Department of Translational Medicine and Therapeutics, Healthcare Global Enterprises Limited, Bengaluru, Karnataka, India.
Department of Medical Oncology, Healthcare Global Enterprises Limited, Bengaluru, Karnataka, India.
J Cancer Res Ther. 2021 Jan-Mar;17(1):114-121. doi: 10.4103/jcrt.JCRT_910_20.
This study is an overall clinical analysis of anti-programmed cell death 1 (PD1) antibodies used in a single institution, emphasizing the role of baseline peripheral blood markers as a prognostic or predictor biomarker of immunotherapy.
Sixty-one patients were retrospectively analyzed from hospital medical records. The endpoint of this study was death from any cause and the survival time was calculated from the date of start of immunotherapy to the date of death. Descriptive and survival statistics was performed using SPSS version 23. Cutoff values for baseline biomarkers (neutrophil-to-lymphocyte ratio [NLR], platelet-to-lymphocyte ratio [PLR], neutrophil-to-eosinophil ratio [NER], and lymphocyte-to-monocyte ratio [LMR]) were obtained using cutp function of Evaluate Cutpoints software (R survMisc package). Pearson and Pearman correlation coefficients were used to examine the relationship of peripheral blood biomarkers.
Nighty-eight percent of the study population had Stage IV disease and total median overall survival postanti-PD1 therapy was 10.7 months. Patients receiving more than 5 doses of anti-PD1 therapy (12.6 m vs. 4.4 m, P < 0.001) and used in front lines (18.9 m vs. 10.7 m vs. 10.1 m vs. 2.8 m in first line, second line, third line, and >3 lines, respectively, P = 0.049) were found to have an impact in overall survival. Pembrolizumab showed a better survival compared to nivolumab (17.4 m vs. 8.2 m, P = 0.049) in our study. Among baseline biomarkers assessed, NLR (cutoff - 2.81, P = 0.003) and LMR (cutoff - 5.76, P = 0.017) has shown a statistically significant relationship with immunotherapy response. NER (cutoff - 24.32, P = 0.051) and PLR (cutoff - 190.8, P = 0.072) were also found to exhibit a strong relationship with anti-PD1 therapy response. NLR exhibits a statistically significant positive correlation with PLR (r = 0.917 P < 0.001) and NER (r = 0.400 P = 0.014).
Real-life data analysis of anti-PD1 use for solid cancers highlights that baseline NLR, PLR, NER, and LMR have a significant role as immunotherapy biomarkers. However, larger studies are required to further prove the specificity and sensitivity.
本研究是对单中心使用抗程序性死亡 1(PD1)抗体的整体临床分析,强调基线外周血标志物作为免疫治疗预后或预测生物标志物的作用。
回顾性分析 61 例患者的病历。本研究的终点为任何原因导致的死亡,生存时间从免疫治疗开始到死亡日期计算。使用 SPSS 版本 23 进行描述性和生存统计。使用 Evaluate Cutpoints 软件(R survMisc 包)的 cutp 函数获得基线生物标志物(中性粒细胞与淋巴细胞比值[NLR]、血小板与淋巴细胞比值[PLR]、中性粒细胞与嗜酸性粒细胞比值[NER]和淋巴细胞与单核细胞比值[LMR])的截断值。使用 Pearson 和 Pearman 相关系数来检查外周血生物标志物之间的关系。
研究人群中 98%为 IV 期疾病,抗 PD1 治疗后总中位总生存期为 10.7 个月。接受超过 5 剂抗 PD1 治疗(12.6 个月比 4.4 个月,P < 0.001)和一线治疗(18.9 个月比 10.7 个月比 10.1 个月比 2.8 个月,分别用于一线、二线、三线和>3 线治疗,P = 0.049)的患者总体生存有影响。在本研究中,与 nivolumab 相比,pembrolizumab 显示出更好的生存(17.4 个月比 8.2 个月,P = 0.049)。在评估的基线生物标志物中,NLR(截断值-2.81,P = 0.003)和 LMR(截断值-5.76,P = 0.017)与免疫治疗反应呈统计学显著相关。NER(截断值-24.32,P = 0.051)和 PLR(截断值-190.8,P = 0.072)也与抗 PD1 治疗反应呈强相关。NLR 与 PLR(r = 0.917,P < 0.001)和 NER(r = 0.400,P = 0.014)呈统计学显著正相关。
实体瘤抗 PD1 治疗的真实数据分析表明,基线 NLR、PLR、NER 和 LMR 作为免疫治疗生物标志物具有重要作用。然而,需要更大的研究来进一步证明其特异性和敏感性。