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The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of tumour-derived signals at dilution factors as low as 1 in 10,000. For a total of 308 patients with surgery-resectable lung cancer and 261 age- and sex-matched non-cancer control individuals recruited from two hospitals, the assay detected 52-81% of the patients at disease stages IA to III with a specificity of 96% (95% confidence interval (CI) 93-98%). In a subgroup of 115 individuals, the assay identified, at 100% specificity (95% CI 91-100%), nearly twice as many patients with cancer as those identified by ultradeep mutation sequencing analysis. The low amounts of ctDNA permitted by machine-learning-aided deep methylation sequencing could provide advantages in cancer screening and the assessment of treatment efficacy.

Circulating tumor DNA (ctDNA) sequencing is being rapidly adopted in precision oncology, but the accuracy, sensitivity, and reproducibility of ctDNA assays is poorly understood. Here we report the findings of a multi-site, cross-platform evaluation of the analytical performance of five industry-leading ctDNA assays. We evaluated each stage of the ctDNA sequencing workflow with simulations, synthetic DNA spike-in experiments, and proficiency testing on standardized cell line–derived reference samples. Above 0.5% variant allele frequency, ctDNA mutations were detected with high sensitivity, precision and reproducibility by all five assays, whereas below this limit detection became unreliable and varied widely between assays, especially when input material was limited. Missed mutations (false-negatives) were more common than erroneous candidates (false-positives), indicating that the reliable sampling of rare ctDNA fragments is the key challenge for ctDNA assays. This comprehensive evaluation of the analytical performance of ctDNA assays serves to inform best-practice guidelines and provides a resource for precision oncology., Editorial summary: Reliable detection of mutations below 0.5% variant allele frequency remains a key challenge for circulating tumor DNA sequencing assays.

Background Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. Results In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5–100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. Conclusion These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

We report the synthesis and basic characterization details of bulk Bi1 − xPbxFe1 − xTixO3 (x = 0.05 and 0.1) polycrystalline samples, which have been synthesized using the conventional solid-state route. We studied the effects of partially doping of Pb and Ti ion on structural, vibrational, and magnetic properties of multiferroic BiFeO3. X-ray diffraction (XRD) was used for crystallographic studies, followed by Rietveld refinement, and phase formation of the compounds was confirmed, which indicates that the sample has rhombohedral (R3c, 100%) symmetry for x = 0.05 and R3c (98%) + P4mm (2%) symmetry for x = 0.1. X-ray absorption spectroscopy has been probed at Fe L2,3 and O K edges to determine the valence (charge) state of Fe in BiFeO3. Interestingly, the magnetic measurement results revealed the existence of spin reorientation transition in Pb and Ti-modified BiFeO3, which indicates that the BiFeO3 samples studied may find promising applications in memory and spintronic devices.

