Utility of Homologous Recombination Deficiency Biomarkers Across Cancer Types

PURPOSE Homologous recombination DNA repair deficiency (HRD) is associated with sensitivity to platinum and poly (ADP-ribose) polymerase inhibitors in certain cancer types, including breast, ovarian, pancreatic, and prostate. In these cancers, BRCA1/2 alterations and genomic scar signatures are useful indicators for assessing HRD. However, alterations in other homologous recombination repair (HRR)-related genes and their clinical significance in other cancer types have not been adequately and systematically investigated. METHODS We obtained data sets of all solid tumors in The Cancer Genome Atlas and Cancer Cell Line Encyclopedia, and comprehensively analyzed HRR pathway gene alterations, their loss-of-heterozygosity status, and per-sample genomic scar scores, that is, the HRD score and mutational signature 3 ratio, DNA methylation profiles, gene expression profiles, somatic TP53 mutations, sex, and clinical or in vitro response to chemical exposure. RESULTS Biallelic alterations in HRR genes other than BRCA1/2 were also associated with elevated genomic scar scores. The association between HRR-related gene alterations and genomic scar scores differed significantly by sex and the presence of somatic TP53 mutations. HRD tumors determined by a combination of indices also showed HRD features in gene expression analysis and exhibited significantly higher sensitivity to DNA-damaging agents than non-HRD cases in both clinical samples and cell lines. CONCLUSION This study provides evidence for the usefulness of HRD analysis in all cancer types, improves chemotherapy decision making and its efficacy in clinical settings, and represents a substantial advancement in precision oncology.

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Validation of Genomic and Transcriptomic Models of Homologous Recombination Deficiency in a Real-World Pan-Cancer Cohort

10.1101/2021.12.20.21267985 ◽

2021 ◽

Author(s):

Benjamin D Leibowitz ◽

Bonnie V Dougherty ◽

Joshua SK Bell ◽

Joshuah Kapilivsky ◽

Jackson Michuda ◽

...

Keyword(s):

Gene Expression ◽

Homologous Recombination ◽

Performance Metrics ◽

Statistical Tests ◽

Homologous Recombination Deficiency ◽

Mutational Status ◽

Pathway Gene ◽

A Genome ◽

Cancer Types ◽

Tumor Types

Background: With the introduction of DNA-damaging therapies into standard of care cancer treatment, there is a growing need for predictive diagnostics assessing homologous recombination deficiency (HRD) status across tumor types. Following the strong clinical evidence for the utility of DNA-sequencing-based HRD testing in ovarian cancer, and growing evidence in breast cancer, we present analytical validation of the Tempus|HRD-DNA test. We further developed, validated, and explored the Tempus|HRD-RNA model, which uses gene expression data from 16,470 RNA-seq samples to predict HRD status from formalin-fixed paraffin-embedded (FFPE) tumor samples across numerous cancer types. Methods: Genomic and transcriptomic profiling was performed using next-generation sequencing from Tempus|xT, Tempus|xO, Tempus|xE, Tempus|RS, and Tempus|RS.v2 assays on 48,843 samples. Samples were labeled based on their BRCA1, BRCA2 and selected Homologous Recombination Repair (HRR) pathway gene (CDK12, PALB2, RAD51B, RAD51C, RAD51D) mutational status to train and validate HRD-DNA, a genome-wide loss-of-heterozygosity biomarker, and HRD-RNA, a logistic regression model trained on gene expression, using several performance metrics and statistical tests. Results: In a sample of 2,058 breast and 1,216 ovarian tumors, BRCA status was predicted by HRD-DNA with F1-scores of 0.98 and 0.96, respectively. Across an independent set of 1,363 samples across solid tumor types, the HRD-RNA model was predictive of BRCA status in prostate, pancreatic, and non-small cell lung cancer, with F1-scores of 0.88, 0.69, and 0.62, respectively. Conclusions: We predict HRD-positive patients across many cancer types and believe both HRD models may generalize to other mechanisms of HRD outside of BRCA loss. HRD-RNA complements DNA-based HRD detection methods, especially for indications with low prevalence of BRCA alterations.

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DIPG-40. TARGETING MASTER REGULATOR DEPENDENCIES IN DIFFUSE INTRINSIC PONTINE GLIOMA (DIPG)

Neuro-Oncology ◽

10.1093/neuonc/noaa222.087 ◽

2020 ◽

Vol 22 (Supplement_3) ◽

pp. iii294-iii295

Author(s):

