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Molecular Cancer Research
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Cancer "-omics"

Genomic Alterations during the In Situ to Invasive Ductal Breast Carcinoma Transition Shaped by the Immune System

Anne Trinh, Carlos R. Gil Del Alcazar, Sachet A. Shukla, Koei Chin, Young Hwan Chang, Guillaume Thibault, Jennifer Eng, Bojana Jovanović, C. Marcelo Aldaz, So Yeon Park, Joon Jeong, Catherine Wu, Joe Gray and Kornelia Polyak
Anne Trinh
1Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
2Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
3Department of Medicine, Harvard Medical School, Boston, Massachusetts.
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  • ORCID record for Anne Trinh
Carlos R. Gil Del Alcazar
1Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
2Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
3Department of Medicine, Harvard Medical School, Boston, Massachusetts.
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Sachet A. Shukla
1Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
2Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
3Department of Medicine, Harvard Medical School, Boston, Massachusetts.
4Broad Institute of Harvard and MIT, Cambridge, Massachusetts.
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  • ORCID record for Sachet A. Shukla
Koei Chin
5Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Oregon Health and Science University, Portland, Oregon.
6Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon.
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Young Hwan Chang
5Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Oregon Health and Science University, Portland, Oregon.
6Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon.
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Guillaume Thibault
5Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Oregon Health and Science University, Portland, Oregon.
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  • ORCID record for Guillaume Thibault
Jennifer Eng
5Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Oregon Health and Science University, Portland, Oregon.
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Bojana Jovanović
1Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
2Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
3Department of Medicine, Harvard Medical School, Boston, Massachusetts.
4Broad Institute of Harvard and MIT, Cambridge, Massachusetts.
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C. Marcelo Aldaz
7Department of Epigenetics and Molecular Carcinogenesis, University of Texas MD Anderson Cancer Center, Houston, Texas.
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  • ORCID record for C. Marcelo Aldaz
So Yeon Park
8Department of Pathology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
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Joon Jeong
9Department of Surgery, Gangnam Severance Hospital, Yonsei University Medical College, Seoul, Korea.
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Catherine Wu
1Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
2Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
3Department of Medicine, Harvard Medical School, Boston, Massachusetts.
4Broad Institute of Harvard and MIT, Cambridge, Massachusetts.
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Joe Gray
5Department of Biomedical Engineering and OHSU Center for Spatial Systems Biomedicine (OCSSB), Oregon Health and Science University, Portland, Oregon.
6Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon.
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Kornelia Polyak
1Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
2Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
3Department of Medicine, Harvard Medical School, Boston, Massachusetts.
4Broad Institute of Harvard and MIT, Cambridge, Massachusetts.
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  • For correspondence: kornelia_polyak@dfci.harvard.edu
DOI: 10.1158/1541-7786.MCR-20-0949 Published April 2021
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    Figure 1.

    Copy-number alterations and somatic mutations in the DCIS-to-IDC transition. A, Genome-wide summary of proportion of patients with observed CNAs in the Recurrence (our data), Abba (5), and Lesurf (16) cohorts. A z-score of 2 in GATK CNV was set to call gains and losses in the recurrence and Abba cohorts (exome-seq), and a threshold of ±0.3 in the Lesurf set (aCGH). Both DCIS and IDC samples are shown in the recurrence cohort. B, Summary of CNAs in breast cancer–associated oncogenes and tumor suppressors in the recurrence cohort. C, Summary of cancer-related CNAs and coding mutations in the recurrence cohort, and corresponding gene expression profiles. D, Variants found in adjacent normal mammary tissue. E, Summary of cancer-related CNAs and mutations in the Abba and Lesurf cohorts grouped by PAM50 subtype. F, Pathways implicated by genetic changes in all three cohorts and by RNA expression in the Abba cohort comparing DCIS to normal. Circle size reflective of the average enrichment score and line width reflective of the number of common genes in two pathways.

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    Figure 2.

    Immune composition and spatial distribution in DCIS and IDC. A–C, Compositional and spatial features in the recurrence set based on whole-slide H&E images. A, Cellular composition. Significance computed using a beta-regression for bounded fractions (P = 0.009) and by paired one-tailed t test (P = 0.045). B, Proportion of immune cells within 10 μm of an epithelial cell within digitally macrodissected DCIS, IDC, or normal regions. C, Proportion of cells with k-Nearest neighbor (k = 3) distances less than 50 μm. Significance computed using Wilcoxon rank sum test and beta-regression for bounded fractions (PImmune-Immune = 0.003, PImmune-Stroma = 0.02). D, Morisita–Horn index of tumor-lymphocyte and stroma-lymphocyte mixing in digitally macrodissected DCIS, IDC, or normal regions. Significance between groups computed using Wilcoxon rank-sum test.

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    Figure 3.

    Immune expression signature analysis in DCIS and IDC. A, Enriched pathways in DCIS compared with IDC across all cases, case 8 and case 9 (FDR < 0.1). B, Heatmap of ssGSEA scores for published immune signatures in the recurrence and Abba cohort. C, Immune composition of tumors inferred by CIBERSORT in the recurrence cohort. D, Heatmap showing relative contribution of ER status and TILs to immune signatures, and the enrichment of immune cell types in normal compared with DCIS using several deconvolution methods. Only significant contributors are shown (P < 0.05, Wilcoxon rank-sum test for enrichment scores, beta-regression for proportion data). E, Comparison of enriched immune subsets in ER+ and ER− DCIS and IDC. Only significant contributors are shown (P < 0.05, Wilcoxon rank sum test for enrichment scores, beta-regression for proportion data).

