{"id":32107,"date":"2024-08-09T17:13:02","date_gmt":"2024-08-09T21:13:02","guid":{"rendered":"https:\/\/www.cancerresearch.org\/?post_type=cri_scientists&#038;p=32107"},"modified":"2024-08-09T17:13:03","modified_gmt":"2024-08-09T21:13:03","slug":"ghamdan-al-eryani-phd","status":"publish","type":"cri_scientists","link":"https:\/\/www.cancerresearch.org\/es\/cri-funded-scientists\/ghamdan-al-eryani-phd","title":{"rendered":"Ghamdan Al-Eryani, PhD"},"content":{"rendered":"\n<p>Modeling the complexities of cancer ecosystems is crucial for developing effective cancer treatments, particularly for advancing personalized therapeutic strategies. However, current lab models often struggle to capture this complexity. This may contribute to why over 90% of drugs developed in the lab fail to achieve clinical efficacy. These results highlight an urgent need for a paradigm shift in the study of cancers. In this fellowship, Dr. Al-Eryani proposes a significant advancement in how we model the interactions found in patient tumors, by directly experimenting on whole human tumor sections that are kept alive in the lab for drug testing, instead of using fragments. Using this approach, the Hacohen lab is improving the preservation of the native architecture of tumors, which contain crucial forces found to be important in driving malignancy. <\/p>\n\n\n\n<p>Furthermore, where previously scientists were breaking down tissues to retrieve the genetic material for further analysis, they will develop a technology pipeline to obtain the genetic information from intact tissue that has been experimented on. Additionally, the pipeline will allow them to link mutations to gene expression of the same cancer cells in a spatially aware manner. By charting how cancer mutations affect their surrounding cells, we can identify new mechanisms for how cancers evade the immune system, and therefore develop alternative therapeutic strategies. In summary, Dr. Al-Eryani provides a pre-clinical model methodology that allows improved prediction of therapeutic efficacy prior to patient outcome, and the identification of new therapeutic strategies.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Projects and Grants<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>The spatial dynamics of cells in native tumors using live sections with perturbations<\/strong><\/h3>\n\n\n\n<p><strong>Broad Institute of MIT and Harvard<\/strong> | All Cancers | 2024 | <strong>Nir Hacohen, PhD<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Modeling the complexities of cancer ecosystems is crucial for developing effective cancer treatments, particularly for advancing personalized therapeutic strategies. However, current lab models often struggle to capture this complexity. This may contribute [&hellip;]<\/p>\n","protected":false},"featured_media":32108,"template":"","tax_cancer_type":[455],"tax_grant_type":[486],"tax_award_year":[741],"tax_institutions":[507],"tax_location_states":[490],"class_list":["post-32107","cri_scientists","type-cri_scientists","status-publish","has-post-thumbnail","hentry"],"acf":{"scientist_subhead":"Postdoctoral Fellow","quote":"","scientist_last_name":"Al-Eryani","show_on_landing":true,"scientist_publish_until":"20280831"},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Ghamdan Al-Eryani, PhD - Cancer Research Institute<\/title>\n<meta name=\"description\" content=\"Dr. Al-Eryani provides a pre-clinical model methodology that allows improved prediction of therapeutic efficacy prior to patient outcome, and the identification of new therapeutic strategies.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.cancerresearch.org\/cri-funded-scientists\/ghamdan-al-eryani-phd\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Ghamdan Al-Eryani, PhD - 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