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Background: Although surgical resection is the only potentially curative treatment for pancreatic cancer (PC), long-term outcomes of this treatment remain poor. The aim of this study is to describe the feasibility of a neoadjuvant treatment with induction polychemotherapy (IPCT) followed by chemoradiation (CRT) in resectable PC, and to develop a machine-learning algorithm to predict risk of relapse. Methods: Forty patients with resectable PC treated in our institution with IPCT (based on mFOLFOXIRI, GEMOX or GEMOXEL) followed by CRT (50 Gy and concurrent Capecitabine) were retrospectively analyzed. Additionally, clinical, pathological and analytical data were collected in order to perform a 2-year relapse-risk predictive population model using machine-learning techniques. Results: A R0 resection was achieved in 90% of the patients. After a median follow-up of 33.5 months, median progression-free survival (PFS) was 18 months and median overall survival (OS) was 39 months. The 3 and 5-year actuarial PFS were 43.8% and 32.3%, respectively. The 3 and 5-year actuarial OS were 51.5% and 34.8%, respectively. Forty-percent of grade 3-4 IPCT toxicity, and 29.7% of grade 3 CRT toxicity were reported. Considering the use of granulocyte colony-stimulating factors, the number of resected lymph nodes, the presence of perineural invasion and the surgical margin status, a logistic regression algorithm predicted the individual 2-year relapse-risk with an accuracy of 0.71 (95% confidence interval [CI] 0.56–0.84, p = 0.005). The model-predicted outcome matched 64% of the observed outcomes in an external dataset. Conclusion: An intensified multimodal neoadjuvant approach (IPCT + CRT) in resectable PC is feasible, with an encouraging long-term outcome. Machine-learning algorithms might be a useful tool to predict individual risk of relapse. A small sample size and therapy heterogeneity remain as potential limitations.

BackgroundRadioimmunotherapy combines irradiation of tumor lesions with immunotherapy to achieve local and abscopal control of cancer. Most immunotherapy agents are given systemically, but strategies for delivering immunotherapy locally are under clinical scrutiny to maximize efficacy and avoid toxicity. Local immunotherapy, by injecting various pathogen-associated molecular patterns, has shown efficacy both preclinically and clinically. BO-112 is a viral mimetic based on nanoplexed double-stranded RNA (poly I:C) which exerts immune-mediated antitumor effects in mice and humans on intratumoral delivery. BO-112 and focal irradiation were used to make the proof-of-concept for local immunotherapy plus radiation therapy combinations.MethodsMurine transplantable tumor cell lines (TS/A, MC38 and B16-OVA) were used to show increased immunogenic features under irradiation, as well as in bilateral tumor models in which only one of the lesions was irradiated or/and injected with BO-112. Flow cytometry and multiplex tissue immunofluorescence were used to determine the effects on antitumor immunity. Depletions of immune cell populations and knockout mice for the IFNAR and BATF-3 genes were used to delineate the immune system requirements for efficacy.ResultsIn cultures of TS/A breast cancer cells, the combination of irradiation and BO-112 showed more prominent features of immunogenic tumor cell death in terms of calreticulin exposure. Injection of BO-112 into the tumor lesion receiving radiation achieved excellent control of the treated tumor and modest delays in contralateral tumor progression. Local effects were associated with more prominent infiltrates of antitumor cytotoxic tumor lymphocytes (CTLs). Importantly, local irradiation plus BO-112 in one of the tumor lesions that enhanced the therapeutic effects of radiotherapy on distant irradiated lesions that were not injected with BO-112. Hence, this beneficial effect of local irradiation plus BO-112 on a tumor lesion enhanced the therapeutic response to radiotherapy on distant non-injected lesions.ConclusionThis study demonstrates that local BO-112 immunotherapy and focal irradiation may act in synergy to achieve local tumor control. Irradiation plus BO-112 in one of the tumor lesions enhanced the therapeutic effects on distant irradiated lesions that were not injected with BO-112, suggesting strategies to treat oligometastatic patients with lesions susceptible to radiotherapy and with at least one tumor accessible for repeated BO-112 intratumoral injections.

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Double checking of the monitor units (MU) is an important step in the quality assurance (QA) process in radiosurgery. In this paper we propose the use of an independent algorithm constructed using the ellipsoid which best fits the measurements taken with the bubble head frame. The monitor units calculated by this independent algorithm and the commercial planning system were compared in 40 patients treated with radiosurgery (57 isocenters, 320 arcs). The average relative difference was -0.2% +/- 2.1 (k=1). These results are better for the variance, -0.4% +/- 1.8 (k=1), when all the depths of the bubble head frame are measured and no arcs are calculated by extrapolation or when only one of these factors appear. If there are missing values in the bubble head frame measurements and the model is extrapolated, the variance of the results is greater, 0.4% +/- 3.9 (k=1). The algorithm is reliable as a QA tool for linac radiosurgery.

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