The fine-scale cell-free DNA fragmentation patterns in early-stage cancers are poorly understood. We developed a de novo approach to characterize the cell-free DNA fragmentation hotspots from plasma whole-genome sequencing. Hotspots are enriched in open chromatin regions, and, interestingly, 3'end of transposons. Hotspots showed global hypo-fragmentation in early-stage liver cancers and are associated with genes involved in the initiation of hepatocellular carcinoma and associated with cancer stem cells. The hotspots varied across multiple early-stage cancers and demonstrated high performance for the diagnosis and identification of tissue-of-origin in early-stage cancers. We further validated the performance with a small number of independent case-control-matched early-stage cancer samples. <<<

Zachariah H Foda, Akshaya V Annapragada, Kavya Boyapati, Daniel C Bruhm, Nicholas A Vulpescu, Jamie E Medina, Dimitrios Mathios, Stephen Cristiano, Noushin Niknafs, Harry T Luu, Michael G Goggins, Robert A Anders, Jing Sun, Shruti H Meta, David L Thomas, Gregory D Kirk, Vilmos Adleff, Jillian Phallen, Robert B Scharpf, Amy K Kim, Victor E Velculescu <<<

Liver cancer is a major cause of cancer mortality worldwide. Screening individuals at high risk, including those with cirrhosis and viral hepatitis, provides an avenue for improved survival, but current screening methods are inadequate. In this study, we used whole-genome cell-free DNA (cfDNA) fragmentome analyses to evaluate 724 individuals from the United States, the European Union, or Hong Kong with hepatocellular carcinoma (HCC) or who were at average or high-risk for HCC. Using a machine learning model that incorporated multifeature fragmentome data, the sensitivity for detecting cancer was 88% in an average-risk population at 98% specificity and 85% among high-risk individuals at 80% specificity. We validated these results in an independent population. cfDNA fragmentation changes reflected genomic and chromatin changes in liver cancer, including from transcription factor binding sites. These findings provide a biological basis for changes in cfDNA fragmentation in patients with liver cancer and provide an accessible approach for noninvasive cancer detection. <<<

Arash Jamshidi, Minetta C Liu, Eric A Klein, Oliver Venn, Earl Hubbell, John F Beausang, Samuel Gross, Collin Melton, Alexander P Fields, Qinwen Liu, Nan Zhang, Eric T Fung, Kathryn N Kurtzman, Hamed Amini, Craig Betts, Daniel Civello, Peter Freese, Robert Calef, Konstantin Davydov, Saniya Fayzullina, Chenlu Hou, Roger Jiang, Byoungsok Jung, Susan Tang, Vasiliki Demas, Joshua Newman, Onur Sakarya, Eric Scott, Archana Shenoy, Seyedmehdi Shojaee, Kristan K Steffen, Virgil Nicula, Tom C Chien, Siddhartha Bagaria, Nathan Hunkapiller, Mohini Desai, Zhao Dong, Donald A Richards, Timothy J Yeatman, Allen L Cohn, David D Thiel, Donald A Berry, Mohan K Tummala, Kristi McIntyre, Mikkael A Sekeres, Alan Bryce, Alexander M Aravanis, Michael V Seiden, Charles Swanton <<<

In the Circulating Cell-free Genome Atlas (NCT02889978) substudy 1, we evaluate several approaches for a circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) test by defining clinical limit of detection (LOD) based on circulating tumor allele fraction (cTAF), enabling performance comparisons. Among 10 machine-learning classifiers trained on the same samples and independently validated, when evaluated at 98% specificity, those using whole-genome (WG) methylation, single nucleotide variants with paired white blood cell background removal, and combined scores from classifiers evaluated in this study show the highest cancer signal detection sensitivities. Compared with clinical stage and tumor type, cTAF is a more significant predictor of classifier performance and may more closely reflect tumor biology. Clinical LODs mirror relative sensitivities for all approaches. The WG methylation feature best predicts cancer signal origin. WG methylation is the most promising technology for MCED and informs development of a targeted methylation MCED test. <<<

Mary L Stackpole, Weihua Zeng, Shuo Li, Chun-Chi Liu, Yonggang Zhou, Shanshan He, Angela Yeh, Ziye Wang, Fengzhu Sun, Qingjiao Li, Zuyang Yuan, Asli Yildirim, Pin-Jung Chen, Paul Winograd, Benjamin Tran, Yi-Te Lee, Paul Shize Li, Zorawar Noor, Megumi Yokomizo, Preeti Ahuja, Yazhen Zhu, Hsian-Rong Tseng, James S Tomlinson, Edward Garon, Samuel French, Clara E Magyar, Sarah Dry, Clara Lajonchere, Daniel Geschwind, Gina Choi, Sammy Saab, Frank Alber, Wing Hung Wong, Steven M Dubinett, Denise R Aberle, Vatche Agopian, Steven-Huy B Han, Xiaohui Ni, Wenyuan Li, Xianghong Jasmine Zhou <<<