Background: Patient-specific flexible gene panels designed based on whole-exome sequencing (WES) of resected tumor tissues is a promising strategy for ctDNA-based detection of molecular residual disease (MRD) in early-stage NSCLC. Flexible gene panels could potentially overcome the limitations of fixed panels by incorporating more unique genomic regions that might be absent in fixed panels; however, no study has reported a head-to-head comparison of these two approaches in postoperative disease monitoring. In this study, we investigated the clinical utility of a novel Patient-specific pROgnostic and Potential tHErapeutic marker Tracking (PROPHET) tumor-informed ctDNA assay. Using the same set of longitudinal blood samples, we further compared the performance of PROPHET assay with tumor-informed (TI) and tumor-naïve (TN) fixed panels for predicting MRD and prognosis in surgical NSCLC patients. Methods: Fifty-three patients with stage I-III resected NSCLC from the MEDAL study (NCT03634826) with adequate samples and median follow-up of 647 days were analyzed. Matched surgical tumor tissue and blood samples collected at various time points, including before surgery (baseline), 3-days (B) and 1-month (C) postoperative time points before any adjuvant therapy and subsequent follow-up time points (F) were analyzed. PROPHET assay involved four major steps: identify somatic mutations using WES, customized design of a patient-specific panel consisting of 50 single nucleotide variants, ultra-deep unique molecular-identifier-based next-generation sequencing (UMI-NGS) of serial blood samples using the patient-specific panel, and MRD risk prediction. Fixed panel assay of serial blood samples was performed using UMI-NGS with 168 gene panel spanning 273 kb of human genome. Results: At 1-month post-surgery, PROPHET assay accurately predicted MRD-positive cases among relapsed patients (50%, 13/26), whereas all disease-free patients were MRD-negative (100%, 21/21). Three-year prognostication with PROPHET assay at B+C yielded higher sensitivity (59% vs 26% vs 22%), negative predictive value (66% vs 51% vs 50%), and hazard ratio (7.15, 95%CI [3.2-15.9] vs 4.48 [1.9-10.9] vs 5.58 [2.1-14.7]) as compared with TI and TN fixed panel assays. Disease monitoring using PROPHET assay at B/C/F accurately predicted the MRD risk in 70% (21/30) of relapsed patients at a median lead time of 318 days (range: 20-751), whereas TI assay predicted 43% (13/30) at 282 days (range 20-716) and TN assay predicted 37% (11/30) at 282 days (range: 20-634). Conclusion: Patient-specific tumor-informed ctDNA-based postoperative monitoring enables risk stratification at early postoperative settings better than fixed panel, which paves an alternative strategy in the individualized management of surgical NSCLC patients. Citation Format: Kezhong Chen, Haifeng Shen, Shuailai Wu, Pengfei Zhu, Chenyang Wang, Analyn Lizaso, Guannan Kang, Yang Wang, Juan Lv, Shuai Fang, Wenjun Wu, Fujun Qiu, Yuan Sun, Qiang Lu, Heng Zhao, Shannon Chuai, Fan Yang, Zhihong Zhang. Tumor-informed patient-specific panel outperforms tumor-naïve and tumor-informed fixed panel for circulating tumor DNA (ctDNA)-based postoperative monitoring of non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5916.

Introduction: Detection of cancer is its early stages can potentially improve therapeutic effectiveness. Previously, we demonstrated that ELSA-seq, a machine-learning-aided methylation profiling test, can detect and locate multiple cancers with high accuracy in plasma samples. Here, we evaluated the analytical performance of a refined test version of ELSA-seq, including analytical sensitivity, specificity, repeatability/reproducibility, and robustness. Methods: The classification algorithm and cut-offs for the ELSA-seq test were established in the THUNDER study (NCT04820868). Here, we describe the analytical performance using both plasma samples from the THUNDER study and DNA blends from cell lines. Analytical sensitivity was established by defining the limit of detection (LoD) using in-house cfDNA Methylation Reference Standards (MRS) with lowest DNA input for the assay. In brief, fragmented genomic control DNA (NA24385, Coriell Institute) was used as a diluent to contrive human cancer cell lines with defined mixing ratios (tumor fractions), which was further verified by digital droplet PCR (ddPCR). The LoD was determined by the lowest tumor fractions at which accurate DOC and TOO was reported in at least 95% of replicates. As variant allele frequency (VAF) is widely used as a surrogate measurement for tumour fractions, we also used ultra-deep mutation sequencing to attain VAF as an independent piece of evidence. Analytical specificity was assessed by the true negative rate in 120 plasma samples from age-matched healthy donors. A batch-to-batch repeatability/reproducibility study was carried out using 168 clinical samples processed across multiple reagent lots, instruments, and operators. Robustness was evaluated using common interfering substances that potentially could be present in plasma samples such as hemoglobin, bilirubin, triglycerides, and genomic DNA. Testing was performed using 24 cancer and 24 non-cancer samples, with or without interfering substances. Results: At 5ng input mass (approximating from 1ml plasma), the LOD95 was estimated down to 0.05% (tumor fractions) or 0.02% (VAF) among 6 cancer cell lines. 2 false positives were detected in 120 age-matched healthy donor samples, yielding a specificity of 98.3% (95%CI: 93.5-99.7%). All test results were concordant across multiple reagent lots, instruments, and operators (100% repeatability and reproducibility), and high Pearson correlation coefficients (>99%) were observed in the pair-wise comparison. In addition, none of the substances tested interfered with the assay. Conclusions: These results suggest that ELSA-seq is a highly sensitive test for detection of the trace amount of tumor-derived methylation signals in plasma samples. The performance is highly reproducible and robust, which is critical for clinical implementations. Citation Format: Bingsi Li, Jing Su, Guangliang Zhang, Jiayue Xu, Jianlong Peng, Ya Zhou, Fujun Qiu, Shuai Fang, Xiaofang Wen, Guoqiang Wang, Jing Zhao, Hao Wang, Shangli Cai, Zhihong Zhang. Analytical performance of ELSA-seq, a blood-based test for early detection of multiple cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5116.

