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INTRODUCTION: Men with African ancestry have the highest incidence and mortality rates of prostate cancer (PCa) worldwide. METHODS: This study aimed to identify differentially methylated genes between tumor vs. adjacent normal and aggressive vs. indolent PCa in 121 African American patients. Epigenome-wide DNA methylation patterns in tumor DNA were assessed using the human Illumina Methylation EPIC V1 array. RESULTS: Around 5,139 differentially methylated CpG-sites (q < 0.01, lΔßl > 0.2) were identified when comparing normal vs. tumor, with an overall trend of hypermethylation in prostate tumors. Multiple representative differentially methylated regions (DMRs), including immune-related genes, such as CD40, Galectin3, OX40L, and STING, were detected in prostate tumors when compared to adjacent normal tissues. Based on an epigenetic clock model, we observed that tumors' total number of stem cell divisions and the stem cell division rate were significantly higher than adjacent normal tissues. Regarding PCa aggressiveness, 2,061 differentially methylated CpG-sites (q < 0.05, lΔßl > .05) were identified when the grade group (GG)1 was compared with GG4/5. Among these 2,061 CpG sites, 155 probes were consistently significant in more than one comparison. Among these genes, several immune system genes, such as COL18A1, S100A2, ITGA4, HLA-C, and ADCYAP1, have previously been linked to tumor progression in PCa. CONCLUSION: Several differentially methylated genes involved in immune-oncologic pathways associated with disease risk or aggressiveness were identified. In addition, 261 African American-specific differentially methylated genes related to the risk of PCa were identified. These results can shedlight on potential mechanisms contributing to PCa disparities in the African American Population.
Subject(s)
Black or African American , DNA Methylation , Genome-Wide Association Study , Prostatic Neoplasms , Humans , Male , Black or African American/genetics , Prostatic Neoplasms/genetics , Prostatic Neoplasms/pathology , Prostatic Neoplasms/ethnology , Middle Aged , Aged , Epigenome , CpG Islands , Epigenesis, Genetic , Gene Expression Regulation, Neoplastic , Biomarkers, Tumor/geneticsABSTRACT
Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential is often underutilized due to the advanced data analysis and programming skills required. To address this, we present spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provides a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enables comparative analysis among samples and supports various ST technologies. We demonstrate the utility of spatialGE through its application in studying the tumor microenvironment of melanoma brain metastasis and Merkel cell carcinoma. Our results highlight the ability of spatialGE to identify spatial gene expression patterns and enrichments, providing valuable insights into the tumor microenvironment and its utility in democratizing ST data analysis for the wider scientific community.
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BACKGROUND: Locally advanced nasopharyngeal cancer (NPC) patients undergoing radiotherapy are at risk of treatment failure, particularly locoregional recurrence. To optimize the individual radiation dose, we hypothesize that the genomic adjusted radiation dose (GARD) can be used to correlate with locoregional control. METHODS: A total of 92 patients with American Joint Committee on Cancer / International Union Against Cancer stage III to stage IVB recruited in a randomized phase III trial were assessed (NPC-0501) (NCT00379262). Patients were treated with concurrent chemo-radiotherapy plus (neo) adjuvant chemotherapy. The primary endpoint is locoregional failure free rate (LRFFR). RESULTS: Despite the homogenous physical radiation dose prescribed (Median: 70 Gy, range 66-76 Gy), there was a wide range of GARD values (median: 50.7, range 31.1-67.8) in this cohort. In multivariable analysis, a GARD threshold (GARDT) of 45 was independently associated with LRFFR (p = 0.008). By evaluating the physical dose required to achieve the GARDT (RxRSI), three distinct clinical subgroups were identified: (1) radiosensitive tumors that RxRSI at dose < 66 Gy (N = 59, 64.1 %) (b) moderately radiosensitive tumors that RxRSI dose within the current standard of care range (66-74 Gy) (N = 20, 21.7 %), (c) radioresistant tumors that need a significant dose escalation above the current standard of care (>74 Gy) (N = 13, 14.1 %). CONCLUSION: GARD is independently associated with locoregional control in radiotherapy-treated NPC patients from a Phase 3 clinical trial. GARD may be a potential framework to personalize radiotherapy dose for NPC patients.
