Early-stage Lung Cancer Patients Research Articles

Disparities in Access to Multidisciplinary Cancer Consultations and Treatment for Patients With Early-Stage Non-Small Cell Lung Cancer: A SEER-Medicare Analysis

PurposeDisparities in access to multidisciplinary cancer consultations (MDCc) persist, and the role of physician relationships remains understudied. This study examined the extent to which multilevel factors, including patient characteristics and patient-sharing network measures reflecting the structure of physician relationships, are associated with MDCc and receipt of stereotactic body radiation therapy (SBRT) versus surgery among early-stage non-small cell lung cancer (NSCLC) patients. Materials and MethodsIn this cross-sectional study, we analyzed Surveillance, Epidemiology, and End-Result (SEER)-Medicare data for patients diagnosed with stage I-IIA NSCLC in 2016-2017. We assembled patient-sharing networks and identified cancer specialists who were locally unique for their specialty, herein referred to as “linchpins”. The proportion of linchpin cancer specialists for each hospital referral region (HRR) was calculated as a network-based measure of specialist scarcity. We used multilevel multinomial logistic regression to estimate associations between study variables and the receipt of MDCc and multilevel logistic regression to examine the relationship between MDCc and patient's first treatment. ResultsOur study included 6,120 patients with stage I-IIA NSCLC, of which 751 (12.3%) received MDCc, 1,729 (28.3%) only consulted a radiation oncologist, 2,010 (32.8%) only consulted a surgeon, and 1,630 (26.6%) had no consultations with either specialist within two months following diagnosis. Compared with patients residing in an HRR with a low proportion of linchpin surgeons, those in an HRR with a high proportion of linchpin surgeons had a 2.99 (95% CI: 1.87-4.78) greater relative risk of exclusively consulting a radiation oncologist (vs. MDCc) and a 2.70 (95% CI: 1.68-4.35) greater relative risk consulting neither specialist (vs. MDCc). Patients who received MDCc were 5.32 (95% CI: 4.27-6.63) times more likely to receive SBRT (vs. surgery). ConclusionsPhysician networks are associated with receipt of MDCc and treatment, underscoring the potential for leveraging patient-sharing network analysis to improve access to lung cancer care.

Transcriptome profiling and metabolic pathway analysis towards reliable biomarker discovery in early-stage lung cancer.

Earlier diagnosis of lung cancer is crucial for reducing mortality and morbidity in high-risk patients. Liquid biopsy is a critical technique for detecting the cancer earlier and tracking the treatment outcomes. However, noninvasive biomarkers are desperately needed due to the lack of therapeutic sensitivity and early-stage diagnosis. Therefore, we have utilized transcriptomic profiling of early-stage lung cancer patients to discover promising biomarkers and their associated metabolic functions. Initially, PCA highlights the diversity level of gene expression in three stages of lung cancer samples. We have identified two major clusters consisting of highly variant genes among the three stages. Further, a total of 7742, 6611, and 643 genes were identified as DGE for stages I-IIIrespectively. Topological analysis of the protein-protein interaction network resulted in seven candidate biomarkers such as JUN, LYN, PTK2, UBC, HSP90AA1, TP53, and UBB cumulatively for the three stages of lung cancers. Gene enrichment and KEGG pathway analyses aid in the comprehension of pathway mechanisms and regulation of identified hub genes in lung cancer. Importantly, the medial survival rates up to ~ 70months were identified for hub genes during the Kaplan-Meier survival analysis. Moreover, the hub genes displayed the significance of risk factors during gene expression analysis using TIMER2.0 analysis. Therefore, we have reason that these biomarkers may serve as a prospective targeting candidate with higher treatment efficacy in early-stage lung cancer patients.

Meta-lasso: new insight on infection prediction after minimally invasive surgery.

