Abstract
Each tumour contains diverse cellular states that underlie intratumour heterogeneity (ITH), a central challenge of cancer therapeutics1. Dozens of recent studies have begun to describe ITH by single-cell RNA sequencing, but each study typically profiled only a small number of tumours and provided a narrow view of transcriptional ITH2. Here we curate, annotate and integrate the data from 77 different studies to reveal the patterns of transcriptional ITH across 1,163 tumour samples covering 24 tumour types. Among the malignant cells, we identify 41 consensus meta-programs, each consisting of dozens of genes that are coordinately upregulated in subpopulations of cells within many tumours. The meta-programs cover diverse cellular processes including both generic (for example, cell cycle and stress) and lineage-specific patterns that we map into 11 hallmarks of transcriptional ITH. Most meta-programs of carcinoma cells are similar to those identified in non-malignant epithelial cells, suggesting that a large fraction of malignant ITH programs are variable even before oncogenesis, reflecting the biology of their cell of origin. We further extended the meta-program analysis to six common non-malignant cell types and utilize these to map cell–cell interactions within the tumour microenvironment. In summary, we have assembled a comprehensive pan-cancer single-cell RNA-sequencing dataset, which is available through the Curated Cancer Cell Atlas website, and leveraged this dataset to carry out a systematic characterization of transcriptional ITH.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
This work relied on curation and integrative analysis of external studies and did not involve generation of new primary data. The curated data from 71 studies are available at https://www.weizmann.ac.il/sites/3CA, including the primary datasets and results of multiple downstream analyses. Datasets from one curated study (PDAC study by Chan-Seng-Yue et al. 2020 (ref. 41)) are available only through EGA with accession code EGAS00001002543 (permissions for sharing through 3CA were denied). Additional datasets from unpublished studies will be added when possible.
Code availability
The code for generating MPs and inferring MP distribution across and within samples in a study (Extended Data Fig. 11) is provided in https://github.com/tiroshlab/3ca/tree/main/ITH_hallmarks. Additional code for downstream analysis will be provided in the future in the 3CA website and through GitHub (https://github.com/tiroshlab/3ca)42.
References
Marusyk, A., Almendro, V. & Polyak, K. Intra-tumour heterogeneity: a looking glass for cancer? Nat. Rev. Cancer 12, 323–334 (2012).
Suva, M. L. & Tirosh, I. Single-cell RNA sequencing in cancer: lessons learned and emerging challenges. Mol. Cell 75, 7–12 (2019).
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
Rambow, F. et al. Toward minimal residual disease-directed therapy in melanoma. Cell 174, 843–855 (2018).
Neftel, C. et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell 178, 835–849 (2019).
Puram, S. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624 (2017).
Kinker, G. S. et al. Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity. Nat. Genet. 52, 1208–1218 (2020).
Baron, M. et al. The stress-like cancer cell state is a consistent component of tumorigenesis. Cell Syst. 11, 536–546 (2020).
Barkley, D. et al. Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment. Nat. Genet. 54, 1192–1201 (2022).
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
Meister, N. et al. Interferon-gamma mediated up-regulation of caspase-8 sensitizes medulloblastoma cells to radio- and chemotherapy. Eur. J. Cancer 43, 1833–1841 (2007).
Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).
Denisenko, E. et al. Spatial transcriptomics reveals ovarian cancer subclones with distinct tumour microenvironments. Preprint at https://doi.org/10.1101/2022.08.29.505206 (2022).
Ji, A. L. et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182, 1661–1662 (2020).
Ravi, V. M. et al. Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma. Cancer Cell 40, 639–655 e613 (2022).
Moffitt, R. A. et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat. Genet. 47, 1168–1178 (2015).
Raghavan, S. et al. Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer. Cell 184, 6119–6137 (2021).
Shim, H. S. et al. Unique genetic and survival characteristics of invasive mucinous adenocarcinoma of the lung. J. Thorac. Oncol. 10, 1156–1162 (2015).
Asada, R. et al. The endoplasmic reticulum stress transducer OASIS is involved in the terminal differentiation of goblet cells in the large intestine. J. Biol. Chem. 287, 8144–8153 (2012).
Chen, G. et al. Foxa3 induces goblet cell metaplasia and inhibits innate antiviral immunity. Am. J. Respir. Crit. Care Med. 189, 301–313 (2014).
Chen, B. et al. Differential pre-malignant programs and microenvironment chart distinct paths to malignancy in human colorectal polyps. Cell 184, 6262–6280 (2021).
Guilliams, M. et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell 185, 379–396 (2022).
