Novel nomogram for predicting survival in advanced non-small cell lung cancer receiving anti-PD-1 plus chemotherapy with or without antiangiogenic therapy.
This study aimed to develop and validate a novel nomogram to predict survival in advanced non-small cell lung cancer (NSCLC) receiving programmed cell death 1 (PD-1) inhibitor plus chemotherapy with or without antiangiogenic therapy.
A total of 271 patients with advanced NSCLC who received anti-PD-1 plus chemotherapy with or without antiangiogenic therapy were enrolled in our center and randomized into the training cohort (n = 133) and the internal validation cohort (n = 138). Forty-five patients from another center were included as an independent external validation cohort. The nomogram was created based on the multivariate Cox regression analysis to predict overall survival (OS) and progression-free survival (PFS). The performance of the nomogram was assessed using the concordance index (C-index), the time-dependent area under the receiver operating (ROC) curves (AUCs), calibration curves, and decision curve analysis (DCA).
Four factors significantly associated with OS were utilized to create a nomogram to predict OS: Eastern Cooperative Oncology Group performance status (ECOG PS), programmed cell death-ligand 1 (PD-L1) expression, chemotherapy cycle, and pretreatment lactate dehydrogenase-albumin ratio (LAR). Six variables significantly associated with PFS were incorporated into the development of a nomogram for predicting PFS: ECOG PS, histology, PD-L1 expression, chemotherapy cycle, pretreatment platelet to lymphocyte (PLR), and pretreatment LAR. The C-indexes of the nomogram for predicting OS and PFS were 0.750 and 0.747, respectively. The AUCs for predicting the 6-month, 12-month, and 18-month OS and PFS were 0.847, 0.791, and 0.776 and 0.810, 0.787, and 0.861, respectively. The calibration curves demonstrated a good agreement between predictions and actual observations. The DCA curves indicated that the nomograms had good net benefits. Furthermore, the nomogram model was well-validated in the internal and external cohorts.
The novel nomogram for predicting the prognosis of advanced NSCLC receiving anti-PD-1 plus chemotherapy with or without antiangiogenic therapy may help guide clinical treatment decisions.
Wu Y
，Lv C
，Lin M
，Hong Y
，Du B
，Yao N
，Zhu Y
，Ji X
，Li J
，Lai J
... -
《Frontiers in Immunology》
Predictive value of inflammation and nutritional index in immunotherapy for stage IV non-small cell lung cancer and model construction.
Identifying individuals poised to gain from immune checkpoint inhibitor (ICI) therapies is a pivotal element in the realm of tailored healthcare. The expression level of Programmed Death Ligand 1 (PD-L1) has been linked to the response to ICI therapy, but its assessment typically requires substantial tumor tissue, which can be challenging to obtain. In contrast, blood samples are more feasible for clinical application. A number of promising peripheral biomarkers have been proposed to overcome this hurdle. This research aims to evaluate the prognostic utility of the albumin-to-lactate dehydrogenase ratio (LAR), the Pan-immune-inflammation Value (PIV), and the Prognostic Nutritional Index (PNI) in predicting the response to ICI therapy in individuals with advanced non-small cell lung cancer (NSCLC). Furthermore, the study seeks to construct a predictive nomogram that includes these markers to facilitate the selection of patients with a higher likelihood of benefiting from ICI therapy. A research initiative scrutinized the treatment records of 157 advanced NSCLC patients who received ICI therapy across two Jiangxi medical centers. The cohort from Jiangxi Provincial People's Hospital (comprising 108 patients) was utilized for the training dataset, while the contingent from Jiangxi Cancer Hospital (49 patients) served for validation purposes. Stratification was based on established LAR, PIV, and PNI benchmarks to explore associations with DCR and ORR metrics. Factorial influences on ICI treatment success were discerned through univariate and multivariate Cox regression analysis. Subsequently, a Nomogram was devised to forecast outcomes, its precision gauged by ROC and calibration curves, DCA analysis, and cross-institutional validation. In the training group, the optimal threshold values for LAR, PIV, and PNI were identified as 5.205, 297.49, and 44.6, respectively. Based on these thresholds, LAR, PIV, and PNI were categorized into high (≥ Cut-off) and low (< Cut-off) groups. Patients with low LAR (L-LAR), low PIV (L-PIV), and high PNI (H-PNI) exhibited a higher disease control rate (DCR) (P < 0.05) and longer median progression-free survival (PFS) (P < 0.05). Cox multivariate analysis indicated that PS, malignant pleural effusion, liver metastasis, high PIV (H-PIV), and low PNI (L-PNI) were risk factors adversely affecting the efficacy of immunotherapy (P < 0.05). The Nomogram model predicted a concordance index (C-index) of 0.78 (95% CI: 0.73-0.84). The areas under the ROC curve (AUC) for the training group at 6, 9, and 12 months were 0.900, 0.869, and 0.866, respectively, while the AUCs for the external validation group at the same time points were 0.800, 0.886, and 0.801, respectively. Throughout immunotherapy, PIV and PNI could act as prospective indicators for forecasting treatment success in NSCLC patients, while the devised Nomogram model exhibits strong predictive performance for patient prognoses.
Lei W
，Wang W
，Qin S
，Yao W
... -
《Scientific Reports》
Two nomograms constructed for predicting the efficacy and prognosis of advanced non‑small cell lung cancer patients treated with anti‑PD‑1 inhibitors based on the absolute counts of lymphocyte subsets.
Patients treated with immune checkpoint inhibitors (ICIs) are at risk of considerable adverse events, and the ongoing struggle is to accurately identify the subset of patients who will benefit. Lymphocyte subsets play a pivotal role in the antitumor response, this study attempted to combine the absolute counts of lymphocyte subsets (ACLS) with the clinicopathological parameters to construct nomograms to accurately predict the prognosis of advanced non-small cell lung cancer (aNSCLC) patients treated with anti-PD-1 inhibitors.
This retrospective study included a training cohort (n = 200) and validation cohort (n = 100) with aNSCLC patients treated with anti-PD-1 inhibitors. Logistic and Cox regression were conducted to identify factors associated with efficacy and progression-free survival (PFS) respectively. Nomograms were built based on independent influencing factors, and assessed by the concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve.
In training cohort, lower baseline absolute counts of CD3+ (P < 0.001) and CD4+ (P < 0.001) were associated with for poorer efficacy. Hepatic metastases (P = 0.019) and lower baseline absolute counts of CD3+ (P < 0.001), CD4+ (P < 0.001), CD8+ (P < 0.001), and B cells (P = 0.042) were associated with shorter PFS. Two nomograms to predict efficacy at 6-week after treatment and PFS at 4-, 8- and 12-months were constructed, and validated in validation cohort. The area under the ROC curve (AUC-ROC) of nomogram to predict response was 0.908 in training cohort and 0.984 in validation cohort. The C-index of nomogram to predict PFS was 0.825 in training cohort and 0.832 in validation cohort. AUC-ROC illustrated the nomograms had excellent discriminative ability. Calibration curves showed a superior consistence between the nomogram predicted probability and actual observation.
We constructed two nomogram based on ACLS to help clinicians screen of patients with possible benefit and make individualized treatment decisions by accurately predicting efficacy and PFS for advanced NSCLC patient treated with anti-PD-1 inhibitors.
Liu A
，Zhang G
，Yang Y
，Xia Y
，Li W
，Liu Y
，Cui Q
，Wang D
，Yu J
... -
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ResearchGate
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钛学术
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