{"id":1094,"date":"2025-01-25T13:58:51","date_gmt":"2025-01-25T13:58:51","guid":{"rendered":"https:\/\/staging.irccs.com\/?post_type=laboratory&p=1094"},"modified":"2026-01-30T13:21:22","modified_gmt":"2026-01-30T12:21:22","slug":"cancer-cell-migration","status":"publish","type":"laboratory","link":"https:\/\/irccs.com\/it\/laboratori\/cancer-cell-migration\/","title":{"rendered":"Quantitative Cancer Cell Biology (QCCB)"},"content":{"rendered":"\n

<\/span>Research topic<\/strong><\/h2>\n\n\n\n

Cell and population dynamics in cancer. Phenotypic intra-tumor heterogeneity. Mechanisms of drug tolerance. Quantitative, computational and mathematical approaches in cancer cell biology. Optimal therapeutic approaches for multi-population tumors. 3D models of cancer and cancer treatment (pharmacological and immune-cell based)<\/p>\n\n\n\n

<\/span>Background<\/strong><\/h2>\n\n\n\n

It is widely recognized that a small number of cells within a tumor can have a disproportionate impact on how the tumor develops, grows, and responds to pharmacological therapies. Cancer research has been revolutionized by the discovery that multiple genetically distinct sub-clones can coexist within the same tumor and that, depending on environmental conditions and interactions with other cells, these sub-clones, even if very small, can shape the overall fate of the tumor during treatment.<\/p>\n\n\n\n

Beyond genetic diversity, phenotypic differences also strongly influence tumor behavior and therapeutic response. Similar to normal tissues, cancer cells can be organized into different cell types or states (even when genetically identical), some of which can fuel relapse or treatment resistance even if present at very low percentages.<\/p>\n\n\n\n

Our lab employs a diverse set of methods\u2014including molecular biology, genomics, quantitative live microscopy, and computational approaches\u2014to investigate how single-cell heterogeneity scales up to tumor-level outcomes. In particular, we aim to identify mechanisms of drug tolerance and develop treatment strategies that exploit the phenotypic diversity found in real tumors. By combining experimental and computational approaches, we seek to uncover emergent vulnerabilities and therapeutic opportunities relevant at the multicellular scale. Recognizing differences within tumors is therefore tightly linked to identifying vulnerabilities and limitations of current therapies, with the goal of designing more effective treatment strategies.<\/p>\n\n\n\n

<\/span>Research achievements<\/strong><\/h2>\n\n\n\n

Our previous research includes the development of 3D cancer models based on cell lines, patient-derived samples, and co-cultures that incorporate different components of the tumor microenvironment. In particular, we have focused on developing quantitative imaging-based approaches to monitor tumor dynamics with single-cell resolution in three-dimensional cancer models.<\/p>\n\n\n\n

We built a biological 3D model of cellular immunotherapy that enables the quantification of immune-cell infiltration and cytotoxicity and can be used to optimize therapeutic yield. We found that tumor cells can move collectively and spatially organize in ways that generate spatial heterogeneity through collective migration. We also determined how to artificially bias the migration of cancer cells using polarized light. More recently, we have focused on the heterogeneous response to therapy of cancer cells using patient-derived tumoroids (PDTs) from colorectal cancer. We identified specific subsets of tumor cells that are either altered or emerge in response to treatment, showing that partial responses to targeted therapies can be explained by the coexistence of distinct phenotypes within tumors.<\/p>\n\n\n\n

To dissect phenotypic transitions, we developed a machine learning approach to infer tumor lineage hierarchies, aimed at quantifying and understanding phenotypic plasticity in cancer.<\/p>\n\n\n\n

<\/span>Conclusions and perspectives<\/strong><\/h2>\n\n\n\n

Our approaches allow us to deepen the molecular characterization of phenotypic subtypes and link them to their dynamic impact on drug treatment.<\/p>\n\n\n\n

We are currently exploiting a range of techniques to resolve single-cell dynamics in PDTs and address pivotal mechanisms of cancer progression, including single cell RNA sequencing, single molecule RNAFISH, fluorescent reporters, light-sheet and confocal live microscopy, in order to access spatio-temporal single-cell resolution of phenotypic switching. In parallel, we use machine learning\u2013based computational approaches to decode cellular dynamics within tumors and to rationally design optimized therapeutic strategies and treatment protocols.<\/p>\n\n\n\n

Our current research has two axes: i) study which treatment strategy is more effective based on the knowledge of lineage hierarchies ruling tumor growth ii) optimizing cellular immunotherapy by leveraging the knowledge of how immune cell recruitment is modulated by the presence of heterogeneous cell populations.<\/p>\n\n\n\n

<\/span>Publications<\/h2>\n\n\n\n

At this link<\/a><\/strong>, you can find all the scientific publications of the Principal Investigator.<\/p>\n\n\n\t

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Selected Publications<\/h2>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\tCollective directional migration drives the formation of heteroclonal cancer cell clusters.<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t

Palmiero M, Cantarosso I, di Blasio L, Monica V, Peracino B, Primo L, Puliafito A.\r\nMol Oncol. 2023 Sep;17(9):1699-1725. doi: 10.1002\/1878-0261.13369. \r\n<\/p>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\t\t\t\t\t\t\t\tThree-dimensional dynamics of mesothelin-targeted CAR.CIK lymphocytes against ovarian cancer peritoneal carcinomatosis <\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t

Galvagno, F., Leuci, V., Massa, A., Donini, C., Rotolo, R., Capellero, S., Proment A., Vitali L., Lombardi A.M., Tuninetti V., D’Ambrosio L., Merlini A., Vigna E., Valabrega G., Primo L., Puliafito A. & Sangiolo, D. (2024). Cancer Immunology, Immunotherapy, 74(1), 6.\r\n<\/p>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\t\t\t\t\t\t\t\tDriving cells with light\u2010controlled topographies<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t

Puliafito, A., Ricciardi, S., Pirani, F., \u010cermochov\u00e1, V., Boarino, L., De Leo, N., Primo, L. & Descrovi, E. (2019). Advanced Science, 6(14), 1801826.\r\n<\/p>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\t\t\t\t\t\t\t\tSelf-Organized Nuclear Positioning Synchronizes the Cell Cycle in Drosophila Embryos.<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t

Deneke VE, Puliafito A, Krueger D, Narla AV, De Simone A, Primo L, Vergassola M, De Renzis S, Di Talia S. Cell. 2019 May 2;177(4):925-941.e17. doi: 10.1016\/j.cell.2019.03.007. Epub 2019 Apr 11.\r\n<\/p>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t

\n\t\t\t\t\t\t\t\t\t\t\t\t\tCollective and single cell behavior in epithelial contact inhibition<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t

Puliafito, A., Hufnagel, L., Neveu, P., Streichan, S., Sigal, A., Fygenson, D. K., & Shraiman, B. I. (2012). Proceedings of the National Academy of Sciences, 109(3), 739-744.<\/p>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\t<\/div>\n","protected":false},"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-1094","laboratory","type-laboratory","status-publish","hentry"],"acf":[],"yoast_head":"\nQuantitative Cancer Cell Biology (QCCB) - Istituto di Candiolo<\/title>\n<meta name=\"description\" content=\"Research topic Cell and population dynamics in cancer. Phenotypic intra-tumor heterogeneity. Mechanisms of drug tolerance. 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