Subject(s)
Nasopharyngeal Neoplasms , Radiotherapy Dosage , Humans , Male , Nasopharyngeal Neoplasms/radiotherapy , Nasopharyngeal Neoplasms/genetics , Nasopharyngeal Neoplasms/pathology , Female , Middle Aged , Adult , Aged , Precision Medicine , Chemoradiotherapy/methods , Neoplasm Staging , Genomics , Neoplasm Recurrence, LocalABSTRACT
BACKGROUND: Soft tissue sarcomas (STS), have significant inter- and intra-tumoral heterogeneity, with poor response to standard neoadjuvant radiotherapy (RT). Achieving a favorable pathologic response (FPR ≥ 95%) from RT is associated with improved patient outcome. Genomic adjusted radiation dose (GARD), a radiation-specific metric that quantifies the expected RT treatment effect as a function of tumor dose and genomics, proposed that STS is significantly underdosed. STS have significant radiomic heterogeneity, where radiomic habitats can delineate regions of intra-tumoral hypoxia and radioresistance. We designed a novel clinical trial, Habitat Escalated Adaptive Therapy (HEAT), utilizing radiomic habitats to identify areas of radioresistance within the tumor and targeting them with GARD-optimized doses, to improve FPR in high-grade STS. METHODS: Phase 2 non-randomized single-arm clinical trial includes non-metastatic, resectable high-grade STS patients. Pre-treatment multiparametric MRIs (mpMRI) delineate three distinct intra-tumoral habitats based on apparent diffusion coefficient (ADC) and dynamic contrast enhanced (DCE) sequences. GARD estimates that simultaneous integrated boost (SIB) doses of 70 and 60 Gy in 25 fractions to the highest and intermediate radioresistant habitats, while the remaining volume receives standard 50 Gy, would lead to a > 3 fold FPR increase to 24%. Pre-treatment CT guided biopsies of each habitat along with clip placement will be performed for pathologic evaluation, future genomic studies, and response assessment. An mpMRI taken between weeks two and three of treatment will be used for biological plan adaptation to account for tumor response, in addition to an mpMRI after the completion of radiotherapy in addition to pathologic response, toxicity, radiomic response, disease control, and survival will be evaluated as secondary endpoints. Furthermore, liquid biopsy will be performed with mpMRI for future ancillary studies. DISCUSSION: This is the first clinical trial to test a novel genomic-based RT dose optimization (GARD) and to utilize radiomic habitats to identify and target radioresistance regions, as a strategy to improve the outcome of RT-treated STS patients. Its success could usher in a new phase in radiation oncology, integrating genomic and radiomic insights into clinical practice and trial designs, and may reveal new radiomic and genomic biomarkers, refining personalized treatment strategies for STS. TRIAL REGISTRATION: NCT05301283. TRIAL STATUS: The trial started recruitment on March 17, 2022.
Subject(s)
Hot Temperature , Sarcoma , Humans , Radiomics , Sarcoma/diagnostic imaging , Sarcoma/genetics , Sarcoma/radiotherapy , Genomics , Radiation DosageABSTRACT
Penile squamous cell carcinoma (PSCC) is a rare and deadly malignancy. Therapeutic advances have been stifled by a poor understanding of disease biology. Specifically, the immune microenvironment is an underexplored component in PSCC and the activity of immune checkpoint inhibitors observed in a subset of patients suggests immune escape may play an important role in tumorigenesis. Herein, we explored for the first time the immune microenvironment of 57 men with PSCC and how it varies with the presence of human papillomavirus (HPV) infection and across tumor stages using multiplex immunofluorescence of key immune cell markers. We observed an increase in the density of immune effector cells in node-negative tumors and a progressive rise in inhibitory immune players such as type 2 macrophages and upregulation of the PD-L1 checkpoint in men with N1 and N2-3 disease. There were no differences in immune cell densities with HPV status.
