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. 2024 Aug;23(3):233-246.
doi: 10.1007/s10689-024-00397-w. Epub 2024 May 23.

The best linear unbiased prediction (BLUP) method as a tool to estimate the lifetime risk of pancreatic ductal adenocarcinoma in high-risk individuals with no known pathogenic germline variants

Cristina-Marianini-Rios #  1 , María E Castillo Sanchez #  2 , Ana García García de Paredes  2   3   4 , Mercedes Rodríguez  2   5   6   7 , Emma Barreto  2   6   7 , Jorge Villalón López  2 , Raquel Fuentes  5 , María Muñoz Beltrán  8 , Alfonso Sanjuanbenito  2   6   9 , Eduardo Lobo  2   9 , Alejandra Caminoa  10 , Ignacio Ruz-Caracuel  2   6   10 , Sergio López Durán  2   3 , José Ramón Foruny Olcina  2   3 , Javier Blázquez  2   8 , Enrique Vázquez Sequeros  2   3   6 , Alfredo Carrato  2   6   7   11 , Jose Carlos Martínez Ávila  12 , Julie Earl  13   14
Affiliations

The best linear unbiased prediction (BLUP) method as a tool to estimate the lifetime risk of pancreatic ductal adenocarcinoma in high-risk individuals with no known pathogenic germline variants

Cristina-Marianini-Rios et al. Fam Cancer. 2024 Aug.

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death in the Western world. The number of diagnosed cases and the mortality rate are almost equal as the majority of patients present with advanced disease at diagnosis. Between 4 and 10% of pancreatic cancer cases have an apparent hereditary background, known as hereditary pancreatic cancer (HPC) and familial pancreatic cancer (FPC), when the genetic basis is unknown. Surveillance of high-risk individuals (HRI) from these families by imaging aims to detect PDAC at an early stage to improve prognosis. However, the genetic basis is unknown in the majority of HRIs, with only around 10-13% of families carrying known pathogenic germline mutations. The aim of this study was to assess an individual's genetic cancer risk based on sex and personal and family history of cancer. The Best Linear Unbiased Prediction (BLUP) methodology was used to estimate an individual's predicted risk of developing cancer during their lifetime. The model uses different demographic factors in order to estimate heritability. A reliable estimation of heritability for pancreatic cancer of 0.27 on the liability scale, and 0.07 at the observed data scale as obtained, which is different from zero, indicating a polygenic inheritance pattern of PDAC. BLUP was able to correctly discriminate PDAC cases from healthy individuals and those with other cancer types. Thus, providing an additional tool to assess PDAC risk HRI with an assumed genetic predisposition in the absence of known pathogenic germline mutations.

Keywords: Genetic cancer risk; Heritability; High-risk screening; Pancreatic cancer.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
(a). Histogram of any cancer prevalence within the families and (b) pancreatic cancer prevalence within the families
Fig. 2
Fig. 2
MCMC traces of variance (corresponding to VA) and density due to individual, family and generation for model III
Fig. 3
Fig. 3
(a) Boxplot of comparison of EGVs according to the any cancer BLUP model and (b) PDAC BLUP model; 0 = no cancer, 1 = any cancer. (c) Correlation between family prevalence and median EGV of the family according to the any cancer BLUP model (Pearson´s correlation coefficient 0.65 [0.5–0.76]; p value < 0.001) and PDAC BLUP model (Pearson´s correlation coefficient 0.61 [0.45–0.73]; p value < 0.001)
Fig. 4
Fig. 4
Average EGV of parents versus calculated EGV of offspring, individuals with cancer are shown in red and individuals without cancer are shown in black, (a) using the any cancer BLUP model and (b) using the PDAC BLUP model. ROC curve using the mean EGV of the parents (c) for the any cancer BLUP model and (d) for the PDAC BLUP model. EGV was used as a cancer predictor, using either the EGV value assigned to the individual or the parental mean of the EGV. Both ROC curves have an AUC above 0.5
Fig. 5
Fig. 5
Comparison of the EGV in individuals with different types of cancer and individuals without cancer, including the number of individuals in each group using the (a) Any cancer BLUP model and (b) PDAC BLUP model
Fig. 6
Fig. 6
Comparison of EGV in individuals with different types of lesions detected during follow-up as a function of the detection technique used, according to the no cancer-any cancer BLUP model by (a) EUS and (b) MRI and the PDAC BLUP model by (c) EUS and (d) MRI
Fig. 7
Fig. 7
Comparison of EGV in individuals with different types of extra-pancreatic lesions detected on MRI and EUS. (a) No cancer-any cancer model (b) No pancreatic cancer-pancreatic cancer model
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