Pub Date : 2024-08-01DOI: 10.1016/j.cancergen.2024.08.017
Thuy Phung, John Larrimore, Kathy Navia, Kelley Doug Hebert
Molecular profiling is critical in identifying genomic mutations for targeted cancer therapy. This study assesses the outcome of in-house molecular testing at a healthcare organization that cares for underserved population in southern Alabama.
We assessed the clinical impact of in-house single-gene mutation assays for BRAF V600 for metastatic melanoma, microsatellite instability (MSI) for colorectal carcinoma, and quantitative BCR::ABL1 p210 for chronic myelogenous leukemia. We determined test result turnaround time (TAT), and QNS and specimen rejection rate.
For BRAF V600 assay, there were 63 cases with 43 (68%) BRAF wild type, 20 (32%) BRAF mutant, 0 (0%) QNS and specimen rejection, and <24 hours TAT. For MSI assay, there were 100 cases with 91 (91%) MSI-Stable, 9 (9%) MSI-High, 0 (0%) QNS and specimen rejection, and <24 hours TAT. For quantitative p210 assay, there were 185 cases with 126 (68%) p210 detected, 59 (32%) p210 not detected, 10 (5.4%) cases were rejected due to pre-analytical errors, and <24 hours TAT. For BRAF and MSI assays, QNS and specimen rejection rate was 0% for in-house testing vs. 12.5% for send-out. For quantitative BCR::ABL1 p210 assay, the rate was 5.4% for in-house testing vs. 20% for send-out. The TAT for all in-house assays was <24 hours vs. >7 days for send-outs.
In-house molecular testing has significant positive clinical impact. Faster TAT, cost effectiveness and better management of testing are major advantages of local testing that would enable broader access to precision therapy for underserved cancer patients.
{"title":"15. Clinical impact of in-house molecular testing for underserved cancer patients in southern Alabama","authors":"Thuy Phung, John Larrimore, Kathy Navia, Kelley Doug Hebert","doi":"10.1016/j.cancergen.2024.08.017","DOIUrl":"10.1016/j.cancergen.2024.08.017","url":null,"abstract":"<div><div>Molecular profiling is critical in identifying genomic mutations for targeted cancer therapy. This study assesses the outcome of in-house molecular testing at a healthcare organization that cares for underserved population in southern Alabama.</div><div>We assessed the clinical impact of in-house single-gene mutation assays for <em>BRAF</em> V600 for metastatic melanoma, microsatellite instability (MSI) for colorectal carcinoma, and quantitative <em>BCR::ABL1</em> p210 for chronic myelogenous leukemia. We determined test result turnaround time (TAT), and QNS and specimen rejection rate.</div><div>For <em>BRAF</em> V600 assay, there were 63 cases with 43 (68%) <em>BRAF</em> wild type, 20 (32%) <em>BRAF</em> mutant, 0 (0%) QNS and specimen rejection, and <24 hours TAT. For MSI assay, there were 100 cases with 91 (91%) MSI-Stable, 9 (9%) MSI-High, 0 (0%) QNS and specimen rejection, and <24 hours TAT. For quantitative p210 assay, there were 185 cases with 126 (68%) p210 detected, 59 (32%) p210 not detected, 10 (5.4%) cases were rejected due to pre-analytical errors, and <24 hours TAT. For <em>BRAF</em> and MSI assays, QNS and specimen rejection rate was 0% for in-house testing vs. 12.5% for send-out. For quantitative <em>BCR::ABL1</em> p210 assay, the rate was 5.4% for in-house testing vs. 20% for send-out. The TAT for all in-house assays was <24 hours vs. >7 days for send-outs.</div><div>In-house molecular testing has significant positive clinical impact. Faster TAT, cost effectiveness and better management of testing are major advantages of local testing that would enable broader access to precision therapy for underserved cancer patients.</div></div>","PeriodicalId":49225,"journal":{"name":"Cancer Genetics","volume":"286 ","pages":"Pages S5-S6"},"PeriodicalIF":1.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.cancergen.2024.08.059
Kilannin Krysiak , Jason Saliba , Chimène Kesserwan , Yassmine Akkari , Mark D. Ewalt , Ilaria Iacobucci , Paulo Campregher , Paul G. Ekert , Deepa Bhojwani , Laura B. Corson , Nikita Mehta , Shivani Golem , Rashmi Kanagal-Shamanna , Alexandra E. Kovach , Kristy Lee , Arpad Danos , Hannah Helber , Sandeep Gurbuxani , Christine J. Harrison , Nitin Jain , Charles Mullighan
B-lymphoblastic leukemia/lymphoma (B-ALL) includes multiple distinct genetic subtypes. BCR::ABL1-like B-ALL is associated with high-risk disease with alterations impacting cytokine receptors and kinases that drive a gene expression profile which mimics BCR::ABL1-positive B-ALL. Given the significant genetic heterogeneity and various methodologies used to identify gene fusions, clinical diagnosis and decision-making for patients with this B-ALL subtype remain challenging.
