tbuckley/cabot-search huggingface dataset:
Decision model for durable clinical benefit from front- or late-line immunotherapy alone or with chemotherapy in large population of non-small cell lung cancer.
Authors: Jie Zhao, Lu Wang, Shidi Wen, Anda Zhou, Wenfeng Fang, Li Zhang, Jianchun Duan, Hua Bai, Jia Zhong, Rui Wan, Boyang Sun, Wei Zhuang, Yiwen Lin, Danming He, Zhijie Wang, Jie Wang
Journal: Journal of Clinical Oncology (2024)

e20533 Background: Predictive biomarkers and models of immune checkpoint inhibitors (ICI) have been extensively studied in NSCLC. However, evidence for many biomarkers remains inconclusive, and the opaqueness of machine learning models hinders their practicality. We aimed to provide compelling evidence for biomarkers and develop a transparent decision-tree model. Methods: We consolidated data from ICI-treated patients with NSCLC (n = 3,288) across real-world multicentre studies, public cohorts, and the Choice-01 trial (NCT03607539). Over 50 features were gathered to examine the predictive capacity of durable clinical benefits (DCB) from mono-ICIs and ICIs plus chemotherapy. Noteworthy biomarkers were identified to establish a decision tree model. We also explored the tumour microenvironment and peripheral PD-1+ CD8+ T-cell receptor profiles. Results: Multivariate logistic regression analysis identified tumour histology, PD-L1 expression, tumour mutational burden, line, and regimen of ICI treatment as significant factors. Mutation subtypes of EGFR, KRAS, KEAP1, STK11, and disruptive TP53 mutations demonstrated significant association with DCB. The decision-tree (DT10) model, using the aforementioned five clinicopathological markers and five genomic mutations, predicted DCB with superior performance in the training set (N = 286, AUC = 0.82), and consistently outperformed the logistic regression, random forest, and support vector machine models, as well as FDA-approved biomarkers of PD-L1 and TMB in the internal and three external test sets (Table). We visualized the decision routes for DCB from ICI-based treatment based on the DT10 model, which was interpretable for clinical use. Furthermore, DT10 predicted-DCB patients manifested significantly longer progression-free survival and overall survival than -NDB patients across the train and test tests (HR &lt; 1, p &lt; 0.05). Additionally, the DT10 predicted-DCB group presented an enriched inflamed tumour immune phenotype (67%) and higher peripheral TCR diversity, whereas the -NDB group showed an enriched desert immune phenotype (86%) and higher peripheral TCR clonality. Conclusions: Based on the large population, we provided persuasive evidence for the predictive reliability of biomarkers in NSCLC. The development of the DT10 model introducing an easily interpretable, visually engaging, and clinically relevant model, which assisted the decision making in varied clinical scenarios. [Table: see text]
The efficacy and safety of a novel PD-1/CTLA-4 bispecific antibody cadonilimab (AK104) in advanced non-small cell lung cancer: A multicenter retrospective observational study in China.
Authors: Jisheng Li, Shuwen Yu, Wen Zhao, Hongxin Li, Chengming Li, Hongchang Shen, Meiying Li, Chengjun Wang, Jian Wang, Cuihua Yi, Chunyan Han, Xue Meng, Jun Wang, Xiuwen Wang, Lian Liu
Journal: Journal of Clinical Oncology (2024)

e20518 Background: Treatment choice for advanced non-small cell lung cancer (NSCLC) after front-line immuno-chemotherapy therapy is limited. As the first approved dual programmed cell death-1(PD-1) / cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) bispecific antibody worldwide, cadonilimab combined with anlotinib, a vascular epidermal growth factor tyrosine kinase inhibitor, showed potential anti-tumor activity in advanced NSCLC patients resistant to anti-PD-1/ programmed cell death ligand-1(PD-L1) antibodies in a phase Ib/II clinical trial. Methods: We retrospectively collectedthe efficacy and safety data in advanced NSCLC patients treated with cadonilimab in later therapy lines. The primary endpoints were objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS) and safety results. The secondary endpoints were overall survival (OS). Results: Totally, 41 advanced NSCLC patients who were refractory to previous chemotherapy and anti-PD-1/PD-L1 therapy