RNA-seq from archival FFPE breast cancer samples: molecular pathway fidelity and novel discovery

Hepatocellular Carcinoma Epigenetic Patterns Correspond to Differences in Ethnoracial Status and Treatment Response in a Single-Center Retrospective Study

PURPOSE

To correlate epigenetic patterns with ethnoracial status and locoregional therapy (LRT) response in patients with hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

DNA and RNA were extracted from 47 distinct formalin-fixed paraffin-embedded tumor samples from 42 patients with HCC (n = 14 Black, n = 19 White, n = 9 Hispanic). LRT response was determined using computed tomography (CT) or magnetic resonance (MR) imaging 3 months posttreatment of 35 tumors (n = 22 complete response, n = 13 retreatment candidates). RNA expression and DNA methylation were used to stratify patients by ethnoracial status and treatment response using partial least-squares discriminant analysis (PLS-DA). Results were validated using hierarchical clustering. Ingenuity pathway analysis was performed to identify upstream regulators and pathways.

RESULTS

PLS-DA identified 100 genes and 12 methylated regions that differentiated tumors from Black from White/Hispanic patients. Hierarchical clustering clustered samples with the top 16 genes or the top 5 methylation regions. Dysregulated pathways included adrenomedullin pathway (P = .030), EIF2 signaling (P = .007), and several metabolic pathways. AGTR1 (log2fold = 1.59) and GSTM3 (log2fold = 2.53) represented potential differentially expressed therapeutic targets. PLS-DA identified 100 genes and 150 methylation regions that differentiated between complete responders and retreatment candidates. Hierarchical clustering clustered samples with the top 30 genes or the top 13 methylation regions. Dysregulated pathways included metabolic and DNA repair-related pathways. ASAP2 (log2fold = 0.29) and RAD50 (log2fold = 0.22) represented potential differentially expressed therapeutic targets.

CONCLUSIONS

Variation in gene expression and DNA methylation patterns in patients with HCC corresponded to ethnoracial status and LRT response. These initial results suggest tumor profiling has the potential to close ethnoracial disparities and improve treatment stratification.

RNA sequencing of formalin fixed paraffin-embedded heart tissue provides transcriptomic information about chemotherapy-induced cardiotoxicity

Gene expression of formalin-fixed paraffin-embedded (FFPE) tissue may serve for molecular studies on cardiovascular diseases. Chemotherapeutics, such as doxorubicin (DOX) may cause heart injury, but the mechanisms of these side effects of DOX are not well understood. This study aimed to investigate whether DOX-induced gene expression in archival FFPE heart tissue in experimental rats would correlate with the gene expression in fresh-frozen heart tissue by applying RNA sequencing technology. The results showed RNA from FFPE samples was degraded, resulting in a lower number of uniquely mapped reads. However, DOX-induced differentially expressed genes in FFPE were related to molecular mechanisms of DOX-induced cardiotoxicity, such as inflammation, calcium binding, endothelial dysfunction, senescence, and cardiac hypertrophy signaling. Our data suggest that, despite the limitations, RNA sequencing of archival FFPE heart tissue supports utilizing FFPE tissues from retrospective studies on cardiovascular disorders, including DOX-induced cardiotoxicity.

Optimization of FFPE preparation and identification of gene attributes associated with RNA degradation

Formalin-fixed paraffin-embedded (FFPE) tissues are widely available specimens for clinical studies. However, RNA degradation in FFPE tissues often restricts their utility. In this study, we determined optimal FFPE preparation conditions, including tissue ischemia at 4°C (<48 h) or 25°C for a short time (0.5 h), 48-h fixation at 25°C and sampling from FFPE scrolls instead of sections. Notably, we observed an increase in intronic reads and a significant change in gene rank based on expression level in the FFPE as opposed to fresh-frozen (FF) samples. Additionally, we found that more reads were mapped to genes associated with chemical stimulus in FFPE samples. Furthermore, we demonstrated that more degraded genes in FFPE samples were enriched in genes with short transcripts and high free energy. Besides, we found 40 housekeeping genes exhibited stable expression in FF and FFPE samples across various tissues. Moreover, our study showed that FFPE samples yielded comparable results to FF samples in dimensionality reduction and pathway analyses between case and control samples. Our study established the optimal conditions for FFPE preparation and identified gene attributes associated with degradation, which would provide useful clues for the utility of FFPE tissues in clinical practice and research.

