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Li K, Guo Z, Li F, Lu S, Zhang M, Gong Y, Tan J, Sheng C, Hao W, Yang X. Non-invasive determination of gene expression in placental tissue using maternal plasma cell-free DNA fragmentation characters. Gene 2024; 928:148789. [PMID: 39047956 DOI: 10.1016/j.gene.2024.148789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 07/04/2024] [Accepted: 07/19/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND The expression profiles of placental genes are crucial for understanding the pathogenesis of fetal development and placental-origin pregnancy syndromes. However, owing to ethical limitations and the risks of puncture sampling, it is difficult to obtain placental tissue samples repeatedly, continuously, multiple times, or in real time. Establishing a non-invasive method for predicting placental gene expression profiles through maternal plasma cell-free DNA (cfDNA) sequencing, which carries information about the source tissue and gene expression, can potentially solve this problem. METHODS Peripheral blood and placental samples were collected simultaneously from pregnant women who underwent cesarean section. Deep sequencing was performed on the separated plasma cfDNA and single-cell sequencing was performed on peripheral blood mononuclear cells (PBMC), chorioamniotic membranes (CAM), placental villi (PV), and decidua basalis (DB). The aggregation of corresponding information for each gene was combined with the transcriptome of PBMCs and a differential resolution transcriptome of the placenta. This combined information was then utilized for the construction of gene expression prediction models. After training, all models evaluated the correlation between the predicted and actual gene expression levels using external test set data. RESULTS From five women, more than 20 million reads were obtained using deep sequencing for plasma cfDNA; PBMCs obtained 32,401 single-cell expression profiles; and placental tissue obtained 156,546 single-cell expression profiles (59,069, 44,921, and 52,556 for CAM, PV, and DB, respectively). The cells in the PBMC and placenta were clustered and annotated into five and eight cell types, respectively. A "DEPICT" gene expression prediction model was successfully constructed using deep neural networks. The predicted correlation coefficients were 0.75 in PBMCs, 0.84 in the placenta, and 0.78, 0.80, and 0.77 in CAM, BP, and PV respectively, and greater than 0.68 in different cell lines in the placenta. CONCLUSION The DEPICT model, which can noninvasively predict placental gene expression profiles based on maternal plasma cfDNA fragmentation characteristics, was constructed to overcome the limitation of the inability to obtain real-time placental gene expression profiles and to improve research on noninvasive prediction of placental origin pregnancy syndrome.
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Affiliation(s)
- Kun Li
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Zhiwei Guo
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Fenxia Li
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Shijing Lu
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Min Zhang
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou 510515, Guangdong, China
| | - Yuyan Gong
- Beijing SeekGene BioSciences Co., Ltd, Beijing, China
| | - Jiayu Tan
- ICU of Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan 528403, China
| | - Chao Sheng
- Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong, China.
| | - Wenbo Hao
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou 510515, Guangdong, China.
| | - Xuexi Yang
- Institute of Antibody Engineering, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou 510515, Guangdong, China.
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2
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Magbanua MJM, Ahmed Z, Sayaman RW, Brown Swigart L, Hirst GL, Yau C, Wolf DM, Li W, Delson AL, Perlmutter J, Pohlmann P, Symmans WF, Yee D, Hylton NM, Esserman LJ, DeMichele AM, Rugo HS, van 't Veer LJ. Cell-free DNA Concentration as a Biomarker of Response and Recurrence in HER2-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy. Clin Cancer Res 2024; 30:2444-2451. [PMID: 38470545 PMCID: PMC11147708 DOI: 10.1158/1078-0432.ccr-23-2928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/08/2023] [Accepted: 03/08/2024] [Indexed: 03/14/2024]
Abstract
PURPOSE We previously demonstrated the clinical significance of circulating tumor DNA (ctDNA) in patients with HER2-negative breast cancer receiving neoadjuvant chemotherapy (NAC). Here, we compared its predictive and prognostic value with cell-free DNA (cfDNA) concentration measured in the same samples from the same patients. EXPERIMENTAL DESIGN 145 patients with hormone receptor (HR)-positive/HER2-negative and 138 triple-negative breast cancer (TNBC) with ctDNA data from a previous study were included in the analysis. Associations of serial cfDNA concentration with residual cancer burden (RCB) and distant recurrence-free survival (DRFS) were examined. RESULTS In TNBC, we observed a modest negative correlation between cfDNA concentration 3 weeks after treatment initiation and RCB, but none of the other timepoints showed significant correlation. In contrast, ctDNA was significantly positively correlated with RCB at all timepoints (all R > 0.3 and P < 0.05). In the HR-positive/HER2-negative group, cfDNA concentration did not associate with response to NAC, but survival analysis showed that high cfDNA shedders at pretreatment had a significantly worse DRFS than low shedders (hazard ratio, 2.12; P = 0.037). In TNBC, the difference in survival between high versus low cfDNA shedders at all timepoints was not statistically significant. In contrast, as previously reported, ctDNA at all timepoints was significantly correlated with DRFS in both subtypes. CONCLUSIONS In TNBC, cfDNA concentrations during therapy were not strongly correlated with response or prognosis. In the HR-positive/HER2-negative group, pretreatment cfDNA concentration was prognostic for DRFS. Overall, the predictive and prognostic value of cfDNA concentration was more limited than that of ctDNA.
