Adrenal-Permissive Germline HSD3B1 Allele and Prostate Cancer Outcomes

Importance

The adrenal androgen-metabolizing 3β-hydroxysteroid dehydrogenase-1 enzyme, encoded by the HSD3B1 gene, catalyzes the rate-limiting step necessary for synthesizing nontesticular testosterone and dihydrotestosterone production. The common adrenal-permissive HSD3B1(1245C) allele is responsible for encoding the 3β-HSD1 protein with decreased susceptibility to degradation resulting in higher extragonadal androgen synthesis. Retrospective studies have suggested an association of the HSD3B1 adrenal-permissive homozygous genotype with androgen deprivation therapy resistance in prostate cancer.

Objective

To evaluate differences in mortality outcomes by HSD3B1 genetic status among men with prostate cancer.

Design, Setting, and Participants

This cohort study of patients with prostate cancer who were enrolled in the Million Veteran Program within the Veterans Health Administration (VHA) system between 2011 and 2023 collected genotyping and phenotyping information.

Exposure

HSD3B1 genotype status was categorized as AA (homozygous adrenal-restrictive), AC (heterozygous adrenal-restrictive), or CC (homozygous adrenal-permissive).

Main Outcomes and Measures

The primary outcome of this study was prostate cancer-specific mortality (PCSM), defined as the time from diagnosis to death from prostate cancer, censored at the date of last VHA follow-up. Secondary outcomes included incidence of metastases and PCSM in predefined subgroups.

Results

Of the 5287 participants (median [IQR] age, 69 [64-74] years), 402 (7.6%) had the CC genotype, 1970 (37.3%) had the AC genotype, and 2915 (55.1%) had the AA genotype. Overall, the primary cause of death for 91 patients (1.7%) was prostate cancer. Cumulative incidence of PCSM at 5 years after prostate cancer diagnosis was higher among men with the CC genotype (4.0%; 95% CI, 1.7%-6.2%) compared with the AC genotype (2.1%; 95% CI, 1.3%-2.8%) and AA genotype (1.9%; 95% CI, 1.3%-2.4%) (P = .02). In the 619 patients who developed metastatic disease at any time, the cumulative incidence of PCSM at 5 years was higher among patients with the CC genotype (36.0%; 95% CI, 16.7%-50.8%) compared with the AC genotype (17.9%; 95% CI, 10.5%-24.7%) and AA genotype (18.5%; 95% CI, 12.0%-24.6%) (P = .01).

Conclusions and Relevance

In this cohort study of US veterans undergoing treatment for prostate cancer at the VHA, the HSD3B1 CC genotype was associated with inferior outcomes. The HSD3B1 biomarker may help identify patients who may benefit from therapeutic targeting of 3β-hydroxysteroid dehydrogenase-1 and the androgen-signaling axis.

PD-1 Limits IL-2 Production and Thymic Regulatory T Cell Development

Inhibitory proteins, such as programmed cell death protein 1 (PD-1), have been studied extensively in peripheral T cell responses to foreign Ags, self-Ags, and neoantigens. Notably, these proteins are first expressed during T cell development in the thymus. Reports suggest that PD-1 limits regulatory T cell (Treg) development, but the mechanism by which PD-1 exerts this function remains unknown. The present study expands the evaluation of murine PD-1 and its ligands in the thymus, demonstrating that some of the highest expressers of PD-1 and programmed death-ligand 1 are agonist selected cells. Surprisingly, we reveal a selective role for PD-1 in regulating the developmental niche only for Tregs because other agonist selected cell populations, such as NK T cells, remain unchanged. We also ruled out PD-1 as a regulator of proliferation or cell death of agonist selected Tregs and further demonstrated that PD-1-deficient Tregs have reduced TCR signaling. Unexpectedly, the data suggest that PD-1-deficient thymocytes produce elevated levels of IL-2, a Treg niche-limiting cytokine. Collectively, these data suggest a novel role for PD-1 in regulating IL-2 production and the concurrent agonist selection of murine thymic Tregs. This observation has implications for the use of checkpoint blockade in the context of cancer and infection.

A rigorous benchmarking of alignment-based HLA typing algorithms for RNA-seq data

Accurate identification of human leukocyte antigen (HLA) alleles is essential for various clinical and research applications, such as transplant matching and drug sensitivities. Recent advances in RNA-seq technology have made it possible to impute HLA types from sequencing data, spurring the development of a large number of computational HLA typing tools. However, the relative performance of these tools is unknown, limiting the ability for clinical and biomedical research to make informed choices regarding which tools to use. Here we report the study design of a comprehensive benchmarking of the performance of 12 HLA callers across 682 RNA-seq samples from 8 datasets with molecularly defined gold standard at 5 loci, HLA-A, -B, -C, -DRB1, and -DQB1. For each HLA typing tool, we will comprehensively assess their accuracy, compare default with optimized parameters, and examine for discrepancies in accuracy at the allele and loci levels. We will also evaluate the computational expense of each HLA caller measured in terms of CPU time and RAM. We also plan to evaluate the influence of read length over the HLA region on accuracy for each tool. Most notably, we will examine the performance of HLA callers across European and African groups, to determine discrepancies in accuracy associated with ancestry. We hypothesize that RNA-Seq HLA callers are capable of returning high-quality results, but the tools that offer a good balance between accuracy and computational expensiveness for all ancestry groups are yet to be developed. We believe that our study will provide clinicians and researchers with clear guidance to inform their selection of an appropriate HLA caller.

Survival Among Veterans Receiving Steroids for Immune-Related Adverse Events After Immune Checkpoint Inhibitor Therapy

Importance

Systemic steroids are commonly used to manage immune-related adverse events (irAEs), but it remains unclear whether they may undermine immune checkpoint inhibitor (ICI) therapy outcomes. Few studies have assessed the impact of steroid timing and its association with continuation or cessation of ICI therapy.

Objective

To characterize how systemic steroids and steroid timing for irAEs are associated with survival in patients receiving ICI therapy.

Design, Setting, and Participants

This multicenter retrospective cohort study encompassed veterans receiving ICI for cancer between January 1, 2010, and December 31, 2021. Data analysis was conducted September 8, 2023.

Exposures

Identifiable primary diagnosis of cancer. Patients were categorized into 3 cohorts: those receiving no steroids, systemic steroids for irAEs, and steroids for non-irAE-associated reasons. All eligible patients received 1 or more doses of an ICI (atezolizumab, avelumab, cemiplimab, durvalumab, ipilimumab, nivolumab, or pembrolizumab). Eligible patients in the steroid group received at least 1 dose (intravenous, intramuscular, or oral) of dexamethasone, hydrocortisone, methylprednisolone, prednisone, or prednisolone. Steroid use at baseline for palliation or infusion prophylaxis or delivered as a single dose was deemed to be non-irAE associated. All other patterns of steroid use were assumed to be for irAEs.