Linking gene expression to clinical outcomes in pediatric Crohn's disease using machine learning

Pediatric Crohn's disease (CD) is characterized by a severe disease course with frequent complications. We sought to apply machine learning-based models to predict risk of developing future complications in pediatric CD using ileal and colonic gene expression. Gene expression data was generated from 101 formalin-fixed, paraffin-embedded (FFPE) ileal and colonic biopsies obtained from treatment-naïve CD patients and controls. Clinical outcomes including development of strictures or fistulas and progression to surgery were analyzed using differential expression and modeled using machine learning. Differential expression analysis revealed downregulation of pathways related to inflammation and extra-cellular matrix production in patients with strictures. Machine learning-based models were able to incorporate colonic gene expression and clinical characteristics to predict outcomes with high accuracy. Models showed an area under the receiver operating characteristic curve (AUROC) of 0.84 for strictures, 0.83 for remission, and 0.75 for surgery. Genes with potential prognostic importance for strictures (REG1A, MMP3, and DUOX2) were not identified in single gene differential analysis but were found to have strong contributions to predictive models. Our findings in FFPE tissue support the importance of colonic gene expression and the potential for machine learning-based models in predicting outcomes for pediatric CD.

Archival skin biopsy specimens as a tool for miRNA-based diagnosis: Technical and post-analytical considerations

Archived specimens, taken by standardized procedures in clinical practice, represent a valuable resource in translational medicine. Their use in retrospective molecular-based studies could provide disease and therapy predictors. Microfluidic array is a user-friendly and cost-effective method allowing profiling of hundreds of microRNAs (miRNAs) from a low amount of RNA. However, even though tissue miRNAs may include potentially robust biomarkers, non-uniformed post-analytical pipelines could hinder translation into clinics. In this study, epidermal RNA from archival skin biopsy specimens was isolated from patients with peripheral neuropathy and healthy individuals. Unbiased miRNA profiling was performed using RT-qPCR-based microfluidic array. We demonstrated that RNA obtained from archival tissue is appropriate for miRNA profiling, providing evidence that different practices in threshold selection could significantly influence the final results. We showed the utility of software-based quality control for amplification curves. We revealed that selection of the most stable reference and the calculation of geometric mean are suitable when utilizing microfluidic arrays without known references. By applying appropriate post-analytical settings, we obtained miRNA profile of human epidermis associated with biological processes and a list of suitable references. Our results, which outline technical and post-analytical considerations, support the broad use of archived specimens for miRNA analysis to unravel disease-specific molecular signatures.

A Novel Metastatic Estrogen Receptor-Expressing Breast Cancer Model with Antiestrogen Responsiveness

Most women diagnosed with breast cancer (BC) have estrogen receptor alpha-positive (ER+) disease. The current mouse models of ER+ BC often rely on exogenous estrogen to encourage metastasis, which modifies the immune system and the function of some tissues like bone. Other studies use genetically modified or immunocompromised mouse strains, which do not accurately replicate the clinical disease. To create a model of antiestrogen responsive BC with spontaneous metastasis, we developed a mouse model of 4T1.2 triple-negative (TN) breast cancer with virally transduced ER expression that metastasizes spontaneously without exogenous estrogen stimulation and is responsive to antiestrogen drugs. Our mouse model exhibited upregulated ER-responsive genes and multi-organ metastasis without exogenous estrogen administration. Additionally, we developed a second TN BC cell line, E0771/bone, to express ER, and while it expressed ER-responsive genes, it lacked spontaneous metastasis to clinically important tissues. Following antiestrogen treatment (tamoxifen, ICI 182,780, or vehicle control), 4T1.2- and E0771/bone-derived tumor volumes and weights were significantly decreased, exemplifying antiestrogen responsivity in both cell lines. This 4T1.2 tumor model, which expresses the estrogen receptor, metastasizes spontaneously, and responds to antiestrogen treatment, will allow for further investigation into the biology and potential treatment of metastasis.

A Comparison of Two Different FFPE Tissue Dissection Methods for Routine Diagnostics in Molecular Pathology: Manual Macrodissection versus Automated Microdissection Using the Roche "AVENIO Millisect" System

Currently, in routine diagnostics, most molecular testing is performed on formalin-fixed, paraffin-embedded tissue after a histomorphological assessment. In order to find the best possible and targeted individual therapy, knowing the mutational status of the tumour is crucial. The "AVENIO Millisect" system Roche introduced an automation solution for the dissection of tissue on slides. This technology allows the precise and fully automated dissection of the tumour area without wasting limited and valuable patient material. In this study, the digitally guided microdissection was directly compared to the manual macrodissection regarding the precision and duration of the procedure, their DNA concentrations as well as DNA qualities, and the overall costs in 24 FFPE samples. In 21 of 24 cases (87.5%), the DNA yields of the manually dissected samples were higher in comparison to the automatically dissected samples. Shorter execution times and lower costs were also benefits of the manual scraping process. Nevertheless, the DNA quality achieved with both methods was comparable, which is essential for further molecular testing. Therefore, it could be used as an additional tool for precise tumour enrichment.

