Potential non-invasive biomarkers in tumor immune checkpoint inhibitor therapy: response and prognosis prediction DOI Creative Commons

Ruixia Song,

Fengsen Liu,

Yu Ping

et al.

Biomarker Research, Journal Year: 2023, Volume and Issue: 11(1)

Published: June 2, 2023

Abstract Immune checkpoint inhibitors (ICIs) have dramatically enhanced the treatment outcomes for diverse malignancies. Yet, only 15–60% of patients respond significantly. Therefore, accurate responder identification and timely ICI administration are critical issues in tumor therapy. Recent rapid developments at intersection oncology, immunology, biology, computer science provided an abundance predictive biomarkers efficacy. These can be invasive or non-invasive, depending on specific sample collection method. Compared with markers, a host non-invasive markers been confirmed to superior availability accuracy efficacy prediction. Considering outstanding advantages dynamic monitoring immunotherapy response potential widespread clinical application, we review recent research this field aim contributing who may derive greatest benefit from

Language: Английский

From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment DOI Creative Commons
Kyle Swanson, Eric Q. Wu, Angela Zhang

et al.

Cell, Journal Year: 2023, Volume and Issue: 186(8), P. 1772 - 1791

Published: March 10, 2023

Language: Английский

Citations

263
deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing DOI Open Access
J. Logeshwaran, Nirmal Adhikari,

Sidharth Srikant Joshi

et al.

International Journal of Health Sciences, Journal Year: 2022, Volume and Issue: unknown, P. 9364 - 9375

Published: July 18, 2022

In general, the various medical systems currently available provide insights into changes in tumor genome of patients with sequencing. Most DNA sequencing can also be referred to as genetic specification or testing. The sequence results help clinical decision-making develop a personalized cancer treatment plan based on molecular characteristics rather than one-size-fits-all approach. plays major role research. this paper, an improved method machine learning was proposed analyze and patterns human gene. This analyzes circulatory problems different types for analysis public domain. It constantly monitors large data sets sequences calculate size location. allows doctor get accurate report type it cause patient. Analysis these datasets gene reveals that makeup each patient is no two cancers are same.

Language: Английский

Citations

139
Deep whole-genome ctDNA chronology of treatment-resistant prostate cancer DOI
Cameron Herberts, Matti Annala, Joonatan Sipola

et al.

Nature, Journal Year: 2022, Volume and Issue: 608(7921), P. 199 - 208

Published: July 20, 2022

Language: Английский

Citations

117
Genomic profiling for clinical decision making in lymphoid neoplasms DOI Open Access
Laurence de Leval, Ash A. Alizadeh, P. Leif Bergsagel

et al.

Blood, Journal Year: 2022, Volume and Issue: 140(21), P. 2193 - 2227

Published: Aug. 24, 2022

Language: Английский

Citations

109
Epigenetic analysis of cell-free DNA by fragmentomic profiling DOI Creative Commons
Qing Zhou, Guannan Kang, Peiyong Jiang

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2022, Volume and Issue: 119(44)

Published: Oct. 26, 2022

Cell-free DNA (cfDNA) fragmentation patterns contain important molecular information linked to tissues of origin. We explored the possibility using predict cytosine-phosphate-guanine (CpG) methylation cfDNA, obviating use bisulfite treatment and associated risks degradation. This study investigated cfDNA cleavage profile surrounding a CpG (i.e., within an 11-nucleotide [nt] window) analyze methylation. The proportion across positions window appeared nonrandom exhibited correlation with status. mean was ∼twofold higher at cytosine methylated CpGs than unmethylated ones in healthy controls. In contrast, rapidly decreased 1-nt position immediately preceding CpGs. Such differential cleavages resulted characteristic change relative presentations CGN NCG motifs 5′ ends, where N represented any nucleotide. CGN/NCG motif ratios were correlated levels tissue-specific (e.g., placenta or liver) (Pearson’s absolute r > 0.86). profiles thus informative for tissue-of-origin analyses. Using CG-containing end motifs, we achieved area under receiver operating curve (AUC) 0.98 differentiating patients without hepatocellular carcinoma enhanced positive predictive value nasopharyngeal screening (from 19.6 26.8%). Furthermore, elucidated feasibility deduce single resolution deep learning algorithm AUC 0.93. FRAGmentomics-based Methylation Analysis (FRAGMA) presents many possibilities noninvasive prenatal, cancer, organ transplantation assessment.