Surgical site infection (SSI) after minimally invasive lung cancer surgery constitutes an important factor influencing the direct and indirect economic implications, patient prognosis, and the 5-year survival rate for early-stage lung cancer patients. In the realm of predictive healthcare, machine learning algorithms have been instrumental in anticipating various surgical outcomes, including SSI. However, accurately predicting infection after minimally invasive surgery remains a clinical challenge due to the multitude of physiological and surgical factors associated with it. Furthermore, clinical patient data, in addition to being high-dimensional, often exists the long-tail problem, posing difficulties for traditional machine learning algorithms in effectively processing such data. Based on this insight, we propose a novel approach called meta-lasso for infection prediction following minimally invasive surgery. Our approach leverages the sparse learning algorithm lasso regression to select informative features and introduces a meta-learning framework to mitigate bias towards the dominant class. We conducted a retrospective cohort study on patients who had undergone minimally invasive surgery for lung cancer at Shanghai Chest Hospital between 2018 and 2020. The evaluation encompassed key performance metrics, including sensitivity, specificity, precision (PPV), negative predictive value (NPV), and accuracy. Our approach has surpassed the performance of logistic regression, random forest, Naive Bayes classifier, gradient boosting decision tree, ANN, and lasso regression, with sensitivity at 0.798, specificity at 0.779, precision at 0.789, NPV at 0.798, and accuracy at 0.788 and has greatly improved the classification performance of the inferior class.

Abstract PR011: Comprehensive longitudinal tracking of lung cancer evolutionary clonal dynamics during therapy using circulating tumour DNA

Abstract Background: Outgrowth of resistance cancer cell populations is a common mechanism of therapy failure in oncology. Effective personalised medicine relies on targeting of ubiquitous aberrations; however, tumours are highly heterogenous. Liquid biopsies have the potential to provide representative tumour sampling at regular intervals through disease course, but extensive interrogation of longitudinal clonal evolution through therapy has been lacking, particularly for patients on a curative disease pathway. Methods: We analysed 1102 plasma samples from 202 early-stage non-small cell lung cancer patients who underwent multiregional whole exome sequencing of primary tumour, relapse and, where available, comprehensive post-mortem samples. We designed personalised panels targeting a median of 200 mutations for each patient and sequenced cell-free DNA (cfDNA) around these variant sites to saturation. For four patients we designed personalised panels including all clonal and subclonal mutations discovered (up to 600). One of these four patients received a personalised neoantigen vaccine. We used ECLIPSE to determine the clonal composition of low tumour fraction ctDNA. Results: We found a directly proportional correlation between the cancer cell fraction (CCF) of subclones estimated in plasma and multiregional tissue samples at surgery. Lower plasma CCFs were observed for clonal illusion mutations from randomly selected single samples, one for each TRACERx patient, when compared to truly clonal mutations (P < 0.001, OR = 0.44), distinguishing appropriate therapeutic targets without multiregional sampling. In 24 patients with both cfDNA and tissue available at relapse we detected 28/29 relapse tissue subclones in the cfDNA with an additional 8 cfDNA unique subclones. We found a strong bias for cfDNA unique subclones to be present in only subset of cells (P = 0.008, OR = 5.5), consistent with insufficient tissue sampling. We observed longitudinal clonal dynamics which were often, but not always, associated with treatment. Metastatic competent subclones had higher CCFs in the primary tumour, as measured in pre-operative plasma (P < 0.001, OR = 4.5). Upon receiving a personalised neoantigen vaccine a patient had regression of clones harbouring a targeted EGFR mutation. However, copy number loss of the targeted EGFR mutation occurred in a liver lesion which grew over the treatment period on surveillance radiology alongside a novel ctDNA subclone. Upon cessation vaccine treatment the resistance-associated subclone and liver lesion underwent regression. EGFR mutant clones become dominant in the tumour thereafter suggesting a fitness cost associated with vaccine resistant EGFR mutation loss in this patient, measured using ctDNA. Conclusions: We find evidence that comprehensive tracking of subclonal mutations in plasma can accurately profile the clonal structure of tumours over time, revealing clonal dynamics in response to treatment and biological determinants of metastasis with the potential to guide personalised medicine. Citation Format: Alexander M. Frankell, Christopher Abbosh, Kristiana Grigoriadis, Emilia Lim, Selvaraju Veeriah, Sophia Ward, Tak Karasaki, James R. M. Black, Mariam Jamal-Hanjani, Nicholas McGranahan, Charles Swanton. Comprehensive longitudinal tracking of lung cancer evolutionary clonal dynamics during therapy using circulating tumour DNA [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR011.

A novel neoadjuvant therapy for early-stage non-small cell lung cancer in a mouse model.