High-depth sequencing characterization of viral dynamics across tissues in fatal COVID-19 reveals compartmentalized infection

SARS-CoV-2 distribution and circulation dynamics are not well understood due to challenges in assessing genomic data from tissue samples. We develop experimental and computational workflows for high-depth viral sequencing and high-resolution genomic analyses from formalin-fixed, paraffin-embedded tissues and apply them to 120 specimens from six subjects with fatal COVID-19. To varying degrees, viral RNA is present in extrapulmonary tissues from all subjects. The majority of the 180 viral variants identified within subjects are unique to individual tissue samples. We find more high-frequency (>10%) minor variants in subjects with a longer disease course, with one subject harboring ten such variants, exclusively in extrapulmonary tissues. One tissue-specific high-frequency variant was a nonsynonymous mutation in the furin-cleavage site of the spike protein. Our findings suggest adaptation and/or compartmentalized infection, illuminating the basis of extrapulmonary COVID-19 symptoms and potential for viral reservoirs, and have broad utility for investigating human pathogens.

Using PDX animal models to identify and stratify adenoid cystic carcinoma patients presenting an enhanced response to HDAC inhibitors

Adenoid cystic carcinoma (ACC) patients face a highly infiltrative and metastatic disease characterized by poor survival rates and suboptimal response to available therapies. We have previously shown that sensitization of ACC tumors to chemotherapy using histone deacetylase inhibitors (HDACi) constitutes a promising therapeutic strategy to manage tumor growth. Here, we used patient-derived xenografts (PDX) from ACC tumors to evaluate the effects of in vivo administration of the HDAC inhibitor Entinostat combined with Cisplatin over tumor growth. RNA from PDX tumor samples receiving the proposed therapy were analyzed using NanoString technology to identify molecular signatures capable of predicting ACC response to the therapy. We also used an RNAseq dataset from 68 ACC patients to validate the molecular signature identified by the NanoString platform. We found that the administration of Entinostat combined with Cisplatin resulted in a potent tumor growth inhibition (TGI) ranging from 38% to 106% of the original tumor mass. Enhanced response to therapy is consistent with the reactivation of tumor suppressor genes, including SFRP1, and the downregulation of oncogenes like FGF8 and CCR7. Nanostring data from PDX tumors identified a genetic signature capable of predicting tumor response to therapy. We further stratified 68 ACC patients containing RNAseq data accordingly to the activity levels of the identified genetic signature. We found that 23% of all patients exhibit a genetic signature consistent with a high ACC tumor response rate to Entinostat and Cisplatin. Our study provides compelling preclinical data supporting the deployment of a powerful systemic anticancer therapy crafted and explicitly tested for ACC tumors.

Clinically relevant fusion oncogenes: detection and practical implications

Mechanistically, chimeric genes result from DNA rearrangements and include parts of preexisting normal genes combined at the genomic junction site. Some rearranged genes encode pathological proteins with altered molecular functions. Those which can aberrantly promote carcinogenesis are called fusion oncogenes. Their formation is not a rare event in human cancers, and many of them were documented in numerous study reports and in specific databases. They may have various molecular peculiarities like increased stability of an oncogenic part, self-activation of tyrosine kinase receptor moiety, and altered transcriptional regulation activities. Currently, tens of low molecular mass inhibitors are approved in cancers as the drugs targeting receptor tyrosine kinase (RTK) oncogenic fusion proteins, that is, including ALK, ABL, EGFR, FGFR1-3, NTRK1-3, MET, RET, ROS1 moieties. Therein, the presence of the respective RTK fusion in the cancer genome is the diagnostic biomarker for drug prescription. However, identification of such fusion oncogenes is challenging as the breakpoint may arise in multiple sites within the gene, and the exact fusion partner is generally unknown. There is no gold standard method for RTK fusion detection, and many alternative experimental techniques are employed nowadays to solve this issue. Among them, RNA-seq-based methods offer an advantage of unbiased high-throughput analysis of only transcribed RTK fusion genes, and of simultaneous finding both fusion partners in a single RNA-seq read. Here we focus on current knowledge of biology and clinical aspects of RTK fusion genes, related databases, and laboratory detection methods.

