{"id":62641,"date":"2024-02-26T16:14:04","date_gmt":"2024-02-26T14:14:04","guid":{"rendered":"https:\/\/www.iemn.fr\/?p=62641"},"modified":"2024-02-26T16:14:04","modified_gmt":"2024-02-26T14:14:04","slug":"these-ahmadian-b-classification-et-modelisations-des-cellules-cancereuses-par-leurs-signatures-biophysiques","status":"publish","type":"post","link":"https:\/\/www.iemn.fr\/en\/these-2023\/these-ahmadian-b-classification-et-modelisations-des-cellules-cancereuses-par-leurs-signatures-biophysiques.html","title":{"rendered":"THESIS: AHMADIAN B. - Classification and modelling of cancer cells by their biophysical signatures"},"content":{"rendered":"<div id='layer_slider_1'  class='avia-layerslider main_color avia-shadow  avia-builder-el-0  el_before_av_heading  avia-builder-el-first  container_wrap sidebar_right'  style='height: 261px;'  ><div id=\"layerslider_58_9j17ys4s3fgr\" data-ls-slug=\"homepageslider\" class=\"ls-wp-container fitvidsignore ls-selectable\" style=\"width:1140px;height:260px;margin:0 auto;margin-bottom: 0px;\"><div class=\"ls-slide\" data-ls=\"duration:6000;transition2d:5;\"><img loading=\"lazy\" decoding=\"async\" width=\"2600\" height=\"270\" src=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1.jpg\" class=\"ls-bg\" alt=\"\" srcset=\"https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1.jpg 2600w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-300x31.jpg 300w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-768x80.jpg 768w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-1030x107.jpg 1030w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-1500x156.jpg 1500w, https:\/\/www.iemn.fr\/wp-content\/uploads\/2019\/01\/sliders_news1-705x73.jpg 705w\" sizes=\"auto, (max-width: 2600px) 100vw, 2600px\" \/><ls-layer style=\"font-size:14px;text-align:left;font-style:normal;text-decoration:none;text-transform:none;font-weight:700;letter-spacing:0px;border-style:solid;border-color:#000;background-position:0% 0%;background-repeat:no-repeat;width:180px;height:30px;left:0px;top:231px;line-height:32px;color:#ffffff;border-radius:6px 6px 6px 6px;padding-left:50px;background-color:rgba(0, 0, 0, 0.57);\" class=\"ls-l ls-ib-icon ls-text-layer\" data-ls=\"minfontsize:0;minmobilefontsize:0;\"><i class=\"fa fa-quote-right\" style=\"color:#ffffff;margin-right:0.8em;font-size:1em;transform:translateY( -0.125em );\"><\/i>ACTUALITES<\/ls-layer><\/div><\/div><\/div><div id='after_layer_slider_1'  class='main_color av_default_container_wrap container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-62641'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-lt30pf8u-941d1686e87f9511a48429aaf81df4aa\">\n#top .av-special-heading.av-lt30pf8u-941d1686e87f9511a48429aaf81df4aa{\nmargin:0 0 10px 0;\npadding-bottom:4px;\n}\nbody .av-special-heading.av-lt30pf8u-941d1686e87f9511a48429aaf81df4aa .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-lt30pf8u-941d1686e87f9511a48429aaf81df4aa .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-lt30pf8u-941d1686e87f9511a48429aaf81df4aa av-special-heading-h2  avia-builder-el-1  el_after_av_layerslider  el_before_av_hr  avia-builder-el-first'><h2 class='av-special-heading-tag'  itemprop=\"headline\"  >THESIS: AHMADIAN B. - Classification and modelling of cancer cells by their biophysical signatures <\/h2><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-18u73nj-dad6a947580930e400fc42ba200e80f1\">\n#top .hr.av-18u73nj-dad6a947580930e400fc42ba200e80f1{\nmargin-top:5px;\nmargin-bottom:5px;\n}\n.hr.av-18u73nj-dad6a947580930e400fc42ba200e80f1 .hr-inner{\nwidth:100%;\n}\n<\/style>\n<div  class='hr av-18u73nj-dad6a947580930e400fc42ba200e80f1 hr-custom  avia-builder-el-2  el_after_av_heading  el_before_av_textblock  hr-left hr-icon-no'><span class='hr-inner inner-border-av-border-thin'><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<section  class='av_textblock_section av-jriy64i8-2f4600354c0449b610997916bbd9b6bc'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" >\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-13ewzjw-68e036126b913e5028f77311dc66b825\">\n.av_font_icon.av-13ewzjw-68e036126b913e5028f77311dc66b825{\ncolor:#bfbfbf;\nborder-color:#bfbfbf;\n}\n.av_font_icon.av-13ewzjw-68e036126b913e5028f77311dc66b825 .av-icon-char{\nfont-size:60px;\nline-height:60px;\n}\n<\/style>\n<span  class='av_font_icon av-13ewzjw-68e036126b913e5028f77311dc66b825 avia_animate_when_visible av-icon-style- avia-icon-pos-left avia-icon-animate'><span class='av-icon-char' aria-hidden='true' data-av_icon='\ue8c9' data-av_iconfont='entypo-fontello' ><\/span><\/span>\n<p><strong><span class=\"auteurs\"><u>AHMADIAN B.