Characterisation of developmental pathways that drive metastatic progression of breast cancer at single cell resolution

2019 ◽  
Author(s):  
Fatima Valdes-Mora ◽  
Robert Salomon ◽  
Brian Gloss ◽  
Andrew MK Law ◽  
Kendelle Murphy ◽  
...  
2020 ◽  
Author(s):  
Lena Wullkopf ◽  
Louis E. Jensen ◽  
Edward R. Horton ◽  
Alejandro E. Mayorca Guiliani ◽  
Chris D. Madsen ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Byunghee Yoo ◽  
Alana Ross ◽  
Pamela Pantazopoulos ◽  
Zdravka Medarova

AbstractRNA interference represents one of the most appealing therapeutic modalities for cancer because of its potency, versatility, and modularity. Because the mechanism is catalytic and affects the expression of disease-causing antigens at the post-transcriptional level, only small amounts of therapeutic need to be delivered to the target in order to exert a robust therapeutic effect. RNA interference is also advantageous over other treatment modalities, such as monoclonal antibodies or small molecules, because it has a much broader array of druggable targets. Finally, the complementarity of the genetic code gives us the opportunity to design RNAi therapeutics using computational, rational approaches. Previously, we developed and tested an RNAi-targeted therapeutic, termed MN-anti-miR10b, which was designed to inhibit the critical driver of metastasis and metastatic colonization, miRNA-10b. We showed in animal models of metastatic breast cancer that MN-anti-miR10b accumulated into tumors and metastases in the lymph nodes, lungs, and bone, following simple intravenous injection. We also found that treatment incorporating MN-anti-miR10b was effective at inhibiting the emergence of metastases and could regress already established metastases in the lymph nodes, lungs, and bone. In the present study, we extend the application of MN-anti-miR10b to a model of breast cancer metastatic to the brain. We demonstrate delivery to the metastatic lesions and obtain evidence of a therapeutic effect manifested as inhibition of metastatic progression. This investigation represents an additional step towards translating similar RNAi-targeted therapeutics for the systemic treatment of metastatic disease.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Vidya C. Sinha ◽  
Amanda L. Rinkenbaugh ◽  
Mingchu Xu ◽  
Xinhui Zhou ◽  
Xiaomei Zhang ◽  
...  

AbstractThere is an unmet clinical need for stratification of breast lesions as indolent or aggressive to tailor treatment. Here, single-cell transcriptomics and multiparametric imaging applied to a mouse model of breast cancer reveals that the aggressive tumor niche is characterized by an expanded basal-like population, specialization of tumor subpopulations, and mixed-lineage tumor cells potentially serving as a transition state between luminal and basal phenotypes. Despite vast tumor cell-intrinsic differences, aggressive and indolent tumor cells are functionally indistinguishable once isolated from their local niche, suggesting a role for non-tumor collaborators in determining aggressiveness. Aggressive lesions harbor fewer total but more suppressed-like T cells, and elevated tumor-promoting neutrophils and IL-17 signaling, disruption of which increase tumor latency and reduce the number of aggressive lesions. Our study provides insight into tumor-immune features distinguishing indolent from aggressive lesions, identifies heterogeneous populations comprising these lesions, and supports a role for IL-17 signaling in aggressive progression.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Fang Wang ◽  
Qihan Wang ◽  
Vakul Mohanty ◽  
Shaoheng Liang ◽  
Jinzhuang Dou ◽  
...  

AbstractWe present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles, and a statistical routine named lineage speciation analysis (LSA), whichty facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees. MEDALT appears more accurate than phylogenetics approaches in reconstructing copy number lineage. From data from 20 triple-negative breast cancer patients, our approaches effectively prioritize genes that are essential for breast cancer cell fitness and predict patient survival, including those implicating convergent evolution.The source code of our study is available at https://github.com/KChen-lab/MEDALT.


2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A799-A799
Author(s):  
Dhiraj Kumar ◽  
Sreeharsha Gurrapu ◽  
Hyunho Han ◽  
Yan Wang ◽  
Seongyeon Bae ◽  
...  

BackgroundLong non-coding RNAs (lncRNAs) are involved in various biological processes and diseases. Malat1 (metastasis-associated lung adenocarcinoma transcript 1), also known as Neat2, is one of the most abundant and highly conserved nuclear lncRNAs. Several studies have shown that the expression of lncRNA Malat1 is associated with metastasis and serving as a predictive marker for various tumor progression. Metastatic relapse often develops years after primary tumor removal as a result of disseminated tumor cells undergoing a period of latency in the target organ.1–4 However, the correlation of tumor intrinsic lncRNA in regulation of tumor dormancy and immune evasion is largely unknown.MethodsUsing an in vivo screening platform for the isolation of genetic entities involved in either dormancy or reactivation of breast cancer tumor cells, we have identified Malat1 as a positive mediator of metastatic reactivation. To functionally uncover the role of Malat1 in metastatic reactivation, we have developed a knock out (KO) model by using paired gRNA CRISPR-Cas9 deletion approach in metastatic breast and other cancer types, including lung, colon and melanoma. As proof of concept we also used inducible knockdown system under in vivo models. To delineate the immune micro-environment, we have used 10X genomics single cell RNA-seq, ChIRP-seq, multi-color flowcytometry, RNA-FISH and immunofluorescence.ResultsOur results reveal that the deletion of Malat1 abrogates the tumorigenic and metastatic potential of these tumors and supports long-term survival without affecting their ploidy, proliferation, and nuclear speckles formation. In contrast, overexpression of Malat1 leads to metastatic reactivation of dormant breast cancer cells. Moreover, the loss of Malat1 in metastatic cells induces dormancy features and inhibits cancer stemness. Our RNA-seq and ChIRP-seq data indicate that Malat1 KO downregulates several immune evasion and stemness associated genes. Strikingly, Malat1 KO cells exhibit metastatic outgrowth when injected in T cells defective mice. Our single-cell RNA-seq cluster analysis and multi-color flow cytometry data show a greater proportion of T cells and reduce Neutrophils infiltration in KO mice which indicate that the immune microenvironment playing an important role in Malat1-dependent immune evasion. Mechanistically, loss of Malat1 is associated with reduced expression of Serpinb6b, which protects the tumor cells from cytotoxic killing by the T cells. Indeed, overexpression of Serpinb6b rescued the metastatic potential of Malat1 KO cells by protecting against cytotoxic T cells.ConclusionsCollectively, our data indicate that targeting this novel cancer-cell-initiated domino effect within the immune system represents a new strategy to inhibit tumor metastatic reactivation.Trial RegistrationN/AEthics ApprovalFor all the animal studies in the present study, the study protocols were approved by the Institutional Animal Care and Use Committee(IACUC) of UT MD Anderson Cancer Center.ConsentN/AReferencesArun G, Diermeier S, Akerman M, et al., Differentiation of mammary tumors and reduction in metastasis upon Malat1 lncRNA loss. Genes Dev 2016 Jan 1;30(1):34–51.Filippo G. Giancotti, mechanisms governing metastatic dormancy and reactivation. Cell 2013 Nov 7;155(4):750–764.Gao H, Chakraborty G, Lee-Lim AP, et al., The BMP inhibitor Coco reactivates breast cancer cells at lung metastatic sites. Cell 2012b;150:764–779.Gao H, Chakraborty G, Lee-Lim AP, et al., Forward genetic screens in mice uncover mediators and suppressors of metastatic reactivation. Proc Natl Acad Sci U S A 2014 Nov 18; 111(46): 16532–16537.


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