Deprez, M. et al. A single-cell atlas of the human healthy airways. Am. J. Respir. Crit. Care Med. 202, 1636–1645 (2020).
Burclaff, J. et al. A proximal-to-distal survey of healthy adult human small intestine and colon epithelium by single-cell transcriptomics. Cell Mol. Gastroenterol. Hepatol. 13, 1554–1589 (2022).
The Tabula Sapiens Consortium. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376, eabl4896 (2022).
Toulmin, S. A. et al. Type II alveolar cell MHCII improves respiratory viral disease outcomes while exhibiting limited antigen presentation. Nat. Commun. 12, 3993 (2021).
Lugano, R., Ramachandran, M. & Dimberg, A. Tumor angiogenesis: causes, consequences, challenges and opportunities. Cell. Mol. Life Sci. 77, 1745–1770 (2020).
Schurch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341–1359 (2020).
Zhang, W. et al. Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA. Nat. Methods 19, 759–769 (2022).
Blise, K. E., Sivagnanam, S., Banik, G. L., Coussens, L. M. & Goecks, J. Single-cell spatial architectures associated with clinical outcome in head and neck squamous cell carcinoma. NPJ Precis. Oncol. 6, 10 (2022).
Borzi, C. et al. c-Myc shuttled by tumour-derived extracellular vesicles promotes lung bronchial cell proliferation through miR-19b and miR-92a. Cell Death Dis. 10, 759 (2019).
Hua, Y. et al. Cancer immunotherapies transition endothelial cells into HEVs that generate TCF1+ T lymphocyte niches through a feed-forward loop. Cancer cell 40, 1600–1618 (2022).
Lee, H.-O. et al. Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer. Nat. Genet., https://doi.org/10.1038/s41588-020-0636-z (2020).
Steele, N. G. et al. Multimodal mapping of the tumor and peripheral blood immune landscape in human pancreatic cancer. Nat. Cancer 1, 1097–1112 (2020).
Luca, B. A. et al. Atlas of clinically distinct cell states and ecosystems across human solid tumors. Cell, https://doi.org/10.1016/j.cell.2021.09.014 (2021).
Tirosh, I. et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016).
Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).
Liu, J. et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell 173, 400–416 (2018).
Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).
Ramilowski, J. A. et al. A draft network of ligand-receptor-mediated multicellular signalling in human. Nat. Commun. 6, 7866 (2015).
Chan-Seng-Yue, M. et al. Transcription phenotypes of pancreatic cancer are driven by genomic events during tumor evolution. Nat. Genet. 52, 231–240 (2020).
Tyler, M. & Gavish, A. tiroshlab/3ca: First release (v1.0.0). Zenodo, https://doi.org/10.5281/zenodo.7688626 (2023).
Acknowledgements
This work was supported by grants from the Israel Science Foundation, the Zuckerman STEM Leadership Program, the Mexican Friends New Generation, the Neuroendocrine Tumor Research Foundation, the Israel Cancer Research Fund, the Benoziyo Endowment Fund and E. Harari. I.T. is the incumbent of the Dr. Celia Zwillenberg-Fridman and Dr. Lutz Zwillenberg Career Development Chair.
Author information
Authors and Affiliations
Contributions
A.G. and I.T. conceived and designed the study, interpreted the data and wrote the manuscript. Data curation for the 3CA website was carried out by A.G., M.T., A.C.G., R.H., D.S., R. Tschernichovsky, C.B., D.K., D.H., R.C.-M., J.L., M.M., A.W., R. Tal. and A.S. Computational analysis relating to scRNA-seq data was mainly carried out by A.G., including defining the MPs and their abundance in malignant and TME cells, inferring MP regulators and correlations with one another. M.T. carried out the computational analysis relating to TCGA bulk RNA data, determined the associations between MPs and various clinical outcomes and interpreted the data. R.H., D.S. and N.G.D. carried out computational analysis relating to spatial transcriptomics and proteomics data. E.S. carried out the subclone analysis. C.B. tested whether MP abundance is influenced by the sequencing platform. T.A. determined MPs in non-cancer scRNA-seq data. A.C.G. and R.H. carried out the IHC experiments and interpreted the data. T.B., L.N.G.C., T.H., M.R.-G., C.S., T.G., A.T., M.L.S. and S.V.P. contributed reagents and samples. M.T., A.C.G., M.L.S. and S.V.P. reviewed the manuscript and provided feedback. The study was supervised by I.T.
Corresponding author
Ethics declarations
Competing interests
I.T. is an advisory board member of Immunitas Therapeutics. M.L.S. is an equity holder, scientific co-founder and advisory board member of Immunitas Therapeutics.
