Clinical and imaging features of interstitial lung disease in cancer patients treated with trastuzumab deruxtecan

Author:

Baba Tomohisa,Kusumoto Masahiko,Kato Terufumi,Kurihara Yasuyuki,Sasaki Shinichi,Oikado Katsunori,Saito Yoshinobu,Endo Masahiro,Fujiwara Yutaka,Kenmotsu Hirotsugu,Sata Masafumi,Takano Toshimi,Kato Ken,Hirata Koji,Katagiri Tomomi,Saito Hanako,Kuwano Kazuyoshi

Abstract

Abstract Background Interstitial lung disease/pneumonitis (ILD/pneumonitis) has been identified as a drug-related adverse event of special interest of trastuzumab deruxtecan (T-DXd), but there were a few reports of T-DXd-related ILD/pneumonitis in clinical practice. Methods Between May 25, 2020 (the launch of T-DXd in Japan) and February 24, 2022, there were 287 physician-reported potential ILD/pneumonitis cases from the Japanese post-marketing all-case surveillance. By February 27, 2022, an independent adjudication committee assessed 138 cases and adjudicated 130 cases as T-DXd-related ILD/pneumonitis. The clinical features and imaging characteristics of these cases were evaluated. Results The majority of adjudicated T-DXd-related ILD/pneumonitis cases were grade 1 or 2 (100/130, 76.9%). The most common radiological pattern types observed were organizing pneumonia patterns (63.1%), hypersensitivity pneumonitis patterns (16.9%), and diffuse alveolar damage (DAD) patterns (14.6%). Eleven cases (8.5%) from 130 resulted in death; the majority of these (8/11, 72.7%) had DAD patterns. The overall proportion of recovery (including the outcomes of recovered, recovered with sequelae, and recovering) was 76.9%, and the median time to recovery was 83.5 days (interquartile range: 42.25–143.75 days). Most cases (59/71, 83.1%) that were treated with corticosteroids were considered responsive to treatment. Conclusions This is the first report to evaluate T-DXd-related ILD/pneumonitis cases in clinical practice. Our findings are consistent with previous reports and suggest that patients with DAD patterns have poor outcomes. Evaluation of a larger real-world dataset may further identify predictors of clinical outcome.

Funder

Daiichi-Sankyo

Publisher

Springer Science and Business Media LLC

Subject

Oncology,Hematology,General Medicine,Surgery

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