Publication: Predictive analysis to find germline genetic susceptibility associated with the tumoral immune infiltration in pancreatic cancer
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2021-07
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Abstract
The immune system plays an important role in the tumor microenvironment since there is an interaction between tumor cells and immune cells that affects the tumor development. In particular, in pancreatic cancer, it has been studied that after characterizing B and T cell repertoire, patients have shown a large heterogeneity among them. Additionally, it was previously demonstrated that genetic susceptibility may explain around 40% of the immune system differences across individuals. Thus, in this project, the main objective was to predict tumoral immune infiltration in pancreatic cancer patients using germline genetic variants (SNPs). T and B cell receptors were extracted from RNAseq data in 120 individuals with pancreatic cancer and richness and diversity were assessed using Expression and Entropy measures. Then, four machine learning methods were proposed (Elastic Net, Ridge Regression, Random Forest and Neural Network) focus on dealing with high dimensionality and multicollinearity problems present in high-throughput data. The performance of the four different methods was assessed through Pearson correlation. Predictions obtained by these methods were benchmarked across 10 testing subsets in three different scenarios. Neural Network which showed the highest and the most consistent correlations between observed and predicted values, overcomes the overfitting and over-specificity problems. Being able to predict the immune infiltration with genetic variants will allow us to integrate and decipher new biological insights extremely necessary in pancreatic cancer research.
El sistema inmunológico desempeña un papel fundamental en el microentorno del tumor, ya que, existe una interacción entre las células tumorales y las inmunes influyendo en su desarrollo. En particular, en cáncer de páncreas. Previamente, se ha estudiado que tras caracterizar el repertorio de las células B y T, los pacientes han mostrado una gran heterogeneidad entre ellos. Además, se ha demostrado que la susceptibilidad genética puede explicar hasta un 40% de las diferencias inmunes observadas entre individuos. Así, en este trabajo, se plantea el objetivo de predecir la infiltración tumoral inmune en individuos con cáncer de páncreas usando variantes genéticas en línea germinal (SNPs). Los receptores de las células B y T se extrajeron de RNAseq de 120 individuos con cáncer de páncreas y la riqueza y diversidad se midieron mediante las medidas de Expresión y Entropía. Se proponen entonces cuatro métodos de machine learning (Elastic Net, Ridge Regression, Random Forest y Neural Network) enfocados a lidiar con los problemas de alta dimensionalidad y multicolinealidad presentes en nuestros datos. La actuación de los cuatro métodos se evaluó a través de la correlación de Pearson. Las predicciones obtenidas por estos métodos fueron comparadas a lo largo de 10 subconjuntos de testing en tres escenarios diferentes. Neural Network, el cual mostró las correlaciones más altas y consistentes entre los valores predichos y observados, superó los problemas de sobreajuste y sobre-especificidad. Ser capaz de predecir la infiltración inmunológica mediante variantes genéticas nos permitirá integrar y descifrar nuevo conocimiento muy necesario para avanzar en el cáncer de páncreas.
El sistema inmunológico desempeña un papel fundamental en el microentorno del tumor, ya que, existe una interacción entre las células tumorales y las inmunes influyendo en su desarrollo. En particular, en cáncer de páncreas. Previamente, se ha estudiado que tras caracterizar el repertorio de las células B y T, los pacientes han mostrado una gran heterogeneidad entre ellos. Además, se ha demostrado que la susceptibilidad genética puede explicar hasta un 40% de las diferencias inmunes observadas entre individuos. Así, en este trabajo, se plantea el objetivo de predecir la infiltración tumoral inmune en individuos con cáncer de páncreas usando variantes genéticas en línea germinal (SNPs). Los receptores de las células B y T se extrajeron de RNAseq de 120 individuos con cáncer de páncreas y la riqueza y diversidad se midieron mediante las medidas de Expresión y Entropía. Se proponen entonces cuatro métodos de machine learning (Elastic Net, Ridge Regression, Random Forest y Neural Network) enfocados a lidiar con los problemas de alta dimensionalidad y multicolinealidad presentes en nuestros datos. La actuación de los cuatro métodos se evaluó a través de la correlación de Pearson. Las predicciones obtenidas por estos métodos fueron comparadas a lo largo de 10 subconjuntos de testing en tres escenarios diferentes. Neural Network, el cual mostró las correlaciones más altas y consistentes entre los valores predichos y observados, superó los problemas de sobreajuste y sobre-especificidad. Ser capaz de predecir la infiltración inmunológica mediante variantes genéticas nos permitirá integrar y descifrar nuevo conocimiento muy necesario para avanzar en el cáncer de páncreas.
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