Early cancer detection by cell-free DNA faces multiple challenges: low fraction of tumor cell-free DNA, molecular heterogeneity of cancer, and sample sizes that are not sufficient to reflect diverse patient populations. Here, we develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of the cell-free DNA methylome (with > 12-fold enrichment over whole genome bisulfite sequencing in CpG islands), and a computational method to extract methylation information and diagnose patients. Applying our approach to 408 colon, liver, lung, and stomach cancer patients and controls, at 97.9% specificity we achieve 80.7% and 74.5% sensitivity in detecting all-stage and early-stage cancer, and 89.1% and 85.0% accuracy for locating tissue-of-origin of all-stage and early-stage cancer, respectively. Our approach cost-effectively retains methylome profiles of cancer abnormalities, allowing us to learn new features and expand to other cancer types as training cohorts grow. <<<

The ability to select short DNA oligonucleotide sequences capable of binding solely to their intended target is of great importance in developing nucleic acid based detection technologies. Applications such as multiplex PCR rely on primers binding to unique regions in a genome. Competing side reactions with other primer pairs or template DNA decrease PCR efficiency. Freely available primer design software such as Primer3 screens for potential hairpin and primer-dimer interactions while selecting a single primer pair. The development of multiplex PCR assays (in the range of 5 to 20 loci) requires the screening of all primer pairs for potential cross-reactivity. However, a logistical problem results due to the number of total number of comparisons required. Comparing the primer set for a 10-plex assay (20 total primer sequences) results in 210 primer-primer combinations that must be screened. The ability to screen sets of candidate oligomers rapidly for potential cross-reactivity reduces overall assay development time. Here we report the application of a familiar sliding algorithm for comparing two strands of DNA in an overlapping fashion. The algorithm has been employed in a software package wherein the user can compare multiple sequences in a single computational run. After the screening is completed, a score is assigned to potential duplex interactions exceeding a user-defined threshold. Additional criteria of predicted melting temperature (Tm) and free energy of melting (ΔG) are included for further ranking. Sodium counterion and total stand concentrations can be adjusted for the Tm and ΔG calculations. The predicted interactions are saved in a text file for further evaluation. <<<

By simultaneously amplifying more than one locus in the same reaction, multiplex PCR is becoming a rapid and convenient screening assay in both the clinical and the research laboratory. While numerous papers and manuals discuss in detail conditions influencing the quality of PCR in general, relatively little has been published about the important experimental factors and the common difficulties frequently encountered with multiplex PCR. We have examined various conditions of the multiplex PCR, using a large number of primer pairs. Especially important for a successful multiplex PCR assay are the relative concentrations of the primers at the various loci, the concentration of the PCR buffer, the cycling temperatures and the balance between the magnesium chloride and deoxynucleotide concentrations. Based on our experience, we propose a protocol for developing a multiplex PCR assay and suggest ways to overcome commonly encountered problems. <<<

Identification of somatic mutations in tumor samples is commonly based on statistical methods in combination with heuristic filters. Here we develop VarNet, an end-to-end deep learning approach for identification of somatic variants from aligned tumor and matched normal DNA reads. VarNet is trained using image representations of 4.6 million high-confidence somatic variants annotated in 356 tumor whole genomes. We benchmark VarNet across a range of publicly available datasets, demonstrating performance often exceeding current state-of-the-art methods. Overall, our results demonstrate how a scalable deep learning approach could augment and potentially supplant human engineered features and heuristic filters in somatic variant calling. <<<

M Ryan Corces, Jeffrey M Granja, Shadi Shams, Bryan H Louie, Jose A Seoane, Wanding Zhou, Tiago C Silva, Clarice Groeneveld, Christopher K Wong, Seung Woo Cho, Ansuman T Satpathy, Maxwell R Mumbach, Katherine A Hoadley, A Gordon Robertson, Nathan C Sheffield, Ina Felau, Mauro A A Castro, Benjamin P Berman, Louis M Staudt, Jean C Zenklusen, Peter W Laird, Christina Curtis, Cancer Genome Atlas Analysis Network, William J Greenleaf, Howard Y Chang <<<