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Background: Treatment decision-making in oropharyngeal squamous cell carcinoma (OPSCC) includes clinical stage, HPV status, and smoking history. Despite improvements in staging with separation of HPV-positive and -negative OPSCC in AJCC 8th edition (AJCC8), patients are largely treated with a uniform approach, with recent efforts focused on de-intensification in low-risk patients. We have previously shown, in a pooled analysis, that the genomic adjusted radiation dose (GARD) is predictive of radiation treatment benefit and can be used to guide RT dose selection. We hypothesize that GARD can be used to predict overall survival (OS) in HPV-positive OPSCC patients treated with radiotherapy (RT). Methods: Gene expression profiles (Affymetrix Clariom D) were analyzed for 234 formalin-fixed paraffin-embedded samples from HPV-positive OPSCC patients within an international, multi-institutional, prospective/retrospective observational study including patients with AJCC 7th edition stage III-IVb. GARD, a measure of the treatment effect of RT, was calculated for each patient as previously described. In total, 191 patients received primary RT definitive treatment (chemoradiation or RT alone, and 43 patients received post-operative RT. Two RT dose fractionations were utilized for primary RT cases (70 Gy in 35 fractions or 69.96 Gy in 33 fractions). Median RT dose was 70 Gy (range 50.88-74) for primary RT definitive cases and 66 Gy (range 44-70) for post-operative RT cases. The median follow up was 46.2 months (95% CI, 33.5-63.1). Cox proportional hazards analyses were performed with GARD as both a continuous and dichotomous variable and time-dependent ROC analyses compared the performance of GARD with the NRG clinical nomogram for overall survival. Results: Despite uniform radiation dose utilization, GARD showed significant heterogeneity (range 30-110), reflecting the underlying genomic differences in the cohort. On multivariable analysis, each unit increase in GARD was associated with an improvement in OS (HR = 0.951 (0.911, 0.993), p = 0.023) compared to AJCC8 (HR = 1.999 (0.791, 5.047)), p = 0.143). ROC analysis for GARD at 36 months yielded an AUC of 80.6 (69.4, 91.9) compared with an AUC of 73.6 (55.4, 91.7) for the NRG clinical nomogram. GARD≥64.2 was associated with improved OS (HR = 0.280 (0.100, 0.781), p = 0.015). In a virtual trial, GARD predicts that uniform RT dose de-escalation results in overall inferior OS but proposes two separate genomic strategies where selective RT dose de-escalation in GARD-selected populations results in clinical equipoise. Conclusions: In this multi-institutional cohort of patients with HPV-positive OPSCC, GARD predicts OS as a continuous variable, outperforms the NRG nomogram and provides a novel genomic strategy to modern clinical trial design. We propose that GARD, which provides the first opportunity for genomic guided personalization of radiation dose, should be incorporated in the diagnostic workup of HPV-positive OPSCC patients.
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Data modeling requires a sufficient sample size for reproducibility. A small sample size can inhibit model evaluation. A synthetic data generation technique addressing this small sample size problem is evaluated: from the space of arbitrarily distributed samples, a subgroup (class) has a latent multivariate normal characteristic; synthetic data can be generated from this class with univariate kernel density estimation (KDE); and synthetic samples are statistically like their respective samples. Three samples (n = 667) were investigated with 10 input variables (X). KDE was used to augment the sample size in X. Maps produced univariate normal variables in Y. Principal component analysis in Y produced uncorrelated variables in T, where the probability density functions were approximated as normal and characterized; synthetic data was generated with normally distributed univariate random variables in T. Reversing each step produced synthetic data in Y and X. All samples were approximately multivariate normal in Y, permitting the generation of synthetic data. Probability density function and covariance comparisons showed similarity between samples and synthetic samples. A class of samples has a latent normal characteristic. For such samples, this approach offers a solution to the small sample size problem. Further studies are required to understand this latent class.
