scholarly journals Multi-dimensional fragmentomic assay for ultrasensitive early detection of colorectal advanced adenoma and adenocarcinoma

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Xiaoji Ma ◽  
Yikuan Chen ◽  
Wanxiangfu Tang ◽  
Hua Bao ◽  
Shaobo Mo ◽  
...  
Keyword(s):  
Early Detection ◽  
Early Stage ◽  
Area Under The Curve ◽  
Detection Sensitivity ◽  
Advanced Adenoma ◽  
Healthy Controls ◽  
Training Cohort ◽  
Fecal Occult ◽  
Independent Test ◽  
Low Sensitivity

AbstractPrevious studies on liquid biopsy-based early detection of advanced colorectal adenoma (advCRA) or adenocarcinoma (CRC) were limited by low sensitivity. We performed a prospective study to establish an integrated model using fragmentomic profiles of plasma cell-free DNA (cfDNA) for accurately and cost-effectively detecting early-stage CRC and advCRA. The training cohort enrolled 310 participants, including 149 early-stage CRC patients, 46 advCRA patients and 115 healthy controls. Plasma cfDNA samples were prepared for whole-genome sequencing. An ensemble stacked model differentiating healthy controls from advCRA/early-stage CRC patients was trained using five machine learning models and five cfDNA fragmentomic features based on the training cohort. The model was subsequently validated using an independent test cohort (N = 311; including 149 early-stage CRC, 46 advCRA and 116 healthy controls). Our model showed an area under the curve (AUC) of 0.988 for differentiating advCRA/early-stage CRC patients from healthy individuals in an independent test cohort. The model performed even better for identifying early-stage CRC (AUC 0.990) compared to advCRA (AUC 0.982). At 94.8% specificity, the sensitivities for detecting advCRA and early-stage CRC reached 95.7% and 98.0% (0: 94.1%; I: 98.5%), respectively. Promisingly, the detection sensitivity has reached 100% and 97.6% in early-stage CRC patients with negative fecal occult or CEA blood test results, respectively. Finally, our model maintained promising performances (AUC: 0.982, 94.4% sensitivity at 94.8% specificity) even when sequencing depth was down-sampled to 1X. Our integrated predictive model demonstrated an unprecedented detection sensitivity for advCRA and early-stage CRC, shedding light on more accurate noninvasive CRC screening in clinical practice.

scholarly journals A MYC-Driven Plasma Polyamine Signature for Early Detection of Ovarian Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 913
Author(s):  
Johannes Fahrmann ◽  
Ehsan Irajizad ◽  
Makoto Kobayashi ◽  
Jody Vykoukal ◽  
Jennifer Dennison ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Early Stage ◽  
Area Under The Curve ◽  
Polyamine Metabolism ◽  
Healthy Controls ◽  
Test Set ◽  
Exact Test ◽  
Oncogenic Driver ◽  
Characteristic Area ◽  
Validation Set

MYC is an oncogenic driver in the pathogenesis of ovarian cancer. We previously demonstrated that MYC regulates polyamine metabolism in triple-negative breast cancer (TNBC) and that a plasma polyamine signature is associated with TNBC development and progression. We hypothesized that a similar plasma polyamine signature may associate with ovarian cancer (OvCa) development. Using mass spectrometry, four polyamines were quantified in plasma from 116 OvCa cases and 143 controls (71 healthy controls + 72 subjects with benign pelvic masses) (Test Set). Findings were validated in an independent plasma set from 61 early-stage OvCa cases and 71 healthy controls (Validation Set). Complementarity of polyamines with CA125 was also evaluated. Receiver operating characteristic area under the curve (AUC) of individual polyamines for distinguishing cases from healthy controls ranged from 0.74–0.88. A polyamine signature consisting of diacetylspermine + N-(3-acetamidopropyl)pyrrolidin-2-one in combination with CA125 developed in the Test Set yielded improvement in sensitivity at >99% specificity relative to CA125 alone (73.7% vs 62.2%; McNemar exact test 2-sided P: 0.019) in the validation set and captured 30.4% of cases that were missed with CA125 alone. Our findings reveal a MYC-driven plasma polyamine signature associated with OvCa that complemented CA125 in detecting early-stage ovarian cancer.

scholarly journals Automated Assay of a Four-Protein Biomarker Panel for Improved Detection of Ovarian Cancer