Main Outcomes and Measures

The primary outcome was overall survival, with a 5-year follow-up after ICI initiation. Kaplan-Meier survival analyses were performed with pairwise log-rank tests to determine significance. Risk was modeled with Cox proportional hazard regression.

Results

The cohort consisted of 20 163 veterans receiving ICI therapy including 12 221 patients (mean [SD] age, 69.5 [8.0] years; 11 830 male patients [96.8%]; 9394 White patients [76.9%]) who received systemic steroids during ICI treatment and 7942 patients (mean [SD] age, 70.3 [8.5] years; 7747 male patients [97.5%]; 6085 White patients [76.6%]) who did not. Patients with an irAE diagnosis had significantly improved overall survival (OS) compared with those without (median [IQR] OS, 17.4 [6.6 to 48.5] months vs 10.5 [3.5 to 36.8] months; adjusted hazard ratio, 0.84; 95% CI, 0.81-0.84; P < .001). For patients with irAEs, systemic steroids for irAEs were associated with significantly improved survival compared with those who received steroids for non-irAE-related reasons or no steroid treatment (median [IQR] OS, 21.3 [9.3 to 58.2] months vs 13.6 [5.5 to 33.7] months vs 15.8 [4.9 to not reached] months; P <.001). However, among those who received steroids for irAEs, early steroid use (<2 months after ICI initiation) was associated with reduced relative survival benefit vs later steroid use, regardless of ICI continuation or cessation following steroid initiation (median [IQR] OS after ICI cessation 4.4 [1.9 to 19.5] months vs 16.0 [8.0 to 42.2] months; median [IQR] OS after ICI continuation, 16.0 [7.1 to not reached] months vs 29.2 [16.5 to 53.5] months; P <.001).

Conclusions and Relevance

This study suggests that steroids for irAE management may not abrogate irAE-associated survival benefits. However, early steroid administration within 2 months of ICI initiation is associated with shorter survival despite continuation of ICI therapy.

Health Equity Achievement in Radiation Therapy (HEART) Score: A Social Prognosis

PURPOSE/OBJECTIVE(S)

The aim of this study was to develop a Health Equity Achievement in Radiation Therapy (HEART) score that can help identify patients at risk of experiencing suboptimal quality-of-care (QoC) early on in the patient-provider encounter and prior to initiation of treatment. Such a score may improve shared decision making to improve QoC.

MATERIALS/METHODS

A retrospective analysis was conducted using the National Cancer Database (NCDB) for prostate cancer cases between 2004-2017. Sociodemographic factors, clinical characteristics, and treatment information were collected. A composite HEART score was built to predict suboptimal QoC, defined as treatment refusal, incomplete treatment, or treatment delay. 70% of the data was allocated to training and 30% to validating a logistic regression model through which a nomogram was constructed.

RESULTS

A total of 1,599,785 patients were included in the analysis, of whom 126,917 (7.9%) had at least one suboptimal QoC. The strongest predictors were Black race, uninsured status, lower educational status, geographic location, and nodal disease (Table). The nomogram demonstrated a fair ability to predict quality metrics, with an area under the receiver operating characteristic curve (AUC) of 0.57 in the test group. The nomogram facilitated graphic interpretation of systemic factors in contributing to suboptimal QoC.

CONCLUSION

With observed potential for predicting suboptimal QoC outcomes in patients with prostate cancer by considering systemic barriers, this NCDB-based nomogram has potential utility as a tool for identifying patients who may benefit from additional social support, including the financial resources associated with these services, to improve access to care. Further validation in diverse datasets is needed to improve performance and generalizability to broader patient populations and different disease sites.

recountmethylation enables flexible analysis of public blood DNA methylation array data

Summary

Thousands of DNA methylation (DNAm) array samples from human blood are publicly available on the Gene Expression Omnibus (GEO), but they remain underutilized for experiment planning, replication and cross-study and cross-platform analyses. To facilitate these tasks, we augmented our recountmethylation R/Bioconductor package with 12 537 uniformly processed EPIC and HM450K blood samples on GEO as well as several new features. We subsequently used our updated package in several illustrative analyses, finding (i) study ID bias adjustment increased variation explained by biological and demographic variables, (ii) most variation in autosomal DNAm was explained by genetic ancestry and CD4+ T-cell fractions and (iii) the dependence of power to detect differential methylation on sample size was similar for each of peripheral blood mononuclear cells (PBMC), whole blood and umbilical cord blood. Finally, we used PBMC and whole blood to perform independent validations, and we recovered 38-46% of differentially methylated probes between sexes from two previously published epigenome-wide association studies.

Availability and implementation

Source code to reproduce the main results are available on GitHub (repo: recountmethylation_flexible-blood-analysis_manuscript; url: https://github.com/metamaden/recountmethylation_flexible-blood-analysis_manuscript). All data was publicly available and downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). Compilations of the analyzed public data can be accessed from the website recount.bio/data (preprocessed HM450K array data: https://recount.bio/data/remethdb_h5se-gm_epic_0-0-2_1589820348/; preprocessed EPIC array data: https://recount.bio/data/remethdb_h5se-gm_epic_0-0-2_1589820348/).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Stress-testing pelvic autosegmentation algorithms using anatomical edge cases

Commercial autosegmentation has entered clinical use, however real-world performance may suffer in certain cases. We aimed to assess the influence of anatomic variants on performance. We identified 112 prostate cancer patients with anatomic variations (edge cases). Pelvic anatomy was autosegmented using three commercial tools. To evaluate performance, Dice similarity coefficients, and mean surface and 95% Hausdorff distances were calculated versus clinician-delineated references. Deep learning autosegmentation outperformed atlas-based and model-based methods. However, edge case performance was lower versus the normal cohort (0.12 mean DSC reduction). Anatomic variation presents challenges to commercial autosegmentation.

Retained introns in long RNA-seq reads are not reliably detected in sample-matched short reads

BACKGROUND

There is growing interest in retained introns in a variety of disease contexts including cancer and aging. Many software tools have been developed to detect retained introns from short RNA-seq reads, but reliable detection is complicated by overlapping genes and transcripts as well as the presence of unprocessed or partially processed RNAs.

