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Background: Metastasis is the leading cause of cancer related deaths in breast cancer patients. Lymphovascular invasion represents one of the earliest stages of metastasis wherein the cells are introduced to a very different and distinct microenvironment. Methods: We leveraged spatial techniques developed for limited specimens in archival tissue to study patient matched cross-sectional tumor samples from different stages of breast neoplasia including normal breast, ductal carcinoma in situ (DCIS), primary invasive carcinoma (IBC), lymphovascular invasion (LVI) and regional lymph node metastasis. We selected a set of 21 patients with ER+ breast cancer to generate cross-sectional samples of each of these stages, for a total of 331 samples. The areas of LVI were identified by a combination of H&E review and immunohistochemistry for podoplanin. We performed smart-3SEQ for gene expression profiling and light pass whole genome sequencing for DNA copy number alterations. Results: We profiled the spectrum of neoplasia for transcriptome-wide gene expression. Principal component analysis of all 252 DCIS, LVI, IBC, or metastasis samples using the top 500 genes with the highest variance demonstrated that clustering was roughly based on the diagnostic stage (i.e. DCIS, LVI, IBC, or metastasis). Differential gene expression profiling identified thousands of genes increased or decreased in expression across the transitional stages with the largest change in gene expression being the transition from normal breast to DCIS, dominated by gene expression down regulation. We next performed NMF clustering on 62 samples of LVI from 18 cases and identified two patterns of gene expression which define two subgroups. Gene ontology analysis revealed that one cluster was associated with increased proliferation and metabolism, whereas the second cluster was dominated by an immune response. When we analyzed the immune and proliferative LVI subgroups separately, we found that the immune profiles in the patient matched IBC and LVI samples from the LVI Immune cluster were similar, whereas the immune profiles in the patient matched IBC and LVI samples from the Proliferative cluster were significantly different. At the LVI stage, all immune cell populations estimated by CibersortX were decreased in the Proliferative LVI cluster. These changes were validated using immunofluorescence for proliferation (Ki67), T cells (CD3) and macrophages (CD68) on the same samples. Using the LVI centroids, we built a model that could predict the same clusters in the METABRIC IBC. Kaplan-Meier analysis showed a significant difference between groups, with the Proliferative-like IBC group having a worse prognosis than the Immune-like IBC group. Conclusions: We observed a dichotomy at the LVI stage with a more proliferative cluster that may escape the immune response and an immune cluster which has a microenvironment with a similar pattern to its primary IBC. The recognition of two groups of LVI, differing in immune association and proliferation, raises the possibility that the risk of metastasis could be different in these two groups, leading to different biological pathways of progression. Citation Format: Belén Rivero-Gutiérrez, Diego Mallo, Almudena Espín-Pérez, Sujay Vennam, Chunfang Zhu, Sushama Varma, Greg Scott, Joseph Foley, E Shelley Hwang, Carlo Maley, Robert West. Characterization of the lymphovascular invasion microenvironment reveals immune response dichotomy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD2-09.

