Early Adverse Event Derived Biomarkers in Predicting Clinical Outcomes in Patients with Advanced Non-Small Cell Lung Cancer Treated with Immunotherapy.
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ABSTRACT:
SUBMITTER: Chen DT
PROVIDER: S-EPMC10177532 | biostudies-literature | 2023 Apr
REPOSITORIES: biostudies-literature
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Early Adverse Event Derived Biomarkers in Predicting Clinical Outcomes in Patients with Advanced Non-Small Cell Lung Cancer Treated with Immunotherapy.
Chen Dung-Tsa DT Saltos Andreas N AN Rose Trevor T Thompson Zachary J ZJ Thapa Ram R Chiappori Alberto A Gray Jhanelle E JE
Cancers 20230428 9
<h4>Rationale</h4>Adverse events (AEs) have been shown to have clinical associations, in addition to patient safety assessments of drugs of interest. However, due to their complex content and associated data structure, AE evaluation has been restricted to descriptive statistics and small AE subset for efficacy analysis, limiting the opportunity for global discovery. This study takes a unique approach to utilize AE-associated parameters to derive a set of innovative AE metrics. Comprehensive anal ...[more]
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Project description:BackgroundThis study aimed to investigate the feasibility of using circulating tumor cells (CTCs), peripheral blood cells (PBCs), and circulating cell-free DNA (cfDNA) as biomarkers of immune checkpoint inhibitor treatment response in patients with advanced non-small cell lung cancer (NSCLC).MethodsWe recruited patients diagnosed with advanced NSCLC who received pembrolizumab or atezolizumab between July 2019 and June 2020. Blood was collected before each treatment cycle (C1-C4) to calculate absolute neutrophil count (ANC), neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), and platelet-to-lymphocyte ratio (PLR). CTCs, isolated using the CD-PRIMETM system, exhibited EpCAM/CK+/CD45- phenotype in BioViewCCBSTM. The cfDNA was extracted from plasma at the beginning of C1 and C4.ResultsThe durable clinical benefit (DCB) rate among 83 response-evaluable patients was 34%. CTC, PBC, and cfDNA levels at baseline (C1) were not significantly correlated with treatment response, although patients with DCB had lower CTC counts from C2 to C4. However, patients with low NLR, dNLR, PLR, and cfDNA levels at C1 had improved progression-free survival (PFS) and overall survival (OS). Patients with decreased CTC counts from C1 to C2 had higher median PFS (6.2 vs. 2.3 months; P=0.078) and OS (not reached vs. 6.8 months, P=0.021) than those with increased CTC counts. Low dNLR (≤2.0) at C1 and decreased CTC counts were independent factors for predicting survival.ConclusionsComprehensive analysis of CTC, PBC, and cfDNA levels at baseline and during treatment demonstrated they might be biomarkers for predicting survival benefit. This finding could aid in risk stratification of patients with advanced NSCLC who are undergoing immune checkpoint inhibitor treatment.
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Project description:Targeted therapy for advanced renal cell carcinoma (RCC) has recently expanded the available treatment options for patients with these malignancies. The rapid introduction of novel treatment options into clinical practice within a relatively short time frame has created some new challenges pertaining to adverse event (AE) management in patients with advanced RCC. Accumulating safety data from the pivotal phase III clinical trials of the anti-vascular endothelial growth factor (VEGF) antibody bevacizumab plus interferon, VEGF receptor tyrosine kinase inhibitors (sunitinib, sorafenib, and pazopanib), and mammalian target of rapamycin inhibitors (temsirolimus and everolimus) have served to characterize the toxicity profiles of these novel agents. Overall, it is evident that RCC-directed targeted therapy differs from immunotherapy and cytotoxic chemotherapy in terms of a number of unique nonhematologic AEs (some of which have not been traditionally encountered in oncology practice) and that there are distinctions within and across the various classes of agents with respect to the most prominent AEs and the risk for less common but serious complications. Although treatment-associated AEs are common, the majority of AEs reported during clinical trial experiences were grade 1 or 2 in severity and manageable with intervention in the form of supportive measures and/or dosage modification. Therefore, despite the relatively complex AE profiles of RCC-directed targeted therapy, patient education, consistent monitoring with a focus on early detection by health care providers (oncologists, general physicians, nurses), and the application of emerging AE management strategies may allow for prolonged treatment in most patients with advanced RCC.
