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            <itunes:name>tv.qiagenbioinformatics.com</itunes:name>
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        <title>tv.qiagenbioinformatics.com</title>
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        <description>Watch tutorials, interviews and much more on our web based TV channel!</description>
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        <itunes:author>tv.qiagenbioinformatics.com</itunes:author>
        <itunes:subtitle>CLC bio TV</itunes:subtitle>
        <itunes:summary>Watch tutorials, interviews and much more on our web based TV channel!</itunes:summary>
        <itunes:keywords>clc bio tv, bioinformatics, genomics, research</itunes:keywords>
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            <title>Targeting MUC16 Neoantigens in Pancreatic Cancer: Precision Oncology and...</title>
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            <description>&lt;p&gt;&lt;p&gt;This webinar shows how we enable identification and prioritization of a novel drug target—&lt;strong&gt;MUC16&lt;/strong&gt;—using data from The Cancer Genome Atlas (TCGA), all without requiring deep bioinformatics expertise. The analyst shows how to mine frequently mutated genes across solid tumors, filter for druggable targets based on subcellular localization and extracellular domain structure and evaluate mutation prevalence and protein context. MUC16 emerged as a compelling candidate, particularly in &lt;strong&gt;pancreatic adenocarcinoma&lt;/strong&gt;, where mutations and high expression levels correlated with poorer patient survival. Despite its relevance, few clinical trials currently target MUC16 in this disease, revealing a potentially untapped therapeutic niche. Network analysis and in silico perturbation simulations further suggested that modulating MUC16 expression could meaningfully influence oncogenic pathways. The session concluded by encouraging attendees to explore digital services and underscoring the speed and accessibility of tools for hypothesis generation and target validation.&lt;/p&gt;&lt;/p&gt;&lt;p&gt;&lt;a href="http://tv.qiagenbioinformatics.com/photo/110918797/targeting-muc16-neoantigens-in"&gt;&lt;img src="http://tv.qiagenbioinformatics.com/64968558/110918797/06e123bd1b18cf2eaf2b0bb2570a04a6/standard/download-11-thumbnail.jpg" width="75" height=""/&gt;&lt;/a&gt;&lt;/p&gt;</description>
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            <pubDate>Tue, 18 Mar 2025 10:09:40 GMT</pubDate>
            <media:title>Targeting MUC16 Neoantigens in Pancreatic Cancer: Precision Oncology and...</media:title>
            <itunes:summary>This webinar shows how we enable identification and prioritization of a novel drug target—MUC16—using data from The Cancer Genome Atlas (TCGA), all without requiring deep bioinformatics expertise. The analyst shows how to mine frequently mutated genes across solid tumors, filter for druggable targets based on subcellular localization and extracellular domain structure and evaluate mutation prevalence and protein context. MUC16 emerged as a compelling candidate, particularly in pancreatic adenocarcinoma, where mutations and high expression levels correlated with poorer patient survival. Despite its relevance, few clinical trials currently target MUC16 in this disease, revealing a potentially untapped therapeutic niche. Network analysis and in silico perturbation simulations further suggested that modulating MUC16 expression could meaningfully influence oncogenic pathways. The session concluded by encouraging attendees to explore digital services and underscoring the speed and accessibility of tools for hypothesis generation and target validation.</itunes:summary>
            <itunes:subtitle>This webinar shows how we enable identification and prioritization of a novel drug target—MUC16—using data from The Cancer Genome Atlas (TCGA), all without requiring deep bioinformatics expertise. The analyst shows how to mine frequently mutated...</itunes:subtitle>
            <itunes:author>tv.qiagenbioinformatics.com</itunes:author>
            <itunes:duration>13:51</itunes:duration>
