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A first-generation pediatric cancer dependency map

Nature Genetics volume 53, pages 529–538 (2021)Cite this article

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Abstract

Exciting therapeutic targets are emerging from CRISPR-based screens of high mutational-burden adult cancers. A key question, however, is whether functional genomic approaches will yield new targets in pediatric cancers, known for remarkably few mutations, which often encode proteins considered challenging drug targets. To address this, we created a first-generation pediatric cancer dependency map representing 13 pediatric solid and brain tumor types. Eighty-two pediatric cancer cell lines were subjected to genome-scale CRISPR–Cas9 loss-of-function screening to identify genes required for cell survival. In contrast to the finding that pediatric cancers harbor fewer somatic mutations, we found a similar complexity of genetic dependencies in pediatric cancer cell lines compared to that in adult models. Findings from the pediatric cancer dependency map provide preclinical support for ongoing precision medicine clinical trials. The vulnerabilities observed in pediatric cancers were often distinct from those in adult cancer, indicating that repurposing adult oncology drugs will be insufficient to address childhood cancers.

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Fig. 1: Pediatric solid tumor cancer models represent high-risk disease.
Fig. 2: Cancer cell line selective dependencies are not correlated with mutation burden.
Fig. 3: Genetic dependencies and potential therapeutic targeting.
Fig. 4: Selective dependencies in pediatric and adult solid tumor lines.

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Data availability

CRISPR–Cas9 screening results for DepMap version 20Q1 (including raw data) and the genomic characterization of cancer cell lines (WES and RNA-seq) used in this study are publicly available at https://depmap.org and also on figshare (https://figshare.com/articles/dataset/DepMap_20Q1_Public/11791698). Subsets of the raw sequencing data from WES and RNA-seq used in this study are available at the Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra) and the European Genome–phenome Archive (https://www.ebi.ac.uk/ega/) under accession numbers SRA PRJNA523380 (Cancer Cell Line Encyclopedia), SRA PRJNA261990 (Ewing sarcoma) and EGAS00001000978 (Sanger) (Supplementary Table 1). The remainder of the raw sequencing data is in the process of being deposited in the SRA via dbGaP (https://dbgap.ncbi.nlm.nih.gov/), delayed in part because these are legacy cell lines. In the interim, we will work with specific requests to expedite the process (contact depmap@broadinstitute.org). Additionally, the pediatric-specific subsets of the processed DepMap version 20Q1 data presented in this study (dependency, mutations, copy number, expression, fusions) are available on our companion website at https://depmap.org/peddep. Source data are provided with this paper.

Code availability

Code to complete the analyses presented in this manuscript and generate corresponding figure panels and tables is publicly available on GitHub at https://github.com/ndharia-broad/peddep.

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Acknowledgements

This work was supported by the National Cancer Institute (NCI) (R35 CA210030, R01 CA204915, P01 CA217959), a St. Baldrick’s Foundation Robert J. Arceci Innovation Award, the Four C’s Fund and PMC Team Eradicate (K.S.). This work was funded in part by the Slim Initiative in Genomic Medicine for the Americas (SIGMA), a joint US–Mexico project funded by the Carlos Slim Foundation (T.R.G.). This work was supported in part by Walter and Marina Bornhorst (T.R.G.). This work was supported by Team Sciarappa Strong (Jimmy Fund Walk) (K.S., A.D.D.). This work was funded in part by the Alexandra Simpson Pediatric Research Fund (C.W.M.R., K.S.). This work was supported by the NBTII Foundation (J.S.B.). This work was supported by the NCI (U01 CA176058) (W.C.H.). N.V.D. was a Julia’s Legacy of Hope St. Baldrick’s Foundation Fellow and received support from the Rally Foundation for Childhood Cancer Research. L.M.G. is a William Raveis Charitable Fund Physician–Scientist of the Damon Runyon Cancer Research Foundation (PST-20-18) and receives support from the Rally Foundation for Childhood Cancer Research, as well as received support from the Boston Children’s Hospital Office of Faculty Development. C.F.M. was supported by a Helen Gurley Brown Presidential Initiative Fellowship and by the National Institutes of Health under a Ruth L. Kirschstein National Research Service Award (F32CA243266). A.D.D. was supported by a Damon Runyon Sohn Fellowship from the Damon Runyon Cancer Research Foundation (DRSG-24-18), the Alex’s Lemonade Stand Foundation, the Rally Foundation for Childhood Cancer Research, CureSearch for Children’s Cancer and the American Society for Clinical Oncology. A.L.H. was supported by grants from the American Cancer Society (MRSG-18-202-01) and the Department of Defense (CDMRP W81XWH-19-1-0281). T.P.H. was supported by the National Institutes of Health (grants T32GM007753 and T32GM007226). P.B. was supported by the Pediatric Brain Tumor Foundation, the Jared Branfman Sunflowers for Life Fund, the Isabel V. Marxuach Fund for Medulloblastoma Research and the NCI (R00CA201592).

