Cancer Informatics最新文献

{"title":"Innovative Approach for a Typology of Treatment Sequences in Early Stage HER2 Positive Breast Cancer Patients Treated With Trastuzumab in the French National Hospital Database.","authors":"Olivier Tredan,&nbsp;Marie Laurent,&nbsp;Melina Gilberg,&nbsp;Rim Ghorbal,&nbsp;Alexandre Vainchtock,&nbsp;Joannie Lortet-Tieulent,&nbsp;Martin Prodel,&nbsp;Julien Dupin","doi":"10.1177/11769351221135134","DOIUrl":"https://doi.org/10.1177/11769351221135134","url":null,"abstract":"<p><strong>Background: </strong>Our objective was to describe the hospital-based systemic treatment sequences in early stage HER2+ breast cancer patients treated with trastuzumab in France in 2016.</p><p><strong>Methods: </strong>This retrospective observational study was based on the national hospital discharge database (PMSI). Patients hospitalized for breast cancer in 2016 and administration of trastuzumab between 6 months prior and 1 year after surgery were included. The following treatments were identified: (1) trastuzumab ± chemotherapy; (2) chemotherapy alone; (3) q3w trastuzumab weekly chemotherapy. Hospital admissions for cardiac events before and after the surgery were investigated. An unsupervised machine learning technic called TAK (Time-sequence Analysis through K-clustering) was used to identify and visualize typical systemic treatment sequences.</p><p><strong>Results: </strong>Overall, 3531 patients were included: 2619 adjuvant cohort patients (74.2%) and 912 neoadjuvant cohort patients (25.8%). The mean age was 56.4 years (±12.3), 99.7% patients were female. Treatment initiation occurred within 6 weeks of the surgery in 58% and 92% of patients, and trastuzumab treatment lasted 12 months (±1 month) in 75% and 66% of patients in the adjuvant and neoadjuvant cohorts, respectively. Nevertheless, 12% and 22% of patients were treated with trastuzumab for <11 months in the adjuvant and neoadjuvant cohorts, respectively. There was not one standard sequence of treatments per cohort, but 4 and 3 typical treatment sequences in the adjuvant and the neoadjuvant cohorts, respectively, plus 2 treatment sequences with an early treatment withdrawal. The frequency of patients with ⩾1 hospital stay with a cardiac event was higher among patients with an early treatment withdrawal.</p><p><strong>Conclusions: </strong>The treatment sequences of most patients were in line with the recommendations in force. The machine learning approach provided a telling visual display of the results, thereby allowing healthcare professionals, health authorities, patients, and care givers to see the whole picture of the hospital-administered drug strategies.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":" ","pages":"11769351221135134"},"PeriodicalIF":2.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/80/e8/10.1177_11769351221135134.PMC9661546.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40468087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methylation of CpG Sites as Biomarkers Predictive of Drug-Specific Patient Survival in Cancer.","authors":"Bridget Neary,&nbsp;Shuting Lin,&nbsp;Peng Qiu","doi":"10.1177/11769351221131124","DOIUrl":"https://doi.org/10.1177/11769351221131124","url":null,"abstract":"<p><strong>Background: </strong>Though the development of targeted cancer drugs continues to accelerate, doctors still lack reliable methods for predicting patient response to standard-of-care therapies for most cancers. DNA methylation has been implicated in tumor drug response and is a promising source of predictive biomarkers of drug efficacy, yet the relationship between drug efficacy and DNA methylation remains largely unexplored.</p><p><strong>Method: </strong>In this analysis, we performed log-rank survival analyses on patients grouped by cancer and drug exposure to find CpG sites where binary methylation status is associated with differential survival in patients treated with a specific drug but not in patients with the same cancer who were not exposed to that drug. We also clustered these drug-specific CpG sites based on co-methylation among patients to identify broader methylation patterns that may be related to drug efficacy, which we investigated for transcription factor binding site enrichment using gene set enrichment analysis.</p><p><strong>Results: </strong>We identified CpG sites that were drug-specific predictors of survival in 38 cancer-drug patient groups across 15 cancers and 20 drugs. These included 11 CpG sites with similar drug-specific survival effects in multiple cancers. We also identified 76 clusters of CpG sites with stronger associations with patient drug response, many of which contained CpG sites in gene promoters containing transcription factor binding sites.