Background: Heterogeneity is a hallmark of human cancers that is apparent both between and within individual tumors. Intra-tumor heterogeneity provides the genetic fuel for natural selection in clonal evolution and cancer progression. Tumors with high levels of genetic heterogeneity are hypothesized to be more likely to progress to invasion and metastasis Methods: We measured the mutational loads from separate areas of pure DCIS and compared this to genetic heterogeneity in DCIS lesions that co-exist with invasive cancer, as a surrogate for progression. Cases of pure DCIS and DCIS diagnosed concurrent with invasive cancer were identified. Two areas of DCIS from each case a minimum of 0.8 cm apart and control tissues were macro-dissected and the DNA extracted from FFPE samples. To analyze the data, we developed new bioinformatics methods that allowed analysis of small amounts of degraded DNA extracted from FFPE samples across multiple regions. Our bioinformatics pipeline was optimized on a series of 28 independent technical replicates of the same DNA sample sequenced twice, as training tools to find the best filtering parameters. Results: Whole exome sequencing was performed on two geospatially separated blocks for each case (41 pure DCIS and 30 DCIS adjacent to invasive disease). Minimum coverage for inclusion in this study was 40X over at least 50% of the exome. We used the ratio of private mutations (only in 1 area) to public (found in both areas) mutations as a measure of intra-tumor heterogeneity. Overall mutational load of DCIS was not a significant predictor to progression; however notably, DCIS adjacent to invasive disease patients had a higher ratio of private/public mutations (heterogeneity) in coding domains (Mann-Whitney p=0.016. Functional analysis of mutated coding genes (DAVID 6.8) shows a statistically significant enrichment in signal transduction, olfactory receptors (G-protein-coupled receptors) and cell-matrix interactions in both tumor types, after FDR correction. DCIS adjacent to invasive disease had an enrichment of mutated genes involved in additional cellular functions such as microtubule activity (fold enrichment =7.6, FDR=0.002), protein-protein interactions (fold enrichment =3.65, FDR=5.11E-04) and extracellular matrix remodeling (fold enrichment =8.3, FDR=0.02). Conclusion: We present an approach to measure clonal heterogeneity using a bulk sequencing strategy applied to geospatially distinct foci of DCIS. Our findings suggest that functional heterogeneity may play an important evolutionary role as a driver for invasive progression. Citation Format: Fortunato A, Mallo D, King L, Hardman T, Hall A, Marks JR, Hwang S, Maley CC. Genetic heterogeneity of DCIS is a predictor of invasive cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-06-12.

Pure ductal carcinoma in situ (DCIS) is being diagnosed more frequently through breast screening programmes and is associated with an increased risk of developing invasive breast cancer. We assessed the clonal relatedness of 143 cases of pure DCIS and their subsequent events using a combination of whole exome, targeted and copy number sequencing, supplemented by single cell analysis. Unexpectedly, 18% of all invasive events after DCIS were clonally unrelated to the primary DCIS. Single cell sequencing of selected pairs confirmed our findings. In contrast, synchronous DCIS and invasive disease (n=44) were almost always (93%) clonally related. This challenges the dogma that almost all invasive events after DCIS represent invasive transformation of the initial DCIS and suggests that DCIS could be an independent risk factor for developing invasive disease as well as a precursor lesion. Our findings support a paradigm shift that confirms a more complex role for DCIS than previously recognized, and that the future management of DCIS should take into account both the precursor and risk factor implications of this diagnosis.

This abstract was not presented at the symposium.

Intra-tumor heterogeneity drives neoplastic progression by supplying the fuel for natural selection among neoplastic cells. It also complicates screening and treatment of neoplasms. We hypothesize that the degree of intra-tumor heterogeneity in DCIS should predict which tumors are likely to become invasive and metastatic. We initiated a pilot project to test this hypothesis by comparing 9 cases of pure DCIS to 9 cases of DCIS with adjacent invasive disease. For each case, we sequenced the exome from two spatially distinct regions of DCIS as well as normal tissue taken from a lymph node with no tumor involvement. This required the development of new methods to extract high quality sequencing data from small amounts of DNA extracted from FFPE samples. We calculated the genetic divergence between the two tumor regions, defined as percent of the sequenced regions of the genome showing differences between the two samples that had sufficient sequencing coverage and quality scores for confident scoring. We also employed automated imaging analysis to score microenvironmental differences between the two tumor regions. These microenvironmental measures are based on ecological methods for measuring organismal interactions and habitats. We will present initial data on differences in phenotypic and genotypic intra-tumor heterogeneity comparing pure DCIS to DCIS associated with invasive breast cancer. Our methods can be readily translated to large tissue banks of FFPE samples from DCIS. Citation Format: Fortunato A, King L, Mallo D, Kovacheva V, Yuan Y, Boddy A, Graham T, Aktipis A, Mardis ER, Hall A, Marks JR, Hwang S, Maley CC. Genomic and microenvironmental intra-tumor heterogeneity in DCIS [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-05-30.