RESULTS

We compared introns detected by 8 tools using short RNA-seq reads with introns observed in long RNA-seq reads from the same biological specimens. We found significant disagreement among tools (Fleiss' [Formula: see text]) such that 47.7% of all detected intron retentions were not called by more than one tool. We also observed poor performance of all tools, with none achieving an F1-score greater than 0.26, and qualitatively different behaviors between general-purpose alternative splicing detection tools and tools confined to retained intron detection.

CONCLUSIONS

Short-read tools detect intron retention with poor recall and precision, calling into question the completeness and validity of a large percentage of putatively retained introns called by commonly used methods.

Statin use is associated with improved overall survival in patients with melanoma

Melanoma remains a leading cause of cancer morbidity and mortality. Recent literature suggests that statin use may improve outcomes in patients with cancer. In order to determine whether statins may improve survival in melanoma patients, we analyzed data from the Veterans Health Administration Corporate Data Warehouse that contains individually identifiable clinical and demographic information from the 1990s to the present for over 19 million individual veterans. We found that melanoma patients who were taking a statin had better 5-year OS when compared with veterans not taking statins. This relationship remained significant in a multivariate model (hazard ratio, 0.38; 95% confidence interval, 0.34-0.43 for statin user vs. nonuser). Importantly, this effect was much larger than the effect of statins in the general population and was remained after controlling for the use of other medications (beta-blocker), implying that statins may have a direct effect on survival in melanoma patients.

Androgen receptor activity in T cells limits checkpoint blockade efficacy

Immune checkpoint blockade has revolutionized the field of oncology, inducing durable anti-tumour immunity in solid tumours. In patients with advanced prostate cancer, immunotherapy treatments have largely failed1-5. Androgen deprivation therapy is classically administered in these patients to inhibit tumour cell growth, and we postulated that this therapy also affects tumour-associated T cells. Here we demonstrate that androgen receptor (AR) blockade sensitizes tumour-bearing hosts to effective checkpoint blockade by directly enhancing CD8 T cell function. Inhibition of AR activity in CD8 T cells prevented T cell exhaustion and improved responsiveness to PD-1 targeted therapy via increased IFNγ expression. AR bound directly to Ifng and eviction of AR with a small molecule significantly increased cytokine production in CD8 T cells. Together, our findings establish that T cell intrinsic AR activity represses IFNγ expression and represents a novel mechanism of immunotherapy resistance.

Minimal observed impact of HLA genotype on hospitalization and severity of SARS-CoV-2 infection

HLA is a critical component of the viral antigen presentation pathway. We investigated the relationship between the severity of SARS-CoV-2 disease and HLA type in 3235 individuals with confirmed SARS-CoV-2 infection. We found only the DPB1 locus to be associated with the binary outcome of whether an individual developed any COVID-19 symptoms. The number of peptides predicted to bind to an HLA allele had no significant relationship with disease severity both when stratifying individuals by ancestry or age and in a pooled analysis. Overall, at the population level, we found HLA type is significantly less predictive of COVID-19 disease severity than certain demographic factors and clinical comorbidities.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

pepsickle rapidly and accurately predicts proteasomal cleavage sites for improved neoantigen identification

MOTIVATION

Proteasomal cleavage is a key component in protein turnover, as well as antigen processing and presentation. Although tools for proteasomal cleavage prediction are available, they vary widely in their performance, options, and availability.

RESULTS

Herein we present pepsickle, an open-source tool for proteasomal cleavage prediction with better in vivo prediction performance (AUC) and computational speed than current models available in the field and with the ability to predict sites based on both constitutive and immunoproteasome profiles. Post-hoc filtering of predicted patient neoepitopes using pepsickle significantly enriches for immune-responsive epitopes and may improve current epitope prediction and vaccine development pipelines.

AVAILABILITY

pepsickle is open source and available at https://github.com/pdxgx/pepsickle.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Thromboembolic risk in patients with lung cancer receiving systemic therapy

In this retrospective study, we investigated the influence of chemotherapy and immunotherapy on thromboembolic risk among United States Veterans with lung cancer during their first 6 months (180 days) following initiation of systemic therapy. Included patients received treatment with common front-line agents that were divided into four groups: chemotherapy alone, immunotherapy alone, combination of chemo- and immunotherapies, and molecularly targeted therapies (control group). The cohort experienced a 7·4% overall incidence of thrombosis, but the analysis demonstrated significantly different rates among the different groups. We explored models incorporating multiple confounding variables as well as the competing risk of death, and these results indicated that both chemo- and immunotherapies were associated with an increased incidence of thrombosis, either alone or combined, compared with the control group (7·56%, P = 2.2 × 10^-16 ; 10·2%, P = 2.2 × 10^-16 ; and 7·87%, P = 2.4 × 10^-14 respectively vs. 4·10%). The Khorana score was found to be associated with increased risk, as were vascular disease and metastases. We found an association between risk of thrombosis and the use of anticoagulation, accounting for several confounders, including history of thrombosis. Further study is warranted to better determine the drivers of thromboembolic risk and to identify ways to mitigate this risk for patients.

Impact of the VA opioid safety initiative on pain management for cancer patients

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Background: Limited research exists on how risk reduction policies in response to the opioid epidemic have impacted pain management among cancer patients. This study investigated the impact of the Veteran’s Health Administration (VHA) Opioid Safety Initiative (OSI) on opioid prescribing patterns and opioid-related toxicity among patients undergoing definitive cancer treatment. Methods: This retrospective cohort study included 42,064 opioid-naïve patients receiving definitive local therapy for prostate, lung, breast, and colorectal cancer at the VHA from 2011-2016. Interrupted time series analysis with segmented regression was used to evaluate the impact of the OSI, which launched October 2013. The primary outcome was the incidence of new opioid prescriptions with diagnosis or treatment. Secondary outcomes included rates of high daily dose opioid (≥ 100 morphine milligram equivalent) and concomitant benzodiazepine prescriptions. Additional long-term outcomes included persistent opioid use, opioid abuse diagnoses, pain-related ED visits, and opioid-related admissions. Results: Prior to OSI implementation, the incidence of opioid prescriptions among new cancer patients increased from 26.7% (95% CI 25.0 – 28.4) in the first quarter (Q1) of 2011 to 50.6% (95% CI 48.3 – 53.0) in Q3 2013. There was a monthly increase in opioid prescription rate pre-OSI followed by a monthly decrease post-OSI (Table). High-dose opioid prescriptions were rare, and the monthly rate was stable before and after the OSI. Monthly incidence of concomitant benzodiazepine prescriptions was stable pre-OSI and decreased post-OSI. Persistent opioid use increased pre-OSI and decreased post-OSI. Pain-related ED visits had an incidence of 0.8% (95% CI 0.4 – 1.0) in Q1 2011, 0.3% (95% CI 0.1 – 0.6) in Q3 2013, and 1.8% (95% CI 0.9 – 2.7) in Q4 2016, with an increasing monthly rate after the OSI. At three years, the cumulative incidence of opioid abuse was 1.2% for both the pre- and post-OSI groups but opioid-related admissions were greater in the pre-OSI cohort than the post-OSI cohort (0.9% vs. 0.5%, p < 0.001). Conclusions: The OSI was associated with a decrease in new, persistent, and certain high-risk opioid prescribing as well as an increase in pain-related ED visits. Further research on patient-centered outcomes is required to optimize opioid prescribing policies for patients with cancer.[Table: see text]