Existing professional guidelines for BCR::ABL1-like B-ALL are not sufficiently detailed for consistent variant interpretation and routine practice between labs, which may impact both treatment decisions and clinical trial enrollment. To promote consensus, ClinGen has assembled a BCR::ABL1-like B-ALL Somatic Cancer Variant Curation Expert Panel (SC-VCEP) for variant interpretation related to this high-risk disease based on guidelines from the Somatic Cancer Clinical Domain Working Group.
Initially using ABL1 fusions not involving BCR, we have drafted oncogenicity guidelines for fusions in this subtype of B-ALL. Adapting the NTRK SC-VCEP fusion guidelines we are evaluating fusion structure, cancer association, and functional evidence to support variant classification. To establish the scope of our pilot fusions for oncogenicity guidelines and AMP/ASCO/CAP categorization, we have expanded our evaluation to include other ABL-class genes (e.g., ABL2, CSF1R). These guidelines will be publicly available with finalized interpretations relevant to B-ALL. Ultimately, these efforts aim to provide community consensus related to the diagnostic, prognostic, and therapeutic implications of genetic changes in B-ALL.
{"title":"57. Developing oncogenicity guidelines for BCR::ABL1-like B-lymphoblastic leukemia/lymphoma through expert consensus","authors":"Kilannin Krysiak , Jason Saliba , Chimène Kesserwan , Yassmine Akkari , Mark D. Ewalt , Ilaria Iacobucci , Paulo Campregher , Paul G. Ekert , Deepa Bhojwani , Laura B. Corson , Nikita Mehta , Shivani Golem , Rashmi Kanagal-Shamanna , Alexandra E. Kovach , Kristy Lee , Arpad Danos , Hannah Helber , Sandeep Gurbuxani , Christine J. Harrison , Nitin Jain , Charles Mullighan","doi":"10.1016/j.cancergen.2024.08.059","DOIUrl":"10.1016/j.cancergen.2024.08.059","url":null,"abstract":"<div><div>B-lymphoblastic leukemia/lymphoma (B-ALL) includes multiple distinct genetic subtypes. <em>BCR::ABL1-</em>like B-ALL is associated with high-risk disease with alterations impacting cytokine receptors and kinases that drive a gene expression profile which mimics <em>BCR::ABL1</em>-positive B-ALL. Given the significant genetic heterogeneity and various methodologies used to identify gene fusions, clinical diagnosis and decision-making for patients with this B-ALL subtype remain challenging.</div><div>Existing professional guidelines for <em>BCR::ABL1</em>-like B-ALL are not sufficiently detailed for consistent variant interpretation and routine practice between labs, which may impact both treatment decisions and clinical trial enrollment. To promote consensus, ClinGen has assembled a <em>BCR::ABL1</em>-like B-ALL Somatic Cancer Variant Curation Expert Panel (SC-VCEP) for variant interpretation related to this high-risk disease based on guidelines from the Somatic Cancer Clinical Domain Working Group.</div><div>Initially using <em>ABL1</em> fusions not involving BCR, we have drafted oncogenicity guidelines for fusions in this subtype of B-ALL. Adapting the NTRK SC-VCEP fusion guidelines we are evaluating fusion structure, cancer association, and functional evidence to support variant classification. To establish the scope of our pilot fusions for oncogenicity guidelines and AMP/ASCO/CAP categorization, we have expanded our evaluation to include other ABL-class genes (e.g., <em>ABL2, CSF1R</em>). These guidelines will be publicly available with finalized interpretations relevant to B-ALL. Ultimately, these efforts aim to provide community consensus related to the diagnostic, prognostic, and therapeutic implications of genetic changes in B-ALL.</div></div>","PeriodicalId":49225,"journal":{"name":"Cancer Genetics","volume":"286 ","pages":"Pages S18-S19"},"PeriodicalIF":1.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.cancergen.2024.08.022
Kristin Deeb, Linsheng Zhang, Tatiana Tvrdik, Deniz Peker Barclift, Saja Asakrah, Shiyong Li
Myeloid/lymphoid neoplasms with eosinophilia and defining gene rearrangements commonly involve FIP1L1::PDGFRA. Eosinophilia is not an invariable feature. Neoplastic myeloid/lymphoid populations may be present at the same or different sites, with T-cell neoplasms being the conventional lymphoid component.
