Super-MSI: CfDNA-based pan-cancer microsatellite instability detection.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e13672-e13672
Author(s):  
Xiaomeng Sun ◽  
Xiaohong Xu ◽  
Rongbo Lin ◽  
Guodong Zhao

e13672 Background: Microsatellite instability (MSI) is a molecular subtype found in many cancers, which is often associated with the benefits of immunotherapy. However, the application of traditional testing strategies (such as PCR or IHC) is largely limited due to their dependencies on sufficient tumor tissues. Here, we introduced Super-MSI, a cfDNA-based pan-cancer MSI detection method using next-generation sequencing (NGS). Methods: 5500 detectable microsatellite loci from 250 white blood cells (WBCs) and 29 PCR-validated MSI-H tissue samples were sequenced and characterized, while the former represented for MSS pattern, and the later, for MSI-H. 91 microsatellite loci with the most variable motif repeat numbers between patterns were filtered and the Super-MSI method was established. Plasma samples were collected from 608 patients with known MSI status assessed by PCR (593 MSS and 15 MSI-H) to validate the method. All samples were sequenced with a 642-gene panel, and initial microsatellite reads count distributions were profiled by MSIsensor. Results: We characterized each microsatellite locus by REF alleles (MSI-H:WBC ratio less than 1) and ALT alleles (MSI-H:WBC ratio more than 20), then defined the sum of ALT alleles frequency in WBC group as the baseline for each candidate locus. For plasma samples with unknown MSI status, hypergeometric distribution and FDR calibration were employed to assess the difference of ALT alleles frequency between the WBC baseline and plasma samples. Locus with FDR adjusted p-value less than 0.05 was defined as an unstable locus based on prior knowledge, and negative log-transformed FDR-p was defined as MSscore for the locus. The plasma sample with both unstable microsatellite proportion larger than 20% and total MSscore larger than 270 was defined as MSI-H, and contrariwise, MSS. For all evaluable samples in the validation cohort, Super-MSI showed high sensitivity and specificity of 60% (9/15) and 100% (593/593) for an overall accuracy of 99.0% (602/608), superior to its kind bMSISEA (83.5%, 106/127) and Guardant360 (98.4%, 934/949). Within LOD of 0.5% maxAF (approximately 1% ctDNA fraction), Super-MSI was able to detect 81.8% (9/11) of MSI-H samples, demonstrating high concordance with tissue-based PCR tests. Conclusions: Super-MSI can genotype pan-cancer patients with blood cfDNA samples and give highly accurate MSI evaluation with 81.8% sensitivity and 100% specificity above 1% ctDNA fraction.

2020 ◽  
Vol 15 ◽  
Author(s):  
Zheng Jiang ◽  
Hui Liu ◽  
Siwen Zhang ◽  
Jia Liu ◽  
Weitao Wang ◽  
...  

Background: Microsatellite instability (MSI) is a prognostic biomarker used to guide medication selection in multiple cancers, such as colorectal cancer. Traditional PCR with capillary electrophoresis and next-generation sequencing using paired tumor tissue and leukocyte samples are the main approaches for MSI detection due to their high sensitivity and specificity. Currently, patient tissue samples are obtained through puncture or surgery, which causes injury and risk of concurrent disease, further illustrating the need for MSI detection by liquid biopsy. Methods: We propose an analytic method using paired plasma/leukocyte samples and MSI detection using next-generation sequencing technology. Based on the theoretical progress of oncogenesis, we hypothesized that the microsatellite site length in plasma equals the combination of the distribution of tumor tissue and leukocytes. Thus, we defined a window-judgement method to identify whether biomarkers were stable. Results: Compared to traditional PCR as the standard, we evaluated three methods in 20 samples (MSI-H:3/MSS:17): peak shifting method using tissue vs. leukocytes, peak shifting method using plasma vs. leukocytes, and our method using plasma vs. leukocytes. Compared to traditional PCR, we observed a sensitivity of 100%, 0%, and 100%, and a specificity of 100.00%, 94.12%, and 88.24%, respectively. Conclusion: Our method has the advantage of possibly detecting MSI in a liquid biopsy and provides a novel direction for future studies to increase the specificity of the method.


2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 384-384
Author(s):  
Hussein Mustafa Khaled ◽  
Abdel-Rahman Zekri ◽  
Mai B Mohamed ◽  
Fatma M Diab ◽  
Mona Abdellateif ◽  
...  

