| Literature DB >> 33194654 |
Paolo Andrea Zucali1,2, Nadia Cordua1, Federica D'Antonio1, Federica Borea1, Matteo Perrino1, Fabio De Vincenzo1, Armando Santoro1,2.
Abstract
Patients with muscle-infiltrating bladder cancer (MIBC) present a high risk of postoperative recurrence and death from metastatic urothelial cancer despite surgical resection. Before the use of peri-operative chemotherapy, about half (52%) of patients undergoing radical cystectomy had had a relapse of tumor disease within 5 years of surgery. However, when peri-operative cisplatin-based chemotherapy is added to radical cystectomy for patients with MIBC it provides limited benefit in terms of survival, disease recurrence and development of metastases, at the expense of toxic effects. In fact, a significant proportion of patients still recurs and die to metastatic disease. Given the success of immune-oncological drugs in metastatic urothelial cancer, several trials started to test them in patients with non-metastatic MIBC either in neo-adjuvant and adjuvant setting. The preliminary results of these studies in neo-adjuvant setting are showing great promise, confirming the potential benefits of immunotherapy also in patients with non-metastatic MIBC. The aim of this review is to present an overview of developments happening on the introduction of immunotherapy in peri-operative setting in non-metastatic urothelial cancer. Moreover, an analysis of the critical issues regarding how best customize the delivery of immunotherapy to optimize efficacy and minimize the adverse effects, with particular focus on potential prognostic and predictive molecular biomarkers, is done.Entities:
Keywords: adjuvant; immunotherapy; muscle-infiltrating bladder cancer (MIBC); neoadjuvant; predictive biomarkers
Year: 2020 PMID: 33194654 PMCID: PMC7609911 DOI: 10.3389/fonc.2020.568279
Source DB: PubMed Journal: Front Oncol ISSN: 2234-943X Impact factor: 6.244
Overview of selected neoadjuvant trials in MIBC testing mono-immunotherapy.
| Avelumab | 10 | Phase II | Change in T-cell subpopulations | Not yet recruiting | NCT03498196 (BL-AIR) |
| Open Label | |||||
| Single arm | |||||
| Atezolizumab | 96 | Phase II | pCR rate | Active, not recruiting | NCT02662309 (ABACUS) |
| Open Label | |||||
| Single arm | |||||
| Atezolizumab | 20 | Phase II | pCR rate | Recruiting | NCT03577132 |
| Open Label | |||||
| Single arm | |||||
| Atezolizumab | 42 | Phase II | pCR rate | Active, not recruiting | NCT02451423 |
| Open Label | |||||
| Pembrolizumab | 114 | Phase II | pCR rate | Has results | NCT02736266 (PURE-01) |
| Open Label | |||||
| Pembrolizumab | 40 | Phase II | pCR rate | Recruiting | NCT03212651 (PANDORE) |
| Open Label | |||||
| Single arm | |||||
ID, identification number; pCR, pathologic complete response.
Overview of selected neoadjuvant trials in MIBC testing immune combination therapy.
| Nivolumab + Ipilimumab | 54 | Phase Ib | Safety, pCR rate | Recruiting | NCT03387761 (NABUCCO) |
| Open label | |||||
| Nivolumab +/– Ipilimumab | 45 | Phase II | Number of RC within 60 days after neoadjuvant therapy | Recruiting | NCT03520491 (CA209-9DJ) |
| Durvalumab + Tremelimumab | 54 | Phase I | Incidence of AEs determined by extreme toxicity | Active, not recruiting | NCT02812420 |
| Durvalumab + Tremelimumab | 6 | Phase II | ORR | Active, not recruiting | NCT03234153 (NITIMIB) |
| Durvalumab + Tremelimumab vs. GC or MVAC or PaGC | 99 | Phase II | pCR rate | Recruiting | NCT03472274 (DUTRENEO) |
| Nivolumab +/– Urelumab | 44 | II | Tumor infiltrating CD8+ T-cells density at RC | Recruiting | NCT02845323 |
ID, identification number; pCR, pathologic complete response; RC, radical cystectomy; AEs, adverse events; ORR, overall response rate; GC, gemcitabine, cisplatin; MVAC, methotrexate, vinblastine, doxorubicin, and cisplatin; PaGC, paclitaxel, gemcitabine, cisplatin.
