Integrating Multi-omics Data to Dissect Mechanisms of DNA repair Dysregulation in Breast Cancer

Systematic exploration of the underlying mechanism of gemcitabine resistance in pancreatic adenocarcinoma

Resistance to gemcitabine is the main challenge of chemotherapy for pancreatic ductal adenocarcinoma (PDAC). Hence, the development of a response signature to gemcitabine is essential for precision therapy of PDAC. However, existing quantitative signatures of gemcitabine are susceptible to batch effects and variations in sequencing platforms. Therefore, based on within-sample relative expression ordering of pairwise genes, we developed a transcriptome-based gemcitabine signature consisting of 28 gene pairs (28-GPS) that could predict response to gemcitabine for PDAC at the individual level. The 28-GPS was superior to previous quantitative signatures in terms of classification accuracy and prognostic performance. Resistant samples classified by 28-GPS showed poorer overall survival, higher genomic instability, lower immune infiltration, higher metabolic level and higher-fidelity DNA damage repair compared with sensitive samples. In addition, we found that gemcitabine combined with phosphoinositide 3-kinase (PI3K) inhibitor may be an alternative treatment strategy for PDAC. Single-cell analysis revealed that cancer cells in the same PDAC sample showed both the characteristics of sensitivity and resistance to gemcitabine, and the activation of the TGFβ signalling pathway could promote progression of PDAC. In brief, 28-GPS could robustly determine whether PDAC is resistant or sensitive to gemcitabine, and may be an auxiliary tool for clinical treatment.

Ferroptosis-based molecular prognostic model for adrenocortical carcinoma based on least absolute shrinkage and selection operator regression

Background

This study aimed to find ferroptosis‐related genes linked to clinical outcomes of adrenocortical carcinoma (ACC) and assess the prognostic value of the model.

Methods

We downloaded the mRNA sequencing data and patient clinical data of 78 ACC patients from the TCGA data portal. Candidate ferroptosis‐related genes were screened by univariate regression analysis, machine‐learning least absolute shrinkage, and selection operator (LASSO). A ferroptosis‐related gene‐based prognostic model was constructed. The effectiveness of the prediction model was accessed by KM and ROC analysis. External validation was done using the GSE19750 cohort. A nomogram was generated. The prognostic accuracy was measured and compared with conventional staging systems (TNM stage). Functional analysis was conducted to identify biological characterization of survival‐associated ferroptosis‐related genes.

Results

Seventy genes were identified as survival‐associated ferroptosis‐related genes. The prognostic model was constructed with 17 ferroptosis‐related genes including STMN1, RRM2, HELLS, FANCD2, AURKA, GABARAPL2, SLC7A11, KRAS, ACSL4, MAPK3, HMGB1, CXCL2, ATG7, DDIT4, NOX1, PLIN4, and STEAP3. A RiskScore was calculated for each patient. KM curve indicated good prognostic performance. The AUC of the ROC curve for predicting 1‐, 3‐, and 5‐ year(s) survival time was 0.975, 0.913, and 0.915 respectively. The nomogram prognostic evaluation model showed better predictive ability than conventional staging systems.

Conclusion

We constructed a prognosis model of ACC based on ferroptosis‐related genes with better predictive value than the conventional staging system. These efforts provided candidate targets for revealing the molecular basis of ACC, as well as novel targets for drug development.

A transcriptional signature detects homologous recombination deficiency in pancreatic cancer at the individual level

Pancreatic cancer (PC) with homologous recombination deficiency (HRD) has been reported to benefit from poly ADP-ribose polymerase (PARP) inhibitors. However, accurate identification of HRD status for PC patients from the transcriptional level is still a great challenge. Here, based on a relative expression ordering (REO)-based algorithm, we developed an HRD signature including 24 gene pairs (24-GPS) using PC transcriptional profiles from The Cancer Genome Atlas (TCGA). HRD samples classified by 24-GPS showed worse overall survival (p = 4.4E-3 for TCGA; p = 1.2E-3 for International Cancer Genome Consortium-Australia cohort; p = 6.4E-2 for GSE17891; p = 7.5E-2 for GSE57495) and higher HRD scores than non-HRD samples (p = 1.4E-4). HRD samples showed highly unstable genomic characteristics and also displayed HRD-related alterations at the epigenomic and proteomic levels. Moreover, HRD cell lines identified by 24-GPS tended to be sensitive to PARP inhibitors (p = 6.6E-2 for olaparib; p = 2.6E-3 for niraparib). Compared with the non-HRD group, the HRD group presented lower immune scores and CD4/CD8 T cell infiltration proportion. Interestingly, PC tumor cells with co-inhibition of PARP-related genes and ATR showed reduced survival ability. In conclusion, 24-GPS can robustly identify PC patients with HRD status at the individualized level.

Revealing biomarkers associated with PARP inhibitors based on genetic interactions in cancer genome

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Candidate genomic biomarkers were revealed for PARPis from genetic interactions.

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Gain-of-function mutation of EGFR induced resistance to PARP inhibitors.

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Lung cancer may benefit from combination of PARP inhibitor and EGFR inhibitor.

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Gene set of biomarkers for PARPis contributes to the prognosis of cancer patients.