</p><p><strong>Conclusion: </strong>These findings are promising biomarkers of drug response for a variety of drugs and contribute to our understanding of drug-methylation interactions in cancer. Investigation and validation of these results could lead to the development of targeted co-therapies aimed at manipulating methylation in order to improve efficacy of commonly used therapies and could improve patient survival and quality of life by furthering the effort toward drug response prediction.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":" ","pages":"11769351221131124"},"PeriodicalIF":2.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9c/b7/10.1177_11769351221131124.PMC9634212.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40669964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SurviveAI: Long Term Survival Prediction of Cancer Patients Based on Somatic RNA-Seq Expression.","authors":"Omri Nayshool,&nbsp;Nitzan Kol,&nbsp;Elisheva Javaski,&nbsp;Ninette Amariglio,&nbsp;Gideon Rechavi","doi":"10.1177/11769351221127875","DOIUrl":"https://doi.org/10.1177/11769351221127875","url":null,"abstract":"<p><strong>Motivation: </strong>Prediction of cancer outcome is a major challenge in oncology and is essential for treatment planning. Repositories such as The Cancer Genome Atlas (TCGA) contain vast amounts of data for many types of cancers. Our goal was to create reliable prediction models using TCGA data and validate them using an external dataset.</p><p><strong>Results: </strong>For 16 TCGA cancer type cohorts we have optimized a Random Forest prediction model using parameter grid search followed by a backward feature elimination loop for dimensions reduction. For each feature that was removed, the model was retrained and the area under the curve of the receiver operating characteristic (AUC-ROC) was calculated using test data. Five prediction models gave AUC-ROC bigger than 80%. We used Clinical Proteomic Tumor Analysis Consortium v3 (CPTAC3) data for validation. The most enriched pathways for the top models were those involved in basic functions related to tumorigenesis and organ development. Enrichment for 2 prediction models of the TCGA-KIRP cohort was explored, one with 42 genes (AUC-ROC = 0.86) the other is composed of 300 genes (AUC-ROC = 0.85). The most enriched networks for both models share only 5 network nodes: DMBT1, IL11, HOXB6, TRIB3, PIM1. These genes play a significant role in renal cancer and might be used for prognosis prediction and as candidate therapeutic targets.</p><p><strong>Availability and implementation: </strong>The prediction models were created and tested using Python SciKit-Learn package. They are freely accessible via a friendly web interface we called surviveAI at https://tinyurl.com/surviveai.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":" ","pages":"11769351221127875"},"PeriodicalIF":2.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/7a/c4/10.1177_11769351221127875.PMC9549197.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33503547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovery of Promising Inhibitors of Epidermal Growth Factor Receptor (EGFR), Human Epidermal Growth Factor Receptor 2 (HER2), Estrogen Receptor (ER), and Phosphatidylinositol-3-kinase a (PI3Ka) for Personalized Breast Cancer Treatment.","authors":"Precious A Akinnusi,&nbsp;Samuel O Olubode,&nbsp;Ayomide O Adebesin,&nbsp;Toluwani A Nana,&nbsp;Sidiqat A Shodehinde","doi":"10.1177/11769351221127862","DOIUrl":"https://doi.org/10.1177/11769351221127862","url":null,"abstract":"<p><p>Despite the rapid developments and advancements to improve treatments, Breast cancer remains one of the deadliest health challenges and the most frequently diagnosed tumor. One of the major problems with treatment is the unique difference that each cancerous cell exhibits. As a result, treatment of breast cancer has now become more personalized based on the specific features of the tumor such as overexpression of growth factor receptors (Epidermal growth factor receptor (EGFR), Human Epidermal Growth Factor Receptor 2 (HER2)), hormone receptors (Human Estrogen receptor alpha (ER)) and kinases involved in pivotal signaling associated with growth (Phosphatidylinositol 3-kinase (PI3K)). Several chemotherapeutic agents have been developed to curb the menace, but the associated adverse drug effects cannot be overlooked. To this end, this study employed a molecular modeling approach to identify novel compounds of natural origin that can potentially antagonize the receptors (mentioned above) associated with the pathophysiology of breast cancer and at the same time pose very little or no side effects. The results of the molecular model of biological interactions between a library of 118 anthocyanins and the binding pockets of the protein targets identified 5 compounds (Pelargonin, Delphinidin 3-<i>O</i>-rutinoside, Malvin, Cyanidin-3-(6-acetylglucoside), and Peonidin 3-<i>O</i>-rutinoside) with good binding affinities to the protein targets. Further MM-GBSA calculations returned high binding energies. The specific molecular interactions between the compounds and the targets were analyzed and reported herein. Also, all the compounds exhibited good pharmacokinetic profiles and are therefore recommended for further analyses as they could be explored as new treatment options for a broad range and personalized breast cancer treatments.