and treated with later line cadonilimab-based regimens were enrolled. More than half of patients received cadonilimab as a fourth or later line of treatment. Until the data cut-off date (August 29, 2023), the treatment efficacy of 23 patients could be evaluated. One (4.3%) patient had partial response, 8 (34.8%) patients had stable disease, and 14 (60.9%) patients progressed. The ORR and DCR were 4.3% and 39.1%, respectively. The median PFS of all evaluated patients was 108.0 days (95% confidence interval: 62.3-153.7). Due to the short follow-up, median OS has not been reached yet. Treatment-related adverse events (TRAEs) and immune-related adverse events (irAEs) occurred in 26(63.4%) and 9(22%) patients, respectively. The most common AEs included gamma-glutamyl transferase elevation (17.1%), coughing (14.6%), and fatigue (12.2%). Five patients (12.2%) experienced grade≥3 AEs. Two patients temporarily paused medication due to Grade 2 rash. One patient and 2 patients permanently discontinued medication due to Grade 3 myocarditis and Grade 2 infusion reactions, respectively. Metabolomics results showed that lipids and lipid-like molecules were rich in stools of patients responsive to cadonilimab treatment. Conclusions: In this heavily treated advanced NSCLC patient cohort, cadonilimab-based regimens only showed moderate anti-tumor efficacy with a generally tolerable and manageable safety profile. More evidence supporting administration of cadonilimab is needed in NSCLC patients refractory to previous anti-PD-1/PD-L1 therapy.
Implementing personalized lung cancer care with spatial profiling, patient derived organoids, and rationally designed drug testing.
Authors: Sharon R. Pine, Alexander S. Watson, Tejas Patil, Erin L. Schenk, Daniel Merrick, David Ross Camidge, Hatim E. Sabaawy
Journal: Journal of Clinical Oncology (2024)

e20521 Background: Lung cancer (LC) remains the leading cause of cancer-related death in 2024, with most LC diagnoses with non-small cell lung cancer (NSCLC) subtype. Tyrosine kinase inhibitors (TKI) and targeted therapy against oncogenic drivers (KRAS, EGFR, or ALK fusions, others) are effective initial therapies; however, nearly all treated patients eventually develop resistance to therapy. Key reasons are the focus on limited predictive biomarkers and lack of patient derived LC models that reflect the underlying heterogeneity and the various phenotypic cell states among subclones within and between patients. Methods: We report the establishment of a novel platform for personalized LC care by refining the resolution of molecular interrogation of diagnostic and resistance biopsies to the single cell level, and with spatial profiling using single cell spatial multiomic (scSpMO) and coupling these enhanced diagnostic tools with functional validation with rational designed drug screens in patient-derived organoids (PDOs). Results: For deriving LC PDOs in epithelial and tumor microenvironment (TME) conditions to faithfully maintain the histological and genetic features of their respective LC tissues, we developed enrichment conditions for LC PDOs guided by tumor mutation variants and activated pathways. PDOs are maintained under the same treatment condition in the clinic (e.g., Lorlatinib) and drug testing is tailored to identify potentially most effective next lines of therapy. With nine patients enrolled and others in ongoing studies, single cell PDOs (at &gt; 80% establishment rate) were used to determine activated pathways, lineage plasticity and acquired resistance. Functional assays for acquired resistance such as PI3K/AKT signaling, Src kinase, BRAF fusion and MET hyperactivity allow the identification of potentially novel lines of therapy and treatment sequence and/or combinations for each patient, through the compassionate care program and/or trial participation. Conclusions: Our platform when empowered by combining datasets from scRNA-seq and scSpMO signatures, with validated functional drug responses in PDOs, offers the ability to perform high-content assays, predict and/or act on resistance to therapy for each patient, and provides a path for precision medicine-guided impact on patient care.
Using machine learning approach based on gene expression profiling to predict the risk of bone metastasis in lung cancer.
