DNA 5-hydroxymethylcytosine (5hmC) modification is known to be associated with gene transcription and frequently used as a mark to investigate dynamic DNA methylation conversion during mammalian development and in human diseases. However, the lack of genome-wide 5hmC profiles in different human tissue types impedes drawing generalized conclusions about how 5hmC is implicated in transcription activity and tissue specificity. To meet this need, we describe the development of a 5hmC tissue map by characterizing the genomic distributions of 5hmC in 19 human tissues derived from ten organ systems. Subsequent sequencing results enabled the identification of genome-wide 5hmC distributions that uniquely separates samples by tissue type. Further comparison of the 5hmC profiles with transcriptomes and histone modifications revealed that 5hmC is preferentially enriched on tissue-specific gene bodies and enhancers. Taken together, the results provide an extensive 5hmC map across diverse human tissue types that suggests a potential role of 5hmC in tissue-specific development; as well as a resource to facilitate future studies of DNA demethylation in pathogenesis and the development of 5hmC as biomarkers.

3051 Background: Orphan non-coding RNAs (oncRNAs) are a novel category of small RNAs (smRNAs) that are present in tumors and largely absent in healthy tissue. We investigated the utility of oncRNAs extracted from serum for early cancer detection across seven cancer types. Methods: We collected 2,882 serum samples from individuals with known cancers of the bladder ( n=152), breast (220), colon and rectum (141), kidney (283), lung (281), pancreas (287), and stomach (280) as well as donors with no history of cancer (1,238). We used 0.5 mL serum aliquots to generate and sequence smRNA libraries at an average depth of 20 million 50-bp single-end reads. Samples were split into age-, sex-, and smoking status-matched training (1,232 cancer; 922 control) and validation (412 cancer; 316 control) cohorts. A large catalog of oncRNAs specific to each cancer was created using tumor and adjacent normal samples from The Cancer Genome Atlas (TCGA) smRNA-seq database. Using TCGA-derived oncRNAs, we trained a machine learning model to predict cancer presence and tissue of origin (TOO) in a 5-fold cross validation setup using our training cohort. For the validation cohort, we averaged the predictions from the five training cohort models. Results: The model ROC-AUC for detecting cancer was 0.95 (95% CI: 0.94–0.95 for training and 0.94–0.97 for validation cohorts). Sensitivities for detecting cancer at 95% specificity were 0.74 (0.70–0.76) for early stage (I/II) and 0.80 (0.76–0.84) for late stage (III/IV) cancers in the training cohort, and 0.77 (0.71–0.81) and 0.81 (0.73–0.87) in the validation cohort. Sensitivities of detection for each cancer type are shown. For samples with cancer and TOO predictions, our top 1 and top 2 TOO accuracy was 0.76 (0.68–0.84) and 0.83 (0.76–0.90) for the validation set. Conclusions: These results demonstrate that oncRNAs detected in serum can be used for accurate, early detection, and localization of multiple cancers. [Table: see text]

