Radiotherapy is widely used in the clinical treatment of cancer patients and it may be used alone or in combination with surgery or chemotherapy to inhibit tumor development. However, radiotherapy may at times not kill all cancer cells completely, as certain cells may develop radioresistance that counteracts the effects of radiation. The emergence of radioresistance is associated with the genetic background and epigenetic regulation of cells. p53 is an important tumor suppressor gene that is expressed at low levels in cells. However, when cells are subjected to stress-induced stimulation, the expression level of p53 increases, thereby preventing genomic disruption. This mechanism has important roles in maintaining cell stability and inhibiting carcinogenesis. However, mutation and deletion destroy the anticancer function of p53 and may induce carcinogenesis. In tumor radiotherapy, the status of p53 expression in cancer cells has a close relationship with radiotherapeutic efficacy. Therefore, understanding how p53 expression affects the cellular response to radiation is of great significance for solving the problem of radioresistance and improving radiotherapeutic outcomes. For the present review, the literature was searched for studies published between 1979 and 2021 using the PubMed database (https://pubmed.ncbi.nlm.nih.gov/) with the following key words: Wild-type p53, mutant-type p53, long non-coding RNA, microRNA, gene mutation, radioresistance and radiosensitivity. From the relevant studies retrieved, the association between different p53 mutants and cellular radiosensitivity, as well as the molecular mechanisms of p53 affecting the radiosensitivity of cells, were summarized. The aim of the present study was to provide useful information for understanding and resolving radioresistance, to help clinical researchers develop more accurate treatment strategies and to improve radiotherapeutic outcomes for cancer patients with p53 mutations.

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Relationship between p53 status and the bioeffect of ionizing radiation (Review)

With a widely attended virtual kickoff event on January 29, 2021, the National Cancer Institute (NCI) and the Department of Energy (DOE) launched a series of 4 interactive, interdisciplinary workshops—and a final concluding “World Café” on March 29, 2021—focused on advancing computational approaches for predictive oncology in the clinical and research domains of radiation oncology. These events reflect 3,870 human hours of virtual engagement with representation from 8 DOE national laboratories and the Frederick National Laboratory for Cancer Research (FNL), 4 research institutes, 5 cancer centers, 17 medical schools and teaching hospitals, 5 companies, 5 federal agencies, 3 research centers, and 27 universities. Here we summarize the workshops by first describing the background for the workshops. Participants identified twelve key questions—and collaborative parallel ideas—as the focus of work going forward to advance the field. These were then used to define short-term and longer-term “Blue Sky” goals. In addition, the group determined key success factors for predictive oncology in the context of radiation oncology, if not the future of all of medicine. These are: cross-discipline collaboration, targeted talent development, development of mechanistic mathematical and computational models and tools, and access to high-quality multiscale data that bridges mechanisms to phenotype. The workshop participants reported feeling energized and highly motivated to pursue next steps together to address the unmet needs in radiation oncology specifically and in cancer research generally and that NCI and DOE project goals align at the convergence of radiation therapy and advanced computing. 

Predictive Radiation Oncology – A New NCI–DOE Scientific Space and Community

Repurposing Proton Beam Therapy through Novel Insights into Tumour Radioresistance.

Angelica gigas exerts powerful anti-tumor and anti-cancer effects in various cancer cell types. However, there have been few studies regarding the anti-cancer effect of nodakenin, a bioactive compound of Angelica gigas, in vivo and in vitro on breast cancers. I found that nodakenin, in a concentration-dependent manner, inhibits breast cancer cell viability and decreases the tumor volume in mice. Additionally, nodakenin induces caspase-3-dependent apoptosis in breast cancer cells; however, the combination of Z-VAD-FMK and nodakenin suppresses the caspase-3-dependent apoptotic cell death. Furthermore, nodakenin mediates apoptotic cell death via the PERK-mediated signaling pathway and calcium (Ca2+) release, and nodakenin combined with thapsigargin induces synergistic cell death by inhibiting sarco/endoplasmic reticulum (ER) Ca2+-ATPase. However, knockdown of PERK or CHOP inhibits Ca2+ generation and caspase-dependent apoptosis in nodakenin-treated breast cancer cells. Nodakenin induces ROS and Ca2+ generation, ER stress, and apoptotic cell death; however, the knockdown of Nox4 inhibits ROS generation and ER stress- and caspase-dependent apoptotic cell death. In addition, nodakenin combined with radiation overcomes radioresistance in radioresistant breast cancer cells by suppressing epithelial–mesenchymal transition phenotypes, including the decrease in E-cadherin and the increase in N-cadherin and vimentin. Therefore, these findings indicate that nodakenin may be a novel therapeutic strategy for breast cancers.

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Nodakenin Induces ROS-Dependent Apoptotic Cell Death and ER Stress in Radioresistant Breast Cancer

The substantial increase in global population and climate change, among other factors, have led to global food security and supply chain challenges. The United Nations has laid out an agenda to sustainably achieve zero hunger by 2030 as one of its sustainable development goals. However, sustainably achieving improved food yield has become a challenge as excessive use of fertilizers has also led to adverse environmental impact. To address the aforementioned challenges, WisDM Green, an artificial intelligence (AI)‐based platform that aims to pinpoint and prioritize compound (e.g., biostimulants) combinations in peat moss, is harnessed to sustainably improve the yield of Amaranthus cruentus (red spinach). In this proof‐of‐concept study, from a pool of eight compounds, WisDM Green‐pinpointed combinations (6‐benzylaminopurine/ethylenediaminetetraacetic acid iron (III) (6‐BAP/EDTA‐Fe) and humic acid/seaweed extract (HA/SWE)) achieved 26.34 ± 15.80 and 33.59 ± 14.60 increase in %Yield, respectively. The study also indicates that compound combinations may exhibit concentration‐dependent synergies and thus, properly adjusting the concentration ratios of combinations may further improve plant yield in the context of sustainable farming. An interactive preprint version of the article can be found at: https://www.authorea.com/doi/full/10.22541/au.165244695.56681780.

WisDM Green: Harnessing Artificial Intelligence to Design and Prioritize Compound Combinations in Peat Moss for Sustainable Farming Applications

Improving the therapeutic ratio of radiotherapy against radioresistant cancers: Leveraging on novel artificial intelligence-based approaches for drug combination discovery.

Li Min Tay

Papers

Improving the therapeutic ratio of radiotherapy against radioresistant cancers: Leveraging on novel artificial intelligence-based approaches for drug combination discovery.