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Project description:Taking into account the huge epidemiologic impact of lung cancer (in 2020, lung cancer accounted for 2,206,771 of the cases and for 1,796,144 of the cancer-related deaths, representing the second most common cancer in female patients, the most common cancer in male patients, and the second most common cancer in male and female patients) and the current lack of recommendations in terms of prognostic factors for patients selection and management, this article aims to provide an overview of the current landscape in terms of currently available immunotherapy treatments and the most promising assessed prognostic biomarkers, highlighting the current state-of-the-art and hinting at future challenges.
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Project description:BackgroundRadiotherapy may cause grade ≥3 cardiac events, necessitating a better understanding of risk factors. The potential predictive role of imaging biomarkers with radiotherapy doses for cardiac event occurrence has not been studied.ObjectivesThe aim of this study was to establish the associations between cardiac substructure dose and coronary artery calcium (CAC) scores and cardiac event occurrence.MethodsA retrospective cohort analysis included patients with locally advanced non-small cell lung cancer treated with radiotherapy (2006-2018). Cardiac substructures, including the left anterior descending coronary artery, left main coronary artery, left circumflex coronary artery, right coronary artery, and TotalLeft (left anterior descending, left main, and left circumflex coronary arteries), were contoured. Doses were measured in 2-Gy equivalent units, and visual CAC scoring was compared with automated scoring. Grade ≥3 adverse cardiac events were recorded. Time-dependent receiver-operating characteristic modeling, the log-rank statistic, and competing-risk models were used to measure prediction performance, threshold modeling, and the cumulative incidence of cardiac events, respectively.ResultsOf the 233 eligible patients, 61.4% were men, with a median age of 68.1 years (range: 34.9-90.7 years). The median follow-up period was 73.7 months (range: 1.6-153.9 months). Following radiotherapy, 22.3% experienced cardiac events, within a median time of 21.5 months (range: 1.7-118.9 months). Visual CAC scoring showed significant correlation with automated scoring (r = 0.72; P < 0.001). In a competing-risk multivariable model, TotalLeft volume receiving 15 Gy (per 1 cc; HR: 1.38; 95% CI: 1.11-1.72; P = 0.004) and CAC score >5 (HR: 2.51; 95% CI: 1.08-5.86; P = 0.033) were independently associated with cardiac events. A model incorporating age, TotalLeft CAC (score >5), and volume receiving 15 Gy demonstrated a higher incidence of cardiac events for a high-risk group (28.9%) compared with a low-risk group (6.9%) (P < 0.001).ConclusionsAdverse cardiac events associated with radiation occur in more than 20% of patients undergoing thoracic radiotherapy within a median time of <2 years. The present findings provide further evidence to support significant associations between TotalLeft radiotherapy dose and cardiac events and define CAC as a predictive risk factor.
