Artificial intelligence in genomics - improving detection of fusion genes in soft tissue sarcomas

Sep 29, 2026

Integration of artificial intelligence (AI) into clinical workflows for soft tissue sarcomas (STS) advances precision oncology as diagnostic complexity increases. AI-driven genomics, especially fusion gene detection, improves sensitivity, speed, and accuracy, strengthening patient stratification and supporting targeted therapy decisions. Using AI with multi-omic data—transcriptomic, epigenomic, and imaging—provides a holistic view of sarcoma biology for earlier diagnosis and more personalized treatment. Realizing these benefits requires global collaboration, standardized protocols, and ongoing clinician education to enable safe clinical adoption.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages2

Reading Time5 min

AccessFree to download

Abstract

Letter to the Editor

Significance of fusion genes in STS

Limitations of traditional diagnostic methods

Advances in next-generation sequencing and AI

International integration efforts and examples

Challenges and requirements for clinical adoption

Conclusion

Letter to the Editor Artificial intelligence in genomics: improvingdetection of fusion genes in soft tissue sarcomas Muhammad Khizar, MDa , Muhammad Zaib, MDa , Savera Ejaz, MBBSb , Hasibullah Aminpoor, MDc ,*,Hasiba Karimi, MDd # Abstract The integration of artificial intelligence (AI) into clinical workflows for soft tissue sarcomas (STS) marks a major step forward inprecision oncology. As diagnostic complexity grows, AI-driven genomics – particularly in detecting fusion genes – enhancessensitivity, speed, and accuracy, improving patient stratification and guiding targeted therapies. The convergence of AI withmulti-omic datasets, including transcriptomic, epigenomic, and imaging data, offers a holistic view of sarcoma biology,enabling earlier detection and more personalized treatment. Achieving these benefits, however, requires global collaboration,standardized protocols, and continuous clinician education. Ultimately, AI’s transformative potential in STS managementdepends not only on technological innovation but also on the establishment of robust frameworks for its clinical adoption.Keywords: artificial intelligence, fusion genes, genomics, soft tissue sarcomas Dear Editor, Soft tissue sarcomas (STS) represent a heterogeneous group ofmesenchymal malignancies with variable prognosis andtherapeu -tic responsiveness. A significant subset of STS is driven by specificfusion genes, such as SS18-SSX1 in synovial sarcoma andEWSR1-FLI1 in Ewing sarcoma, which serve as both diagnosticbiomarkers and potential therapeutic targets[1,2] . Early and accu -rate detection of these fusionsis critical for precise classification,prognostication, and treatment selection. Traditional diagnosticmethods, including fluorescence in situ hybridization (FISH) andreverse transcription polymerase chain reaction (RT-PCR), areconstrained by prior knowledge of fusion partners and may over -look rare or novel rearrangements, highlighting a pressing needfor more comprehensive approaches[3] . Recent advancesin next-generation sequencing (NGS) technolo -gies have revolutionized the detection of fusion genes in STS.Targeted RNA panels, whole-transcriptome sequencing, and inte -grative bioinformatic pipelines now enable the identification ofboth canonical and previously uncharacterized fusions, enhancing aFaculty of Medicine, Georgian American University, Manifest Medical ResearchCo., Tbilisi, Georgia, bMedicine Department, Shaheed Mohtarma Benazir BhuttoMedical College Karachi, Karachi, Pakistan, cFaculty of Medicine, Kabul Universityof Medical Sciences “Abu Ali Ibn Sina”, Kabul, Afghanistan and dFaculty ofMedicine, Bezmialem Vakif University, Istanbul, Turkey Sponsorships or competing interests that maybe relevant to content are disclosedatthe end of this article. *Corresponding author. Address: Faculty of Medicine, Kabul University of MedicalSciences “Abu Ali Ibn Sina”, Ata Turk Avenue, Jamal Mena, 3rd District, Kabul1001, Afghanistan. Tel.: +93730097097. E-mail: hasibaminpoor786@gmail.com(H. Aminpoor). Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc. This isanopen access article distributed under the Creative Commons Attribution License 4.0(CCBY), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work is properly cited. Annals of Medicine & Surgery (2026) 88:935–936Received 14 October 2025; Accepted 17 October 2025Published online 1 December 2025http://dx.doi.org/10.1097/MS9.0000000000004206 diagnostic sensitivity and informing precision oncology strategies[4] .However, the increasing complexity and volume of genomic datanecessitate computational tools capable of accurate interpreta -tion. AI approaches, including machine learning and deep learningmodels, have demonstrated remarkable utility in parsinghigh-dimensional sequencing data, distinguishing driver fusionsfrom passenger alterations, and predicting functional impact[5,6] . Internationally, several coun

FAQs

How does AI-driven genomics improve fusion gene detection in soft tissue sarcomas?

It enhances diagnostic sensitivity, speed, and accuracy, improving patient stratification and informing targeted therapeutic strategies.

What are the limitations of traditional fusion gene diagnostic methods mentioned in the letter?

FISH and RT-PCR rely on prior knowledge of fusion partners and may miss rare or novel rearrangements.

Why is clinical adoption of AI in STS genomics challenging?

AI algorithms require large, high-quality datasets and rigorous validation across diverse populations, along with regulatory guidance, standardized reporting, and clinician education.

Sep 29, 2026

Uploaded by eBook King