Radiomic biomarkers of tumor immune biology and immunotherapy response

Immunotherapies are leading to improved outcomes for many cancers, including those with devastating prognoses. As therapies like immune checkpoint inhibitors (ICI) become a mainstay in treatment regimens, many concurrent challenges have arisen - for instance, delineating clinical responders from non-responders. Predicting response has proven to be difficult given a lack of consistent and accurate biomarkers, heterogeneity of the tumor microenvironment (TME), and a poor understanding of resistance mechanisms. For the most part, imaging data have remained an untapped, yet abundant, resource to address these challenges. In recent years, quantitative image analyses have highlighted the utility of medical imaging in predicting tumor phenotypes, prognosis, and therapeutic response. These studies have been fueled by an explosion of resources in high-throughput mining of image features (i.e. radiomics) and artificial intelligence. In this review, we highlight current progress in radiomics to understand tumor immune biology and predict clinical responses to immunotherapies. We also discuss limitations in these studies and future directions for the field, particularly if high-dimensional imaging data are to play a larger role in precision medicine.

Human methylome variation across Infinium 450K data on the Gene Expression Omnibus

While DNA methylation (DNAm) is the most-studied epigenetic mark, few recent studies probe the breadth of publicly available DNAm array samples. We collectively analyzed 35 360 Illumina Infinium HumanMethylation450K DNAm array samples published on the Gene Expression Omnibus. We learned a controlled vocabulary of sample labels by applying regular expressions to metadata and used existing models to predict various sample properties including epigenetic age. We found approximately two-thirds of samples were from blood, one-quarter were from brain and one-third were from cancer patients. About 19% of samples failed at least one of Illumina's 17 prescribed quality assessments; signal distributions across samples suggest modifying manufacturer-recommended thresholds for failure would make these assessments more informative. We further analyzed DNAm variances in seven tissues (adipose, nasal, blood, brain, buccal, sperm and liver) and characterized specific probes distinguishing them. Finally, we compiled DNAm array data and metadata, including our learned and predicted sample labels, into database files accessible via the recountmethylation R/Bioconductor companion package. Its vignettes walk the user through some analyses contained in this paper.

National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation

PURPOSE

Artificial intelligence (AI) is about to touch every aspect of radiation therapy, from consultation to treatment planning, quality assurance, therapy delivery, and outcomes modeling. There is an urgent need to train radiation oncologists and medical physicists in data science to help shepherd AI solutions into clinical practice. Poorly trained personnel may do more harm than good when attempting to apply rapidly developing and complex technologies. As the amount of AI research expands in our field, the radiation oncology community needs to discuss how to educate future generations in this area.

METHODS AND MATERIALS

The National Cancer Institute (NCI) Workshop on AI in Radiation Oncology (Shady Grove, MD, April 4-5, 2019) was the first of 2 data science workshops in radiation oncology hosted by the NCI in 2019. During this workshop, the Training and Education Working Group was formed by volunteers among the invited attendees. Its members represent radiation oncology, medical physics, radiology, computer science, industry, and the NCI.

RESULTS

In this perspective article written by members of the Training and Education Working Group, we provide and discuss action points relevant for future trainees interested in radiation oncology AI: (1) creating AI awareness and responsible conduct; (2) implementing a practical didactic curriculum; (3) creating a publicly available database of training resources; and (4) accelerating learning and funding opportunities.

CONCLUSION

Together, these action points can facilitate the translation of AI into clinical practice.

neoepiscope improves neoepitope prediction with multivariant phasing

MOTIVATION

The vast majority of tools for neoepitope prediction from DNA sequencing of complementary tumor and normal patient samples do not consider germline context or the potential for the co-occurrence of two or more somatic variants on the same mRNA transcript. Without consideration of these phenomena, existing approaches are likely to produce both false-positive and false-negative results, resulting in an inaccurate and incomplete picture of the cancer neoepitope landscape. We developed neoepiscope chiefly to address this issue for single nucleotide variants (SNVs) and insertions/deletions (indels).

RESULTS

Herein, we illustrate how germline and somatic variant phasing affects neoepitope prediction across multiple datasets. We estimate that up to ∼5% of neoepitopes arising from SNVs and indels may require variant phasing for their accurate assessment. neoepiscope is performant, flexible and supports several major histocompatibility complex binding affinity prediction tools.

AVAILABILITY AND IMPLEMENTATION

neoepiscope is available on GitHub at https://github.com/pdxgx/neoepiscope under the MIT license. Scripts for reproducing results described in the text are available at https://github.com/pdxgx/neoepiscope-paper under the MIT license. Additional data from this study, including summaries of variant phasing incidence and benchmarking wallclock times, are available in Supplementary Files 1, 2 and 3. Supplementary File 1 contains Supplementary Table 1, Supplementary Figures 1 and 2, and descriptions of Supplementary Tables 2-8. Supplementary File 2 contains Supplementary Tables 2-6 and 8. Supplementary File 3 contains Supplementary Table 7. Raw sequencing data used for the analyses in this manuscript are available from the Sequence Read Archive under accessions PRJNA278450, PRJNA312948, PRJNA307199, PRJNA343789, PRJNA357321, PRJNA293912, PRJNA369259, PRJNA305077, PRJNA306070, PRJNA82745 and PRJNA324705; from the European Genome-phenome Archive under accessions EGAD00001004352 and EGAD00001002731; and by direct request to the authors.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A phase II single-arm study of pembrolizumab with enzalutamide in men with metastatic castration-resistant prostate cancer progressing on enzalutamide alone

BACKGROUND

Checkpoint inhibitors can induce profound anticancer responses, but programmed cell death protein-1 (PD-1) inhibition monotherapy has shown minimal activity in prostate cancer. A published report showed that men with prostate cancer who were resistant to the second-generation androgen receptor inhibitor enzalutamide had increased programmed death-ligand 1 (PD-L1) expression on circulating antigen-presenting cells. We hypothesized that the addition of PD-1 inhibition in these patients could induce a meaningful cancer response.