Peer review
Peer review information
Nature thanks Dominic Gruen, Sydney Shaffer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Genes and functional annotations of malignant MPs.
(a) Heatmap showing NMF scores for all MP genes (rows) across all robust NMF programs (columns, arranged as in Fig. 2a). Dashed lines separate the genes of different MPs; MP numbers (right) and selected genes (left) are indicated. (b) Significance (-log10(FDR)) of the enrichment of selected functional annotations (columns) for all of the MPs (rows, ordered as in a). Annotations are colored by their database origin, as indicated (right legend). See Table S3 for additional functional annotations.
Extended Data Fig. 2 MP regulators inferred by SCENIC.
(a) Heatmap showing the mean correlations between regulon scores and MP scores (correlations were calculated separately within each sample, and then averaged across samples). Regulons were included if they had a consistent association with at least one MP, defined as correlation above 0.5 (or below −0.5) in at least 10 samples or in at least 30% of the samples with that MP (see Table S6 for a list of such regulons). (b) Distributions of the correlations whose means are shown in a for selected regulons associated with nine MPs (each shown in a separate panel). ex., extended.
Extended Data Fig. 3 Genetic subclones explain limited variability in MP expression.
(a-b) CNA patterns for two samples from the Lee et al. 2020 colorectal study, in which 3 and 2 subclones were detected, respectively. Few cells were unresolved with respect to subclone assignment. (c-d) Boxplots depicting MP scores across cells in subclones detected in a and b, for all MPs detected in the respective samples. Also shown is the significance of the difference between subclones. In sample SMC01, only the Interferon/MHC-II (I) was significantly different between subclone A and the other two subclones; in sample SMC18, none of the MPs was significantly different between subclones. Boxes indicate the median and 1st and 3rd quartiles, while the upper and lower whiskers respectively extend to maximal and minimal values which are no further than 1.5 times the interquartile range from the 3rd and 1st quartiles. e Percentage of all 481 detected subclones in which zero, one or >1 MPs were found as significantly differentially expressed in comparison to the other subclones from the same tumor (as exemplified in panel c). f For each MP, the X-axis shows the percentage of subclones in which that MP was significantly different from other subclones in the same tumor (calculated only among subclones in which the MP is detected); for that subset of significant subclones, the Y-axis shows the mean fraction of cells with at least a 2-fold difference from the average of the tumor: cells with score above 1 for subclones with increased MP expression, or cells with score below −1 for subclones with decreased MP expression. Thus, even in subclones with a significant upregulation/downregulation, only a minority of cells have larger than 2-fold effect. Red dot (‘ALL’) indicates the average across all MPs.
Extended Data Fig. 4 Associations of MPs with genomic alterations in TCGA data.
(a-b) Heatmaps showing significant associations between MP scores (columns) and mutations or focal amplifications/deletions (rows), colored by the cancer types in which they were observed, or in red if observed in multiple cancer types. Panels separate positive and negative effects, that is, associations where the difference in score between tumors with and without a given genomic alteration is positive or negative, respectively. Significance levels were computed by two-sided t-test and adjusted by Benjamini-Hochberg correction. Exact p values are shown in Table S7. (c-d) Same as a-b for chromosome arm gains and losses. (e) Boxplot showing scores (Y-axis) for the Interferon/MHC-II (I) meta-program in HNSCC tumors (points, n = 480 biologically independent samples), comparing CASP8-mut tumors, or CASP8-mut/HRAS-mut/TP53-wt tumors, with all others (X-axis). Significance levels were computed by two-sided t-test, without adjustment. Boxes indicate the median and 1st and 3rd quartiles, while the upper and lower whiskers respectively extend to maximal and minimal values which are no further than 1.5 times the interquartile range from the 3rd and 1st quartiles. (f) Boxplot showing scores (Y-axis) for the Interferon/MHC-II (I) meta-program in LUAD tumors (points, n = 492 biologically independent samples), comparing tumors with and without STK11 and KEAP1 mutations, respectively. Significance