The case involves a 43-year-old male with chronic intractable disseminated skin rashes. Blood flow cytometry showed an aberrant T-cell population with no surface CD3 expression. Atypical T-cell infiltrates were present in the bone marrow, skin, and inguinal lymph node biopsy, and clonal TRG rearrangements were detected in blood, skin, and lymph node. However, there were no specific features to definitively classify the abnormal T-cell infiltrates. Bone marrow was fibrotic and hypercellular with only focal eosinophilia. Next-generation sequencing of blood and lymph node detected no significant mutations, and bone marrow cells demonstrated a normal karyotype. Fluorescence in situ hybridization demonstrated loss of both the CHIC2 and PDGFRA signals, with retention of the FIP1L1 signal. Whole-genome microarray analysis revealed an ∼1.3 Mb loss in the 4q12 region with breakpoints within the FIP1L1 and KIT genes. A novel FIP1L1::KIT fusion was confirmed by RNA-sequencing demonstrating in-frame retention of the KIT tyrosine kinase domain. The patient had a poor response to chemotherapy but superb response to the tyrosine kinase inhibitor, dasatinib.
FIP1L1::KIT fusion has not been described in systemic peripheral T-cell neoplasms without significant abnormality in myeloid lineage. This case indicates that KIT fusions are targetable genetic lesions and supports the inclusion of KIT fusions in the myeloid/lymphoid neoplasms with defining gene rearrangement.
{"title":"20. FIP1L1::KIT fusion in a case of peripheral T-cell lymphoproliferative neoplasm responsive to tyrosine kinase inhibitor","authors":"Kristin Deeb, Linsheng Zhang, Tatiana Tvrdik, Deniz Peker Barclift, Saja Asakrah, Shiyong Li","doi":"10.1016/j.cancergen.2024.08.022","DOIUrl":"10.1016/j.cancergen.2024.08.022","url":null,"abstract":"<div><div>Myeloid/lymphoid neoplasms with eosinophilia and defining gene rearrangements commonly involve <em>FIP1L1::PDGFRA</em>. Eosinophilia is not an invariable feature. Neoplastic myeloid/lymphoid populations may be present at the same or different sites, with T-cell neoplasms being the conventional lymphoid component.</div><div>The case involves a 43-year-old male with chronic intractable disseminated skin rashes. Blood flow cytometry showed an aberrant T-cell population with no surface CD3 expression. Atypical T-cell infiltrates were present in the bone marrow, skin, and inguinal lymph node biopsy, and clonal <em>TRG</em> rearrangements were detected in blood, skin, and lymph node. However, there were no specific features to definitively classify the abnormal T-cell infiltrates. Bone marrow was fibrotic and hypercellular with only focal eosinophilia. Next-generation sequencing of blood and lymph node detected no significant mutations, and bone marrow cells demonstrated a normal karyotype. Fluorescence in situ hybridization demonstrated loss of both the <em>CHIC2</em> and <em>PDGFRA</em> signals, with retention of the <em>FIP1L1</em> signal. Whole-genome microarray analysis revealed an ∼1.3 Mb loss in the 4q12 region with breakpoints within the <em>FIP1L1</em> and <em>KIT</em> genes. A novel <em>FIP1L1::KIT</em> fusion was confirmed by RNA-sequencing demonstrating in-frame retention of the <em>KIT</em> tyrosine kinase domain. The patient had a poor response to chemotherapy but superb response to the tyrosine kinase inhibitor, dasatinib.</div><div><em>FIP1L1::KIT</em> fusion has not been described in systemic peripheral T-cell neoplasms without significant abnormality in myeloid lineage. This case indicates that <em>KIT</em> fusions are targetable genetic lesions and supports the inclusion of <em>KIT</em> fusions in the myeloid/lymphoid neoplasms with defining gene rearrangement.</div></div>","PeriodicalId":49225,"journal":{"name":"Cancer Genetics","volume":"286 ","pages":"Page S7"},"PeriodicalIF":1.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.cancergen.2024.08.026
Alyssa Parker, Joseph Van Amburg, Alexander Bick
Somatic mutations in hematopoietic stem cells give rise to clonal hematopoiesis (CH), a pre-malignant state that precedes hematologic malignancy. In current clinical practice, CH clone size as quantified by the variant allele fraction (VAF) is serially monitored with DNA sequencing. Increases in VAF are often interpreted as portending progression to malignancy. CH leads to a myeloid bias and VAF measurements can be confounded by cell-type proportions, which also vary according to immune demands. We developed a targeted enzymatic DNA methylation sequencing assay that costs ∼$80/sample (including reagents, library preparation and sequencing) and captures ∼4 million CpGs and applied it to 91 samples from patients with CH. We used the resulting methylation data to infer cell-type proportions. We found that predicted cell-type proportions for lymphocytes and granulocytes correlated highly with complete blood cell counts (R^2 = 0.84 and p-value = 2.65 × 10^-14; R2 = 0.88 and p-value = 4.31 × 10^-16), but predictions for monocytes were much less correlated (R^2 = 0.26, p-value = 2.04 × 10^-3). Furthermore, we observed that as monocyte proportion increased, so did reported percent change in VAF. Correlation was highest for clones driven by mutations in TET2, which have been shown to have more extreme degrees of myeloid bias. This work raises concerns about current methods of monitoring CH based solely on VAF. Given the low cost, cell-type proportion prediction from DNA methylation is a feasible addition to CH assays. Our work suggests that cell-type proportions would provide vital context for accurate interpretation of VAF throughout hematologic malignancy progression and treatment.
