Circulating cell-free DNA in breast cancer: size profiling, levels, and methylation patterns lead to prognostic and predictive classifiers

Παναγοπούλου, Μαρία; Karaglani, Makrina; Balgkouranidou, Ioanna; Biziota, Eirini; Koukaki, Triantafillia; Karamitrousis, Evaggelos; Nena, Evangelia; Tsamardinos, Ioannis; Kolios, George; Lianidou, Evi; Kakolyris, Stylianos; Chatzaki, Εkaterini

doi:10.1038/s41388-018-0660-y

Cited by 118 publications

(106 citation statements)

References 48 publications

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“…It shields against typical methodological pitfalls in data analysis that lead to overfitting and overestimating performance and therefore to misleading results. The novel machine learning algorithms of JADBIO have been validated by the machine learning and statistical community [10][11][12][13][14][15][16][17][18][19][20] , while the system has produced novel results in nanomaterial property predictions 21 , suicide prediction 22 , speech classification 23 , bank failure prediction 24 , function protein prediction 25 , and breast cancer prognosis and drug response prediction 26 , to name a few.…”

Section: Introductionmentioning

confidence: 99%

Just Add Data: Automated Predictive Modeling and BioSignature Discovery

Tsamardinos

Charonyktakis²,

Lakiotaki

et al. 2020

Preprint

Self Cite

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Fully automated machine learning, statistical modelling, and artificial intelligence for predictive modeling is becoming a reality, giving rise to the field of Automated Machine Learning (Au-toML). AutoML systems promise to democratize data analysis to non-experts, drastically increase productivity, improve replicability of the statistical analysis, facilitate the interpretation of results, and shield against common methodological analysis pitfalls. We present the basic ideas and principles of Just Add Data Bio (JADBIO), an AutoML technology applicable to the lowsample, high-dimensional omics data that arise in translational medicine and bioinformatics applications. In addition to predictive and diagnostic models ready for clinical use, JADBIO also returns the corresponding biosignatures, i.e., minimal-size subsets of biomarkers that are jointly predictive of the outcome of interest. A use-case on thymic epithelial tumors is presented, along with an extensive evaluation on 374 public biological datasets. Results show that long-standing challenges with overfitting and overestimation of complex non-linear machine learning pipelines on high-dimensional, low small sample data can be overcome.

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Section: Introductionmentioning

confidence: 99%

Just Add Data: Automated Predictive Modeling and BioSignature Discovery

Tsamardinos

Charonyktakis²,

Lakiotaki

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, RASSF1A me and PITX2 me were found to be independent biomarkers of poor OS and RASSF1A me together with lymph node and ER status indicated poor distant DFS in BrC patients' ccfDNA [100]. Recently, a study analyzing methylation patterns of several genes in ccfDNA showed that BrC patients with positive SOX17 me and WNT5A me displayed shorter OS, whereas KLK10 me associated with higher number of relapses and shorter disease-free interval [101]. Apart from being a detection biomarker, SOX17 me associated with lymph node metastasis, poor DFS and OS in plasma samples from BrC's patients [102].…”

Section: Prognosis Prediction and Monitoringmentioning

confidence: 99%

DNA Methylation-Based Testing in Liquid Biopsies as Detection and Prognostic Biomarkers for the Four Major Cancer Types

Constâncio

Nunes

Henrique

et al. 2020

Cells

138

117

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Lung, breast, colorectal, and prostate cancers are the most incident worldwide. Optimal population-based cancer screening methods remain an unmet need, since cancer detection at early stages increases the prospects of successful and curative treatment, leading to a lower incidence of recurrences. Moreover, the current parameters for cancer patients’ stratification have been associated with divergent outcomes. Therefore, new biomarkers that could aid in cancer detection and prognosis, preferably detected by minimally invasive methods are of major importance. Aberrant DNA methylation is an early event in cancer development and may be detected in circulating cell-free DNA (ccfDNA), constituting a valuable cancer biomarker. Furthermore, DNA methylation is a stable alteration that can be easily and rapidly quantified by methylation-specific PCR methods. Thus, the main goal of this review is to provide an overview of the most important studies that report methylation biomarkers for the detection and prognosis of the four major cancers after a critical analysis of the available literature. DNA methylation-based biomarkers show promise for cancer detection and management, with some studies describing a “PanCancer” detection approach for the simultaneous detection of several cancer types. Nonetheless, DNA methylation biomarkers still lack large-scale validation, precluding implementation in clinical practice.

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“…However, because even after significant sample concentration, white blood cells (WBCs) contamination increases the cost/benefit ratio of this technique, CTCs detection strategies based on NGS still require isolation of single CTCs 73‐75 . Interestingly, studies on ctDNA have recently implied that methylation pattern analyses might be more informative than mutation screening, at least in the context of early disease detection, 76‐78 suggesting that in the future, CTCs epigenome might be a promising parameter to account for.…”

Section: Marker‐based Approaches For Ctcs Identificationmentioning

confidence: 99%

Perspectives on liquid biopsy for label‐free detection of “circulating tumor cells” through intelligent lab‐on‐chips

Miccio

Cimmino

Kurelac

et al. 2020

VIEW

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Circulating tumor cells (CTCs) are rare tumor cells released from primary, metastatic, or recurrent tumors in the peripheral blood of cancer patients. CTCs isolation from peripheral blood and their molecular characterization represent a new marker in cancer screening, a diagnostic tool called “liquid biopsy” (LB). Compared to traditional tissue biopsy that is invasive and does not reveal tumor heterogeneity, LB is noninvasive and reflects in “real‐time” tumor dynamism and drug sensitivity. In the frame of LB, a new paradigm based on single‐cell and label‐free analysis based on morphological analysis is emerging. Here, we review the latest research developments in this emerging vision of LB. In particular, we survey and discuss recent improvements in microfluidics, imaging label‐free diagnosis and cell classification by artificial intelligence and how to combine them to realize an intelligent platform based on lab‐on‐chip technology. This prospect appears to open up promising and intriguing new scenarios for cancer management through single‐cell analysis that will revolutionize the future of early cancer diagnosis and therapeutic choice with disruptive impact on the society.

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Circulating cell-free DNA in breast cancer: size profiling, levels, and methylation patterns lead to prognostic and predictive classifiers

Cited by 118 publications

References 48 publications

Just Add Data: Automated Predictive Modeling and BioSignature Discovery

Just Add Data: Automated Predictive Modeling and BioSignature Discovery

DNA Methylation-Based Testing in Liquid Biopsies as Detection and Prognostic Biomarkers for the Four Major Cancer Types

Perspectives on liquid biopsy for label‐free detection of “circulating tumor cells” through intelligent lab‐on‐chips

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