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Penile squamous cell carcinoma (PSCC) is a rare malignancy in most parts of the world and the underlying mechanisms of this disease have not been fully investigated. About 30-50% of cases are associated with high-risk human papillomavirus (HPV) infection, which may have prognostic value. When PSCC becomes resistant to upfront therapies there are limited options, thus further research is needed in this venue. The extracellular domain-facing protein profile on the cell surface (i.e., the surfaceome) is a key area for biomarker and drug target discovery. This research employs computational methods combined with cell line translatomic (n = 5) and RNA-seq transcriptomic data from patient-derived tumors (n = 18) to characterize the PSCC surfaceome, evaluate the composition dependency on HPV infection, and explore the prognostic impact of identified surfaceome candidates. Immunohistochemistry (IHC) was used to validate the localization of select surfaceome markers. This analysis characterized a diverse surfaceome within patient tumors with 25% and 18% of the surfaceome represented by the functional classes of receptors and transporters, respectively. Significant differences in protein classes were noted by HPV status, with the most change being seen in transporter proteins (25%). IHC confirmed the robust surface expression of select surfaceome targets in the top 85% of expression and a superfamily immunoglobulin protein called BSG/CD147 was prognostic of survival. This study provides the first description of the PSCC surfaceome and its relation to HPV infection and sets a foundation for novel biomarker and drug target discovery in this rare cancer.
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PSCC is a rare cancer, with approximately half of all cases related to HPV. While HPV and p16 IHC testing have proven their prognostic value for oropharyngeal cancer, this is not yet established for PSCC. The current level of evidence exploring the relation between PSCC and HPV is moderate, so we conducted a systematic review following PRISMA guidelines to evaluate the prognostic role of HPV and p16 IHC in PSCC clinical outcomes. We searched the PubMed, Embase, and Cochrane databases and identified 34 relevant studies that met our inclusion criteria. Of these, 33 were retrospective cohort studies, and one was a cross-sectional study. Nine studies reported that HPV-positive and p16-positive PSCC had better overall survival (OS) and disease-free survival (DFS). This study highlights the need for a meta-analysis to determine the role of routine HPV status or p16 staining testing as part of the initial diagnosis and staging of PSCC patients worldwide.
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The genomic era has significantly changed the practice of clinical oncology. The use of genomic-based molecular diagnostics including prognostic genomic signatures and new-generation sequencing has become routine for clinical decisions regarding cytotoxic chemotherapy, targeted agents and immunotherapy. In contrast, clinical decisions regarding radiation therapy (RT) remain uninformed about the genomic heterogeneity of tumors. In this review, we discuss the clinical opportunity to utilize genomics to optimize RT dose. Although from the technical perspective, RT has been moving towards a data-driven approach, RT prescription dose is still based on a one-size-fits all approach, with most RT dose based on cancer diagnosis and stage. This approach is in direct conflict with the realization that tumors are biologically heterogeneous, and that cancer is not a single disease. Here, we discuss how genomics can be integrated into RT prescription dose, the clinical potential for this approach and how genomic-optimization of RT dose could lead to new understanding of the clinical benefit of RT.
Subject(s)
Antineoplastic Agents , Neoplasms , Humans , Neoplasms/genetics , Neoplasms/radiotherapy , Neoplasms/pathology , Medical Oncology , Prognosis , GenomicsABSTRACT
Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient's clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.
Subject(s)
Leukemia, Myeloid, Acute , Melanoma , Child , Humans , Drug Repositioning , Medical Oncology , Melanoma/drug therapy , Melanoma/genetics , Algorithms , Leukemia, Myeloid, Acute/drug therapy , Leukemia, Myeloid, Acute/geneticsABSTRACT
Liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM) has widespread clinical use for detection of inborn errors of metabolism, therapeutic drug monitoring, and numerous other applications. This technique detects proteolytic peptides as surrogates for protein biomarker expression, mutation, and post-translational modification in individual clinical assays and in cancer research with highly multiplexed quantitation across biological pathways. LC-MRM for protein biomarkers must be translated from multiplexed research-grade panels to clinical use. LC-MRM panels provide the capability to quantify clinical biomarkers and emerging protein markers to establish the context of tumor phenotypes that provide highly relevant supporting information. An application to visualize and communicate targeted proteomics data will empower translational researchers to move protein biomarker panels from discovery to clinical use. Therefore, we have developed a web-based tool for targeted proteomics that provides pathway-level evaluations of key biological drivers (e.g., EGFR signaling), signature scores (representing phenotypes) (e.g., EMT), and the ability to quantify specific drug targets across a sample cohort. This tool represents a framework for integrating summary information, decision algorithms, and risk scores to support Physician-Interpretable Phenotypic Evaluation in R (PIPER) that can be reused or repurposed by other labs to communicate and interpret their own biomarker panels.