Author information

Author notes

  1. Guillaume Kugener

    Present address: Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA

  2. Adam D. Durbin

    Present address: Department of Oncology, Comprehensive Cancer Center, St. Jude Children’s Research Hospital, Memphis, TN, USA

  3. Andrew L. Hong

    Present address: Department of Pediatrics, Emory University and Department of Hematology and Oncology, Children’s Healthcare of Atlanta, Atlanta, GA, USA

  4. Phoebe Moh

    Present address: University of Maryland, College Park, MD, USA

Authors and Affiliations

  1. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Neekesh V. Dharia, Lillian M. Guenther, Clare F. Malone, Adam D. Durbin, Andrew L. Hong, Thomas P. Howard, Pratiti Bandopadhayay, Caroline S. Wechsler, Todd R. Golub & Kimberly Stegmaier

  2. Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA, USA

    Neekesh V. Dharia, Lillian M. Guenther, Clare F. Malone, Adam D. Durbin, Andrew L. Hong, Thomas P. Howard, Pratiti Bandopadhayay, Caroline S. Wechsler, Todd R. Golub & Kimberly Stegmaier

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Neekesh V. Dharia, Guillaume Kugener, Lillian M. Guenther, Clare F. Malone, Adam D. Durbin, Andrew L. Hong, Thomas P. Howard, Pratiti Bandopadhayay, Caroline S. Wechsler, Iris Fung, Allison C. Warren, Joshua M. Dempster, John M. Krill-Burger, Brenton R. Paolella, Phoebe Moh, Nishant Jha, Andrew Tang, Philip Montgomery, Jesse S. Boehm, William C. Hahn, James M. McFarland, Aviad Tsherniak, Todd R. Golub, Francisca Vazquez & Kimberly Stegmaier

  4. Harvard Medical School, Boston, MA, USA

    Neekesh V. Dharia, Lillian M. Guenther, Clare F. Malone, Adam D. Durbin, Andrew L. Hong, Thomas P. Howard, Pratiti Bandopadhayay, Caroline S. Wechsler, William C. Hahn, Todd R. Golub & Kimberly Stegmaier

  5. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Thomas P. Howard, William C. Hahn & Francisca Vazquez

  6. Department of Oncology, Comprehensive Cancer Center, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Charles W. M. Roberts

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  1. Neekesh V. Dharia
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Contributions

J.S.B., W.C.H., C.W.M.R., A. Tsherniak, T.R.G., F.V. and K.S. conceptualized the study. N.V.D., G.K., L.M.G., C.F.M., A.D.D., A.L.H., T.P.H., P.B., A.C.W., J.M.D., J.M.K.-B., B.R.P., J.M.M., A. Tsherniak, T.R.G., F.V. and K.S. devised the study methodology. N.V.D., G.K., A.C.W., J.M.D. and J.M.K.-B. performed computational analyses. L.M.G. and C.S.W. validated the MCL1 inhibitor. I.F. managed the project. P. Moh, N.J., A. Tang and P. Montgomery created the companion website for the project. N.V.D., T.R.G., F.V. and K.S. wrote the original draft. G.K., L.M.G., C.F.M., A.D.D., A.L.H., T.P.H., P.B., C.S.W., I.F., A.C.W., J.M.D., J.M.K.-B., B.R.P., P. Moh, A. Tang, P. Montgomery, J.S.B., W.C.H., C.W.M.R., J.M.M. and A. Tsherniak reviewed and edited the manuscript. C.W.M.R., A. Tsherniak, T.R.G., F.V. and K.S. supervised the study. J.S.B., W.C.H., C.W.M.R., T.R.G. and K.S. acquired the funding.