Lung cancer is the common malignancy with high mortality rate in the world. Even with curative resection for early-stage lung cancer patients, the rate of postoperative recurrence and metastasis is still high. Neoadjuvant nivolumab combined with chemotherapy leads to improved pathological complete response rate and event-free survival in resectable non-small cell lung cancer (NSCLC) patients. However, the neoadjuvant therapy is not only accompanied by grade 3 or above adverse events which resulting in the potential missing out on the window for curative surgery for the patients, but also has low efficacy especially in patients with low programmed death ligand 1 (PD-L1) expression. Hence, it is particularly important to explore innovative ways to inhibit tumour recurrence and metastasis. In the present study, we investigated whether neoadjuvant therapy with intralesional Rose Bengal (RB) elicited specific immune responses compared with control group, and then the lung cancer mouse model was used to evaluated the immunological mechanism. The secondary Lewis lung cancer cells (LLCs) tumour growth was significantly suppressed by RB intralesional injection into subcutaneous tumour; the formation rate of secondary tumours induced by the B16 melanoma cell injection was 100%. Intralesional RB neoadjuvant therapy before surgical resection exhibited effectively enhanced T central memory cells (Tcm) and T memory stem cells (Tscm) + naïve T cells (Tn) infiltration, elicited stronger cytotoxic T lymphocyte (CTL) responses against LLCs, and displayed markedly higher proportions of splenic lymphocytes that produce tumour necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ) upon restimulation in a lung cancer mouse model. Based on our preclinical data, neoadjuvant therapy with intralesional RB injection generated immune memory and prevented the recurrence and metastasis of tumour in a lung cancer mouse model, which provides a new strategy for neoadjuvant treatment of early-stage NSCLC.

Factors associated with cavity formation after stereotactic body radiation therapy for peripheral early-stage lung cancer.

This retrospective study aimed to identify the factors associated with cavity formation after SBRT in peripheral early-stage lung cancer patients. We analyzed the occurrence of cavity changes after SBRT. We examined 99 cases with T1-T2aN0 peripheral non-small cell lung cancer treated with SBRT from 2004 to 2021. Patients underwent respiratory function tests, including diffusing capacity for carbon monoxide (DLco), before treatment. The median observation period was 35months (IQR 18-47.5months). Treatment involved fixed multi-portal irradiation in 67% of cases and VMAT in 33%. The total radiation doses ranged from 42 to 55Gy, delivered over 4 to 5 fractions. Cavity formation occurred in 14 cases (14.1%), appearing a median of 8months after SBRT. The cavity disappeared in a median of 4months after formation. High DLco and total radiation dose were identified as factors significantly associated with cavity formation. There have been no confirmed recurrences to date, but one patient developed a lung abscess. Although cavity formation after SBRT for peripheral early-stage lung cancer is infrequent, it can occur. This study showed high DLco and total radiation dose to be factors significantly associated with cavity formation. These findings can be applied to optimizing radiation therapy (RT) and improving patient outcomes. Further research is needed to determine the optimal radiation dose for patients with near-normal DLco for whom surgery is an option. This study provides valuable insights into image changes after RT.

A PET/CT radiomics model for predicting distant metastasis in early-stage non–small cell lung cancer patients treated with stereotactic body radiotherapy: a multicentric study

ObjectivesStereotactic body radiotherapy (SBRT) is a treatment option for patients with early-stage non-small cell lung cancer (NSCLC) who are unfit for surgery. Some patients may experience distant metastasis. This study aimed to develop and validate a radiomics model for predicting distant metastasis in patients with early-stage NSCLC treated with SBRT.MethodsPatients at five institutions were enrolled in this study. Radiomics features were extracted based on the PET/CT images. After feature selection in the training set (from Tianjin), CT-based and PET-based radiomics signatures were built. Models based on CT and PET signatures were built and validated using external datasets (from Zhejiang, Zhengzhou, Shandong, and Shanghai). An integrated model that included CT and PET radiomic signatures was developed. The performance of the proposed model was evaluated in terms of its discrimination, calibration, and clinical utility. Multivariate logistic regression was used to calculate the probability of distant metastases. The cutoff value was obtained using the receiver operator characteristic curve (ROC), and the patients were divided into high- and low-risk groups. Kaplan-Meier analysis was used to evaluate the distant metastasis-free survival (DMFS) of different risk groups.ResultsIn total, 228 patients were enrolled. The median follow-up time was 31.4 (2.0-111.4) months. The model based on CT radiomics signatures had an area under the curve (AUC) of 0.819 in the training set (n = 139) and 0.786 in the external dataset (n = 89). The PET radiomics model had an AUC of 0.763 for the training set and 0.804 for the external dataset. The model combining CT and PET radiomics had an AUC of 0.835 for the training set and 0.819 for the external dataset. The combined model showed a moderate calibration and a positive net benefit. When the probability of distant metastasis was greater than 0.19, the patient was considered to be at high risk. The DMFS of patients with high- and low-risk was significantly stratified (P < 0.001).ConclusionsThe proposed PET/CT radiomics model can be used to predict distant metastasis in patients with early-stage NSCLC treated with SBRT and provide a reference for clinical decision-making.Plain language summaryIn this study, the model was established by combining CT and PET radiomics signatures in a moderate-quantity training cohort of early-stage NSCLC patients treated with SBRT and was successfully validated in independent cohorts. Physicians could use this easy-to-use model to assess the risk of distant metastasis after SBRT. Identifying subgroups of patients with different risk factors for distant metastasis is useful for guiding personalized treatment approaches.

