260 patients, with enrollment currently ongoing. Key eligibility includes: aged ≥18 years, histologically confirmed advanced solid tumors, including those with colorectal cancer, esophageal squamous cell carcinoma, and pancreatic cancer, with measurable disease per Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1), Eastern Cooperative Oncology Group (ECOG) performance status of 0–1, and no prior B7-H3 treatment. Eligible patients will receive intravenous GSK’227 every 3 weeks (Q3W) until progression, toxicity, loss to follow-up, or death. For Phase 1a, a Bayesian optimal interval design will be used to determine the maximum tolerated dose. Phase 1a primary endpoints are safety and tolerability, including incidences of adverse events (AEs) and serious AEs. Secondary endpoints include objective response rate (ORR), disease control rate, duration of response, immunogenicity, and PK. The Phase 1b primary endpoint is progression-free survival (extensive-stage small cell lung cancer cohort) or ORR (GI and other solid tumors cohort). Secondary endpoints include additional efficacy assessments, PK, safety and tolerability. For Phase 1a and 1b, efficacy will be assessed per RECIST v1.1, with imaging conducted Q6W from first dose then Q12W after 24 weeks. Safety follow-up will be conducted at 30, 60, and 90 (±7) days after the last dose. Safety, tolerability, and efficacy analyses will be conducted using descriptive statistics and, for efficacy analyses, point estimates with 2-sided 95% confidence intervals. This abstract was previously submitted to the European Society for Medical Oncology (ESMO) Immuno-Oncology Congress 2024 and is submitted on behalf of the original authors with their permission. Funding: GSK (Study 223054). Medical writing support was provided by Avalere Health, funded by GSK. Clinical trial information: NCT05830123 .

Drivers and barriers to health-seeking behaviors and interactions: a qualitative study of Black patients with lung cancer and with peripheral artery disease

Objective To identify factors that may influence health-seeking behaviors and health system interactions from the perspective of Black patients with lung cancer (LC) or peripheral artery disease (PAD). Methods Semi-structured interviews were conducted virtually with Black patients in the United States. Thematic analysis of interview transcripts was performed. The Sense-Think-ACT-Relate (STAR) behavioral framework was used to map emerging themes of drivers and barriers to health-seeking behaviors and health system interactions. Results Thirty Black patients with LC (n = 15) and PAD (n = 15) participated in this study. The mean age of participants was 53.4 years, 22 were female, and half lived in an urban area or large city. Factors that shape health-seeking behaviors spanned several framework domains including Trust, Rational and Emotional Associations, Cultural, and Situational. Having a provider who was friendly, knowledgeable, and understood the patient’s lived experience was a key driver to seeking care. Barriers to care included patients not recognizing disease symptoms, reservations about seeking care, having previous negative interactions with healthcare systems, and feeling stigmatized or excluded. Situational influences, such as financial cost of accessing and receiving healthcare, also acted as barriers. Similar themes emerged for the LC versus PAD cohorts. Conclusion Multiple drivers and barriers to health-seeking behaviors exist for Black patients with LC versus PAD, including patients’ perceptions of previous health system interactions. Because of known inequities experienced by Black patients, these results highlight the need for interventions that address more than just medical needs but that also encourage patients to seek care when they experience early symptoms and prioritize establishing patient-provider relationships built on trust, respect, and cultural understanding.

Association of radiation-induced normal tissue toxicity with a high genetic risk for rheumatoid arthritis.