Poly (ADPribose) polymerase inhibitors (PARPis) are clinically approved drugs designed according to the concept of synthetic lethality (SL) interaction. It is crucial to expand the scale of patients who can benefit from PARPis, and overcome drug resistance associated with it. Genetic interactions (GIs) include SL and synthetic viability (SV) that participate in drug response in cancer cells. Based on the hypothesis that mutated genes with SL or SV interactions with PARP1/2/3 are potential sensitive or resistant PARPis biomarkers, respectively, we developed a novel computational method to identify them. We analyzed fitness variation of cell lines to identify PARP1/2/3-related GIs according to CRISPR/Cas9 and RNA interference functional screens. Potential resistant/sensitive mutated genes were identified using pharmacogenomic datasets. We identified 41 candidate resistant and 130 candidate sensitive PARPi-response related genes, and observed that EGFR with gain-of-function mutation induced PARPi resistance, and predicted a combination therapy with PARP inhibitor (veliparib) and EGFR inhibitor (erlotinib) for lung cancer. We also revealed that a resistant gene set (TNN, PLEC, and TRIP12) in lower grade glioma and a sensitive gene set (BRCA2, TOP3A, and ASCC3) in ovarian cancer, which were associated with prognosis. Thus, cancer genome-derived GIs provide new insights for identifying PARPi biomarkers and a new avenue for precision therapeutics.

Differential Regulation of Lacto-/Neolacto- Glycosphingolipid Biosynthesis Pathway Reveals Transcription Factors as Potential Candidates in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive breast cancer with limited treatment options. Glycosylation has been implicated in cancer development, but TNBC-specific glycosylation pathways have not been examined. Here, we applied bioinformatic analyses on public datasets to discover TNBC-specific glycogenes and pathways, as well as their upstream regulatory mechanisms. Unsupervised clustering of 345 glycogene expressions in breast cancer datasets revealed a relative homogenous expression pattern in basal-like TNBC subtype. Differential expression analyses of the 345 glycogenes between basal-like TNBC (hereafter termed TNBC) and other BC subtypes, or normal controls, revealed 84 differential glycogenes in TNBC. Pathway enrichment showed two common TNBC-enriched pathways across all three datasets, cell cycle and lacto-/neolacto- glycosphingolipid (GSL) biosynthesis, while a total of four glycosylation-related pathways were significantly enriched in TNBC. We applied a selection criterion of the top 50% differential anabolic/catabolic glycogenes in the enriched pathways to define 34 TNBC-specific glycogenes. The lacto-/neolacto- GSL biosynthesis pathway was the most highly enriched, with seven glycogenes all up-regulated in TNBC. This data led us to investigate the hypothesis that a common upstream mechanism in TNBC up-regulates the lacto-/neolacto-GSL biosynthesis pathway. Using public multi-omic datasets, we excluded the involvement of copy-number alteration and DNA methylation, but identified three transcription factors (AR, GATA3 and ZNG622) that each target three candidate genes in the lacto-/neolacto- GSL biosynthesis pathway. Interestingly, a subset of TNBC has been reported to express AR and GATA3, and AR antagonists are being trialed for TNBC. Our findings suggest that AR and GATA3 may contribute to TNBC via GSL regulation, and provide a list of candidate glycogenes for further investigation.

Fluorescence Sheds Light on DNA Damage, DNA Repair, and Mutations

DNA damage can lead to carcinogenic mutations and toxicity that promotes diseases. Therefore, having rapid assays to quantify DNA damage, DNA repair, mutations, and cytotoxicity is broadly relevant to health. For example, DNA damage assays can be used to screen chemicals for genotoxicity, and knowledge about DNA repair capacity has applications in precision prevention and in personalized medicine. Furthermore, knowledge of mutation frequency has predictive power for downstream cancer, and assays for cytotoxicity can predict deleterious health effects. Tests for all of these purposes have been rendered faster and more effective via adoption of fluorescent readouts. Here, we provide an overview of established and emerging cell-based assays that exploit fluorescence for studies of DNA damage and its consequences.

Targeting DNA Damage Response in Prostate and Breast Cancer

Steroid hormone signaling induces vast gene expression programs which necessitate the local formation of transcription factories at regulatory regions and large-scale alterations of the genome architecture to allow communication among distantly related cis-acting regions. This involves major stress at the genomic DNA level. Transcriptionally active regions are generally instable and prone to breakage due to the torsional stress and local depletion of nucleosomes that make DNA more accessible to damaging agents. A dedicated DNA damage response (DDR) is therefore essential to maintain genome integrity at these exposed regions. The DDR is a complex network involving DNA damage sensor proteins, such as the poly(ADP-ribose) polymerase 1 (PARP-1), the DNA-dependent protein kinase catalytic subunit (DNA-PKcs), the ataxia-telangiectasia-mutated (ATM) kinase and the ATM and Rad3-related (ATR) kinase, as central regulators. The tight interplay between the DDR and steroid hormone receptors has been unraveled recently. Several DNA repair factors interact with the androgen and estrogen receptors and support their transcriptional functions. Conversely, both receptors directly control the expression of agents involved in the DDR. Impaired DDR is also exploited by tumors to acquire advantageous mutations. Cancer cells often harbor germline or somatic alterations in DDR genes, and their association with disease outcome and treatment response led to intensive efforts towards identifying selective inhibitors targeting the major players in this process. The PARP-1 inhibitors are now approved for ovarian, breast, and prostate cancer with specific genomic alterations. Additional DDR-targeting agents are being evaluated in clinical studies either as single agents or in combination with treatments eliciting DNA damage (e.g., radiation therapy, including targeted radiotherapy, and chemotherapy) or addressing targets involved in maintenance of genome integrity. Recent preclinical and clinical findings made in addressing DNA repair dysfunction in hormone-dependent and -independent prostate and breast tumors are presented. Importantly, the combination of anti-hormonal therapy with DDR inhibition or with radiation has the potential to enhance efficacy but still needs further investigation.