FANCD2 Maintains Fork Stability in BRCA1/2-Deficient Tumors and Promotes Alternative End-Joining DNA Repair

BRCA1/2 proteins function in homologous recombination (HR)-mediated DNA repair and cooperate with Fanconi anemia (FA) proteins to maintain genomic integrity through replication fork stabilization. Loss of BRCA1/2 proteins results in DNA repair deficiency and replicative stress, leading to genomic instability and enhanced sensitivity to DNA-damaging agents. Recent studies have shown that BRCA1/2-deficient tumors upregulate Polθ-mediated alternative end-joining (alt-EJ) repair as a survival mechanism. Whether other mechanisms maintain genomic integrity upon loss of BRCA1/2 proteins is currently unknown. Here we show that BRCA1/2-deficient tumors also upregulate FANCD2 activity. FANCD2 is required for fork protection and fork restart in BRCA1/2-deficient tumors. Moreover, FANCD2 promotes Polθ recruitment at sites of damage and alt-EJ repair. Finally, loss of FANCD2 in BRCA1/2-deficient tumors enhances cell death. These results reveal a synthetic lethal relationship between FANCD2 and BRCA1/2, and they identify FANCD2 as a central player orchestrating DNA repair pathway choice at the replication fork

Weighted Frequent Gene Co-expression Network Mining to Identify Genes Involved in Genome Stability

<div><p>Gene co-expression network analysis is an effective method for predicting gene functions and disease biomarkers. However, few studies have systematically identified co-expressed genes involved in the molecular origin and development of various types of tumors. In this study, we used a network mining algorithm to identify tightly connected gene co-expression networks that are frequently present in microarray datasets from 33 types of cancer which were derived from 16 organs/tissues. We compared the results with networks found in multiple normal tissue types and discovered 18 tightly connected frequent networks in cancers, with highly enriched functions on cancer-related activities. Most networks identified also formed physically interacting networks. In contrast, only 6 networks were found in normal tissues, which were highly enriched for housekeeping functions. The largest cancer network contained many genes with genome stability maintenance functions. We tested 13 selected genes from this network for their involvement in genome maintenance using two cell-based assays. Among them, 10 were shown to be involved in either homology-directed DNA repair or centrosome duplication control including the well- known cancer marker MKI67. Our results suggest that the commonly recognized characteristics of cancers are supported by highly coordinated transcriptomic activities. This study also demonstrated that the co-expression network directed approach provides a powerful tool for understanding cancer physiology, predicting new gene functions, as well as providing new target candidates for cancer therapeutics.</p> </div

Validated protein-protein interactions on genes from networks identified from cancer datasets using IPA.

<p>The edges represent validated protein-protein interactions obtained from Ingenuity Knowledge Base. The nodes are gene members. Only members with connection to other members are shown. A: Validated protein-protein interactions on genes from Cancer Network 1 (cell proliferation/cell cycle control network) using IPA. The red circles indicate the genes further selected for genome stability function assays using RNAi. B: Validated protein-protein interactions on genes from Cancer Network 6 (extracellular matrix network) using IPA.</p

Workflow to mine frequent co-expression network using QCM from cancer and normal tissue microarray datasets.

<p>Blue ovals indicate gene members shared by different networks, ovals in other colors indicate genes unique to each network.</p

Comparison of Cancer Network 1 with gene signatures from other cancer microarray studies.

<p>Comparison of Cancer Network 1 with gene signatures from other cancer microarray studies.</p

Comparison of networks identified from multiple cancer vs. normal tissue microarray datasets.

<p>Top 13 networks (ranked by size) were shown. The size of each circle represents the relative size of each network. The numbers inside the circles indicate the size of the network. The numbers above the connection line indicate the numbers of common genes shared by the two networks. Different top-enriched biological functions in each network were assigned with different colors. ECM: extracellular matrix construction. Parameter settings are: β = 0.8, γ = 0.8, λ = 2.0, t = 1.0 (for networks from cancer datasets); β = 0.8, γ = 0.7, λ = 2.0, t = 1.0 (for networks from normal tissue datasets).</p

Summary of co-expression networks identified from multiple cancer datasets vs. normal tissue datasets.

<p>Summary of co-expression networks identified from multiple cancer datasets vs. normal tissue datasets.</p

Kaplan-Meier curve of breast cancer, glioblastoma (GBM) and ovarian cancer (OV) using network genes identified from cancer datasets.

<p>The p-values are computed using Log- rank test with 100 repeats. A: using Network 1 genes on NKI mixed cohort; B: using Van't Veer 70-gene signature <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002656#pcbi.1002656-VantVeer1" target="_blank">[1]</a> on NKI mixed cohort; C: using Network 1 genes on NKI LN+ cohort; D: using van't Veer 70-gene signature <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002656#pcbi.1002656-VantVeer1" target="_blank">[1]</a> on NKI LN+ cohort; E: using Network1 genes on NKI ER− cohort; F: using Van't Veer 70-gene signature on NKI ER− data. G: using Network 18 genes on TCGA GBM dataset; H: using 23-gene signature on TCGA GBM cohort <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002656#pcbi.1002656-Zhang3" target="_blank">[28]</a>. I: using Network 17 genes on TCGA OV cohort. J: using 19-gene signature on TCGA OV dataset <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002656#pcbi.1002656-Konstantinopoulos1" target="_blank">[34]</a>. Blue lines: good survival outcome group; Red lines: poor survival outcome group. LN+: lymph node positive. ER−: estrogen receptor negative.</p