WITHDRAWN: Impact of spatial clustering of cytotoxic and tumor infiltrating lymphocytes on overall survival in women with high grade serous ovarian cancer.

The authors have withdrawn their manuscript owing to incorrect handling of multiple measures in the survival analyses. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

Novel Use of Circulating Tumor DNA to Identify Muscle-invasive and Non-organ-confined Upper Tract Urothelial Carcinoma.

BACKGROUND: Optimal patient selection for neoadjuvant chemotherapy prior to surgical extirpation is limited by the inaccuracy of contemporary clinical staging methods in high-risk upper tract urothelial carcinoma (UTUC). OBJECTIVE: To investigate whether the detection of plasma circulating tumor DNA (ctDNA) can predict muscle-invasive (MI) and non-organ-confined (NOC) UTUC. DESIGN, SETTING, AND PARTICIPANTS: Plasma cell-free DNA was prospectively collected from chemotherapy-naïve, high-risk UTUC patients undergoing surgical extirpation and sequenced using a 152-gene panel and low-pass whole-genome sequencing. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: To test for concordance, whole-exome sequencing was performed on matching tumor samples. The performance of ctDNA for predicting MI/NOC UTUC was summarized using the area under a receiver-operating curve, and a variant count threshold for predicting MI/NOC disease was determined by maximizing Youden's J statistic. Kaplan-Meier methods estimated survival, and Mantel-Cox log-rank testing assessed the association between preoperative ctDNA positivity and clinical outcomes. RESULTS AND LIMITATIONS: Of 30 patients enrolled prospectively, 14 were found to have MI/NOC UTUC. At least one ctDNA variant was detected from 21/30 (70%) patients, with 52% concordance with matching tumor samples. Detection of at least two panel-based molecular alterations yielded 71% sensitivity at 94% specificity to predict MI/NOC UTUC. Imposing this threshold in combination with a plasma copy number burden score of >6.5 increased sensitivity to 79% at 94% specificity. Furthermore, the presence of ctDNA was strongly prognostic for progression-free survival (PFS; 1-yr PFS 69% vs 100%, p < 0.001) and cancer-specific survival (CSS; 1-yr CSS 56% vs 100%, p = 0.016). CONCLUSIONS: The detection of plasma ctDNA prior to extirpative surgery was highly predictive of MI/NOC UTUC and strongly prognostic of PFS and CSS. Preoperative ctDNA demonstrates promise as a biomarker for selecting patients to undergo neoadjuvant chemotherapy prior to nephroureterectomy. PATIENT SUMMARY: Here, we show that DNA from upper tract urothelial tumors can be detected in the blood prior to surgical removal of the kidney or ureter. This circulating tumor DNA can be used to predict that upper tract urothelial carcinoma is invasive into the muscular lining of the urinary tract and may help identify those patients who could benefit from chemotherapy prior to surgery.

Increased spatial coupling of integrin and collagen IV in the immunoresistant clear cell renal cell carcinoma tumor microenvironment.

Background: Immunotherapy (IO) has improved survival for patients with advanced clear cell renal cell carcinoma (ccRCC), but resistance to therapy develops in most patients. We use cellular-resolution spatial transcriptomics in patients with IO naïve and IO exposed primary ccRCC tumors to better understand IO resistance. Spatial molecular imaging (SMI) was obtained for tumor and adjacent stroma samples. Spatial gene set enrichment analysis (GSEA) and autocorrelation (coupling with high expression) of ligand-receptor transcript pairs were assessed. Multiplex immunofluorescence (mIF) validation was used for significant autocorrelative findings and the cancer genome atlas (TCGA) and the clinical proteomic tumor analysis consortium (CPTAC) databases were queried to assess bulk RNA expression and proteomic correlates. Results: 21 patient samples underwent SMI. Viable tumors following IO harbored more stromal CD8+ T cells and neutrophils than IO naïve tumors. YES1 was significantly upregulated in IO exposed tumor cells. The epithelial-mesenchymal transition pathway was enriched on spatial GSEA and the associated transcript pair COL4A1-ITGAV had significantly higher autocorrelation in the stroma. Fibroblasts, tumor cells, and endothelium had the relative highest expression. More integrin αV+ cells were seen in IO exposed stroma on mIF validation. Compared to other cancers in TCGA, ccRCC tumors have the highest expression of both COL4A1 and ITGAV. In CPTAC, collagen IV protein was more abundant in advanced stages of disease. Conclusions: On spatial transcriptomics, COL4A1 and ITGAV were more autocorrelated in IO-exposed stroma compared to IO-naïve tumors, with high expression amongst fibroblasts, tumor cells, and endothelium. Integrin represents a potential therapeutic target in IO treated ccRCC.

