Alterations of the epigenome, including histone tail post-translational modifications (PTMs), have been shown to regulate the expression of genes important for driving multiple cellular processes including cellular differentiation, aging and tumorigenesis. Although the ability to measure histone tail PTM levels is integral to the understanding of chromatin biology, because of technological limitations, the proportion of nucleosomes that have histone tail PTMs at genomic loci remain undefined. To overcome this limitation, we developed the immuno-profiling of dCas9 targeted chromatin (I-CATCH) system, a technology designed to measure and normalize the levels of nucleosomes and co-occurring histone tail PTMs at targeted loci. Here, we show that the I-CATCH system has high analytical performance for measuring the levels of histone tail PTMs and we report the normalized levels of multiple histone tail PTMs at different genomic loci from both HeLa cells and human plasma. While further studies are required to determine the full utility of the I-CATCH system, we are hopeful that the I-CATCH system will help enable the ability to investigate new important features of chromatin biology.

The ability to accurately measure aberrant DNA methylation levels is integral to the understanding of DNA methylation biology. It is well-established that in cancer, the largest, and thus, most biologically important absolute gains of DNA methylation levels occur at CpG sites with low native levels while the largest losses occur at CpG sites with high native levels. Conventional wisdom assumes that the observed association between the degree of the alterations and the native levels are largely due to the limitations of change within the DNA methylation scale. Here, we present evidence that this association is largely caused by alterations occurring as a global rate of change relative to the native level. We show that DNA methylation alterations can be accurately compared by calculating the rate of change relative to the native level. Most importantly, this approach enables the identification of more biologically significant DNA methylation alterations.

Alterations in cellular metabolism are known to play a crucial role in the development and progression of cancer. The lipoxygenase pathway, which controls unsaturated fatty acid metabolism, has been shown to regulate tumour progression and is commonly altered in breast cancer. In this study, we first obtained 11 lipoxygenase pathway genes from the Molecular Signatures Database (MSigDB). We then explored the lipoxygenase pathway-related long non-coding RNAs (lncRNAs) in breast cancer tissues from The Cancer Genome Atlas (TCGA) database. Information from our analysis was used to construct a risk prediction model (ProlncSig) to predict breast cancer prognosis. We found that ProlncSig could accurately identify breast cancer patients that had significantly shorter overall survival from those that had longer overall survival. Moreover, ProlncSig performs better in predicting prognosis than the clinicopathological features. Furthermore, GO and KEGG enrichment analysis showed that ProlncSig risk score had a high correlation with immune signature. We developed and validated an accurate prognostic risk prediction model (ProlncSig) based on lipoxygenase pathway-related lncRNAs, which has strong potential to provide prognostic information and may provide novel guidance for immunotherapeutic strategies for breast cancer patients.

Detecting protein biomarkers with high sensitivity is essential for early disease diagnosis, treatment optimization, and basic biomedical research. Conventional enzyme-linked immunosorbent assays (ELISA) often lack the sensitivity required for low-abundance protein detection, whereas digital ELISA offers ultrahigh sensitivity but faces limitations in multiplexing and accessibility due to reliance on specialized instrumentation. To address these challenges, we developed FluoMag-dPEA (Fluorescence-coded, Magnetic bead-enhanced digital Proximity Extension Assay), a streamlined platform that integrates magnetic bead-enhanced proximity extension assay with digital PCR (dPCR) for highly sensitive and multiplexed protein detection. FluoMag-dPEA platform employs a ratiometric fluorescence coding scheme to achieve scalable multiplexing that can be decoded with any standard two-color dPCR readouts. Target proteins are converted into fluorescence-coded DNA templates on magnetic beads, which are then released for bead-free digital analysis, ensuring broad compatibility with common dPCR systems. The approach achieves attomolar sensitivity and enables precise, simultaneous quantification of multiple proteins. We validated FluoMag-dPEA using an eight-plex cytokine assay to profile secretions from peripheral blood mononuclear cells, demonstrating strong concordance with the benchmark Luminex multiplex protein assay in measuring secreted cytokines while achieving superior sensitivity at lower concentrations, reliably detecting proteins at single-cell equivalents. By offering ultrahigh sensitivity, scalable multiplexing, and accessibility, FluoMag-dPEA represents a powerful tool for protein biomarker detection and holds significant promise for diverse biomedical and clinical applications by many users.

