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<p>Chimeric antigen receptor (CAR) T-cell therapy holds great promise for patients with cancer, and the identification of predictive biomarkers is crucial in finding new ways to guide therapy. Major challenges to the application of informatics and machine learning in CAR T-cell therapy include limited sample sizes and non-uniformity in data generation across cancer indications and trials. Here we took a global, pan-haematologic cancer approach, analysing 256 patients across 5 cancer types and 13 clinical trials. We generated data using a framework that included pre-infusion clinical features, over 2 million apheresis T cells analysed by flow cytometry using 17 unique markers, ex vivo T-cell expansion during CAR T-cell manufacture, more than 90,000 measurements of 30 serum markers and serial tracking of circulating CAR T cells using qPCR. From this data resource, we demonstrate the potential of pan-cancer predictive biomarkers that capture generalizable characteristics of treatment response and non-response in CAR T-cell therapy.</p>

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Predictive biomarkers of response to chimeric antigen receptor (CAR) T-cell therapy for pan-haematologic cancer

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Gregory M. Chen,
  • Ankita Jain,
  • David T. Gering,
  • Javier Satulovsky,
  • Sarbani Datta,
  • Peng Lai,
  • Jayashree Karar,
  • Vanessa E. Gonzalez,
  • Kathleen Alexander,
  • Anne Chew,
  • Julie K. Jadlowsky,
  • Marco Ruella,
  • Luca Paruzzo,
  • Kevin R. Amses,
  • Andrew J. Rech,
  • Edward A. Stadtmauer,
  • Noelle V. Frey,
  • Elizabeth O. Hexner,
  • David L. Porter,
  • Adam D. Cohen,
  • Saar I. Gill,
  • Alfred L. Garfall,
  • Stephen J. Schuster,
  • Kelvin C. Mo,
  • Samantha I. Liang,
  • Marko Spasic,
  • Bruce L. Levine,
  • Don L. Siegel,
  • Angel Ramírez-Fernández,
  • Christopher R. Cabanski,
  • EnJun Yang,
  • Crystal L. Mackall,
  • Frederic D. Bushman,
  • Zinaida Good,
  • E. John Wherry,
  • Carl H. June,
  • Joseph A. Fraietta

摘要

Chimeric antigen receptor (CAR) T-cell therapy holds great promise for patients with cancer, and the identification of predictive biomarkers is crucial in finding new ways to guide therapy. Major challenges to the application of informatics and machine learning in CAR T-cell therapy include limited sample sizes and non-uniformity in data generation across cancer indications and trials. Here we took a global, pan-haematologic cancer approach, analysing 256 patients across 5 cancer types and 13 clinical trials. We generated data using a framework that included pre-infusion clinical features, over 2 million apheresis T cells analysed by flow cytometry using 17 unique markers, ex vivo T-cell expansion during CAR T-cell manufacture, more than 90,000 measurements of 30 serum markers and serial tracking of circulating CAR T cells using qPCR. From this data resource, we demonstrate the potential of pan-cancer predictive biomarkers that capture generalizable characteristics of treatment response and non-response in CAR T-cell therapy.