{"title":"24. Methylation sequencing enhances interpretation of clonal hematopoiesis dynamics","authors":"Alyssa Parker, Joseph Van Amburg, Alexander Bick","doi":"10.1016/j.cancergen.2024.08.026","DOIUrl":"10.1016/j.cancergen.2024.08.026","url":null,"abstract":"<div><div>Somatic mutations in hematopoietic stem cells give rise to clonal hematopoiesis (CH), a pre-malignant state that precedes hematologic malignancy. In current clinical practice, CH clone size as quantified by the variant allele fraction (VAF) is serially monitored with DNA sequencing. Increases in VAF are often interpreted as portending progression to malignancy. CH leads to a myeloid bias and VAF measurements can be confounded by cell-type proportions, which also vary according to immune demands. We developed a targeted enzymatic DNA methylation sequencing assay that costs ∼$80/sample (including reagents, library preparation and sequencing) and captures ∼4 million CpGs and applied it to 91 samples from patients with CH. We used the resulting methylation data to infer cell-type proportions. We found that predicted cell-type proportions for lymphocytes and granulocytes correlated highly with complete blood cell counts (R^2 = 0.84 and p-value = 2.65 × 10^-14; R2 = 0.88 and p-value = 4.31 × 10^-16), but predictions for monocytes were much less correlated (R^2 = 0.26, p-value = 2.04 × 10^-3). Furthermore, we observed that as monocyte proportion increased, so did reported percent change in VAF. Correlation was highest for clones driven by mutations in <em>TET2</em>, which have been shown to have more extreme degrees of myeloid bias. This work raises concerns about current methods of monitoring CH based solely on VAF. Given the low cost, cell-type proportion prediction from DNA methylation is a feasible addition to CH assays. Our work suggests that cell-type proportions would provide vital context for accurate interpretation of VAF throughout hematologic malignancy progression and treatment.</div></div>","PeriodicalId":49225,"journal":{"name":"Cancer Genetics","volume":"286 ","pages":"Page S8"},"PeriodicalIF":1.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.cancergen.2024.08.033
Alex Wagner , Reece Hart , Johan den Dunnen , Elspeth Bruford , Ros Hastings , Marina DiStefano , Myungshin Kim , Sarah Moore , Gordana Raca
Despite the well-established role of gene fusions in oncogenic processes, current practices for characterizing and annotating gene fusion events in the clinical setting and in biomedical literature are inconsistent. Consequently, evidence-based interpretation of functional and clinical significance of fusion variants requires laborious and time-consuming gathering and review of putative evidence. Differences between community standards inhibit the uniform communication of fusion events as well as the interoperability of tools, resources, and pipelines, ultimately impeding data sharing and downstream utility.
To address these challenges, a cross-consortia initiative between the Variant Interpretation for Cancer Consortium (VICC), CGC, ClinGen Somatic, and AMP was formed to develop a unified, standard nomenclature for representing the product of gene fusions (fusions.cancervariants.org). Invested participants across academic, government, and industry sectors engaged with these challenges to propose solutions via participation in community surveys and discussions to define and develop a standard for this diverse class of alterations. Our recent efforts to align these pre-release recommendations for fusion representation with the recommendations of the HGNC, ISCN, and HGVS nomenclature committees have resulted in consensus definitions and interoperable nomenclature systems for the description of gene fusions.
In January 2024, the first major release (21.0.0) of the HGVS nomenclature since 2020 includes the results of this work, which is also reflected in the cross-consortia recommendations. We discuss the vocabulary and nomenclature alignment between these related and cross-referenced standards, and provide recommendations for characterization and representation of gene fusions across these systems.