Comparison of Treatment Outcomes Between Thoracoscopic Surgery and Stereotactic Body Radiotherapy for Early-Stage Non-Small Cell Lung Cancer.

Objective: To compare the risk of death, tumor recurrence, metastasis, and disease progression in early-stage non-small cell lung cancer (NSCLC) patients treated with thoracoscopic surgery and stereotactic body radiotherapy (SBRT). Methods: Patients who underwent radical surgery and SBRT for NSCLC between April 2010 and November 2021 were retrospectively analyzed. Continuous and categorical variables were compared using the Mann-Whitney U and Chi-square test, respectively. Kaplan-Meier curves were used to evaluate the survival outcomes of each patient group. Cox proportional hazard regression analyses were performed to estimate the risk of death, tumor recurrence, metastasis, and disease progression. Results: A total of 167 patients were enrolled, of whom 75 and 92 underwent SBRT and surgery, respectively. The median follow-up was 45 months (range, 4-105 months). SBRT patients were observed to be significantly older (median, 76.0 vs 67.0 years; P < .001), and associated with significantly higher mortality rate (42.7% vs 26.1%, P = .024). However, no significant difference in overall survival duration was seen between the SBRT and surgery groups (45.0 vs 41.0 months; P = .199). SBRT patients demonstrated significantly lower rates of metastasis (12.0% vs 30.4%, P = .004), and significantly longer metastasis-free survival (39.0 months vs 35.5 months, P = .020). The remaining outcomes, including tumor recurrence and disease progression rates, were similar between the groups. Compared to surgery, SBRT did not significantly associate with death, recurrence, or disease progression. Kaplan-Meier curves showed significant differences in overall, tumor recurrence-free, and disease progression-free survival between the groups (log-rank P < .05). Conclusions: SBRT demonstrated similar overall survival compared to radical surgery, and associated with significantly reduced risk of tumor metastasis. Our study thereby suggests SBRT as the best treatment option for patients with inoperable NSCLC.

Preoperative splenic area as a prognostic biomarker of early-stage non-small cell lung cancer

BackgroundThe correlation between the preoperative splenic area measured on CT scans and the overall survival (OS) of early-stage non-small cell lung cancer (NSCLC) patients remains unclear.MethodsA retrospective discovery cohort and validation cohort consisting of consecutive NSCLC patients who underwent resection and preoperative CT scans were created. The patients were divided into two groups based on the measurement of their preoperative splenic area: normal and abnormal. The Cox proportional hazard model was used to analyse the correlation between splenic area and OS.ResultsThe discovery and validation cohorts included 2532 patients (1374 (54.27%) males; median (IQR) age 59 (52–66) years) and 608 patients (403 (66.28%) males; age 69 (62–76) years), respectively. Patients with a normal splenic area had a 6% higher 5-year OS (n = 727 (80%)) than patients with an abnormal splenic area (n = 1805 (74%)) (p = 0.007) in the discovery cohort. A similar result was obtained in the validation cohort. In the univariable analysis, the OS hazard ratios (HRs) for the patients with abnormal splenic areas were 1.32 (95% confidence interval (CI): 1.08, 1.61) in the discovery cohort and 1.59 (95% CI: 1.01, 2.50) in the validation cohort. Multivariable analysis demonstrated that abnormal splenic area was independent of shorter OS in the discovery (HR: 1.32, 95% CI: 1.08, 1.63) and validation cohorts (HR: 1.84, 95% CI: 1.12, 3.02).ConclusionPreoperative CT measurements of the splenic area serve as a prognostic indicator for early-stage NSCLC patients, offering a novel metric with potential implications for personalized therapeutic strategies in top-tier oncology research.

