1
DCIS genomic signatures define biology and correlate with clinical outcome: a Human
Tumor Atlas Network (HTAN) analysis of TBCRC 038 and RAHBT cohorts
Siri H Strand
1,2
, Belén Rivero-Gutiérrez
1#
, Kathleen E Houlahan
3#
, Jose A Seoane
3
, Lorraine
King
4
, Tyler Risom
1
, Lunden A Simpson
4
, Sujay Vennam
1
, Aziz Khan
3
, Luis Cisneros
5
,
Timothy Hardman
4
, Bryan Harmon
6,7
, Fergus Couch
7,8
, Kristalyn Gallagher
7,9
, Mark
Kilgore
7,10
,
Shi Wei
7,11
,
Angela DeMichele
7,12
, Tari King
7,13,14
, Priscilla F McAuliffe
7,15
,
Julie
Nangia
7,16
, Joanna Lee
7,17
, Jennifer Tseng
7,18
, Anna Maria Storniolo
7,19
, Alastair
Thompson
7,20
, Gaorav Gupta
7,21
, Robyn Burns
7,22
, Deborah J Veis
23,24
, Katherine
DeSchryver
24
, Chunfang Zhu
1
, Magdalena Matusiak
1
, Jason Wang
1
, Shirley X Zhu
1
, Jen
Tappenden
25
, Daisy Yi Ding
26
, Dadong Zhang
27
, Jingqin Luo
25
, Shu Jiang
25
, Sushama
Varma
1
, Lauren Anderson
4
, Cody Straub
4
, Sucheta Srivastava
1
, Christina Curtis
3,28
, Rob
Tibshirani
26,29
, Robert Michael Angelo
1
, Allison Hall
30
, Kouros Owzar
31
, Kornelia Polyak
32
,
Carlo Maley
5
, Jeffrey R Marks
4
, Graham A Colditz
25
, E Shelley Hwang
4
*, Robert B West
1
*
1. Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
2. Department of Molecular Medicine, Aarhus University Hospital, 8200 Aarhus N, Denmark
3. Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
4. Department of Surgery, Duke University School of Medicine, Durham, NC 27708, USA
5. School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
6. Department of Pathology, Montefiore Medical Center, Bronx, NY 10467, USA
7. TBCRC Loco-Regional Working Group
8. Department of Pathology, Mayo Clinic, Rochester, MN 55902, USA
9. Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
10. Department of Pathology, University of Washington, Seattle, WA 98195, USA
11. Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
12. Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
13. Breast Oncology Program, Dana-Farber Cancer Institute, Boston, MA 02215, USA
14. Department of Surgery, Brigham and Women’s Hospital, Boston, MA 02115, USA
15. Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
16. Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston TX 77030, USA
17. Department of Surgery, MD Anderson Cancer Center, Houston, TX 77030, USA
18. Department of Surgery, University of Chicago, Chicago, IL 60637, USA
19. Department of Medicine, Indiana University, Indianapolis, IN 46202, USA
20. Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA
21. Department of Radiation and Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC
27599, USA
22. TBCRC, The EMMES Corporation, Rockville, MD 20850, USA
23. Department of Medicine, Washington University School of Medicine, St. Louis, MO 63108, USA
24. Departments of Pathology & Immunology, Washington University School of Medicine, St. Louis, MO
63108, USA
25. Department of Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA
26. Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
27. Duke Cancer Institute, Duke University School of Medicine, Durham, NC 27708, USA
28. Department of Medicine and Genetics, Stanford University, Stanford, CA 94305, USA
29. Department of Statistics, Stanford University, Stanford, CA 94305, USA
30. Department of Pathology, Duke University School of Medicine, Durham, NC 27708, USA
31. Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC 27708,
USA
32. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (whichthis version posted July 24, 2021. ; https://doi.org/10.1101/2021.06.16.448585doi: bioRxiv preprint
2
#
These authors contributed equally
*Correspondence: rbwest@stanford.edu, shelley.hwang@duke.edu
HIGHLIGHTS
• New transcriptomic classification solution reveals 3 major subgroups in DCIS.
• Four stroma-specific signatures identified.
• Outcome analysis identifies pathways involved in DCIS progression.
• CNAs characterize high risk of distant relapse IBC subtypes observed in DCIS.
SUMMARY
Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer
(IBC), with variable propensity for progression. We have performed the first multiscale,
integrated profiling of DCIS with clinical outcomes by analyzing 677 DCIS samples from 481
patients with 7.1 years median follow-up from the Translational Breast Cancer Research
Consortium (TBCRC) 038 study and the Resource of Archival Breast Tissue (RAHBT)
cohorts. We made observations on DNA, RNA, and protein expression, and generated a de
novo clustering scheme for DCIS that represents a fundamental transcriptomic organization
at this early stage of breast neoplasia. Distinct stromal expression patterns and immune cell
compositions were identified. We found RNA expression patterns that correlate with later
events. Our multiscale approach employed in situ methods to generate a spatially resolved
atlas of breast precancers, where complementary modalities can be directly compared and
correlated with conventional pathology findings, disease states, and clinical outcome.
