Toward Precision Radiotherapy: A Nonlinear Optimization Framework and an Accelerated Machine Learning Algorithm for the Deconvolution of Tumor-Infiltrating Immune Cells

Cells. 2022 Nov 14;11(22):3604. doi: 10.3390/cells11223604.

Abstract

Tumor-infiltrating immune cells (TIICs) form a critical part of the ecosystem surrounding a cancerous tumor. Recent advances in radiobiology have shown that, in addition to damaging cancerous cells, radiotherapy drives the upregulation of immunosuppressive and immunostimulatory TIICs, which in turn impacts treatment response. Quantifying TIICs in tumor samples could form an important predictive biomarker guiding patient stratification and the design of radiotherapy regimens and combined immune-radiation treatments. As a result of several limitations associated with experimental methods for quantifying TIICs and the availability of extensive gene sequencing data, deconvolution-based computational methods have appeared as a suitable alternative for quantifying TIICs. Accordingly, we introduce and discuss a nonlinear regression approach (remarkably different from the traditional linear modeling approach of current deconvolution-based methods) and a machine learning algorithm for approximating the solution of the resulting constrained optimization problem. This way, the deconvolution problem is treated naturally, given that the gene expression levels of pure and heterogenous samples do not have a strictly linear relationship. When applied across transcriptomics datasets, our approach, which also allows the coupling of different loss functions, yields results that closely match ground-truth values from experimental methods and exhibits superior performance over popular deconvolution-based methods.

Keywords: bioinformatics; bulk RNA-seq; constrained optimization; digital cytometry; error analysis; immune contexture; inverse problem; nonlinear functional analysis; nonlinear regression; predictive biomarkers.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Ecosystem*
  • Humans
  • Linear Models
  • Machine Learning
  • Neoplasms* / genetics
  • Neoplasms* / radiotherapy

Grants and funding

This research was funded by the UK government through the Commonwealth Scholarship, grant number NGCN-2020-263. L.C.O. acknowledges financial endowment from Foundation L’Oreal and UNESCO through the 2021 L’Oreal–UNESCO For Women in Science Young Talent Award Sub-Saharan Africa.