Exploring published and novel pre-treatment CT and PET radiomics to stratify risk of progression among early-stage non-small cell lung cancer patients treated with stereotactic radiation

PurposeDisease progression after definitive stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC) occurs in 20–40% of patients. Here, we explored published and novel pre-treatment CT and PET radiomics features to identify patients at risk of progression. Materials/MethodsPublished CT and PET features were identified and explored along with 15 other CT and PET features in 408 consecutively treated early-stage NSCLC patients having CT and PET < 3 months pre-SBRT (training/set-aside validation subsets: n = 286/122). Features were associated with progression-free survival (PFS) using bootstrapped Cox regression (Bonferroni-corrected univariate predictor: p ≤ 0.002) and only non-strongly correlated predictors were retained (|Rs|<0.70) in forward-stepwise multivariate analysis. ResultsTumor diameter and SUVmax were the two most frequently reported features associated with progression/survival (in 6/20 and 10/20 identified studies). These two features and 12 of the 15 additional features (CT: 6; PET: 6) were candidate PFS predictors. A re-fitted model including diameter and SUVmax presented with the best performance (c-index: 0.78; log-rank p-value < 0.0001). A model built with the two best additional features (CTspiculation1 and SUVentropy) had a c-index of 0.75 (log-rank p-value < 0.0001). ConclusionsA re-fitted pre-treatment model using the two most frequently published features – tumor diameter and SUVmax – successfully stratified early-stage NSCLC patients by PFS after receiving SBRT.

EP07.05-09 Oncologic Outcomes of Stereotactic Body Radiotherapy in Early-Stage Non-Small Cell Lung Cancer Patients with Interstitial Lung Disease

EP07.05-09 Oncologic Outcomes of Stereotactic Body Radiotherapy in Early-Stage Non-Small Cell Lung Cancer Patients with Interstitial Lung Disease

EP07.01-27 The Impact of Comorbidities on Exercise Level after Treatment for Early-Stage Lung Cancer Patients

EP07.01-27 The Impact of Comorbidities on Exercise Level after Treatment for Early-Stage Lung Cancer Patients

Impact of time-to-treatment on survival for early-stage non-small cell lung cancer in The Netherlands-a nationwide observational cohort study.

Varied outcomes on the relation between time-to-treatment and survival in early-stage non-small cell lung cancer (NSCLC) patients are reported. We examined this relation in a large multicentric retrospective cohort study and identified factors associated with extended time-to-treatment. We included 9,536 patients with clinical stage I-II NSCLC, diagnosed and treated in 2014-2019, from the Netherlands Cancer Registry that includes nation-wide data. Time-to-treatment was defined as the number of days between first outpatient visit for suspected lung cancer and start of treatment. The effect of extended time-to-treatment beyond the first quartile and survival was studied with Cox proportional hazard regression. Analyses were stratified for stage and type of therapy. Time-to-treatment was adjusted for multiple covariates including performance status and socioeconomic status. Factors associated with treatment delay were identified by multilevel logistic regression. Median time-to-treatment was 47 days [interquartile range (IQR): 34-65] for stage I and 46 days (IQR: 34-62) for stage II. The first quartile extended to 33 days for both stages. Risk of death increased significantly with extended time-to-treatment for surgical treatment of clinical stage II patients [adjusted hazard ratio (aHR) >33 days: 1.36, 95% confidence intervals (CI): 1.09-1.70], but not in stage II patients treated with radiotherapy or in stage I patients. Causes of prolonged time-to-treatment were multifactorial including diagnostic tests, such as endoscopic ultrasound (EUS) or endobronchial ultrasound (EBUS). Clinical stage II patients benefit from fast initiation of surgical treatment. Surprisingly this appears to be accounted for by patients who are clinically stage II but pathologically stage I. Further study is needed on characterizing these patients and the significance of lymph node- or distant micrometastasis in guiding time-to-treatment and treatment strategy.

Recurrence prediction of lung adenocarcinoma using an immune gene expression and clinical data trained and validated support vector machine classifier.