384 Background: Microsatellite alterations in urine sediments have proved to be a promising tool for detection of bladder cancer (BC) due to its high sensitivity and specificity. Methods: We assessed the possible prognostic and predictive values of microsatellite alterations in tissue samples and urine sediments obtained from Egyptian patients with BC, and their utility as diagnostic, prognostic and predictive value. Microsatellite instability (MSI) and loss of heterozygosity (LOH) were assessed using 13 microsatellite markers in tumor tissue and urine sediments of 30 patients with BC. The concordance between MSI in tissue and urine samples was determined. Results: We found that MSI was more frequent than LOH (100% and 46.7%; respectively). D16S310, MBP and IFN-α showed the highest MSI frequency in urine samples (70%, 70% and 66.67%; respectively), while MBP, ACTBP2 and D9S171 (66.67%, 63.33%, and 60%; respectively) were the most frequently detected in tumor tissue. All markers correlated significantly with the pathological subtypes (more frequent in TCC) and hematuria. The concordance between tissue and urine was statistically significant for , D9S171, D16S476, FGA and ACTBP2 (P = 0.04, 0.015, 0.02 and 0.007; respectively). When we combined D16S476 and D9S171, the sensitivity, specificity, PPV and NPV reached (80.0%, 75.0%, 82.8% and 71.4%; respectively) for diagnosis of BC . Conclusions: Thus MSI in urine sediments could be a sensitive and reliable method for diagnosis of BC.


2016 ◽  
Author(s):  
Isidro Cortes-Ciriano ◽  
Sejoon Lee ◽  
Woong-Yang Park ◽  
Tae-Min Kim ◽  
Peter J. Park

ABSTRACTMicrosatellite instability (MSI) refers to the hypermutability of the cancer genome due to impaired DNA mismatch repair. Although MSI has been studied for decades, the large amount of sequencing data now available allows us to examine the molecular fingerprints of MSI in greater detail. Here, we analyze ~8000 exome and ~1000 whole-genome pairs across 23 cancer types. Our pan-cancer analysis reveals that the prevalence of MSI events is highly variable within and across tumor types including some in which MSI is not typically examined. We also identify genes in DNA repair and oncogenic pathways recurrently subject to MSI and uncover non-coding loci that frequently display MSI events. Finally, we propose an exomebased predictive model for the MSI phenotype that achieves high sensitivity and specificity. These results advance our understanding of the genomic drivers and consequences of MSI, and a comprehensive catalog of tumor-type specific MSI loci we have generated enables efficient panel-based MSI testing to identify patients who are likely to benefit from immunotherapy.


1997 ◽  
Vol 41 (3) ◽  
pp. 661-664 ◽  
Author(s):  
C Bethune ◽  
T Bui ◽  
M L Liu ◽  
M A Kay ◽  
R J Ho

We have developed a chromatographic assay with high sensitivity and specificity to quantify G418 sulfate (Geneticin), an antibiotic used routinely in molecular genetics experiments for selecting eukaryotic transformants. With this method, G418 in tissues and plasma samples can be quantitated without the confounding factors often associated with biological assays. After removal of proteins in homogenized tissue or plasma samples with methanol (2:1, vol/vol), the amino group of G418 was derivatized with 1-fluoro-2,4-dinitrobenzene (DNFB) to form the UV-visible G418-DNFB product. The DNFB-derivatized G418 was separated on a reversed-phase C18 column with an acetonitrile and water gradient as the mobile phase. Under these assay conditions, the detection limit for G418 sulfate in buffer, plasma, and tissues was recorded at 78 ng/ml and the linearity was recorded for concentrations up to 100 micrograms/ml. The data obtained from this analysis indicate that this assay can be used for the quantitative determination of G418 sulfate in plasma and tissue samples.


2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 7546-7546
Author(s):  
Alexander F. Lovejoy ◽  
Hai Lin ◽  
Ehsan Tabari ◽  
Seng Lor Saelee ◽  
David Matthew Kurtz ◽  
...  