Language: Английский

Citations

70
Risk of Second Tumors and T-Cell Lymphoma after CAR T-Cell Therapy DOI
Mark Hamilton, Takeshi Sugio, Troy Noordenbos

et al.

New England Journal of Medicine, Journal Year: 2024, Volume and Issue: 390(22), P. 2047 - 2060

Published: June 12, 2024

The risk of second tumors after chimeric antigen receptor (CAR) T-cell therapy, especially the neoplasms related to viral vector integration, is an emerging concern.

Language: Английский

Citations

67
Bridging biological cfDNA features and machine learning approaches DOI Creative Commons
Tina Moser, Stefan Kühberger, Isaac Lazzeri

et al.

Trends in Genetics, Journal Year: 2023, Volume and Issue: 39(4), P. 285 - 307

Published: Feb. 13, 2023

Liquid biopsies (LBs), particularly using circulating tumor DNA (ctDNA), are expected to revolutionize precision oncology and blood-based cancer screening. Recent technological improvements, in combination with the ever-growing understanding of cell-free (cfDNA) biology, enabling detection tumor-specific changes extremely high resolution new analysis concepts beyond genetic alterations, including methylomics, fragmentomics, nucleosomics. The interrogation a large number markers complexity data render traditional correlation methods insufficient. In this regard, machine learning (ML) algorithms increasingly being used decipher disease- tissue-specific signals from cfDNA. Here, we review recent insights into biological ctDNA features how these incorporated sophisticated ML applications.

Language: Английский

Citations

65
DNA methylation analysis explores the molecular basis of plasma cell-free DNA fragmentation DOI Creative Commons
Yunyun An, Xin Zhao, Ziteng Zhang

et al.

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Jan. 18, 2023

Plasma cell-free DNA (cfDNA) are small molecules generated through a non-random fragmentation procedure. Despite commendable translational values in cancer liquid biopsy, however, the biology of cfDNA, especially principles cfDNA fragmentation, remains largely elusive. Through orientation-aware analyses patterns against nucleosome structure and integration with multidimensional functional genomics data, here we report methylation - nuclease preference cutting end size distribution axis, demonstrating role as molecular regulator fragmentation. Hence, low-level could increase accessibility alter activities nucleases during which further leads to variation sites cfDNA. We develop ending preference-based metric for diagnosis, whose performance has been validated by multiple pan-cancer datasets. Our work sheds light on basis towards broader applications biopsy.

Language: Английский

Citations

53
Distinct Hodgkin lymphoma subtypes defined by noninvasive genomic profiling DOI
Stefan Alig, Mohammad Shahrokh Esfahani,

Andrea Garofalo

et al.

Nature, Journal Year: 2023, Volume and Issue: 625(7996), P. 778 - 787

Published: Dec. 11, 2023

Language: Английский

Citations

50
Cell-Free DNA Fragmentomics: The Novel Promising Biomarker DOI Open Access
Ting Qi, Min Pan, Huajuan Shi

et al.

International Journal of Molecular Sciences, Journal Year: 2023, Volume and Issue: 24(2), P. 1503 - 1503

Published: Jan. 12, 2023

Cell-free DNA molecules are released into the plasma via apoptotic or necrotic events and active release mechanisms, which carry genetic epigenetic information of its origin tissues. However, cfDNA is mixture various cell fragments, efficient enrichment fragments with diagnostic value remains a great challenge for application in clinical setting. Evidence from recent years shows that fragmentomics' characteristics differ normal diseased individuals without need to distinguish source makes it promising novel biomarker. Moreover, fragmentomics can identify tissue origins by inferring information. Thus, further insights shed light on fragmentation mechanisms during physiological pathological processes diseases enhance our ability take advantage as molecular tool. In this review, we focus fragment potential application, such length, end motifs, jagged ends, preferred coordinates, well nucleosome footprints, open chromatin region, gene expression inferred pattern across genome. Furthermore, summarize methods deducing fragmentomics.

Language: Английский

Citations

43