Overview of selected neoadjuvant trials in MIBC testing chemo-immuntherapy combinations.
| Nivolumab + GC | 43 | Phase II | pCR rate | Has results | NCT03294304 (BLTASST-1) |
| Nivolumab + GC | 76 | Phase II | pCR rate | Active, not recruiting | NCT03558087 |
| Pembrolizumab + GC | 39 | Phase II | pD rate | Active, not recruiting | NCT02690558 |
| Pembrolizumab + GC or G | 83 | Phase I/II | Safety pMI-RR | Recruiting | NCT02365766 |
| Avelumab vs. Avelumab + MVAC vs. Avelumab + CG vs. Avelumab + PaG | 166 | II | pCR rate | Recruiting | NCT03674424 (AURA) |
| Durvalumab + MVAC vs. Durvalumab + Tremelimumab + MVAC | 120 | I/II | pCR rate | Recruiting | NCT03549715 (NEMIO) |
ID, identification number; GC, gemcitabine, cisplatin; pCR, pathologic complete response; 2 yrs MFS: two years metastasis-free survival; pD, pathologic down-staging; G, gemcitabine; pMI-RR, pathologic muscle invasive response rate; MVAC, methotrexate, vinblastine, doxorubicin, and cisplatin; PaG, paclitaxel, gemcitabine.
Overview of selected neoadjuvant trials in MIBC testing immunotherapy combined with novel therapeutic agents.
| Durvalumab + Olaparib | 29 | Phase II | pCR rate | Completed | NCT03534492 (NEODURVARIB) |
| Atezolizumab + Cabozantinib | 42 | Phase II | pRR rate | Not yet recruiting | NCT04289779 (ABATE) |
| Pembrolizumab + Epacadostat | 38 | Phase II | pCR rate | Not yet recruiting | NCT03832673 |
ID, identification number; GC, gemcitabine, cisplatin; pCR, pathologic complete response; pRR, pathologic response rate.
Overview of adjuvant trials in MIBC testing immunotherapy.
| Pembrolizumab | 739 | Phase III | OS, DFS | Recruiting | NCT03244384 (AMBASSADOR) |
| Atezolizumab | 809 | Phase III | DFS | Active, non recruiting | NCT02450331 (IMvigor010) |
| Nivolumab | 700 | Phase III | DFS | Active, non recruiting | NCT02632409 (Checkmate 274) |
| NA Durvalumab + GC and A Durvalumab vs. NA GC | 1050 | Phase III | pCR rate, EFS | Recruiting | NCT03732677 (NIAGARA) |
| NA Pembrolizumab + GC and A Pembrolizumab vs. NA GC | 790 | Phase III | pCR rate, EFS | Recruiting | NCT03924856 (KEYNOTE-866) |
| NA Nivolumab/BMS-986205 + GC and A Nivolumab/BMS-986205 vs. NA Nivolumab/BMS-986205 + GC and A Nivolumab/placebo + A BMS-986205/placebo vs. NA GC | 1,200 | Phase III | pCR rate, EFS | Recruiting | NCT03661320 |
ID, identification number; OS, Overall survival; DFS, Disease Free Survival; NA, Neoadjuvant; GC, gemcitabine, cisplatin; A, Adjuvant; pCR, pathologic complete response; EFS, Event-Free Survival.
Potential predictive biomarkers for immunotherapy with immune CPIs.
| PD-L1 | Hypothesis that high levels of PD-L1 in tumor and/or immunological cells in tumor microenvironment may predict clinical response to CPIs with good evidence of correlation in NSCLC, melanoma, renal cell carcinoma | • Discordant results across different trials |
| TMB (tumor mutational burden) | Tumors with a higher TMB seem more likely to express neoantigens, inducing a more robust response if treated with CPIs | • Discordant results across different trials |
| Immune cell gene expression profiling | It is considered a comprehensive biomarker that can enable to assess tumor microenvironment and its inflammatory status to distinguish hot tumors from cold ones | • Lack of standardized commercially available gene panel |
| Granzyme B | It acts as a mediator of target cell apoptosis induced by immune effectors and might be used as a surrogate marker of CD8+ cells activation | • No standardized method of evaluation (levels of soluble marker rather than double staining for CD8+ cells and granzyme B) |
| DNA damage response (DDR) genes alterations | Association with better response to neoadjuvant chemotherapy and higher TMB and copy number alteration. Plausible a good relation also to CPIs response | • Lack of solid data |
| Retinoblastoma 1 (RB1) gene alterations | In addition to being fundamental in cell cycle regulation, it has been discovered to be involved in immune function | • Lack of solid data |
| Epithelial-mesenchymal transition (EMT) markers | In some studies higher EMT-related gene expression was linked to a major benefit from immune checkpoint blockade | • Lack of solid data and contradictory correlations with CPIs response in different studies |
| TGF-β pathway | It acts as a key factor in cancer development and progression. In some studies high levels of expression were related with resistance to CPIs | • Lack of solid data |
| Molecular subtyping | Heterogeneous tumors may be grouped by molecular features in several subtypes, different for treatment response and prognosis | • Need for a consensus classification |