Corresponding authors

Correspondence to Francisca Vazquez or Kimberly Stegmaier.

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Competing interests

N.V.D. is a current employee of Genentech, Inc., a member of the Roche Group. P.B. receives funding from the Novartis Institute of BioMedical Research for an unrelated project and serves as a consultant for QED Therapeutics. W.C.H. is a consultant for Thermo Fisher, Solvasta Ventures, MPM Capital, KSQ Therapeutics, iTeos, Tyra Biosciences, Frontier Medicines, Paraxel and Jubilant Therapeutics. A. Tsherniak is a consultant for Tango Therapeutics. T.R.G. receives research funding unrelated to this project from Bayer HealthCare, Calico Life Sciences and Novo Ventures. T.R.G. was formerly a consultant and equity holder in Foundation Medicine, which was acquired by Roche. T.R.G. is a consultant to GlaxoSmithKline and is a founder and equity holder of Sherlock Biosciences and FORMA Therapeutics. F.V. receives research support from Novo Ventures unrelated to this project. K.S. has funding from the Novartis Institute of BioMedical Research, consults for and has stock options in Auron Therapeutics and served as an advisor for Kronos Bio. The remaining authors declare no competing interests.

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Peer review information Nature Genetics thanks Richard Gilbertson, Stefan Pfister, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Pediatric solid tumor cancer models represent primary tumors.

Two-dimensional representation of RNA-sequencing data using uniform manifold approximation and projection (UMAP) following alignment by Celligner for all primary tumors (triangles) and cancer cell lines (circles) with each cancer type separated for clarity. Cell line names are labelled.

Extended Data Fig. 2 Pediatric solid tumor cancer models represent high-risk disease.

a, Two-dimensional representation of RNA-sequencing data using uniform manifold approximation and projection (UMAP) after alignment by Celligner for primary tumors (triangles) and cancer cell lines (circles). Cell lines and primary tumors that were classified as belonging to the undifferentiated cluster are outlined by a black border. b, Two-dimensional representation of RNA-sequencing data using UMAP prior to alignment by Celligner for primary tumors (triangles) and cancer cell lines (circles). c, The total count of mutations in whole exome sequencing (WES) (y-axis) grouped by solid tumor type (x-axis) with diseases ordered by median burden. d, Number of mutations in WES (y-axis) of pediatric solid tumor cell lines (red, n = 166 biologically independent cell lines) compared to adult solid tumor (gray, n = 1099 biologically independent cell lines) (p<2.22e−16 by two-sided Wilcoxon test) and fibroblast cell lines (black, n = 28 biologically independent cell lines) (p = 1.8e−13). e, The count of mutations in WES filtered to only include hotspot, missense or damaging mutations in COSMIC genes (y-axis) grouped by solid tumor type (x-axis) with diseases ordered by median burden. Each circle in panels (c, e) represents an individual cell line with pediatric tumors colored by type; the black line represents the median mutation burden per tumor type. f, Mutations in WES filtered to only include hotspot, missense or damaging mutations in COSMIC genes (y-axis) of pediatric solid tumor cell lines (red, n = 166 biologically independent cell lines) compared to adult solid tumor (gray, n = 1099 biologically independent cell lines) (p<2.22e−16 by two-sided Wilcoxon test) and fibroblast cell lines (black, n = 28 biologically independent cell lines) (p = 3.5e−11). Horizontal lines in panels (d, f) demonstrate the median (center) with minima and maxima box boundaries demonstrating the 25 and 75th percentiles. Upper and lower bounds (whiskers) in panels (d, f) represent the 10 and 90th percentiles respectively.