Cancers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 325
Author(s):  
Christopher Walker ◽  
Tuan-Minh Nguyen ◽  
Shlomit Jessel ◽  
Ayesha B. Alvero ◽  
Dan-Arin Silasi ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Early Detection ◽  
Predictive Value ◽  
Early Stage ◽  
Ca 125 ◽  
Healthy Controls ◽  
Classification Models ◽  
Serum Samples ◽  
Protein Biomarker ◽  
Biomarker Panel

Background: Mortality from ovarian cancer remains high due to the lack of methods for early detection. The difficulty lies in the low prevalence of the disease necessitating a significantly high specificity and positive-predictive value (PPV) to avoid unneeded and invasive intervention. Currently, cancer antigen- 125 (CA-125) is the most commonly used biomarker for the early detection of ovarian cancer. In this study we determine the value of combining macrophage migration inhibitory factor (MIF), osteopontin (OPN), and prolactin (PROL) with CA-125 in the detection of ovarian cancer serum samples from healthy controls. Materials and Methods: A total of 432 serum samples were included in this study. 153 samples were from ovarian cancer patients and 279 samples were from age-matched healthy controls. The four proteins were quantified using a fully automated, multi-analyte immunoassay. The serum samples were divided into training and testing datasets and analyzed using four classification models to calculate accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results: The four-protein biomarker panel yielded an average accuracy of 91% compared to 85% using CA-125 alone across four classification models (p = 3.224 × 10−9). Further, in our cohort, the four-protein biomarker panel demonstrated a higher sensitivity (median of 76%), specificity (median of 98%), PPV (median of 91.5%), and NPV (median of 92%), compared to CA-125 alone. The performance of the four-protein biomarker remained better than CA-125 alone even in experiments comparing early stage (Stage I and Stage II) ovarian cancer to healthy controls. Conclusions: Combining MIF, OPN, PROL, and CA-125 can better differentiate ovarian cancer from healthy controls compared to CA-125 alone.

scholarly journals Nomogram to identify severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics: a multi-center study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yixing Yu ◽  
Ximing Wang ◽  
Min Li ◽  
Lan Gu ◽  
Zongyu Xie ◽  
...  
Keyword(s):  
Risk Factors ◽  
Calibration Curve ◽  
Severity Score ◽  
Early Stage ◽  
Area Under The Curve ◽  
Clinical Impact ◽  
Imaging Data ◽  
Net Benefit ◽  
Training Cohort ◽  
Independent Risk Factors

Abstract Background To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics. Methods The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. Two hundred seventeen patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart. Results In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5–9) was significantly higher than that of the mild group (4, IQR,2–5) (P < 0.001). Age, density, mosaic perfusion sign and severity score of lung were independent risk factors for severe COVID-19. The nomogram had a AUC of 0.929 (95% CI, 0.889–0.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867–1.000), sensitivity of 90.5% and specificity of 88.6% in the validation cohort. The calibration curve, decision curve, clinical impact curve and risk chart showed that nomogram had high accuracy and superior net benefit in predicting severe COVID-19. Conclusion The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.

scholarly journals A Nomogram-Based Model to Predict Neoplastic Risk For Patients With Gallbladder Polyps

2021 ◽  
Author(s):  
Xudong Zhang ◽  
Jin-Cheng Wang ◽  
Baoqiang Wu ◽  
Tao Li ◽  
Lei Jin ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Early Stage ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Training Group ◽  
Clinical Decision ◽  
Polyp Size ◽  
Training Cohort ◽  
Discrimination Ability ◽  
Gallbladder Polyps

Abstract Background: Gallbladder polyps (GBPs) assessment seeks to identify early-stage gallbladder carcinoma (GBC). Many studies have analyzed the risk factors for malignant GBPs, and we try to establish a more accurate predictive model for potential neoplastic polyps in patients with GBPs.Methods: This retrospective study developed a nomogram-based model in a training cohort of 233 GBP patients. Clinical information, ultrasonographic findings, and blood tests were retrospectively analyzed. Spearman correlation and logistic regression analysis were used to identify independent predictors and establish a nomogram model. An internal validation was conducted in 225 consecutive patients. Performance of models was evaluated through the receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Results: Age, cholelithiasis, CEA, polyp size and sessile were confirmed as independent predictors for neoplastic potential of GBPs in the training group. Compared with other proposed prediction methods, the established nomogram model presented good discrimination ability in the training cohort (area under the curve [AUC]: 0.845) and the validation cohort (AUC: 0.836). DCA demonstrated the most clinical benefits can be provided by the nomogram. Conclusions: Our developed preoperative nomogram model can successfully evaluate the neoplastic potential of GBPs based on simple clinical variables, that maybe useful for clinical decision-making.