Overlapping genes are involved with rheumatoid arthritis (RA) and DNA repair pathways. Therefore, we hypothesised that patients with a high polygenic risk score (PRS) for RA will have an increased risk of radiotherapy (RT) toxicity given the involvement of DNA repair. Primary analysis was performed on 1494 prostate cancer, 483 lung cancer and 1820 breast cancer patients assessed for development of RT toxicity in the REQUITE study. Validation cohorts were available from the Radiogenomics Consortium. All patients had undergone curative-intent radiotherapy and were assessed prospectively for toxicity. Germline genomic data was available for all patients, allowing a PRS to be calculated using 101 RA risk variants. PRS was analysed as a continuous variable and with >90th percentile cut-off. Associations with acute and late standardised total average toxicity (STAT) scores and individual toxicity end-points were analysed in multivariable models with preselected adjustment variables. Increasing PRS for RA did not increase the risk of acute or late STAT in any cohort. There was an increased risk of late oesophagitis in the lung cancer cohort (coefficient 0.018, p = .01), however this was not validated (p = .79). No individual acute or late toxicity endpoints were significantly associated with PRS for the prostate or breast cohorts. No significant results were found in the validation cohorts in multivariable models. Patients with a high genetic risk for RA do not show increased levels of toxicity after radiotherapy suggesting treatment planning does not need to be modified for such patients.

Assessing the feasibility and external validity of natural language processing-extracted data for advanced lung cancer patients.

Manual extraction of real-world clinical data for research can be time-consuming and prone to error. We assessed the feasibility of using natural language processing (NLP), an AI technique, to automate data extraction for patients with advanced lung cancer (aLC). We assessed the external validity of our NLP-extracted data by comparing our findings to those reported in the literature. Patients diagnosed with stage IIIB or IV lung cancer between January 2015 to December 2017 at Princess Margaret Cancer Centre who received at least one dose of systemic therapy were included. Their electronic health records were provided to Pentavere's NLP platform, DARWENTM, in March 2019. Descriptive statistics summarized baseline patient and cancer characteristics, molecular biomarkers, and first-line systemic therapies. Cox multivariate models were used to evaluate prognostic factors for advanced non-small cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) cohort. NLP extracted clinical information (n=333 patients) in a total of 8hours, with only a few missing data for smoking status (n=2), and Eastern Cooperative Oncology Group (ECOG) status (n=5). Baseline patient and cancer characteristics summarized from NLP-extracted data were comparable to those in previous studies and population reports. For NSCLC patients, being male (HR 1.44, 95% CI [1.04, 2.00]), having worse ECOG (1.48 [1.22, 1.81]), and having liver (2.24 [1.45, 3.46]), bone (2.09 [1.48, 2.96]), or lung metastases (2.54 [1.05, 2.26]) were associated with worse survival outcomes. For SCLC patients, having older age (HR 1.70 per 10years, 95% CI [1.10, 2.63]) and liver metastases (3.81 [1.61, 9.01]) were associated with worse survival outcomes. Our study demonstrated that automated data extraction using NLP is feasible and time efficient. Additionally, the NLP-extracted data can be used to identify valid and useful clinical endpoints for research. NLP holds significant potential to accelerate the extraction of real-world data for future observational studies.

TRACERx analysis identifies a role for FAT1 in regulating chromosomal instability and whole-genome doubling via Hippo signalling

Chromosomal instability (CIN) is common in solid tumours and fuels evolutionary adaptation and poor prognosis by increasing intratumour heterogeneity. Systematic characterization of driver events in the TRACERx non-small-cell lung cancer (NSCLC) cohort identified that genetic alterations in six genes, including FAT1, result in homologous recombination (HR) repair deficiencies and CIN. Using orthogonal genetic and experimental approaches, we demonstrate that FAT1 alterations are positively selected before genome doubling and associated with HR deficiency. FAT1 ablation causes persistent replication stress, an elevated mitotic failure rate, nuclear deformation and elevated structural CIN, including chromosome translocations and radial chromosomes. FAT1 loss contributes to whole-genome doubling (a form of numerical CIN) through the dysregulation of YAP1. Co-depletion of YAP1 partially rescues numerical CIN caused by FAT1 loss but does not relieve HR deficiencies, nor structural CIN. Importantly, overexpression of constitutively active YAP15SA is sufficient to induce numerical CIN. Taken together, we show that FAT1 loss in NSCLC attenuates HR and exacerbates CIN through two distinct downstream mechanisms, leading to increased tumour heterogeneity.