METHODS

We evaluated enzalutamide plus the PD-1 inhibitor pembrolizumab in a single-arm phase II study of 28 men with metastatic castration-resistant prostate cancer (mprogressing on enzalutamide alone. Pembrolizumab 200 mg intravenous was given every 3 weeks for four doses with enzalutamide. The primary endpoint was prostate-specific antigen (PSA) decline of ≥50%. Secondary endpoints were objective response, PSA progression-free survival (PFS), time to subsequent treatment, and time to death. Baseline tumor biopsies were obtained when feasible, and samples were sequenced and evaluated for the expression of PD-L1, microsatellite instability (MSI), mutational and neoepitope burdens.

RESULTS

Five (18%) of 28 patients had a PSA decline of ≥50%. Three (25%) of 12 patients with measurable disease at baseline achieved an objective response. Of the five responders, two continue with PSA and radiographic response after 39.3 and 37.8 months. For the entire cohort, median follow-up was 37 months, and median PSA PFS time was 3.8 months (95% CI: 2.8 to 9.9 months). Time to subsequent treatment was 7.21 months (95% CI: 5.1 to 11.1 months). Median overall survival for all patients was 21.9 months (95% CI: 14.7 to 28 .4 months), versus 41.7 months (95% CI: 22.16 to not reached (NR)) in the responders. Of the three responders with baseline biopsies, one had MSI high disease with mutations consistent with DNA-repair defects. None had detectable PD-L1 expression.

CONCLUSIONS

Pembrolizumab has activity in mCRPC when added to enzalutamide. Responses were deep and durable and did not require tumor PD-L1 expression or DNA-repair defects.

TRIAL REGISTRATION NUMBER

clinicaltrials.gov (NCT02312557).

Human Leukocyte Antigen Susceptibility Map for Severe Acute Respiratory Syndrome Coronavirus 2

Genetic variability across the three major histocompatibility complex (MHC) class I genes (human leukocyte antigen A [HLA-A], -B, and -C genes) may affect susceptibility to and severity of the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19). We performed a comprehensive in silico analysis of viral peptide-MHC class I binding affinity across 145 HLA-A, -B, and -C genotypes for all SARS-CoV-2 peptides. We further explored the potential for cross-protective immunity conferred by prior exposure to four common human coronaviruses. The SARS-CoV-2 proteome was successfully sampled and was represented by a diversity of HLA alleles. However, we found that HLA-B*46:01 had the fewest predicted binding peptides for SARS-CoV-2, suggesting that individuals with this allele may be particularly vulnerable to COVID-19, as they were previously shown to be for SARS (M. Lin, H.-T. Tseng, J. A. Trejaut, H.-L. Lee, et al., BMC Med Genet 4:9, 2003, https://bmcmedgenet.biomedcentral.com/articles/10.1186/1471-2350-4-9). Conversely, we found that HLA-B*15:03 showed the greatest capacity to present highly conserved SARS-CoV-2 peptides that are shared among common human coronaviruses, suggesting that it could enable cross-protective T-cell-based immunity. Finally, we reported global distributions of HLA types with potential epidemiological ramifications in the setting of the current pandemic.IMPORTANCE Individual genetic variation may help to explain different immune responses to a virus across a population. In particular, understanding how variation in HLA may affect the course of COVID-19 could help identify individuals at higher risk from the disease. HLA typing can be fast and inexpensive. Pairing HLA typing with COVID-19 testing where feasible could improve assessment of severity of viral disease in the population. Following the development of a vaccine against SARS-CoV-2, the virus that causes COVID-19, individuals with high-risk HLA types could be prioritized for vaccination.

Burden of tumor mutations, neoepitopes, and other variants are weak predictors of cancer immunotherapy response and overall survival

BACKGROUND

Tumor mutational burden (TMB; the quantity of aberrant nucleotide sequences a given tumor may harbor) has been associated with response to immune checkpoint inhibitor therapy and is gaining broad acceptance as a result. However, TMB harbors intrinsic variability across cancer types, and its assessment and interpretation are poorly standardized.

METHODS

Using a standardized approach, we quantify the robustness of TMB as a metric and its potential as a predictor of immunotherapy response and survival among a diverse cohort of cancer patients. We also explore the additive predictive potential of RNA-derived variants and neoepitope burden, incorporating several novel metrics of immunogenic potential.

RESULTS

We find that TMB is a partial predictor of immunotherapy response in melanoma and non-small cell lung cancer, but not renal cell carcinoma. We find that TMB is predictive of overall survival in melanoma patients receiving immunotherapy, but not in an immunotherapy-naive population. We also find that it is an unstable metric with potentially problematic repercussions for clinical cohort classification. We finally note minimal additional predictive benefit to assessing neoepitope burden or its bulk derivatives, including RNA-derived sources of neoepitopes.

CONCLUSIONS

We find sufficient cause to suggest that the predictive clinical value of TMB should not be overstated or oversimplified. While it is readily quantified, TMB is at best a limited surrogate biomarker of immunotherapy response. The data do not support isolated use of TMB in renal cell carcinoma.

Putatively cancer-specific exon-exon junctions are shared across patients and present in developmental and other non-cancer cells

This study probes the distribution of putatively cancer-specific junctions across a broad set of publicly available non-cancer human RNA sequencing (RNA-seq) datasets. We compared cancer and non-cancer RNA-seq data from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) Project and the Sequence Read Archive. We found that (i) averaging across cancer types, 80.6% of exon–exon junctions thought to be cancer-specific based on comparison with tissue-matched samples (σ = 13.0%) are in fact present in other adult non-cancer tissues throughout the body; (ii) 30.8% of junctions not present in any GTEx or TCGA normal tissues are shared by multiple samples within at least one cancer type cohort, and 87.4% of these distinguish between different cancer types; and (iii) many of these junctions not found in GTEx or TCGA normal tissues (15.4% on average, σ = 2.4%) are also found in embryological and other developmentally associated cells. These findings refine the meaning of RNA splicing event novelty, particularly with respect to the human neoepitope repertoire. Ultimately, cancer-specific exon–exon junctions may have a substantial causal relationship with the biology of disease.