Background: Identifying molecular residual disease (MRD) with tailored tumor-informed ctDNA based next-generation sequencing (NGS) assays after curative surgery could facilitate the individualized management of resected CRC patients. Here, we investigated the clinical utility of tumor-informed ctDNA mutation analysis using a novel Patient-specific pROgnostic and Potential tHErapeutic marker Tracking (PROPHET) approach for accessing MRD in resected CRC patients. Using the same set of baseline and post-operative blood samples, we compared the performance of PROPHET assay with tumor-naïve fixed panel for detecting MRD and predicting recurrence in resected CRC patients. Methods: The prospective study recruited 42 patients diagnosed with stage I-III CRC from May 2019 to Jun 2020 at the First Affiliated Hospital of Soochow University. Tumor tissue samples were collected at surgery. Blood samples collected before surgery (baseline), 8-day post-operative time point before any adjuvant therapy were analyzed. The detection and quantification of ctDNA for MRD assessment was investigated using PROPHET, a personalized, tumor-informed ctDNA assay designed to track up to 50 top-ranked patient-specific somatic variants based on whole-exome sequencing (WES) of the tumor tissue and matched white blood cells (WBCs). Tumor- naïve fixed assay was performed using targeted NGS panel, containing 41 gastrointestinal cancer-related genes. Results: Baseline ctDNA status was detected in 95.23% (40/42) of the patients with PROPHET assay, and 69.05% (29/42) of the patients with fixed panel. Of 42 patients included in the analysis, 1, 25, and 16 patients had pathological stages I, II, and III CRC with baseline ctDNA detected in 100% (1/1), 92% (23/25), 100% (16/16) patients with PROPHET assay, and 100% (1/1), 64% (16/25), 75% (12/16) patients with fixed panel. Post-operative ctDNA-positive status with PROPHET assay was associated with 3-year DFS, compared with ctDNA-negative group (hazard ratio [HR], 16.57, 95% confidence interval [CI]:3.01-91.36, p=0.014). 15% (6/40) patients were identified to be MRD- positive and 83.33% (5/6) patients eventually relapsed at 3-years follow-up. Although fixed panel also showed high performance in predicting relapse HR, 4.48, 95% CI:1.9-10.9; p< 0.001, 27.5% (11/40) patients were identified to be MRD-positive and only 45.45% (5/11) patients eventually relapsed. 3-year prognostication with PROPHET assay at 8-day post-operation yielded higher positive predictive value (83.33% vs 45.45%), negative predictive value (91.18% vs 89.66%), and specificity (96.88% vs 81.25%) as compared with tumor-naïve fixed panel. Conclusion: Patient-specific PROPHET assay based on unique somatic mutation profiles detects patients with high-risk of recurrence, which achieved higher specificity than tumor-naïve fixed panel. Citation Format: Jian Zhou, Jian Yang, Zixiang Zhang, Ye Li, Bin Yi, Yuchen Tang, Dechun Li, Xiaozhe Li, Di Peng, XI Li, Yang Wang, Haiyan Li, Bing Li, Chenyang Wang, Pengfei Zhu, Longfei Chen, Shuailai Wu, Shuai Fang, Chenxi Li, Fujun Qiu, Shannon Chuai, Zhihong Zhang. Patient-specific tumor-informed circulating tumor DNA (ctDNA) analysis for postoperative monitoring of patients with stages I-III colorectal cancer (CRC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5917.

