Archives

  • 2022-09
  • 2022-08
  • 2022-07
  • 2022-05
  • 2022-04
  • 2021-03
  • 2020-08
  • 2020-07
  • 2020-03
  • 2019-11
  • 2019-10
  • 2019-09
  • 2019-08
  • 2019-07
  • br Du K Reinhardt JM Christensen GE et al Respiratory

    2022-04-25


    33. Du K, Reinhardt JM, Christensen GE, et al. Respiratory effort correction strategies to improve the reproducibility of lung expansion measurements. Med Phys 2013;40:123504.
    34. Kadoya N, Cho SY, Kanai T, et al. Dosimetric impact of 4-dimensional computed tomography ventilation imaging-based func-tional treatment planning for stereotactic body BAY-598 therapy with 3-dimensional conformal radiation therapy. Pract Radiat Oncol 2015; 5:e505-e512.
    35. Gergel TJ, Leichman L, Nava HR, et al. Effect of concurrent radiation therapy and chemotherapy on pulmonary function in patients with esoph-ageal cancer: Dose-volume histogram analysis. Cancer J 2002;8:451-460.
    36. Kelsen DP, Minsky B, Smith M, et al. Preoperative therapy for esophageal cancer: A randomized comparison of chemotherapy versus radiation therapy. J Clin Oncol 1990;8:1352-1361.
    37. Hart JP, McCurdy MR, Ezhil M, et al. Radiation pneumonitis: Cor-relation of toxicity with pulmonary metabolic radiation response. Int J Radiat Oncol Biol Phys 2008;71:967-971. 
    38. Castillo R, Pham N, Castillo E, et al. Pre-radiation therapy fluorine 18 fluorodeoxyglucose pet helps identify patients with esophageal cancer at high risk for radiation pneumonitis. Radiology 2015;275:822-831.
    39. Morimoto S, Takeuchi N, Imanaka H, et al. Gravity-dependent atelec-tasis. Radiologic, physiologic and pathologic correlation in rabbits on high-frequency oscillation ventilation. Invest Radiol 1989;24:522-530.
    40. Nyre´n S, Radell P, Lindahl SGE, et al. Lung ventilation and perfusion in prone and supine postures with reference to anesthetized and me-chanically ventilated healthy volunteers. Anesthesiology 2010;112: 682-687.
    41. Henderson AC, Sa´ RC, Theilmann RJ, et al. The gravitational distri-bution of ventilation-perfusion ratio is more uniform in prone than supine posture in the normal human lung. J Appl Physiol 2013;115: 313-324.
    42. Yamamoto T, Kabus S, von Berg J, et al. 4D-CT pulmonary ventilation image-guided radiotherapy planning is significantly influenced by deformable image registration algorithms and metrics. Int J Radiat Oncol Biol Phys 2010;78:S185.
    44. Mistry NN, Diwanji T, Shi X, et al. Evaluation of fractional regional ventilation using 4D-CT and effects of breathing maneuvers on ventilation. Int J Radiat Oncol Biol Phys 2013;87:825-831.
    46. Castillo E, Castillo R, Vinogradskiy Y, et al. The numerical stability of transformation-based CT ventilation. Int J Comput Assist Radiol Surg 2017;12:569-580. Article
    CHASMplus Reveals the Scope of Somatic Missense Mutations Driving Human Cancers
    Graphical Abstract
    Highlights
    d CHASMplus identifies driver missense mutations in a cancer-type-specific manner
    d Rare driver mutations are common in cancer when considered as a group
    d In some cancers, further sequencing will yield insight into rare driver mutations
    d Comprehensive resource of driver propensity for all possible missense mutations 
    Authors
    Collin Tokheim, Rachel Karchin
    Correspondence
    [email protected]
    In Brief
    Tokheim et al. introduce a computational approach to accurately separate driver from passenger mutations in cancer. Their analysis revealed that most driver mutations occur only in a few patients, presenting a challenge for precision medicine, and several cancer types will benefit from additional sequencing to identify these rare driver mutations.
    Cell Systems Article
    CHASMplus Reveals the Scope of Somatic Missense Mutations Driving Human Cancers
    Collin Tokheim1,2 and Rachel Karchin1,2,3,4,* 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA 2Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA 3Department of Oncology, Johns Hopkins University, Baltimore, MD 21204, USA 4Lead Contact *Correspondence: [email protected]
    SUMMARY
    Large-scale cancer sequencing studies of patient co-horts have statistically implicated many genes driving cancer growth and progression, and their identifica-tion has yielded substantial translational impact. However, a remaining challenge is to increase the res-olution of driver prediction from the level of genes to mutations because mutation-level predictions are more closely aligned with the goal of precision cancer medicine. Here, we present CHASMplus, a computa-tional method that is uniquely capable of identifying driver missense mutations, including those specific to a cancer type, as evidenced by significantly supe-rior performance on diverse benchmarks. Applied to 8,657 tumor samples across 32 cancer types in The Cancer Genome Atlas (TCGA), CHASMplus identifies over 4,000 unique driver missense mutations in 240 genes, supporting a prominent role for rare driver mu-tations. We show which TCGA cancer types are likely to yield discovery of new driver missense mutations by additional sequencing, which has important impli-cations for public policy.