Extended Data Fig. 3 Pediatric solid tumors have fewer total copy number events and gene fusions than adult tumor cell lines with expected profiles for disease subtypes.

a, Total number of genes with copy number alterations (CNA) as identified by genes that had a relative change in ploidy of 0.5 is plotted on the y-axis with tumor types along the x-axis. Each circle represents an individual cell line with pediatric tumors colored by type; the black line represents the median number of CNAs per tumor type. Of note, rhabdoid tumors have very few CNAs, consistent with primary patient tumors. b, CNAs (y-axis) in pediatric solid tumor cell lines (red, n = 166 biologically independent cell lines) compared to adult solid tumor (gray, n = 1177 biologically independent cell lines) (p = 5.3e−06 by two-sided Wilcoxon test) and fibroblast cell lines (black, n = 42 biologically independent cell lines) (p<2.22e−16). c, Copy number heatmap across the genome for pediatric cancer cell lines demonstrates multiple CNAs in osteosarcoma as expected with few events in rhabdoid tumors. d, Total number of genes fusions per cell line from RNA sequencing is plotted on the y-axis with tumor types along the x-axis. Each circle represents an individual cell line with pediatric tumors colored by type; the black line represents the median number of gene fusions per tumor type. Of note, osteosarcoma cell lines have high numbers of gene fusions, consistent with primary patient tumors. e, Gene fusion calls from RNA sequencing (y-axis) in pediatric solid tumor cell lines (red, n = 123 biologically independent cell lines) compared to adult solid and brain tumor (gray, n = 896 biologically independent cell lines) and fibroblast cell lines (black, n = 39 biologically independent cell lines) by two-sided Wilcoxon test. Horizontal lines in panels (b, e) demonstrate the median (center) with minima and maxima box boundaries demonstrating the 25 and 75th percentiles. Upper and lower bounds (whiskers) in panels (b, e) represent the 10 and 90th percentiles respectively.

Extended Data Fig. 4 Selective dependencies in pediatric cell lines and the relationship to mutation burden.

a, Mutational burden count of mutations in whole exome sequencing (WES) (y-axis) compared to the number of selective dependencies per cell line (x-axis) in the screen. b, Mutational burden count of mutations in WES filtered to only include hotspot, missense or damaging mutations in COSMIC genes (y-axis) compared to the number of selective dependencies per cell line (x-axis) in the screen. c, Total number of genes with copy number alterations (CNA) (y-axis) compared to the number of selective dependencies per cell line (x-axis) in the screen. d, Total number of unique gene fusions (y-axis) compared to the number of selective dependencies per cell line (x-axis) in the screen. The circles in panels (a-d) represent individual cell lines with tumor types colored as in panel (e). The blue lines in panels (a-d) represent a linear model fit to this data with the gray shaded area representing the 95% confidence interval around the fit. e, Number of selective dependencies per cell line (y-axis) grouped by tumor type ordered by number of cell lines (x-axis). Each circle represents an individual cell line with pediatric tumors colored by type; the black line represents the median number of selective dependencies per tumor type.

Extended Data Fig. 5 Selective dependencies in pediatric cell lines and the relationship to confounders.

a, Screen quality measured by null-normalized mean difference (NNMD) between positive and negative controls (y-axis) compared to number of selective dependencies per cell line (x-axis). b, Cas9 activity expressed as percent of GFP remaining after CRISPR-Cas9-mediated disruption of exogenous GFP (y-axis) compared to number of selective dependencies per cell line (x-axis). c, Cell line doubling time (y-axis) compared to number of selective dependencies per cell line (x-axis). d, Estimated false positive rate calculated as the fraction of genetic dependencies in a cell line that are not expressed in RNA sequencing data (y-axis) compared to the number of selective dependencies per cell line (x-axis). Circles in panels (a–d) represent individual cell lines with tumor types colored as in panel (Extended Data Fig. 4e). Blue lines in panels (a–d) represent a linear model fit to this data with gray shaded area representing the 95% confidence interval around the fit. e, Number of selective dependencies in cell lines cultured in DMEM-based media (red, n = 135 biologically independent cell lines), RPMI-based media (black, n = 295 biologically independent cell lines), or other media (gray, n = 199 biologically independent cell lines). f, Number of selective dependencies per cell line annotated as derived from metastatic samples (red, n = 213 biologically independent cell lines), primary tumors (black, n = 289 biologically independent cell lines), or unknown (gray, n = 127 biologically independent cell lines). g, Number of selective dependencies per pediatric cancer cell line annotated by literature search as derived from a patient with no pre-treatment (‘none’, red, n = 28 biologically independent cell lines), after treatment (‘pre-treated’, black, n = 17 biologically independent cell lines), or unknown (gray, n = 33 biologically independent cell lines). Horizontal lines in panels (e-g) demonstrate the median (center) with minima and maxima box boundaries demonstrating the 25 and 75th percentiles. Upper and lower bounds (whiskers) in panels (e-g) represent 10 and 90th percentiles respectively.