The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of tumour-derived signals at dilution factors as low as 1 in 10,000. For a total of 308 patients with surgery-resectable lung cancer and 261 age- and sex-matched non-cancer control individuals recruited from two hospitals, the assay detected 52-81% of the patients at disease stages IA to III with a specificity of 96% (95% confidence interval (CI) 93-98%). In a subgroup of 115 individuals, the assay identified, at 100% specificity (95% CI 91-100%), nearly twice as many patients with cancer as those identified by ultradeep mutation sequencing analysis. The low amounts of ctDNA permitted by machine-learning-aided deep methylation sequencing could provide advantages in cancer screening and the assessment of treatment efficacy.

e22518 Background: Early detection of cancer has the potential to provide a clinical benefit to patients, especially for those with hard-to-treat diseases like ovarian and pancreatic cancer. The THUNDER (the unintrusive detection of early-stage cancers) program was designed to develop and validate a multicancer detection blood test (ELSA-seq) through a series of prospective, observational case-control studies. The second sub-study of THUNDER program (THUNDER-II) includes a discovery, training/validation, and single-blind test (SBT) phase. This new analysis, comprising data from the SBT set of THUNDER-II, evaluated performance of ELSA-seq in an independent group of participants, including individuals at high risk of certain cancer types for the first time. Methods: In the SBT group reported here, all participants were recruited from sites that were not represented in training or validation. The cancer group focused on six deadly cancer types (lung, colorectal, liver, pancreatic, esophageal, and ovarian cancer), and the non-cancer controls were stratified to ‘healthy’ and ‘high-risk’ subgroups based on examination results of low-dose spiral chest CT scan (LDCT), abdominal ultrasound, and Hepatitis B serologic test (HBsAg). 10ml of blood from each participant was collected and methylation-sequencing libraries were prepared by ELSA-seq as described previously. The detection results were automatically reported by a locked computational pipeline to mimic a real-world test scenario (Burning Rock Biotech, China). All samples were delinked from the donors’ cancer status and experiments were performed by blinded personnel. Results: In general, the cancer and non-cancer groups were comparable with respect to age, sex, and tobacco/alcohol-consumption history. The assessment is still in progress, and we have studied 360 eligible participants including 202 cancer cases and 158 non-cancer controls (76 ‘healthy’; 82 ‘high-risk’). Overall, ELSA-seq achieved a specificity of 98.1% (95%CI: 94.1-99.5%) and a sensitivity of 74.8% (95%CI: 68.1-80.5%), consistent with the performance in training and validation1. Among six pre-specified cancer types, sensitivity was 53.0% (95%CI: 40.4-76.3%) in stage I, 73.3% (95%CI: 57.8-84.9%) in stage II, 90.4% (95%CI: 78.2-96.4%) in stage III, and 92.3% (95%CI: 78.0-98.0%) in stage IV. In terms of identifying the diseased organ(s), 80.8% (95%CI: 73.4-86.6%) of the predications matched the true TOO status. Conclusions: ELSA-seq demonstrated consistent results in sites that did not contribute to training, supporting that performance was not subject to sample-/site-dependent bias. It also showed a very low false positive rate in asymptomatic individuals with or without certain cancer-prone conditions, suggesting a potential applicability to broad populations.

459 Background: Early detection of cancer can potentially offer clinical benefits, particularly for those without effective screening methods. The PREDICT study (pan-cancer early detection project, NCT 04383353) is a prospective, multi-center, longitudinal study that aims to identify multiple cancers non-invasively before symptoms become apparent. As a pilot project, the THUNDER (the unintrusive detection of early-stage cancer) study is designed for development and validation of ELSA-seq, a sensitive targeted methylation sequencing assay that interrogates epigenetic alterations from circulating cell-free DNA (cfDNA). Herein we report results from the second THUNDER sub study (THUNDER-II), which focused on malignancies developed in liver, colon/rectum, esophagus, pancreas, lung and ovary, of which four are gastrointestinal (GI) cancer types. Methods: THUNDER-II comprises four independent steps: marker discovery, model training, validation, and single-blind test. The marker discovery work was conducted by profiling 5.5 million CpG sites in 247 tissue samples (116 cancer, 131 normal/benign, 6 cancer types) using a SeqCap Epi CpGiant system (Roche). By combining data generated in-house and from public sources, a custom hybridization capture panel was designed to target 161,984 CpG sites. For cfDNA applications, 625 patients and 483 non-cancer controls were enrolled and divided into a training set (274 cancer and 195 non-cancer) and an independent validation set (351 cancer and 288 non-cancer). Results: The cancer patients and non-cancer controls were generally comparable with respect to age, gender, and smoking status. Various stages were represented in the cancer group, and 79.5% patients were diagnosed at early stages (I-III). At 99.5% training specificity (95%CI: 96.7-100%), the cross-validated sensitivity was 79.9% (95%CI: 74.6-84.4%). The results were consistent in the validation set, with 98.3% specificity (95%CI: 95.8-99.4%) and 80.6% (76.0-84.6%) sensitivity across stages and cancer types. In terms of tracking diseased organ(s), the classifier returned a tissue-of-origin (TOO) result in 98.6% cases, and 81.0% (95%CI: 77.2-84.3%) of these predictions were correct. Conclusions: Results from the THUNDER-II study demonstrated that early cancer signals could be identified by ELSA-seq with high specificity. This method also enabled accurate prediction of TOO, offering guidance for subsequent diagnostic work-up. Together these findings highlight the potential implementation of this sensitive and robust assay as a multi-cancer detection test.

