Immune checkpoint inhibitors (ICI) have created an advanced shift in the treatment of lung cancer (LC), but the existing biomarkers were not in clinical and widespread use. The purpose of this study was to develop a new nomogram with immune factors used for monitoring the response to ICI therapy. LC patients with PD-1/PD-L1 inhibitors treatment were included in this analysis. The immune biomarkers and clinicopathological characteristic values at baseline were used to estimate the tumor response. The nomogram was based on the factors that were determined by univariate and multivariate Cox hazard analysis. For internal validation, bootstrapping with 1000 resamples was used. The concordance index ( C -index) and calibration curve were used to determine the predictive accuracy and discriminative ability of the nomogram. Overall survival (OS) was estimated using the Kaplan-Meier method. Patients with lung metastasis ( P = 0.010 ), higher baseline neutrophil-lymphocyte ratio (NLR) level ( P < 0.001 ), lower baseline lymphocyte-monocyte (LMR) ( P = 0.019 ), and lower CD3+CD8+ T cell count ( P = 0.009 ) were significantly related to the tumor response. The above biomarkers were contained into the nomogram. The calibration plot for the probability of OS showed an optimal agreement between the actual observation and prediction by nomogram at 3 or 5 years after therapy. The C -index of nomogram for OS prediction was 0.804 (95% CI: 0.739-0.869). Decision curve analysis demonstrated that the nomogram was clinically useful. Moreover, patients were divided into two distinct risk groups for OS by the nomogram: low-risk group (OS: 17.27 months, 95% CI: 14.75-19.78) and high-risk group (OS: 6.11 months, 95% CI: 3.57-8.65), respectively. A nomogram constructed with lung metastasis baseline NLR, LMR, and CD3+CD8+ T cell count could be used to monitor and predict clinical benefit and prognosis in lung cancer patients within ICI therapy.
Introduction: Various studies have reported that anti-PD-1/PD-L1 treatment may lead to the rapid development of tumors called hyperprogressive disease (HPD). A nomogram for HPD prediction in NSCLC patients is urgently needed. Methods: This retrospective cohort study included 176 cases for establishing a model of HPD prediction and 85 cases for validation in advanced NSCLC patients treated with PD-1/PD-L1 inhibitors. HPD was defined as tumor growth rate (TGR, ≥ 2), tumor growth kinetics (TGK, ≥ 2) or time to treatment failure (TTF, ≤ 2 months). Univariate and multivariate logistic regression were used to estimate the specified factors associated with HPD. Then, the nomogram was developed and validated. Results: Anti-PD-1/PD-L1 therapy resulted in a 9.66% (17/176) incidence of HPD in advanced NSCLC. The overall survival (OS) and progression-free survival (PFS) in patients with HPD were significantly shorter than those in patients without HPD (OS: 7.00 vs 12.00 months, P<0.01; PFS: 2.00 vs 5.00 months, P<0.001, respectively). The HPD prediction nomogram included APTT (P<0.01), CD4+ CD25+ CD127-low cells (Treg cells) (P<0.01), the presence of liver metastasis (P<0.05), and more than two metastatic sites (P<0.05). Then, patients were divided into two groups by the "HPD score" calculated by the nomogram. The C-index was 0.845, while the area under the curve (AUC) was 0.830 (sensitivity 75.00%, specificity 91.70%). The calibration plot of HPD probability showed an optimal agreement between the actual observation and prediction by the nomogram. In the validation cohort, the AUC was up to 0.960 (sensitivity 88.70%, specificity 89.80%). Conclusions:The nomogram was constructed with the presence of liver metastasis, more than two metastatic sites, lengthened APTT and a high level of Treg cells, which could be used to predict HPD risk.
BackgroundImmune checkpoint inhibitors (ICIs)-based treatments have been recommended as the first line for refractory recurrent and/or metastatic nasopharyngeal carcinoma (NPC) patients, yet responses vary, and predictive biomarkers are urgently needed. We selected serum interleukin-15 (sIL-15) out of four interleukins as a candidate biomarker, while most patients’ sIL-15 levels were too low to be detected by conventional methods, so it was necessary to construct a highly sensitive method to detect sIL-15 in order to select NPC patients who would benefit most or least from ICIs.MethodsCombining a primer exchange reaction (PER), transcription-mediated amplification (TMA), and a immuno-PER-TMA-CRISPR/Cas13a system, we developed a novel multiple signal amplification platform with a detection limit of 32 fg/mL, making it 153-fold more sensitive than ELISA.ResultsThis platform demonstrated high specificity, repeatability, and versatility. When applied to two independent cohorts of 130 NPC sera, the predictive value of sIL-15 was accurate in both cohorts (area under the curve: training, 0.882; validation, 0.898). Additionally, lower sIL-15 levels were correlated with poorer progression-free survival (training, HR: 0.080, p<0.0001; validation, HR: 0.053, p<0.0001).ConclusionThis work proposes a simple and sensitive approach for sIL-15 detection to provide insights for personalized immunotherapy of NPC patients.
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