The tumor microenvironment (TME) encompasses the complex and diverse surroundings in which tumors arise. Emerging insights highlight the TME's critical role in tumor development, progression, metastasis, and treatment response. Consequently, the TME has attracted significant research and clinical interest, leading to the identification of numerous novel therapeutic targets. Advances in molecular technologies now enable detailed genomic and transcriptional analysis of cancer cells and the TME and the integration of microenvironmental data to the tumor genomic landscape. This comprehensive review discusses current progress in targeting the TME for drug development, addressing associated challenges, strategies for modulating the pro-tumor microenvironment, and the discovery of new targets.

Immune checkpoint inhibitors may be associated with multiple immune-related toxicities. Cardiovascular adverse effects are underreported in clinical trials. We conducted a systematic review and meta-analysis to evaluate cardiovascular adverse effects incidence among patients with solid tumors receiving immune checkpoint inhibitors in randomized clinical trials and the relative risk of presenting these effects compared to placebo or best supportive care. The search was conducted through MEDLINE, Embase, and Scopus databases from January 1st, 2010 until July 1st, 2020. Outcomes were reported following Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. 57 randomized clinical trials including 12,118 patients were included. All grade CV AEs incidence rate was 8.32% (95% CI = 6.35%-10.53%). When only grade 3–5 CV AEs were considered, ICIs were significantly associated with increased risk than placebo or BSC (RR = 1.36; 95% CI = 1.06–1.73; p = 0.01). This meta-analysis corroborates the hypothesis of increased CV risk related to immune checkpoint inhibitors.

Purpose: Solid tumors harboring tumor mutational burden (TMB) ≥10 mutations per megabase (mut/Mb) received agnostic approval for pembrolizumab. This work aims to analyze the somatic mutational profile’s influence on the outcomes of patients with TMB-high tumors treated with immune checkpoint inhibitors (ICIs). Methods: This post-hoc analysis evaluated clinical and molecular features of patients with solid tumors treated with ICIs that could be either monoclonal antibody directed against programmed cell death protein-1 or monoclonal antibody directed against programmed cell death ligand 1 (anti-PD-1/anti-PD-L1), monoclonal antibody directed against cytotoxic T lymphocyte-associated antigen (anti-CTLA-4) or a combined treatment regimen including one anti-PD-1/anti-PD-L1 and one anti-CTLA-4 (ICIs combination). We performed OS analysis for TMB thresholds of ≥10, ≥20, and <10 mut/Mb. We assessed OS according to the mutational profile for a TMB ≥ 10 mut/Mb cutoff. For genes correlated with OS at the univariate assessment, we conducted a Cox multivariate analysis adjusted by median TMB, sex, age, microsatellite instability (MSI), and histology. Results: A total of 1661 patients were investigated; 488 with a TMB ≥10 mut/Mb (29.4%). The median OS was 42 months for TMB ≥10 or 20 mut/Mb, and 15 months for TMB <10 mut/Mb (p < 0.005). Among TMB ≥10 mut/Mb patients, mutations in E2F3 or STK11 correlated with worse OS, and mutations in NTRK3, PTPRD, RNF43, TENT5C, TET1, or ZFHX3 with better OS. These associations were confirmed with univariate and multivariate analyses (p < 0.05). Melanoma histology and TMB above the median endowed patients with better OS (p < 0.05), while MSI status, age, and gender did not have a statistically significant effect on OS. Conclusion: Combining TMB and mutation profiles in key cancer genes can better qualify patients for ICI treatment and predict their OS.

Background: PRRT can be an option for all-grade GEP-NETs, but selecting patients is challenging. In this scenario, clinical-pathological and radiological characteristics, such as pre-treatment Ga-68 DOTA PET/CT, might have the potential to help. Methods: A retrospective chart review was conducted on advanced GEP-NETs treated with at least one PRRT dose. Overall survival (OS) and progression-free survival (PFS) were calculated using the Kaplan–Meier method. Krenning Score (KS), and the maximum standardized uptake value (SUVmax) were derived from the pre-treatment scans. A maximally selected rank statistics test was used for SUVmax simple cut point estimate. Results: Among 36 patients, 19 had primary pancreatic tumors. The numbers of G1, G2, and G3 tumors were 10, 18, and 7, respectively. During a median follow-up of 90.5 months, 4 patients died. Median OS was not reached for G1 and G2 tumors, and it was 30 months for G3 (p = 0.001). Median PFS was 23 months, with G3 showing lower PFS compared to G1 [7 versus 30 months; HR 8.41 (95%CI 2.2–31.0; p = 0.001)]. Conclusions: PRRT provides long-term PFS in patients with G1/G2 GEP-NETs independent of clinical characteristics and primary site. G3 has worse survival, but selected patients may experience long OS after PRRT treatment.