Algorithms for reconstructing within-patient tumor histories to support precision oncology

Alex Gavryushkin

IVPP (Chinese Academy of Sciences), 7 July 2024, Beijing

Disclaimers

Precision oncology

Phylogenetic trees on tumor cells

Problem

This result is not consistent across datasets — why?

Finding important genes

Model-based approaches to genotype-phenotype data

$ f(x_1,\, \ldots,\, x_p) = \beta_0 + \sum_{i} x_i \beta_i + \sum_{i \le j} x_i x_j \beta_{i,j} $ $ + \sum_{i \le j \le k} x_i x_j x_k \beta_{i,j,k} + \ldots $

State of the art in 2018


Kieran Elmes et al. Learning epistatic gene interactions from perturbation screens. 2021

Problem #1: The hierarchy assumptions


Sarah Howles et al. Genetic variants of calcium and vitamin D metabolism in kidney stone disease. 2019

Problem #2: Scalability


Kieran Elmes et al. Learning epistatic gene interactions from perturbation screens. 2021
Hierarchy and scalability are largely solved (until you sequence more)
Kieran Elmes et al. A fast lasso-based method for inferring higher-order interactions

Problem #3 (unsolved!):

Model assumptions

$ f(x_1,\, \ldots,\, x_p) = \beta_0 + \sum_{i} x_i \beta_i + \sum_{i \le j} x_i x_j \beta_{i,j} $ $ + \sum_{i \le j \le k} x_i x_j x_k \beta_{i,j,k} + \ldots $
Kieran Elmes et al. A fast lasso-based method for inferring higher-order interactions

SNVformer


Kieran Elmes et al. SNVformer: an attention-based deep neural network for GWAS data

"eQTL" simulations (work in progress)

  • Multi-Layer Perceptron [ AI ]
  • Logistic Regression [ :) ]
  • Transformer-encoder [ AI ]
  • LightGBM (Random Forests) [ AI? ]
  • Differentiable Logic [ AI ]
  • Support Vector Machines [ AI? ]

Why don't you just do AI?


What AI?

bioDS lab @UCNZ

Thank you!