<\/u><\/span><\/strong><\/p>\n<p>Soutenance : 11 D\u00e9cembre 2023<br \/>\nT<span class=\"titre\">D. thesis in Micro-nanosystems and Sensors, University of Lille, ENGSYS Engineering and Systems Sciences<\/span><strong><br \/>\n<\/strong>IEMN Amphitheatre - Central Laboratory - Villeneuve d'Ascq<\/p>\n<\/div><\/section>\n<section  class='av_textblock_section av-jtefqx33-628129dba2299b2ecd65ebfc92eac29d'   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><div  class='hr av-kjh3zw-4dff888f744b728a1aca9b3a0971493a hr-default  avia-builder-el-6  avia-builder-el-no-sibling'><span class='hr-inner'><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<h5><\/h5>\n<h5>Summary:<\/h5>\n<p>Liquid biopsy is the term used to describe the use of peripheral blood as a source of cancer diagnosis. This minimally invasive technique targets biological samples as tumour signatures in the blood, such as circulating tumour cells (CTCs). Since CTCs are heterogeneous and rare, detecting them is a major challenge. As CTCs undergo mechanical stress during the process of metastasis, studying the response of the cell and its subcellular elements to different mechanical stimuli provides a reliable and practical method for early diagnosis, evaluation of therapy and monitoring of the disease. Interest in the link between the mechanical response of the cell at subcellular level and biological properties requires certain characteristics to be obtained from an appropriate measurement method. Atomic force microscopy (AFM), the reference technique, measures the deformation of an adherent cell with great sensitivity for elastic and viscoelastic properties. Its limitations for cells in suspension prevent its use with liquid biopsy products. Microfluidics-based approaches analyse the mechanical characteristics of cells in suspension. However, the absence of active compression prevents practical control of cell deformation for in-depth analysis. In summary, existing techniques show limitations when it comes to obtaining a practical and reliable means for all the required characteristics, e.g. mechanical characterisation of cells in suspension and controlled compression even with high deformation without compromising subcellular visualisation. I developed a method combining MEMS technology, artificial intelligence (AI), confocal imaging and finite element modelling to study the link between the mechanical characteristics and the metastatic potential of a cell. The first step was to use the biophysical signatures of cells to predict their metastatic potential. I chose six breast cancer cell lines, forming three groups according to their metastatic potential: high (SUM159PT &amp; MDA-MB-231), low (MCF7 &amp; T47D), and not (MCF10A &amp; HTRET). The cell lines in each group did not differ significantly, but each group was significantly different. The application of AI algorithms resulted in a prediction rate of nearly 95% between cancerous and non-cancerous cells and over 80% between the three groups, demonstrating that biophysical properties are good candidates for predicting metastatic potential. I then adapted the method to integrate MEMS tweezers with confocal microscopy in order to visualise the subcellular elements of a cancer cell simultaneously with its mechanical characterisation. Finally, the real-time visualisation of subcellular elements combined with measurements of the mechanical characteristics of the cells was used to develop a cell-specific finite element model for further analysis of subcellular elements such as the nucleus. The method demonstrated makes it possible to obtain models representing exact cellular structures in order to predict their biological properties on the basis of mechanical properties. The link between the mechanical response of cells and subcellular elements eliminates time-consuming and costly biological steps. The specific models developed by the method demonstrated can lead to critical applications commonly used for early diagnosis, drug testing and therapy monitoring with minimally invasive sample handling, e.g. liquid biopsy.