Introduction. The natural history of preinvasive breast cancer, or ductal carcinoma in situ (DCIS) remains poorly understood. Overcoming this gap would allow risk-appropriate treatment for patients diagnosed with DCIS. We used a multiregional sequencing approach in combination with mathematical modeling to characterize the evolutionary dynamics of DCIS initiation and progression. Methods. We analyzed a cohort of 18 patients diagnosed with DCIS, either with (n=9) or without (n=9) synchronous invasive cancer. Based on whole exome sequencing, tumor-specific mutation panels were constructed, each targeting 29-75 mutations (median: 60). From each tumor, and using selective ultraviolet radiation fractionation (SURF), we microdissected small spots (encompassing 1-3 duct cross-sections) from 3-4 spatially separated microscope sections (mean slide separation: 1.25cm, range: 0.34-6.0cm). Spots were spatially registered and genotyped based on targeted sequencing of the tumor-specific mutation panels. For each tumor, we performed unsupervised clonal deconvolution of the spot genotypes (CloneFinder) and constructed phylogenetic subclone trees. To quantify the spatial patterns of subclonal mutations, we introduced a dispersion index (DI), ranging from low (DI=0%) to high (DI=100%). To provide a spatio-temporal context for the heterogeneity patterns we developed a family of stochastic mathematical models of DCIS initiation and progression. Thereby, we embedded the evolutionary dynamics of tumor cell expansion in the branching topology of mammary ductal trees. Results. A total of 485 microdissected spots (median per tumor: 23, range: 10-50) were spatially registered and sequenced (median depth: 9,000x). All tumors were multiclonal, containing a median of 5 subclones (range: 2-14). Surprisingly, the correlation between spatial and genomic distances of spots was low. Individual subclones were diffusely dispersed across tumors. DCIS with synchronous DCIS and invasive cancer (mixed DCIS) had a higher mutation dispersion (DI=84.7%) than those without (pure DCIS, DI=70.5%; p=0.03, Wilcoxon rank-sum test). Mixed DCIS also had a higher fraction of spots containing more than one subclone than pure DCIS (median: 30.4% vs 0%, p=0.03). Among 7 mixed DCIS with invasive spots, 5 showed evidence of multiclonal invasion, that is more than one invading subclones were found in both in situ and invasive regions of the tumor. Mathematical modeling analyses show that the observed spatial patterns of genetic heterogeneity are consistent with a single expansion of mixing subclones across the ductal tree architecture. Conclusions. Our findings provide novel insights into the early growth and invasion dynamics of DCIS lesions. Further, we identified potential evolutionary markers for the delineation between indolent (pure) and aggressive (mixed) DCIS. This constitutes an important step towards identification of patients with low-risk DCIS who could be appropriately managed with less aggressive treatment. Citation Format: Marc D. Ryser, Inmaculada C. Sorribes, Matthew Greenwald, Ethan Wu, Allison Hall, Diego Mallo, Lorraine M. King, Timothy Hardman, Lunden Simpson, Carlo C. Maley, Jeffrey R. Marks, Darryl Shibata, E. Shelley Hwang. Inferring the evolutionary dynamics of ductal carcinoma in situ through multi-regional sequencing and mathematical modeling [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PR02.

Genetic diversity both between and within individual tumors constitutes a challenge to personalized cancer medicine. Intra-tumor heterogeneity provides the genetic fuel for natural selection in clonal evolution and cancer progression. Tumors with high levels of genetic heterogeneity are hypothesized to be more likely to demonstrate aggressive behavior and progress to invasion and metastasis. We analyzed the mutational loads from separate areas of pure DCIS and compared this to genetic heterogeneity in DCIS lesions found adjacent to invasive and metastatic cancer. Two spatially distinct areas of DCIS from each case were macro-dissected and the DNA extracted from FFPE samples. To analyze the data, we developed new bioinformatics methods that allowed analysis of small amounts of degraded DNA extracted from FFPE samples across multiple regions. Our bioinformatics pipeline was optimized on a series of 28 independent technical replicates of the same DNA sample sequenced twice, as training tools to find the best filtering parameters. Whole exome sequencing was performed on each of the two geospatially separated samples for each case. Minimum coverage for inclusion in this study was 40X over at least 50% of the exome. We used the ratio of private mutations (only in 1 area) to public (found in both areas) mutations as a measure of intra-tumor heterogeneity. We present an approach to measure clonal heterogeneity using a bulk sequencing strategy applied to geospatially distinct foci of DCIS. We found statistically significant difference between DCIS adjacent to invasive disease and metastatic patients' genetic divergence (t-test, p=0.013). Our findings suggest that genetic and functional heterogeneity may play an important evolutionary role as a driver for invasive progression. Citation Format: Angelo Fortunato, Diego Mallo, Lorraine King, Timothy Hardman, Allison Hall, Jeffrey R. Marks, Shelley Shelley Hwang, Carlo C. Maley. Genetic and functional heterogeneity of DCIS as predictors of invasive cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2502.