{"title":"31. A cross-consortia initiative for aligning the definitions and descriptions of gene fusions","authors":"Alex Wagner , Reece Hart , Johan den Dunnen , Elspeth Bruford , Ros Hastings , Marina DiStefano , Myungshin Kim , Sarah Moore , Gordana Raca","doi":"10.1016/j.cancergen.2024.08.033","DOIUrl":"10.1016/j.cancergen.2024.08.033","url":null,"abstract":"<div><div>Despite the well-established role of gene fusions in oncogenic processes, current practices for characterizing and annotating gene fusion events in the clinical setting and in biomedical literature are inconsistent. Consequently, evidence-based interpretation of functional and clinical significance of fusion variants requires laborious and time-consuming gathering and review of putative evidence. Differences between community standards inhibit the uniform communication of fusion events as well as the interoperability of tools, resources, and pipelines, ultimately impeding data sharing and downstream utility.</div><div>To address these challenges, a cross-consortia initiative between the Variant Interpretation for Cancer Consortium (VICC), CGC, ClinGen Somatic, and AMP was formed to develop a unified, standard nomenclature for representing the product of gene fusions (fusions.cancervariants.org). Invested participants across academic, government, and industry sectors engaged with these challenges to propose solutions via participation in community surveys and discussions to define and develop a standard for this diverse class of alterations. Our recent efforts to align these pre-release recommendations for fusion representation with the recommendations of the HGNC, ISCN, and HGVS nomenclature committees have resulted in consensus definitions and interoperable nomenclature systems for the description of gene fusions.</div><div>In January 2024, the first major release (21.0.0) of the HGVS nomenclature since 2020 includes the results of this work, which is also reflected in the cross-consortia recommendations. We discuss the vocabulary and nomenclature alignment between these related and cross-referenced standards, and provide recommendations for characterization and representation of gene fusions across these systems.</div></div>","PeriodicalId":49225,"journal":{"name":"Cancer Genetics","volume":"286 ","pages":"Page S10"},"PeriodicalIF":1.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.cancergen.2024.08.072
Yavuz Sahin, Jianming Pei, Don A. Baldwin, Nashwa Mansoor, Lori Koslosky, Peter Abdelmessieh, Y. Lynn Wang, Reza Nejati, Joseph Testa
Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy associated with various combinations of gene mutations, epigenetic abnormalities, and chromosome rearrangement-related gene fusions. Despite the significant degree of heterogeneity in its pathogenesis, many gene fusions and point mutations are recurrent in AML and have been employed in risk stratification over the last several decades. Gene fusions have long been recognized for understanding tumorigenesis and their proven roles in clinical diagnosis and targeted therapies. Advances in DNA sequencing technologies and computational biology have contributed significantly to the detection of known fusion genes as well as for the discovery of novel ones. Several recurring gene fusions in AML have been linked to prognosis, treatment response, and disease progression. Here, we present a case with a long history of essential thrombocythemia and hallmark CALR mutation transforming to AML characterized by a previously unreported AKAP9::PDGFRA fusion gene. We propose mechanisms by which this fusion may contribute to the pathogenesis of AML and its potential as a molecular target for tyrosine kinase inhibitors.
{"title":"70. Acute myeloid leukemia with a novel AKAP9::PDGFRA fusion transformed from essential thrombocythemia","authors":"Yavuz Sahin, Jianming Pei, Don A. Baldwin, Nashwa Mansoor, Lori Koslosky, Peter Abdelmessieh, Y. Lynn Wang, Reza Nejati, Joseph Testa","doi":"10.1016/j.cancergen.2024.08.072","DOIUrl":"10.1016/j.cancergen.2024.08.072","url":null,"abstract":"<div><div>Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy associated with various combinations of gene mutations, epigenetic abnormalities, and chromosome rearrangement-related gene fusions. Despite the significant degree of heterogeneity in its pathogenesis, many gene fusions and point mutations are recurrent in AML and have been employed in risk stratification over the last several decades. Gene fusions have long been recognized for understanding tumorigenesis and their proven roles in clinical diagnosis and targeted therapies. Advances in DNA sequencing technologies and computational biology have contributed significantly to the detection of known fusion genes as well as for the discovery of novel ones. Several recurring gene fusions in AML have been linked to prognosis, treatment response, and disease progression. Here, we present a case with a long history of essential thrombocythemia and hallmark CALR mutation transforming to AML characterized by a previously unreported <em>AKAP9::PDGFRA</em> fusion gene. We propose mechanisms by which this fusion may contribute to the pathogenesis of AML and its potential as a molecular target for tyrosine kinase inhibitors.</div><div>Journal: Leukemia Research Reports</div></div>","PeriodicalId":49225,"journal":{"name":"Cancer Genetics","volume":"286 ","pages":"Pages S22-S23"},"PeriodicalIF":1.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.cancergen.2024.08.039
Hussam Al Kateb, Lisa Mullineaux, Adam McClung, Amber Pryzbylski, Claire Teigen, Beth Pitel, Kevin Halling
Clinical Laboratories reimbursement for large cancer panels has been challenging with many health care payers arguing about the clinical utility of such panels. We sought to investigate the clinical utility of the TSO500 kit in patients with solid tumors (ST) by determining its ability to detect a target for an FDA-approved drug in tumor type (FDA-in-TT), and in other tumor type (FDA-in-OTT).
The TSO500 kit which is commercially offered by Illumina company along with an analysis pipeline has been clinically verified in our laboratory and has been offered since 2021 for clinical care. A total of 1194 cases representing over 25 solid tumor types that were tested on the TSO500 platform were included in the study.