Introduction: Ovarian cancer (OC), often diagnosed at an advanced stage, lacks effective screening strategy. It is the most lethal gynecological cancer, with a 5-year survival ranging from 35-50% depending on the subtype. A clear correlation between stage and survival has been demonstrated; therefore, early detection represents the best hope for mortality reduction. This study interrogates the potential of ctDNA methylation status as an early diagnostic marker for OC. Methods: Panel Design: Public data sets such as TCGA and GEO databases (tumor=4,014, normal=352) and in-house generated functional methylome (5.5 million CpG sites, 50X) sequencing data (n=561) were used to profile methylation alterations in 12 common cancer types. The panel covers 225,369 CpG sites, spanning ~3.5Mb of human genome. Deep targeted bisulfite sequencing (ELSA-seq) was performed on plasma samples of 126 Chinese ovarian cancer patients, 59 patients with benign ovarian diseases, including but not limited to endometrial cyst, ovarian cyst and serous cystadenoma and 49 age-matched asymptomatic females to interrogate their methylation statuses with an average sequencing depth of 1,000x. The plasma samples were collected and processed from two centers independently, and randomly assigned into the training (n=123) or validation group (n=113) at 1:1 ratio. Samples that failed quality control specifications were removed from the downstream analysis and the diagnostic information was not disclosed for the validation group until all analyses were completed. Results: First, ovarian tissues samples from 22 OC (FIGO stage II-III) patients, 11 ovarian benign diseases patients and 11 healthy donors were profiled to derive markers that can differentiate the 3 groups. In this study, 45,753 OC-specific differentially methylated CpG sites/loci (DMLs) were selected using modified wald-test with an adjusted p-value 2, and subsequently grouped into 3,160 differentially methylated blocks (DMBs) based on the co-methylation levels and genomic distances of adjacent CpG sites. Blood samples from 63 patients with OC (4 stage I, 6 stage II and 53 stage III), 33 patients with benign ovarian disease and 27 healthy individuals were obtained to train the diagnostic classification model using a support vector machine (SVM)-based machine learning classifier. Subsequently, iterated 5-fold cross-validation were performed to gain a robust estimation of model performance, achieving a specificity of 91% for benign ovarian diseases and sensitivity of 25%, 50% and 87% for stage I, II and III OC, respectively at a specificity of 93% and area under curve (AUC) of 91.3%. The model was subsequently validated in a hold-out cohort, consisting of 22 healthy individuals, 26 patients with benign ovarian disease and 63 stage I-III OC patients with comparable clinical characteristics as the patients in the training cohort. The model yielded a specificity of 93% for benign disease and a sensitivity of 84% for OC at a specificity of 96% in the validation cohort, suggesting its robustness. Conclusion: Our findings demonstrated the potential of ctDNA methylation profiling, which can effectively distinguish ovarian cancer, benign diseases and healthy, to serve as a diagnostic tool in ovarian cancer early detection. The general concept can be further extended to other types of cancer diagnostics. Citation Format: Ning Li, Yu Zhang, Xin Zhu, Zhenjing Zhang, Jiayue Xu, Bingsi Li, Han Han-Zhang, Fujun Qiu, Shuai Fang, Hao Liu, Zhihong Zhang. Methylation profiling of circulating tumor DNA for the detection of ovarian cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2302.