7546 Background: Detection of an initial molecular response to therapy in DLBCL could help differentiate patients who will relapse (30-40% of frontline subjects) from those who will not. Recent studies in DLBCL showed ability to detect residual disease and molecular response to therapy from analysis of circulating tumor DNA (ctDNA). Here we performed targeted next generation sequencing (NGS) of baseline ctDNA vs. tumor tissue, and on-treatment ctDNA samples in 32 relapse/refractory DLBCL subjects from the ROMULUS study to assess correlation of outcome with molecular response. Methods: We sequenced plasma, plasma depleted whole blood (PDWB), and tumor DNA from 32 subjects (range 2-6 samples / subject). Library preparation and NGS were performed using hybrid capture-based workflows, with a panel of ~300 kb targeting regions relevant for disease detection in DLBCL. Variants were called from tissue and plasma data, and PDWB data were used to filter out non-tumor specific variants. Results: 83% of variants detected in tissue (1441/1745) were found in the corresponding plasma samples, and 78% of variants detected in plasma (1441/1846) were found in corresponding tissue samples, in line with previous reports. To follow ctDNA changes with treatment, tumor-specific variants were determined from tissue or cycle 1 day 1 (C1D1) plasma samples. These variants were then monitored in C1D1 and later timepoints, with similar ctDNA levels based on variants determined from C1D1 plasma or tissue (R2=0.99). Change in ctDNA levels from C1D1 to C2D1 separated subjects that responded from subjects that progressed (Wilcoxon p-value: 9.39×10-4). Subjects that showed a 10-fold or higher drop in ctDNA levels between C1D1 and C2D1 had significantly longer PFS than those with a smaller ctDNA fold change (HR: 8.06; p=0.0008). Conclusions: This study showed that tumor-specific variants can be identified in baseline plasma with similar performance as from tumor tissue, and that monitoring molecular response as an early change in ctDNA levels after one cycle of treatment correlated with outcomes in this DLBCL study. Clinical trial information: NCT01691898.


2018 ◽  
Author(s):  
Akihiro Fujimoto ◽  
Masashi Fujita ◽  
Takanori Hasegawa ◽  
Jing Hao Wong ◽  
Kazuhiro Maejima ◽  
...  

AbstractMicrosatellites are repeats of 1-6bp units and ∼10 million microsatellites have been identified across the human genome. Microsatellites are vulnerable to DNA mismatch errors, and have thus been used to detect cancers with mismatch repair deficiency. To reveal the mutational landscape of the microsatellite repeat regions at the genome level, we analyzed approximately 20.1 billion microsatellites in 2,717 whole genomes of pan-cancer samples across 21 tissue types. Firstly, we developed a new insertion and deletion caller (MIMcall) that takes into consideration the error patterns of different types of microsatellites. Among the 2,717 pan-cancer samples, our analysis identified 31 samples, including colorectal, uterus, and stomach cancers, with higher microsatellite mutation rate (≥ 0.03), which we defined as microsatellite instability (MSI) cancers in genome-wide level. Next, we found 20 highly-mutated microsatellites that can be used to detect MSI cancers with high sensitivity. Third, we found that replication timing and DNA shape were significantly associated with mutation rates of the microsatellites. Analysis of germline variation of the microsatellites suggested that the amount of germline variations and somatic mutation rates were correlated. Lastly, analysis of mutations in mismatch repair genes showed that somatic SNVs and short indels had larger functional impact than germline mutations and structural variations. Our analysis provides a comprehensive picture of mutations in the microsatellite regions, and reveals possible causes of mutations, as well as provides a useful marker set for MSI detection.


2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 5046-5046
Author(s):  
Pier Vitale Nuzzo ◽  
Sandor Spisak ◽  
Jacob E Berchuck ◽  
Sylvan Baca ◽  
Keegan Korthauer ◽  
...  

5046 Background: Methylation profiling of circulating cell-free DNA (cfDNA) is a promising approach for non-invasive tumor detection due to the presence of tissue-specific epigenetic signatures that are detectable in cfDNA. Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMedDIP-seq) is a sensitive, low-input, cost-effective, bisulfite-free approach to profiling cfDNA methylomes, capable of detecting and classifying various tumor types. We tested the feasibility of cfMeDIP-seq to detect urothelial carcinoma (UC) in plasma samples. Methods: We performed cfMeDIP-seq on plasma samples from 43 patients (pts): 18 metastatic UC (UC) pts, 12 pre-cystectomy non-metastatic UC pts, and 13 cancer-free controls. Six (50%) of pre-cystectomy cases were non-muscle invasive UC. cfDNA was immunoprecipitated and enriched using an antibody targeting 5-methylcytosine and PCR-amplified to create a sequence-ready library. The top differentially methylated regions (DMRs) between UC and control samples were used to train a regularized binomial generalized linear model using 80% of the samples as a training set. The 20% of withheld test samples were then assigned a probability of being UC or control. This process was repeated 100 times. Results: The average amount (standard deviation) of cfDNA isolated from 1 ml of UC plasma samples was 29.2 (27.4) ng/µL and 8.02 (3.58) ng/µL in cancer-free controls. We identified 9,826 DMRs in plasma samples at an adjusted p-value of < 0.01, which partitioned UC and control samples. Iterative training and classification of held out samples using the top 300 DMRs resulted in a mean AUROC of 0.987. Conclusions: cfMeDIP-seq is an interesting new approach for non-invasive detection of UC. cfMeDIP-seq demonstrates high sensitivity to detect UC across all stages of UC, including non-muscle invasive disease.


