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      • Record: found
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      Is Open Access

      Combined metformin-salicylate treatment provides improved anti-tumor activity and enhanced radiotherapy response in prostate cancer; drug synergy at clinically relevant doses

      research-article
      Author(s): a , b , 1 , a , b , 1 , a , b , 1 , a , c , a , c , a , c , a , c , c , e , a , c , a , c , c , c , a , b , f , a , c , d , e , *
      Publication date (Electronic):
      Journal: Translational Oncology
      Publisher: Neoplasia Press
      Keywords: AMPK, Lipogenesis, mTOR, HIF1a, Histone-H3, Xenografts, PrCa, prostate cancer, MET, metformin, SAL, salicylate, MET + SAL, metformin and salicylate used together, RT, radiotherapy / ionizing irradiation with clinical radiotherapy units, DNL, de novo lipogenesis, mTOR, the mammalian target of rapamycin, mTORC1, mTOR complex 1, AMPK, AMP-activated protein kinase, LKB1, Liver Kinase B 1, p70s6k, ribosomal p70 S6 kinase, ACC, acetyl-CoA carboxylase, TSC2, Tuberin Sclerosis Complex 2, 4EBP1, Eukaryotic Translation Initiation Factor 4E Binding Protein 1, HIF1α, hypoxia inducible factor 1, OxPhos, oxidative phosphorylation, OCR, oxygen consumption rate, ECAR, Extracellular Acidification Rate, P-H3, phosphorylated histone H3, IHC, immunohistochemistry

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          Highlights

          • •

            Combined metformin + salicylate treatment has improved anti-tumor efficacy in prostate cancer.

          • •

            At clinically achievable doses, it induces increased metabolic stress and sensitizes tumors to radiation.

          • •

            Metformin + salicylate blocks pathways of de novo lipogenesis and protein synthesis.

          • •

            In combination with radiation suppresses HIF1a and DNA replication.

          • •

            This work supports clinical investigation of metformin + salicylate in combination with radiotherapy.

          Abstract

          Background

          There is need for well-tolerated therapies for prostate cancer (PrCa) secondary prevention and to improve response to radiotherapy (RT). The anti-diabetic agent metformin (MET) and the aspirin metabolite salicylate (SAL) are shown to activate AMP-activated protein kinase (AMPK), suppress de novo lipogenesis (DNL), the mammalian target of rapamycin (mTOR) pathway and reduce PrCa proliferation in-vitro. The purpose of this study was to examine whether combined MET+SAL treatment could provide enhanced PrCa tumor suppression and improve response to RT.

          Methods

          Androgen-sensitive (22RV1) and resistant (PC3, DU-145) PrCa cells and PC3 xenografts were used to examine whether combined treatment with MET+SAL can provide improved anti-tumor activity compared to each agent alone in non-irradiated and irradiated PrCa cells and tumors. Mechanisms of action were investigated with analysis of signaling events, mitochondria respiration and DNL activity assays.

          Results

          We observed that PrCa cells are resistant to clinically relevant doses of MET. Combined MET + SAL treatment provides synergistic anti-proliferative activity at clinically relevant doses and enhances the anti-proliferative effects of RT. This was associated with suppression of oxygen consumption rate (OCR), activation of AMPK, suppression of acetyl-CoA carboxylase (ACC)-DNL and mTOR-p70 s6k/4EBP1 and HIF1α pathways. MET + SAL reduced tumor growth in non-irradiated tumors and enhanced the effects of RT.

          Conclusion

          MET+SAL treatment suppresses PrCa cell proliferation and tumor growth and enhances responses to RT at clinically relevant doses. Since MET and SAL are safe, widely-used and inexpensive agents, these data support the investigation of MET+SAL in PrCa clinical trials alone and in combination with RT.