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":" ","pages":"11769351221127862"},"PeriodicalIF":2.0,"publicationDate":"2022-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/69/89/10.1177_11769351221127862.PMC9536107.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33498319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcription Elongation Factor A (SII)-Like (TCEAL) Gene Family Member-TCEAL2: A Novel Prognostic Marker in Pan-Cancer.","authors":"Yu Sun,&nbsp;Jun Zhao","doi":"10.1177/11769351221126285","DOIUrl":"https://doi.org/10.1177/11769351221126285","url":null,"abstract":"<p><strong>Background: </strong>Cancer is the leading cause of death in the world. The mechanism is not fully elucidated and the therapeutic effect is also unsatisfactory. In our study, we aim to find new target gene in pan-cancer.</p><p><strong>Methods: </strong>Differently expressed genes (DEGs) was screened out in various types of cancers from GEO database. The expression of DEG (TCEAL2) in tumor cell lines, normal tissues and tumor tissues was calculated. Then the clinical characteristics, DNA methylation, tumor infiltration and gene enrichment of TCEAL2 was studied.</p><p><strong>Results: </strong>TCEAL2 expressions were down-regulated in most cancers. Its expression and methylation were positively or negatively associated with prognosis in different cancers. The tumor infiltration results revealed that TCEAL2 was significantly related with many immune cells especially NK cells and immune-related genes in majority cancers. Furthermore, tau protein and tubulin binding were involved in the molecular function mechanisms of TCEAL2.</p><p><strong>Conclusion: </strong>TCEAL2 may be a novel prognostic marker in different cancers and may affect tumor through immune infiltration.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":" ","pages":"11769351221126285"},"PeriodicalIF":2.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2e/f3/10.1177_11769351221126285.PMC9527986.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33489675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-omics Data Integration Model Based on UMAP Embedding and Convolutional Neural Network.","authors":"Bashier ElKarami,&nbsp;Abedalrhman Alkhateeb,&nbsp;Hazem Qattous,&nbsp;Lujain Alshomali,&nbsp;Behnam Shahrrava","doi":"10.1177/11769351221124205","DOIUrl":"https://doi.org/10.1177/11769351221124205","url":null,"abstract":"<p><strong>Introduction: </strong>Multi-omics data integration facilitates collecting richer understanding and perceptions than separate omics data. Various promising integrative approaches have been utilized to analyze multi-omics data for biomedical applications, including disease prediction and disease subtypes, biomarker prediction, and others.</p><p><strong>Methods: </strong>In this paper, we introduce a multi-omics data integration method that is constructed using the combination of gene similarity network (GSN) based on uniform manifold approximation and projection (UMAP) and convolutional neural networks (CNNs). The method utilizes UMAP to embed gene expression, DNA methylation, and copy number alteration (CNA) to a lower dimension creating two-dimensional RGB images. Gene expression is used as a reference to construct the GSN and then integrate other omics data with the gene expression for better prediction. We used CNNs to predict the Gleason score levels of prostate cancer patients and the tumor stage in breast cancer patients.</p><p><strong>Results: </strong>The model proposed near perfection with accuracy above 99% with all other performance measurements at the same level. The proposed model outperformed the state-of-art iSOM-GSN model that constructs the GSN map based on the self-organizing map.</p><p><strong>Conclusion: </strong>The results show that UMAP as an embedding technique can better integrate multi-omics maps into the prediction model than SOM. The proposed model can also be applied to build a multi-omics prediction model for other types of cancer.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":" ","pages":"11769351221124205"},"PeriodicalIF":2.0,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/0a/5e/10.1177_11769351221124205.PMC9523837.