<\/p>\n<h5>Abstract:<\/h5>\n<p>Liquid biopsy is the term used to describe the use of peripheral blood as a source of cancer diagnosis. This minimally invasive technique targets biological samples as tumour signatures in the blood, such as circulating tumour cells (CTCs). Since CTCs are heterogeneous and rare, detecting them is a major challenge. As CTCs undergo mechanical stress during the process of metastasis, studying the response of the cell and its subcellular elements to different mechanical stimuli provides a reliable and practical method for early diagnosis, evaluation of therapy and monitoring of the disease. Interest in the link between the mechanical response of the cell at subcellular level and biological properties requires certain characteristics to be obtained from an appropriate measurement method. Atomic force microscopy (AFM), the reference technique, measures the deformation of an adherent cell with great sensitivity for elastic and viscoelastic properties. Its limitations for cells in suspension prevent its use with liquid biopsy products. Microfluidics-based approaches analyse the mechanical characteristics of cells in suspension. However, the absence of active compression prevents practical control of cell deformation for in-depth analysis. In summary, existing techniques show limitations when it comes to obtaining a practical and reliable means for all the required characteristics, e.g. mechanical characterisation of cells in suspension and controlled compression even with high deformation without compromising subcellular visualisation. I developed a method combining MEMS technology, artificial intelligence (AI), confocal imaging and finite element modelling to study the link between the mechanical characteristics and the metastatic potential of a cell. The first step was to use the biophysical signatures of cells to predict their metastatic potential. I chose six breast cancer cell lines, forming three groups according to their metastatic potential: high (SUM159PT &amp; MDA-MB-231), low (MCF7 &amp; T47D), and not (MCF10A &amp; HTRET). The cell lines in each group did not differ significantly, but each group was significantly different. The application of AI algorithms resulted in a prediction rate of almost 95% between cancerous and non-cancerous cells and over 80% between the three groups, demonstrating that biophysical properties are good candidates for predicting metastatic potential. I then adapted the method to integrate MEMS tweezers with confocal microscopy in order to visualise the subcellular elements of a cancer cell simultaneously with its mechanical characterisation. Finally, the real-time visualisation of subcellular elements combined with measurements of the mechanical characteristics of the cells was used to develop a cell-specific finite element model for further analysis of subcellular elements such as the nucleus. The method demonstrated makes it possible to obtain models representing exact cellular structures in order to predict their biological properties on the basis of mechanical properties. The link between the mechanical response of cells and subcellular elements eliminates time-consuming and costly biological steps. The specific models developed by the method demonstrated can lead to critical applications commonly used for early diagnosis, drug testing and therapy monitoring with minimally invasive sample handling, e.g. liquid biopsy.<\/p>\n<\/div><\/section>","protected":false},"excerpt":{"rendered":"","protected":false},"author":20,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[318],"tags":[],"class_list":["post-62641","post","type-post","status-publish","format-standard","hentry","category-these-2023"],"_links":{"self":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts\/62641","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/users\/20"}],"replies":[{"embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/comments?post=62641"}],"version-history":[{"count":0,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/posts\/62641\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/media?parent=62641"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/categories?post=62641"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.iemn.fr\/en\/wp-json\/wp\/v2\/tags?post=62641"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}