Immune microenvironment plays a critical role in cancer from onset to relapse. Machine learning (ML) algorithm can facilitate the analysis of lab and clinical data to predict lung cancer recurrence. Prompt detection and intervention are crucial for long-term survival in lung cancer relapse. Our study aimed to evaluate the clinical and genomic prognosticators for lung cancer recurrence by comparing the predictive accuracy of four ML models. A total of 41 early-stage lung cancer patients who underwent surgery between June 2007 and October 2014 at New York University Langone Medical Center were included (with recurrence, n=16; without recurrence, n=25). All patients had tumor tissue and buffy coat collected at the time of resection. The CIBERSORT algorithm quantified tumor-infiltrating immune cells (TIICs). Protein-protein interaction (PPI) network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to unearth potential molecular drivers of tumor progression. The data was split into training (75%) and validation sets (25%). Ensemble linear kernel support vector machine (SVM) ML models were developed using optimized clinical and genomic features to predict tumor recurrence. Activated natural killer (NK) cells, M0 macrophages, and M1 macrophages showed a positive correlation with progression. Conversely, T CD4+ memory resting cells were negatively correlated. In the PPI network, TNF and IL6 emerged as prominent hub genes. Prediction models integrating clinicopathological prognostic factors, tumor gene expression (45 genes), and buffy coat gene expression (47 genes) yielded varying receiver operating characteristic (ROC)-area under the curves (AUCs): 62.7%, 65.4%, and 59.7% in the training set, 58.3%, 83.3%, and 75.0% in the validation set, respectively. Notably, merging gene expression with clinical data in a linear SVM model led to a significant accuracy boost, with an AUC of 92.0% in training and 91.7% in validation. Using ML algorithm, immune gene expression data from tumor tissue and buffy coat may enhance the precision of lung cancer recurrence prediction.

Prognostic Value of Hypoxia Gene Expression Signature in Early-Stage Non-Small Cell Lung Cancer Patients Treated with Stereotactic Body Radiation Therapy

Stereotactic body radiation therapy (SBRT) is an effective alternative to surgery in the treatment of early-stage non-small cell lung cancer (NSCLC). However, disease recurrence occurs in approximately 20-30% of patients. Hypoxia is a well-known factor promoting solid tumor progression, radiation resistance, and immune evasion. In this study, we utilize a previously published hypoxia gene expression signature (Buffa) and measure its association with clinical outcomes in patients with NSCLC treated with SBRT. Our study focused on patients with localized, node-negative NSCLC treated with SBRT, and we identified 92 patients treated at our institution between 2008 and 2018 with available gene expression data. Total RNA from formalin-fixed paraffin-embedded archival biopsy specimens (pre-therapy) was isolated and mRNA expression was analyzed using an assay for human samples. For each gene in the Buffa signature, the top 50% of mRNA abundance values were given a score of +1, and the bottom 50% were given a score of -1 to generate a tumor hypoxia score. High scores suggest a hypoxic tumor and low scores imply normoxia. Kaplan-Meier curves were used to assess overall survival, disease-free survival, local recurrence, regional nodal recurrence, and distant recurrence. Cox proportional hazards were performed to account for confounding due to age, T stage, ECOG performance status, biologically effective dose, patient sex, and histology (squamous vs non-squamous). The patient's gene expression data were dichotomized based on their median hypoxia score (Table 1). Median follow-up was 23.9 months (95% CI 20.6 - 26.6). A high hypoxia score was associated with squamous histology (p = 0.003). On univariate analysis, a high hypoxia score was significantly associated with local recurrence (hazard ratio [HR] = 5.63; 95% CI 1.03 - 30.78; p = 0.046) and distant recurrence (HR = 1.85; 95% CI 1.14 - 3.02; p = 0.013). There was no significant difference in overall survival (HR = 1.93; 95% CI 0.98 - 3.82; p = 0.058), disease-free survival (HR = 2.13; 95% CI 0.93 - 4.89; p = 0.074), or regional nodal recurrence (HR = 1.45; 95% CI 0.41 - 5.14; p = 0.57). On multivariate analysis, a high hypoxia score was associated with worse overall survival (HR = 2.73; 95% CI 1.23 - 6.07; p = 0.014), local recurrence (HR = 12.85; 95% CI 1.08 - 152.49; p = 0.043) and distant recurrence (HR = 1.91; 95% CI 1.13 - 3.22; p = 0.016). Our findings demonstrate that a hypoxia gene signature is significantly associated with worse survival and increased local and distant recurrence after SBRT for localized NSCLC. This hypoxia signature may be useful in prognostication, selecting patients for surgery versus radiation, or selecting patients for treatment intensification with radiation or other systemic agents, and should be prospectively validated.

