Han L, Shi H, Luo Y, et al. - Although there has been improved prognosis in lung adenocarcinoma (LUAD) due to emerging treatments, clinical therapies are beneficial only in a small fraction of patients, so researchers developed a signature based on 308 tumor‐infiltrating B lymphocyte‐specific genes (TILBSig) employing a machine learning method after analyzing radiotherapy (RT), immunization, mRNA, and clinical information to predict response to these therapies. Six B cell‐specific genes (PARP15, BIRC3, RUBCNL, SP110, TLE1, and FADS3) were incorporated in TILBSig, and these genes were highly related to overall survival as independent factors. TILBSig enabled discrimination between better survival and worse survival in various patients, and was identified as an independent factor for clinical features. A greater likelihood of responding to RT and immunotherapy was observed in patients with lower risk scores. In patients suffering from LUAD, TILBSig was able to predict prognosis as well as response to immunotherapy and RT.