Oral microbiota as a biomarker for predicting the risk of malignancy in indeterminate pulmonary nodules: a prospective multicenter study.

Determining the benign or malignant status of indeterminate pulmonary nodules (IPN) with intermediate malignancy risk is a significant clinical challenge. Oral microbiota-lung cancer interactions have qualified oral microbiota as a promising non-invasive predictive biomarker in IPN. Prospectively collected saliva, throat swabs, and tongue coating samples from 1040 IPN patients and 70 healthy controls across three hospitals. Following up, the IPNs were diagnosed as benign (BPN) or malignant pulmonary nodules (MPN). Through 16S rRNA sequencing, bioinformatics analysis, fluorescence in situ hybridization (FISH), and seven machine learning algorithms (support vector machine, logistic regression, naïve bayes, multi-layer perceptron, random forest, gradient-boosting decision tree, and LightGBM), we revealed the oral microbiota characteristics at different stages of HC-BPN-MPN, identified the sample types with the highest predictive potential, constructed and evaluated the optimal MPN prediction model for predictive efficacy, and determined microbial biomarkers. Additionally, based on the SHAP algorithm interpretation of the ML model's output, we have developed a visualized IPN risk prediction system on the web. Saliva, tongue coating, and throat swab microbiotas exhibit site-specific characteristics, with saliva microbiota being the optimal sample type for disease prediction. The saliva-LightGBM model demonstrated the best predictive performance (AUC=0.887, 95%CI: 0.865-0.918), and identified Actinomyces, Rothia, Streptococcus, Prevotella, Porphyromonas, and Veillonella as biomarkers for predicting MPN. FISH was used to confirm the presence of a microbiota within tumors, and external data from a lung cancer cohort, along with three non-IPN disease cohorts, were employed to validate the specificity of the microbial biomarkers. Notably, coabundance analysis of the ecological network revealed that microbial biomarkers exhibit richer interspecies connections within the MPN, which may contribute to the pathogenesis of MPN. This study presents a new predictive strategy for the clinic to determine MPNs from BPNs, which aids in the surgical decision-making for IPN.

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.

Metabolic pathways from the gut metatranscriptome are associated with COPD and respiratory function in lung cancer patients.

Changes in the human gut microbiome have been linked to various chronic diseases, including chronic obstructive pulmonary disease (COPD). While substantial knowledge is available on the genomic features of fecal communities, little is known about the microbiome's transcriptional activity. Here, we analyzed the metatranscriptomic (MTR) abundance of MetaCyc pathways, SuperPathways, and protein domain families (PFAM) represented by the gut microbiome in a cohort of non-small cell lung cancer (NSCLC) patients with- or without COPD comorbidity. Fecal samples of 40 NSCLC patients with- or without COPD comorbidity were collected at the time of diagnosis. Data was preprocessed using the Metaphlan3/Humann3 pipeline and BioCyc© to identify metabolic SuperPathways. LEfSe analysis was conducted on Pathway- and PFAM abundance data to determine COPD- and non-COPD-related clusters. Key genera Streptococcus, Escherichia, Gemella, and Lactobacillus were significantly more active transcriptionally compared to their metagenomic presence. LEfSe analysis identified 11 MetaCyc pathways that were significantly overrepresented in patients with- and without COPD comorbidity. According to Spearman's rank correlation, Smoking PY showed a significant negative correlation with Glycolysis IV, Purine Ribonucleoside Degradation and Glycogen Biosynthesis I, and a significant positive correlation with Superpathway of Ac-CoA Biosynthesis and Glyoxylate cycle, whereas forced expiratory volume in the first second (FEV1) showed a significant negative correlation with Glycolysis IV and a significant positive correlation with Glycogen Biosynthesis I. Furthermore, COPD patients showed a significantly increased MTR abundance in ~60% of SuperPathways, indicating a universally increased MTR activity in this condition. FEV1 showed a significant correlation with SuperPathways Carbohydrate degradation, Glycan biosynthesis, and Glycolysis. Taxonomic analysis suggested a more prominent MTR activity from multiple Streptococcus species, Enterococcus (E.) faecalis, E. faecium and Escherichia (E.) coli than expected from their metagenomic abundance. Multiple protein domain families (PFAMs) were identified as more associated with COPD, E. faecium, E.coli, and Streptococcus salivarius, contributing the most to these PFAMs. Metatranscriptome analysis identified COPD-related subsets of lung cancer with potential therapeutic relevance.