A harmonized resource of integrated prostate cancer clinical, -omic, and signature features.

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or  not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

Detectable Lipidomes and Metabolomes by Different Plasma Exosome Isolation Methods in Healthy Controls and Patients with Advanced Prostate and Lung Cancer.

Circulating exosomes in the blood are promising tools for biomarker discovery in cancer. Due to their heterogeneity, different isolation methods may enrich distinct exosome cargos generating different omic profiles. In this study, we evaluated the effects of plasma exosome isolation methods on detectable multi-omic profiles in patients with non-small cell lung cancer (NSCLC), castration-resistant prostate cancer (CRPC), and healthy controls, and developed an algorithm to quantify exosome enrichment. Plasma exosomes were isolated from CRPC (n = 10), NSCLC (n = 14), and healthy controls (n = 10) using three different methods: size exclusion chromatography (SEC), lectin binding, and T-cell immunoglobulin domain and mucin domain-containing protein 4 (TIM4) binding. Molecular profiles were determined by mass spectrometry of extracted exosome fractions. Enrichment analysis of uniquely detected molecules was performed for each method with MetaboAnalyst. The exosome enrichment index (EEI) scores methods based on top differential molecules between patient groups. The lipidomic analysis detected 949 lipids using exosomes from SEC, followed by 246 from lectin binding and 226 from TIM4 binding. The detectable metabolites showed SEC identifying 191 while lectin binding and TIM4 binding identified 100 and 107, respectively. When comparing uniquely detected molecules, different methods showed preferential enrichment of different sets of molecules with SEC enriching the greatest diversity. Compared to controls, SEC identified 28 lipids showing significant difference in NSCLC, while only 1 metabolite in NSCLC and 5 metabolites in CRPC were considered statistically significant (FDR < 0.1). Neither lectin-binding- nor TIM4-binding-derived exosome lipids or metabolites demonstrated significant differences between patient groups. We observed the highest EEI from SEC in lipids (NSCLC: 871.33) which was also noted in metabolites. These results support that the size exclusion method of exosome extraction implemented by SBI captures more heterogeneous exosome populations. In contrast, lectin-binding and TIM4-binding methods bind surface glycans or phosphatidylserine moieties of the exosomes. Overall, these findings suggest that specific isolation methods select subpopulations which may significantly impact cancer biomarker discovery.

Proteogenomic, Epigenetic, and Clinical Implications of Recurrent Aberrant Splice Variants in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Alternative mRNA splicing can be dysregulated in cancer, resulting in the generation of aberrant splice variants (SVs). Given the paucity of actionable genomic mutations in clear cell renal cell carcinoma (ccRCC), aberrant SVs may be an avenue to novel mechanisms of pathogenesis. OBJECTIVE: To identify and characterize aberrant SVs enriched in ccRCC. DESIGN, SETTING, AND PARTICIPANTS: Using RNA-seq data from the Cancer Cell Line Encyclopedia, we identified neojunctions uniquely expressed in ccRCC. Candidate SVs were then checked for expression across normal tissue in the Genotype-Tissue Expression Project and primary tumor tissue from The Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and our institutional Total Cancer Care database. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Clinicopathologic, genomic, and survival data were available for all cohorts. Epigenetic data were available for the TCGA and CPTAC cohorts. Proteomic data were available for the CPTAC cohort. The association of aberrant SV expression with these variables was examined using the Kruskal-Wallis test, pairwise t test, Spearman correlation test, and Cox regression analysis. RESULTS AND LIMITATIONS: Our pipeline identified 16 ccRCC-enriched SVs. EGFR, HPCAL1-SV and RNASET2-SV expression was negatively correlated with gene-specific CpG methylation. We derived a survival risk score based primarily on the expression of five SVs (RNASET2, FGD1, PDZD2, COBLL1, and PTPN14), which was consistent and applicable across multiple cohorts on multivariate analysis. The splicing factor RBM4, which modulates splicing of HIF-1α, exhibited significantly lower expression at the protein level in the high-risk group, as defined by our SV-based score. CONCLUSIONS: We describe 16 aberrant SVs enriched in ccRCC, many of which are associated with disease biology and/or clinical outcomes. This study provides a novel strategy for identifying and characterizing disease-specific aberrant SVs. PATIENT SUMMARY: We describe a method to identify disease targets and biomarkers using transcriptomic analysis beyond somatic mutations or gene expression. Kidney tumors express unique splice variants that may provide additional prognostic information following surgery.