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Affiliation(s)
- Mark Jesus M Magbanua
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, California
| | - Ziad Ahmed
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, California
| | - Rosalyn W Sayaman
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, California
| | - Lamorna Brown Swigart
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, California
| | - Gillian L Hirst
- Department of Surgery, University of California San Francisco, San Francisco, California
| | - Christina Yau
- Department of Surgery, University of California San Francisco, San Francisco, California
| | - Denise M Wolf
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, California
| | - Wen Li
- Department of Radiology, University of California San Francisco, San Francisco, California
| | - Amy L Delson
- Breast Science Advocacy Core, University of California San Francisco, San Francisco, California
| | - Jane Perlmutter
- Breast Science Advocacy Core, University of California San Francisco, San Francisco, California
| | - Paula Pohlmann
- Department of Breast Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - W Fraser Symmans
- Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Douglas Yee
- Division of Hematology, Oncology, and Transplantation, University of Minnesota, Minneapolis, Minnesota
| | - Nola M Hylton
- Department of Radiology, University of California San Francisco, San Francisco, California
| | - Laura J Esserman
- Department of Surgery, University of California San Francisco, San Francisco, California
| | - Angela M DeMichele
- Division of Hematology/Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Hope S Rugo
- Division of Hematology/Oncology, University of California San Francisco, San Francisco, California
| | - Laura J van 't Veer
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, California
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3
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Jacob DR, Guiblet WM, Mamayusupova H, Shtumpf M, Ciuta I, Ruje L, Gretton S, Bikova M, Correa C, Dellow E, Agrawal SP, Shafiei N, Drobysevskaja A, Armstrong CM, Lam JDG, Vainshtein Y, Clarkson CT, Thorn GJ, Sohn K, Pradeepa MM, Chandrasekharan S, Brooke GN, Klenova E, Zhurkin VB, Teif VB. Nucleosome reorganisation in breast cancer tissues. Clin Epigenetics 2024; 16:50. [PMID: 38561804 PMCID: PMC10986098 DOI: 10.1186/s13148-024-01656-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 03/11/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Nucleosome repositioning in cancer is believed to cause many changes in genome organisation and gene expression. Understanding these changes is important to elucidate fundamental aspects of cancer. It is also important for medical diagnostics based on cell-free DNA (cfDNA), which originates from genomic DNA regions protected from digestion by nucleosomes. RESULTS We have generated high-resolution nucleosome maps in paired tumour and normal tissues from the same breast cancer patients using MNase-assisted histone H3 ChIP-seq and compared them with the corresponding cfDNA from blood plasma. This analysis has detected single-nucleosome repositioning at key regulatory regions in a patient-specific manner and common cancer-specific patterns across patients. The nucleosomes gained in tumour versus normal tissue were particularly informative of cancer pathways, with ~ 20-fold enrichment at CpG islands, a large fraction of which marked promoters of genes encoding DNA-binding proteins. The tumour tissues were characterised by a 5-10 bp decrease in the average distance between nucleosomes (nucleosome repeat length, NRL), which is qualitatively similar to the differences between pluripotent and differentiated cells. This effect was correlated with gene activity, differential DNA methylation and changes in local occupancy of linker histone variants H1.4 and H1X. CONCLUSIONS Our study offers a novel resource of high-resolution nucleosome maps in breast cancer patients and reports for the first time the effect of systematic decrease of NRL in paired tumour versus normal breast tissues from the same patient. Our findings provide a new mechanistic understanding of nucleosome repositioning in tumour tissues that can be valuable for patient diagnostics, stratification and monitoring.
