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Correlation of transcriptional subtypes with a validated CT radiomics score in resectable pancreatic ductal adenocarcinoma

  • Hepatobiliary-Pancreas
  • Published:
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Abstract

Objectives

Transcriptional classifiers (Bailey, Moffitt and Collison) are key prognostic factors of pancreatic ductal adenocarcinoma (PDAC). Among these classifiers, the squamous, basal-like, and quasimesenchymal subtypes overlap and have inferior survival. Currently, only an invasive biopsy can determine these subtypes, possibly resulting in treatment delay. This study aimed to investigate the association between transcriptional subtypes and an externally validated preoperative CT-based radiomic prognostic score (Rad-score).

Methods

We retrospectively evaluated 122 patients who underwent resection for PDAC. All treatment decisions were determined at multidisciplinary tumor boards. Tumor Rad-score values from preoperative CT were dichotomized into high or llow categories. The primary endpoint was the correlation between the transcriptional subtypes and the Rad-score using multivariable linear regression, adjusting for clinical and histopathological variables (i.e., tumor size). Prediction of overall survival (OS) was secondary endpoint.

Results

The Bailey transcriptional classifier significantly associated with the Rad-score (coefficient = 0.31, 95% confidence interval [CI]: 0.13–0.44, p = 0.001). Squamous subtype was associated with high Rad-scores while non-squamous subtype was associated with low Rad-scores (adjusted p = 0.03). Squamous subtype and high Rad-score were both prognostic for OS at multivariable analysis with hazard ratios (HR) of 2.79 (95% CI: 1.12–6.92, p = 0.03) and 4.03 (95% CI: 1.42–11.39, p = 0.01), respectively.

Conclusions

In patients with resectable PDAC, an externally validated prognostic radiomic model derived from preoperative CT is associated with the Bailey transcriptional classifier. Higher Rad-scores were correlated with the squamous subtype, while lower Rad-scores were associated with the less lethal subtypes (immunogenic, ADEX, pancreatic progenitor).

Key Points

• The transcriptional subtypes of PDAC have been shown to have prognostic importance but they require invasive biopsy to be assessed.

• The Rad-score radiomic biomarker, which is obtained non-invasively from preoperative CT, correlates with the Bailey squamous transcriptional subtype and both are negative prognostic biomarkers.

• The Rad-score is a promising non-invasive imaging biomarker for personalizing neoadjuvant approaches in patients undergoing resection for PDAC, although additional validation studies are required.

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Abbreviations

ADEX:

Aberrantly differentiated endocrine exocrine

AJCC:

American Joint Committee on Cancer

CCP:

Cell cycle progression

CECT:

Contrast-enhanced computed tomography

CI:

Confidence interval

CT:

Computed tomography

DFS:

Disease-free survival

HR:

Hazard ratio

IBSI:

Image Biomarker Standardization Initiative

IHC:

Immunohistochemistry

OS:

Overall survival

PDAC:

Pancreatic ductal adenocarcinoma

PV:

Portal venous

ROI:

Region of interest

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Funding

Ontario Institute for Cancer Research Translational Research Initiative in Pancreatic Cancer and Clinician Investigator Program (M.A.H.); research scholarship from the Faculty of Radiologists, Royal College of Surgeons in Ireland (GMH); Deutsche Forschungsgemeinschaft (DFG) Fellowship DE 3207/1–1 (DD).

