PURPOSE: To ascertain if preoperative short-term radiotherapy followed by chemotherapy is not inferior to a standard schedule of long-term chemoradiotherapy in patients with locally advanced rectal cancer. MATERIALS AND METHODS: Patients with distal or middle-third, clinical primary tumor stage 3-4 and/or regional lymph node–positive rectal cancer were randomly assigned (1:1) to short-term radiotherapy (25 Gy in five fractions over 1 week) followed by four cycles of chemotherapy (total neoadjuvant therapy [TNT]) or chemoradiotherapy (50 Gy in 25 fractions over 5 weeks, concurrently with capecitabine [chemoradiotherapy; CRT]). Total mesorectal excision was undertaken 6-8 weeks after preoperative treatment, with two additional cycles of CAPOX (intravenous oxaliplatin [130 mg/m², once a day] on day 1 and capecitabine [1,000 mg/m², twice a day] from days 1 to 14) in the TNT group and six cycles of CAPOX in the CRT group. The primary end point was 3-year disease-free survival (DFS). RESULTS: Between August 2015 and August 2018, a total of 599 patients were randomly assigned to receive TNT (n = 302) or CRT (n = 297). At a median follow-up of 35.0 months, 3-year DFS was 64.5% and 62.3% in TNT and CRT groups, respectively (hazard ratio, 0.883; one-sided 95% CI, not applicable to 1.11; P < .001 for noninferiority). There was no significant difference in metastasis-free survival or locoregional recurrence, but the TNT group had better 3-year overall survival than the CRT group (86.5% v 75.1%; P = .033). Treatment effects on DFS and overall survival were similar regardless of prognostic factors. The prevalence of acute grade III-V toxicities during preoperative treatment was 26.5% in the TNT group versus 12.6% in the CRT group ( P < .001). CONCLUSION: Short-term radiotherapy with preoperative chemotherapy followed by surgery was efficacious with acceptable toxicity and could be used as an alternative to CRT for locally advanced rectal cancer.

Background: We hypothesized that the Effective radiation Dose to the Immune Cells (EDIC) in circulating blood is a significant factor for the treatment outcome in patients with locally advanced non-small-cell lung cancer (NSCLC). Methods: This is a secondary study of a phase III trial, NRG/RTOG 0617, in patients with stage III NSCLC treated with radiation-based treatment. The EDIC was computed as equivalent uniform dose to the entire blood based on radiation doses to all blood-containing organs, with consideration of blood flow and fractionation effect. The primary endpoint was overall survival (OS), and the secondary endpoints were progression-free survival (PFS) and local progression-free survival (LPFS). The EDIC–survival relationship was analyzed with consideration of clinical significant factors. Results: A total of 456 patients were eligible. The median EDIC values were 5.6 Gy (range, 2.1–12.2 Gy) and 6.3 Gy (2.1–11.6 Gy) for the low- and high-dose groups, respectively. The EDIC was significantly associated with OS (hazard ratio [HR] = 1.12, p = 0.005) and LPFS (HR = 1.09, p = 0.02) but PFS (HR = 1.05, p = 0.17) after adjustment for tumor dose, gross tumor volume and other factors. OS decreased with an increasing EDIC in a non-linear pattern: the two-year OS decreased first with a slope of 8%/Gy when the EDIC < 6 Gy, remained relatively unchanged when the EDIC was 6–8 Gy, and followed by a further reduction with a slope of 12%/Gy when the EDIC > 8 Gy. Conclusions: The EDIC is a significant independent risk factor for poor OS and LPFS in RTOG 0617 patients with stage III NSCLC, suggesting that radiation dose to circulating immune cells is critical for tumor control. Organ at risk for the immune system should be considered during RT plan.

The factors that influence nanoparticle fate in vivo following systemic delivery remain an area of intense interest. Of particular interest is whether labeling with a cancer-specific antibody ligand (“active targeting”) is superior to its unlabeled counterpart (“passive targeting”). Using models of breast cancer in three immune variants of mice, we demonstrate that intratumor retention of antibody-labeled nanoparticles was determined by tumor-associated dendritic cells, neutrophils, monocytes, and macrophages and not by antibody-antigen interactions. Systemic exposure to either nanoparticle type induced an immune response leading to CD8⁺ T cell infiltration and tumor growth delay that was independent of antibody therapeutic activity. These results suggest that antitumor immune responses can be induced by systemic exposure to nanoparticles without requiring a therapeutic payload. We conclude that immune status of the host and microenvironment of solid tumors are critical variables for studies in cancer nanomedicine and that nanoparticle technology may harbor potential for cancer immunotherapy.

Gastroesophageal cancer dynamics and drivers of clinical responses with immune checkpoint inhibitors (ICI) remain poorly understood. Potential synergistic activity of dual programmed cell death protein 1 (PD-1) and lymphocyte-activation gene 3 (LAG-3) inhibition may help improve immunotherapy responses for these tumors. We report a phase Ib trial that evaluated neoadjuvant nivolumab (Arm A, n = 16) or nivolumab–relatlimab (Arm B, n = 16) in combination with chemoradiotherapy in 32 patients with resectable stage II/stage III gastroesophageal cancer together with an in-depth evaluation of pathological, molecular and functional immune responses. Primary endpoint was safety; the secondary endpoint was feasibility; exploratory endpoints included pathological complete (pCR) and major pathological response (MPR), recurrence-free survival (RFS) and overall survival (OS). The study met its primary safety endpoint in Arm A, although Arm B required modification to mitigate toxicity. pCR and MPR rates were 40% and 53.5% for Arm A and 21.4% and 57.1% for Arm B. Most common adverse events were fatigue, nausea, thrombocytopenia and dermatitis. Overall, 2-year RFS and OS rates were 72.5% and 82.6%, respectively. Higher baseline programmed cell death ligand 1 (PD-L1) and LAG-3 expression were associated with deeper pathological responses. Exploratory analyses of circulating tumor DNA (ctDNA) showed that patients with undetectable ctDNA post-ICI induction, preoperatively and postoperatively had a significantly longer RFS and OS; ctDNA clearance was reflective of neoantigen-specific T cell responses. Our findings provide insights into the safety profile of combined PD-1 and LAG-3 blockade in gastroesophageal cancer and highlight the potential of ctDNA analysis to dynamically assess systemic tumor burden during neoadjuvant ICI that may open a therapeutic window for future intervention. ClinicalTrials.gov registration: NCT03044613.

In this precision oncology era, where molecular profiling at the individual patient level becomes increasingly accessible and affordable, more and more clinical trials are now driven by biomarkers, with an overarching objective to optimize and personalize disease management. As compared with the conventional clinical development paradigms, where the key is to evaluate treatment effects in histology-defined populations, the choices of biomarker-driven clinical trial designs and analysis plans require additional considerations that are heavily dependent on the nature of biomarkers (eg, prognostic or predictive, integral or integrated) and the credential of biomarkers’ performance and clinical utility. Most recently, another major paradigm change in biomarker-driven trials is to conduct multi-agent and/or multihistology master protocols or platform trials. These trials, although they may enjoy substantial infrastructure and logistical advantages, also face unique operational and conduct challenges. Here we provide a concise overview of design options for both the setting of single-biomarker/single-disease and the setting of multiple-biomarker/multiple-disease types. We focus on explaining the trial design and practical considerations and rationale of when to use which designs, as well as how to incorporate various adaptive design components to provide additional flexibility, enhance logistical efficiency, and optimize resource allocation. Lessons learned from real trials are also presented for illustration.