Utilizing machine learning to identify multimodal signatures for patients who would benefit from the addition of tremelimumab to durvalumab and chemotherapy (TRIDENT).

Ferdinandos Skoulidis¹, Salma K Jabbour², Edward B Garon³, Puneeth Iyengar⁴, Giorgio Scagliotti⁵, Loïc Ferrer⁶, Guillaume Etchepare⁶, Olivier Gallinato⁶, Jérôme Faure⁶, Paul Bernard⁶, Thierry Colin⁶, Philippe Menu⁷, Yian Lin⁸, Ling Cai⁸, Ammar Ahmed Chaudhry⁹, Amanda Remorino¹⁰, Ross Stewart¹¹, Luisa Luciani-Silverman⁹, Katy Miller¹², David Dellamonica¹³, Jolyon Faria¹¹, Yiduo Zhang¹⁴

¹The University of Texas MD Anderson Cancer Center Houston, TX United States.

²Rutgers Cancer Institute New Brunswick, New Jersey United States.

³University of California, Los Angeles Santa Monica, CA United States.

⁴Memorial Sloan Kettering Cancer Center New York, NY United States.

⁵University of Turin Torino Italy.

⁶SOPHiA GENETICS Pessac France.

⁷Sophia Genetics Rolle Switzerland.

⁸AstraZeneca South San Franciso United States.

⁹AstraZeneca Gaithersburg United States.

¹⁰AstraZeneca Barcelona Spain.

¹¹AstraZeneca (United Kingdom) Cambridge United Kingdom.

¹²AstraZeneca (United States) Gaithersburg United States.

¹³AstraZeneca (Switzerland) Baar Switzerland.

¹⁴AstraZeneca Spain Spain.

PubMed DOI

Abstract

PURPOSE: POSEIDON (NCT03164616) was a randomized, open-label, multicenter phase 3 trial comparing first-line durvalumab with or without tremelimumab in combination with chemotherapy versus chemotherapy alone in patients with metastatic non-small-cell lung cancer (NSCLC). Overall survival (OS) and progression-free survival were significantly increased in the tremelimumab plus durvalumab and chemoth…

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