Single-cell technologies uncover intra-tumor heterogeneity in childhood cancers

Proteomic insights into paediatric cancer: Unravelling molecular signatures and therapeutic opportunities

“…Such data provide opportunities for understanding tumour heterogeneity and mechanisms of relapse and resistance. Proteins can be measured in single cells using methods such as cytometry by time‐of‐flight (CyTOF), CITE‐seq (cellular indexing of transcriptomes and epitopes by sequencing) or several spatial proteomics approaches, with potential utility in paediatric cancer as reviewed in ref 112 …”

Harnessing the n+1 dimensions of single-cell omics data for the prediction and prevention of human diseases

“…This issue further explores how single-cell technologies, particularly those that afford spatial resolution, are currently implemented in clinical outcome studies for the discovery of predictive biomarkers, novel pathobiological mechanisms, or therapeutic targets. Drawing from examples across multiple technologies, such as single-cell RNA sequencing (scR-NAseq), spatial transcriptomics, COdetection by indexing (CODEX), and suspension and imaging mass cytometry (IMC), and diseases (from infectious and neurovascular diseases to transplantation and cancer), all authors contributing to this special issue emphasize important considerations with respect to clinical study design, sample processing, and the choice of machine learning-based analytical pipelines [1][2][3][4][5][6][7][8][9][10].…”

“…As the authors point out, existing NSCLC biomarkers are already employed to assign patients to treatment despite being inaccurate predictors of patient clinical outcomes. Similarly, Funingana et al discuss the challenges of conventional chemotherapy resistance and non-response to immunotherapy in treating ovarian tumors [4], while Lo et al emphasize the critical need for predictive biomarkers of treatment response and new therapeutic targets in the context of pediatric oncology [5]. In each clinical use-case, the potential of high-dimensional single-cell analysis of the tumor microenvironment combined with other omics modalities, such as genomics and radiomics, for improving cancer screening and surveillance of individualized therapeutic treatment is emphasized.…”

Computational single cell oncology: state of the art

“…Intra-tumor heterogeneity must be considered, tumors are often composed of subclones with distinct genetic and phenotypic characteristics. To capture intra-tumor heterogeneity, researchers need to profile multiple single cells from different regions within a tumor (Martelotto et al, 2014;Dong et al, 2021;Lo et al, 2023). On the other hand, different individuals are also quite heterogeneous even in analogous regions/ organs/tissues, designs must deal with inter-patient heterogeneity because comparing single cells from different patients adds another layer of heterogeneity.…”

“…Some critical applications of single-cell technologies in cancer research and how their continued use is expected to further transform the field are shown below: Single-cell RNA sequencing (scRNA-seq) has revealed the immense heterogeneity within tumors, identifying various cell types and transcriptional states (Nieto et al, 2021;Blise et al, 2022;Li C. et al, 2023). Researchers have used this technology to dissect clonal evolution (Losic et al, 2020;Miles et al, 2020;Morita et al, 2020;Nam et al, 2021), identifying driver mutations (Li et al, 2012;Roerink et al, 2018;Huang Z. et al, 2022), and tracking the emergence of drug-resistant subclones (Prieto-Vila et al, 2019;Liu L. et al, 2023;Li X. et al, 2023). Continued use of single-cell technologies will provide deeper insights into the evolution of tumors over time.…”