Reproducible and sensitive micro-tissue RNA sequencing from formalin-fixed paraffin-embedded tissues for spatial gene expression analysis

Spatial transcriptome analysis of formalin-fixed paraffin-embedded (FFPE) tissues using RNA-sequencing (RNA-seq) provides interactive information on morphology and gene expression, which is useful for clinical applications. However, despite the advantages of long-term storage at room temperature, FFPE tissues may be severely damaged by methylene crosslinking and provide less gene information than fresh-frozen tissues. In this study, we proposed a sensitive FFPE micro-tissue RNA-seq method that combines the punching of tissue sections (diameter: 100 μm) and the direct construction of RNA-seq libraries. We evaluated a method using mouse liver tissues at two years after fixation and embedding and detected approximately 7000 genes in micro-punched tissue-spots (thickness: 10 μm), similar to that detected with purified total RNA (2.5 ng) equivalent to the several dozen cells in the spot. We applied this method to clinical FFPE specimens of lung cancer that had been fixed and embedded 6 years prior, and found that it was possible to determine characteristic gene expression in the microenvironment containing tumor and non-tumor cells of different morphologies. This result indicates that spatial gene expression analysis of the tumor microenvironment is feasible using FFPE tissue sections stored for extensive periods in medical facilities.

MiRNA expression deregulation correlates with the Oncotype DX® DCIS score

Background

Current clinical criteria do not discriminate well between women who will or those who will not develop ipsilateral invasive breast cancer (IBC), or a DCIS recurrence after a ductal carcinoma in situ (DCIS) diagnosis. The 12-gene Oncotype DX® DCIS assay (RT qPCR gene-based scoring system) was established and shown to predict the risk of subsequent ipsilateral IBC or DCIS recurrence. Recent studies have shown that microRNA (miRNA) expression deregulation can contribute to the development of IBC, but very few have evaluated miRNA deregulation in DCIS lesions. In this study, we sought to determine whether specific miRNA expression changes may correlate with Oncotype DX® DCIS scores.

Methods

For this study, we used archived formalin-fixed, paraffin-embedded (FFPE) specimens from 41 women diagnosed with DCIS between 2012 and 2018. The DCIS lesions were stratified into low (n = 26), intermediate (n = 10), and high (n = 5) risk score groups using the Oncotype DX® DCIS assay. Total RNA was extracted from DCIS lesions by macro-dissection of unstained FFPE sections, and next-generation small-RNA sequencing was performed. We evaluated the correlation between miRNA expression data and Oncotype score, as well as patient age. RT-qPCR validations were performed to validate the topmost differentially expressed miRNAs identified between the different risk score groups.

Results

MiRNA sequencing of 32 FFPE DCIS specimens from the three different risk group scores identified a correlation between expression deregulation of 17 miRNAs and Oncotype scores. Our analyses also revealed a correlation between the expression deregulation of 9 miRNAs and the patient’s age. Based on these results, a total of 15 miRNAs were selected for RT-qPCR validation. Of these, miR-190b (p = 0.043), miR-135a (p = 0.05), miR-205 (p = 0.00056), miR-30c (p = 0.011), and miR-744 (p = 0.038) showed a decreased expression in the intermediate/high Oncotype group when compared to the low-risk score group. A composite risk score was established using these 5 miRNAs and indicated a significant association between miRNA expression deregulation and the Oncotype DX® DCIS Score (p < 0.0021), between high/intermediate and low risk groups.

Conclusions

Our analyses identified a subset of 5 miRNAs able to discriminate between Oncotype DX® DCIS score subgroups. Together, our data suggest that miRNA expression analysis may add value to the predictive and prognostic evaluation of DCIS lesions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-022-01558-4.

Genes expressed at low levels raise false discovery rates in RNA samples contaminated with genomic DNA

Background

RNA preparations contaminated with genomic DNA (gDNA) are frequently disregarded by RNA-seq studies. Such contamination may generate false results; however, their effect on the outcomes of RNA-seq analyses is unknown. To address this gap in our knowledge, here we added different concentrations of gDNA to total RNA preparations and subjected them to RNA-seq analysis.

Results

We found that the contaminating gDNA altered the quantification of transcripts at relatively high concentrations. Differentially expressed genes (DEGs) resulting from gDNA contamination may therefore contribute to higher rates of false enrichment of pathways compared with analogous samples lacking numerous DEGs. A strategy was developed to correct gene expression levels in gDNA-contaminated RNA samples, which assessed the magnitude of contamination to improve the reliability of the results.

Conclusions

Our study indicates that caution must be exercised when interpreting results associated with low-abundance transcripts. The data provided here will likely serve as a valuable resource to evaluate the influence of gDNA contamination on RNA-seq analysis, particularly related to the detection of putative novel gene elements.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08785-1.

Weakly-supervised tumor purity prediction from frozen H&E stained slides

Background

Estimating tumor purity is especially important in the age of precision medicine. Purity estimates have been shown to be critical for correction of tumor sequencing results, and higher purity samples allow for more accurate interpretations from next-generation sequencing results. Molecular-based purity estimates using computational approaches require sequencing of tumors, which is both time-consuming and expensive.

Methods

Here we propose an approach, weakly-supervised purity (wsPurity), which can accurately quantify tumor purity within a digitally captured hematoxylin and eosin (H&E) stained histological slide, using several types of cancer from The Cancer Genome Atlas (TCGA) as a proof-of-concept.