Initial Report of a Randomized Trial Comparing Conventional vs. Novel Treatment Planning Technique to Ameliorate Immunosuppression from Lung SBRT

SBRT is highly effective against early-stage non-small cell lung cancer. Radiation Therapy (RT) is known to modulate the immune system and contribute to the generation of anti-tumor T cells and stimulate T cell infiltration into tumors. However, this anti-tumor activity is offset by radiation-induced immunosuppression (RIIS) which results in lower tumor control and survival. Lymphocytes are highly radiosensitive and RIIS means destroying existing as well as newly created cytotoxic and helper T lymphocytes. We hypothesized that optimizing RT treatment planning by considering circulating blood and lymphatics as a critical Organ at Risk (OAR) may mitigate RIIS. We conducted an IRB approved NCI funded clinical trial for 50 early-stage lung cancer patients treated with SBRT alone, from 2020 to 2023, to investigate the ability to reduce RIIS by reducing dose to circulating blood and lymphatics with the aid of a predictive algorithm. All SBRT plans adhered to treatment parameters from RTOG 0813 (central) or RTOG 0915 (peripheral). Patients were randomized to two arms: experimental optimization for RIIS (to reduce dose to blood and lymphatic rich organs) versus standard SBRT planning (without optimization for RIIS). Peripheral blood samples were collected at baseline, end of Tx, 4 weeks and 6 months post Tx. Patients with baseline absolute lymphocyte counts (ALC) less than 0.5x109 cells/L were ineligible for the trial. Data acquired for all blood cell types as well as lymphocyte sub populations CD3+, CD4+, CD8+, CD19+, CD56+. Two sample t-test was used to determine the statistical significance between the cohorts at the time points. The standard arm had an ALC reduction of 28% at one week post Tx and a nadir at 4 weeks with a 34% reduction. Absolute percentage reductions in ALC from baseline in the optimized arm compared to the standard arm are: end of treatment point (13%, p = 0.03), 4 weeks (12%, p = 0.08), 6 months (15%, p = 0.1), and all three time points together 13% (p = 0.001). ALC recovery appears to be faster in the optimized arm. Radiation induced suppression of all blood cell types are also reduced in the optimized arm with respect to standard arm (relative percentages): ALC (34%), WBC (47%), RBC (46%), platelets (40%), monocytes (100%), and neutrophils (62%) at 4-week mark. Average percentage reductions on integral doses, and V5 (volume receiving a 5 Gy dose) of optimized compared to standard plans are: aorta: 26%, 41% heart: 8%, 33%, vena cava: 32%, 52%, T spine: 51%, 81%, lymph nodes: 35%, 57%, total lung- ITV: 1.6%, 1%, body: 10%, 14%. For the first time, we have shown that it is possible to reduce RIIS in a statistically significant manner, compared to standard of care, via optimized RT planning using a predictive model. This has implications in increasing the efficacy of immunotherapy by preserving the existing tumor reactive T cells in the immune system to enhance anti-tumor activity, and in reducing hospitalizations and improving survival.

Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients

Recurring sequences of genomic alterations occurring across patients can highlight repeated evolutionary processes with significant implications for predicting cancer progression. Leveraging the ever-increasing availability of cancer omics data, here we unveil cancer’s evolutionary signatures tied to distinct disease outcomes, representing “favored trajectories” of acquisition of driver mutations detected in patients with similar prognosis. We present a framework named ASCETIC (Agony-baSed Cancer EvoluTion InferenCe) to extract such signatures from sequencing experiments generated by different technologies such as bulk and single-cell sequencing data. We apply ASCETIC to (i) single-cell data from 146 myeloid malignancy patients and bulk sequencing from 366 acute myeloid leukemia patients, (ii) multi-region sequencing from 100 early-stage lung cancer patients, (iii) exome/genome data from 10,000+ Pan-Cancer Atlas samples, and (iv) targeted sequencing from 25,000+ MSK-MET metastatic patients, revealing subtype-specific single-nucleotide variant signatures associated with distinct prognostic clusters. Validations on several datasets underscore the robustness and generalizability of the extracted signatures.

598 Video-Assisted Thoracoscopic Surgery Versus Robot-Assisted Thoracoscopic Surgery in Early-Stage Lung Cancer: A Systematic Review and Meta-Analysis