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40391609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular Modeling Studies of Natural Inhibitors of Androgen Signaling in Prostate Cancer.","authors":"Samuel O Olubode,&nbsp;Mutolib O Bankole,&nbsp;Precious A Akinnusi,&nbsp;Olayinka S Adanlawo,&nbsp;Kehinde I Ojubola,&nbsp;Daniel O Nwankwo,&nbsp;Onome E Edjebah,&nbsp;Ayomide O Adebesin,&nbsp;Abigail O Ayodele","doi":"10.1177/11769351221118556","DOIUrl":"https://doi.org/10.1177/11769351221118556","url":null,"abstract":"<p><p>Prostate cancer is the second most common disease in men and the sixth leading cause of death from cancer globally, with 20 million men expected to be affected by 2024 thus considered as chronic illness which requires immediate attention. As an androgen-dependent illness that relies on the androgen receptor for development and progression, inhibition of the androgen receptor can lead to a therapeutic solution, hence serving as a vital therapeutic target. This study focused on the computational analysis of the inhibitory potentials of Vitis vinifera, a reported plant with anti-cancer properties, against androgen receptor employing molecular docking, ADMET studies, Binding energy study, pharmacophore modeling, and molecular dynamics simulation approaches. After the investigation, it was determined that 5 compounds: cis-piceid, cis-astrigin, gallocatechin, phlorizin, and trans-polydatin, might be possible androgen receptor inhibitors since they had higher docking scores and ADMET qualities than compared standards, with cis-piceid being the best-predicted inhibitor.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":" ","pages":"11769351221118556"},"PeriodicalIF":2.0,"publicationDate":"2022-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/1d/9a/10.1177_11769351221118556.PMC9379963.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40624259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cancer Treatment Data in Central Cancer Registries: When Are Supplemental Data Needed?","authors":"Cathy J Bradley, Rifei Liang, Jagar Jasem, Richard C Lindrooth, Lindsay M Sabik, Marcelo C Perraillon","doi":"10.1177/11769351221112457","DOIUrl":"10.1177/11769351221112457","url":null,"abstract":"<p><strong>Background: </strong>We evaluated treatment concordance between the Colorado All Payer Claims Database (APCD) and the Colorado Central Cancer Registry (CCCR) to explore whether APCDs can augment registry data. We compare treatment concordance for breast cancer, an extensively studied site with an inpatient reporting source and select leukemias that are often diagnosed outpatient.</p><p><strong>Methods: </strong>We analyzed concordance by cancer type and treatment, patient demographics, reporting source, and health insurance, calculating the sensitivity, specificity, positive predictive values (PPV) and Kappa statistics. We estimated an adjusted logistic regression model to assess whether the APCD statistically significantly reports additional cancer-directed treatments.</p><p><strong>Results: </strong>Among women with breast cancer, 14% had chemotherapy treatments that were absent from the CCCR. Missing treatments were more common among women younger than age 50 (15%) and patients aged 75 and older (19%), rural residents (17%), and when the reporting source was outpatient (22%). Similar and more pronounced patterns for people with leukemia were observed. Concordance for oral treatments was lower for each cancer. Sensitivity and PPVs were high, with moderate Kappa statistics. The APCD was 5.3 percentage points less likely to identify additional treatments for breast cancer patients and 10 percentage points more likely to identify additional treatments when the reporting source was an outpatient facility.</p><p><strong>Conclusion: </strong>A robust data infrastructure is needed to investigate research questions that require population-level analyses, particularly for questions seeking to reduce health inequity and comparisons across payers, including Medicare Advantage and fee-for-service. APCD data are a step toward creating an infrastructure for cancer, particularly for patients who reside in rural areas and/or receive care from outpatient centers.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"21 ","pages":"11769351221112457"},"PeriodicalIF":2.4,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/69/58/10.1177_11769351221112457.PMC9340909.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9700819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global Gene Expression Regulation Mediated by TGFβ Through H3K9me3 Mark.","authors":"Ankit Naik,&nbsp;Nidhi Dalpatraj,&nbsp;Noopur Thakur","doi":"10.1177/11769351221115135","DOIUrl":"https://doi.org/10.1177/11769351221115135","url":null,"abstract":"<p><strong>Background: </strong>Epigenetic alterations play an important part in carcinogenesis. Different biological responses, including cell proliferation, migration, apoptosis, invasion, and senescence, are affected by epigenetic alterations in cancer. In addition, growth factors, such as transforming growth factor beta (TGFβ) are important regulators of tumorigenesis. Our understanding of the interplay between the epigenetic bases of tumorigenesis and growth factor signaling in tumorigenesis is rudimentary. Some studies suggest a link between TGFβ signaling and the heterochromatinizing histone mark H3K9me3. There is evidence for signal-dependent interactions between R-Smads and histone methyltransferases. However, the effects of TGFβ signaling on genome wide H3K9me3 landscape remains unknown. Our research examines TGFβ -induced genome-wide H3K9me3 in prostate cancer.