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    Figure 4.

    Cyclic immunofluorescence in DCIS-to-IDC. A, Whole-slide image of classified immune cells in cases 8 and 9, representative image, and relative proportions of immune cells in each sample. Scale bar whole slide image: 1 mm, insert: 100 μm. Differences were computed using proportionality test, *, P < 0.05. B, Whole-slide image of classified tumor cells in cases 8 and 9, representative image, and relative proportions of tumor cells in each sample. Scale bar whole slide image: 1 mm, insert: 100 μm. C, Cellular composition in each sample. D, Z-score of the interacting fraction of immune cells to tumor cells. Null distribution was calculated from 1,000 permutations of immune cell labels. E, Morisita–Horn index of spatial correlation of two interacting-cell populations (*, P < 0.05).

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    Figure 5.

    CNAs associated with immune changes. A, CNAs and RNA-expression of in MHC-I presentation and immune checkpoint proteins. In purple are genes involved in MHC-I presentation and in red are immune checkpoint proteins. B, Association between copy-number and gene-expression in the Abba and Lesurf cohorts of the genes shown in A. Colored genes show a spearman correlation P < 0.1, blue indicates significance in both sets. C, Frequency of CNAs at immune-enriched loci in the Abba cohort. Significant enrichment defined by hypergeometric testing FDR < 0.1. D, Heatmaps of associations between immune signatures and copy number at loci shown in C in DCIS (Abba cohort), ER+ IDC (TCGA) and ER− IDC (TCGA) determined by generalized linear models. (Significantly associated beta-scores are shown, P < 0.05). E, CNAs of the regions highlighted in C in the recurrence cohort.

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    Figure 6.

    Neoantigen prediction in DCIS and IDC. A and B, Predicted neoantigens supported by expression of the mutation in RNA-sequencing data in Recurrence cohort (A) and Abba cohort (B). C, Frequency of mutation, neoantigen and rsSNP sites in the most commonly mutated genes in breast cancer in DCIS (Abba and recurrence cohorts) compared with IDC (TCGA cohort). Differences computed using proportion test *, P < 0.05. D, HLAs predicted to bind to the most common TP53 and PIK3CA mutant peptides in the TCGA cohort. Asterisked are HLAs predicted to recognize these peptides in the Recurrence/Abba cohort. E, Heatmap showing relative contribution of specific neoantigen to immune signaling pathways in TCGA ER+ patients using a generalized linear model (P < 0.1 shown). F, Changes in BCR repertoire diversity in DCIS and IDC in case 8 and 9. G, Changes in BCR repertoire. Only clonotypes appearing at frequency > 1% are shown, and colored clonotypes are shared between samples.

Additional Files

  • Figures
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    • Supplementary Methods and Figures 1-5 - S1. Overview of patient cohort. S2. Whole exome sequencing of matched DCISand IDC transition. S3. Mutationalprofiling of the DCIS-to-IDC transition. S4.Phenotypic immune properties of patient cohort. S5.Immune-related genomicchanges and neoantigen load.
    • Supplementary Tables 1-7 - Summary of Supplementary Tables Supplementary Table S1. Clinicopathological characteristics of patient cohort Supplementary Table S2. Antibody panel for cyclic IF Supplementary Table S3. Summary of mutations and neoantigen expression in the recurrence cohort (Related to 1C, 6A) Supplementary Table S4. Summary of altered pathways in DCIS/IDC (Fig 1F) Supplementary Table S5. Enriched gene sets between DCIS and IDC (Fig 3A) Supplementary Table S6. Identified chromosomal regions associated with immune changes (Related to Fig. 5) Supplementary Table S7. Mutational, neoantigen and rsSNP frequency of commonly mutated genes in TCGA and Abba cohorts (related to Fig 6C,E)
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Molecular Cancer Research: 19 (4)
April 2021
Volume 19, Issue 4
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Genomic Alterations during the In Situ to Invasive Ductal Breast Carcinoma Transition Shaped by the Immune System
Anne Trinh, Carlos R. Gil Del Alcazar, Sachet A. Shukla, Koei Chin, Young Hwan Chang, Guillaume Thibault, Jennifer Eng, Bojana Jovanović, C. Marcelo Aldaz, So Yeon Park, Joon Jeong, Catherine Wu, Joe Gray and Kornelia Polyak
Mol Cancer Res April 1 2021 (19) (4) 623-635; DOI: 10.1158/1541-7786.MCR-20-0949

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Genomic Alterations during the In Situ to Invasive Ductal Breast Carcinoma Transition Shaped by the Immune System
Anne Trinh, Carlos R. Gil Del Alcazar, Sachet A. Shukla, Koei Chin, Young Hwan Chang, Guillaume Thibault, Jennifer Eng, Bojana Jovanović, C. Marcelo Aldaz, So Yeon Park, Joon Jeong, Catherine Wu, Joe Gray and Kornelia Polyak
Mol Cancer Res April 1 2021 (19) (4) 623-635; DOI: 10.1158/1541-7786.MCR-20-0949
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