            <media:description type="html">&lt;p&gt;&lt;p&gt;This webinar shows how we enable identification and prioritization of a novel drug target—&lt;strong&gt;MUC16&lt;/strong&gt;—using data from The Cancer Genome Atlas (TCGA), all without requiring deep bioinformatics expertise. The analyst shows how to mine frequently mutated genes across solid tumors, filter for druggable targets based on subcellular localization and extracellular domain structure and evaluate mutation prevalence and protein context. MUC16 emerged as a compelling candidate, particularly in &lt;strong&gt;pancreatic adenocarcinoma&lt;/strong&gt;, where mutations and high expression levels correlated with poorer patient survival. Despite its relevance, few clinical trials currently target MUC16 in this disease, revealing a potentially untapped therapeutic niche. Network analysis and in silico perturbation simulations further suggested that modulating MUC16 expression could meaningfully influence oncogenic pathways. The session concluded by encouraging attendees to explore digital services and underscoring the speed and accessibility of tools for hypothesis generation and target validation.&lt;/p&gt;&lt;/p&gt;&lt;p&gt;&lt;a href="http://tv.qiagenbioinformatics.com/photo/110918797/targeting-muc16-neoantigens-in"&gt;&lt;img src="http://tv.qiagenbioinformatics.com/64968558/110918797/06e123bd1b18cf2eaf2b0bb2570a04a6/standard/download-11-thumbnail.jpg" width="75" height=""/&gt;&lt;/a&gt;&lt;/p&gt;</media:description>
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            <category>cancer_drug_discovery</category>
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            <title>Indication Expansion and Repurposing of PIK3CA Kinase Inhibitors: Systems...</title>
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            <description>&lt;p&gt;&lt;p&gt;Here we demo tools to rapidly guide &lt;strong&gt;drug repurposing and indication expansion&lt;/strong&gt;, using &lt;em&gt;alpelisib&lt;/em&gt;—a PI3Kα inhibitor approved for metastatic breast cancer—as a case study. The speaker shows how activating mutations in the &lt;strong&gt;PIK3CA&lt;/strong&gt; gene, a key oncogene, not only prevalent in breast cancer but also significantly present in oral cancers, an area with few active clinical trials and limited therapeutic development. By merging real-world mutation data, clinical outcomes, and pathway-based network modeling, the analysis shows that &lt;strong&gt;PIK3CA mutations in oral cancer correlate with poorer survival&lt;/strong&gt;, and that inhibiting PIK3CA activity could reduce disease progression. Further, the session used public dataset comparisons to identify non-oncologic diseases (e.g., lupus, heart disease) that share similar biology, suggesting future repurposing potential. It concluded by identifying possible &lt;strong&gt;combination therapy partners&lt;/strong&gt; through anti-matching expression patterns, showcasing a full workflow from hypothesis generation to target validation in under an hour.&lt;/p&gt;&lt;/p&gt;&lt;p&gt;&lt;a href="http://tv.qiagenbioinformatics.com/photo/88808161/indication-expansion-and"&gt;&lt;img src="http://tv.qiagenbioinformatics.com/64968577/88808161/1118ac40915ddac874e574aef12a376e/standard/download-10-thumbnail.jpg" width="75" height=""/&gt;&lt;/a&gt;&lt;/p&gt;</description>
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            <pubDate>Wed, 20 Sep 2023 07:38:56 GMT</pubDate>
            <media:title>Indication Expansion and Repurposing of PIK3CA Kinase Inhibitors: Systems...</media:title>
            <itunes:summary>Here we demo tools to rapidly guide drug repurposing and indication expansion, using alpelisib—a PI3Kα inhibitor approved for metastatic breast cancer—as a case study. The speaker shows how activating mutations in the PIK3CA gene, a key oncogene, not only prevalent in breast cancer but also significantly present in oral cancers, an area with few active clinical trials and limited therapeutic development. By merging real-world mutation data, clinical outcomes, and pathway-based network modeling, the analysis shows that PIK3CA mutations in oral cancer correlate with poorer survival, and that inhibiting PIK3CA activity could reduce disease progression. Further, the session used public dataset comparisons to identify non-oncologic diseases (e.g., lupus, heart disease) that share similar biology, suggesting future repurposing potential. It concluded by identifying possible combination therapy partners through anti-matching expression patterns, showcasing a full workflow from hypothesis generation to target validation in under an hour.</itunes:summary>
            <itunes:subtitle>Here we demo tools to rapidly guide drug repurposing and indication expansion, using alpelisib—a PI3Kα inhibitor approved for metastatic breast cancer—as a case study. The speaker shows how activating mutations in the PIK3CA gene, a key oncogene,...</itunes:subtitle>
            <itunes:author>tv.qiagenbioinformatics.com</itunes:author>
            <itunes:duration>24:12</itunes:duration>