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Project description:ImportanceOnly a small fraction of patients with advanced non-small cell lung cancer (NSCLC) respond to immune checkpoint inhibitor (ICI) treatment. For optimal personalized NSCLC care, it is imperative to identify patients who are most likely to benefit from immunotherapy.ObjectiveTo develop a supervised deep learning-based ICI response prediction method; evaluate its performance alongside other known predictive biomarkers; and assess its association with clinical outcomes in patients with advanced NSCLC.Design, setting, and participantsThis multicenter cohort study developed and independently validated a deep learning-based response stratification model for predicting ICI treatment outcome in patients with advanced NSCLC from whole slide hematoxylin and eosin-stained images. Images for model development and validation were obtained from 1 participating center in the US and 3 in the European Union (EU) from August 2014 to December 2022. Data analyses were performed from September 2022 to May 2024.ExposureMonotherapy with ICIs.Main outcomes and measuresModel performance measured by clinical end points and objective response rate (ORR) differentiation power vs other predictive biomarkers, ie, programmed death-ligand 1 (PD-L1), tumor mutational burden (TMB), and tumor-infiltrating lymphocytes (TILs).ResultsA total of 295 581 image tiles from 958 patients (mean [SD] age, 66.0 [10.6] years; 456 [48%] females and 502 [52%] males) treated with ICI for NSCLC were included in the analysis. The US-based development cohort consisted of 614 patients with median (IQR) follow-up time of 54.5 (38.2-68.1) months, and the EU-based validation cohort, 344 patients with 43.3 (27.4-53.9) months of follow-up. The ORR to ICI was 26% in the developmental cohort and 28% in the validation cohort. The deep learning model's area under the receiver operating characteristic curve (AUC) for ORR was 0.75 (95% CI, 0.64-0.85) in the internal test set and 0.66 (95% CI, 0.60-0.72) in the validation cohort. In a multivariable analysis, the deep learning model's score was an independent predictor of ICI response in the validation cohort for both progression-free (hazard ratio, 0.56; 95% CI, 0.42-0.76; P < .001) and overall survival (hazard ratio, 0.53; 95% CI, 0.39-0.73; P < .001). The tuned deep learning model achieved a higher AUC than TMB, TILs, and PD-L1 in the internal set; in the validation cohort, it was superior to TILs and comparable with PD-L1 (AUC, 0.67; 95% CI, 0.60-0.74), with a 10-percentage point improvement in specificity. In the validation cohort, combining the deep learning model with PD-L1 scores achieved an AUC of 0.70 (95% CI, 0.63-0.76), outperforming either marker alone, with a response rate of 51% compared to 41% for PD-L1 (≥50%) alone.Conclusions and relevanceThe findings of this cohort study demonstrate a strong and independent deep learning-based feature associated with ICI response in patients with NSCLC across various cohorts. Clinical use of this deep learning model could refine treatment precision and better identify patients who are likely to benefit from ICI for treatment of advanced NSCLC.
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Project description:BackgroundBiomarkers that effectively predict response to anti-PD-1 mAb therapy in cancer patients are an unmet need. We evaluated the utility of small extracellular vesicles (sEV) as biomarkers of response to immunotherapy in recurrent/metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) patients.MethodsPlasma sEV were isolated from 24 R/M HNSCC patients prior to immunotherapy initiation. sEV were separated by immune capture into T cell-derived CD3(+) and tumor-enriched CD3(-) subsets. Stimulatory and suppressive profiles of CD3(-) sEV were determined by on-bead flow cytometry. Differences were assessed using nonparametric tests. Multivariable Cox regression was used to evaluate the relationship with overall (OS) and progression free survival (PFS).ResultsCD3(-)CD44v3(+) sEV represented the majority of plasma sEV; the T-cell-derived CD3(+) fraction was significantly smaller. High CD3(+) sEV was associated with better OS and PFS. Total CD3(-)CD44v3(+) sEV was not associated with outcome. However, suppressive and stimulatory profiles were associated with OS; the suppressive/stimulatory ratio was associated with best response. Exploration of individual proteins on CD3(-) sEV showed that high PD-L1 and high CTLA-4 were associated with better outcomes.ConclusionsEvaluation of the T cell-derived-CD3(+) and tumor-enriched CD3(-) plasma sEV subsets indicated their potential utility as biomarkers of response to immunotherapy.