Findings

Our model predicts cancer type with high accuracy on unseen cancer slides from TCGA and shows promising generalizability to unseen data from an external cohort (F1-score of 0.83 for prostate adenocarcinoma). In addition we compare performance of our model on tumor purity prediction with a comparable fully-supervised approach on our TCGA held-out cohort and show our model has improved performance, as well as generalizability to unseen frozen slides (0.1543 MAE on an independent test cohort). In addition to tumor purity prediction, our approach identified high resolution tumor regions within a slide, and can also be used to stratify tumors into high and low tumor purity, using different cancer-dependent thresholds.

Interpretation

Overall, we demonstrate our deep learning model's different capabilities to analyze tumor H&E sections. We show our model is generalizable to unseen H&E stained slides from data from TCGA as well as data processed at Weill Cornell Medicine.

Funding

Starr Cancer Consortium Grant (SCC I15-0027) to Iman Hajirasouliha.

A Highly Sensitive Method to Efficiently Profile the Histone Modifications of FFPE Samples

The majority of biopsies in both basic research and translational cancer studies are preserved in the format of archived formalin-fixed paraffin-embedded (FFPE) samples. Profiling histone modifications in archived FFPE tissues is critically important to understand gene regulation in human disease. The required input for current genome-wide histone modification profiling studies from FFPE samples is either 10-20 tissue sections or whole tissue blocks, which prevents better resolved analyses. Nevertheless, it is desirable to consume a minimal amount of FFPE tissue sections in the analysis as clinical tissue of interest are limited. Here, we present F FPE tissue with a ntibody-guided c hromatin t agmentation with sequencing (FACT-seq), highly sensitive method to efficiently profile histone modifications in FFPE tissue by combining a novel fusion protein of hyperactive Tn5 transposase and protein A (T7-pA-Tn5) transposition and T7 in vitro transcription. FACT-seq generates high-quality chromatin profiles from different histone modifications with low number of FFPE nuclei. We showed a very small piece of FFPE tissue section containing ~4000 nuclei is sufficient to decode H3K27ac modifications with FACT-seq. In archived FFPE human colorectal and human glioblastoma cancer tissue, H3K27ac FACT-seq revealed disease specific super enhancers. In summary, FACT-seq allows researchers to decode histone modifications like H3K27ac and H3K27me3 in archival FFPE tissues with high sensitivity, thus allowing us to understand epigenetic regulation. Graphical abstract: ( i ) FFPE tissue section; ( ii ) Isolated nuclei; ( iii ) Primary antibody, secondary antibody and T7-pA-Tn5 bind to targets; ( iv ) DNA purification; ( v ) In vitro transcription and sequencing library preparation; ( vi ) Sequencing.

Wholistic approach - transcriptomic analysis and beyond using archival material for molecular diagnosis

Many neoplasms remain unclassified after histopathological examination, which requires further molecular analysis. To this regard, mesenchymal neoplasms are particularly challenging due to the combination of their rarity and the large number of subtypes, and many entities still lack robust diagnostic hallmarks. RNA transcriptomic profiles have proven to be a reliable basis for the classification of previously unclassified tumors and notably for mesenchymal neoplasms. Using exome-based RNA capture sequencing on more than 5000 samples of archival material (FFPE), the combination of expression profiles analyzes (including several clustering methods), fusion genes, and small nucleotide variations has been developed at the Centre Léon Bérard (CLB) in Lyon for the molecular diagnosis of challenging neoplasms and the discovery of new entities. The molecular basis of the technique, the protocol, and the bioinformatics algorithms used are described herein, as well as its advantages and limitations.

Artificial intelligence, molecular subtyping, biomarkers, and precision oncology

A targeted cancer therapy is only useful if there is a way to accurately identify the tumors that are susceptible to that therapy. Thus rapid expansion in the number of available targeted cancer treatments has been accompanied by a robust effort to subdivide the traditional histological and anatomical tumor classifications into molecularly defined subtypes. This review highlights the history of the paired evolution of targeted therapies and biomarkers, reviews currently used methods for subtype identification, and discusses challenges to the implementation of precision oncology as well as possible solutions.

Validation of HER2 Status in Whole Genome Sequencing Data of Breast Cancers with the Ploidy-Corrected Copy Number Approach

BACKGROUND AND OBJECTIVE

Human epidermal growth factor receptor 2 (HER2) protein overexpression is one of the most significant biomarkers for breast cancer diagnostics, treatment prediction, and prognostics. The high accessibility of HER2 inhibitors in routine clinical practice directly translates into the diagnostic need for precise and robust marker identification. Even though multigene next-generation sequencing methodologies have slowly taken over the field of single-biomarker molecular tests, the copy number alterations such as amplification of the HER2-coding ERBB2 gene are hard to validate on next-generation sequencing platforms as they are characterized by chromosomal structural heterogeneity, polysomy, and genomic context of ploidy. In our study, we tested the approach of using whole genome sequencing instead of next-generation sequencing panels to determine HER2 status in the clinical set-up.