Highly sensitive and multiplexed protein detection is crucial across various biological contexts. Although the digital enzyme-linked immunosorbent assay (dELISA) has enabled 1000-fold increase in sensitivity over traditional immunoassays, it involves sophisticated microfluidic devices for single bead trapping, has limited multiplexing capability, and lacks broad accessibility. To address these challenges, we present MagDroplex, an innovative approach that integrates magnetic beads-based proximity extension assay and fluorescence-coding droplet digital polymerase chain reaction (ddPCR) to transform the detection of protein into the quantification of fluorescence-coded DNA molecules. By detecting DNA rather than enzyme labels, our approach eliminates precise bead control as required in dELISA, facilitates concurrent detection of multiple targets through PCR multiplexing, and leverages the widespread adoption of ddPCR technology for broader implementation. As a proof of principle, we apply MagDroplex towards an 8-plex detection targeting human cytokines. Our approach demonstrates exceptional sensitivities for all the targets, while operating only across 2 fluorescent channels.

Abstract Background Ductal carcinoma in-situ (DCIS) is a pre-invasive form of invasive breast cancer (IBC). Due to improved breast cancer screening, it now accounts for ~ 25% of all breast cancers. While the treatment success rates are over 90%, this comes at the cost of considerable morbidity, considering that the majority of DCIS never become invasive and our understanding of the molecular changes occurring in DCIS that predispose to invasive disease is limited. The aim of this study is to characterize molecular changes that occur in DCIS, with the goal of improving DCIS risk stratification. Methods We identified and obtained a total of 197 breast tissue samples from 5 institutions (93 DCIS progressors, 93 DCIS non-progressors, and 11 adjacent normal breast tissues) that had at least 10-year follow-up. We isolated DNA and RNA from archival tissue blocks and characterized genome-wide mRNA expression, DNA methylation, DNA copy number variation, and RNA splicing variation. Results We obtained all four genomic data sets in 122 of the 197 samples. Our intrinsic expression subtype-stratified analyses identified multiple molecular differences both between DCIS subtypes and between DCIS and IBC. While there was heterogeneity in molecular signatures and outcomes within intrinsic subtypes, several gene sets that differed significantly between progressing and non-progressing DCIS were identified by Gene Set Enrichment Analysis. Conclusion DCIS is a molecularly highly heterogenous disease with variable outcomes, and the molecular events determining DCIS disease progression remain poorly defined. Our genome-wide multi-omic survey documents DCIS-associated alterations and reveals molecular heterogeneity within the intrinsic DCIS subtypes. Further studies investigating intrinsic subtype-stratified characteristics and molecular signatures are needed to determine if these may be exploitable for risk assessment and mitigation of DCIS progression. The highly significant associations of specific gene sets with IBC progression revealed by our Gene Set Enrichment Analysis may lend themselves to the development of a prognostic molecular score, to be validated on independent DCIS cohorts.

Table contains sequences of primers and probes used to amplify the nine markers and ACTB in the LBx-BCM assay

In this study, our goal was to determine probe-specific thresholds for identifying aberrant, or outlying, DNA methylation and to provide guidance on the relative merits of using continuous or outlier methylation data. To construct a reference database, we downloaded Illumina Human 450K array data for more than 2,000 normal samples, characterized the distribution of DNA methylation and derived probe-specific thresholds for identifying aberrations. We made the decision to restrict our reference database to solid normal tissue and morphologically normal tissue found adjacent to solid tumours, excluding blood which has very distinctive patterns of DNA methylation. Next, we explored the utility of our outlier thresholds in several analyses that are commonly performed on DNA methylation data. Outliers are as effective as the full continuous dataset for simple tasks, like distinguishing tumour tissue from normal, but becomes less useful as the complexity of the problem increases. We developed an R package called OutlierMeth containing our thresholds, as well as functions for applying them to data.