The top 3 indications for testing were lung cancer (35.6%), unknown primary (UNP) (14.5%), and other tumor types (12.5%). FDA-in-TT and FDA-in-OTT variants were identified in 531/1194(44.5%), and (257/1194)(15.4%), total (788/1194)(66%) cases, respectively. Immunotherapy markers including microsatellite instability (MSI-H) and/or high tumor mutation burden (TMB-H) were detected in 324 cases representing 60% of cases with FDA-in-TT and FDA-in-OTT, and 30% of all tested cases, respectively. Interestingly, 43/172(25%) of UKP cases had FDA-approved-drug, of which 35/43(81%) had TMB-H.
The detection of an FDA-in-TT and FDA-in-OTT target in 66% of patients, 60% of whom had TMB-H, a biomarker for FDA-approved immunotherapy drugs that requires interrogating over one megabase of the genome for accurate detection, provides strong evidence for the clinical utility of a large panels testing like the TSO500 in patients with solid tumors.
{"title":"37. The clinical utility of the TSO500 clinically-verified test in patients with solid tumors: The Mayo Clinic experience","authors":"Hussam Al Kateb, Lisa Mullineaux, Adam McClung, Amber Pryzbylski, Claire Teigen, Beth Pitel, Kevin Halling","doi":"10.1016/j.cancergen.2024.08.039","DOIUrl":"10.1016/j.cancergen.2024.08.039","url":null,"abstract":"<div><div>Clinical Laboratories reimbursement for large cancer panels has been challenging with many health care payers arguing about the clinical utility of such panels. We sought to investigate the clinical utility of the TSO500 kit in patients with solid tumors (ST) by determining its ability to detect a target for an FDA-approved drug in tumor type (FDA-in-TT), and in other tumor type (FDA-in-OTT).</div><div>The TSO500 kit which is commercially offered by Illumina company along with an analysis pipeline has been clinically verified in our laboratory and has been offered since 2021 for clinical care. A total of 1194 cases representing over 25 solid tumor types that were tested on the TSO500 platform were included in the study.</div><div>The top 3 indications for testing were lung cancer (35.6%), unknown primary (UNP) (14.5%), and other tumor types (12.5%). FDA-in-TT and FDA-in-OTT variants were identified in 531/1194(44.5%), and (257/1194)(15.4%), total (788/1194)(66%) cases, respectively. Immunotherapy markers including microsatellite instability (MSI-H) and/or high tumor mutation burden (TMB-H) were detected in 324 cases representing 60% of cases with FDA-in-TT and FDA-in-OTT, and 30% of all tested cases, respectively. Interestingly, 43/172(25%) of UKP cases had FDA-approved-drug, of which 35/43(81%) had TMB-H.</div><div>The detection of an FDA-in-TT and FDA-in-OTT target in 66% of patients, 60% of whom had TMB-H, a biomarker for FDA-approved immunotherapy drugs that requires interrogating over one megabase of the genome for accurate detection, provides strong evidence for the clinical utility of a large panels testing like the TSO500 in patients with solid tumors.</div></div>","PeriodicalId":49225,"journal":{"name":"Cancer Genetics","volume":"286 ","pages":"Page S12"},"PeriodicalIF":1.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.cancergen.2024.08.013
Kartik Singhal , Susanna Kiwala , Peter S. Goedegebuure , Christopher Miller , Evelyn Schmidt , Huiming Xia , My Hoang , Mariam Khanfar , Shelly O'Laughlin , Nancy Myers , Tammi Vickery , Kelsy C. Cotto , Sherri Davies , Feiyu Du , Thomas B. Mooney , Gue Su Chang , Jasreet Hundal , John Garza , Mike D. McLellan , Joshua McMichael , Malachi Griffith
Personalized cancer vaccines (PCVs) leverage immunogenomics strategies to combat cancer. Somatic mutations in tumor cells generate neoantigens that may get presented on the tumor cell's surface by MHC molecules. Immunotherapies target neoantigens to stimulate tumor-specific immune responses. Our bioinformatics workflow has designed vaccines for over 170 patients across 11 of the 180 neoantigen vaccine trials on clinicaltrials.gov.
Despite the rise in PCV-related interventions, gaps in established protocols addressing the complexities associated with the design of PCVs still remain. Here, we summarize our bioinformatics pipeline and describe measures taken to ensure robust support for clinical trials at Washington University. Our Google Cloud immunotherapy pipeline (open MIT license) to predict neoantigen epitopes is implemented in Workflow Definition Language and containerized using Docker to ensure portability and reliability. The pVACtools software suite (pvactools.org) that carries out neoantigen identification and prioritization, is developed and updated following industry best practices including version control (Git), formal code review, automated unit and integration tests, and benchmark tests. The final steps of the bioinformatics workflow generate files recording the analysis parameters and QC results tailored to the FDA's requests. Candidates generated by the pipeline are reviewed at an Immunogenomics Tumor Board using the pVACview tool. Prioritized candidates undergo a rigorous examination of data QC metrics, variant support at genomic and transcriptomic levels, MHC binding prediction algorithms, and HLA allele concordance between the clinical data and in-silico prediction tools. Finally, a long-peptide order form generated by the pipeline is sent to the vaccine manufacturer for synthesis.