Abstract 4136755: Associations of Pulmonary Hypertension and Lung Cancer: A Prospective Cohort Study of Half a Million UK Biobank Participants

Background: Pulmonary hypertension (PH) and lung cancer (LC) are both significant public health issues with substantial morbidity and mortality. Recent studies suggest potential associations between pulmonary vascular diseases and LC due to shared risk factors and pathophysiological mechanisms, yet comprehensive research in large populations is scarce. Methods: This prospective cohort study utilized data from the UK Biobank, with baseline assessment between 2006 and 2010 and follow-up until November 30, 2023, for England and Scotland, and May 31, 2022, for Wales. We included 6,425 participants with LC, identified using linked cancer registry data, and followed them for the incidence of PH over a period of 10 years. We adjusted for potential confounders including age, ethnics, sex, smoking status, BMI, and histologic subtypes of LC. Statistical analyses were performed using Time-dependent Cox proportional hazards models to estimate the associations between PH and LC. Results: Of 502,173 participants in this prospective cohort, 6,425 were diagnosed with LC, while 494,451 did not receive the LC diagnosis. The prevalence of PH in the LC cohort and the no-LC cohort were 0.64% (1,494 of 3,142 women [47.55%]) and 2.40% (59 of 154 women [38.31%]), respectively, with significant difference between the two groups (p<0.001). In the LC cohort, the mortality rates of patients without PH and with PH were 69.18% and 77.92% (p=0.020). Among the patients diagnosed with LC and PH, 33.12% were diagnosed with LC after PH (PH-LC), while 66.88% were diagnosed with LC before PH (LC-PH). And the mortality rates of the PH-LC group and the LC-PH group were 78.43% and 77.67%, respectively, with no significant difference between the two groups (p=0.915). Time-dependent Cox regression analysis showed that PH shows an increasing trend in risk (5th year HR: 3.22, 95% CI: 2.09-4.99, p-value < 0.001), highlighting its significant impact on LC long-term prognosis. Conclusion: To conclude, the presence of PH significantly increased the risk of mortality in participants with LC, especially for the long-term LC survivors. And certain percentage of patients were diagnosed with LC before PH, but they had no significant difference between the patients with LC after PH.

Sitravatinib in patients with solid tumors selected by molecular alterations: results from a Phase Ib study.

Aim: We report clinical activity and safety of sitravatinib in patients with advanced cancer from basket cohorts with specific molecular alterations, in a Phase Ib study.Materials & methods: Patients with advanced solid tumors harboring amplification, mutation, or rearrangement of MET, AXL, RET, NTRK, DDR2, KDR, PDGFRA, KIT or CBL received sitravatinib once daily. Primary end point was confirmed objective response rate (ORR).Results: In total, 113 patients were enrolled following a median of 3 (range 1-18) prior systemic regimens. Altered RET (n = 31), CBL (n = 31) and MET (n = 17) were most frequent cohorts. Overall, 68.9% had reduced tumor volume and most (61.5%) had a best objective response of stable disease. ORR was highest in patients with RET-rearranged non-small cell lung cancer (21.1%) but did not differ significantly from the null hypothesis (ORR ≤15%; p = 0.316). Median progression-free survival and overall survival (5.7 and 24.2 months, respectively) were also longest in the RET-rearranged non-small cell lung cancer cohort. Diarrhea (61.1%), fatigue (50.4%) and hypertension (46.9%) were the most frequent treatment-emergent adverse events. Most treatment-emergent adverse events were mild-to-moderate in severity. The study closed before the planned number of patients were enrolled in all cohorts.Conclusion: Sitravatinib had a manageable safety profile with modest signals of clinical activity in patients with molecularly selected solid tumors.Clinical trial registration: www.clinicaltrials.gov identifier is NCT02219711.

