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The Evolution of Anticancer Therapies
Published in David E. Thurston, Ilona Pysz, Chemistry and Pharmacology of Anticancer Drugs, 2021
Finally, signature-based approaches rely on gene signatures derived from disease-based omics data from patients before and after treatment to discover unexplored off-target or unidentified disease mechanisms. As microarray and next-generation sequencing techniques advance, vast amounts of genomics data relevant to Drug Repurposing are being accumulated which can be used to explore unknown disease-altering pathways. Examples of databases available for obtaining genomics data include NCBI-GEO, SRA (Sequence Read Archive), the CMAP (Connectivity Map), and CCLE (Cancer Cell Line Encyclopedia). As the efficacy and toxicity profile of a drug is usually associated with unique gene signatures in individuals, a gene signature database is helpful for Drug Repurposing through computational methods.
Multiparameter Gene Expression Assays and Breast Cancer Management
Published in Sherry X. Yang, Janet E. Dancey, Handbook of Therapeutic Biomarkers in Cancer, 2021
Della Makower, Joseph A. Sparano
While the majority of the 70-gene signature validation trials focused on the assay’s prognostic ability, some studies evaluated the test’s ability to predict chemotherapy benefit. Knauer et al. assessed the ability of the MammaPrint assay to predict benefit from adjuvant chemotherapy in a cohort derived from seven previously reported studies, comprised of 541 patients with operable breast cancer and up to three involved axillary nodes who were treated either with endocrine therapy alone or with chemotherapy plus endocrine therapy. Receipt of adjuvant chemotherapy was associated with significant improvements in breast cancer specific survival (BCSS) (94% vs. 81%, p = 0.02) and distant disease free survival (DDFS) (88% vs. 76%, p < 0.01) at 5 years in the 289 patients with poor prognosis signatures. No difference in BCSS or DDFS was seen in the 252 patients with good prognosis signatures. BCSS was 97% for low-risk patients receiving endocrine therapy alone, compared with 99% for those receiving chemotherapy, and DDFS was 93% for endocrine therapy alone vs. 99% for chemoendocrine therapy (p = 0.20) [37].
Hereditary Breast and Ovarian Cancer
Published in Dongyou Liu, Handbook of Tumor Syndromes, 2020
Gene expression profiling investigates on the activity of multiple genes (21 in Oncotype DX test, 70 in MammaPrint test, and 58 in Prosigna breast cancer prognostic gene signature assay or PAM50 test), which consist of not only BRCA1/2, but also ATM, CHEK2, and PALB2, etc. This helps predict likelihood of cancer spread to other parts of the body or recurrence as well as potential response to chemotherapy.
Identification and validation of a novel prognostic circadian rhythm-related gene signature for stomach adenocarcinoma
Published in Chronobiology International, 2023
Lei Qian, Xiaochen Ding, Xiaoyan Fan, Shisen Li, Yihuan Qiao, Xiaoqun Zhang, Jipeng Li
Despite the tremendous advances in conventional treatment, the prognosis of patients with STAD remains poor, with 30–50% of patients experiencing recurrence within 5 years after surgical treatment and adjuvant chemotherapy (Kamangar et al. 2006). In contrast, patients with early STAD have a good prognosis after radical gastrectomy, with a 5-year survival rate of more than 97% (Zheng et al. 2021). Therefore, early diagnosis and treatment are essential to prolong the survival of these patients. In this study, we identified six circadian rhythm-related genes (GNA11, PER1, SOX14, EZH2, MAGED1, and NR1D1) and established a gene-base prognostic tool for STAD. This newly proposed gene signature showed an acceptable predicting ability in two independent datasets. Noteworthily, genes associated with the circadian rhythm were substantially correlated with clinical outcomes (grade, sex, and M stage) in patients with STAD. In addition, the circadian rhythm-related gene signature was significantly associated with MAPK and Notch signaling pathways, which are well-known contributors for cancer progression. Hence, the prognostic model herein described, which was based on six circadian rhythm-associated genes, may represent a valuable tool for clinical decision-making and personalized treatment for STAD.
Transcriptomics for radiation biodosimetry: progress and challenges
Published in International Journal of Radiation Biology, 2023
With the increasing number of published radiation biodosimetry studies and the public availability of primary transcriptomic data in repositories such as the Gene Expression Omnibus (Barrett et al. 2009) and ArrayExpress (Brazma et al. 2003), several systematic literature reviews and meta-analyses have derived consensus human signatures for radiation biodosimetry. Despite using some of the same data sets for gene selection, their different approaches resulted in signatures with only moderate overlap (Figure 2). The three studies that used correlation with dose as one of their gene selection criteria (Li et al. 2017; Lacombe et al. 2018; Ghandhi et al. 2019a) had the most genes in common with each other, while the two meta-analyses (Zhao et al. 2018; Ghandhi et al. 2019a) that built classification or dose reconstruction models and tested them against independent data sets as part of the gene selection process included the most genes not selected by other approaches. Differences in the specific genes and models used by different groups are not unexpected in the field of gene signature selection, and should not undermine confidence in the robustness of individual approaches (Ein-Dor et al. 2006). Other factors besides those currently being used in gene selection may need to be considered, however. For instance, comparison of the genes from these four studies with the analysis by Park et al. (2017) shows that between 42 and 77% of the consensus signature genes did not show cross-species correlation between humans and NHPs. This may pose a potential obstacle for in vivo validation studies using a NHP model.
Managing sepsis in the era of precision medicine: challenges and opportunities
Published in Expert Review of Anti-infective Therapy, 2022
Richard R. Watkins, Robert A. Bonomo, Jordi Rello
Recent advances in computational and molecular methods have allowed further progress in technology for molecular phenotyping in patients with sepsis. For example, Sweeney et al. performed a unified clustering analysis on transcriptomic samples (n = 700) in 14 datasets to reveal three subtypes, which they termed ‘inflammopathic,’ ‘adaptive,’ and ‘coagulopathic’ [25]. They then validated these subtypes on nine independent datasets from five different countries (n = 600). In both the discovery and validation data, the adaptive subtype was associated with a lower clinical severity and lower mortality rate while the coagulopathic subtype was associated with higher mortality and clinical coagulopathy. Another study used a 140-gene signature profile and found four molecular phenotypes that shared a common change in gene expression relative to healthy patients [26]. These studies suggest that gene signatures could be an effective tool to identify patients at high risk of mortality due to sepsis in future clinical trials [27]. Finally, single nucleotide polymorphisms (SNPs) have been identified in the human genome that affect susceptibility to infection and alter the expression of the host's response during sepsis [28]. The interacting genomic elements induced by sepsis are not well understood and will require further advancements in computational biology to maximize their potential in precision medicine.