| S-EPMC11390474 | biostudies-literature
Project description:Immune checkpoint inhibitors, as nivolumab, are used in advanced non-small cell lung cancer (NSCLC). However, no associated biomarker is validated in clinical practice with this drug. We investigated herein immune-related blood markers in patients with advanced NSCLC treated with nivolumab. Plasma of 43 consecutive patients were prospectively collected at time of the diagnosis of cancer, at the initiation of nivolumab and at the first tumour evaluation (2 months). Concentrations of PD-L1 (sPD-L1), soluble PD-L2 (sPD-L2), Interleukine-2 (sIl-2), Interferon-gamma (sIFN-γ), and Granzyme B (sGranB) were quantified by ELISA. Cell free RNA was quantified by Reverse Transcriptase -PCR), and plasmatic microRNAs (miRNAs) were evaluated by targeted sequencing. Expression of PD-L1 on tumour biopsies was performed by immunohistochemistry using E13LN. High sPD-L1 at 2 months and increase of sPD-L1 concentrations were associated with poor response and absence of clinical benefit (nivolumab treatment less than 6 months). The variation of sPD-L1 concentrations were confirmed by RNA quantification. sPD-L1 concentrations were not correlated with PD-L1 expression on corresponding tumour samples. Low sGranB at nivolumab initiation was also associated with poor response. High sPD-L1 and low sGranB were associated with poor progression-free survival (PFS) and overall survival (OS). Low sPD-L2, low sIl-2 and high sIFN-γ were associated with grade 3-4 toxicities. Finally, miRNA screening showed that patients with clinical benefit (n = 9) had down-expression of miRNA-320b and -375 compared to patients with early progression at 2 months (n = 9). In conclusion, our results highlight the interest of circulating biomarkers in patients treated with nivolumab.
| S-EPMC6136870 | biostudies-literature
Project description:BackgroundAdvanced non-small cell lung cancer (NSCLC) presents significant treatment challenges, with chemo-immunotherapy emerging as a promising approach. This study explores the potential of lipidomic biomarkers to predict responses to chemo-immunotherapy in advanced non-small cell lung cancer (NSCLC) patients.MethodsA prospective analysis was conducted on 68 NSCLC patients undergoing chemo-immunotherapy, divided into disease control (DC) and progressive disease (PD) groups based on treatment response. Pre-treatment serum samples were subjected to lipidomic profiling using liquid chromatography-mass spectrometry (LC-MS). Key predictive lipids (biomarkers) were identified through projection to latent structures discriminant analysis. A biomarker combined model and a clinical combined model were developed to enhance the prediction accuracy. The predictive performances of the clinical combined model in different histological subtypes were also performed.ResultsSix lipids were identified as the key lipids. The expression levels of PC(16:0/18:2), PC(16:0/18:1), PC(16:0/18:0), CE(20:1), and PC(14:0/18:1) were significantly up-regulated. While the expression level of TAG56:7-FA18:2 was significantly down-regulated. The biomarker combined model demonstrated a receiver operating characteristic (ROC) curve of 0.85 (95% CI: 0.75-0.95) in differentiating the PD from the DC. The clinical combined model exhibited an AUC of 0.87 (95% CI: 0.79-0.96) in differentiating the PD from the DC. The clinical combined model demonstrated good discriminability in DC and PD patients in different histological subtypes with the AUC of 0.78 (95% CI: 0.62-0.96), 0.79 (95% CI: 0.64-0.94), and 0.86 (95% CI: 0.52-1.00) in squamous cell carcinoma, large cell carcinoma, and adenocarcinoma subtype, respectively. Pathway analysis revealed the metabolisms of linoleic acid, alpha-linolenic acid, glycerolipid, arachidonic acid, glycerophospholipid, and steroid were implicated in the chemo-immunotherapy response in advanced NSCLC.ConclusionLipidomic profiling presents a highly accurate method for predicting responses to chemo-immunotherapy in patients with advanced NSCLC, offering a potential avenue for personalized treatment strategies.
| S-EPMC11260645 | biostudies-literature