1366P Natural Language Processing (NLP) as promising artificial intelligence (AI) tool to improve patients (pts) enrollment in clinical trials (CT): Analysis in real-world conditions on a lung cancer cohort

1366P Natural Language Processing (NLP) as promising artificial intelligence (AI) tool to improve patients (pts) enrollment in clinical trials (CT): Analysis in real-world conditions on a lung cancer cohort

23P Germline testing in a selected cohort of non-small cell lung cancer (NSCLC) patients: Final results from the INHERITY LC study

23P Germline testing in a selected cohort of non-small cell lung cancer (NSCLC) patients: Final results from the INHERITY LC study

Evaluation of risk prediction models to select lung cancer screening participants in Europe: a prospective cohort consortium analysis

Lung cancer risk prediction models might efficiently identify individuals who should be offered lung cancer screening. However, their performance has not been comprehensively evaluated in Europe. We aimed to externally validate and evaluate the performance of several risk prediction models that predict lung cancer incidence or mortality in prospective European cohorts. We analysed 240 137 participants aged 45-80 years with a current or former smoking history from nine European countries in four prospective cohorts from the pooled database of the Lung Cancer Cohort Consortium: the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (Finland), the Nord-Trøndelag Health Study (Norway), CONSTANCES (France), and the European Prospective Investigation into Cancer and Nutrition (Denmark, Germany, Italy, Spain, Sweden, the Netherlands, and Norway). We evaluated ten lung cancer risk models, which comprised the Bach, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 model (PLCOm2012), the Lung Cancer Risk Assessment Tool (LCRAT), the Lung Cancer Death Risk Assessment Tool (LCDRAT), the Nord-Trøndelag Health Study (HUNT), the Optimized Early Warning Model for Lung Cancer Risk (OWL), the University College London-Death (UCLD), the University College London-Incidence (UCLI), the Liverpool Lung Project version 2 (LLP version 2), and the Liverpool Lung Project version 3 (LLP version 3) models. We quantified model calibration as the ratio of expected to observed cases or deaths and discrimination using the area under the receiver operating characteristic curve (AUC). For each model, we also identified risk thresholds that would screen the same number of individuals as each of the US Preventive Services Task Force 2021 (USPSTF-2021), the US Preventive Services Task Force 2013 (USPSTF-2013), and the Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) criteria. Among the participants, 1734 lung cancer cases and 1072 lung cancer deaths occurred within five years of enrolment. Most models had reasonable calibration in most countries, although the LLP version 2 overpredicted risk by more than 50% in eight countries (expected to observed ≥1·50). The PLCOm2012, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI models showed similar discrimination in most countries, with AUCs ranging from 0·68 (95% CI 0·59-0·77) to 0·83 (0·78-0·89), whereas the LLP version 2 and LLP version 3 showed lower discrimination, with AUCs ranging from 0·64 (95% CI 0·57-0·72) to 0·78 (0·74-0·83). When pooling data from all countries (but excluding the HUNT cohort), 33·9% (73 313 of 216 387) of individuals were eligible by USPSTF-2021 criteria, which included 74·8% (1185) of lung cancers and 76·3% (730) of lung cancer deaths occurring over 5 years. Fewer individuals were selected by USPSTF-2013 and NELSON criteria. After applying thresholds to select a population of equal size to USPSTF-2021, the PLCOm2012, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI, models identified 77·6%-79·1% of future cases, although they selected slightly older individuals compared with USPSTF-2021 criteria. Results were similar for USPSTF-2013 and NELSON. Several lung cancer risk prediction models showed good performance in European countries and might improve the efficiency of lung cancer screening if used in place of categorical eligibility criteria. US National Cancer Institute, l'Institut National du Cancer, Cancer Research UK.