Subject(s)
Proteins , Translational Research, Biomedical , Proteins/analysis , Peptides/metabolism , Biomarkers/analysis , PhenotypeABSTRACT
Precision medicine offers remarkable potential for the treatment of cancer, but is largely focused on tumors that harbor actionable mutations. Gene expression signatures can expand the scope of precision medicine by predicting response to traditional (cytotoxic) chemotherapy agents without relying on changes in mutational status. We present a new signature extraction method, inspired by the principle of convergent phenotypes, which states that tumors with disparate genetic backgrounds may evolve similar phenotypes independently. This evolutionary-informed method can be utilized to produce consensus signatures predictive of response to over 200 chemotherapeutic drugs found in the Genomics of Drug Sensitivity in Cancer (GDSC) Database. Here, we demonstrate its use by extracting the Cisplatin Response Signature (CisSig). We show that this signature can predict cisplatin response within carcinoma-based cell lines from the GDSC database, and expression of the signatures aligns with clinical trends seen in independent datasets of tumor samples from The Cancer Genome Atlas (TCGA) and Total Cancer Care (TCC) database. Finally, we demonstrate preliminary validation of CisSig for use in muscle-invasive bladder cancer, predicting overall survival in a small cohort of patients who undergo cisplatin-containing chemotherapy. This methodology can be used to produce robust signatures that, with further clinical validation, may be used for the prediction of traditional chemotherapeutic response, dramatically increasing the reach of personalized medicine in cancer.
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BACKGROUND: Massively parallel sequencing includes many liquid handling steps which introduce the possibility of sample swaps, mixing, and duplication. The unique profile of inherited variants in human genomes allows for comparison of sample identity using sequence data. A comparison of all samples vs. each other (all vs. all) provides both identification of mismatched samples and the possibility of resolving swapped samples. However, all vs. all comparison complexity grows as the square of the number of samples, so efficiency becomes essential. RESULTS: We have developed a tool for fast all vs. all genotype comparison using low level bitwise operations built into the Perl programming language. Importantly, we have also developed a complete workflow allowing users to start with either raw FASTQ sequence files, aligned BAM files, or genotype VCF files and automatically generate comparison metrics and summary plots. The tool is freely available at https://github.com/teerjk/TimeAttackGenComp/ . CONCLUSIONS: A fast and easy to use method for genotype comparison as described here is an important tool to ensure high quality and robust results in sequencing studies.
Subject(s)
High-Throughput Nucleotide Sequencing , Software , Humans , Workflow , Genotype , High-Throughput Nucleotide Sequencing/methods , Sequence Analysis, RNA/methods , DNA , Sequence Analysis, DNA/methodsABSTRACT
Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, DRPPM-PATH-SURVEIOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis (GSEA) and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient's clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.
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Penile squamous cell carcinoma (PSCC) remains a worldwide healthcare concern with poor outcomes and inadequate therapeutic options. Molecular characterization continues to describe the intricacies of PSCC biology, which vary by human papillomavirus (HPV) infection. Despite these advancements in our understanding, utilization of targeted therapies remains limited and underexplored. In this study, we evaluated the transcript and protein expression of Nectin-4 (PVRL4) in PSCC tumors and evaluated whether this is related to tumor features or clinical outcomes. Using two separate PSCC cohorts, we demonstrate that the majority of tumors have active transcription of Nectin-4. We then validated our findings using immunohistochemistry in a tissue microarray representing 57 patients with PSCC. We identified that Nectin-4 was expressed at higher levels in patients with high-risk HPV infection. No significant differences were identified in tumor characteristics or various clinical endpoints when comparing tumors with high and low Nectin-4 expression. This study demonstrates that Nectin-4 is expressed in PSCC and may represent a novel therapeutic target. Patient summary: In this study, we evaluated the expression of Nectin-4, a cell surface protein, in tumors from patients with nonmetastatic penile squamous cell carcinoma (PSCC). To our knowledge, this is the first study to describe elevated expression of Nectin-4 in PSCC, which may suggest its utility as a therapeutic target.
