The transcriptional hallmarks of intra-tumor heterogeneity across a thousand tumors

Abstract

Each tumor contains malignant cells that differ in genotype, phenotype, and in their interactions with the tumor micro-environment (TME). This results in distinct integrated cellular states that govern intra-tumor heterogeneity (ITH), a central challenge of cancer therapeutics. Dozens of recent studies have begun to describe ITH by single cell RNA-seq, but each study typically profiledonly a small number of tumors and provided a narrow view of transcriptional ITH. Here, we curate, annotate and integrate the data from 77 different studies to reveal the patterns of ITH across 1,163 tumor samples covering 24 tumor types. Focusing on the malignant cells, we find thousands of transcriptional ITH programs that can be described by 41 consensus meta-programs (MPs), each consisting of dozens of genes that are coordinately upregulated in subpopulations of cells within many different tumors. The MPs cover diverse cellular processes and differ in their cancer-type distribution. General MPs associated with processes such as cell cycle and stress vary within most tumors, while context-specific MPs reflect the unique biology of particular cancer types, often resembling developmental cell types and suggesting the co-existence of variable differentiation states within tumors. Some of the MPs are further associated with overall tumor proliferation or immune state, highlighting their potential clinical significance. Based on functional similarities among MPs, we propose a set of 11 hallmarks that together account for the majority of observed ITH programs. Given the breadth and scope of the investigated cohort, the MPs and hallmarks described here reflect the first comprehensive pan-cancer description of transcriptional ITH.

An atlas of epithelial cell states and plasticity in lung adenocarcinoma

“…2a, b ). Consistently, cells from Meta_C1 exhibited the highest expression of a gene module associated with KRAS G12D present in pancreatic ductal adenocarcinoma (MP30) 21 , epithelial to mesenchymal transition (EMT-III; MP14) and epithelial senescence (MP19), lowest levels of alveolar differentiation (MP31), in line with the findings above ( Fig. 2c ), overall increased cellular respiration (MP21) particularly in cells with reduced differentiation ( Fig.…”

“…We measured the expression levels of previously defined ITH meta-programs (MPs) by Gavish et al . 21 which analyzed 1456 samples covering 24 cancer types and profiling 2,591,545 cells. Among a total of 41 consensus ITH MPs identified, MPs with unassigned functional annotations (Unassigned MPs 38-41; n = 4), neural/ hematopoietic lineage specific MPs (MPs 25-29, MPs 33-37; n = 10) as well as cell type specific MPs irrelevant to LUAD (MPs 22-24 secreted/cilia, MP 32 skin-pigmentation; n = 4) were filtered out, resulting in 23 MPs which intimately correlated with the hallmarks of cancer 50 for further analysis.…”

“…Detected genes were further required to be expressed by at least 50 cells. To show the expression dynamics of ITH meta-programs 21 as pseudotime progresses, ITH meta-program scores were plotted along the pseudotime axis and the smoothed lines was generated using the smoother tool in JMP Pro v15. CytoTRACE 20 was applied with default parameters to infer the cellular differentiation states to complement the trajectory analysis and robustly understand the cellular differentiation hierarchy.…”

“…To better understand malignant cell transcriptional programs that are shared across tumors and govern tumor cell heterogeneity, we measured, in malignant cells from our cohort, the expression levels of 23 recurrent malignant meta-programs (MPs) that were recently defined in the comprehensive study by Gavish and colleagues 21 and which interrogated >1,000 human tumor samples across a large number of scRNA-seq datasets. Unsupervised clustering identified 5 MP- driven cell clusters (Meta_C1- Meta_C5) based on expression 21 ( Fig. 2d, Supplementary Table 3 ).…”

Computational estimation of quality and clinical relevance of cancer cell lines

“…These different levels of intra‐tumour heterogeneity can arise from genetic instability followed by subclonal evolution, as well as epigenetic plasticity, diverse microenvironmental factors, and heterotypic interactions with immune and stromal cells (Hinohara & Polyak, 2019 ; Vitale et al , 2021 ). Recent work suggests that distinct genetic and molecular subtypes can often co‐exist within the same tumour (Patel et al , 2014 ; Roerink et al , 2018 ; preprint: Gavish et al , 2021 ; Raghavan et al , 2021 ). Such intra‐tumour heterogeneity plays a role in governing cancer progression and metastasis, as well as therapeutic response and resistance (Roider et al , 2020 ; Hong et al , 2019 ; Kim et al , 2018 ).…”