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Carter JM, Chumsri S, Hinerfeld DA, Ma Y, Wang X, Zahrieh D, Hillman DW, Tenner KS, Kachergus JM, Brauer HA, Warren SE, Henderson D, Shi J, Liu Y, Joensuu H, Lindman H, Leon-Ferre RA, Boughey JC, Liu MC, Ingle JN, Kalari KR, Couch FJ, Knutson KL, Goetz MP, Perez EA, Thompson EA. Distinct spatial immune microlandscapes are independently associated with outcomes in triple-negative breast cancer. Nat Commun 2023; 14:2215. [PMID: 37072398 PMCID: PMC10113250 DOI: 10.1038/s41467-023-37806-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 03/30/2023] [Indexed: 04/20/2023] Open
Abstract
The utility of spatial immunobiomarker quantitation in prognostication and therapeutic prediction is actively being investigated in triple-negative breast cancer (TNBC). Here, with high-plex quantitative digital spatial profiling, we map and quantitate intraepithelial and adjacent stromal tumor immune protein microenvironments in systemic treatment-naïve (female only) TNBC to assess the spatial context in immunobiomarker-based prediction of outcome. Immune protein profiles of CD45-rich and CD68-rich stromal microenvironments differ significantly. While they typically mirror adjacent, intraepithelial microenvironments, this is not uniformly true. In two TNBC cohorts, intraepithelial CD40 or HLA-DR enrichment associates with better outcomes, independently of stromal immune protein profiles or stromal TILs and other established prognostic variables. In contrast, intraepithelial or stromal microenvironment enrichment with IDO1 associates with improved survival irrespective of its spatial location. Antigen-presenting and T-cell activation states are inferred from eigenprotein scores. Such scores within the intraepithelial compartment interact with PD-L1 and IDO1 in ways that suggest prognostic and/or therapeutic potential. This characterization of the intrinsic spatial immunobiology of treatment-naïve TNBC highlights the importance of spatial microenvironments for biomarker quantitation to resolve intrinsic prognostic and predictive immune features and ultimately inform therapeutic strategies for clinically actionable immune biomarkers.
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Affiliation(s)
- Jodi M Carter
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Canada
| | - Saranya Chumsri
- Department of Medicine, Division of Hematology and Oncology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Yaohua Ma
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Jacksonville, FL, USA
| | - Xue Wang
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Jacksonville, FL, USA
| | - David Zahrieh
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - David W Hillman
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Kathleen S Tenner
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | - Ji Shi
- Department of Cancer Biology, Mayo Clinic, Jacksonville, FL, USA
| | - Yi Liu
- Department of Cancer Biology, Mayo Clinic, Jacksonville, FL, USA
| | - Heikki Joensuu
- Department of Oncology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Henrik Lindman
- Department of Oncology, University of Uppsala, Uppsala, Sweden
| | - Roberto A Leon-Ferre
- Department of Oncology, Division of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | | | | | - James N Ingle
- Department of Oncology, Division of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | - Krishna R Kalari
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Keith L Knutson
- Department of Immunology, Mayo Clinic, Jacksonville, FL, USA
| | - Matthew P Goetz
- Department of Oncology, Division of Medical Oncology, Mayo Clinic, Rochester, MN, USA
| | - Edith A Perez
- Department of Medicine, Division of Hematology and Oncology, Mayo Clinic, Jacksonville, FL, USA
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Zhong PS, Li R, Santo S. Homogeneity tests of covariance matrices with high-dimensional longitudinal data. Biometrika 2019; 106:619-634. [PMID: 31427823 DOI: 10.1093/biomet/asz011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Indexed: 12/22/2022] Open
Abstract
This paper deals with the detection and identification of changepoints among covariances of high-dimensional longitudinal data, where the number of features is greater than both the sample size and the number of repeated measurements. The proposed methods are applicable under general temporal-spatial dependence. A new test statistic is introduced for changepoint detection, and its asymptotic distribution is established. If a changepoint is detected, an estimate of the location is provided. The rate of convergence of the estimator is shown to depend on the data dimension, sample size, and signal-to-noise ratio. Binary segmentation is used to estimate the locations of possibly multiple changepoints, and the corresponding estimator is shown to be consistent under mild conditions. Simulation studies provide the empirical size and power of the proposed test and the accuracy of the changepoint estimator. An application to a time-course microarray dataset identifies gene sets with significant gene interaction changes over time.
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Affiliation(s)
- Ping-Shou Zhong
- Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, 851 S. Morgan Street, Chicago, Illinois 60607, USA
| | - Runze Li
- Department of Statistics and The Methodology Center, Pennsylvania State University, University Park, Pennsylvania 16801, USA
| | - Shawn Santo
- Department of Statistics and Probability, Michigan State University, 619 Red Cedar Road, East Lansing, Michigan 48824, USA
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Cologne J, Loo L, Shvetsov YB, Misumi M, Lin P, Haiman CA, Wilkens LR, Le Marchand L. Stepwise approach to SNP-set analysis illustrated with the Metabochip and colorectal cancer in Japanese Americans of the Multiethnic Cohort. BMC Genomics 2018; 19:524. [PMID: 29986644 PMCID: PMC6038257 DOI: 10.1186/s12864-018-4910-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Accepted: 06/29/2018] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Common variants have explained less than the amount of heritability expected for complex diseases, which has led to interest in less-common variants and more powerful approaches to the analysis of whole-genome scans. Because of low frequency (low statistical power), less-common variants are best analyzed using SNP-set methods such as gene-set or pathway-based analyses. However, there is as yet no clear consensus regarding how to focus in on potential risk variants following set-based analyses. We used a stepwise, telescoping approach to analyze common- and rare-variant data from the Illumina Metabochip array to assess genomic association with colorectal cancer (CRC) in the Japanese sub-population of the Multiethnic Cohort (676 cases, 7180 controls). We started with pathway analysis of SNPs that are in genes and pathways having known mechanistic roles in colorectal cancer, then focused on genes within the pathways that evidenced association with CRC, and finally assessed individual SNPs within the genes that evidenced association. Pathway SNPs downloaded from the dbSNP database were cross-matched with Metabochip SNPs and analyzed using the logistic kernel machine regression approach (logistic SNP-set kernel-machine association test, or sequence kernel association test; SKAT) and related methods. RESULTS The TGF-β and WNT pathways were associated with all CRC, and the WNT pathway was associated with colon cancer. Individual genes demonstrating the strongest associations were TGFBR2 in the TGF-β pathway and SMAD7 (which is involved in both the TGF-β and WNT pathways). As partial validation of our approach, a known CRC risk variant in SMAD7 (in both the TGF-β and WNT pathways: rs11874392) was associated with CRC risk in our data. We also detected two novel candidate CRC risk variants (rs13075948 and rs17025857) in TGFBR2, a gene known to be associated with CRC risk. CONCLUSIONS A stepwise, telescoping approach identified some potentially novel risk variants associated with colorectal cancer, so it may be a useful method for following up on results of set-based SNP analyses. Further work is required to assess the statistical characteristics of the approach, and additional applications should aid in better clarifying its utility.
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Affiliation(s)
- John Cologne
- Department of Statistics, Radiation Effects Research Foundation, Hiroshima, 732-0815, Japan.
| | - Lenora Loo
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Yurii B Shvetsov
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Munechika Misumi
- Department of Statistics, Radiation Effects Research Foundation, Hiroshima, 732-0815, Japan
| | - Philip Lin
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Christopher A Haiman
- Department of Preventive Medicine and Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Lynne R Wilkens
- Biostatistics and Informatics Shared Resource, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Loïc Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
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