scholarly journals Diagnostic Value of Plasma Annexin A2 in Early-Stage High-Grade Serous Ovarian Cancer

Diagnostics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 69
Author(s):  
Noor A. Lokman ◽  
Carmela Ricciardelli ◽  
Andrew N. Stephens ◽  
Thomas W. Jobling ◽  
Peter Hoffmann ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Early Detection ◽  
Early Stage ◽  
Diagnostic Value ◽  
Annexin A2 ◽  
High Grade ◽  
Healthy Controls ◽  
Operating Characteristics ◽  
Logistic Regression Models ◽  
Stage Ia

Ovarian cancer (OC) is commonly diagnosed at advanced stage when prognosis is poor. Consequently, there is an urgent clinical need to identify novel biomarkers for early detection to improve survival. We examined the diagnostic value of the calcium phospholipid binding protein annexin A2 (ANXA2), which plays an important role in OC metastasis. Annexin A2 plasma levels in patients with high grade serous OC (n = 105), benign ovarian lesions (n = 55) and healthy controls (n = 143) were measured by ELISA. Annexin A2 levels were found to be significantly increased in patients with stage I (p < 0.0001) and stage IA (p = 0.0027) OC when compared to healthy controls. In the logistic regression models followed by receiver operating characteristics (ROC) curve analyses, plasma annexin A2 showed 46.7% sensitivity at 99.6% specificity in distinguishing stage IA OC patients from healthy controls and 75% sensitivity at 65.5% specificity in the diagnosis of stage IA versus benign ovarian tumors. In the diagnosis of stage IA OC versus normal controls, the combination of plasma annexin A2 and CA125 showed 80% sensitivity at 99.6% specificity (AUC = 0.970) which was significantly higher than for CA125 (53.3% sensitivity at 99.6% specificity; AUC = 0.891) alone. The diagnostic accuracy in distinguishing stage IA OC from benign ovarian disease when combining annexin A2 and CA125 (71.4% accuracy at 100% sensitivity) was almost twice as high compared to CA125 (37.1% accuracy at 100% sensitivity) alone. In conclusion, annexin A2 in combination with CA125 has potential as a biomarker for the early detection of OC and to predict malignancy in patients with ovarian lesions, warranting further investigations.

Serum Exosomal Dicer Is a Useful Biomarker for Early Detection of Differentiated Gastric Adenocarcinoma

Digestion ◽  
2020 ◽  
pp. 1-10
Author(s):  
Yusuke Okuda ◽  
Takaya Shimura ◽  
Hiroyasu Iwasaki ◽  
Takahito Katano ◽  
Mika Kitagawa ◽  
...  
Keyword(s):  
Early Detection ◽  
Gastric Adenocarcinoma ◽  
Early Stage ◽  
Area Under The Curve ◽  
Diagnostic Biomarker ◽  
Basic Study ◽  
Particle Size Analyzer ◽  
H Pylori ◽  
Serum Exosomes

<b><i>Background and Aim:</i></b> A recent basic study identified that Dicer is contained in exosomes derived from cancer cells and plays crucial roles in microRNA maturation and cancer development. Based on this novel basic concept, we analyzed the usefulness of serum exosomal Dicer as a diagnostic biomarker for gastrointestinal cancers. <b><i>Methods:</i></b> Enrolled participants (691) were categorized into 3 groups: gastric cancer (GC) cohort, 183 patients (90 healthy controls (HCs) and 93 GC patients); esophageal cancer (EC) cohort, 115 patients (90 HCs and 25 EC patients); and colorectal cancer (CRC) cohort, 188 patients (92 HCs and 96 CRC patients) after age- and sex matching using the propensity score. The quality of isolated serum exosomes was validated with an electron microscope, particle size analyzer, and exosome marker, CD63. <b><i>Results:</i></b> Serum exosomal Dicer was significantly higher in the GC group than in the HC group (<i>p</i> = 0.004), whereas no significant differences were found in both EC and CRC cohorts. Serum exosomal Dicer was significantly higher in only differentiated gastric adenocarcinoma and not in the undifferentiated type. Moreover, serum exosomal Dicer showed no significant differences regardless of <i>Helicobacter pylori</i> (<i>H. pylori</i>) status. The biomarker panel combining serum exosomal Dicer with <i>H. pylori</i> status distinguished between HC and differentiated GC patients with an area under the curve (AUC) of 0.762. As for early-stage diagnosis, this combination distinguished between HC and stage I differentiated GC with an AUC = 0.758. <b><i>Conclusions:</i></b> Serum exosomal Dicer is a potential noninvasive diagnostic biomarker for early detection of differentiated gastric adenocarcinoma.