{"title":"11. Developing a robust bioinformatics workflow to support personalized neoantigen vaccine clinical trials","authors":"Kartik Singhal , Susanna Kiwala , Peter S. Goedegebuure , Christopher Miller , Evelyn Schmidt , Huiming Xia , My Hoang , Mariam Khanfar , Shelly O'Laughlin , Nancy Myers , Tammi Vickery , Kelsy C. Cotto , Sherri Davies , Feiyu Du , Thomas B. Mooney , Gue Su Chang , Jasreet Hundal , John Garza , Mike D. McLellan , Joshua McMichael , Malachi Griffith","doi":"10.1016/j.cancergen.2024.08.013","DOIUrl":"10.1016/j.cancergen.2024.08.013","url":null,"abstract":"<div><div>Personalized cancer vaccines (PCVs) leverage immunogenomics strategies to combat cancer. Somatic mutations in tumor cells generate neoantigens that may get presented on the tumor cell's surface by MHC molecules. Immunotherapies target neoantigens to stimulate tumor-specific immune responses. Our bioinformatics workflow has designed vaccines for over 170 patients across 11 of the 180 neoantigen vaccine trials on clinicaltrials.gov.</div><div>Despite the rise in PCV-related interventions, gaps in established protocols addressing the complexities associated with the design of PCVs still remain. Here, we summarize our bioinformatics pipeline and describe measures taken to ensure robust support for clinical trials at Washington University. Our Google Cloud immunotherapy pipeline (open MIT license) to predict neoantigen epitopes is implemented in Workflow Definition Language and containerized using Docker to ensure portability and reliability. The pVACtools software suite (pvactools.org) that carries out neoantigen identification and prioritization, is developed and updated following industry best practices including version control (Git), formal code review, automated unit and integration tests, and benchmark tests. The final steps of the bioinformatics workflow generate files recording the analysis parameters and QC results tailored to the FDA's requests. Candidates generated by the pipeline are reviewed at an Immunogenomics Tumor Board using the pVACview tool. Prioritized candidates undergo a rigorous examination of data QC metrics, variant support at genomic and transcriptomic levels, MHC binding prediction algorithms, and HLA allele concordance between the clinical data and in-silico prediction tools. Finally, a long-peptide order form generated by the pipeline is sent to the vaccine manufacturer for synthesis.</div></div>","PeriodicalId":49225,"journal":{"name":"Cancer Genetics","volume":"286 ","pages":"Page S4"},"PeriodicalIF":1.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.cancergen.2024.08.074
Kathryn Stahl, Wesley Goar, Kori Kuzma, Alex Wagner
The Variation Categorizer (VarCat) is a tool for classifying variant oncogenicity for variant-disease pairings in a clinical laboratory workflow. VarCat implements the ClinGen/CGC/VICC oncogenicity guidelines to assist in the classification of a variant's capability for driving cancer formation and growth. VarCat provides an intuitive interface for structured data sharing and produces classification assessments compliant with genomic knowledge standards specified by the Global Alliance for Genomics and Health (GA4GH).
Here, we present the models, structures, and capabilities provided by VarCat's API and demonstrate its ability to create standardized assessments. VarCat leverages harmonized data from several genomic knowledge sources collated by the VICC MetaKB service. VarCat ensures comprehensive analysis by incorporating standardized gene, variant, therapeutic, disease, and evidence data, and it is driving the development of GA4GH genomic knowledge formats for oncogenicity data. We also describe the suite of normalization microservices used by MetaKB and VarCat to harmonize genomic knowledge concepts. We illustrate how VarCat reduces barriers to interoperable variant-associated evidence through the adoption of the GA4GH Variation Representation Specification (VRS). We also present standardized evidence data using the AMP/ASCO/CAP guidelines for clinical actionability. Overall, our work illustrates how GA4GH Genomic Knowledge Standards drive data interoperability and successful knowledge exchange, ultimately enhancing genetic disease comprehension and advancing patient care.