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Molecular epidemiology evidence indicates racial and ethnic differences in the aggressiveness and survival of breast cancer. Hispanics/Latinas (H/Ls) and non-Hispanic Black women (NHB) are at higher risk of breast cancer (BC)-related death relative to non-Hispanic white (NHW) women in part because they are diagnosed with hormone receptor-negative (HR) subtype and at higher stages. Since the cell cycle is one of the most commonly deregulated cellular processes in cancer, we propose that the mitotic kinases TTK (or Mps1), TBK1, and Nek2 could be novel targets to prevent breast cancer progression among NHBs and H/Ls. In this study, we calculated levels of TTK, p-TBK1, epithelial (E-cadherin), mesenchymal (Vimentin), and proliferation (Ki67) markers through immunohistochemical (IHC) staining of breast cancer tissue microarrays (TMAs) that includes samples from 6 regions in the Southeast of the United States and Puerto Rico -regions enriched with NHB and H/L breast cancer patients. IHC analysis showed that TTK, Ki67, and Vimentin were significantly expressed in triple-negative (TNBC) tumors relative to other subtypes, while E-cadherin showed decreased expression. TTK correlated with all of the clinical variables but p-TBK1 did not correlate with any of them. TCGA analysis revealed that the mRNA levels of multiple mitotic kinases, including TTK, Nek2, Plk1, Bub1, and Aurora kinases A and B, and transcription factors that are known to control the expression of these kinases (e.g. FoxM1 and E2F1-3) were upregulated in NHBs versus NHWs and correlated with higher aneuploidy indexes in NHB, suggesting that these mitotic kinases may be future novel targets for breast cancer treatment in NHB women.
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BACKGROUND: Image-based biomarkers could have translational implications by characterizing tumor behavior of lung cancers diagnosed during lung cancer screening. In this study, peritumoral and intratumoral radiomics and volume doubling time (VDT) were used to identify high-risk subsets of lung patients diagnosed in lung cancer screening that are associated with poor survival outcomes. METHODS: Data and images were acquired from the National Lung Screening Trial. VDT was calculated between two consequent screening intervals approximately 1 year apart; peritumoral and intratumoral radiomics were extracted from the baseline screen. Overall survival (OS) was the main endpoint. Classification and Regression Tree analyses identified the most predictive covariates to classify patient outcomes. RESULTS: Decision tree analysis stratified patients into three risk-groups (low, intermediate, and high) based on VDT and one radiomic feature (compactness). High-risk patients had extremely poor survival outcomes (hazard ratio [HR] = 8.15; 25% 5-year OS) versus low-risk patients (HR = 1.00; 83.3% 5-year OS). Among early-stage lung cancers, high-risk patients had poor survival outcomes (HR = 9.07; 44.4% 5-year OS) versus the low-risk group (HR = 1.00; 90.9% 5-year OS). For VDT, the decision tree analysis identified a novel cut-point of 279 days and using this cut-point VDT alone discriminated between aggressive (HR = 4.18; 45% 5-year OS) versus indolent/low-risk cancers (HR = 1.00; 82.8% 5-year OS). CONCLUSION: We utilized peritumoral and intratumoral radiomic features and VDT to generate a model that identify a high-risk group of screen-detected lung cancers associated with poor survival outcomes. These vulnerable subset of screen-detected lung cancers may be candidates for more aggressive surveillance/follow-up and treatment, such as adjuvant therapy.