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          Most cited references43

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          Cancer statistics, 2020

          Rebecca Siegel, Kimberly D Miller, Ahmedin Jemal (2020)
          Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2016) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2017) were collected by the National Center for Health Statistics. In 2020, 1,806,590 new cancer cases and 606,520 cancer deaths are projected to occur in the United States. The cancer death rate rose until 1991, then fell continuously through 2017, resulting in an overall decline of 29% that translates into an estimated 2.9 million fewer cancer deaths than would have occurred if peak rates had persisted. This progress is driven by long-term declines in death rates for the 4 leading cancers (lung, colorectal, breast, prostate); however, over the past decade (2008-2017), reductions slowed for female breast and colorectal cancers, and halted for prostate cancer. In contrast, declines accelerated for lung cancer, from 3% annually during 2008 through 2013 to 5% during 2013 through 2017 in men and from 2% to almost 4% in women, spurring the largest ever single-year drop in overall cancer mortality of 2.2% from 2016 to 2017. Yet lung cancer still caused more deaths in 2017 than breast, prostate, colorectal, and brain cancers combined. Recent mortality declines were also dramatic for melanoma of the skin in the wake of US Food and Drug Administration approval of new therapies for metastatic disease, escalating to 7% annually during 2013 through 2017 from 1% during 2006 through 2010 in men and women aged 50 to 64 years and from 2% to 3% in those aged 20 to 49 years; annual declines of 5% to 6% in individuals aged 65 years and older are particularly striking because rates in this age group were increasing prior to 2013. It is also notable that long-term rapid increases in liver cancer mortality have attenuated in women and stabilized in men. In summary, slowing momentum for some cancers amenable to early detection is juxtaposed with notable gains for other common cancers.
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            Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors.

            P Talalay, T C Chou (1984)
            A generalized method for analyzing the effects of multiple drugs and for determining summation, synergism and antagonism has been proposed. The derived, generalized equations are based on kinetic principles. The method is relatively simple and is not limited by whether the dose-effect relationships are hyperbolic or sigmoidal, whether the effects of the drugs are mutually exclusive or nonexclusive, whether the ligand interactions are competitive, noncompetitive or uncompetitive, whether the drugs are agonists or antagonists, or the number of drugs involved. The equations for the two most widely used methods for analyzing synergism, antagonism and summation of effects of multiple drugs, the isobologram and fractional product concepts, have been derived and been shown to have limitations in their applications. These two methods cannot be used indiscriminately. The equations underlying these two methods can be derived from a more generalized equation previously developed by us (59). It can be shown that the isobologram is valid only for drugs whose effects are mutually exclusive, whereas the fractional product method is valid only for mutually nonexclusive drugs which have hyperbolic dose-effect curves. Furthermore, in the isobol method, it is laborious to find proper combinations of drugs that would produce an iso-effective curve, and the fractional product method tends to give indication of synergism, since it underestimates the summation of the effect of mutually nonexclusive drugs that have sigmoidal dose-effect curves. The method described herein is devoid of these deficiencies and limitations. The simplified experimental design proposed for multiple drug-effect analysis has the following advantages: It provides a simple diagnostic plot (i.e., the median-effect plot) for evaluating the applicability of the data, and provides parameters that can be directly used to obtain a general equation for the dose-effect relation; the analysis which involves logarithmic conversion and linear regression can be readily carried out with a simple programmable electronic calculator and does not require special graph paper or tables; and the simplicity of the equation allows flexibility of application and the use of a minimum number of data points. This method has been used to analyze experimental data obtained from enzymatic, cellular and animal systems.
            Article 0 comments Cited 857 times – based on 0 reviews     Review now
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              AMPK phosphorylation of raptor mediates a metabolic checkpoint.