Validity of Veterans Health Administration structured data to determine accurate smoking status

We compared smoking status from Veterans Health Administration (VHA) structured data with text in electronic health record (EHR) to assess validity. We manually abstracted the smoking status of 5,610 VHA patients. Only those with a smoking status found in both EHR text data and VHA structured data were included (n=5,289). We calculated agreement and kappa statistics to compare structured data vs. manually abstracted EHR text smoking status. We found a kappa statistic of 0.70 and total agreement of 81.1% between EHR text data and structured data for Current, Former, and Never smoking categories. Comparing EHR text data and structured data between Never and Ever smokers revealed a kappa statistic of 0.62 and total agreement of 89.1%. For comparison between Current and Never/Former smokers, the kappa statistic was 0.80 and total agreement was 90.2%. We found substantial and significant agreement between smoking status in EHR text data and structured data that may aid in future research.

Artificial Intelligence in Radiation Oncology

The integration of artificial intelligence in the radiation oncologist's workflow has multiple applications and significant potential. From the initial patient encounter, artificial intelligence may aid in pretreatment disease outcome and toxicity prediction. It may subsequently aid in treatment planning, and enhanced dose optimization. Artificial intelligence may also optimize the quality assurance process and support a higher level of safety, quality, and efficiency of care. This article describes components of the radiation consultation, planning, and treatment process and how the thoughtful integration of artificial intelligence may improve shared decision making, planning efficiency, planning quality, patient safety, and patient outcomes.

Clinical Documentation and Patient Care Using Artificial Intelligence in Radiation Oncology

Detailed clinical documentation is required in the patient-facing specialty of radiation oncology. The burden of clinical documentation has increased significantly with the introduction of electronic health records and participation in payer-mandated quality initiatives. Artificial intelligence (AI) has the potential to reduce the burden of data entry associated with clinical documentation, provide clinical decision support, improve quality and value, and integrate patient data from multiple sources. The authors discuss key elements of an AI-enhanced clinic and review some emerging technologies in the industry. Challenges regarding data privacy, regulation, and medicolegal liabilities must be addressed for such AI technologies to be successful.

Stereotactic body proton therapy for liver tumors: Dosimetric advantages and their radiobiological and clinical implications

Background and Purpose

Photon Stereotactic Body Radiotherapy (SBRT) for primary and metastatic tumors of the liver is challenging for larger lesions. An in silico comparison of paired SBRT and Stereotactic Body Proton Therapy (SBPT) plans was performed to understand the potential advantages of SBPT as a function of tumor size and location.

Methods and materials

Theoretical tumor volumes with maximum diameter of 1–10 cm were contoured in the dome, right inferior, left medial, and central locations. SBRT and SBPT plans were generated to deliver 50 Gy in 5 fractions, max dose <135%. When organs-at-risk (OAR) constraints were exceeded, hypothetical plans (not clinically acceptable) were generated for comparison. Liver normal tissue complication probability (NTCP) models were applied to evaluate differences between treatment modalities.

Results

SBRT and SBPT were able to meet target goals and OAR constraints for lesions up to 7 cm and 9 cm diameter, respectively. SBPT plans resulted in a higher integral gross target dose for all lesions up to 7 cm (mean dose 57.8 ± 2.3 Gy to 64.1 ± 2.2 Gy, p < 0.01). Simultaneously, SBPT spared dose to the uninvolved liver in all locations (from 11.5 ± 5.3 Gy to 8.6 ± 4.4 Gy, p < 0.01), resulting in lower NTCP particularly for larger targets in the dome and central locations. SBPT also spared duodenal dose across all sizes and positions (from 7.3 ± 1.1 Gy to 1.1 ± 0.3 Gy, p < 0.05).

Conclusion

The main advantages of SBPT over SBRT is meeting plan goals and constrains for larger targets, particularly dome and central locations, and sparing dose to uninvolved liver. For such patients, SBPT may allow improvements in tumor control and treatment safety.

Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manufacturing to advertising and facial recognition to fully autonomous transportation. Advances in each of these domains have led some to call AI the "fourth" industrial revolution [1]. In healthcare, AI is emerging as both a productive and disruptive force across many disciplines. This is perhaps most evident in Diagnostic Radiology and Pathology, specialties largely built around the processing and complex interpretation of medical images, where the role of AI is increasingly seen as both a boon and a threat. In Radiation Oncology as well, AI seems poised to reshape the specialty in significant ways, though the impact of AI has been relatively limited at present, and may rightly seem more distant to many, given the predominantly interpersonal and complex interventional nature of the specialty. In this overview, we will explore the current state and anticipated future impact of AI on Radiation Oncology, in detail, focusing on key topics from multiple stakeholder perspectives, as well as the role our specialty may play in helping to shape the future of AI within the larger spectrum of medicine.

Precision Oncology and Genomically Guided Radiation Therapy: A Report From the American Society for Radiation Oncology/American Association of Physicists in Medicine/National Cancer Institute Precision Medicine Conference

PURPOSE

To summarize important talking points from a 2016 symposium focusing on real-world challenges to advancing precision medicine in radiation oncology, and to help radiation oncologists navigate the practical challenges of precision, radiation oncology.

METHODS AND MATERIALS

The American Society for Radiation Oncology, American Association of Physicists in Medicine, and National Cancer Institute cosponsored a meeting on precision medicine in radiation oncology. In June 2016 numerous scientists, clinicians, and physicists convened at the National Institutes of Health to discuss challenges and future directions toward personalized radiation therapy. Various breakout sessions were held to discuss particular components and approaches to the implementation of personalized radiation oncology. This article summarizes the genomically guided radiation therapy breakout session.

RESULTS

A summary of existing genomic data enabling personalized radiation therapy, ongoing clinical trials, current challenges, and future directions was collected. The group attempted to provide both a current overview of data that radiation oncologists could use to personalize therapy, along with data that are anticipated in the coming years. It seems apparent from the provided review that a considerable opportunity exists to truly bring genomically guided radiation therapy into clinical reality.

CONCLUSIONS

Genomically guided radiation therapy is a necessity that must be embraced in the coming years. Incorporating these data into treatment recommendations will provide radiation oncologists with a substantial opportunity to improve outcomes for numerous cancer patients. More research focused on this topic is needed to bring genomic signatures into routine standard of care.

Population-level distribution and putative immunogenicity of cancer neoepitopes

Background

Tumor neoantigens are drivers of cancer immunotherapy response; however, current prediction tools produce many candidates requiring further prioritization. Additional filtration criteria and population-level understanding may assist with prioritization. Herein, we show neoepitope immunogenicity is related to measures of peptide novelty and report population-level behavior of these and other metrics.