Tumor immune cell clustering and its association with survival in African American women with ovarian cancer.

New technologies, such as multiplex immunofluorescence microscopy (mIF), are being developed and used for the assessment and visualization of the tumor immune microenvironment (TIME). These assays produce not only an estimate of the abundance of immune cells in the TIME, but also their spatial locations. However, there are currently few approaches to analyze the spatial context of the TIME. Therefore, we have developed a framework for the spatial analysis of the TIME using Ripley's K, coupled with a permutation-based framework to estimate and measure the departure from complete spatial randomness (CSR) as a measure of the interactions between immune cells. This approach was then applied to epithelial ovarian cancer (EOC) using mIF collected on intra-tumoral regions of interest (ROIs) and tissue microarrays (TMAs) from 160 high-grade serous ovarian carcinoma patients in the African American Cancer Epidemiology Study (AACES) (94 subjects on TMAs resulting in 263 tissue cores; 93 subjects with 260 ROIs; 27 subjects with both TMA and ROI data). Cox proportional hazard models were constructed to determine the association of abundance and spatial clustering of tumor-infiltrating lymphocytes (CD3+), cytotoxic T-cells (CD8+CD3+), and regulatory T-cells (CD3+FoxP3+) with overall survival. Analysis was done on TMA and ROIs, treating the TMA data as validation of the findings from the ROIs. We found that EOC patients with high abundance and low spatial clustering of tumor-infiltrating lymphocytes and T-cell subsets in their tumors had the best overall survival. Additionally, patients with EOC tumors displaying high co-occurrence of cytotoxic T-cells and regulatory T-cells had the best overall survival. Grouping women with ovarian cancer based on both cell abundance and spatial contexture showed better discrimination for survival than grouping ovarian cancer cases only by cell abundance. These findings underscore the prognostic importance of evaluating not only immune cell abundance but also the spatial contexture of the immune cells in the TIME. In conclusion, the application of this spatial analysis framework to the study of the TIME could lead to the identification of immune content and spatial architecture that could aid in the determination of patients that are likely to respond to immunotherapies.

spatialGE: quantification and visualization of the tumor microenvironment heterogeneity using spatial transcriptomics.

SUMMARY: Spatially resolved transcriptomics promises to increase our understanding of the tumor microenvironment and improve cancer prognosis and therapies. Nonetheless, analytical methods to explore associations between the spatial heterogeneity of the tumor and clinical data are not available. Hence, we have developed spatialGE, a software that provides visualizations and quantification of the tumor microenvironment heterogeneity through gene expression surfaces, spatial heterogeneity statistics that can be compared against clinical information, spot-level cell deconvolution and spatially informed clustering, all using a new data object to store data and resulting analyses simultaneously. AVAILABILITY AND IMPLEMENTATION: The R package and tutorial/vignette are available at https://github.com/FridleyLab/spatialGE. A script to reproduce the analyses in this manuscript is available in Supplementary information. The Thrane study data included in spatialGE was made available from the public available from the website https://www.spatialresearch.org/resources-published-datasets/doi-10-1158-0008-5472-can-18-0747/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cell-free DNA methylome profiling by MBD-seq with ultra-low input.

