Vashisht Vishakha, Vashisht Ashutosh, Mondal Ashis K, Woodall Jana, Kolhe Ravindra
Department of Pathology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA.
Curr Issues Mol Biol. 2024 Nov 6;46(11):12527-12549. doi: 10.3390/cimb46110744.
Next-generation sequencing (NGS) has revolutionized personalized oncology care by providing exceptional insights into the complex genomic landscape. NGS offers comprehensive cancer profiling, which enables clinicians and researchers to better understand the molecular basis of cancer and to tailor treatment strategies accordingly. Targeted therapies based on genomic alterations identified through NGS have shown promise in improving patient outcomes across various cancer types, circumventing resistance mechanisms and enhancing treatment efficacy. Moreover, NGS facilitates the identification of predictive biomarkers and prognostic indicators, aiding in patient stratification and personalized treatment approaches. By uncovering driver mutations and actionable alterations, NGS empowers clinicians to make informed decisions regarding treatment selection and patient management. However, the full potential of NGS in personalized oncology can only be realized through bioinformatics analyses. Bioinformatics plays a crucial role in processing raw sequencing data, identifying clinically relevant variants, and interpreting complex genomic landscapes. This comprehensive review investigates the diverse NGS techniques, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and single-cell RNA sequencing (sc-RNA-Seq), elucidating their roles in understanding the complex genomic/transcriptomic landscape of cancer. Furthermore, the review explores the integration of NGS data with bioinformatics tools to facilitate personalized oncology approaches, from understanding tumor heterogeneity to identifying driver mutations and predicting therapeutic responses. Challenges and future directions in NGS-based cancer research are also discussed, underscoring the transformative impact of these technologies on cancer diagnosis, management, and treatment strategies.
下一代测序（NGS）通过对复杂的基因组格局提供非凡的见解，彻底改变了个性化肿瘤治疗。NGS提供全面的癌症分析，使临床医生和研究人员能够更好地理解癌症的分子基础，并据此制定治疗策略。基于通过NGS鉴定出的基因组改变的靶向治疗，在改善各种癌症类型患者的预后、规避耐药机制和提高治疗效果方面已显示出前景。此外，NGS有助于识别预测性生物标志物和预后指标，辅助患者分层和个性化治疗方法。通过揭示驱动突变和可操作的改变，NGS使临床医生能够在治疗选择和患者管理方面做出明智的决策。然而，NGS在个性化肿瘤学中的全部潜力只有通过生物信息学分析才能实现。生物信息学在处理原始测序数据、识别临床相关变异以及解读复杂的基因组格局方面发挥着关键作用。这篇综述全面研究了多种NGS技术，包括全基因组测序（WGS）、全外显子组测序（WES）和单细胞RNA测序（sc-RNA-Seq），阐明了它们在理解癌症复杂的基因组/转录组格局中的作用。此外，该综述探讨了将NGS数据与生物信息学工具整合，以促进个性化肿瘤学方法，从了解肿瘤异质性到识别驱动突变和预测治疗反应。还讨论了基于NGS的癌症研究中的挑战和未来方向，强调了这些技术对癌症诊断、管理和治疗策略的变革性影响。