Abstract Aim Video-assisted thoracic surgery (VATS) remains the main surgical procedure for lung cancer resection. However, it is associated with technical limitations and a steep learning curve. Hence, alternative approaches like robot-assisted thoracic surgery (RATS) have been explored. This review aims to compare the clinical outcomes of VATS versus RATS in early-stage lung cancer patients. Method A systematic, electronic search was performed according to PRISMA guidelines to identify relevant articles that compared outcomes of the VATS versus RATS procedures in patients with early-stage lung cancer. Results Seventeen studies were identified, enrolling 17,111 patients. The mean tumor size was 12.58±7.21cm in the RATS group and 5±2.5cm in the VATS group, with most patients in the Stage 1 cancer group. The primary outcomes, length of hospital stay (MD 0.37, 95% CI [-0.15, 0.89], and conversion (OR 0.99, 95% CI [0.68, 1.43]), were not statistically significant. Although more lymph nodes were retrieved during the RATS procedure (MD -0.70, 95% CI [-1.18, -0.21]), no significant differences were observed in other secondary outcomes such as operative duration (MD -0.14, 95% CI [-1.00, 0.73]), reintervention (OR 1.47, 95% CI[0.22, 9.79]), infection (OR 0.89, 95% CI [0.52, 1.54]), and bleeding (OR 1.15, 95% CI [0.52, 2.55]). Conclusions The RATS and VATS procedures have comparable outcomes, thus proving the RATS procedure to be a safe alternative. However, the technical feasibility and cost-effectiveness need to be thoroughly studied. This review calls for robust trials comparing the two techniques to further evaluate the value of the RATS procedure in early-stage lung cancer.

Patient-reported outcomes and functional exercise capacity in a real-life setting in non-small cell lung cancer patients undergoing stereotactic body radiotherapy: the Lung PLUS study.

To better understand the impact of stereotactic body radiotherapy (SBRT) and its treatment-related toxicity on early-stage non-small cell lung cancer (ES-NSCLC) patients, we conducted the Lung PLUS study in a real-world setting. This is a monocentric prospective longitudinal study up to 12 months post-treatment, evaluating clinician- and patient-reported toxicity (resp. CTCAE and PRO-CTCAE), health-related quality of life (HRQoL) (EORTC QLQ-C30 and LC-13), activities of daily living (HAQ-DI) and functional exercise capacity (6 Minute Walking Test (6MWT)). A mixed model approach was applied to analyze the data. At baseline, clinicians and patients (n=51) reported mostly fatigue (63% vs 79%), cough (49% vs 75%) and dyspnea (65% vs 73%) of any grade. Dyspnea (p=.041) increased over time. Meaningful clinical improvements were particularly seen in pain, fatigue, and cough. Clinician reported clinically meaningful improvements and deteriorations over time in fatigue, cough, and dyspnea. Almost at every timepoint, more people reported deterioration to the clinician than improvement in aforementioned toxicities. Overall HRQoL (p=.014), physical (p=.011) and emotional (p<.001) functioning improved over time. At baseline, patients had a moderate daily functioning score and walked an average distance of 360 meters. No statistically significant differences were found in daily functioning and exercise capacity over time. Our study showed an increase in patient-reported toxicity and dyspnea, without impacting functional status, following SBRT. Overall HRQoL, physical and emotional functioning improved over time. Understanding the impact of treatment on patient-reported outcomes is crucial to identify the needs/problems of patients to enhance their HRQoL.

Plasmonic internal standard-decorated nitrocellulose membranes for duplex detection of circulating tumor biomarkers

Surface-enhanced Raman spectroscopy (SERS) technology has been increasingly integrated with lateral flow immunoassay (SERS-LFA) to enable sensitive and quantitative detection, but SERS-LFA still faces notable challenges. One of the biggest problems is signal variations caused by random distributions of plasmonic SERS immunoprobes. Herein, we develop a sensitive, accurate quantification platform that overcomes this problem with a plasmonic internal standard (PIS), referred to as PIS-LFA. The PIS is achieved by decorating nitrocellulose membranes with Raman reporter molecule-embedded silver-gold alloy nanoparticles. By simultaneously measuring signals of plasmonic immunoprobes and IS and calculating their intensity ratios for signal normalization, we can mitigate signal variations and improve assay sensitivity. Comparing with SERS-LFA in carcinoembryonic antigen (CEA) detection, we show that PIS-LFA achieves a 1.8-fold higher uniformity and 3.2-fold lower limit of detection. We further demonstrate that PIS-LFA exhibits accurate quantification capability in duplex detection of CEA and neuron-specific enolase (NSE) in human sera by performing spike-recovery experiments and validating with electrochemiluminescence immunoassays. As a proof-of-concept, PIS-LFA differentiates levels of serum CEA and NSE between 12 early-stage lung cancer patients and 12 healthy individuals. We believe that PIS-LFA will open up avenues for affordable, rapid, and non-invasive disease screening in point-of-care settings.