            <media:description type="html">&lt;p&gt;&lt;p&gt;Here we demo tools to rapidly guide &lt;strong&gt;drug repurposing and indication expansion&lt;/strong&gt;, using &lt;em&gt;alpelisib&lt;/em&gt;—a PI3Kα inhibitor approved for metastatic breast cancer—as a case study. The speaker shows how activating mutations in the &lt;strong&gt;PIK3CA&lt;/strong&gt; gene, a key oncogene, not only prevalent in breast cancer but also significantly present in oral cancers, an area with few active clinical trials and limited therapeutic development. By merging real-world mutation data, clinical outcomes, and pathway-based network modeling, the analysis shows that &lt;strong&gt;PIK3CA mutations in oral cancer correlate with poorer survival&lt;/strong&gt;, and that inhibiting PIK3CA activity could reduce disease progression. Further, the session used public dataset comparisons to identify non-oncologic diseases (e.g., lupus, heart disease) that share similar biology, suggesting future repurposing potential. It concluded by identifying possible &lt;strong&gt;combination therapy partners&lt;/strong&gt; through anti-matching expression patterns, showcasing a full workflow from hypothesis generation to target validation in under an hour.&lt;/p&gt;&lt;/p&gt;&lt;p&gt;&lt;a href="http://tv.qiagenbioinformatics.com/photo/88808161/indication-expansion-and"&gt;&lt;img src="http://tv.qiagenbioinformatics.com/64968577/88808161/1118ac40915ddac874e574aef12a376e/standard/download-10-thumbnail.jpg" width="75" height=""/&gt;&lt;/a&gt;&lt;/p&gt;</media:description>
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            <category>biomarker</category>
            <category>cancer_drug_discovery</category>
            <category>hsmd</category>
            <category>oncology</category>
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            <title>Data curation as a key element in successful data science strategy</title>
            <link>http://tv.qiagenbioinformatics.com/photo/79058773/data-curation-as-a-key-element-in</link>
            <description>&lt;p&gt;&lt;p&gt;Learn the 7 habits of effective manual curation that QIAGEN Digital Insights uses to drive and build its data content to advance data science applications in biopharma companies.&lt;br&gt;&lt;br&gt;Uses include:&lt;br&gt;&lt;/p&gt;&lt;ul&gt;&lt;li&gt;&lt;p&gt;biomarker and target identification&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Disease networks&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Pathway construction&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;upstream and downstream relationships&lt;/p&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/p&gt;&lt;p&gt;&lt;a href="http://tv.qiagenbioinformatics.com/photo/79058773/data-curation-as-a-key-element-in"&gt;&lt;img src="http://tv.qiagenbioinformatics.com/64968556/79058773/5a66f548855b7de1065cd97040cb3f11/standard/download-12-thumbnail.jpg" width="75" height=""/&gt;&lt;/a&gt;&lt;/p&gt;</description>
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            <pubDate>Tue, 07 Feb 2023 17:12:02 GMT</pubDate>
            <media:title>Data curation as a key element in successful data science strategy</media:title>
            <itunes:summary>Learn the 7 habits of effective manual curation that QIAGEN Digital Insights uses to drive and build its data content to advance data science applications in biopharma companies.Uses include:biomarker and target identificationDisease networksPathway constructionupstream and downstream relationships</itunes:summary>
            <itunes:subtitle>Learn the 7 habits of effective manual curation that QIAGEN Digital Insights uses to drive and build its data content to advance data science applications in biopharma companies.Uses include:biomarker and target identificationDisease...</itunes:subtitle>
            <itunes:author>tv.qiagenbioinformatics.com</itunes:author>
            <itunes:duration>16:14</itunes:duration>
            <media:description type="html">&lt;p&gt;&lt;p&gt;Learn the 7 habits of effective manual curation that QIAGEN Digital Insights uses to drive and build its data content to advance data science applications in biopharma companies.&lt;br&gt;&lt;br&gt;Uses include:&lt;br&gt;&lt;/p&gt;&lt;ul&gt;&lt;li&gt;&lt;p&gt;biomarker and target identification&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Disease networks&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Pathway construction&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;upstream and downstream relationships&lt;/p&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/p&gt;&lt;p&gt;&lt;a href="http://tv.qiagenbioinformatics.com/photo/79058773/data-curation-as-a-key-element-in"&gt;&lt;img src="http://tv.qiagenbioinformatics.com/64968556/79058773/5a66f548855b7de1065cd97040cb3f11/standard/download-12-thumbnail.jpg" width="75" height=""/&gt;&lt;/a&gt;&lt;/p&gt;</media:description>