We present the genome-wide chromatin accessibility profiles of 410 tumor samples spanning 23 cancer types from The Cancer Genome Atlas (TCGA). We identify 562,709 transposase-accessible DNA elements that substantially extend the compendium of known cis-regulatory elements. Integration of ATAC-seq (the assay for transposase-accessible chromatin using sequencing) with TCGA multi-omic data identifies a large number of putative distal enhancers that distinguish molecular subtypes of cancers, uncovers specific driving transcription factors via protein-DNA footprints, and nominates long-range gene-regulatory interactions in cancer. These data reveal genetic risk loci of cancer predisposition as active DNA regulatory elements in cancer, identify gene-regulatory interactions underlying cancer immune evasion, and pinpoint noncoding mutations that drive enhancer activation and may affect patient survival. These results suggest a systematic approach to understanding the noncoding genome in cancer to advance diagnosis and therapy. <<<

AID/APOBEC family enzymes are best known for deaminating cytosine bases to uracil in single-stranded DNA, with characteristic sequence preferences that can produce mutational signatures in targets such as retroviral and cancer cell genomes. These deaminases have also been proposed to function in DNA demethylation via deamination of either 5-methylcytosine (mC) or TET-oxidized mC bases (ox-mCs), which include 5-hydroxymethylcytosine, 5-formylcytosine and 5-carboxylcytosine. One specific family member, APOBEC3A (A3A), has been shown to readily deaminate mC, raising the prospect of broader activity on ox-mCs. To investigate this claim, we developed a novel assay that allows for parallel profiling of activity on all modified cytosines. Our steady-state kinetic analysis reveals that A3A discriminates against all ox-mCs by >3700-fold, arguing that ox-mC deamination does not contribute substantially to demethylation. A3A is, by contrast, highly proficient at C/mC deamination. Under conditions of excess enzyme, C/mC bases can be deaminated to completion in long DNA segments, regardless of sequence context. Interestingly, under limiting A3A, the sequence preferences observed with targeting unmodified cytosine are further exaggerated when deaminating mC. Our study informs how methylation, oxidation, and deamination can interplay in the genome and suggests A3A's potential utility as a biotechnological tool to discriminate between cytosine modification states. <<<

Biyuan Luo, Fang Ma, Hao Liu, Jixiong Hu, Le Rao, Chun Liu, Yongfang Jiang, Shuyu Kuangzeng, Xuan Lin, Chenyang Wang, Yiyu Lei, Zhongzhou Si, Guangshun Chen, Ning Zhou, Chengbai Liang, Fangqing Jiang, Fenge Liu, Weidong Dai, Wei Liu, Yawen Gao, Zhihong Li, Xi Li, Guangyu Zhou, Bingsi Li, Zhihong Zhang, Weiqi Nian, Lihua Luo, Xianling Liu <<<

BACKGROUND: Aberrant DNA methylation may offer opportunities in revolutionizing cancer screening and diagnosis. We sought to identify a non-invasive DNA methylation-based screening approach using cell-free DNA (cfDNA) for early detection of hepatocellular carcinoma (HCC).METHODS: Differentially, DNA methylation blocks were determined by comparing methylation profiles of biopsy-proven HCC, liver cirrhosis, and normal tissue samples with high throughput DNA bisulfite sequencing. A multi-layer HCC screening model was subsequently constructed based on tissue-derived differentially methylated blocks (DMBs). This model was tested in a cohort consisting of 120 HCC, 92 liver cirrhotic, and 290 healthy plasma samples including 65 hepatitis B surface antigen-seropositive (HBsAg+) samples, independently validated in a cohort consisting of 67 HCC, 111 liver cirrhotic, and 242 healthy plasma samples including 56 HBsAg+ samples.RESULTS: Based on methylation profiling of tissue samples, 2321 DMBs were identified, which were subsequently used to construct a cfDNA-based HCC screening model, achieved a sensitivity of 86% and specificity of 98% in the training cohort and a sensitivity of 84% and specificity of 96% in the independent validation cohort. This model obtained a sensitivity of 76% in 37 early-stage HCC (Barcelona clinical liver cancer [BCLC] stage 0-A) patients. The screening model can effectively discriminate HCC patients from non-HCC controls, including liver cirrhotic patients, asymptomatic HBsAg+ and healthy individuals, achieving an AUC of 0.957(95% CI 0.939-0.975), whereas serum α-fetoprotein (AFP) only achieved an AUC of 0.803 (95% CI 0.758-0.847). Besides detecting patients with early-stage HCC from non-HCC controls, this model showed high capacity for distinguishing early-stage HCC from a high risk population (AUC=0.934; 95% CI 0.905-0.963), also significantly outperforming AFP. Furthermore, our model also showed superior performance in distinguishing HCC with normal AFP (< 20ng ml-1) from high risk population (AUC=0.93; 95% CI 0.892-0.969).CONCLUSIONS: We have developed a sensitive blood-based non-invasive HCC screening model which can effectively distinguish early-stage HCC patients from high risk population and demonstrated its performance through an independent validation cohort.TRIAL REGISTRATION: The study was approved by the ethic committee of The Second Xiangya Hospital of Central South University (KYLL2018072) and Chongqing University Cancer Hospital (2019167). The study is registered at ClinicalTrials.gov(# NCT04383353 ). <<<