Introduction: Our current understanding of the molecular changes predisposing Ductal carcinoma in situ (DCIS) to progress to invasive breast cancer (IBC) is limited. The aim of this study is to characterize the molecular changes of DCIS to improve DCIS risk assessment. Methods: We obtained paraffin-embedded tissue of 210 DCIS samples with no concurrent or antecedent IBC from 5 cancer institutions, and extracted DNA and RNA. Transcriptome analysis: Illumina’s TruSeq RNA Exome kit was used for library construction, followed by sequencing. Methylome analysis: Bisulfite treated genomic DNA was restored and arrayed using the Illumina 450K methylation chips. DNA Copy number (CNV) analysis: CNV was estimated from the methylation data set using the EpiCopy R package. Results: Samples passing Q/C metrics: Transcriptome: 59 cases, 63 controls. Methylome & CNV: 93 cases, 98 controls. We classified samples into their intrinsic PAM50 subsets. Cases showed an increase in the Her2 subtype, and the controls were enriched in LuminalA (LumA) samples, while Basal and LuminalB (LumB) subtypes were evenly distributed. Compared to the TCGA IBC dataset, proportions of Her2 and LumB subtypes in DCIS were increased while the LumA cohort was decreased. Unsupervised clustering of the transcriptome data resulted in 3 clusters with key differences between PAM50 subtypes: one cluster predominated in Basal and Her2 subtypes, and a second was enriched in hormone-positive samples (LumA and LumB). For the methylome data, the optimal number of clusters was 6. Again, several clusters showed correlation with PAM50 subtype, e.g., one was enriched for hormone-negative subtypes (Basal and Her2), while another was enriched for hormone-positive subtypes (LumA and LumB). For the CNV data, the optimal number of clusters was 4. Here, one CNV cluster appeared predominantly in the Basal subtype, and there were multiple regions showing significant subtype-specific differences with the TCGA IBC data set. While the well-known heterogeneity of DCIS prevents the above broad molecular categories from strongly stratifying DCIS by outcome, we are continuing to analyze our data sets for predictive molecular signatures. Since the long-term outcome of all DCIS in this cohort is known, we are in the unique position to pursue additional questions such as PAM50 subtype differences in times-to-events or lineage fidelity (i.e., whether the subtype of the subsequent IBC matches the preceding DCIS), and it appears that the time to IBC diagnosis is shortest in Basal DCIS, while at the same time, lineage fidelity is lowest in Basal DCIS, as has been previously reported. Conclusions: Our subtype-stratified analyses identified multiple molecular differences both between intrinsic subtypes as well as between DCIS and IBC that suggest subtype-specific characteristics that may be exploitable for risk stratification of DCIS. Citation Format: Marija Debeljak, Soonweng Cho, Bradley Downs, Michael Considine, Brittany Avin-McKelvey, Yongchun Wang, William Grizzle, Katherine Hoadley, Charles Lynch, Brenda Hernandez, Paul van Diest, Wendy Cozen, Ann Hamilton, Debra Hawes, Edward Gabrielson, Ashley Cimino-Mathews, Liliana Florea, Leslie Cope, Christopher B. Umbricht. Multimodal genome-wide survey of progressing and non-progressing DCIS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3126.

Figure shows changes in LBx-BCM methylation in response to chemotherapy in 7 additional patient longitudinal serial samples. LBx-BCM was performed and cumulative methylation (CM) (Y-axis) is plotted from serum samples drawn at baseline (0 days) and immediately before each treatment cycle (X-axis). For each patient, treatment cycles are shown as a shaded area. PD, progressive disease; SD, stable disease (SD).

Figure shows ACTB reference gene DNA levels plotted for each of the 132 study samples. Stage IV breast cancer patient sera had significantly higher total ACTB DNA (lower Ct) compared to normal (Mann Whitney P < 0.0001). Descriptive statistics are shown for normal vs. cancer and for methylation Cartridge A vs Cartridge B

Figure shows the relationship between PCR Cycle threshold (Ct) and target DNA copy (0-300 copies) input

Figure shows the analytical sensitivity of LBx-BCM based detection of fully methylated DNA (0-300 copies) spiked into normal serum separately for each gene in the panel in 10-11 replicate assays

Supplementary Data from HOXA5-Mediated Stabilization of IκBα Inhibits the NF-κB Pathway and Suppresses Malignant Transformation of Breast Epithelial Cells

Multiple studies have shown that extracellular vesicles (EVs) play a key role in the process of information transfer and material transport between cells. EVs are classified into different types according to their sizes, which includes the class of exosomes. In comparison to normal EVs, tumor-derived EVs (TDEs) have both altered components and quantities of contents. TDEs have been shown to help facilitate an environment conducive to the occurrence and development of tumor by regulation of glucose, lipids and amino acids. Furthermore, TDEs can also affect the host metabolism and immune system. EVs have been shown to have multiple clinically useful properties, including the use of TDEs as biomarkers for the early diagnosis of diseases and using the transport properties of exosomes for drug delivery. Targeting the key bioactive cargoes of exosomes could be applied to provide new strategies for the treatment of tumors. In this review, we summarize the finding of studies focused on measuring the effects of TDE on tumor-related microenvironment and systemic metabolism.