Jovana Pavisic ◽

Chankrit Sethi ◽

Chris Jones ◽

Stergios Zacharoulis ◽

Andrea Califano

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Treatment Options ◽

Diffuse Intrinsic Pontine Glioma ◽

Low Grade ◽

Precision Oncology ◽

Pontine Glioma ◽

Convection Enhanced Delivery ◽

Master Regulator

Abstract Diffuse intrinsic pontine glioma (DIPG) remains a fatal disease with no effective drugs to date. Mutation-based precision oncology approaches are limited by lack of targetable mutations and genetic heterogeneity. We leveraged systems biology methodologies to discover common targetable disease drivers—master regulator proteins (MRs)—in DIPG to expand treatment options. Using the metaVIPER algorithm, we interrogated an integrated low grade glioma and GBM gene regulatory network with 31 DIPG-gene expression signatures to identify tumor-specific MRs by differential expression of their transcriptional targets. Unsupervised clustering identified MR signatures of upregulated activity in RRM2/TOP2A in 13 patients, CD3D in 5 patients, and MMP7, TACSTD2, RAC2 and SLC15A1/SLC34A2 in individual patients, all of which can be targeted. Notably, intratumoral administration of etoposide by convection enhanced delivery was effective in murine proneural gliomas in which TOP2 was identified as a MR while RRM2—targetable by drugs such as cladribine—has been shown to be a positive regulator of glioma progression whose knock-down inhibits tumor growth. We also prioritized drugs by their ability to reverse MR-activity signatures using a large drug-perturbation database. Patients clustered by predicted drug sensitivities with distinct groups of tumors predicted to respond to proteasome inhibitors, Thiotepa or Volasertib all of which have early evidence in treating gliomas. We will refine this analysis in a multi-institutional study of >100 patient gene expression profiles to define MR signatures driving known biological/molecular disease subtypes, use DIPG cell lines recapitulating common MR architectures to optimize therapy prioritization, and validate our findings in vivo.

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Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

Cancer Informatics ◽

10.1177/11769351211002494 ◽

2021 ◽

Vol 20 ◽

pp. 117693512110024

Author(s):

Jason D Wells ◽

Jacqueline R Griffin ◽

Todd W Miller

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Human Tumors ◽

Molecular Characteristics ◽

Survival Times ◽

Chemotherapy Drugs ◽

Testing Dataset ◽

Cancer Models ◽

Cancer Types ◽

Pan Cancer

Motivation: Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor. Results: Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line–derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times ( P = .048) and in patients with pancreatic cancer treated with gemcitabine ( P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

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LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data

BMC Genomics ◽

10.1186/s12864-021-07900-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yanan Ren ◽

Ting-You Wang ◽

Leah C. Anderton ◽

Qi Cao ◽

Rendong Yang

Keyword(s):

Gene Expression ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Clinical Samples ◽

Sequencing Data ◽

Multiple Cancer ◽

Regulatory Pathways ◽

Cancer Transcriptome ◽

Versatile Tool

Abstract Background Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs. Results As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancer patient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types. Conclusions LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA.

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Analyzing cancer gene expression data through the lens of normal tissue-specificity

10.1101/2021.01.25.428166 ◽

2021 ◽

Author(s):

H. Robert Frost

Keyword(s):

Gene Expression ◽

Normal Tissue ◽

Tissue Specificity ◽

Cancer Genomics ◽

Expression Profiles ◽

Genetic Alterations ◽

Cancer Type ◽

Tissue Specific ◽

Normal Tissues ◽

Cancer Types

AbstractThe genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

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Profiling of Epigenetic Features in Clinical Samples Reveals Novel Widespread Changes in Cancer

Cancers ◽

10.3390/cancers11050723 ◽

2019 ◽

Vol 11 (5) ◽

pp. 723 ◽

Cited By ~ 5

Author(s):

Roberta Noberini ◽

Camilla Restellini ◽

Evelyn Oliva Savoia ◽

Francesco Raimondi ◽

Lavinia Ghiani ◽

...

Keyword(s):

Cell Cycle ◽

Expression Profiles ◽

Histone H3 ◽

Gene Expression Profiles ◽

Culture Conditions ◽

Clinical Samples ◽

Post Translational Modifications ◽

A Cell ◽

Cancer Types ◽

Histone Modifying Enzymes

Aberrations in histone post-translational modifications (PTMs), as well as in the histone modifying enzymes (HMEs) that catalyze their deposition and removal, have been reported in many tumors and many epigenetic inhibitors are currently under investigation for cancer treatment. Therefore, profiling epigenetic features in cancer could have important implications for the discovery of both biomarkers for patient stratification and novel epigenetic targets. In this study, we employed mass spectrometry-based approaches to comprehensively profile histone H3 PTMs in a panel of normal and tumoral tissues for different cancer types, identifying various changes, some of which appear to be a consequence of the increased proliferation rate of tumors, while others are cell-cycle independent. Histone PTM changes found in tumors partially correlate with alterations of the gene expression profiles of HMEs obtained from publicly available data and are generally lost in culture conditions. Through this analysis, we identified tumor- and subtype-specific histone PTM changes, but also widespread changes in the levels of histone H3 K9me3 and K14ac marks. In particular, H3K14ac showed a cell-cycle independent decrease in all the seven tumor/tumor subtype models tested and could represent a novel epigenetic hallmark of cancer.

