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Schupp PG, Shelton SJ, Brody DJ, Eliscu R, Johnson BE, Mazor T, Kelley KW, Potts MB, McDermott MW, Huang EJ, Lim DA, Pieper RO, Berger MS, Costello JF, Phillips JJ, Oldham MC. Deconstructing Intratumoral Heterogeneity through Multiomic and Multiscale Analysis of Serial Sections. Cancers (Basel) 2024; 16:2429. [PMID: 39001492 PMCID: PMC11240479 DOI: 10.3390/cancers16132429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 06/27/2024] [Accepted: 06/28/2024] [Indexed: 07/16/2024] Open
Abstract
Tumors may contain billions of cells, including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that are consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.
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Affiliation(s)
- Patrick G. Schupp
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Samuel J. Shelton
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Daniel J. Brody
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Rebecca Eliscu
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Brett E. Johnson
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Tali Mazor
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Kevin W. Kelley
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
- Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA 94143, USA
- Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Matthew B. Potts
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Michael W. McDermott
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Eric J. Huang
- Department of Pathology, University of California, San Francisco, San Francisco, CA 94143, USA;
| | - Daniel A. Lim
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Russell O. Pieper
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Mitchel S. Berger
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Joseph F. Costello
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
| | - Joanna J. Phillips
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
- Department of Pathology, University of California, San Francisco, San Francisco, CA 94143, USA;
| | - Michael C. Oldham
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA 94143, USA; (P.G.S.); (S.J.S.); (D.J.B.); (R.E.); (B.E.J.); (T.M.); (K.W.K.); (M.B.P.); (M.W.M.); (D.A.L.); (R.O.P.); (M.S.B.); (J.F.C.); (J.J.P.)
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Schupp PG, Shelton SJ, Brody DJ, Eliscu R, Johnson BE, Mazor T, Kelley KW, Potts MB, McDermott MW, Huang EJ, Lim DA, Pieper RO, Berger MS, Costello JF, Phillips JJ, Oldham MC. Deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial sections. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.21.545365. [PMID: 37645893 PMCID: PMC10461981 DOI: 10.1101/2023.06.21.545365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Tumors may contain billions of cells including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that is consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.
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Affiliation(s)
- Patrick G. Schupp
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
- Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, California, USA
| | - Samuel J. Shelton
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Daniel J. Brody
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Rebecca Eliscu
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Brett E. Johnson
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Tali Mazor
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
- Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, California, USA
- Medical Scientist Training Program and Neuroscience Graduate Program, University of California San Francisco, San Francisco, California, USA
| | - Kevin W. Kelley
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
- Medical Scientist Training Program and Neuroscience Graduate Program, University of California San Francisco, San Francisco, California, USA
- Neuroscience Graduate Program, University of California San Francisco, San Francisco, California, USA
| | - Matthew B. Potts
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Michael W. McDermott
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Eric J. Huang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Daniel A. Lim
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Russell O. Pieper
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Mitchel S. Berger
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Joseph F. Costello
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
| | - Joanna J. Phillips
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
- Department of Pathology, University of California, San Francisco, San Francisco, California, USA
| | - Michael C. Oldham
- Department of Neurological Surgery, University of California, San Francisco, San Francisco,California, USA
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Hejazi NS, Boileau P, van der Laan MJ, Hubbard AE. A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology. Stat Methods Med Res 2023; 32:539-554. [PMID: 36573044 PMCID: PMC11078029 DOI: 10.1177/09622802221146313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The widespread availability of high-dimensional biological data has made the simultaneous screening of many biological characteristics a central problem in computational and high-dimensional biology. As the dimensionality of datasets continues to grow, so too does the complexity of identifying biomarkers linked to exposure patterns. The statistical analysis of such data often relies upon parametric modeling assumptions motivated by convenience, inviting opportunities for model misspecification. While estimation frameworks incorporating flexible, data adaptive regression strategies can mitigate this, their standard variance estimators are often unstable in high-dimensional settings, resulting in inflated Type-I error even after standard multiple testing corrections. We adapt a shrinkage approach compatible with parametric modeling strategies to semiparametric variance estimators of a family of efficient, asymptotically linear estimators of causal effects, defined by counterfactual exposure contrasts. Augmenting the inferential stability of these estimators in high-dimensional settings yields a data adaptive approach for robustly uncovering stable causal associations, even when sample sizes are limited. Our generalized variance estimator is evaluated against appropriate alternatives in numerical experiments, and an open source R/Bioconductor package, biotmle, is introduced. The proposal is demonstrated in an analysis of high-dimensional DNA methylation data from an observational study on the epigenetic effects of tobacco smoking.
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Affiliation(s)
- Nima S Hejazi
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Philippe Boileau
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Mark J van der Laan
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, CA, USA
- Department of Statistics, University of California, Berkeley, CA, USA
| | - Alan E Hubbard
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, CA, USA
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False and true positives in arthropod thermal adaptation candidate gene lists. Genetica 2021; 149:143-153. [PMID: 33963492 DOI: 10.1007/s10709-021-00122-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 04/27/2021] [Indexed: 10/21/2022]
Abstract
Genome-wide studies are prone to false positives due to inherently low priors and statistical power. One approach to ameliorate this problem is to seek validation of reported candidate genes across independent studies: genes with repeatedly discovered effects are less likely to be false positives. Inversely, genes reported only as many times as expected by chance alone, while possibly representing novel discoveries, are also more likely to be false positives. We show that, across over 30 genome-wide studies that reported Drosophila and Daphnia genes with possible roles in thermal adaptation, the combined lists of candidate genes and orthologous groups are rapidly approaching the total number of genes and orthologous groups in the respective genomes. This is consistent with the expectation of high frequency of false positives. The majority of these spurious candidates have been identified by one or a few studies, as expected by chance alone. In contrast, a noticeable minority of genes have been identified by numerous studies with the probabilities of such discoveries occurring by chance alone being exceedingly small. For this subset of genes, different studies are in agreement with each other despite differences in the ecological settings, genomic tools and methodology, and reporting thresholds. We provide a reference set of presumed true positives among Drosophila candidate genes and orthologous groups involved in response to changes in temperature, suitable for cross-validation purposes. Despite this approach being prone to false negatives, this list of presumed true positives includes several hundred genes, consistent with the "omnigenic" concept of genetic architecture of complex traits.
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