METHODS

We used a large dataset of 876 patients with breast cancer whole genomes with curated clinical data and an additional set of 551 patients' external genomic data. We used the decision-tree-based algorithm for optimization of the diagnostic tool for HER2 status assessment by whole genome sequencing.

RESULTS

The most efficient approach to assess HER2 status in whole genome sequencing data was the ploidy-corrected copy number, utilizing ERBB2 copy number and mean tumor ploidy. The classifier achieved sensitivity of 91.18% and specificity of 98.69% on the internal validation dataset and 89.86% and 96.06% on the external data, which is similar to other next-generation sequencing methods, currently tested in the clinic.

CONCLUSIONS

We provide evidence that the HER2 status may be reliably determined by whole genome sequencing and is applicable across different laboratory protocols and pipelines. We suggest using the ploidy-corrected copy number for diagnostic purposes.

Mammary collagen is under reproductive control with implications for breast cancer

Mammographically-detected breast density impacts breast cancer risk and progression, and fibrillar collagen is a key component of breast density. However, physiologic factors influencing collagen production in the breast are poorly understood. In female rats, we analyzed gene expression of the most abundantly expressed mammary collagens and collagen-associated proteins across a pregnancy, lactation, and weaning cycle. We identified a triphasic pattern of collagen gene regulation and evidence for reproductive state-dependent composition. An initial phase of collagen deposition occurred during pregnancy, followed by an active phase of collagen suppression during lactation. The third phase of collagen regulation occurred during weaning-induced mammary gland involution, which was characterized by increased collagen deposition. Concomitant changes in collagen protein abundance were confirmed by Masson's trichrome staining, second harmonic generation (SHG) imaging, and mass spectrometry. We observed similar reproductive-state dependent collagen patterns in human breast tissue obtained from premenopausal women. SHG analysis also revealed structural variation in collagen across a reproductive cycle, with higher packing density and more collagen fibers arranged perpendicular to the mammary epithelium in the involuting rat mammary gland compared to nulliparous and lactating glands. Involution was also characterized by high expression of the collagen cross-linking enzyme lysyl oxidase, which was associated with increased levels of cross-linked collagen. Breast cancer relevance is suggested, as we found that breast cancer diagnosed in recently postpartum women displayed gene expression signatures of increased collagen deposition and crosslinking compared to breast cancers diagnosed in age-matched nulliparous women. Using publically available data sets, we found this involution-like, collagen gene signature correlated with poor progression-free survival in breast cancer patients overall and in younger women. In sum, these findings of physiologic collagen regulation in the normal mammary gland may provide insight into normal breast function, the etiology of breast density, and inform breast cancer risk and outcomes.

Postpartum breast cancer has a distinct molecular profile that predicts poor outcomes

Young women's breast cancer (YWBC) has poor prognosis and known interactions with parity. Women diagnosed within 5-10 years of childbirth, defined as postpartum breast cancer (PPBC), have poorer prognosis compared to age, stage, and biologic subtype-matched nulliparous patients. Genomic differences that explain this poor prognosis remain unknown. In this study, using RNA expression data from clinically matched estrogen receptor positive (ER+) cases (n = 16), we observe that ER+ YWBC can be differentiated based on a postpartum or nulliparous diagnosis. The gene expression signatures of PPBC are consistent with increased cell cycle, T-cell activation and reduced estrogen receptor and TP53 signaling. When applied to a large YWBC cohort, these signatures for ER+ PPBC associate with significantly reduced 15-year survival rates in high compared to low expressing cases. Cumulatively these results provide evidence that PPBC is a unique entity within YWBC with poor prognostic phenotypes.

Functional comparison of exome capture-based methods for transcriptomic profiling of formalin-fixed paraffin-embedded tumors