2019 ◽  
Vol 17 ◽  
Author(s):  
Xiaoli Yu ◽  
Lu Zhang ◽  
Na Li ◽  
Peng Hu ◽  
Zhaoqin Zhu ◽  
...  

Aim: We aimed to identify new plasma biomarkers for the diagnosis of Pulmonary tuberculosis. Background: Tuberculosis is an ancient infectious disease that remains one of the major global health problems. Until now, effective, convenient, and affordable methods for diagnosis of Pulmonary tuberculosis were still lacked. Objective: This study focused on construct a label-free LC-MS/MS based comparative proteomics between six tuberculosis patients and six healthy controls to identify differentially expressed proteins (DEPs) in plasma. Method: To reduce the influences of high-abundant proteins, albumin and globulin were removed from plasma samples using affinity gels. Then DEPs from the plasma samples were identified using a label-free Quadrupole-Orbitrap LC-MS/MS system. The results were analyzed by the protein database search algorithm SEQUEST-HT to identify mass spectra to peptides. The predictive abilities of combinations of host markers were investigated by general discriminant analysis (GDA), with leave-one-out cross-validation. Results: A total of 572 proteins were identified and 549 proteins were quantified. The threshold for differentially expressed protein was set as adjusted p-value < 0.05 and fold change ≥1.5 or ≤0.6667, 32 DEPs were found. ClusterVis, TBtools, and STRING were used to find new potential biomarkers of PTB. Six proteins, LY6D, DSC3, CDSN, FABP5, SERPINB12, and SLURP1, which performed well in the LOOCV method validation, were termed as potential biomarkers. The percentage of cross-validated grouped cases correctly classified and original grouped cases correctly classified is greater than or equal to 91.7%. Conclusion: We successfully identified five candidate biomarkers for immunodiagnosis of PTB in plasma, LY6D, DSC3, CDSN, SERPINB12, and SLURP1. Our work supported this group of proteins as potential biomarkers for pulmonary tuberculosis, and be worthy of further validation.


2020 ◽  
Vol 14 (11) ◽  
pp. 1009-1020
Author(s):  
Ryota Nakano ◽  
Shin Nishiumi ◽  
Takashi Kobayashi ◽  
Takuya Ikegawa ◽  
Yuzo Kodama ◽  
...  

Aim: The aim of this study was to identify whether metabolite biomarker candidates for pancreatic cancer (PC) could aid detection of intraductal papillary mucinous neoplasms (IPMN), recognized as high-risk factors for PC. Materials & methods: The 12 metabolite biomarker candidates, which were found to be useful to detect PC in our previous study, were evaluated for plasma samples from patients with PC (n = 44) or IPMN (n = 24) or healthy volunteers (n = 46). Results: Regarding the performance of individual biomarkers of PC and PC high-risk IPMN, lysine exhibited the best performance (sensitivity: 67.8%; specificity: 86.9%). The multiple logistic regression analysis-based detection model displayed high sensitivity and specificity values of 92.5 and 90.6%, respectively. Conclusion: Metabolite biomarker candidates for PC are useful for detecting high-risk IPMN, which can progress to PC.


Cancers ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 147
Author(s):  
Leticia Díaz-Beltrán ◽  
Carmen González-Olmedo ◽  
Natalia Luque-Caro ◽  
Caridad Díaz ◽  
Ariadna Martín-Blázquez ◽  
...  

Purpose: The aim of this study is to identify differential metabolomic signatures in plasma samples of distinct subtypes of breast cancer patients that could be used in clinical practice as diagnostic biomarkers for these molecular phenotypes and to provide a more individualized and accurate therapeutic procedure. Methods: Untargeted LC-HRMS metabolomics approach in positive and negative electrospray ionization mode was used to analyze plasma samples from LA, LB, HER2+ and TN breast cancer patients and healthy controls in order to determine specific metabolomic profiles through univariate and multivariate statistical data analysis. Results: We tentatively identified altered metabolites displaying concentration variations among the four breast cancer molecular subtypes. We found a biomarker panel of 5 candidates in LA, 7 in LB, 5 in HER2 and 3 in TN that were able to discriminate each breast cancer subtype with a false discovery range corrected p-value < 0.05 and a fold-change cutoff value > 1.3. The model clinical value was evaluated with the AUROC, providing diagnostic capacities above 0.85. Conclusion: Our study identifies metabolic profiling differences in molecular phenotypes of breast cancer. This may represent a key step towards therapy improvement in personalized medicine and prioritization of tailored therapeutic intervention strategies.


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