scholarly journals Nomogram to identify severe Coronavirus Disease 2019 (COVID-19) based on initial clinical and CT characteristics: a multi-center study

2020 ◽  
Author(s):  
Yixing Yu ◽  
Ximing Wang ◽  
Min Li ◽  
Lan Gu ◽  
Zongyu Xie ◽  
...  
Keyword(s):  
Risk Factors ◽  
Calibration Curve ◽  
Severity Score ◽  
Early Stage ◽  
Area Under The Curve ◽  
Clinical Impact ◽  
Imaging Data ◽  
Net Benefit ◽  
Training Cohort ◽  
Independent Risk Factors

Abstract Background: To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics.Methods: The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. 217 patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart.Results: In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5-9) was significantly higher than that of the mild group (4, IQR:2-5) (P < 0.001). Age, density, mosaic perfusion sign and severity score of lung were independent risk factors for severe COVID-19. The nomogram had a AUC of 0.929 (95% CI, 0.889-0.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867-1.000), sensitivity of 90.5% and specificity of 88.6% in the validation cohort. The calibration curve, decision curve, clinical impact curve and risk chart showed that nomogram had high accuracy and superior net benefit in predicting severe COVID-19.Conclusion: The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.

scholarly journals A Plasma-Derived Protein-Metabolite Multiplexed Panel for Early-Stage Pancreatic Cancer

2018 ◽  
Vol 111 (4) ◽  
pp. 372-379 ◽  
Author(s):  
Johannes F Fahrmann ◽  
Leonidas E Bantis ◽  
Michela Capello ◽  
Ghislaine Scelo ◽  
Jennifer B Dennison ◽  
...  
Keyword(s):  
Early Stage ◽  
Ductal Adenocarcinoma ◽  
Pancreatic Cysts ◽  
Healthy Controls ◽  
Protein Marker ◽  
Independent Test ◽  
Comprehensive Metabolomics ◽  
Improved Performance ◽  
Plasma Metabolite ◽  
Independent Cohort

Abstract Background We applied a training and testing approach to develop and validate a plasma metabolite panel for the detection of early-stage pancreatic ductal adenocarcinoma (PDAC) alone and in combination with a previously validated protein panel for early-stage PDAC. Methods A comprehensive metabolomics platform was initially applied to plasmas collected from 20 PDAC cases and 80 controls. Candidate markers were filtered based on a second independent cohort that included nine invasive intraductal papillary mucinous neoplasm cases and 51 benign pancreatic cysts. Blinded validation of the resulting metabolite panel was performed in an independent test cohort consisting of 39 resectable PDAC cases and 82 matched healthy controls. The additive value of combining the metabolite panel with a previously validated protein panel was evaluated. Results Five metabolites (acetylspermidine, diacetylspermine, an indole-derivative, and two lysophosphatidylcholines) were selected as a panel based on filtering criteria. A combination rule was developed for distinguishing between PDAC and healthy controls using the Training Set. In the blinded validation study with early-stage PDAC samples and controls, the five metabolites yielded areas under the curve (AUCs) ranging from 0.726 to 0.842, and the combined metabolite model yielded an AUC of 0.892 (95% confidence interval [CI] = 0.828 to 0.956). Performance was further statistically significantly improved by combining the metabolite panel with a previously validated protein marker panel consisting of CA 19–9, LRG1, and TIMP1 (AUC = 0.924, 95% CI = 0.864 to 0.983, comparison DeLong test one-sided P= .02). Conclusions A metabolite panel in combination with CA19-9, TIMP1, and LRG1 exhibited substantially improved performance in the detection of early-stage PDAC compared with a protein panel alone.

