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.

Adrenal-Permissive Germline HSD3B1 Allele and Prostate Cancer Outcomes.

Importance: The adrenal androgen-metabolizing 3ß-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 3ß-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 3ß-hydroxysteroid dehydrogenase-1 and the androgen-signaling axis.

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.

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.

Plasma cell-free RNA profiling distinguishes cancers from pre-malignant conditions in solid and hematologic malignancies.

Cell-free RNA (cfRNA) in plasma reflects phenotypic alterations of both localized sites of cancer and the systemic host response. Here we report that cfRNA sequencing enables the discovery of messenger RNA (mRNA) biomarkers in plasma with the tissue of origin-specific to cancer types and precancerous conditions in both solid and hematologic malignancies. To explore the diagnostic potential of total cfRNA from blood, we sequenced plasma samples of eight hepatocellular carcinoma (HCC) and ten multiple myeloma (MM) patients, 12 patients of their respective precancerous conditions, and 20 non-cancer (NC) donors. We identified distinct gene sets and built classification models using Random Forest and linear discriminant analysis algorithms that could distinguish cancer patients from premalignant conditions and NC individuals with high accuracy. Plasma cfRNA biomarkers of HCC are liver-specific genes and biomarkers of MM are highly expressed in the bone marrow compared to other tissues and are related to cell cycle processes. The cfRNA level of these biomarkers displayed a gradual transition from noncancerous states through precancerous conditions and cancer. Sequencing data were cross-validated by quantitative reverse transcription PCR and cfRNA biomarkers were validated in an independent sample set (20 HCC, 9 MM, and 10 NC) with AUC greater than 0.86. cfRNA results observed in precancerous conditions require further validation. This work demonstrates a proof of principle for using mRNA transcripts in plasma with a small panel of genes to distinguish between cancers, noncancerous states, and precancerous conditions.

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.

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.

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.

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 (area under the curve) 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 AND IMPLEMENTATION: 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.

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.

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).