Temporal Characterization and Visualization of Revolving Therapy-Events in Lung Cancer Patients.

This paper presents a comprehensive workflow for integrating revolving events into the transitive sequential pattern mining (tSPM+) algorithm and Machine Learning for Health Outcomes (MLHO) framework, emphasizing best practices and pitfalls in its application. We emphasize feature engineering and visualization techniques, demonstrating their efficacy in capturing temporal relationships. Applied to an EGFR lung cancer cohort, our approach showcases reliable temporal insights even in a small dataset. This work highlights the importance of temporal nuances in healthcare data analysis, paving the way for improved disease understanding and patient care.

CT Derived Measurement of Body Composition: Observations from a Comparative Analysis of Patients with Colorectal and Lung Cancer

Background CT-derived measures of body composition have been shown to have prognostic value in patients with cancer. However, few studies have compared these observations across tumor types and stages of disease. The aim of the present study was to compare body composition measures between two types of cancers, i.e. colorectal cancer (CRC), which is less inflammatory and patients maintain body composition over a longitudinal study period, whereas lung cancer (LC) is proinflammatory and patients lose more fat and muscle mass using a standard methodology. Methods Clinicopathological characteristics, including those pertaining to nutritional risk/status and systemic inflammation in patients with colorectal cancer (CRC, n = 1047) and lung cancer (LC, n = 662), were compared. The CT image at L3 was used to assess body composition. Comparison of these cohorts was carried out using the chi-square test. Binary logistic regression analysis was performed to assess the impact of clinico-pathological variables on body composition, and scatter plots were used to examine the relationship between body mass index (BMI) and CT-derived measures of body composition. Results According to CT-derived body composition, high subcutaneous (SFI) and visceral fat index (VFI) were common (>70%) in both CRC and LC. Also, low skeletal muscle index (SMI) and density (SMD) were approximately 40–50% and 60–70% in both CRC and LC. Compared with CRC, patients with LC had a higher American Society of Anaesthesia (ASA) (P < 0.001), Malnutrition Universal Screening Tool (MUST) (P < 0.001), modified frailty index (mFI) (P < 0.001), modified Glasgow Prognostic Score (mGPS) (P < 0.001), and neutrophil lymphocyte ratio (NLR) (P < 0.001) scores. On binary logistic regression analysis, MUST, mFI, and NLR were predictors of subcutaneous adiposity (P < 0.05); type of cancer, MUST, and mFI were predictors of visceral obesity (P < 0.001); age, type of cancer, MUST, and mGPS were predictors of low SMI (P < 0.001); and age, type of cancer, mFI, and mGPS were predictors of low SMD (P < 0.05). There was a similar relationship between BMI and other measures of CT-derived body composition across two types of cancers. Conclusion Obesity and low skeletal muscle mass were common in both CRC and LC cohorts despite large differences in comorbidity, nutritional risk, systemic inflammation, and survival, even when normalized for TNM stage. These observations would support the hypothesis that, although prognostic, CT derived body composition analysis primarily reflects patient constitution rather than the effect of tumor stage in patients with cancer. The systemic inflammatory response, as evidenced by mGPS, can be considered as an important therapeutic target and loss of muscle mass in patients with advanced cancer is related to the systemic inflammatory response.

Association of TP53 Mutation Status and Sex with Clinical Outcome in Non-Small Cell Lung Cancer Treated with Immune Checkpoint Inhibitors: A Retrospective Cohort Study.

