Immune-inflammatory biomarkers as prognostic factors for immunotherapy in pretreated advanced urinary tract cancer patients: an analysis of the Italian SAUL cohort

Fornarini, Giuseppe; Rebuzzi, Sara Elena; Banna, Giuseppe Luigi; Calabrò, Fabio; Scandurra, Giuseppa; Giorgi, Ugo De; Masini, Cristina; Baldessari, Cinzia; Naglieri, Emanuele; Caserta, Claudia; Manacorda, S.; Maruzzo, Marco; Milella, Michèle; Buttigliero, Consuelo; Tambaro, Rosa; Ermacora, Paola; Morelli, Franco; Nolè, Franco; Astolfi, C.; Sternberg, Cora N.

doi:10.1016/j.esmoop.2021.100118

Cited by 40 publications

(55 citation statements)

References 47 publications

(53 reference statements)

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“…The median age of the patients was 61 years (IQR, 55-69). The median follow-up period was 36 months (IQR, [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. In the validation cohort (Shanghai Cancer Cohort n=46), the median age of the patients was 59 years (IQR, 54-71).…”

Section: Resultsmentioning

confidence: 99%

A novel scoring model (SII-MSKCC) to better predict the clinical outcomes of primary central nervous system lymphoma

Xia

Cao

et al. 2022

Preprint

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Currently, risk stratification and prognosis stratification of primary central nervous system lymphoma (PCNSL) is predominantly based on the Memorial Sloan-Kettering Cancer Center (MSKCC) model, but the prognostic value is controversial. Recently, the systemic immune-inflammation index (SII) has been considered as an important prognostic factor in PCNSL. 174 newly diagnosed PCNSL patients treated with methotrexate-based combination immunochemotherapy were analyzed in two retrospective discovery cohorts (n=128) and one prospective validation cohort (n=46) to determine whether the SII-MSKCC model could better predict the clinical outcomes. Generalized additive model, Kaplan–Meier curve, and Cox analysis were performed. The scores of SII-MSKCC model were: 890>SII>450, score=0; SII＜450, score=1; SII>890, score=2; Age≦50, score=0; age >50 + KPS≧70, score=1; age >50 + KPS≦70, score=2. Here, we report the results of discovery cohort that SII-MSKCC model provided higher prognostic performance compared with the MSKCC model. We further validate the prognosis value of the SII-MSKCC model with an external dataset from an independent prospective cohort, which confirmed its robustness. In multivariable analysis, the SII-MSKCC model remained significantly independently associated with clinical outcomes. Thus, SII-MSKCC model could be used across centers to better predict prognosis in PCNSL.

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Section: Resultsmentioning

confidence: 99%

A novel scoring model (SII-MSKCC) to better predict the clinical outcomes of primary central nervous system lymphoma

Xia

Cao

et al. 2022

Preprint

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“…Furthermore, the prediction probability for patients who had no response slightly increased from 5.9–37.3% to 43.1–51.0%. To date, these inflammation indexes as prognostic biomarkers have been widely investigated in a variety of tumors, but there have been few studies on their prediction probability of agent efficacy; for example, SII at baseline could determine patients with advanced urinary tract cancer who might benefit from immunotherapy [ 29 ], and baseline NLR and LMR could predict response to first-line chemotherapy and TAS-102 + bevacizumab in advanced biliary cancer and colorectal cancer [ 30 , 31 ]. In ESCC studies, meta-analysis showed that clinical indicators such as NLR, PLR, LMR, and SII had moderate predictive ability for prognosis [ 32 ], yet their prediction ability of therapeutic efficacy, especially in relation to immunotherapy, remains rarely reported.…”

Section: Discussionmentioning

confidence: 99%

Combining serum inflammation indexes at baseline and post treatment could predict pathological efficacy to anti‑PD‑1 combined with neoadjuvant chemotherapy in esophageal squamous cell carcinoma