{"title":"72. What's under VarCat's hat: Modeling variant oncogenicity classifications with GA4GH Standards","authors":"Kathryn Stahl, Wesley Goar, Kori Kuzma, Alex Wagner","doi":"10.1016/j.cancergen.2024.08.074","DOIUrl":"10.1016/j.cancergen.2024.08.074","url":null,"abstract":"<div><div>The Variation Categorizer (VarCat) is a tool for classifying variant oncogenicity for variant-disease pairings in a clinical laboratory workflow. VarCat implements the ClinGen/CGC/VICC oncogenicity guidelines to assist in the classification of a variant's capability for driving cancer formation and growth. VarCat provides an intuitive interface for structured data sharing and produces classification assessments compliant with genomic knowledge standards specified by the Global Alliance for Genomics and Health (GA4GH).</div><div>Here, we present the models, structures, and capabilities provided by VarCat's API and demonstrate its ability to create standardized assessments. VarCat leverages harmonized data from several genomic knowledge sources collated by the VICC MetaKB service. VarCat ensures comprehensive analysis by incorporating standardized gene, variant, therapeutic, disease, and evidence data, and it is driving the development of GA4GH genomic knowledge formats for oncogenicity data. We also describe the suite of normalization microservices used by MetaKB and VarCat to harmonize genomic knowledge concepts. We illustrate how VarCat reduces barriers to interoperable variant-associated evidence through the adoption of the GA4GH Variation Representation Specification (VRS). We also present standardized evidence data using the AMP/ASCO/CAP guidelines for clinical actionability. Overall, our work illustrates how GA4GH Genomic Knowledge Standards drive data interoperability and successful knowledge exchange, ultimately enhancing genetic disease comprehension and advancing patient care.</div></div>","PeriodicalId":49225,"journal":{"name":"Cancer Genetics","volume":"286 ","pages":"Page S23"},"PeriodicalIF":1.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1016/j.cancergen.2024.08.014
Matthew Cannon , James Stevenson , Kathryn Stahl , Rohit Basu , Adam Coffman , Susanna Kiwala , Joshua McMichael , Elaine Mardis , Obi Griffith , Malachi Griffith , Alex Wagner
The drug-gene interaction database (DGIdb) is a resource that aggregates interaction data from over 40 different resources into one platform with the primary goal of making the druggable genome accessible to clinicians and researchers. By providing a public, computationally accessible database, the DGIdb enables therapeutic insights through broad aggregation of DGI data.
As part of our aggregation process, DGIdb preserves data regarding interaction types, directionality, and other attributes that enable filtering or biochemical insight. However, source data are often incomplete and may not contain the original physiological context of the interaction. Without this context, the therapeutic relevance of an interaction may be compromised or lost. In this report, we address these missing data and extract therapeutic context from free-text sources. We apply existing large language models (LLMs) that have been fine-tuned on additional medical corpuses to tag and extract indications, cancer types, and relevant pharmacogenomics from free-text, FDA approved labels. We are then able to utilize our in-house normalization services to link extracted data back to formally grouped concepts.
In a preliminary test set of 355 FDA labels, we were able to normalize 59.4%, 49.8%, and 49.1% of extracted chemical, disease, and genetic entities back to harmonized concepts. Extracting this data allows us to supplement our existing interactions with relevant context that may inform the therapeutic relevance of a particular interaction. Inclusion of these data will be particularly invaluable for variant interpretation pipelines where mutational status can lead to the identification of a lifesaving therapeutic and a positive patient outcome.
{"title":"12. Contextualizing clinical significance using FDA label supplemented DGI data","authors":"Matthew Cannon , James Stevenson , Kathryn Stahl , Rohit Basu , Adam Coffman , Susanna Kiwala , Joshua McMichael , Elaine Mardis , Obi Griffith , Malachi Griffith , Alex Wagner","doi":"10.1016/j.cancergen.2024.08.014","DOIUrl":"10.1016/j.cancergen.2024.08.014","url":null,"abstract":"<div><div>The drug-gene interaction database (DGIdb) is a resource that aggregates interaction data from over 40 different resources into one platform with the primary goal of making the druggable genome accessible to clinicians and researchers. By providing a public, computationally accessible database, the DGIdb enables therapeutic insights through broad aggregation of DGI data.</div><div>As part of our aggregation process, DGIdb preserves data regarding interaction types, directionality, and other attributes that enable filtering or biochemical insight. However, source data are often incomplete and may not contain the original physiological context of the interaction. Without this context, the therapeutic relevance of an interaction may be compromised or lost. In this report, we address these missing data and extract therapeutic context from free-text sources. We apply existing large language models (LLMs) that have been fine-tuned on additional medical corpuses to tag and extract indications, cancer types, and relevant pharmacogenomics from free-text, FDA approved labels. We are then able to utilize our in-house normalization services to link extracted data back to formally grouped concepts.</div><div>In a preliminary test set of 355 FDA labels, we were able to normalize 59.4%, 49.8%, and 49.1% of extracted chemical, disease, and genetic entities back to harmonized concepts. Extracting this data allows us to supplement our existing interactions with relevant context that may inform the therapeutic relevance of a particular interaction. Inclusion of these data will be particularly invaluable for variant interpretation pipelines where mutational status can lead to the identification of a lifesaving therapeutic and a positive patient outcome.</div></div>","PeriodicalId":49225,"journal":{"name":"Cancer Genetics","volume":"286 ","pages":"Pages S4-S5"},"PeriodicalIF":1.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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