Clinical Data Prediction Model to Identify Patients With Early-Stage Pancreatic Cancer

2021 ◽  
pp. 279-287
Author(s):  
Qinyu Chen ◽  
Daniel R. Cherry ◽  
Vinit Nalawade ◽  
Edmund M. Qiao ◽  
Abhishek Kumar ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Early Detection ◽  
Prediction Model ◽  
Clinical Data ◽  
Late Stage ◽  
Early Stage ◽  
Area Under The Curve ◽  
Screening Tools ◽  
Data Set ◽  
Extreme Gradient Boosting

PURPOSE Pancreatic cancer is an aggressive malignancy with patients often experiencing nonspecific symptoms before diagnosis. This study evaluates a machine learning approach to help identify patients with early-stage pancreatic cancer from clinical data within electronic health records (EHRs). MATERIALS AND METHODS From the Optum deidentified EHR data set, we identified early-stage (n = 3,322) and late-stage (n = 25,908) pancreatic cancer cases over 40 years of age diagnosed between 2009 and 2017. Patients with early-stage pancreatic cancer were matched to noncancer controls (1:16 match). We constructed a prediction model using eXtreme Gradient Boosting (XGBoost) to identify early-stage patients on the basis of 18,220 features within the EHR including diagnoses, procedures, information within clinical notes, and medications. Model accuracy was assessed with sensitivity, specificity, positive predictive value, and the area under the curve. RESULTS The final predictive model included 582 predictive features from the EHR, including 248 (42.5%) physician note elements, 146 (25.0%) procedure codes, 91 (15.6%) diagnosis codes, 89 (15.3%) medications, and 9 (1.5%) demographic features. The final model area under the curve was 0.84. Choosing a model cut point with a sensitivity of 60% and specificity of 90% would enable early detection of 58% late-stage patients with a median of 24 months before their actual diagnosis. CONCLUSION Prediction models using EHR data show promise in the early detection of pancreatic cancer. Although widespread use of this approach on an unselected population would produce high rates of false-positive tests, this technique may be rapidly impactful if deployed among high-risk patients or paired with other imaging or biomarker screening tools.

scholarly journals Feasibility of Methylated CLIP4 in Stool for Early Detection of Colorectal Cancer: A Training Study in Chinese Population

2021 ◽  
Vol 11 ◽  
Author(s):  
Yang Cao ◽  
Guodong Zhao ◽  
Yaping Cao ◽  
Zhiliang Chen ◽  
Xiaoyu Liu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Early Detection ◽  
Methylation Level ◽  
Precancerous Lesion ◽  
Area Under The Curve ◽  
Final Analysis ◽  
Advanced Adenoma ◽  
Promoter Regions ◽  
Potential Biomarker ◽  
Significant Difference

BackgroundEarly detection of colorectal cancer (CRC) and precancerous lesion is vitally important for mitigating CRC morbidity and mortality. Aberrant DNA methylations in certain promoter regions have been identified to be closely associated with CRC development and progression, suggesting their potential as diagnostic biomarkers for early detection. In this study, we evaluated the performance of methylated CLIP4 in stool specimens as a potential biomarker for CRC detection.MethodsA total of 321 subjects out of 365 enrolled participants were included in the final analysis, including 154 CRC patients, 23 advanced adenoma (AA) patients, 49 small polyp (SP) patients, and 95 healthy controls. CLIP4 methylation level was examined by qPCR with bisulfite converted DNA purified from approximately 5 g stool specimen.ResultsMethylated CLIP4 test showed high sensitivities of 78.3% (95% CI: 55.8%–91.7%) and 90.3% (95% CI: 84.2%–94.3%) for detecting AA and CRC, respectively, with a specificity of 88.4% (95% CI: 79.8%–93.8%). CLIP4 methylation level discriminated AA and CRC patients from control subjects with area under the curve values of 0.892 (95% CI: 0.795–0.988) and 0.961 (95% CI: 0.938–0.983). Further analysis indicated no significant difference in sensitivities among different ages, genders, stages, locations, sides, tumor sizes and differentiation statuses.ConclusionsMethylated CLIP4 showed a strong potential as a noninvasive biomarker for early CRC detection.

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