Zhang

Gari

et al. 2022

J Transl Med

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Background The neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) have been used to predict therapeutic response in different tumors. However, no assessments of their usefulness have been performed in esophageal squamous cell carcinoma (ESCC) patients receiving anti‑PD‑1 combined with neoadjuvant chemotherapy. Methods The respective data of 64 ESCC patients receiving anti‑PD‑1 combined with neoadjuvant chemotherapy were analyzed. Whether NLR, LMR, PLR, and SII at baseline and post-treatment might predict pathological response to anti‑PD‑1 plus neoadjuvant chemotherapy, and cutoff values of these parameters were all determined by ROC curve analysis. Results NLR (cutoff = 3.173, AUC = 0.644, 95% CI 0.500–0.788, P = 0.124, sensitivity = 1.000, specificity = 0.373), LMR (cutoff = 1.622, AUC = 0.631, 95% CI 0.477–0.784, P = 0.161, sensitivity = 0.917, specificity = 0.137), PLR (cutoff = 71.108, AUC = 0.712, 95% CI 0.575–0.849, P = 0.023, sensitivity = 1.000, specificity = 0.059), and SII at baseline (cutoff = 559.266, AUC = 0.681, 95% CI 0.533–0.830, P = 0.052, sensitivity = 0.373, specificity = 1.000) seemed to be a useful predictor for distinguishing responders from non-responders. Combining NLR with SII at baseline (AUC = 0.729, 95% CI 0.600–0.858, P = 0.014, sensitivity = 0.917, specificity = 0.510), LMR and SII at baseline (AUC = 0.735, 95% CI 0.609–0.861, P = 0.012, sensitivity = 1.000 specificity = 0.471), PLR and SII at baseline (AUC = 0.716, 95% CI 0.584–0.847, P = 0.021, sensitivity = 1.000 specificity = 0.431), and LMR and PLR at post-treatment in the third period (AUC = 0.761, 95% CI 0.605–0.917, P = 0.010, sensitivity = 0.800, specificity = 0.696) might slightly increase the prediction ability to determine patients who have response or no response. Finally, combining LMR at baseline, SII at post-treatment in the second period with PLR at post-treatment in the third period could be considered a better predictor for discriminating responders and non-responders than single or dual biomarkers (AUC = 0.879, 95% CI 0.788–0.969, P = 0.0001, sensitivity = 0.909, specificity = 0.800). Conclusions The models we constructed allowed for the accurate and efficient stratification of ESCC patients receiving anti-PD-1 plus chemotherapy and are easily applicable for clinical practice at no additional cost.

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“…Inflammatory indices from peripheral blood have been investigated in combination with other clinical prognostic factors within prognostic models for risk-stratification in several cancer types treated with ICIs, especially the NSCLC [ 91 , 98 , 99 , 100 ]. The interest in prognostic models has also recently been increasing for genitourinary tumours, including RCC [ 101 , 102 , 103 ] and UC [ 63 , 65 , 68 , 95 , 96 , 97 , 104 ].…”

Section: Clinical Factorsmentioning

confidence: 99%

“…In patients with mUC treated with ICIs, the most frequently included factors in prognostic models are inflammatory indices (such as NLR, C-reactive protein and albumin) and pretreatment clinical parameters (i.e., the PS and metastatic site) [ 63 , 65 , 68 , 95 , 96 , 97 , 104 , 105 , 106 ]. The molecular factors included in the prognostic scores were PD-L1 expression and genomic parameters (e.g., single-nucleotide variants) [ 96 , 104 ].…”

Section: Clinical Factorsmentioning

confidence: 99%

“…Most of these analyses are derived from retrospective studies, except for the two prognostic scores developed by Fornarini et al and Sonpavde et al, and the machine learning (ML) model developed by Abuhelwa et al [ 104 , 105 , 106 ]. The two prognostic models were developed by the retrospective analysis of the phase IIIb SAUL trial [ 104 ] and the two phase I/II trials [ 105 ], both evaluating second-line ICI monotherapy. The ML model was built using the atezolizumab cohort of the IMvigor210 trial as the training cohort, and the IMvigor211 trial population as the external validation [ 106 ].…”

Section: Clinical Factorsmentioning

confidence: 99%

See 1 more Smart Citation

Prognostic and Predictive Factors in Advanced Urothelial Carcinoma Treated with Immune Checkpoint Inhibitors: A Review of the Current Evidence

Rebuzzi

Banna

Murianni

et al. 2021

Cancers

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In recent years, the treatment landscape of urothelial carcinoma has significantly changed due to the introduction of immune checkpoint inhibitors (ICIs), which are the standard of care for second-line treatment and first-line platinum-ineligible patients with advanced disease. Despite the overall survival improvement, only a minority of patients benefit from this immunotherapy. Therefore, there is an unmet need to identify prognostic and predictive biomarkers or models to select patients who will benefit from ICIs, especially in view of novel therapeutic agents. This review describes the prognostic and predictive role, and clinical readiness, of clinical and tumour factors, including new molecular classes, tumour mutational burden, mutational signatures, circulating tumour DNA, programmed death-ligand 1, inflammatory indices and clinical characteristics for patients with urothelial cancer treated with ICIs. A classification of these factors according to the levels of evidence and grades of recommendation currently indicates both a prognostic and predictive value for ctDNA and a prognostic relevance only for concomitant medications and patients’ characteristics.

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Immune-inflammatory biomarkers as prognostic factors for immunotherapy in pretreated advanced urinary tract cancer patients: an analysis of the Italian SAUL cohort

Cited by 40 publications

References 47 publications

A novel scoring model (SII-MSKCC) to better predict the clinical outcomes of primary central nervous system lymphoma

A novel scoring model (SII-MSKCC) to better predict the clinical outcomes of primary central nervous system lymphoma

Combining serum inflammation indexes at baseline and post treatment could predict pathological efficacy to anti‑PD‑1 combined with neoadjuvant chemotherapy in esophageal squamous cell carcinoma

Prognostic and Predictive Factors in Advanced Urothelial Carcinoma Treated with Immune Checkpoint Inhibitors: A Review of the Current Evidence

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