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Affiliation(s)
- Divya R Jacob
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
| | - Wilfried M Guiblet
- Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Hulkar Mamayusupova
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
| | - Mariya Shtumpf
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
| | - Isabella Ciuta
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
| | - Luminita Ruje
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
| | - Svetlana Gretton
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
- School of Engineering, Arts, Science and Technology, University of Suffolk, James Hehir Building, University Avenue, Ipswich, Suffolk, IP3 0FS, UK
| | - Milena Bikova
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
| | - Clark Correa
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
| | - Emily Dellow
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
| | - Shivam P Agrawal
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
| | - Navid Shafiei
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
| | | | - Chris M Armstrong
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
| | - Jonathan D G Lam
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
| | - Yevhen Vainshtein
- Fraunhofer-Institut für Grenzflächen- und Bioverfahrenstechnik IGB, Nobelstraße 12, 70569, Stuttgart, Germany
| | - Christopher T Clarkson
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
- University College London, Gower St, Bloomsbury, London, WC1E 6BT, UK
| | - Graeme J Thorn
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
- Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Kai Sohn
- Fraunhofer-Institut für Grenzflächen- und Bioverfahrenstechnik IGB, Nobelstraße 12, 70569, Stuttgart, Germany
| | - Madapura M Pradeepa
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, E1 2AT, UK
| | - Sankaran Chandrasekharan
- Colchester General Hospital, East Suffolk and North Essex NHS Foundation Trust, Turner Road, Colchester, CO4 5JL, UK
| | - Greg N Brooke
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
| | - Elena Klenova
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
| | - Victor B Zhurkin
- Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Vladimir B Teif
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK.
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Moser T, Kühberger S, Lazzeri I, Vlachos G, Heitzer E. Bridging biological cfDNA features and machine learning approaches. Trends Genet 2023; 39:285-307. [PMID: 36792446 DOI: 10.1016/j.tig.2023.01.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 01/10/2023] [Accepted: 01/19/2023] [Indexed: 02/15/2023]
Abstract
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 DNA (cfDNA) biology, are enabling the detection of tumor-specific changes with extremely high resolution and new analysis concepts beyond genetic alterations, including methylomics, fragmentomics, and nucleosomics. The interrogation of a large number of markers and the high complexity of data render traditional correlation methods insufficient. In this regard, machine learning (ML) algorithms are increasingly being used to decipher disease- and tissue-specific signals from cfDNA. Here, we review recent insights into biological ctDNA features and how these are incorporated into sophisticated ML applications.
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Affiliation(s)
- Tina Moser
- Institute of Human Genetics, Diagnostic & Research Center for Molecular BioMedicine, Medical University of Graz, Neue Stiftingtalstrasse 6, 8010 Graz, Austria; Christian Doppler Laboratory for Liquid Biopsies for Early Detection of Cancer, Medical University of Graz, Graz, Austria
| | - Stefan Kühberger
- Institute of Human Genetics, Diagnostic & Research Center for Molecular BioMedicine, Medical University of Graz, Neue Stiftingtalstrasse 6, 8010 Graz, Austria; Christian Doppler Laboratory for Liquid Biopsies for Early Detection of Cancer, Medical University of Graz, Graz, Austria
| | - Isaac Lazzeri
- Institute of Human Genetics, Diagnostic & Research Center for Molecular BioMedicine, Medical University of Graz, Neue Stiftingtalstrasse 6, 8010 Graz, Austria; Christian Doppler Laboratory for Liquid Biopsies for Early Detection of Cancer, Medical University of Graz, Graz, Austria
| | - Georgios Vlachos
- Institute of Human Genetics, Diagnostic & Research Center for Molecular BioMedicine, Medical University of Graz, Neue Stiftingtalstrasse 6, 8010 Graz, Austria; Christian Doppler Laboratory for Liquid Biopsies for Early Detection of Cancer, Medical University of Graz, Graz, Austria
| | - Ellen Heitzer
- Institute of Human Genetics, Diagnostic & Research Center for Molecular BioMedicine, Medical University of Graz, Neue Stiftingtalstrasse 6, 8010 Graz, Austria; Christian Doppler Laboratory for Liquid Biopsies for Early Detection of Cancer, Medical University of Graz, Graz, Austria.
