Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer

Vivek Nimgaonkar; Viswesh Krishna; Vrishab Krishna; Ekin Tiu; Anirudh Joshi; Damir Vrabac; Hriday Bhambhvani; Katelyn Smith; Julia S Johansen; Shalini Makawita; Benjamin Musher; Arnav Mehta; Andrew Hendifar; Zev Wainberg; Davendra Sohal; Christos Fountzilas; Aatur Singhi; Pranav Rajpurkar; Eric A Collisson

doi:10.1016/j.xcrm.2023.101013

Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer

Cell Rep Med. 2023 Apr 18;4(4):101013. doi: 10.1016/j.xcrm.2023.101013. Epub 2023 Apr 11.

Authors

Vivek Nimgaonkar¹, Viswesh Krishna², Vrishab Krishna¹, Ekin Tiu¹, Anirudh Joshi¹, Damir Vrabac¹, Hriday Bhambhvani¹, Katelyn Smith³, Julia S Johansen⁴, Shalini Makawita⁵, Benjamin Musher⁵, Arnav Mehta⁶, Andrew Hendifar⁷, Zev Wainberg⁸, Davendra Sohal⁹, Christos Fountzilas¹⁰, Aatur Singhi³, Pranav Rajpurkar¹¹, Eric A Collisson¹²

Affiliations

¹ Valar Labs, Inc., Palo Alto, CA, USA.
² Valar Labs, Inc., Palo Alto, CA, USA. Electronic address: viswesh@valarlabs.com.
³ Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
⁴ Departments of Oncology and Medicine, University of Copenhagen, Copenhagen, Denmark.
⁵ Baylor College of Medicine, Houston, TX, USA.
⁶ Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁷ Cedars Sinai Medical Center, Los Angeles, CA, USA.
⁸ University of California Los Angeles, Los Angeles, CA, USA.
⁹ University of Cincinnati Health, Cincinnati, OH, USA.
¹⁰ Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
¹¹ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
¹² University of California San Francisco, San Francisco, CA, USA.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) has been left behind in the evolution of personalized medicine. Predictive markers of response to therapy are lacking in PDAC despite various histological and transcriptional classification schemes. We report an artificial intelligence (AI) approach to histologic feature examination that extracts a signature predictive of disease-specific survival (DSS) in patients with PDAC receiving adjuvant gemcitabine. We demonstrate that this AI-generated histologic signature is associated with outcomes following adjuvant gemcitabine, while three previously developed transcriptomic classification systems are not (n = 47). We externally validate this signature in an independent cohort of patients treated with adjuvant gemcitabine (n = 46). Finally, we demonstrate that the signature does not stratify survival outcomes in a third cohort of untreated patients (n = 161), suggesting that the signature is specifically predictive of treatment-related outcomes but is not generally prognostic. This imaging analysis pipeline has promise in the development of actionable markers in other clinical settings where few biomarkers currently exist.

Keywords: digital pathology; pancreatic cancer; predictive biomarker.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence
Biomarkers
Carcinoma, Pancreatic Ductal* / drug therapy
Carcinoma, Pancreatic Ductal* / genetics
Deoxycytidine / therapeutic use
Gemcitabine
Humans
Pancreatic Neoplasms* / drug therapy
Pancreatic Neoplasms* / pathology
Treatment Outcome

Substances

Gemcitabine
Deoxycytidine
Biomarkers