Role of intratumoral heterogeneity in metastatic progression and drug resistance

Abstract

Metastatic cancer is the final frontier in disease progression with few avenues of treatment open to patients. The failure of therapeutic options at the end-stage disease is confounded by the presence of diverse clones in the metastatic tumor which contributes to intratumoral heterogeneity (ITH), resulting in intrinsic and acquired drug resistance. We aim to elucidate the contribution of ITH to the development of metastasis with a focus on the origins and molecular mechanisms driving ITH, as well as the clinical and technical challenges of acquiring and studying metastatic cohorts in which we can investigate this phenomenon. Bioinformatic approaches that could help in silico analysis will be discussed, which could shed light on how to design new therapeutic strategies for metastatic cancer and give hope to patients living under the shadow of the sword of Damocles.

Keywords: Drug resistance; Intratumoral heterogeneity; Metastasis; Single-cell profiling; Spatial profiling; Tumor microenvironment.

Fig. 1

Molecular drivers of intratumoral heterogeneity in metastasis. Intratumoral heterogeneity is governed by four major molecular drivers: Genetic mutations, transcriptomic, epigenetic and translational modifications, which gives rise to distinct phenotypes within tumor cells during metastasis. Genetic mutations include point mutation in genes like TP53, APOBEC , PIK3CA and chromosome breakage in genes like CXCL2 leading to upregulation of prominent metastatic pathways which can be studied using Single-cell DNA sequencing. Transcriptomic modifications include dysregulation of transcription factors like miR-600, TWIST, SNAIL and ZEB, can be studied using Single-cell RNA sequencing. Epigenetic modifications include hypermethylation in the CpG islands of genes like c-myc, KRAS and BRCA1 studied using Single-cell ATAC sequencing and translational modifications dysregulation of proteins VEGF, HER2, TMEM97 and FEAT can be studied using Single-cell Proteomics profiling.

Fig. 2

Mechanisms of intrinsic and acquired drug resistance. Drug resistance is classified into two types: intrinsic drug resistance and acquired drug resistance. Intrinsic drug resistance is the inability of the cancer cells to respond to treatment, due to mutations of driver genes as well as selective pressures from tumor microenvironment. Acquired resistance happens when tumor cells develop resistance to the treatment, upon prolonged exposure to the drug, eventually leading to relapse. Several mechanisms leading to the two types of drug resistance are as follows: genetic mutations (e.g. BRACA, PIK3CA, KIF20A, PARP1) and epigenetic modifications (e.g. alternate splicing leading to overexpression of splice variants of genes like MCL-1, VEGF) leads to uncontrolled cell division, tumor proliferation, inability of cells to undergo apoptosis and reduced sensitivity to chemotherapy. Activation of drug efflux pumps (e.g. ATP-binding cassette [ABC] transporters) and alteration in structures of transporters present in the plasma membrane leads to decreased drug uptake by cancer cells. The ABC transporters actively pump out xenobiotics through ATP hydrolysis and reduce intracellular concentrations, leading to treatment failure. Interactions from the TME like hypoxic conditions induced by reactive oxygen species (ROS), altered glucose metabolism (e.g. altered expression of GLUT and upregulation of glycolytic pathways) and increased paracrine signaling of TAMs leads to an immunosuppressive environment, rendering immunotherapy ineffective

Fig. 3

Model systems and technologies used to study intratumoral heterogeneity. The current models like cell lines, genetically engineered mice models, organoids and organ-on-a-chip models have been improvised to study heterogeneity within a tumor. These model systems coupled with single cell profiling technologies can help understand ITH at genomic (Single-cell DNA seq), transcriptomic (Single-cell RNA seq), epigenomic (Single-cell ATAC seq) and post-translational (Single-cell proteomics) levels. Spatial profiling technologies like Multiplex imaging, NanoString (DSP, CosMx), 10X Visium and MERFISH are used to analyze the diverse sub-clonal populations and their cell-specific interactions within TME. Computational modelling helps in analysing large multi omics data obtained through spatial and single cell profiling technologies using machine learning methods like Random Forest plots, and support vector machines as well as multiscale models like Bayesian interference and gene regulatory networks