Methods

We propose four peptide novelty metrics to refine predicted neoantigenicity: tumor vs. paired normal peptide binding affinity difference, tumor vs. paired normal peptide sequence similarity, tumor vs. closest human peptide sequence similarity, and tumor vs. closest microbial peptide sequence similarity. We apply these metrics to neoepitopes predicted from somatic missense mutations in The Cancer Genome Atlas (TCGA) and a cohort of melanoma patients, and to a group of peptides with neoepitope-specific immune response data using an extension of pVAC-Seq (Hundal et al., pVAC-Seq: a genome-guided in silico approach to identifying tumor neoantigens. Genome Med 8:11, 2016).

Results

We show neoepitope burden varies across TCGA diseases and HLA alleles, with surprisingly low repetition of neoepitope sequences across patients or neoepitope preferences among sets of HLA alleles. Only 20.3% of predicted neoepitopes across TCGA patients displayed novel binding change based on our binding affinity difference criteria. Similarity of amino acid sequence was typically high between paired tumor-normal epitopes, but in 24.6% of cases, neoepitopes were more similar to other human peptides, or bacterial (56.8% of cases) or viral peptides (15.5% of cases), than their paired normal counterparts. Applied to peptides with neoepitope-specific immune response, a linear model incorporating neoepitope binding affinity, protein sequence similarity between neoepitopes and their closest viral peptides, and paired binding affinity difference was able to predict immunogenicity (AUROC = 0.66).

Conclusions

Our proposed prioritization criteria emphasize neoepitope novelty and refine patient neoepitope predictions for focus on biologically meaningful candidate neoantigens. We have demonstrated that neoepitopes should be considered not only with respect to their paired normal epitope, but to the entire human proteome, and bacterial and viral peptides, with potential implications for neoepitope immunogenicity and personalized vaccines for cancer treatment. We conclude that putative neoantigens are highly variable across individuals as a function of cancer genetics and personalized HLA repertoire, while the overall behavior of filtration criteria reflects predictable patterns.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4325-6) contains supplementary material, which is available to authorized users.

A dosimetric comparison of proton and photon therapy in unresectable cancers of the head of pancreas

PURPOSE

Uncontrolled local growth is the cause of death in ∼ 30% of patients with unresectable pancreatic cancers. The addition of standard-dose radiotherapy to gemcitabine has been shown to confer a modest survival benefit in this population. Radiation dose escalation with three-dimensional planning is not feasible, but high-dose intensity-modulated radiation therapy (IMRT) has been shown to improve local control. Still, dose-escalation remains limited by gastrointestinal toxicity. In this study, the authors investigate the potential use of double scattering (DS) and pencil beam scanning (PBS) proton therapy in limiting dose to critical organs at risk.

METHODS

The authors compared DS, PBS, and IMRT plans in 13 patients with unresectable cancer of the pancreatic head, paying particular attention to duodenum, small intestine, stomach, liver, kidney, and cord constraints in addition to target volume coverage. All plans were calculated to 5500 cGy in 25 fractions with equivalent constraints and normalized to prescription dose. All statistics were by two-tailed paired t-test.

RESULTS

Both DS and PBS decreased stomach, duodenum, and small bowel dose in low-dose regions compared to IMRT (p < 0.01). However, protons yielded increased doses in the mid to high dose regions (e.g., 23.6-53.8 and 34.9-52.4 Gy for duodenum using DS and PBS, respectively; p < 0.05). Protons also increased generalized equivalent uniform dose to duodenum and stomach, however these differences were small (<5% and 10%, respectively; p < 0.01). Doses to other organs-at-risk were within institutional constraints and placed no obvious limitations on treatment planning.

CONCLUSIONS

Proton therapy does not appear to reduce OAR volumes receiving high dose. Protons are able to reduce the treated volume receiving low-intermediate doses, however the clinical significance of this remains to be determined in future investigations.

Development and evaluation of a standardized method and atlas for contouring primary and permanent dentition

OBJECTIVES

Radiation toxicity of the dentition may present significant treatment-related morbidity in the paediatric head and neck cancer population. However, clear dose-effect relationships remain undetermined and must be predicated upon accurate structure delineation and dosimetry at the individual tooth level. Radiation oncologists generally have limited familiarity or experience with relevant dental anatomy.

METHODS

We therefore developed a detailed CT atlas of permanent and primary dentition. After studying this atlas, five radiation oncology clinicians delineated all teeth for each of eight different cases (selected for breadth of dental maturity and anatomical variability). They were asked to record confidence in their contours on a per-tooth basis as well as the duration of time required per case. Contour accuracy and interclinician variability were assessed by Hausdorff distance and Dice similarity coefficient. All analyses were performed using R v. 3.1.1 and the RadOnc v. 1.0.9 package.

RESULTS

Participating clinicians delineated teeth with varying degrees of completeness and accuracy, stratified primarily by the age of the subject. On a per-tooth basis, delineation of permanent dentition was feasible for incisors, canines, premolars and first molars among all subjects, even at the youngest ages. However, delineation of second and third molars was less consistent, commensurate with approximate timing of tooth development. Within each tooth contour, uncertainty was the greatest at the level of the dental roots.

CONCLUSIONS

Delineation of individual teeth is feasible and serves as a necessary precursor for dental dose assessment and avoidance. Among the paediatric radiation oncology community in particular, this atlas may serve as a useful tool and reference.

Pencil-beam scanning proton therapy for anal cancer: a dosimetric comparison with intensity-modulated radiotherapy

BACKGROUND

Concurrent chemoradiotherapy cures most patients with anal squamous cell carcinoma at the cost of significant treatment-related toxicities. Intensity-modulated radiotherapy (IMRT) reduces side effects compared to older techniques, but whether proton beam therapy (PBT) offers additional advantages is unclear.

MATERIAL AND METHODS

Eight patients treated with PBT for anal cancer were chosen for this study. We conducted detailed plan comparisons between pencil-beam scanning PBT via two posterior oblique fields and seven-field IMRT. Cumulative dose-volume histograms were analyzed by Wilcoxon signed-rank test, and plan delivery robustness was assessed via verification computed tomography (CT) scans obtained during treatment.