The availability of fresh frozen (FF) tissue is a barrier for implementing RNA sequencing (RNA-seq) in the clinic. The majority of clinical samples are stored as formalin-fixed, paraffin-embedded (FFPE) tissues. Exome capture platforms have been developed for RNA-seq from FFPE samples. However, these methods have not been systematically compared. We performed transcriptomic analysis of 32 FFPE tumor samples from 11 patients using three exome capture-based methods: Agilent SureSelect V6, TWIST NGS Exome, and IDT XGen Exome Research Panel. We compared these methods to the TruSeq RNA-seq of fresh frozen (FF-TruSeq) tumor samples from the same patients. We assessed the recovery of clinically relevant biological features. The Spearman's correlation coefficients between the global expression profiles of the three capture-based methods from FFPE and matched FF-TruSeq were high (rho = 0.72-0.9, p < 0.05). A significant correlation between the expression of key immune genes between individual capture-based methods and FF-TruSeq (rho = 0.76-0.88, p < 0.05) was observed. All exome capture-based methods reliably detected outlier expression of actionable gene transcripts, including ERBB2, MET, NTRK1, and PPARG. In urothelial cancer samples, the Agilent assay was associated with the highest molecular subtype concordance with FF-TruSeq (Cohen's k = 0.7, p < 0.01). The Agilent and IDT assays detected all the clinically relevant fusions that were initially identified in FF-TruSeq. All FFPE exome capture-based methods had comparable performance and concordance with FF-TruSeq. Our findings will enable the implementation of RNA-seq in the clinic to guide precision oncology approaches.

Targeted RNA-seq improves efficiency, resolution, and accuracy of allele specific expression for human term placentas

Genomic imprinting is an epigenetic mechanism that results in allele-specific expression (ASE) based on the parent of origin. It is known to play a role in the prenatal and postnatal allocation of maternal resources in mammals. ASE detected by whole transcriptome RNA-seq (wht-RNAseq) has been widely used to analyze imprinted genes using reciprocal crosses in mice to generate large numbers of informative SNPs. Studies in humans are more challenging due to the paucity of SNPs and the poor preservation of RNA in term placentas and other tissues. Targeted RNA-seq (tar-RNAseq) can potentially mitigate these challenges by focusing sequencing resources on the regions of interest in the transcriptome. Here, we compared tar-RNAseq and wht-RNAseq in a study of ASE in known imprinted genes in placental tissue collected from a healthy human cohort in Mali, West Africa. As expected, tar-RNAseq substantially improved the coverage of SNPs. Compared to wht-RNAseq, tar-RNAseq produced on average four times more SNPs in twice as many genes per sample and read depth at the SNPs increased fourfold. In previous research on humans, discordant ASE values for SNPs of the same gene have limited the ability to accurately quantify ASE. We show that tar-RNAseq reduces this limitation as it unexpectedly increased the concordance of ASE between SNPs of the same gene, even in cases of degraded RNA. Studies aimed at discovering associations between individual variation in ASE and phenotypes in mammals and flowering plants will benefit from the improved power and accuracy of tar-RNAseq.

Oncogenic and Tumor Suppressive Components of the Cell Cycle in Breast Cancer Progression and Prognosis

Cancer, a disease of inappropriate cell proliferation, is strongly interconnected with the cell cycle. All cancers consist of an abnormal accumulation of neoplastic cells, which are propagated toward uncontrolled cell division and proliferation in response to mitogenic signals. Mitogenic stimuli include genetic and epigenetic changes in cell cycle regulatory genes and other genes which regulate the cell cycle. This suggests that multiple, distinct pathways of genetic alterations lead to cancer development. Products of both oncogenes (including cyclin-dependent kinase (CDKs) and cyclins) and tumor suppressor genes (including cyclin-dependent kinase inhibitors) regulate cell cycle machinery and promote or suppress cell cycle progression, respectively. The identification of cyclins and CDKs help to explain and understand the molecular mechanisms of cell cycle machinery. During breast cancer tumorigenesis, cyclins A, B, C, D1, and E; cyclin-dependent kinase (CDKs); and CDK-inhibitor proteins p16, p21, p27, and p53 are known to play significant roles in cell cycle control and are tightly regulated in normal breast epithelial cells. Following mitogenic stimuli, these components are deregulated, which promotes neoplastic transformation of breast epithelial cells. Multiple studies implicate the roles of both types of components-oncogenic CDKs and cyclins, along with tumor-suppressing cyclin-dependent inhibitors-in breast cancer initiation and progression. Numerous clinical studies have confirmed that there is a prognostic significance for screening for these described components, regarding patient outcomes and their responses to therapy. The aim of this review article is to summarize the roles of oncogenic and tumor-suppressive components of the cell cycle in breast cancer progression and prognosis.

Molecular intrinsic versus clinical subtyping in breast cancer: A comprehensive review

Breast cancer is a heterogeneous disease manifesting diversity at the molecular, histological and clinical level. The development of breast cancer classification was centered on informing clinical decisions. The current approach to the classification of breast cancer, which categorizes this disease into clinical subtypes based on the detection of estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and proliferation marker Ki67, is not ideal. This is manifested as a heterogeneity of therapeutic responses and outcomes within the clinical subtypes. The newer classification model, based on gene expression profiling (intrinsic subtyping) informs about transcriptional responses downstream from IHC single markers, revealing deeper appreciation for the disease heterogeneity and capturing tumor biology in a more comprehensive way than an expression of a single protein or gene alone. While accumulating evidences suggest that intrinsic subtypes provide clinically relevant information beyond clinical surrogates, it is imperative to establish whether the current conventional immunohistochemistry-based clinical subtyping approach could be improved by gene expression profiling and if this approach has a potential to translate into clinical practice.

