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A model of breast cancer heterogeneity reveals vascular mimicry as a driver of metastasis.

ABSTRACT: Cancer metastasis requires that primary tumour cells evolve the capacity to intravasate into the lymphatic system or vasculature, and extravasate into and colonize secondary sites. Others have demonstrated that individual cells within complex populations show heterogeneity in their capacity to form secondary lesions. Here we develop a polyclonal mouse model of breast tumour heterogeneity, and show that distinct clones within a mixed population display specialization, for example, dominating the primary tumour, contributing to metastatic populations, or showing tropism for entering the lymphatic or vasculature systems. We correlate these stable properties to distinct gene expression profiles. Those clones that efficiently enter the vasculature express two secreted proteins, Serpine2 and Slpi, which were necessary and sufficient to program these cells for vascular mimicry. Our data indicate that these proteins not only drive the formation of extravascular networks but also ensure their perfusion by acting as anticoagulants. We propose that vascular mimicry drives the ability of some breast tumour cells to contribute to distant metastases while simultaneously satisfying a critical need of the primary tumour to be fed by the vasculature. Enforced expression of SERPINE2 and SLPI in human breast cancer cell lines also programmed them for vascular mimicry, and SERPINE2 and SLPI were overexpressed preferentially in human patients that had lung-metastatic relapse. Thus, these two secreted proteins, and the phenotype they promote, may be broadly relevant as drivers of metastatic progression in human cancer.

SUBMITTER: Wagenblast E

PROVIDER: S-EPMC4634366 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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A model of breast cancer heterogeneity reveals vascular mimicry as a driver of metastasis.

Wagenblast Elvin E Soto Mar M Gutiérrez-Ángel Sara S Hartl Christina A CA Gable Annika L AL Maceli Ashley R AR Erard Nicolas N Williams Alissa M AM Kim Sun Y SY Dickopf Steffen S Harrell J Chuck JC Smith Andrew D AD Perou Charles M CM Wilkinson John E JE Hannon Gregory J GJ Knott Simon R V SR

Nature 20150408 7547

Cancer metastasis requires that primary tumour cells evolve the capacity to intravasate into the lymphatic system or vasculature, and extravasate into and colonize secondary sites. Others have demonstrated that individual cells within complex populations show heterogeneity in their capacity to form secondary lesions. Here we develop a polyclonal mouse model of breast tumour heterogeneity, and show that distinct clones within a mixed population display specialization, for example, dominating the ...[more]

PMID: 25855289

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Project description:BackgroundIn the ongoing battle against breast cancer, a leading cause of cancer-related mortality among women globally, the urgent need for innovative prognostic markers and therapeutic targets is undeniable. This study pioneers an advanced methodology by integrating machine learning techniques to unveil a vascular mimicry signature, offering predictive insights into breast cancer outcomes. Vascular mimicry refers to the phenomenon where cancer cells mimic blood vessel formation absent of endothelial cells, a trait associated with heightened tumor aggression and diminished response to conventional treatments.MethodsThe study's comprehensive analysis spanned data from over 6,000 breast cancer patients across 12 distinct datasets, incorporating both proprietary clinical data and single-cell data from 7 patients, accounting for a total of 43,095 cells. By employing an integrative strategy that utilized 10 machine learning algorithms across 108 unique combinations, the research scrutinized 100 existing breast cancer signatures. Empirical validation was sought through immunohistochemistry assays, alongside explorations into potential immunotherapeutic and chemotherapeutic avenues.ResultsThe investigation successfully identified six genes related to vascular mimicry from multi-center cohorts, laying the groundwork for a novel predictive model. This model outstripped the prognostic accuracy of traditional clinical and molecular indicators in forecasting recurrence and mortality risks. High-risk individuals identified by our model faced worse outcomes. Further validation through IHC assays in 30 patients underscored the model's extensive applicability. Notably, the model unveiled varying therapeutic responses; low-risk patients might achieve greater benefits from immunotherapy, whereas high-risk patients demonstrated a particular sensitivity to certain chemotherapies, such as ispinesib.ConclusionsThis model marks a significant step forward in the precise evaluation of breast cancer prognosis and therapeutic responses across different patient groups. It heralds the possibility of refining patient outcomes through tailored treatment strategies, accentuating the potential of machine learning in revolutionizing cancer prognosis and management.

Project description:Although intratumoral genomic heterogeneity can impede cancer research and treatment, less is known about the effects of phenotypic heterogeneities. To investigate the role of cell migration heterogeneities in metastasis, we phenotypically sorted metastatic breast cancer cells into two subpopulations based on migration ability. Although migration is typically considered to be associated with metastasis, when injected orthotopically in vivo, the weakly migratory subpopulation metastasized significantly more than the highly migratory subpopulation. To investigate the mechanism behind this observation, both subpopulations were assessed at each stage of the metastatic cascade, including dissemination from the primary tumor, survival in the circulation, extravasation, and colonization. Although both subpopulations performed each step successfully, weakly migratory cells presented as circulating tumor cell (CTC) clusters in the circulation, suggesting clustering as one potential mechanism behind the increased metastasis of weakly migratory cells. RNA sequencing revealed weakly migratory subpopulations to be more epithelial and highly migratory subpopulations to be more mesenchymal. Depletion of E-cadherin expression from weakly migratory cells abrogated metastasis. Conversely, induction of E-cadherin expression in highly migratory cells increased metastasis. Clinical patient data and blood samples showed that CTC clustering and E-cadherin expression are both associated with worsened patient outcome. This study demonstrates that deconvolving phenotypic heterogeneities can reveal fundamental insights into metastatic progression. More specifically, these results indicate that migratory ability does not necessarily correlate with metastatic potential and that E-cadherin promotes metastasis in phenotypically sorted breast cancer cell subpopulations by enabling CTC clustering. SIGNIFICANCE: This study employs phenotypic cell sorting for migration to reveal a weakly migratory, highly metastatic breast cancer cell subpopulation regulated by E-cadherin, highlighting the dichotomy between cancer cell migration and metastasis.

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A model of breast cancer heterogeneity reveals vascular mimicry as a driver of metastasis.

Publications

A model of breast cancer heterogeneity reveals vascular mimicry as a driver of metastasis.

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