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            <category>bkb</category>
            <category>cancer_drug_discovery</category>
            <category>omicsoft webinar</category>
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            <title>Exploring drug response in low-grade glioma</title>
            <link>http://tv.qiagenbioinformatics.com/photo/77509927/exploring-drug-response-in</link>
            <description>&lt;p&gt;&lt;p&gt;This webinar introduces the Tumor Microenvironment (TME) Pathway in QIAGEN’s Ingenuity Pathway Analysis (IPA), demonstrating how users can explore immune and tumor interactions by constructing networks around targets like IL1B and predicting downstream effects on functions such as tumor proliferation. Using tools like Molecule Activity Predictor and Path Explorer, we show how curated literature-derived relationships can model cytokine activity and TME behavior. Additional features like BioProfiler, Land Explorer, and Activity Plot allow users to analyze public datasets (e.g., TCGA) for expression trends and regulatory roles, with use cases including PD-1–positive and –negative CD8+ T cells in lung cancer. IPA’s upstream regulator analysis further enabled prediction of key drivers influencing immune and tumor cell dynamics.&lt;/p&gt;&lt;/p&gt;&lt;p&gt;&lt;a href="http://tv.qiagenbioinformatics.com/photo/77509927/exploring-drug-response-in"&gt;&lt;img src="http://tv.qiagenbioinformatics.com/64968566/77509927/35be54dca6ae5b84be7ad4207d22dab4/standard/download-13-thumbnail.jpg" width="75" height=""/&gt;&lt;/a&gt;&lt;/p&gt;</description>
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            <pubDate>Wed, 31 Aug 2022 02:58:16 GMT</pubDate>
            <media:title>Exploring drug response in low-grade glioma</media:title>
            <itunes:summary>This webinar introduces the Tumor Microenvironment (TME) Pathway in QIAGEN’s Ingenuity Pathway Analysis (IPA), demonstrating how users can explore immune and tumor interactions by constructing networks around targets like IL1B and predicting downstream effects on functions such as tumor proliferation. Using tools like Molecule Activity Predictor and Path Explorer, we show how curated literature-derived relationships can model cytokine activity and TME behavior. Additional features like BioProfiler, Land Explorer, and Activity Plot allow users to analyze public datasets (e.g., TCGA) for expression trends and regulatory roles, with use cases including PD-1–positive and –negative CD8+ T cells in lung cancer. IPA’s upstream regulator analysis further enabled prediction of key drivers influencing immune and tumor cell dynamics.</itunes:summary>
            <itunes:subtitle>This webinar introduces the Tumor Microenvironment (TME) Pathway in QIAGEN’s Ingenuity Pathway Analysis (IPA), demonstrating how users can explore immune and tumor interactions by constructing networks around targets like IL1B and predicting...</itunes:subtitle>
            <itunes:author>tv.qiagenbioinformatics.com</itunes:author>
            <itunes:duration>21:23</itunes:duration>
            <media:description type="html">&lt;p&gt;&lt;p&gt;This webinar introduces the Tumor Microenvironment (TME) Pathway in QIAGEN’s Ingenuity Pathway Analysis (IPA), demonstrating how users can explore immune and tumor interactions by constructing networks around targets like IL1B and predicting downstream effects on functions such as tumor proliferation. Using tools like Molecule Activity Predictor and Path Explorer, we show how curated literature-derived relationships can model cytokine activity and TME behavior. Additional features like BioProfiler, Land Explorer, and Activity Plot allow users to analyze public datasets (e.g., TCGA) for expression trends and regulatory roles, with use cases including PD-1–positive and –negative CD8+ T cells in lung cancer. IPA’s upstream regulator analysis further enabled prediction of key drivers influencing immune and tumor cell dynamics.&lt;/p&gt;&lt;/p&gt;&lt;p&gt;&lt;a href="http://tv.qiagenbioinformatics.com/photo/77509927/exploring-drug-response-in"&gt;&lt;img src="http://tv.qiagenbioinformatics.com/64968566/77509927/35be54dca6ae5b84be7ad4207d22dab4/standard/download-13-thumbnail.jpg" width="75" height=""/&gt;&lt;/a&gt;&lt;/p&gt;</media:description>
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            <category>cancer_drug_discovery</category>
            <category>FAS Training</category>
            <category>ipa webinar</category>
            <category>omicsoft webinar</category>
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