Background: Detection of cancer in its early stages is likely to be the most effective way to improve clinical outcome. In patients with cancer, a portion of cell-free DNA (cfDNA) in the blood stream is tumor derived, providing an opportunity to analyze the cancer genome in real time noninvasively. MERMAID is a retrospective multicenter case-control study to investigate early tumor signatures in blood using different platforms. Methods: The study was conducted among 452 surgery-resectable patients with lung cancer (N=180), colorectal cancer (N=210), liver cancer (N=62), and 290 age-/sex- matched non-cancer controls. Patients who were recognized to have anemia, autoimmune diseases, treated with neoadjuvant therapy were excluded from the study. The non-cancer controls were recruited with the criteria of showing no clinical symptoms or history of cancer at time of administration. The participants were divided into subgroups and analyzed by ultra-deep mutation sequencing (HS-UMI), droplet digital PCR (ddPCR), and deep methylation sequencing (ELSA-seq) singly or in combination. Results: Overall, the highest accuracy was achieved using ELSA-seq and will be reported in full. Highly similar classification results were obtained in training and test sets, with the area under the curve (AUC) value ranging from 0.90-0.97. The specificity for each cancer type ranged from 96-99%, and the average sensitivities with 95CI were lung cancer (61%, 53-70%), colorectal cancer (82%, 76-87%), and hepatocellular cancer (91%, 80-96%). Moreover, incorporation of somatic variants and epigenetic alterations improved the overall accuracy. Conclusions: This study highlighted the potential of machine learning-aided deep methylation sequencing as a sensitive ctDNA profiling approach for early cancer detection. Further investigation in large-scale clinical studies is ongoing. Citation Format: Bingsi Li, Chenyang Wang, Jiayue Xu, Shuai Fang, Fujun Qiu, Jing Su, Huiling Chu, Han Han-Zhang, Xinru Mao, Hao Liu, Xianling Liu, Wei Zhang, Heng Zhao, Zhihong Zhang. Multiplatform analysis of early-stage cancer signatures in blood [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr A06.

LncRNAs are involved in a wide range of biological processes, such as chromatin remodeling, mRNA splicing, mRNA editing and translation. They can either upregulate or downregulate gene expression, and play key roles in the progression of various human cancers. However, the functional mechanisms of most lncRNAs still remain unknown at present. This paper aims to provide space for the understanding of lncRNAs by proposing a new method to obtain protein-coding genes (PCGs) regulated by lncRNAs, thus identifying candidate cancer-related lncRNAs using bioinformatics approaches. This study presents a method based on sample correlation, which is applied to the expression profiles of lncRNAs and PCGs in prostate cancer in combination with protein interaction data to build a lncRNA-PCG bipartite network. Candidate cancer-related lncRNAs were extracted from the bipartite network by using a random walk. 14 prostate cancer-related lncRNAs were acquired from the LncRNADisease database and MNDR, of which 6 lncRNAs were present in our network. As one of the seed nodes, ENSG00000234741 achieved the highest score among them. The other two cancer-related lncRNAs (ENSG00000225937 and ENSG00000236830) were ranked within the top 30. In addition, the top candidate lncRNA ENSG00000261777 shares an intron with DDX19, and interacts with IGF2 P1, indicating its involvement in prostate cancer. In this paper, we described a new method for predicting candidate lncRNA targets, and obtained candidate therapeutic targets using this method. We hope that this study will bring a new perspective in future lncRNA studies.

