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’sK, 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.

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.

Given their rarity and diversity, a fundamental understanding of the genomic underpinnings for many sarcoma subtypes is still lacking. To better define the molecular landscape of this group of diseases, we perform matched whole exome sequencing and RNA sequencing on a cohort of 1340 sarcoma tumor specimens. We identify recurrent somatic mutations and observe an increased mutational burden in metastatic vs. primary samples (p < 0.001). We observe frequent copy number alterations including whole genome doubling, with this feature being more common in metastatic tumors (p = 0.026). Estimation of immune cell abundances followed by hierarchical clustering identifies five immune subtypes ranging from low to high and we observe inferior overall survival in immune deplete clusters compared to immune enriched (p < 0.01). Interestingly, GIST predominantly form a distinct “immune intermediate” cluster that is marked by a specific enrichment for NK cells (FDR < 0.01).

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.

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.

Although previous studies identified numerous single nucleotide polymorphisms (SNPs) and their target genes predisposed to prostate cancer (PrCa) risks, SNP-related splicing associations are rarely reported. In this study, we applied distance-based sQTL analysis (sQTLseekeR) using RNA-seq and SNP genotype data from benign prostate tissue (n = 467) and identified significant associations in 3344 SNP-transcript pairs (P ≤ 0.05) at PrCa risk loci. We characterized a common SNP (rs7247241) and its target gene (PPP1R14A) located in chr19q13, an sQTL with risk allele T associated with upregulation of long isoform (P = 9.99E−7). We confirmed the associations in both TCGA (P = 2.42E−24) and GTEX prostate cohorts (P = 9.08E−78). To functionally characterize this SNP, we performed chromatin immunoprecipitation qPCR and confirmed stronger CTCF and PLAGL2 binding in rs7247241 C than T allele. We found that CTCF binding enrichment was negatively associated with methylation level at the SNP site in human cell lines (r = −0.58). Bisulfite sequencing showed consistent association of rs7247241-T allele with nearby sequence CpG hypermethylation in prostate cell lines and tissues. Moreover, the methylation level at CpG sites nearest to the CTCF binding and first exon splice-in (ψ) of PPP1R14A was significantly associated with aggressive phenotype in the TCGA PrCa cohort. Meanwhile, the long isoform of the gene also promoted cell proliferation. Taken together, with the most updated gene annotations, we reported a set of sQTL associated with multiple traits related to human prostate diseases and revealed a unique role of PrCa risk SNP rs7247241 on PPP1R14A isoform transition.