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

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.

Graphical abstract

Figure 1

Constructing an experimental approach to identify an AI-derived histologic biomarker associated with outcomes following adjuvant gemcitabine (A) AI-derived biomarkers could be identified from digitized slides of pancreatic tumor resections, which might guide adjuvant treatment selection. (B) Data from three patient cohorts were used for this study: (1) TCGA (n = 93 patients), which served as the source for a training set (n = 46) to develop a histologic signature and for a test set (n = 47) to evaluate the performance of the histologic signature; (2) a retrospective cohort from UPMC, which served as a test set external to the data source used for training; and (3) a cohort from a study in Copenhagen, which included patients who received no adjuvant treatment, serving as a negative control.

Figure 2

An image analysis pipeline yields a histologic signature that stratifies disease-specific survival (DSS) following adjuvant gemcitabine (A) In the image analysis pipeline for this study, whole-slide images (WSIs) from tumor resections were converted to smaller patches before cell-level segmentation and geometric feature extraction describing cellular morphology at the patient level. 816 features are extracted for each individual patient from the available digitized slide. Downstream statistical analysis of these features enables identification of a histologic signature. (B) Kaplan-Meier curves for the test set from TCGA cohort (n = 47) stratified by the presence or absence of the AI-derived histologic signature. The p value (p = 0.01) corresponds to the log rank test. The median DSS for signature+ patients was 67.9 months (95% CI: [16.2, not reached]), and the median DSS for signature− patients was 16 months (95% CI: [9.3, 22.8]).

Figure 3

Known RNA-seq sub-types do not stratify patients by outcomes following adjuvant gemcitabine and do not associate with the histologic signature (A–C) Kaplan-Meier curves describing a sub-group of TCGA test set with RNA-seq data available (n = 39) stratified by (A) RNA-seq clusters previously described by Moffitt et al., (B) RNA-seq clusters previously described by Collisson et al., and (C) RNA-seq clusters previously described by Bailey et al. (D–F) Among patients with RNA-seq data available across the entire TCGA cohort (n = 79), the proportion of patients falling into the (D) Moffitt clusters, (E) Collisson clusters, and (F) Bailey clusters is graphed among all patients, those who are signature+, and those who are signature−.

Figure 4

The signature generalizes to external cohorts of gemcitabine-treated patients but not untreated patients (A) Kaplan-Meier curves describing DSS among patients receiving adjuvant gemcitabine-based therapy in the UPMC cohort (n = 46) when stratified by the histologic signature. The p value (p = 0.02) corresponds to the log rank test. Median DSS of signature+ patients was 43.1 months (95% CI: [26.8, 63.9]), and the median DSS of signature− patients was 16 months (95% CI: [10.8, 50.1]). (B) Kaplan-Meier curves describing DSS among patients in the UPMC cohort who had received no therapy prior to surgery (n = 24) (log rank test p = 0.03). Median DSS of signature+ patients was 40.2 months (95% CI: [16.4, not reached]), and the median DSS of signature− patients was 12.9 months (95% CI: [8.1, not reached]). (C) Kaplan-Meier curves describing time to recurrence among patients who received adjuvant gemcitabine-based therapy (n = 46) (log rank test p = 0.01). Median time to recurrence of signature+ patients was 22.6 months (95% CI: [14.1, 44.8]), and the median time to recurrence among signature− patients of 9.1 months (95% CI: [6.4, 14.7]). (D) Kaplan-Meier curves describing DSS among patients in the Copenhagen cohort who were untreated (log rank test p = 0.59; signature+ median DSS: 13.2 months [10.4, 19.8], signature− median DSS: 12.3 [10.4, 19.8]).