Authors: Tao He
Journal: Journal of Clinical Oncology (2024)

e20522 Background: Bone metastasis (BM) occurs in about 30% of patients with advanced lung cancer, and the prognosis of BM patients is very poor, with a median survival of 6-10 months. Therefore, we attempted to use machine learning methods based on gene expression profiles to construct a model to predict BM. Methods: Gene Expression Omnibus (GEO) database were searched, then these datasets (GSE10096, GSE29367, GSE29391, GSE76194, GSE175601) were included for analysis. Batch effects were removed using the ComBat function from the R package sva (v3.44.0). edgeR was used for differential expression analysis, and genes that had an absolute log2 fold change &gt; 1 and adjusted p-value &lt; 0.05, were selected as differentially expressed genes (DEGs) for modeling. Random forest (RF) modeling was performed using the R package random Forest (v4.7-1.1), and support vector machine (SVM) modeling and SVM Recursive Feature Elimination (SVM-RFE) were performed using the R package e1071 (v1.7-13). Survival curves were generated using the GEPIA2 R packages. Models performance was evaluated using the area under the receiver operating characteristics curve (AUC). Results: A total of 48 qualified cases were subjected to analysis, including 22 BM and 26 non-BM patients. Differential expression analysis revealed that a total of 327 DEGs were identified in the BM group. We divided the sample dataset into a training set and a test set. In the first approach, we attempted to use all 327 DEGs features to train the models. Among the evaluated models, the RF algorithm performed best with an AUC of 0.753, while the SVM algorithm had an AUC of 0.938. The second approach, we applied the SVM-RFE algorithm for feature selection, which ranked the features according to their importance, and the top 10 genes/features (TMC5, S100A4, EEF1A2, CLDN1, RALY, MYH10, MAGEC2, ALDH1A1, PI3, SALL1) were used to build a classification model (in the test set, sensitivity was100%, specificity: 81.8%, AUC:0.909). In addition, TCGA survival analysis of 10 genes was performed and found that the expression levels of five genes, including ALDH1A1, MYH10, RALY, CLDN1 and MAGEC2, were significantly associated with prognosis (p &lt; 0.1). Conclusions: Our study suggests that we have identified ten key genes associated with BM of lung cancer which could be potential therapeutic targets.
Correlation of gut dysbiosis biomarkers and peripheral thyroid hormones conversion in patients with (pts) advanced lung cancer.
Authors: Adele Bonato, Lorenzo Belluomini, Carolina Alves Costa Silva, Marine Fidelle, Roxanne Birebent, Federica Gattazzo, Claudia Parisi, Jordi Remon Masip, Pernelle Lavaud, David Planchard, Mihaela Aldea, Benjamin Besse, Fabrice Barlesi, Fotios Loupakis, Laurence Zitvogel, Lisa Derosa
Journal: Journal of Clinical Oncology (2024)

e20517 Background: Changes in gut microbiota may affect benefit from immunotherapy (IO). Gut dysbiosis can be identified by a fecal metagenomic signature (TOPOSCORE SIG1+ vs SIG2+), and/or by low levels of sMAdCAM-1. Recent studies showed that peripheral thyroid hormones conversion (fT3/fT4 ratio) has a strong prognostic role in various cancers, with low fT3/fT4 ratio predicting worse outcomes. Correlation between gut dysbiosis and metabolic stress, such as altered fT3/fT4 conversion, has not been investigated yet. Methods: A total of 77 lung cancer pts treated at Gustave Roussy Cancer Center with IO-based treatment with available measurement of baseline fT3/fT4 ratio, metagenomic data defined by TOPOSCORE category SIG1+ vs SIG2+, and/or level of sMAdCAM1 (low vs high according to the median) from the ongoing ONCOBIOTICS multicentric prospective study (NCT04567446) were selected. fT3/fT4 low vs high groups were defined adopting the median value as cut-off. Correlation between the fT3/fT4 ratio categories with TOPOSCORE and sMAdCAM1 were tested by means of a 2-sided chi-square test. Results: Baseline fT3/fT4 ratio and TOPOSCORE and/or sMAdCAM-1 data were available for 62 and 57 patients, respectively. A significant correlation was observed between fT3/fT4 ratio and TOPOSCORE category, with a higher proportion of SIG1+ pts (64.5%) vs SIG2+ (35.5%) in fT3/fT4 low group, and higher rate of SIG2+ (61.3%) vs SIG1+ (38.7%) pts in fT3/fT4 high group (p = .042). Similarly, a significant association was observed between low levels of sMAdCAM-1 and the fT3/fT4 low group (p = .047). In a independent dataset of 42 NSCLC treated with 2nd line IO, pts with low fT3/fT4 ratio had a mPFS of 1.7 mos and a mOS of 2.4 mos compared to 6.8 and 18.7 mos respectively for fT3/fT4 high pts (PFS HR = 0.56, 95%CI 0.30-1.05, p = .040; OS HR = 0.50, 95%CI 0.26-0.99, p = .030). Conclusions: Our data confirm the prognostic value of fT3/fT4 in progressing metastatic NSCLC and its correlation with gut dysbiosis markers. These findings open the way to further research for exploring specific therapeutic applications, such as tailored nutritional support and microbiota-driven interventions. Clinical trial information: NCT04567446 .