Some studies suggest that TP53 mutations are associated with the response to immune checkpoint inhibitors (ICI) in patients with non-small cell lung cancer (NSCLC) and also contribute to sex disparities in several cancers. Thus, we hypothesized that TP53 mutations might serve as sex-dependent genomic biomarkers of ICI treatment response in patients with NSCLC. Clinical data of 100 patients with metastatic NSCLC treated with ICI monotherapy at Seoul National University Bundang Hospital (SNUBH) were retrospectively reviewed. Genomic and clinical datasets of The Cancer Genome Atlas and an ICI-treated lung cancer cohort (cBioPortal) were also analyzed. In SNUBH cohort, no statistically significant difference was observed in the median progression-free survival (PFS) according to TP53 mutation status (p=0.930); however, female patients with TP53 mutations (MT) had a significantly prolonged median PFS compared to wild-type (WT) (6.1 months in TP53 MT vs. 2.6 months in TP53 WT; p=0.021). Programmed death-ligand 1 (PD-L1) high (≥ 50%) expression was significantly enriched in female patients with TP53 MT (p=0.005). The analysis from publicly available dataset also revealed that females with NSCLC with TP53 MT showed significantly longer PFS than those with TP53 WT (p < 0.001). In The Cancer Genome Atlas analysis, expression of immune-related genes, and tumor mutation burden score in TP53 MT females were higher than in males without TP53 MT. Female patients with NSCLC with TP53 mutations had high PD-L1 expression and showed favorable clinical outcomes following ICI therapy, suggesting a need for further research to explore the role of TP53 mutations for sex disparities in response to ICI therapy.

Epithelium/imcDC2 axis facilitates the resistance of neoadjuvant anti-PD-1 in human NSCLC

BackgroundTherapeutic resistance is a main obstacle to achieve long-term benefits from immune checkpoint inhibitors. The underlying mechanism of neoadjuvant anti-PD-1 resistance remains unclear.MethodsMulti-omics analysis, including mass cytometry, single-cell RNA-seq, bulk...

Topotecan in a Real-World Small-Cell Lung Cancer Cohort: Prognostic Biomarkers Improve Selection of Patients for Second-Line Treatment.

Small-cell lung cancer (SCLC) is a highly aggressive tumor, and overall survival (OS) remains poor despite intensive efforts to develop new treatment strategies. In second line, topotecan is the only approved drug, with a median OS of 5.9 months. However, real-world SCLC patients are often in worse condition and harbor more comorbidities than study populations. Therefore, the real-world performance of topotecan may differ from that seen in studies. Here, we analyzed outcomes of SCLC patients receiving topotecan and identified predictive and prognostic markers. We retrospectively analyzed 44 consecutive SCLC patients receiving topotecan between 2015 and 2022. We analyzed baseline characteristics (age, ECOG-PS, topotecan cycles, and dosage) and pre-treatment blood values (LDH, CRP, sodium) as well as prognostic scores (neutrophil/lymphocyte ratio (NLR), thrombocyte/lymphocyte ratio (TLR), Glasgow Prognostic Score, prognostic nutritional score, systemic inflammation index (SII), and the prognostic index) extracted from electronic patients' charts to identify predictive and prognostic markers. In our cohort, mPFS and mOS were only 1.9 and 5.6 months, respectively. Gender, ECOG-PS, active brain metastases, NLR, GPS, PNI, and SII significantly influenced PFS and OS in univariate analysis. ECOG-PS (p > 0.001), active brain metastases (p = 0.001), and SII (p = 0.008) were significant independent prognostic variables in a multivariate COX regression model. Selecting patients by these three markers achieved an mPFS of 5.7 months and thus increased the mPFS three-fold. Patients not meeting all criteria had an mPFS of 1.8 months (p = 0.006). Patients identified by prognostic markers had an mOS of 9.1 months (p = 0.002). The efficacy of topotecan in SCLC real-world patients is poor, indicating that many patients were treated without any benefit. Easy-to-obtain markers can predict response and treatment efficacy and should therefore be validated in larger cohorts to identify patients who are more likely to benefit from topotecan.

Next-generation sequencing as a valuable tool for mutational spectrum in advanced-stage NSCLC patients.