Extended Data Fig. 6 Predictive modeling of dependencies.

a, Distribution of Pearson correlations of predictive modeling of all dependencies in the screen when using all solid or brain cancer cell lines (black) versus using only the pediatric solid or brain tumor cell lines (red) demonstrates better overall performance when considering all cell lines. b, Predictive modeling of selective dependencies across all solid and brain tumor cell lines versus pediatric solid and brain cancer cell lines. The y-axis depicts the Pearson correlation of the predictive model for dependency on a gene when only considering pediatric cancer cell lines, and the x-axis depicts the Pearson correlation of the predictive model for dependency on a gene when only considering all solid or brain cancer cell lines. The size of the points corresponds to the -log10(adjusted p-value) comparing the rates of dependency in pediatric versus adult cancer cell lines with the points colored by whether the rate is higher in pediatric or adult cancer cell lines for a particular genetic dependency.

Extended Data Fig. 7 Homogeneity of tumor type in expression space is correlated to homogeneity in dependency space.

a, Two-dimensional representation of selective dependencies using uniform manifold approximation and projection (UMAP) demonstrates clustering of cell lines by tumor type. Each circle represents a cell line with pediatric tumors colored by type and adult tumors not depicted for clarity. b, Median distance from panel (d) (y-axis) compared to median distance from panel (f) (x-axis) demonstrating a trend that tumor types with more homogeneity in expression tend toward more homogeneity in dependency. c, Pairwise Pearson correlation of gene expression of the top 2000 most variable genes across cell line pairs from the same tumor type (y-axis) versus tumor types ordered by median (x-axis). Dotted line represents the median correlation to cell lines not of the same tumor type. d, Distance between each cell line in a tumor type and the center of the tumor type cluster in the first 3 principle components of gene expression of the top 2000 most variable genes (y-axis) versus tumor types ordered by median (x-axis). e, Pairwise Pearson correlation of gene dependency of top 500 most variable dependencies across cell line pairs from the same tumor type (y-axis) versus tumor types ordered by median (x-axis). Dotted line represents median correlation to cell lines not of the same tumor type. f, Distance between each cell line in a tumor type and the center of the tumor type cluster in the first 5 principle components of gene dependency of the top 500 most variable dependencies (y-axis) versus tumor types ordered by median (x-axis). Horizontal lines in panels (c-f) demonstrate the median (center) with minima and maxima box boundaries demonstrating the 25 and 75th percentiles. Upper and lower bounds (whiskers) in panels (c-f) represent the 10 and 90th percentiles respectively.

Extended Data Fig. 8 Validation of MCL1 dependency in pediatric cell lines.

