Prognostic and Predictive Factors in Advanced Urothelial Carcinoma Treated with Immune Checkpoint Inhibitors: A Review of the Current Evidence
Abstract
:Simple Summary
Abstract
1. Introduction
2. Molecular Factors
2.1. Molecular Classes
2.2. Tumour Mutational Burden (TMB)
2.3. Mulecular Signatures
2.4. ctDNA
2.5. Programmed Death Ligand-1 (PD-L1)
3. Clinical Factors
3.1. Patient’s Characteristics
3.2. Concomitant Medications
3.3. Inflammatory Indices
3.4. Combined Prognostic Tools
4. Radiomics
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Category | Description | Prognostic and Predictive Value in UC | Notes |
---|---|---|---|
Molecular classes | 6 molecular (transcriptomic) classes based on a consensus of MIBC: Squamous (Ba/Sq)-35%; luminal/papillary (LumP), 24%; luminal unstable (LumU), 15%; stroma-rich, 15%; luminal non-specified (LumNS), 8%; neuroendocrine (NE)-like, 3% [9]. | Ba/Sq is associated with shorter OS, NE-like is associated with worse prognosis (LumP as reference) [9]. LUmNS, LumU, NE-like are more responsive to ICIs [9]. | Need for prospective validation |
TMB | Total number of non-synonymous mutations per coding area of a tumour genome. UC is characterized by high values of TMB compared to other tumours [23]. | High TMB predicts OS benefit from avelumab maintenance therapy [23] and improved OS with atezolizumab when compared to CT [28]. TMB was higher in responder to neoadjuvant pembrolizumab [27]. | Issues: variability, lack of a validated cut-off, differences related to the sequencing platforms |
Molecular signatures | Study of involved genes (DNA sequencing), messenger ribonucleic acids (mRNAs) (RNA sequencing), and proteins (transcriptome) in tumor samples. | APOBEC mutational signature predicts OS benefit with atezolizumab ± CT compared to CT alone [28]. TGF-β signature predicts worse OS with ICIs [28]. IFN-γ signature correlates with response to nivolumab [38]. JAVELIN -Immuno high signature correlates with increased responses to ICIs [24]. Transcriptional signature of eight genes (IFNG, CXCL9, CD8A, GZMA, GZMB, CXCL10, PRF1 and TBX21) correlates with response to atezolizumab [39]. FGFR mutations predict low response to durvalumab [41]. | Need to standardize molecular assays and find a molecular panel applicable to daily clinical practice |
ctDNA | Quantitative and qualitative analysis of circulating tumoral DNA detected on blood samples. | ctDNA detection is associated with worse prognosis in early stages and could identify metastatic relapses before imaging [47]. ctDNA detection is predictive for adjuvant atezolizumab (both DFS and OS) [48]. ctDNA profiling may be predictive for response to specific therapies [46]. | |
PD-L1 | Expression of the ligand of PD1 receptor has been widely studied as predictive biomarker of response to anti-PD1 and anti-PD-L1 therapies across human cancers. | Possibly predictive for anti-PD1 and anti-PD-L1 agents used in UC patients as adjuvant [54], first-line [49], maintenance after first-line [53] and second-line therapy [56]. | Issues: different diagnostic assays used in clinical trials for each anti-PD1 or anti-PD-L1 agent, discordant efficacy of these agents between studies |
Category | Description | Prognostic and Predictive Value in UC |
---|---|---|
Patient’s characteristics | Performance status (PS), metastatic sites, haemoglobin levels are prognostic factors in human cancer. | Karnofsky PS < 80%, ECOG PS ≥ 2, low haemoglobin levels and the presence of visceral metastases are associated with worse OS in mUC patients treated with CT [59,61]. Prognostic but not predictive in mUC patients treated with ICIs [62,63,64,65,66,67,68,69,70,71,72]. |