Lung cancer remains one of the most threatening malignancies, ranking as the second most diagnosed cancer, and it continues to be the leading cause of cancer-related deaths worldwide. Challenges persist with late diagnosis and the high mutational burden characteristic of lung cancer. Our study focuses on identifying the mutational spectrum of a cohort of advanced-stage non-small cell lung cancer (NSCLC) patients using a minimally invasive method through blood collection. To analyze the mutational landscape of these patients, we employed plasma DNA for the next-generation sequencing (NGS) cancer panel Ion Torrent, which contains 50 of the most mutated genes in lung cancer. All protocols for extraction, quality and quantity control, and library preparation follow the manufacturer's rules. Bioinformatics analysis was performed to select pathogenic mutations versus non-pathogenic-benign ones. This approach is particularly valuable for patients in advanced stages (III and IV, n=10) of lung adenocarcinoma and lung squamous cell carcinoma, who lack surgical options and limited therapeutic avenues. The comprehensive sequencing analysis revealed that nine of the ten lung cancer patients carried a TP53 mutation. Also, several other mutations exist in various cases, showing heterogeneous profiling. Our findings demonstrate the potential of liquid biopsies in providing crucial genetic insights that can guide personalized treatment strategies, improving the management and outcomes for patients with advanced lung cancer.

Exploring the significance of MET overexpression by gene expression profiling versus immunohistochemistry in an EGFR-mutant lung cancer cohort.

e20600 Background: Amivantamab, an innovative EGFR-MET bispecific antibody, approved for use in EGFR exon 20 mutated lung cancers, recently demonstrated substantial clinical benefits in advanced/metastatic lung cancers with EGFR exon 19 deletion and/or L858R mutation according to data from the MARIPOSA clinical trial. Recent findings from the CHRYSALIS 2 trial indicate a potential role for MET overexpression as a predictive biomarker for response to Amivantamab combination therapy, particularly in cases resistant to Osimertinib. Methods: We retrospectively analyzed genomic profiling data in a large lung cancer cohort to characterize EGFR mutations. Additionally, we assessed MET expression via gene expression profiling (GEP) in a smaller EGFR-mutation positive sub-cohort to explore its prevalence. Results: Genomic profiling data from 799 tissue samples was included in our analysis. EGFR mutations were detected in 36%; higher prevalence of EGFR mutations was noted in our cohort (36%) vs the TCGA cohort (26%). Consistent with other studies, exon 19 deletion was the most frequent category of EGFR mutations (46%,), with EGFR p.E746_750del being the most frequent variant among them (60%). Exon 21 L858R mutation was the next most frequently detected variant (28%). Uncommon EGFR mutations were identified in 26% samples, with G719X being the most frequent among these. EGFR Exon 20 insertions contributed 6% of the EGFR variants. EGFR amplification was detected in 4.6% of samples. Of particular significance was the analysis of 21 EGFR-mutated tissue samples for targeted transcriptome analysis as a part Exacta Encyclopedic Tumor Analysis. The MET gene overexpression was detected in 52% of these samples. MET overexpression based on differential GEP identified a higher positivity rate than previously reported using MET immunohistochemistry (IHC) expression (52% vs. 36%), suggesting that GEP may be useful in identifying more patients with MET overexpression. This finding underscores the potential value of MET GEP as a biomarker, especially considering emerging data suggesting MET overexpression as a predictor for enhanced clinical benefit from the combination of Amivantamab and Lazertinib in EGFR-mutated, Osimertinib resistant patients. Furthermore, ongoing clinical trials are exploring MET overexpression as a biomarker for Amivantamab monotherapy. Conclusions: In conclusion, our study not only characterizes the landscape of EGFR mutations in a large lung cancer cohort but also highlights the prevalence of MET overexpression in EGFR-mutated samples, suggesting that MET GEP may serve as a promising predictive biomarker for enhanced clinical outcomes from Amivantamab therapy. In view of small sample size, further exploration and validation of MET overexpression by GEP is warranted in the context of EGFR-mutated advanced lung cancer.