RESULTS

Compared to IMRT, PBT reduced low dose radiation (≤ 30 Gy) to the small bowel, total pelvic bone marrow, external genitalia, femoral heads, and bladder (all p < 0.05) without compromising target coverage. For PBT versus IMRT, mean small bowel volume receiving ≥ 15 Gy (V15) was 81 versus 151 cm(3), mean external genitalia V20 was 14 versus 40%, and mean total pelvic bone marrow V15 was 66 versus 83% (all p = 0.008). The lumbosacral bone marrow dose was higher with PBT due to beam geometry. PBT was delivered with ≤ 1.3% interfraction deviation in the dose received by 98% of the clinical target volumes.

CONCLUSION

Pencil-beam scanning PBT is clinically feasible and can be robustly delivered for anal cancer patients. Compared with IMRT, PBT reduces low dose radiation to important organs at risk in this population. While the clinical benefit of these differences remains to be shown, existing data suggest that limiting low dose to the small bowel and pelvic bone marrow may reduce treatment toxicity.

Epigenetic basis for fetal origins of age-related disease

The current concept of fetal origins of adult diseases describes in utero programming, or adaptation to a spectrum of adverse environmental conditions that ultimately leads to increased susceptibility to age-related diseases (e.g., type 2 diabetes and cardiovascular disease) later in life. Although the precise mechanism of this biological memory remains unclear, mounting evidence suggests an epigenetic basis. The increased susceptibility to chronic disease and involvement of multiple organ systems that is observed is analogous to the decline in resistance to disease that is typical of normal aging. Although the cumulative environment over the course of a lifetime can induce increasing epigenetic dysregulation, we propose that adverse events that occur during early development can induce significant additional dysregulation of the epigenome. Here, we describe the current evidence for fetal origins of adult disease and the associated role of epigenetic dysregulation. In addition, we present a new perspective on the induction of epigenetic alterations in utero, which subsequently lead to an aging phenotype marked by increased susceptibility to age-related diseases.

Late-replicating heterochromatin is characterized by decreased cytosine methylation in the human genome

Heterochromatin is believed to be associated with increased levels of cytosine methylation. With the recent availability of genome-wide, high-resolution molecular data reflecting chromatin organization and methylation, such relationships can be explored systematically. As well-defined surrogates for heterochromatin, we tested the relationship between DNA replication timing and DNase hypersensitivity with cytosine methylation in two human cell types, unexpectedly finding the later-replicating, more heterochromatic regions to be less methylated than early replicating regions. When we integrated gene-expression data into the study, we found that regions of increased gene expression were earlier replicating, as previously identified, and that transcription-targeted cytosine methylation in gene bodies contributes to the positive correlation with early replication. A self-organizing map (SOM) approach was able to identify genomic regions with early replication and increased methylation, but lacking annotated transcripts, loci missed in simple two variable analyses, possibly encoding unrecognized intergenic transcripts. We conclude that the relationship of cytosine methylation with heterochromatin is not simple and depends on whether the genomic context is tandemly repetitive sequences often found near centromeres, which are known to be heterochromatic and methylated, or the remaining majority of the genome, where cytosine methylation is targeted preferentially to the transcriptionally active, euchromatic compartment of the genome.

Widespread hypomethylation occurs early and synergizes with gene amplification during esophageal carcinogenesis

Although a combination of genomic and epigenetic alterations are implicated in the multistep transformation of normal squamous esophageal epithelium to Barrett esophagus, dysplasia, and adenocarcinoma, the combinatorial effect of these changes is unknown. By integrating genome-wide DNA methylation, copy number, and transcriptomic datasets obtained from endoscopic biopsies of neoplastic progression within the same individual, we are uniquely able to define the molecular events associated progression of Barrett esophagus. We find that the previously reported global hypomethylation phenomenon in cancer has its origins at the earliest stages of epithelial carcinogenesis. Promoter hypomethylation synergizes with gene amplification and leads to significant upregulation of a chr4q21 chemokine cluster and other transcripts during Barrett neoplasia. In contrast, gene-specific hypermethylation is observed at a restricted number of loci and, in combination with hemi-allelic deletions, leads to downregulatation of selected transcripts during multistep progression. We also observe that epigenetic regulation during epithelial carcinogenesis is not restricted to traditionally defined “CpG islands,” but may also occur through a mechanism of differential methylation outside of these regions. Finally, validation of novel upregulated targets (CXCL1 and 3, GATA6, and DMBT1) in a larger independent panel of samples confirms the utility of integrative analysis in cancer biomarker discovery.

The incidence of esophageal adenocarcinoma (EA) is increasing at an alarming pace in the United States. Distinct pathological stages of Barrett's metaplasia and low- and high-grade dysplasia can be seen preceding malignant transformation. These precursor lesions provide a unique in vivo model for deepening our understanding the early steps in human neoplasia. By integrating genome-wide DNA methylation, copy number, and transcriptomic datasets obtained from endoscopic biopsies of neoplastic progression within the same individual, we are uniquely able to define the molecular events associated progression of Barrett esophagus. We show that the predominant change during this process is loss of DNA methylation. We show that this global hypomethylation occurs very early during the process and is seen even in preinvasive lesions. This loss of DNA methylation drives carcinogenesis by cooperating with gene amplifications in upregulating proteins during this process. Finally we uncovered proteins that upregulated by loss of methylation or gene amplification (CXCL1 and 3, GATA6, and DMBT1) and show their relevance by validating their levels in larger independent panel of samples, thus confirming the utility of integrative analysis in cancer biomarker discovery.

Tissue-specific dysregulation of DNA methylation in aging

The normal aging process is a complex phenomenon associated with physiological alterations in the function of cells and organs over time. Although an attractive candidate for mediating transcriptional dysregulation, the contribution of epigenetic dysregulation to these progressive changes in cellular physiology remains unclear. In this study, we employed the genome-wide HpaII tiny fragment enrichment by ligation-mediated PCR assay to define patterns of cytosine methylation throughout the rat genome and the luminometric methylation analysis assay to measure global levels of DNA methylation in the same samples. We studied both liver and visceral adipose tissues and demonstrated significant differences in DNA methylation with age at > 5% of sites analyzed. Furthermore, we showed that epigenetic dysregulation with age is a highly tissue-dependent phenomenon. The most distinctive loci were located at intergenic sequences and conserved noncoding elements, and not at promoters nor at CG-dinucleotide-dense loci. Despite this, we found that there was a subset of genes at which cytosine methylation and gene expression changes were concordant. Finally, we demonstrated that changes in methylation occur consistently near genes that are involved in metabolism and metabolic regulation, implicating their potential role in the pathogenesis of age-related diseases. We conclude that different patterns of epigenetic dysregulation occur in each tissue over time and may cause some of the physiological changes associated with normal aging.