Health outcomes of pembrolizumab plus chemotherapy in non-small cell lung cancer.
Authors: Juan Francisco Marin Pozo, Vanesa Cao Viña, Ainoa Plaza Arbeo, Begoña Morales Rivero, Ana Laura Ortega Granados, Jose Antonio Lopez Lopez
Journal: Journal of Clinical Oncology (2024)

e20527 Background: Pembrolizumab (PEM) is a standard of treatment for advanced or metastatic non-small cell lung cancer (m-nSCLC) in tumors without biomarker actionable. The clinical practice protocol for m-nSCLC in our hospital includes PEM monotherapy if PD-L1 ≥50% and PEM+chemotherapy (PEM+CT) if PD-L1 1-49% or negative. The aim of this study is to determine the effectiveness results of PEM+CT in m-nSCLC in real clinical practice. Methods: It was designed a single center, observational and retrospective study. We included all patients (pts) treated with PEM+CT as first line of therapy with m-nSCLC of September-2019 to August-2023 at a hospital from south Spain, cut-off date 11/30/2023. The primary endpoints were overall survival (OS) and progression-free survival (PFS). The results were analyzed for population and by subgroups (histology, ECOG, previous localized disease (LD) and other), using Kaplan-Meier method. Results: 103 pts were enrolled (89% men), mean age was 66.2 years, 48.5% of pts had ECOG 0, 40.8% ECOG 1 and the rest ECOG 2-3. The tumors were 64.1 % non-squamous histology. 28.2% pts were treated for previous localized disease and 39.8% pts received radiation therapy at some point during treatment. At start of therapy the mean WBC count with PEM+CT was 10.0 x10(9)/L (sd=6.2). With a median follow-up of 10.8 months (m) the median for treatment duration was 5.7 m [95%CI 4.3-7.2].PFS was 7.2 m [IC95% 5.9-8.6] for all pts, 6.6 m [IC95% 4.6-8.7] for non-squamous and 8.2 m [IC95% 4.6-11.7] for squamous histology, p=0.158. OS was 14.2 m [IC95% 10.2-18.2] for all pts, 13.1 m [IC95% 7.6-18.6] and 14.2 [IC95% 9.3-19.2] by non-squamous and squamous histology, respectively, p=0.396.We found statistically significant difference in OS between: pts with ECOG 0-1 (median 15.1 m [IC95% 11.6-18.7]) vs pts with EOCG 2-3 (median 6.6 m [IC95% 1.6-11.6]), p=0.046 and pts previously treated pts of LD (median 21.7 m [IC95% 2.5-40.8]) vs pts with advanced or metastatic disease at onset (median 10.5 m [IC95% 5.2-15.9]), p=0.025. Another subgroup analysis is shown in table. Conclusions: The effectiveness of PEM+CT was similar in OS and PFS values to that of the pivotal trials. In contrast to these, our results were slightly better in squamous histology. The OS was better in pts with ECOG 0-1 and previously treated of localized disease, highlighting the importance of early diagnosis. [Table: see text]
The efficacy and safety of furmonertinib in patients with advanced <i>EGFR</i>-mutated NSCLC and leptomeningeal metastases.
