Table of Contents
Cancer Research Highlights
Controversy and Consensus
Understanding the significance of physical dose delivered using energetically different methods of radiation treatment will aid the transition from radionuclide γ-irradiators to orthovoltage X-irradiators.
Genome and Epigenome
Altered DNA methylation in lung cancer brain metastases corresponds with loss of EZH2 occupancy at developmental genes, which could promote stem-like phenotypes permissive of dissemination and survival in different microenvironments.
A novel machine learning approach predicts the impact of tumor mutations on cellular phenotypes, overcomes limited training data, minimizes costly functional validation, and advances efforts to implement cancer precision medicine.
Tumor Biology and Immunology
IL6 and CCL18 Mediate Cross-talk between VHL-Deficient Kidney Cells and Macrophages during Development of Renal Cell Carcinoma
The identification of VHL-deficient kidney tubule cell cross-talk with macrophages regulated by IL6 and CCL18 reveals potential targets for the prevention and treatment of ccRCC.
NELL1 modulates the sarcoma matrisome to promote tumor growth, invasion, and metastasis, identifying the matrix-associated protein as an orchestrator of cell–ECM interactions in sarcomagenesis and disease progression.
The identification of the SETDB1-mediated suppression of radiotherapy-induced viral mimicry reveals SETDB1 inhibition as a potential approach to sensitize tumors to radiotherapy by enhancing the type I interferon response.
A Programmable In Vivo CRISPR Activation Model Elucidates the Oncogenic and Immunosuppressive Functions of MYC in Lung Adenocarcinoma
A streamlined platform for programmable CRISPR gene activation enables rapid evaluation and functional validation of putative oncogenes in vivo.
Newly developed ponatinib analogs retain antitumor efficacy but elicit significantly decreased cardiotoxicity, representing a therapeutic opportunity for safer CML treatment.
Convergence and Technologies
This work demonstrates the potential for deep learning analysis of histopathologic images to serve as a fast, low-cost method to assess genetic intratumoral heterogeneity.