John B Kisiel, Brian A Dukek, Reddappa V S R Kanipakam, Hassan M Ghoz, Tracy C Yab, Calise K Berger, William R Taylor, Patrick H Foote, Nasra H Giama, Kristeen Onyirioha, Mohamed A Abdallah, Kelli N Burger, Seth W Slettedahl, Douglas W Mahoney, Thomas C Smyrk, Jason T Lewis, Maria Giakoumopoulos, Hatim T Allawi, Graham P Lidgard, Lewis R Roberts, David A Ahlquist <<<

Early detection improves hepatocellular carcinoma (HCC) outcomes, but better noninvasive surveillance tools are needed. We aimed to identify and validate methylated DNA markers (MDMs) for HCC detection. Reduced representation bisulfite sequencing was performed on DNA extracted from 18 HCC and 35 control tissues. Candidate MDMs were confirmed by quantitative methylation-specific PCR in DNA from independent tissues (74 HCC, 29 controls). A phase I plasma pilot incorporated quantitative allele-specific real-time target and signal amplification assays on independent plasma-extracted DNA from 21 HCC cases and 30 controls with cirrhosis. A phase II plasma study was then performed in 95 HCC cases, 51 controls with cirrhosis, and 98 healthy controls using target enrichment long-probe quantitative amplified signal (TELQAS) assays. Recursive partitioning identified best MDM combinations. The entire MDM panel was statistically cross-validated by randomly splitting the data 2:1 for training and testing. Random forest (rForest) regression models performed on the training set predicted disease status in the testing set; median areas under the receiver operating characteristics curve (AUCs; and 95% confidence interval [CI]) were reported after 500 iterations. In phase II, a six-marker MDM panel (homeobox A1 [HOXA1], empty spiracles homeobox 1 [EMX1], AK055957, endothelin-converting enzyme 1 [ECE1], phosphofructokinase [PFKP], and C-type lectin domain containing 11A [CLEC11A]) normalized by beta-1,3-galactosyltransferase 6 (B3GALT6) level yielded a best-fit AUC of 0.96 (95% CI, 0.93-0.99) with HCC sensitivity of 95% (88%-98%) at specificity of 92% (86%-96%). The panel detected 3 of 4 (75%) stage 0, 39 of 42 (93%) stage A, 13 of 14 (93%) stage B, 28 of 28 (100%) stage C, and 7 of 7 (100%) stage D HCCs. The AUC value for alpha-fetoprotein (AFP) was 0.80 (0.74-0.87) compared to 0.94 (0.9-0.97) for the cross-validated MDM panel (P < 0.0001). Conclusion: MDMs identified in this study proved to accurately detect HCC by plasma testing. Further optimization and clinical testing of this promising approach are indicated. <<<

Methylation plotter is a Web tool that allows the visualization of methylation data in a user-friendly manner and with publication-ready quality. The user is asked to introduce a file containing the methylation status of a genomic region. This file can contain up to 100 samples and 100 CpGs. Optionally, the user can assign a group for each sample (i.e. whether a sample is a tumoral or normal tissue). After the data upload, the tool produces different graphical representations of the results following the most commonly used styles to display this type of data. They include an interactive plot that summarizes the status of every CpG site and for every sample in lollipop or grid styles. Methylation values ranging from 0 (unmethylated) to 1 (fully methylated) are represented using a gray color gradient. A practical feature of the tool allows the user to choose from different types of arrangement of the samples in the display: for instance, sorting by overall methylation level, by group, by unsupervised clustering or just following the order in which data were entered. In addition to the detailed plot, Methylation plotter produces a methylation profile plot that summarizes the status of the scrutinized region, a boxplot that sums up the differences between groups (if any) and a dendrogram that classifies the data by unsupervised clustering. Coupled with this analysis, descriptive statistics and testing for differences at both CpG and group levels are provided. The implementation is based in R/shiny, providing a highly dynamic user interface that generates quality graphics without the need of writing R code. Methylation plotter is freely available at http://gattaca.imppc.org:3838/methylation_plotter/. <<<