a, MCL1 gene effect scores for overlapping cell lines in DepMap 20Q1 (x-axis) and DRIVE RNAi (y-axis) for adult (gray) and pediatric cancer cell lines (red). b, MCL1 gene effect scores (x-axis) versus gene expression of BCL2L1 (y-axis) for adult (gray) and pediatric cancer cell lines (red). Gray and red lines in panels (a-b) represent linear model fits to adult or pediatric data, respectively. c, CRISPR-Cas9 mediated disruption of MCL1 by two independent sgRNAs reveals decreased cell growth in vitro as demonstrated by CellTiter-Glo luminescence (y-axis) versus time (x-axis), correlated with the larger screen. One representative experiment shown for each cell line; each time-point measured in replicate (n = 8). Data presented as mean values ± SEM. d, Western blotting after MCL1 disruption by CRISPR-Cas9 2 days post-selection (SKNBE2, SKNMC) or 3 days post-selection (Kelly). e, Western blotting after MCL1 inhibition with S63845 at 48 hours demonstrates increased protein expression of MCL1 after inhibition with S63845 at 48 hours with less induction of cleaved PARP or Caspase 3 at lower concentrations in SKNBE2 or EWS502 compared to the more sensitive neuroblastoma or Ewing cell lines, Kelly and SKNMC, respectively. f, Treatment with increasing concentrations of ZVAD, a pan-caspase inhibitor, reveals a concentration-dependent rescue of 2 µM S63845 treatment in Kelly and SKNMC at day 3 as demonstrated by the fraction of CellTiter-Glo luminescence compared to DMSO control (y-axis). One representative experiment shown for each cell line; each time-point measured in replicate (n = 4). Data presented as mean values ± SEM. g, Western blotting after one hour of pre-treatment with either DMSO or 20 μM ZVAD followed by either DMSO or 1 μM S63845 treatment at 48 hours show increased protein expression of MCL1 after inhibition with S63845 at 48 hours with decreased induction of cleaved PARP or Caspase 3 following pre-treatment with ZVAD in SKNMC. Experiments shown in panels (c-g) were performed independently at least in duplicate, with one representative experiment shown.

Source data

Extended Data Fig. 9 Selective and enriched dependencies in pediatric and adult solid tumor lines.

a, The frequency of dependency on the neuroblastoma core regulatory transcription factors (ISL1, HAND2, GATA3, PHOX2A, PHOX2B) and rhabdomyosarcoma regulatory transcription factors (MYOD1) are depicted in pediatric and adult solid tumor types with at least 3 cell lines screened per type in polar bar graphs. The tumor types are colored as in the legend. The neuroblastoma transcription factor dependencies were seen uniquely in neuroblastoma and MYOD1 dependency was seen in rhabdomyosarcoma. b, Feature importance for the predictive models of HDAC2 dependency using data from all solid and brain tumor cell lines (left) or pediatric solid and brain cancer cell lines only (right). The y-axis shows the feature importance as calculated by the predictive model with features listed on the x-axis. c, Feature importance for the predictive models of HDAC2 dependency using data from all solid and brain tumor cell lines (left) or pediatric solid and brain cancer cell lines only (right). The y-axis shows the feature importance as calculated by the predictive model with features listed on the x-axis.

Extended Data Fig. 10 Selective and enriched dependencies in pediatric and adult solid tumor lines.

a, Quantification of tumor type-enriched dependencies per tumor-type (y-axis) compared to number of cell lines screened per tumor type (x-axis). The number of enriched dependencies per tumor type with a q-value <0.05 was calculated by performing a two-class comparison between gene effect scores in each tumor type compared to all other cell lines screened using two-sided t-tests with Benjamini-Hochberg correction. b, Quantification of tumor type-enriched dependencies that are also classified as selective dependencies in the screen per tumor-type (y-axis) compared to number of cell lines screened per tumor type (x-axis). The number of enriched dependencies per tumor type with a q-value <0.05 was calculated by performing a two-class comparison between gene effect scores in each tumor type compared to all other cell lines screened using two-sided t-tests with Benjamini-Hochberg correction. Each circle in panels (a-b) represents a tumor type colored as in the legend. The blue lines in panels (a-b) represent a linear model fit to this data with the gray shaded area representing the 95% confidence interval around the fit. c, Tumor type-enriched dependencies in all solid and brain tumor types with more than 2 cell lines. Plotted on the y-axis is -log10 of the q-value of enrichment as calculated by performing a two-class comparison between gene effect scores in each tumor type compared to all other cell lines screened using two-sided t-tests with Benjamini-Hochberg correction. Tumor types are plotted along the x-axis. The size of the circles reflects the mean difference in dependency score between the tumor type and all other cell lines screened. Gray circles are enriched dependencies in a tumor type that are not classified as transcription factors and colored circles are transcription factor dependencies in the screen.

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Dharia, N.V., Kugener, G., Guenther, L.M. et al. A first-generation pediatric cancer dependency map. Nat Genet 53, 529–538 (2021). https://doi.org/10.1038/s41588-021-00819-w

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