Methylation signatures in cell-free DNA (cfDNA) have shown great sensitivity and specificity in the characterization of tumour status and classification of tumour types, as well as the response to therapy and recurrence. Currently, most cfDNA methylation studies are based on bisulphite conversion, especially targeted bisulphite sequencing, while enrichment-based methods such as cfMeDIP-seq are beginning to show potential. Here, we report an enrichment-based ultra-low input cfDNA methylation profiling method using methyl-CpG binding proteins capture, termed cfMBD-seq. We optimized the conditions for cfMBD capture by adjusting the amount of MethylCap protein along with using methylated filler DNA. Our data show high correlation between low input cfMBD-seq and standard MBD-seq (>1000 ng input). When compared to cfMEDIP-seq, cfMBD-seq demonstrates higher sequencing data quality with more sequenced reads passed filter and less duplicate rate. cfMBD-seq also outperforms cfMeDIP-seq in the enrichment of CpG islands. This new bisulphite-free ultra-low input methylation profiling technology has great potential in non-invasive and cost-effective cancer detection and classification.

spatialTIME and iTIME: R package and Shiny application for visualization and analysis of immunofluorescence data.

SUMMARY: Multiplex immunofluorescence (mIF) staining combined with quantitative digital image analysis is a novel and increasingly used technique that allows for the characterization of the tumor immune microenvironment (TIME). Generally, mIF data is used to examine the abundance of immune cells in the TIME; however, this does not capture spatial patterns of immune cells throughout the TIME, a metric increasingly recognized as important for prognosis. To address this gap, we developed an R package spatialTIME that enables spatial analysis of mIF data, as well as the iTIME web application that provides a robust but simplified user interface for describing both abundance and spatial architecture of the TIME. The spatialTIME package calculates univariate and bivariate spatial statistics (e.g. Ripley's K, Besag's L, Macron's M and G or nearest neighbor distance) and creates publication quality plots for spatial organization of the cells in each tissue sample. The iTIME web application allows users to statistically compare the abundance measures with patient clinical features along with visualization of the TIME for one tissue sample at a time. AVAILABILITY AND IMPLEMENTATION: spatialTIME is implemented in R and can be downloaded from GitHub (https://github.com/FridleyLab/spatialTIME) or CRAN. An extensive vignette for using spatialTIME can also be found at https://cran.r-project.org/web/packages/spatialTIME/index.html. iTIME is implemented within a R Shiny application and can be accessed online (http://itime.moffitt.org/), with code available on GitHub (https://github.com/FridleyLab/iTIME). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cancer Detection and Classification by CpG Island Hypermethylation Signatures in Plasma Cell-Free DNA.

Cell-free DNA (cfDNA) methylation has emerged as a promising biomarker for early cancer detection, tumor type classification, and treatment response monitoring. Enrichment-based cfDNA methylation profiling methods such as cfMeDIP-seq have shown high accuracy in the classification of multiple cancer types. We have previously optimized another enrichment-based approach for ultra-low input cfDNA methylome profiling, termed cfMBD-seq. We reported that cfMBD-seq outperforms cfMeDIP-seq in the enrichment of high-CpG-density regions, such as CpG islands. However, the clinical feasibility of cfMBD-seq is unknown. In this study, we applied cfMBD-seq to profiling the cfDNA methylome using plasma samples from cancer patients and non-cancer controls. We identified 1759, 1783, and 1548 differentially hypermethylated CpG islands (DMCGIs) in lung, colorectal, and pancreatic cancer patients, respectively. Interestingly, the vast majority of DMCGIs were overlapped with aberrant methylation changes in corresponding tumor tissues, indicating that DMCGIs detected by cfMBD-seq were mainly driven by tumor-specific DNA methylation patterns. From the overlapping DMCGIs, we carried out machine learning analyses and identified a set of discriminating methylation signatures that had robust performance in cancer detection and classification. Overall, our study demonstrates that cfMBD-seq is a powerful tool for sensitive detection of tumor-derived epigenomic signals in cfDNA.