Introduction: Lung cancer found at an early stage carries much-improved prognosis. Unfortunately, there has been little success in developing blood-based diagnostic method. Profiling somatic mutations from ctDNA has shown great promise for cancer diagnosis, prognosis, and surveillance. Despite the substantial advances made in ctDNA detection techniques, the detection rate remains low for early stage disease. The role of aberrant DNA methylation in the process of tumorigenesis both at individual genes and a genome-wide scale has been well elucidated. It occurs very early in cancer development, thus capable of serving as a diagnostic biomarker. In this prospective study, we evaluated the potentiality of DNA methylation status obtained from ctDNA as an early diagnostic marker for NSCLC. Methods: Panel Design: Methylation data of tumor samples (12 types, n=4772), adjacent normal (8 types, n=411), and normal white blood cells (WBC, n=656) from TCGA and GSE were compared. Differentially methylated sites were extracted using modified wald-test with an adjusted p-value 2. Our panel covers 80,672 CpG sites, spanning 1.05Mb of human genome. We performed targeted bisulfite sequencing on plasma samples of 359 early stage Chinese NSCLC patients and 144 healthy individuals to interrogate their methylation statuses with an average sequencing depth of 1,000x. The Results: The training cohort consisted of 359 early stage Chinese lung cancer patients (266 stage IA, 25 stage IB, 43 stage II and 25 stage III) with a median age of 51 and 144 Chinese healthy individuals with a median age of 59. We constructed a diagnostic classification model using a support vector machine (SVM)-based machine learning classifier based on top 3,000 differentially methylated regions (DMRs) selected by random forest between tumor and normal plasma samples. Subsequently, 5-fold cross-validation with 100-time repeats were performed to gain a robust estimation of model performance, achieving a sensitivity of 79.4%, specificity of 95.4% and area under curve (AUC) of 95.2%. The model was subsequently validated in an independent cohort, consisting of 79 early stage Chinese NSCLC patients and 74 healthy individuals with comparable clinical characteristics as the patients in the training cohort. The model yielded a sensitivity of 77% and specificity of 90% in the validation cohort, suggesting its robustness. Conclusions: Overall, our findings demonstrated in a large clinical cohort the potential of profiling DNA methylation from ctDNA, which can effectively distinguish cancerous from healthy, for the purpose of diagnosis. This method has potential to serve as a supplementary or alternative approach in lung cancer early detection. The general concept can be further extended to other types of cancer diagnostics. Citation Format: Naixin Liang, Bingsi Li, Chenyang Wang, Tao Zheng, Jiayue Xu, Shuai Fang, Fujun Qiu, Jing Su, Lichen Zhang, Xin Lu, Miaomiao Song, Lingjian Yang, Han Han-Zhang, Xinru Mao, Hao Liu, Shanqing Li, Ke Ma, Zhihong Zhang. DNA methylation profiling from circulating tumor DNA for early diagnosis of non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 816.

Copy number variation (CNV) is a kind of chromosomal structural reorganization that has been detected, in this decade, mainly by high-throughput biological technology. Researchers have found that CNVs are ubiquitous in many species and accumulating evidence indicates that CNVs are closely related with complex diseases. The investigation of chromosomal structural alterations has begun to reveal some important clues to the pathologic causes of diseases and to the disease process. However, many of the published studies have focused on a single disease and, so far, the experimental results have not been systematically collected or organized. Manual text mining from 6301 published papers was used to build the Copy Number Variation in Disease database (CNVD). CNVD contains CNV information for 792 diseases in 22 species from diverse types of experiments, thus, ensuring high confidence and comprehensive representation of the relationship between the CNVs and the diseases. In addition, multiple query modes and visualized results are provided in the CNVD database. With its user-friendly interface and the integrated CNV information for different diseases, CNVD will offer a truly comprehensive platform for disease research based on chromosomal structural variations. The CNVD interface is accessible at http://bioinfo.hrbmu.edu.cn/CNVD.

In this paper, a novel hybrid electric vehicle (HEV) powertrain based on electric variable transmission (EVT) with dual clutches is put forward. In this novel powertrain, one clutch is used to enable the internal combustion engine to directly drive the wheels and the other is to fix the inner rotor of EVT to realize a double-electric-machine powertrain. Firstly, driving modes of this powertrain and its benefits is discussed. Secondly, the simulation is built by using EMR and inversion control strategies are designed. Compared with traditional EVT based HEV powertrain, the novel HEV powertrain is allowed to operate on more different modes, making the EVT based powertrain more flexible and more reasonable for its application.