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (whichthis version posted July 24, 2021. ; https://doi.org/10.1101/2021.06.16.448585doi: bioRxiv preprint
3
KEYWORDS
Ductal carcinoma in situ, RNA gene expression profiling, whole genome sequencing,
multiplex immunohistochemistry, invasive breast cancer, precancer, outcome, human tumor
atlas network, breast, tumor microenvironment.
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (whichthis version posted July 24, 2021. ; https://doi.org/10.1101/2021.06.16.448585doi: bioRxiv preprint
4
INTRODUCTION
As nonobligate precursors of invasive disease, precancers provide a unique vantage point
from which to study the molecular pathways and evolutionary dynamics that lead to the
development of life-threatening cancers. Breast ductal carcinoma in situ (DCIS) is one of the
most common precancers across all tissues, with almost 50,000 women diagnosed each year
in the U.S. alone (American Cancer Society, 2019). Current treatment of DCIS involves
surgical excision with either breast conserving surgery or mastectomy, with the goal of
preventing invasive cancer. However, DCIS consists of a molecularly heterogeneous group
of lesions, with highly variable risk of invasive progression. An improved understanding of
which DCIS is likely to progress could thus spare a subgroup of women unnecessary
treatment.
Identification of factors associated with disease progression has been the subject of
substantial study. Epidemiologic models of cancer progression indicate that clinical features
such as age at diagnosis, tumor grade, and hormone receptor expression may have some
prognostic value; however, they have limited ability to identify the biologic conditions that
govern whether DCIS will progress to invasive cancer. Previous molecular analyses of DCIS
have studied either 1) cohorts of DCIS with known outcomes (e.g. disease-free versus
recurrent), or 2) cross-sectional cohorts of DCIS that either do or do not exhibit adjacent
areas of invasive cancer. Both of these approaches have tested key potentially divergent
assumptions: Recurrence of the DCIS as IBC may arise from neoplastic cells that were left
behind when the DCIS was removed, be related to an initial field effect, or may develop from
independent events. Longitudinal cohorts have provided a perspective of cancer progression
over time. Analysis of DCIS found adjacent to invasive cancer assumes that these
preinvasive areas are a good model for pure DCIS tumors and are the ancestors of the
invasive cancer cells, with synchronous lesions inferring progression. In either case, these
studies have not produced clear evidence for a common set of events that are associated
with invasion (Allinen et al., 2004, Gil Del Alcazar et al., 2017, Heselmeyer-Haddad et al.,
2012, Lesurf et al., 2016, Newburger et al., 2013, Gorringe et al., 2015, Casasent et al.,
2018, Abba et al., 2015, Vincent-Salomon et al., 2008a).
Lessons can be learned from precancerous evolution in other tissues. In Barrett’s esophagus,
the genomic copy number landscape and chromosomal instability predicted esophageal
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (whichthis version posted July 24, 2021. ; https://doi.org/10.1101/2021.06.16.448585doi: bioRxiv preprint
5
cancer years before diagnosis (Killcoyne et al., 2020). In prostate cancer, a stromal signature
reflecting immune and osteoblast activity stratified indolent from clinically significant disease
(Tyekucheva et al., 2017), highlighting the relevance of the tumor microenvironmental
context. These findings suggest diverse trajectories of premalignant to malignant tumor
progression. This diversity is mirrored in DCIS where few genomic aberrations have been
identified that can differentiate DCIS from IBC (Johnson et al., 2012, Heselmeyer-Haddad et
al., 2012, Newburger et al., 2013, Gorringe et al., 2015, Yao et al., 2006, Pareja et al., 2020)
and microenvironmental processes, including collagen organization, myoepithelial changes,
and immune suppression, may contribute to IBC development (Lesurf et al., 2016, Allinen et
al., 2004, Gil Del Alcazar et al., 2017). Presently, it remains unknown how these different
molecular axes together contribute to DCIS evolution.
Here, as part of the NCI Human Tumor Atlas Network (HTAN) we collected and curated two
of the largest DCIS cohorts to date from the Translational Breast Cancer Research
Consortium (TBCRC) 038 study and the Resource of Archival Breast Tissue (RAHBT), on
which to conduct comprehensive molecular analyses. We performed a multimodal integrated
profile of these complementary, longitudinally sampled DCIS cohorts, in order to understand
the spectrum of molecular changes in DCIS and to identify predictors of subsequent events in
both tumor and stroma. We used multidimensional and multiparametric approaches to
address the central conceptual themes of cancer progression, ecology and evolutionary
biology, and molecular subtypes. Multiple data types were applied to create a platform for
complex multi-dimensional data representation. We hypothesize that the breast precancer
atlas (PCA) presented here will allow for the application of phylogenetic tools that can
reconstruct the relationship between DCIS and IBC, the natural history of DCIS, and factors
that underlie progression to invasive disease.
RESULTS
Study Design and Cohorts
We generated two retrospective study cohorts of patients with DCIS. Each cohort was
composed of cases with DCIS who had no later events, and cases with DCIS who had a
subsequent ipsilateral breast event (iBE, either DCIS or IBC) after surgical treatment. Table 1
was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (whichthis version posted July 24, 2021. ; https://doi.org/10.1101/2021.06.16.448585doi: bioRxiv preprint