Stable gene expression for normalisation and single-sample scoring

Gene expression signatures have been critical in defining the molecular phenotypes of cells, tissues, and patient samples. Their most notable and widespread clinical application is stratification of breast cancer patients into molecular (PAM50) subtypes. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical application of thousands of existing gene signatures captured in repositories such as the Molecular Signature Database. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across thousands of samples, allowing signature scoring and supporting general data normalisation for transcriptomic data. Our new method, stingscore, quantifies and summarises relative expression levels of signature genes from individual samples through the inclusion of these ‘stably-expressed genes’. We show that our list of stable genes has better stability across cancer and normal tissue data than previously proposed gene sets. Additionally, we show that signature scores computed from targeted transcript measurements using stingscore can predict docetaxel response in breast cancer patients. This new approach to gene expression signature analysis will facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.

Identification of Differential Tumor Subtypes of T1 Bladder Cancer

Stage T1 bladder cancers have the highest progression and recurrence rates of all non-muscle-invasive bladder cancers (NMIBCs). Most T1 cancers are treated with bacillus Calmette-Guérin (BCG), but many will progress or recur, and some T1 patients will die from bladder cancer. Particularly aggressive tumors could be treated with early cystectomy. To better understand the molecular heterogeneity of T1 cancers, we performed transcriptome profiling and unsupervised clustering, and identified five consensus subtypes of T1 tumors treated with repeat transurethral resection (reTUR) and induction and maintenance BCG. The T1-LumGU subtype was associated with carcinoma in situ (CIS; six/13, 46% of all CIS), had high E2F1 and EZH2 expression, and was enriched in E2F target and G2M checkpoint hallmarks. The T1-Inflam subtype was inflamed and infiltrated with immune cells. While most T1 tumors were classified as luminal papillary, the T1-TLum subtype had the highest median luminal papillary score and FGFR3 expression, no recurrence events, and the fewest copy number gains. T1-Myc and T1-Early subtypes had the most recurrences (14/30 within 24 mo), the highest median MYC expression, and, when combined, had significantly worse recurrence-free survival than the other three subtypes. T1-Early had five (38%) recurrences within the first 6 mo of BCG, and repressed IFN-α and IFN-γ hallmarks and inflammation. We developed a single-patient T1 classifier and validated our subtype biology in a second cohort of T1 tumors. Future research will be necessary to validate the proposed T1 subtypes and to determine if therapies can be individualized for each subtype. PATIENT SUMMARY: We identified and characterized expression subtypes of high-grade stage T1 bladder cancer that are biologically heterogeneous and have variable responses to bacillus Calmette-Guérin treatment. We validated the subtypes and describe a single-patient classifier.

Influence of Cold Ischemia Time and Storage Period on DNA Quality and Biomarker Research in Biobanked Colorectal Cancer Tissues

Objectives Biobanking plays an important role in future research. Assessment and control of the preanalytical variables of biobanked tissues are fundamentals for the optimal use of biospecimens. Methods Forty-five colorectal cancer (CRC) tissues stored at −80°C in Bio-Resource Bank were evaluated to define the influence of cold ischemia time (CIT) and storage period (SP) on DNA quality in biobanked tissues. Three CITs (less than 30 minutes (CIT-1), 30–45 minutes (CIT-2), and 45–60 minutes (CIT-3)) and three SPs (less than 1 year (SP-1), 2–3 years (SP-2), and 4–5 years (SP-3)) were chosen. NanoDrop spectrophotometer was used to determine the 260/280 ratio for DNA purity. DNA integrity was analyzed by a UV transilluminator following electrophoresis on 2% agarose gel. To evaluate the practical usability of DNA for biomarker research, KRAS mutation status was assessed by PCR amplification. Results All DNA specimens had a 260/280 ratio ranging between 1.8 and 2.0 with the exception of one specimen (CIT-2/SP-2 group). For DNA integrity, DNA appeared as a compact, high-molecular-weight band with no or scanty low-molecular-weight smears. The concordance of KRAS mutation status between paired biobanked frozen tissues and formalin-fixed paraffin-embedded tissues was 100%. DNA remained stable in CRC tissues kept at room temperature for up to 1 hour and long-term storage up to 5 years. Conclusions Storage conditions of our biobank are suitable for long-term (at least five years) specimen preservation with high DNA quality. These results have practical implications that could affect banking guidelines.