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5
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Wang Y, Fan X, Bao H, Xia F, Wan J, Shen L, Wang Y, Zhang H, Wei Y, Wu X, Shao Y, Li X, Xu Y, Cai S, Zhang Z. Utility of Circulating Free DNA Fragmentomics in the Prediction of Pathological Response after Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Clin Chem 2023; 69:88-99. [PMID: 36308331 DOI: 10.1093/clinchem/hvac173] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 08/25/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND A "Watch and Wait" (W&W) approach has become an alternative to surgery for locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT). Precise prediction of pathological complete response (pCR) will improve patient selection for W&W. We investigated the utility of cell-free DNA (cfDNA) fragmentomics in predicting pCR. METHODS We recruited 119 LARC patients and evaluated nCRT response by pCR status and pathological or MRI tumor regression grade (mrTRG). Plasma samples before, during, and after nCRT were applied to deep targeted-panel sequencing, with 103 patients having complete samples. cfDNA fragment and 5'-end motif profiles were used to construct elastic-net logistic regression models to predict non-pCR. Predictive performance was measured by area under the receiver operator characteristic curve (AUC), sensitivity, and specificity. RESULTS In the training cohort, the model based on 5'-end motif profile plus mrTRG achieved the highest cross-validation AUC (0.92, 95% CI, 0.91-0.93). The AUC in a testing cohort was 0.96 (95% CI, 0.90-1.00). The models based on 5'-end motif profile alone or in combination with mrTRG both maintained good predictive ability for patients without detectable circulating tumor DNA (AUC 0.94, 95% CI, 0.93-0.95; AUC 0.95, 95% CI, 0.94-0.96). In an external validation cohort, the model trained with a local 5'-end motif profile obtained an AUC of 0.878 (95% CI, 0.801-0.956) in discriminating colorectal cancer from healthy subjects. CONCLUSIONS The combination of a 5'-end motif profile with mrTRG has the potential to predict the response to nCRT, and therefore may improve the patient selection for a W&W approach.
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Affiliation(s)
- Yaqi Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Xiaojun Fan
- Geneseeq Research Institute, Nanjing Geneseeq Technology Inc., Nanjing, China
| | - Hua Bao
- Geneseeq Research Institute, Nanjing Geneseeq Technology Inc., Nanjing, China
| | - Fan Xia
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Juefeng Wan
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Lijun Shen
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Yan Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Hui Zhang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
| | - Yulin Wei
- Geneseeq Research Institute, Nanjing Geneseeq Technology Inc., Nanjing, China
| | - Xue Wu
- Geneseeq Research Institute, Nanjing Geneseeq Technology Inc., Nanjing, China
| | - Yang Shao
- Geneseeq Research Institute, Nanjing Geneseeq Technology Inc., Nanjing, China.,School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xinxiang Li
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ye Xu
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Sanjun Cai
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhen Zhang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, China
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6
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Gianni C, Palleschi M, Merloni F, Bleve S, Casadei C, Sirico M, Di Menna G, Sarti S, Cecconetto L, Mariotti M, De Giorgi U. Potential Impact of Preoperative Circulating Biomarkers on Individual Escalating/de-Escalating Strategies in Early Breast Cancer. Cancers (Basel) 2022; 15:96. [PMID: 36612091 PMCID: PMC9817806 DOI: 10.3390/cancers15010096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/18/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
The research on non-invasive circulating biomarkers to guide clinical decision is in wide expansion, including the earliest disease settings. Several new intensification/de-intensification strategies are approaching clinical practice, personalizing the treatment for each patient. Moreover, liquid biopsy is revealing its potential with multiple techniques and studies available on circulating biomarkers in the preoperative phase. Inflammatory circulating cells, circulating tumor cells (CTCs), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), and other biological biomarkers are improving the armamentarium for treatment selection. Defining the escalation and de-escalation of treatments is a mainstay of personalized medicine in early breast cancer. In this review, we delineate the studies investigating the possible application of these non-invasive tools to give a more enlightened approach to escalating/de-escalating strategies in early breast cancer.