Subject(s)
Lung Neoplasms , Early Detection of Cancer , Humans , Lung/pathology , Lung Neoplasms/diagnostic imaging , Risk Factors , Tomography, X-Ray Computed/methodsABSTRACT
PURPOSE: Radiation therapy (RT) is a mainstay of cancer care, and accumulating evidence suggests the potential for synergism with components of the immune response. However, few data describe the tumor immune contexture in relation to RT sensitivity. To address this challenge, we used the radiation sensitivity index (RSI) gene signature to estimate the RT sensitivity of >10,000 primary tumors and characterized their immune microenvironments in relation to the RSI. METHODS AND MATERIALS: We analyzed gene expression profiles of 10,469 primary tumors (31 types) within a prospective tissue collection protocol. The RT sensitivity of each tumor was estimated by the RSI and respective distributions were characterized. The tumor biology measured by the RSI was evaluated by differentially expressed genes combined with single sample gene set enrichment analysis. Differences in the expression of immune regulatory molecules were assessed and deconvolution algorithms were used to estimate immune cell infiltrates in relation to the RSI. A subset (n = 2368) of tumors underwent DNA sequencing for mutational frequency characterization. RESULTS: We identified a wide range of RSI values within and across various tumor types, with several demonstrating nonunimodal distributions (eg, colon, renal, lung, prostate, esophagus, pancreas, and PAM50 breast subtypes; P < .05). Across all tumor types, stratifying RSI at a tumor type-specific median identified 7148 differentially expressed genes, of which 146 were coordinate in direction. Network topology analysis demonstrates RSI measures a coordinated STAT1, IRF1, and CCL4/MIP-1ß transcriptional network. Tumors with an estimated high sensitivity to RT demonstrated distinct enrichment of interferon-associated signaling pathways and immune cell infiltrates (eg, CD8+ T cells, activated natural killer cells, M1-macrophages; q < 0.05), which was in the context of diverse expression patterns of various immunoregulatory molecules. CONCLUSIONS: This analysis describes the immune microenvironments of patient tumors in relation to the RSI gene expression signature.
Subject(s)
CD8-Positive T-Lymphocytes , Neoplasms , Biomarkers, Tumor/genetics , Gene Expression Regulation, Neoplastic , Humans , Male , Neoplasms/genetics , Neoplasms/radiotherapy , Prognosis , Radiation Tolerance/genetics , Transcriptome , Tumor Microenvironment/geneticsABSTRACT
BACKGROUND: Epigenetic changes associated with human papillomavirus (HPV)-driven tumors have been described; however, HPV type-specific alterations are less well understood. We sought to compare HPV16-specific methylation changes with those in virus-unassociated head and neck squamous cell carcinomas (HNSCC). METHODS: Within The Cancer Genome Atlas, 59 HPV16+ HNSCC, 238 nonviral HNSCC (no detectable HPV or other viruses), and 50 normal head and neck tissues were evaluated. Significant differentially methylated regions (DMR) were selected, and key associated genes were identified. Partial least squares models were generated to predict HPV16 status in additional independent samples. RESULTS: HPV infection in HNSCC is associated with type-specific methylomic profiles. Multiple significant DMRs were identified between HPV16+, nonviral, and normal samples. The most significant differentially methylated genes, SYCP2, MSX2, HLTF, PITX2, and GRAMD4, demonstrated HPV16-associated methylation patterns with corresponding alterations in gene expression. Phylogenetically related HPV types (alpha-9 species; HPV31, HPV33, and HPV35) demonstrated a similar methylation profile to that of HPV16 but differed from those seen in other types, such as HPV18 and 45 (alpha-7). CONCLUSIONS: HNSCC linked to HPV16 and types from the same alpha species are associated with a distinct methylation profile. This HPV16-associated methylation pattern is also detected in cervical cancer and testicular germ cell tumors. We present insights into both shared and unique methylation alterations associated with HPV16+ tumors and may have implications for understanding the clinical behavior of HPV-associated HNSCC. IMPACT: HPV type-specific methylomic changes may contribute to understanding biologic mechanisms underlying differences in clinical behavior among different HPV+ and HPV- HNSCC.