levels were computed by two-sided t-test, without adjustment. Boxes and whiskers are defined as in e. (g) Boxplot showing scores (Y-axis) for the PDAC-classical meta-program in NSD1-mut and NSD1-wt HNSCC tumors (points), first amongst all HNSCC tumors (n = 480 biologically independent samples), then separately for non-laryngeal and laryngeal tumors (n = 373 and n = 107 respectively). Significance levels were computed by two-sided t-test, without adjustment. Boxes and whiskers are defined as in e. (h) Boxplot showing scores (Y-axis) in NSD1-mut and NSD1-wt HNSC tumors (points, n = 480 biologically independent samples) for three MP30-related gene-sets: those specific to the Mucinous LUAD tumors, those specific to Classical PDAC tumors, and those shared between Mucinous LUAD and Classical PDAC tumors. Significance levels were computed by two-sided t test, without adjustment. Boxes and whiskers are defined as in e. (i) Histogram of scores for the Glutathione meta-program in KIRC tumors (n = 532 biologically independent samples). The dashed red line indicates the chosen threshold of −1, used to define Glutathione-low and -high populations. (j) Bar plot showing the percentage of KIRC tumors (Y-axis) in Glutathione-low and -high categories (color) having alterations in VHL, PBRM1 and CDKN2A genes (X-axis). Significance levels were computed by two-sided Fisher test, without adjustment, and the notation ‘*’, ‘**’ and ‘***’ indicates p < 0.05, p < 0.01 and p < 0.001, respectively (exact p-values are 0.00064, 0.0025 and 0.020 for the VHL, PBRM1 and CDKN2A comparisons, respectively). (k) Kaplan-Meier plot comparing survival probability (Y-axis) across time (X-axis) between KIRC tumors in Glutathione-low and -high categories (color), with p-value computed by log-rank test. Error bands represent 95% confidence intervals. (l) Boxplot comparing the average scores for the G1/S and G2/M MPs (Y-axis) between KIRC tumors (points, n = 532 biologically independent samples) in the Glutathione-low and -high categories (color), with P-value computed by two-sided t-test. Boxes and whiskers are defined as in e.
Extended Data Fig. 5 Associations of MPs with clinical features in TCGA data.
(a) Heatmap showing the hazard ratio of scores for MPs (rows) with respect to overall survival in each cancer type (columns). Purple and green represent association with worse and better survival, respectively. For relevant carcinoma types, the columns of results from data deconvolved by CIBERSORTx are presented with the suffix “(d)”, adjacent to the corresponding column for non-deconvolved data. Significant associations are labelled with ‘*’ for p < 0.05, ‘**’ for p < 0.01 and ‘***’ for p < 0.001. Significance levels were computed via Cox regression and likelihood ratio test, and adjusted by Benjamini-Hochberg correction. Exact p values are shown in Table S8. (b-c) Heatmaps as in a showing the difference in average scores between tumors stratified by lymph node metastasis and therapy resistance, respectively. Significance levels were computed by two-sided t-test and adjusted by Benjamini-Hochberg correction. Exact p values are shown in Table S8. (d) Scatterplot showing consistency of associations of MPs with clinical features in TCGA data. Each point represents an MP, its X-value being the sum of the signs (1 for positive effect, −1 for negative effect) of all significant effects (adjusted p value < 0.05, with adjusted p values as in a-c) for that MP across all cancer types and clinical features, and its Y-value being the sum of the signs of all effects for that MP. Selected points are labelled with their corresponding MPs.
Extended Data Fig. 6 Experimental validation related to EpiSen and PDAC-classical MPs.
(a) Example of EpiSen in HNSCC (SPRR1B+) with the senescence-associated secretory-like phenotype (SLPI+) (representative image from 3 independent experiments). (b) The PDAC-classical MP (panCK+TFF1+) is also observed in other cancer types such as invasive mucinous lung adenocarcinoma (also shown in Fig. 3e). In both PDAC and invasive mucinous lung adenocarcinoma, subsets of cancer cells expressing TFF1 are spatially zonated (representative images from 4 independent experiments per cancer type).
Extended Data Fig. 8 Jaccard similarity between MPs of malignant cells and TME cells.
Similar to Fig. 4b for the remaining non-malignant cell types. The most significant overlap was observed for MPs of the cell-cycle, stress, interferon and MYC.
Extended Data Fig. 9 Dissimilarities within MPs across different cancer types and cell types.