Author information

Authors and Affiliations

  1. Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Joseph & Wolf Lebovic Health Complex, 600 University Avenue, 6th Floor, Office 6 200, Toronto, ON, M5G 1X5, Canada

    Emmanuel Salinas-Miranda, Gerard M. Healy, Dominik Deniffel & Masoom A. Haider

  2. Joint Department of Medical Imaging, University Health Network/Sinai Health System, 600 University Ave, 5th Floor, Toronto, ON, M5G1X5, Canada

    Emmanuel Salinas-Miranda, Gerard M. Healy & Masoom A. Haider

  3. Department of Medical Imaging, University of Toronto, 263 McCaul St 4th Floor, Toronto, ON, M5T 1W5, Canada

    Gerard M. Healy & Masoom A. Haider

  4. Department of Pathology, University Health Network, 610 University Ave, Toronto, ON, M5G 2C1, Canada

    Barbara Grünwald & Sandra Fischer

  5. PanCuRx Translational Research Initiative, Ontario Institute for Cancer Research, 661 University Avenue, Suite 510, Toronto, ON, M5G 0A3, Canada

    Barbara Grünwald, Grainne M. O’Kane, Robert Grant, Julie Wilson, Jennifer Knox, Steven Gallinger & Masoom A. Haider

  6. Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, 610 University Ave, Toronto, ON, M5G 2C1, Canada

    Rahi Jain

  7. Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, 610 University Ave, Toronto, ON, M5G 2C1, Canada

    Grainne M. O’Kane, Robert Grant, Jennifer Knox & Steven Gallinger

  8. Hepatobiliary Pancreatic Surgical Oncology Program, University Health Network, 190 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada

    Steven Gallinger

  9. Department of Medical Biophysics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, 610 University Ave, Toronto, ON, M5G 2C1, Canada

    Rama Khokha

Authors
  1. Emmanuel Salinas-Miranda
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  2. Gerard M. Healy
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  3. Barbara Grünwald
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  4. Rahi Jain
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  6. Grainne M. O’Kane
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  8. Julie Wilson
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  9. Jennifer Knox
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  10. Steven Gallinger
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  11. Sandra Fischer
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  12. Rama Khokha
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  13. Masoom A. Haider
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Contributions

Guarantors of integrity of the entire study: E.S.,G.H., M.A.H.; study concepts/study design or data acquisition or data analysis/interpretation: E.S.,G.H., D.D., M.A.H.; manuscript drafting or manuscript revision for important intellectual content: all authors; approval of final version of submitted manuscript: all authors; literature research: E.S.,G.H; clinical studies: E.S, G.H., M.A.H; statistical analysis: R.J; and manuscript editing: all authors.

Corresponding author

Correspondence to Masoom A. Haider.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Masoom A. Haider.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Rahi Jain and Dr. Dominik Deniffel who have significant statistical expertise (both co-authors) provided statistical advice for this manuscript.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

This retrospective single-center study was approved by the institutional review boards.

Study subjects or cohorts overlap

This cohort represents a subset of our prior studies. No pathohistological or transcriptomic information was available for the cohort at the time of the prior analysis. An overlap of study populations is discussed in the cover letter and disclosure paragraph.

References

Healy GM, Salinas-Miranda E, Jain R, et al (2021) Pre-operative radiomics model for prognostication in resectable pancreatic adenocarcinoma with external validation. European Radiology 2021 1–14. https://doi.org/10.1007/S00330-021-08314-W (n = 122)

Khalvati F, Zhang Y, Baig S, et al (2019) Prognostic value of CT radiomic features in resectable pancreatic ductal adenocarcinoma. Scientific Reports 9:5449. https://doi.org/10.1038/s41598-019-41728-7 (n = 13)

Zhang Y, Lobo-Mueller EM, Karanicolas P, et al (2021) Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images. Scientific reports 11:. https://doi.org/10.1038/S41598-021-80998-Y (n = 38)

Zhang Y, Lobo-Mueller EM, Karanicolas P, et al (2020) CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging. BMC Medical Imaging 20:. https://doi.org/10.1186/s12880-020-0418-1 (n = 38)

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• performed at one institution

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Salinas-Miranda, E., Healy, G.M., Grünwald, B. et al. Correlation of transcriptional subtypes with a validated CT radiomics score in resectable pancreatic ductal adenocarcinoma. Eur Radiol 32, 6712–6722 (2022). https://doi.org/10.1007/s00330-022-09057-y

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