Introduction. Ductal carcinoma in situ (DCIS) of the breast has a limited propensity to progress to invasive breast cancer. However, due to a lack of markers that predict the risk of progression to invasive cancer, many DCIS patients may receive unnecessary surgery. Here, we explore whether the multiclonal intratumoral heterogeneity commonly observed in invasive breast cancers is also present in the precursor DCIS, and whether it differs between pure DCIS (without concurrent invasion) and mixed DCIS (with concurrent invasion). Methods. A total of 19 tumors (10 pure, 9 mixed) were analyzed. Based on whole exome sequencing, tumor-specific mutation panels were identified and contained a median of 60 point mutations per tumor (range: 35-80). From each tumor, 3-4 histologic sections were obtained, with a mean distance between slides of 1.26cm (range: 0.34-6.0cm). Individual DCIS ducts were microdissected from the sections using selective ultraviolet radiation fractionation (SURF); median number of ducts per tumor was 23 (range: 10-50). For each duct, the tumor specific mutation panel was assayed using targeted deep sequencing (median depth: 9,000x). Clonal deconvolution within each tumor was performed using the software package CloneFinder. Individual glands were classified as multiclonal if they contained 2 or more tumor subclones. In addition, the following evolutionary measures were computed for each tumor: (i) clone diversity as the mean pairwise Hamming distance between all ducts; (ii) clonal mixing as the number of clones that were present in more than one section. Comparisons between pure and mixed cases were performed using a two-sided Wilcoxon rank sum test. Results. All DCIS lesions were multiclonal. The number of subclones per tumor ranged from 2 to 9 overall, with a median of 5 and 6 subclones in pure and mixed DCIS, respectively (p-value for difference=.1). The median fraction of multiclonal glands per tumor was higher in mixed vs. pure DCIS (10% vs. 0%; p=.03). Median subclone diversity was higher in mixed vs. pure DCIS (1.48 vs. 1.04; p=.03), and the fraction of mixing subclones trended higher in mixed DCIS (median: 63%, range: 38-100%) compared to pure DCIS (median: 37%, range: 0-80%; p=.1). Subclones were spatially extensive: 18/19 tumors had at least one subclone that spanned across the analyzed sections, covering a mean distance of 2.5cm. Conclusions. All analyzed DCIS cases were multiclonal and individual subclones were found to span large regions of the tumor. Despite small sample sizes we found differences between pure and mixed DCIS. In particular, mixed DCIS harbored a higher fraction of multiclonal glands and a higher subclone diversity between individual ducts. Our findings characterize the evolutionary dynamics of breast cancer initiation and may provide evolutionary markers that distinguish indolent (pure) and progressive (mixed) disease. As such, this line of work has the potential to risk-stratify DCIS lesions. Citation Format: Marc D. Ryser, Inmaculada C. Sorribes, Matthew Greenwald, Allison Hall, Diego Mallo, Lorraine M. King, Timothy Hardman, Lunden Simpson, Carlo C. Maley, Jeffrey R. Marks, Darryl Shibata, E. Shelley Hwang. Ductal carcinoma in situ is a multiclonal disease [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2683.

Whole-genome analyses of human medulloblastomas show that the dominant clone at relapse is present as a rare subclone at primary diagnosis.