Figure shows the perfomance of LBx-BCM in the training set samples. A histogram shows cumulative methylation for each sample, and a box plot shows significnt difference of methylation in serum of metastatic breast cancer patients compared to normal individuals (Mann Whitney p= 0.002)

A limiting factor in using blood-based liquid biopsies for cancer detection is the volume of extracted blood required to capture a measurable number of circulating tumor DNA (ctDNA). To overcome this limitation, we developed a technology named the dCas9 capture system to capture ctDNA from unaltered flowing plasma, removing the need to extract the plasma from the body. This technology has provided the first opportunity to investigate whether microfluidic flow cell design can affect the capture of ctDNA in unaltered plasma. With inspiration from microfluidic mixer flow cells designed to capture circulating tumor cells and exosomes, we constructed four microfluidic mixer flow cells. Next, we investigated the effects of these flow cell designs and the flow rate on the rate of captured spiked-in BRAF T1799A (BRAF(Mut)) ctDNA in unaltered flowing plasma using surface-immobilized dCas9. Once the optimal mass transfer rate of ctDNA, identified by the optimal ctDNA capture rate, was determined, we investigated whether the design of the microfluidic device, flow rate, flow time, and the number of spiked-in mutant DNA copies affected the rate of capture by the dCas9 capture system. We found that size modifications to the flow channel had no effect on the flow rate required to achieve the optimal capture rate of ctDNA. However, decreasing the size of the capture chamber decreased the flow rate required to achieve the optimal capture rate. Finally, we showed that, at the optimal capture rate, different microfluidic designs using different flow rates could capture DNA copies at a similar rate over time. In this study, the optimal capture rate of ctDNA in unaltered plasma was identified by adjusting the flow rate in each of the passive microfluidic mixer flow cells. However, further validation and optimization of the dCas9 capture system are required before it is ready to be used clinically.

The table provides descriptive statistics and coefficient of variation for methylation in each gene in replicate LBx-BCM analyses of 300 copies of fully methylated DNA spiked into normal serum or plasma

The table provides a step by step guide to calculate methylation in each gene followed by cumulative methylation using the algorithm derived in this paper.

Current molecular liquid biopsy assays to detect recurrence or monitor response to treatment require sophisticated technology, highly trained personnel, and a turnaround time of weeks. We describe the development and technical validation of an automated Liquid Biopsy for Breast Cancer Methylation (LBx-BCM) prototype, a DNA methylation detection cartridge assay that is simple to perform and quantitatively detects nine methylated markers within 4.5 hours. LBx-BCM demonstrated high interassay reproducibility when analyzing exogenous methylated DNA (75–300 DNA copies) spiked into plasma (coefficient of variation, CV = 7.1%–10.9%) and serum (CV = 19.1%–36.1%). It also demonstrated high interuser reproducibility (Spearman r = 0.887, P < 0.0001) when samples of metastatic breast cancer (MBC, N = 11) and normal control (N = 4) were evaluated independently by two users. Analyses of interplatform reproducibility indicated very high concordance between LBx-BCM and the reference assay, cMethDNA, among 66 paired plasma samples [MBC N = 40, controls N = 26; Spearman r = 0.891; 95% confidence interval (CI) = 0.825–0.933, P < 0.0001]. LBx-BCM achieved a ROC AUC = 0.909 (95% CI = 0.836–0.982), 83% sensitivity and 92% specificity; cMethDNA achieved a ROC AUC = 0.896 (95% CI = 0.817–0.974), 83% sensitivity and 92% specificity in test set samples. The automated LBx-BCM cartridge prototype is fast, with performance levels equivalent to the highly sensitive, manual cMethDNA method. Future prospective clinical studies will evaluate LBx-BCM detection sensitivity and its ability to monitor therapeutic response during treatment for advanced breast cancer.Significance:We technically validated an automated, cartridge-based, liquid biopsy prototype assay, to quantitatively measure breast cancer methylation in serum or plasma of patients with MBC, that demonstrated high sensitivity and specificity.