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Affiliation(s)
- Caterina Gianni
- Department of Medical Oncology, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, 47014 Meldola, Italy
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7
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Zhang M, Li K, Qu S, Guo Z, Wang Y, Yang X, Zhou J, Ouyang G, Weng R, Li F, Wu Y, Yang X. Integrative analyses of maternal plasma cell-free DNA nucleosome footprint differences reveal chromosomal aneuploidy fetuses gene expression profile. J Transl Med 2022; 20:536. [PMID: 36401256 PMCID: PMC9673457 DOI: 10.1186/s12967-022-03735-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 10/30/2022] [Indexed: 11/19/2022] Open
Abstract
Background Chromosomal aneuploidy is the most common birth defect. However, the developmental mechanism and gene expression profile of fetuses with chromosomal aneuploidy are relatively unknown, and the maternal immune changes induced by fetal aneuploidy remain unclear. The inability to obtain the placenta multiple times in real-time is a bottleneck in research on aneuploid pregnancies. Plasma cell-free DNA (cfDNA) carries the gene expression profile information of its source cells and may be used to evaluate the development of fetuses with aneuploidy and the immune changes induced in the mother owing to fetal aneuploidy. Methods Here, we carried out whole-genome sequencing of the plasma cfDNA of 101 pregnant women carrying a fetus with trisomy (trisomy 21, n = 42; trisomy 18, n = 28; trisomy 13, n = 31) based on non-invasive prenatal testing (NIPT) screening and 140 normal pregnant women to identify differential genes according to the cfDNA nucleosome profile in the region around the transcription start sites (TSSs). Results The plasma cfDNA promoter profiles were found to differ between aneuploid and euploid pregnancies. A total of 158 genes with significant differences were identified, of which 43 genes were upregulated and 98 genes were downregulated. Functional enrichment and signaling pathway analysis were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases found that these signal pathways were mainly related to the coordination of developmental signals during embryonic development, the control of cell growth and development, regulation of neuronal survival, and immune regulation, such as the MAPK, Hippo, TGF-β, and Rap1 signaling pathways, which play important roles in the development of embryonic tissues and organs. Furthermore, based on the results of differential gene analysis, a total of 14 immune-related genes with significant differences from the ImmPort database were collected and analyzed. These significantly different immune genes were mainly associated with the maintenance of embryonic homeostasis and normal development. Conclusions These results suggest that the distribution characteristics of cfDNA nucleosomes in maternal plasma can be used to reflect the status of fetal development and changes of the immune responses in trisomic pregnancies. Overall, our findings may provide research ideas for non-invasive detection of the physiological and pathological states of other diseases. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03735-7.
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8
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Han BW, Yang X, Qu SF, Guo ZW, Huang LM, Li K, Ouyang GJ, Cai GX, Xiao WW, Weng RT, Xu S, Huang J, Yang XX, Wu YS. A Deep-Learning Pipeline for TSS Coverage Imputation From Shallow Cell-Free DNA Sequencing. Front Med (Lausanne) 2021; 8:684238. [PMID: 34926480 PMCID: PMC8678047 DOI: 10.3389/fmed.2021.684238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 11/15/2021] [Indexed: 11/13/2022] Open
Abstract
Cell-free DNA (cfDNA) serves as a footprint of the nucleosome occupancy status of transcription start sites (TSSs), and has been subject to wide development for use in noninvasive health monitoring and disease detection. However, the requirement for high sequencing depth limits its clinical use. Here, we introduce a deep-learning pipeline designed for TSS coverage profiles generated from shallow cfDNA sequencing called the Autoencoder of cfDNA TSS (AECT) coverage profile. AECT outperformed existing single-cell sequencing imputation algorithms in terms of improvements to TSS coverage accuracy and the capture of latent biological features that distinguish sex or tumor status. We built classifiers for the detection of breast and rectal cancer using AECT-imputed shallow sequencing data, and their performance was close to that achieved by high-depth sequencing, suggesting that AECT could provide a broadly applicable noninvasive screening approach with high accuracy and at a moderate cost.