              Dana Gwinn, David B. Shackelford, Daniel Egan … (2008)
              AMPK is a highly conserved sensor of cellular energy status that is activated under conditions of low intracellular ATP. AMPK responds to energy stress by suppressing cell growth and biosynthetic processes, in part through its inhibition of the rapamycin-sensitive mTOR (mTORC1) pathway. AMPK phosphorylation of the TSC2 tumor suppressor contributes to suppression of mTORC1; however, TSC2-deficient cells remain responsive to energy stress. Using a proteomic and bioinformatics approach, we sought to identify additional substrates of AMPK that mediate its effects on growth control. We report here that AMPK directly phosphorylates the mTOR binding partner raptor on two well-conserved serine residues, and this phosphorylation induces 14-3-3 binding to raptor. The phosphorylation of raptor by AMPK is required for the inhibition of mTORC1 and cell-cycle arrest induced by energy stress. These findings uncover a conserved effector of AMPK that mediates its role as a metabolic checkpoint coordinating cell growth with energy status.
              Article 0 comments Cited 606 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Theodoros Tsakiridis
                Journal
                Journal ID (nlm-ta): Transl Oncol
                Journal ID (iso-abbrev): Transl Oncol
                Title: Translational Oncology
                Publisher: Neoplasia Press
                ISSN (Electronic): 1936-5233
                Publication date PMC-release: 31 August 2021
                Publication date Collection: November 2021
                Publication date (Electronic): 31 August 2021
                Volume: 14
                Issue: 11
                Electronic Location Identifier: 101209
                Affiliations
                [a ]Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, Ontario, Canada
                [b ]Department of Medicine, McMaster University, Hamilton, Ontario, Canada
                [c ]Department of Oncology, McMaster University, Hamilton, Ontario, Canada
                [d ]Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
                [e ]Department of Radiation Oncology, Juravinski Cancer Center, 699 Concession Street, Hamilton, Ontario L8V 5C2, Canada
                [f ]Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada
                Author notes
                [* ]Corresponding author at: Centre for Metabolism, Obesity and Diabetes Research, Hamilton, Ontario, Canada. theos.tsakiridis@ 123456hhsc.ca
                [1]

                These authors contributed equally to this work.

                Article
                Publisher Item ID: S1936-5233(21)00201-1 Publisher ID: 101209
                DOI: 10.1016/j.tranon.2021.101209
                PMC ID: 8411238
                PubMed ID: 34479029
                SO-VID: fb0c69b6-1a20-4b67-bc1f-44b56e6f6560
                Copyright © © 2021 The Authors. Published by Elsevier Inc.
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 14 June 2021
                Date revision received : 3 August 2021
                Date accepted : 18 August 2021
                Categories
                Subject: Original Research

                Keywords: ampk,lipogenesis,mtor,hif1a,histone-h3,xenografts,prca, prostate cancer,met, metformin,sal, salicylate,met + sal, metformin and salicylate used together,rt, radiotherapy / ionizing irradiation with clinical radiotherapy units,dnl, de novo lipogenesis,mtor, the mammalian target of rapamycin,mtorc1, mtor complex 1,ampk, amp-activated protein kinase,lkb1, liver kinase b 1,p70s6k, ribosomal p70 s6 kinase,acc, acetyl-coa carboxylase,tsc2, tuberin sclerosis complex 2,4ebp1, eukaryotic translation initiation factor 4e binding protein 1,hif1α, hypoxia inducible factor 1,oxphos, oxidative phosphorylation,ocr, oxygen consumption rate,ecar, extracellular acidification rate,p-h3, phosphorylated histone h3,ihc, immunohistochemistry
                Data availability:
                Keywords: ampk, lipogenesis, mtor, hif1a, histone-h3, xenografts, prca, prostate cancer, met, metformin, sal, salicylate, met + sal, metformin and salicylate used together, rt, radiotherapy / ionizing irradiation with clinical radiotherapy units, dnl, de novo lipogenesis, mtor, the mammalian target of rapamycin, mtorc1, mtor complex 1, ampk, amp-activated protein kinase, lkb1, liver kinase b 1, p70s6k, ribosomal p70 s6 kinase, acc, acetyl-coa carboxylase, tsc2, tuberin sclerosis complex 2, 4ebp1, eukaryotic translation initiation factor 4e binding protein 1, hif1α, hypoxia inducible factor 1, oxphos, oxidative phosphorylation, ocr, oxygen consumption rate, ecar, extracellular acidification rate, p-h3, phosphorylated histone h3, ihc, immunohistochemistry

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