Authors: Huan Han, Hong Tang
Journal: Journal of Clinical Oncology (2024)

e20525 Background: Leptomeningeal metastasis (LM) is a common and fatal complication in patients with advanced non-small-cell lung cancer (NSCLC) harboring epidermal growth factor receptor ( EGFR) mutation. This retrospective study aimed to evaluate the efficacy and safety of furmonertinib in patients with advanced EGFR-mutated ( EGFRm) NSCLC and LM. Methods: Eligible patients with confirmed advanced EGFRm NSCLC and LM were treated with furmonertinib. The primary endpoints were LM response rate and progression-free survival (PFS). Secondary endpoints were overall survival (OS) and adverse events (AEs). Results: This retrospective study analyzed the efficacy and safety of 31 EGFRm advanced NSCLC patients with LM at two hospitals between February 2020 to March 2023. The median age was 56 years (rang: 31-75), 22 (71.0%) were females. 19 (61.3%) patients had ECOG PS≥2 and all 31 patients (100%) had adenocarcinoma histology. For the treatment of LM, 19 (61.3%) patients received furmonertinib as first-line treatment, 8 (25.8%) patients as second-line treatment. 16 (51.6%) patients were treated with 240 mg of furmonertinib and 12 (38.7%) patients with 160 mg of furmonertinib. The median follow-up duration was 16.80 months (95% CI 12.71-20.89 months). LM objective response rate (ORR) was 75.0% and LM disease control rate (DCR) was 95.0% according to RANO-LM radiologic criteria. Overall ORR was 22.6% and DCR was 96.8%. Median LM duration of response (DoR) was 12.00 months (95% CI 7.40-16.60 months) and median overall DoR was 7.20 months (95% CI 3.60-10.80 months). 23 (74.2%) of the 31 patients had progressed or died by data cutoff. The median PFS was 11.70 months (95% CI 9.48-13.92 months) and the median OS was 18.40 months (95% CI 12.49-24.21 months). There was no significant difference in survival (median PFS: 11.70 months vs 10.90 months, p = 0.335; median OS: 18.40 months vs 13.50 months, p = 0.196) between the 19 patients who treated with furmonertinib as first-line therapy and those who treated with furmonertinib as second-line therapy. Although data on cerebrospinal fluid (CSF) clearance were not available, we observed improved neurologic function in 18 (75%) of 24 patients with baseline neurologic abnormalities. Logistic regression analysis showed that the therapeutic dose of furmonertinib was a positive predictor of OS in NSCLC patients harboring EGFR mutation with LM [HR: 2.679 (1.004-7.147), P = 0.049]. The most common AEs were rash, diarrhea and decreased appetite. However, most AEs were grade 1-2. Conclusions: Our study provides real-world clinical evidence that furmonertinib shows an encouraging efficacy and a manageable safety profile in patients with EGFRm NSCLC and LM.
Impact of intratumor heterogeneity (ITH) on survival outcomes in patients with non-small cell lung cancer (NSCLC) with high tumor mutational burden (TMB-H).
Authors: Stanislav Fridland, Hye Sung Kim, Young Kwang Chae
Journal: Journal of Clinical Oncology (2024)

e20519 Background: Heterogeneity among cancer cells within a tumor significantly impacts cancer development and resistance to treatment. Currently, no standard measure exists for assessing ITH in routine clinical settings. In this study, we applied a quantitative metric to assess intratumor genetic diversity in NSCLC, using a large, multicenter database. Methods: We accessed the Tempus database for next-generation sequencing results from patients with NSCLC who underwent immune checkpoint inhibitor (ICIs) monotherapy as their first-line treatment. We calculated the mutant-allele tumor heterogeneity (MATH), defined as the ratio of the distribution's range to the median of mutant-allele fractions across tumor-specific mutated loci. The primary outcomes were progression-free survival (PFS) and overall survival (OS). Results: A total of 160 pembrolizumab Significant differences in PFS between high and low MATH groups (MATH-H and MATH-L, respectively), using the 50th percentile as the cutoff, were observed among patients with stage 4 adenocarcinoma who had TMB-H above the 25th percentile (Table). In this subgroup, with 27 patients in the MATH-H/TMB-H group and 22 in the MATH-L/TMB-H group, the MATH-H/TMB-H group showed improved PFS (median PFS 5.19 vs. 1.61 months; p = 0.02). Differences in PFS were also observed among patients with TP53, KEAP1, and MET mutations (p &lt; 0.001, p = 0.02, and p = 0.004, respectively). TP53 mutations were found in 27.6% of the MATH-H group and 27.8% of the MATH-L group, KEAP1 mutations in 8.2% and 10.1%, respectively, and MET mutations in 5.1% and 8.9%, respectively. No significant PFS differences were found in groups with low TMB, other stages, or squamous histology. Conclusions: Our findings suggest that higher genetic diversity within stage 4 TMB-H lung adenocarcinoma, as quantified by MATH, might be linked to improved clinical outcomes among those who receive pembrolizumab as first-line therapy and supports further exploration of MATH as a predictive/prognostic biomarker. [Table: see text]