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Circulating melanoma cells isolated from clinical blood samples and characterized by full-length mRNA sequencing at single-cell level.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.10539 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. 10539-10539 ◽

Cited By ~ 1

Author(s):

Yu-Chieh Wang ◽

Daniel Ramskold ◽

Shujun Luo ◽

Robin Li ◽

Qiaolin Deng ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Expression Patterns ◽

Melanoma Cells ◽

Clinical Samples ◽

Single Cell Level ◽

Cell Level ◽

Blood Samples

10539 Background: Melanoma is the most aggressive type of skin cancer. Late-stage melanoma is highly metastatic and currently lacks effective treatment. This discouraging clinical observation highlights the need for a better understanding of the molecular mechanisms underlying melanoma initiation and progression and for developing new therapeutic approaches based on novel targets. Although genome-wide transcriptome analyses have been frequently used to study molecular alterations in clinical samples, it has been technically challenging to obtain the transcriptomic profiles at single-cell level. Methods: Using antibody-mediated magnetic activated cell separation (MACS), we isolated and individualized putative circulating melanoma cells (CMCs) from the blood samples of the melanoma patients at advance stages. The transcriptomic analysis based on a novel and robust mRNA-Seq protocol (Smart-Seq) was established and applied to the putative CMCs for single-cell profiling. Results: We have discovered distinct gene expression patterns, including new putative markers for CMCs. Meanwhile, the gene expression profiles derived of the CMC candidates isolated from the patient’s blood samples are closely-related to the expression profiles of other cells originated from human melanocytes, including normal melanocytes in primary culture and melanoma cell lines. Compared with existing methods, Smart-Seq has improved read coverage across transcripts, which provides advantage for better analyzing transcript isoforms and SNPs. Conclusions: Our results suggest that the techniques developed in this research for cell isolation and transcriptomic analyses can potentially be used for addressing many biological and clinical questions requiring genomewide transcriptome profiling in rare cells.

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Gene expression profiles of inflammatory breast cancer reveal high heterogeneity across the epithelial-hybrid-mesenchymal spectrum

10.1101/2020.08.27.267609 ◽

2020 ◽

Author(s):

Priyanka Chakraborty ◽

Jason T George ◽

Wendy A Woodward ◽

Herbert Levine ◽

Mohit Kumar Jolly

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Inflammatory Breast Cancer ◽

Predictive Accuracy ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Clinical Samples ◽

Intratumor Heterogeneity ◽

Training Algorithms

AbstractInflammatory breast cancer (IBC) is a highly aggressive breast cancer that metastasizes largely via tumor emboli, and has a 5-year survival rate of less than 30%. No unique genomic signature has yet been identified for IBC nor has any specific molecular therapeutic been developed to manage the disease. Thus, identifying gene expression signatures specific to IBC remains crucial. Here, we compare various gene lists that have been proposed as molecular footprints of IBC using different clinical samples as training and validation sets and using independent training algorithms, and determine their accuracy in identifying IBC samples in three independent datasets. We show that these gene lists have little to no mutual overlap, and have limited predictive accuracy in identifying IBC samples. Despite this inconsistency, single-sample gene set enrichment analysis (ssGSEA) of IBC samples correlate with their position on the epithelial-hybrid-mesenchymal spectrum. This positioning, together with ssGSEA scores, improves the accuracy of IBC identification across the three independent datasets. Finally, we observed that IBC samples robustly displayed a higher coefficient of variation in terms of EMT scores, as compared to non-IBC samples. Pending verification that this patient-to-patient variability extends to intratumor heterogeneity within a single patient, these results suggest that higher heterogeneity along the epithelial-hybrid-mesenchymal spectrum can be regarded to be a hallmark of IBC and a possibly useful biomarker.

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Validation of Reference Genes for Quantitative Real-time PCR in Diaphanosoma Celebensis: For Chemical Exposure Conditions and Different Ages

10.21203/rs.3.rs-847536/v1 ◽

2021 ◽

Author(s):

Young-Mi Lee ◽

Soyeon In ◽

Se-Joo Kim ◽

Eun-Ji Won ◽

Hayoung Cho ◽

...

Keyword(s):

Gene Expression ◽

Real Time ◽

Reference Gene ◽

Reference Genes ◽

Expression Profiles ◽

Chemical Exposure ◽

Mrna Levels ◽

Qrt Pcr ◽

Different Ages ◽

Diaphanosoma Celebensis

Abstract Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR), a primary approach for evaluating gene expression, requires an appropriate normalization strategy to rule out variations in gene expression among samples. The best option is to use a reference gene whose expression level is stable across various experimental conditions to compare the mRNA levels of a target gene. However, there is limited information on how the reference gene is differentially expressed at different ages (growth) in small invertebrates with notable changes such as molting. In this study, expression profiles of nine candidate reference genes from the brackish water flea, Diaphanosoma celebensis, were evaluated under diverse exposure to toxicants and according to growth. As a result, four different algorithms showed similar stabilities of genes for chemical exposures in the case of limited conditions using the same developmental stage (e.g., adult), while the results according to age showed a significantly different pattern in suite of candidate reference genes. This affected the results of genes EcRA and GST, which are involved in development and detoxification mechanisms, respectively. Our finding is the first step towards establishing a standardized real-time qRT-PCR analysis of this environmentally important invertebrate that has potential for aquatic ecotoxicology, particularly in estuarine environments.

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