(a) Comparing the EMT-II MP between PDAC samples and samples from other cancer types. For each gene, the correlation with the MP scores were calculated in each sample with the MP, and those correlations are compared between PDAC and non-PDAC samples, showing their mean (X-axis) and significance (Y-axis). Red and blue reflect genes with significantly higher or lower MP correlations in PDAC, respectively, calculated by a two-sided t-test adjusted by Benjamini-Hochberg correction. (b) Same as a, but instead of correlation with MP, the measure for each gene in each sample is the differential expression (log-ratio) between cells with MP score above 1 and those with score below 1. (c,d) heatmaps corresponding to a and b that show the actual correlation or differential-expression values across the PDAC samples (to the left of the black vertical line) vs. the other samples, showing genes that were high in both approaches (above the dashed line) vs. genes that were low in both approaches. The genes in both heatmaps are the same but are sorted differently (in decreasing order according to their average in PDAC samples). (e) The overlap between MHC-II or Interferon related MPs from different cell types and several curated gene-sets. The only MP with considerable overlap to both MHC-II and Interferon response came from cancer. (f) NMF scores of samples that participated in e averaged according to cancer type. Grey indicates missing values. (g) Average expression of MHC and interferon response genes (rows) across samples (column) in which the Interferon/MHC-II(I) MP was detected. In each sample, the expression was averaged only among cells that scored above 1 to the MP. Samples are sorted by the average difference between MHC-II and Interferon genes. Genes above the top broken line reflect MHC-I, genes between the broken lines reflect MHC-II, and genes below the bottom broken line reflect Interferon response.
Extended Data Fig. 10 Coupling and decoupling of MHC-II and Interferon response.
In malignant cells, MHC-II and interferon response are often co-expressed, unlike in non-malignant cells where they are uncoupled (e.g. asterisks in a, right panel in d). Cancer cells co-expressing MHC-II and interferon response frequently form an expression gradient in which the cancer cells co-express both markers on a continuum (upper panels in a and b; panel c; left panel in d). T cells often cluster around cancer cells with high expression of interferon response and MHC class II (left lower panels in a; left lower panel in b; left and middle lower panels in d) (representative images from 3 independent experiments per cancer type).
Extended Data Fig. 11 MP distribution across the Puram 2017 HNSCC study.
(a) Pie chart depicting the proportions of malignant cell assignment to malignant MPs combining all samples in the study. Each cell was assigned to a single MP to which it scored the highest (given that the maximal score was larger than 1). MPs with total proportions of less than 5% (across the whole study) are not shown. (b,c) Two examples for the MP distribution across samples (samples 26 and 25, bar plots). Heatmaps show the expression of the MPs per sample, with the MP genes in the rows and cells in the columns. Legend lists all MPs that had a maximal score above 1 in at least 5% of the cells in at least one sample (including samples not shown). See Methods (and code availability) for exact definition and code.
Supplementary information
Supplementary Information
This file contains a description of the analytical approach (Supplementary Figs. 1 and 9), other analysis (Supplementary Figs. 2–6, 8 and 10–15) and experimental validations (Supplementary Figs. 7 and 16).
Supplementary Note 1
Detailed description of malignant MPs.
Supplementary Table 1
List of scRNA-seq studies, and cell type composition across studies.
Supplementary Table 2
MPs and robust NMF programs.
Supplementary Table 3
Enrichment of functional gene sets in malignant MPs.
Supplementary Table 4
Comparison between the MPs defined in this work and those defined previously in cell lines (Kinker et al.), and in a tumour cohort (Barkley et al.).
Supplementary Table 5
Enrichment of metabolic gene sets in malignant MPs.
Supplementary Table 6
MP regulators inferred by SCENIC.
Supplementary Table 7
Significant associations between MP expression and genetic events in various TCGA cohorts.
Supplementary Table 8
Significant associations between MP expression and clinical features in various TCGA cohorts.
Supplementary Table 9
Hallmark distribution across different cancer types.
Supplementary Table 10
MPs of each of the six common TME cells types.
Supplementary Table 11
MPs from healthy epithelial tissues.
Supplementary Table 12
Differentially expressed genes related to MPs. Differentially expressed genes: for each MP, the differentially expressed genes between that MP in one cancer type versus the same MP in all other cancer types. MHC II and interferon genes: annotated gene sets related to the MHC II and interferon MPs.
Supplementary Table 13
Interactions between MPs of distinct cell types and associated ligand–receptor interactions.
Supplementary Table 14
Statistics for interactions between MPs of distinct cell types.
Supplementary Table 15
Information on all antibodies and tumour samples used for IHC experiments.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gavish, A., Tyler, M., Greenwald, A.C. et al. Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours. Nature 618, 598–606 (2023). https://doi.org/10.1038/s41586-023-06130-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-023-06130-4
This article is cited by
-
Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes
Genome Biology (2024)
-
Key processes in tumor metastasis and therapeutic strategies with nanocarriers: a review
Molecular Biology Reports (2024)
-
An atlas of epithelial cell states and plasticity in lung adenocarcinoma
Nature (2024)
-
Multiomic analysis of cervical squamous cell carcinoma identifies cellular ecosystems with biological and clinical relevance
Nature Genetics (2023)
-
Pan-cancer atlas of intratumour heterogeneity
Nature Reviews Genetics (2023)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