</p><p><strong>Method: </strong>Chromatin-Immunoprecipitation followed by sequencing was performed to analyze genome-wide association of H3K9me3 epigenetic mark. DAVID Functional annotation tool was utilized to understand the involvement of different Biological Processes and Molecular Function. MEME-ChIP tool was also used to analyze known and novel DNA-binding motifs.</p><p><strong>Results: </strong>H3K9me3 occupancy appears to increase at intronic regions after short-term (6 hours) TGFβ stimulation and at distal intergenic regions during long-term stimulation (24 hours). We also found evidence for a possible association of SLC transporters with H3K9me3 mark in presence of TGFβ during tumorigenesis. No direct correlation was found between the occupancy of H3K9me3 mark and the expression of various genes. The epigenetic mechanisms-mediated regulation of gene expression by TGFβ was concentrated at promoters rich in SRY and FOXJ3 binding sites.</p><p><strong>Conclusion: </strong>Our results point toward a positive association of oncogenic function of TGFβ and the H3K9me3 mark and provide a context to the role of H3K9me3 in TGFβ-induced cell migration and cell adhesion. Interestingly, these functions of TGFβ through H3K9me3 mark regulation seem to depend on transcriptional activation in contrast to the conventionally known repressive nature of H3K9me3.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":" ","pages":"11769351221115135"},"PeriodicalIF":2.0,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/db/1b/10.1177_11769351221115135.PMC9340917.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40579598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Migrating to Long-Read Sequencing for Clinical Routine <i>BCR-ABL1</i> TKI Resistance Mutation Screening.","authors":"Wesley Schaal,&nbsp;Adam Ameur,&nbsp;Ulla Olsson-Strömberg,&nbsp;Monica Hermanson,&nbsp;Lucia Cavelier,&nbsp;Ola Spjuth","doi":"10.1177/11769351221110872","DOIUrl":"https://doi.org/10.1177/11769351221110872","url":null,"abstract":"<p><strong>Objective: </strong>The aim of this project was to implement long-read sequencing for BCR-ABL1 TKI resistance mutation screening in a clinical setting for patients undergoing treatment for chronic myeloid leukemia.</p><p><strong>Materials and methods: </strong>Processes were established for registering and transferring samples from the clinic to an academic sequencing facility for long-read sequencing. An automated analysis pipeline for detecting mutations was established, and an information system was implemented comprising features for data management, analysis and visualization. Clinical validation was performed by identifying BCR-ABL1 TKI resistance mutations by Sanger and long-read sequencing in parallel. The developed software is available as open source via GitHub at https://github.com/pharmbio/clamp.</p><p><strong>Results: </strong>The information system enabled traceable transfer of samples from the clinic to the sequencing facility, robust and automated analysis of the long-read sequence data, and communication of results from sequence analysis in a reporting format that could be easily interpreted and acted upon by clinical experts. In a validation study, all 17 resistance mutations found by Sanger sequencing were also detected by long-read sequencing. An additional 16 mutations were found only by long-read sequencing, all of them with frequencies below the limit of detection for Sanger sequencing. The clonal distributions of co-existing mutations were automatically resolved through the long-read data analysis. After the implementation and validation, the clinical laboratory switched their routine protocol from using Sanger to long-read sequencing for this application.</p><p><strong>Conclusions: </strong>Long-read sequencing delivers results with higher sensitivity compared to Sanger sequencing and enables earlier detection of emerging TKI resistance mutations. The developed processes, analysis workflow, and software components lower barriers for adoption and could be extended to other applications.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":" ","pages":"11769351221110872"},"PeriodicalIF":2.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9290162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40524984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}