Concomitant medications | Commonly used drugs in clinical practice may affect clinical outcomes of cancer patients treated with ICIs. | Use of antibiotics and PPIs have a negative predictive role in mUC patients treated with ICIs but not in those treated with CT [80,81]. |
Inflammatory indices | Inflammatory indices, like NLR, CMP and albumin, have been investigated in several human cancer types as prognostic tools, especially regarding ICIs. | High levels of NLR, PLR, LDH, CRP and low levels of albumin have been correlated with worse survival and efficacy outcomes in mUC patients [62,63,64,70,92,93,94,95,96,97,98]. |
Combined prognostic tools | Combinations of inflammatory indices and clinical factors have been investigated in several human cancer types as prognostic tools, especially regarding ICIs. | Two prognostic models were developed in second-line ICIs mono-therapy [104,105]. A machine learning model was built from IMvigor210 atezolizumab arm and validated in IMvigor211 atezolizumab arm [106]. |
Variable | Prognostic | Predictive | ||||
---|---|---|---|---|---|---|
Parameter | Clinical Value | Strenght of Evidence | Outcome Variable | Clinical Value | Strenght of Evidence | Outcome Variable |
Tumour molecular factors | ||||||
Molecular classes for MIBC | | IV, B | PFS, OS | | IV, B | PFS, OS |
TMB | | IV, C | ORR, PFS, OS | | I, C | ORR, PFS, OS |
Mutational signatures 1 | | IV, C | ORR, PFS, OS | | I, C | ORR, PFS, OS |
ctDNA | | II, A | DFS, OS | | II, A | DFS, OS |
PD-L1 | | I, C | DFS, PFS, OS | | I, B 2 | DFS, PFS, OS |
Clinical factors | ||||||
Patient’s characteristics 3 | | I, A | ORR, PFS, OS | | I, A | ORR, PFS, OS |
Concomitant medications 4 | | I, A | ORR, PFS, OS | | I, B | ORR, PFS, OS |
Inflammatory indices 5 | | IV, A | ORR, PFS, OS | | IV, A | ORR, PFS, OS |
Combined tools 6 | | III, B | ORR, PFS, OS | | IV, C | ORR, PFS, OS |
Radiomics | ||||||
Radiomics-based models 7 | | IV, B | ORR, PFS, OS | | NI | NI |
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Rebuzzi, S.E.; Banna, G.L.; Murianni, V.; Damassi, A.; Giunta, E.F.; Fraggetta, F.; De Giorgi, U.; Cathomas, R.; Rescigno, P.; Brunelli, M.; et al. Prognostic and Predictive Factors in Advanced Urothelial Carcinoma Treated with Immune Checkpoint Inhibitors: A Review of the Current Evidence. Cancers 2021, 13, 5517. https://doi.org/10.3390/cancers13215517
Rebuzzi SE, Banna GL, Murianni V, Damassi A, Giunta EF, Fraggetta F, De Giorgi U, Cathomas R, Rescigno P, Brunelli M, et al. Prognostic and Predictive Factors in Advanced Urothelial Carcinoma Treated with Immune Checkpoint Inhibitors: A Review of the Current Evidence. Cancers. 2021; 13(21):5517. https://doi.org/10.3390/cancers13215517
Chicago/Turabian StyleRebuzzi, Sara Elena, Giuseppe Luigi Banna, Veronica Murianni, Alessandra Damassi, Emilio Francesco Giunta, Filippo Fraggetta, Ugo De Giorgi, Richard Cathomas, Pasquale Rescigno, Matteo Brunelli, and et al. 2021. "Prognostic and Predictive Factors in Advanced Urothelial Carcinoma Treated with Immune Checkpoint Inhibitors: A Review of the Current Evidence" Cancers 13, no. 21: 5517. https://doi.org/10.3390/cancers13215517
APA StyleRebuzzi, S. E., Banna, G. L., Murianni, V., Damassi, A., Giunta, E. F., Fraggetta, F., De Giorgi, U., Cathomas, R., Rescigno, P., Brunelli, M., & Fornarini, G. (2021). Prognostic and Predictive Factors in Advanced Urothelial Carcinoma Treated with Immune Checkpoint Inhibitors: A Review of the Current Evidence. Cancers, 13(21), 5517. https://doi.org/10.3390/cancers13215517