Changes in intermolecular interactions (differential interactions) may influence the progression of cancer. Specific genes and their regulatory networks may be more closely associated with cancer when taking their transcriptional and post-transcriptional levels and dynamic and static interactions into account simultaneously. In this paper, a differential interaction analysis was performed to detect lung adenocarcinoma-related genes. Furthermore, a miRNA-TF (transcription factor) synergistic regulation network was constructed to identify three kinds of co-regulated motifs, namely, triplet, crosstalk and joint. Not only were the known cancer-related miRNAs and TFs (let-7, miR-15a, miR-17, TP53, ETS1, and so on) were detected in the motifs, but also the miR-15, let-7 and miR-17 families showed a tendency to regulate the triplet, crosstalk and joint motifs, respectively. Moreover, several biological functions (i.e., cell cycle, signaling pathways and hemopoiesis) associated with the three motifs were found to be frequently targeted by the drugs for lung adenocarcinoma. Specifically, the two 4-node motifs (crosstalk and joint) based on co-expression and interaction had a closer relationship to lung adenocarcinoma, and so further research was performed on them. A 10-gene biomarker (UBC, SRC, SP1, MYC, STAT3, JUN, NR3C1, RB1, GRB2 and MAPK1) was selected from the joint motif, and a survival analysis indicated its significant association with survival. Among the ten genes, JUN, NR3C1 and GRB2 are our newly detected candidate lung adenocarcinoma-related genes. The genes, regulators and regulatory motifs detected in this work will provide potential drug targets and new strategies for individual therapy.

Copy number alteration (CNA) is known to induce gene expression changes mainly through dosage effect and therefore affect the initiation and progression of tumor. However, tumor samples exhibit heterogeneity in gene dosage sensitivity due to the complicated mechanisms of transcriptional regulation. Currently, no high-throughput method has been available for identifying the regulatory factors affecting the functional consequences of CNA and determining their effects on cancer. In view of the important regulatory role of miRNA, we investigated the influence of miRNAs on the dosage sensitivities of genes within the CNA regions. By integrating copy number, mRNA expression, miRNA expression profiles of three kinds of cancer, we observed a tendency for high dosage-sensitivity genes to be more targeted by miRNAs in cancer and identified the miRNAs regulating the dosage sensitivity of amplified/deleted target genes. The results show that miRNAs can modulate oncogenic biological functions by regulating the genes within the CNA regions and thus play a role as a trigger or balancer in cancer, affecting cancer processes, even survival. This work provided a framework for analyzing the regulation of dosage effect, which will shed a light on understanding the oncogenic and tumor suppressive mechanisms of CNA. Besides, new cancer-related miRNAs were identified.

Phenotypic similarity is correlated with a number of measures of gene function, such as relatedness at the level of direct protein-protein interaction. The phenotypic effect of a deleted or mutated gene, which is one part of gene annotation, has caught broad attention. However, there have been few measures to study phenotypic similarity with the data from Human Phenotype Ontology (HPO) database, therefore more analogous measures should be developed and investigated. We used five semantic similarity-based measures (Jiang and Conrath, Lin, Schlicker, Yu and Wu) to calculate the human phenotypic similarity between genes (PSG) with data from HPO database, and evaluated their accuracy with information of protein-protein interaction, protein complex, protein family, gene function or DNA sequence. Compared with the gene pairs that were random selected, the results of these methods were statistically significant (all P

Copy number variations (CNVs) are one type of the human genetic variations and are pervasive in the human genome. It has been confirmed that they can play a causal role in complex diseases. Previous studies of CNVs focused more on identifying the disease-specific CNV regions or candidate genes on these CNV regions, but less on the synergistic actions between genes on CNV regions and other genes. Our research combined the CNVs with related gene co-expression to reconstruct gene co-expression network by using single nucleotide polymorphism microarray datasets and gene microarray datasets of breast cancer, and then extracted the modules which connected densely inside and analyzed the functions of modules. Interestingly, all of these modules’ functions were related to breast cancer according to our enrichment analysis, and most of the genes in these modules have been reported to be involved in breast cancer. Our findings suggested that integrating CNVs and gene co-expressed relations was an available way to analyze the roles of CNV genes and their synergistic genes in breast cancer, and provided a novel insight into the pathological mechanism of breast cancer.