| Literature DB >> 34557714 |
Zhihao Lu1, Zhi Peng1, Chang Liu1, Zhenghang Wang1, Yakun Wang1, Xi Jiao1, Jian Li1, Lin Shen1.
Abstract
Gastrointestinal (GI) cancers represent a major public health problem worldwide. Due to the late detection and high heterogeneity of GI cancers, traditional treatments, including surgery, radiotherapy, chemotherapy, and targeted therapy, have shown limited effects, and the overall prognosis of these patients remains poor. Recently, immunotherapy, involving programmed cell death-1 (PD-1) and its ligand (PD-L1), has shown promising efficacy in several solid cancers and seems to have become a potential treatment option for GI cancers This review focuses on data on the development of immunotherapy-based clinical trials in esophageal cancer, gastric cancer, and colorectal cancer. The predictive biomarkers and combination strategies in clinical trials and translational medicine are also discussed. Finally, prospects for immunotherapy in the treatment of GI cancers are described. Although only a small proportion of patients with GI cancers respond to PD-1/PD-L1 blockade, we strongly believe that precision immunotherapy might improve the overall survival of many more GI cancer patients in the future.Entities:
Keywords: biomarkers; gastrointestinal cancer; immune checkpoint blockade; precision immunotherapy
Year: 2020 PMID: 34557714 PMCID: PMC8454608 DOI: 10.1016/j.xinn.2020.100041
Source DB: PubMed Journal: Innovation (Camb) ISSN: 2666-6758
Efficacy of Immune Checkpoint Inhibitors in Esophageal Squamous Cell Carcinoma
| KEYNOTE-181 | ATTRACTION-3 | ESCORT | ||||
|---|---|---|---|---|---|---|
| Regimen | Pembrolizumab | chemotherapy | nivolumab | chemotherapy | camrelizumab | chemotherapy |
| Sample size | 198 | 203 | 210 | 209 | 228 | 220 |
| Prior treatment lines | ≥1 | ≥1 | ≥1 | ≥1 | ≥1 | ≥1 |
| ORR, n (%) | 33 (16.7) | 15 (7.4) | 33/171 | 34/158 | 46 (20.2) | 14 (6.4) |
| PFS, months | 2.2 | 3.1 | 1.7 | 3.4 | 1.9 | 1.9 |
| OS, months | 8.2 | 7.1 | 10.9 | 8.4 | 8.3 | 6.2 |
Randomly assigned patients who had target lesion measurements at baseline.
Regimens and Efficacy of Immune Checkpoint Inhibitors in Patients with dMMR or MSI-H Tumors
| Regimen | Nivo 3 mg/kg q2w | Pembro 200 mg q3w | Nivo 3 mg/kg q2w | Nivo 3 mg/kg q2w | Pembro 200 mg q3w | Pembro 10 mg/kg q2w | Durva 10 mg/kg q2w | Durva 10 mg/kg q2w | |
|---|---|---|---|---|---|---|---|---|---|
| ( | ( | ||||||||
| Sample size | 45 | 153 | 119 | 74 | 63 | 40 | 11 | 36 | |
| Prior treatment lines | 0 | 0 | ≥1 | ≥1 | ≥1 | ≥2 | ≥2 | NA | |
| Best overall response, n (%) | |||||||||
| CR | 3 (7) | 17 (11) | 7 (6) | 7 (9) | 2 (3) | 5 (12) | NA | NA | |
| PR | 24 (53) | 50 (33) | 62 (52) | 18 (24) | 18 (29) | 16 (40) | NA | NA | |
| SD | 11 (24) | 32 (21) | 33 (28) | 23 (31) | 16 (25) | 12 (30) | NA | NA | |
| PD | 6 (13) | 45 (30) | 14 (12) | 22 (30) | 25 (40) | 4 (10) | NA | NA | |
| Not evaluable | 1 (2) | 9 (6) | 3 (3) | 4 (5) | 2 (3) | 3 (8) | NA | NA | |
| ORR | 27 (60) | 67 (44) | 69 (58) | 25 (33) | 20 (32) | 21 (52) | 3 (27) | 8 (22) | |
| CR + PR + SD ≥12 weeks | 38 (84) | 99 (65) | 96 (81) | 46 (62) | 36 (57) | 33 (82) | NA | NA | |
| PFS | NR | 16.5 months | NR | 6.6 months | 4.1 months | NR | 6 months | 6 months | |
| 1-year PFS, % | 77 | 55 | 71 | 44 | 41 | NA | 36 | 38 | |
| 2-year PFS, % | NA | 48 | 60 | NA | NA | 59 | NA | NA | |
| OS | NR | NA | NR | NR | NR | NR | NR | NR | |
| 1-year OS, % | 83 | NA | 85 | 72 | 76 | NA | NA | NA | |
| 2-year OS, % | NA | NA | 74 | NA | NA | 72 | NA | 54 | |
| TRAEs, n (%) | |||||||||
| Any | 35 (78) | 122 (80) | 87 (73) | 54 (73) | 40 (64) | NA | NA | NA | |
| Grades 3–5 | 7 (16) | 34 (22) | 37 (31) | 15 (20) | 7 (11) | NA | NA | NA | |
q(n)w, every (n) weeks. Anti-PD-1 antibody: Nivo (nivolumab), Pembro (pembrolizumab). Anti-PD-L1 antibody: Durva (durvalumab). Anti-CTLA-4 antibody: Ipi (ipilimumab). NR, not reached; NA, not available; TRAEs, treatment-related adverse events.
Figure 1Predictive Biomarkers for Immune Checkpoint Inhibitors in GI Cancer.
The current immunotherapeutic biomarkers in GI cancer were developed on the basis of the mechanism of antitumor immunity. Consideration of each step of the cancer immune cycle must be incorporated in the ongoing efforts of biomarker optimization in GI cancer.
Figure 2Future Perspective of Immunotherapy.
For precision immunotherapy, shown above, the following aspects could be focused upon, based on the tumor environment. (1) Identification of potential precise biomarkers. With the current understanding of cancer immunology, biomarkers could be identified in multi-omics, and combinational strategies taken into account to guide precision immuno-oncology. (2) Exploration of the precise combination strategies. Based on the complexity of TME, the different combination strategies of chemotherapy, radiotherapy, targeted therapy, immunotherapy, and so forth that co-target the function of tumor-specific factors could be considered. (3) Identification of the timing of immunotherapy. Neoadjuvant therapy showed promising prevention of tumor relapse, even better than adjuvant therapy in some preclinical mouse models and clinical trials, providing a new option for patients. Therefore, it is necessary to confirm the appropriate timing of immunotherapy to obtain better efficacy. (4) Identification of the strategy to reverse acquired resistance. Acquired resistance to ICIs always discounts the efficiency of immunotherapy and remains a big challenge, so it is imperative to understand the mechanism of acquired resistance. The strategies that analyze the antigen presentation machinery, target mutation in IFN-γ signaling, block additional inhibitory checkpoints, increase neoantigen expression, decrease immunosuppressive cells or factors, increase T cell infiltration, and so forth could be explored to reverse acquired resistance.