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  • Cervera-Carrascon, Victor; Quixabeira, Dafne C. A.; Santos, Joao M.; Havunen, Riikka; Milenova, Ioanna; Verhoeff, Jan; Heinio, Camilla; Zafar, Sadia; Garcia-Vallejo, Juan J.; van Beusechem, Victor W.; de Gruijl, Tanja D.; Kalervo, Aino; Sorsa, Suvi; Kanerva, Anna; Hemminki, Akseli (2021)
    Immune checkpoint inhibitors such as anti-PD-1 have revolutionized the field of oncology over the past decade. Nevertheless, the majority of patients do not benefit from them. Virotherapy is a flexible tool that can be used to stimulate and/or recruit different immune populations. T-cell enabling virotherapy could enhance the efficacy of immune checkpoint inhibitors, even in tumors resistant to these inhibitors. The T-cell potentiating virotherapy used here consisted of adenoviruses engineered to express tumor necrosis factor alpha and interleukin-2 in the tumor microenvironment. To study virus efficacy in checkpoint-inhibitor resistant tumors, we developed an anti-PD-1 resistant melanoma model in vivo. In resistant tumors, adding virotherapy to an anti-PD-1 regimen resulted in increased survival (p=0.0009), when compared to anti-PD-1 monotherapy. Some of the animals receiving virotherapy displayed complete responses, which did not occur in the immune checkpoint-inhibitor monotherapy group. When adenoviruses were delivered into resistant tumors, there were signs of increased CD8 T-cell infiltration and activation, which - together with a reduced presence of M2 macrophages and myeloid-derived suppressor cells - could explain those results. T-cell enabling virotherapy appeared as a valuable tool to counter resistance to immune checkpoint inhibitors. The clinical translation of this approach could increase the number of cancer patients benefiting from immunotherapies.
  • Federico, Antonio; Fratello, Michele; Scala, Giovanni; Möbus, Lena; Pavel, Alisa; del Giudice, Giusy; Ceccarelli, Michele; Costa, Valerio; Ciccodicola, Alfredo; Fortino, Vittorio; Serra, Angela; Greco, Dario (2022)
    Simple Summary Current treatments for complex diseases, including cancer, are generally characterized by high toxicity due to their low selectivity for target cells. Moreover, patients often develop drug resistance, hence becoming less sensitive to the therapy. For this reason, novel, improved, and more specific pharmacological therapies are needed. The high cost and the time required to develop new drugs poses the attention on the development of computational methods for drug repositioning and combination therapy prediction. In this study, we developed an integrated network pharmacology framework that combines mechanistic and chemocentric approaches in order to predict potential drug combinations for cancer therapy. We applied our paradigm in five cancer types, which we used as case studies. Our strategy can be applied to the study of any complex disease by guiding the prioritization of drug combinations. Despite remarkable efforts of computational and predictive pharmacology to improve therapeutic strategies for complex diseases, only in a few cases have the predictions been eventually employed in the clinics. One of the reasons behind this drawback is that current predictive approaches are based only on the integration of molecular perturbation of a certain disease with drug sensitivity signatures, neglecting intrinsic properties of the drugs. Here we integrate mechanistic and chemocentric approaches to drug repositioning by developing an innovative network pharmacology strategy. We developed a multilayer network-based computational framework integrating perturbational signatures of the disease as well as intrinsic characteristics of the drugs, such as their mechanism of action and chemical structure. We present five case studies carried out on public data from The Cancer Genome Atlas, including invasive breast cancer, colon adenocarcinoma, lung squamous cell carcinoma, hepatocellular carcinoma and prostate adenocarcinoma. Our results highlight paclitaxel as a suitable drug for combination therapy for many of the considered cancer types. In addition, several non-cancer-related genes representing unusual drug targets were identified as potential candidates for pharmacological treatment of cancer.