e21505 Background: Liquid biopsies are gaining prominence for not only cancer diagnosis but also patient monitoring. Mutational signals derived from cell-free DNA (cfDNA) show promise to assess response to cancer treatment, including immunotherapy. However, reliance of these methods on mutational data from tissue biopsies limit their applicability when a tumor biopsy is unavailable, or when mutational landscape of tumor changes under the selective pressures of cancer drug treatment. Epigenomic approaches have the potential to address these shortcomings. Methods: Blood draws were obtained from a cohort of non-small cell lung cancer (NSCLC) patients (n = 19) who went on to anti-PD1 treatment prior to therapy start and while on therapy. cfDNA was isolated from plasma and was subsequently processed to generate 5hmC genome-wide profiles. Results: We analyzed cfDNA from NSCLC patients undergoing anti-PD1 therapy to investigate whether immunotherapy response can be detected from plasma. Using a predictive model trained on lung cancer and non-cancer samples, we were able to detect changes in prediction scores in patient treated with immunotherapy that were consistent with RECIST. Patients with progressive disease (n = 3), determined by RECIST, had prediction scores that increased while they received treatment. On the other hand, majority of the patients that exhibited partial response to treatment (n = 12) had predictive scores that decreased with treatment, again consistent with RECIST. Furthermore, score changes consistent with RECIST was observed one cycle prior to the RECIST timepoint in all except one patient, where an extra blood draw after baseline was available (n = 7). Annotation of the regions that account for differential scoring identified enhancer, 5’UTR and promoter regions. Comparison of partial responders to patients with progressive disease revealed genes involved in metastasis, oncogenes and tumor suppressors that change in opposing directions between these patient groups, consistent with the underlying biology. Conclusions: Our results suggest that 5hmC profiles from cfDNA can be used to determine immunotherapy response in non-small cell lung cancer patients. Compared with mutation based liquid biopsy methods to assess response, epigenomics-based methods have the advantage of being agnostic to starting tumor mutations, and not relying on a mutational analysis from tumor biopsy. Future work will help determine applicability of this method to other kinds of therapies and cancer types.

Background: Our feasibility study employed a novel genomic detection methodology that enriches 5-hydroxymethylcytosine (5hmC) loci in cell free DNA (cfDNA) from the plasma of cancer patients using click chemistry coupled with sequencing and machine learning based classification methods. These classification methods were developed to detect the presence of disease in the plasma of cancer and control subjects. Cancer and control patient cfDNA cohorts were accrued from multiple sites consisting of 48 breast, 55 lung, 32 prostate and 2 pancreatic datasets consisting of 41 and 53 cancer subjects (Set 1 and 2). In addition, a control cohort of 260 subjects (non-cancer) was employed to match cancer patient demographics (age, sex and smoking status) in a case-control study design. Methods: Machine learning methods, applied to each cancer case cohort individually, with a balancing non-cancer cohort, were able to classify cancer and control samples. Measures of predictive performance using 5-fold cross validation coupled with out-of-fold Area Under the Receiver Operating Characteristic Curve (AUROC) measures were employed. Gene sets selected as part of biomarker discovery were further analyzed for disease relevance using pathway analysis tools (GSEA, mSigDB). Results: 260 controls and 229 cancers from four disease types (breast, lung, pancreas and prostate) were analyzed; more than 60% of cancer patients had early stage disease (I or II). Predictive performance, employing AUROC measures, was established for breast (0.89), lung (0.84), pancreas (set 1 - 0.95 and 2 - 0.93) and prostate (0.83). The genes defining each of these predictive models were enriched for pathways relevant to disease specific etiology, notably in the control of gene regulation in these same pathways. The breast cancer cohort consisted primarily of stage I and II patients including tumors < 2 cm and these samples exhibited a higher prediction probability score. The prostate cancer cohort consisted of both indolent and aggressive disease sample and prediction performance was equally high for both (AUROC for indolent vs aggressive was 0.81 and 0.77, respectively). Conclusions: These findings suggest that 5hmC changes in cfDNA enable non-invasive detection of early stage breast, pancreatic, prostate, and lung cancers. Furthermore, 5hmC profiling in cfDNA may enable the prediction of clinically relevant features such as tumor size in breast adenocarcinoma or indolent disease in prostate cancer. Finally, this study identified a suite of 5hmC biomarkers that may be further validated in larger, and more diverse, patient cohorts. Citation Format: Anna Bergamaschi, Jeremy Ku, Yuhong Ning, Francois Collin, Chris Ellison, Tierney Phillips, Erin McCarthy, Wendy Wang, Michael Antoine, David Haan, Aaron Scott, Paul Lloyd, Gulfem Guler, Alan Ashworth, Stephen Quake, Samuel Levy. Epigenomic detection of multiple cancers in plasma derived cell free DNA [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 783.

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