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Affiliation(s)
- Bo-Wei Han
- Key Laboratory of Antibody Engineering of Guangdong Higher Education Institutes, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Xu Yang
- Key Laboratory of Antibody Engineering of Guangdong Higher Education Institutes, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Shou-Fang Qu
- Division of in vitro Diagnostic Reagents, National Institutes for Food and Drug Control (NIFDC), Beijing, China
| | - Zhi-Wei Guo
- Key Laboratory of Antibody Engineering of Guangdong Higher Education Institutes, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Li-Min Huang
- Key Laboratory of Antibody Engineering of Guangdong Higher Education Institutes, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Kun Li
- Key Laboratory of Antibody Engineering of Guangdong Higher Education Institutes, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China.,Guangzhou XGene Co., Ltd., Guangzhou, China
| | - Guo-Jun Ouyang
- Guangzhou Darui Biotechnology Co., Ltd., Guangzhou, China
| | - Geng-Xi Cai
- Department of Breast Surgery, The First People's Hospital of Foshan, Foshan, China.,Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wei-Wei Xiao
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Rong-Tao Weng
- Guangzhou Darui Biotechnology Co., Ltd., Guangzhou, China
| | - Shun Xu
- Guangzhou Darui Biotechnology Co., Ltd., Guangzhou, China
| | - Jie Huang
- Division of in vitro Diagnostic Reagents, National Institutes for Food and Drug Control (NIFDC), Beijing, China
| | - Xue-Xi Yang
- Key Laboratory of Antibody Engineering of Guangdong Higher Education Institutes, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
| | - Ying-Song Wu
- Key Laboratory of Antibody Engineering of Guangdong Higher Education Institutes, School of Laboratory Medicine and Biotechnology, Southern Medical University, Guangzhou, China
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Liu L, Chen X, Petinrin OO, Zhang W, Rahaman S, Tang ZR, Wong KC. Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey. Life (Basel) 2021; 11:638. [PMID: 34209249 PMCID: PMC8308091 DOI: 10.3390/life11070638] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 06/23/2021] [Accepted: 06/24/2021] [Indexed: 12/24/2022] Open
Abstract
With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.
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Affiliation(s)
- Linjing Liu
- Department of Computer Science, City University of Hong Kong, Hong Kong, China; (L.L.); (X.C.); (O.O.P.); (W.Z.); (S.R.); (Z.-R.T.)
| | - Xingjian Chen
- Department of Computer Science, City University of Hong Kong, Hong Kong, China; (L.L.); (X.C.); (O.O.P.); (W.Z.); (S.R.); (Z.-R.T.)
| | - Olutomilayo Olayemi Petinrin
- Department of Computer Science, City University of Hong Kong, Hong Kong, China; (L.L.); (X.C.); (O.O.P.); (W.Z.); (S.R.); (Z.-R.T.)
| | - Weitong Zhang
- Department of Computer Science, City University of Hong Kong, Hong Kong, China; (L.L.); (X.C.); (O.O.P.); (W.Z.); (S.R.); (Z.-R.T.)
| | - Saifur Rahaman
- Department of Computer Science, City University of Hong Kong, Hong Kong, China; (L.L.); (X.C.); (O.O.P.); (W.Z.); (S.R.); (Z.-R.T.)
| | - Zhi-Ri Tang
- Department of Computer Science, City University of Hong Kong, Hong Kong, China; (L.L.); (X.C.); (O.O.P.); (W.Z.); (S.R.); (Z.-R.T.)
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Hong Kong, China; (L.L.); (X.C.); (O.O.P.); (W.Z.); (S.R.); (Z.-R.T.)
- Hong Kong Institute for Data Science, City University of Hong Kong, Hong Kong, China
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Association between the nucleosome footprint of plasma DNA and neoadjuvant chemotherapy response for breast cancer. NPJ Breast Cancer 2021; 7:35. [PMID: 33772032 PMCID: PMC7997954 DOI: 10.1038/s41523-021-00237-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 02/26/2021] [Indexed: 12/29/2022] Open
Abstract
Gene expression signatures have been used to predict the outcome of chemotherapy for breast cancer. The nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of the original tissues and thus may be used to predict the response to chemotherapy. Here we carried out the nucleosome positioning on cfDNA from 85 breast cancer patients and 85 healthy individuals and two cancer cell lines T-47D and MDA-MB-231 using low-coverage whole-genome sequencing (LCWGS) method. The patients showed distinct nucleosome footprints at Transcription Start Sites (TSSs) compared with normal donors. In order to identify the footprints of cfDNA corresponding with the responses to neoadjuvant chemotherapy in patients, we mapped on nucleosome positions on cfDNA of patients with different responses: responders (pretreatment, n = 28; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 12) and nonresponders (pretreatment, n = 10; post-1 cycle, post-3/4 cycles, and post-8 cycles of treatment, n = 10). The coverage depth near TSSs in plasma cfDNA differed significantly between responders and nonresponders at pretreatment, and also after neoadjuvant chemotherapy treatment cycles. We identified 232 TSSs with differential footprints at pretreatment and 321 after treatment and found enrichment in Gene Ontology terms such as cell growth inhibition, tumor suppressor, necrotic cell death, acute inflammatory response, T cell receptor signaling pathway, and positive regulation of vascular endothelial growth factor production. These results suggest that cfDNA nucleosome footprints may be used to predict the efficacy of neoadjuvant chemotherapy for breast cancer patients and thus may provide help in decision making for individual patients.
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