A DNA repair pathway-focused score for prediction of outcomes in ovarian cancer treated with platinum-based chemotherapy

Biomarkers associated with survival in patients with platinum-refractory urothelial carcinoma treated with paclitaxel

BACKGROUND

Taxane- based chemotherapy is widely used in patients with platinum- and immunotherapy refractory, metastatic urothelial carcinoma (mUC). Outcomes are poor and biomarkers associated with outcome are lacking. We aim to identify cancer hallmarks associated with survival in patients receiving paclitaxel.

METHODS

Whole-transcriptome profiles were generated for a subset of patients enrolled in a randomised phase II study investigating paclitaxel and pazopanib in platinum refractory mUC (PLUTO, EudraCT 2011-001841-34). Estimates of gene expression were calculated and input into the Almac proprietary analysis pipeline and signature scores were calculated using ClaraT V3.0.0. Ten key gene signatures were assessed: Immuno-Oncology, Epithelial to Mesenchymal Transition, Angiogenesis, Proliferation, Cell Death, Genome Instability, Energetics, Inflammation, Immortality and Evading Growth. Hazard ratios were calculated using Cox regression model and Kaplan-Meier methods were used to estimate progression free survival (PFS) and overall survival (OS).

RESULTS

38 and 45 patients treated with paclitaxel or pazopanib were included. Patients with high genome instability expression treated with paclitaxel had significantly improved survival with a HR of 0.29 (95% CI: 0.14-0.61, p=0.001) and HR 0.34 (95% CI: 0.17-0.69, p=0.003) for PFS and OS, respectively. Similarly, patients with high evading growth suppressor expression treated with paclitaxel had improved PFS and OS with a HR of 0.35 (95% CI: 0.19-0.77, p=0.007) and HR 0.46 (95% CI: 0.23-0.91, p=0.026), respectively. No other gene signatures had significant impact on outcome. In both paclitaxel and pazopanib cohorts, angiogenesis activation was associated with worse PFS and OS, and VEGF targeted therapy did not improve outcomes.

CONCLUSION

High Genome-instability and Evading-growth suppressor biologies are associated with improved survival in patients with platinum refractory mUC receiving paclitaxel. These may refine mUC risk stratification and guide treatment decision in the future.

Opening the Black Box: Spatial Transcriptomics and the Relevance of Artificial Intelligence-Detected Prognostic Regions in High-Grade Serous Carcinoma

Image-based deep learning models are used to extract new information from standard hematoxylin and eosin pathology slides; however, biological interpretation of the features detected by artificial intelligence (AI) remains a challenge. High-grade serous carcinoma of the ovary (HGSC) is characterized by aggressive behavior and chemotherapy resistance, but also exhibits striking variability in outcome. Our understanding of this disease is limited, partly due to considerable tumor heterogeneity. We previously trained an AI model to identify HGSC tumor regions that are highly associated with outcome status but are indistinguishable by conventional morphologic methods. Here, we applied spatially resolved transcriptomics to further profile the AI-identified tumor regions in 16 patients (8 per outcome group) and identify molecular features related to disease outcome in patients who underwent primary debulking surgery and platinum-based chemotherapy. We examined formalin-fixed paraffin-embedded tissue from (1) regions identified by the AI model as highly associated with short or extended chemotherapy response, and (2) background tumor regions (not identified by the AI model as highly associated with outcome status) from the same tumors. We show that the transcriptomic profiles of AI-identified regions are more distinct than background regions from the same tumors, are superior in predicting outcome, and differ in several pathways including those associated with chemoresistance in HGSC. Further, we find that poor outcome and good outcome regions are enriched by different tumor subpopulations, suggesting distinctive interaction patterns. In summary, our work presents proof of concept that AI-guided spatial transcriptomic analysis improves recognition of biologic features relevant to patient outcomes.

Computational identification of DNA damage-relevant lncRNAs for predicting therapeutic efficacy and clinical outcomes in cancer

OBJECTIVES

The role of long non-coding RNAs (lncRNAs) in cancer treatment, particularly in modulating DNA repair programs, is an emerging field that warrants systematic exploration. This study aimed to systematically identify the lncRNA regulators that potentially regulate DNA damage response (DDR).

METHODS

Using genome-wide mRNA and lncRNA expression profiles of the same tumor patients, we proposed a novel computational framework. This framework performed Gene Set Variation Analysis to calculate DDR pathway enrichment score, which relies on weighting by tumor purity to obtain DDR activity score for each patient. Then, an in-depth differential expression profiling was conducted to identify pathway activity lncRNAs between high- and low-activity groups, utilizing a bootstrap-based randomization method.

RESULTS

We comprehensively charted the landscape of DDR-relevant lncRNAs across 23 epithelial-based cancer types. Its effectiveness was validated by assessing the consistency of these lncRNAs within various datasets of the same cancer type (hypergeometric test P < 0.001), examining the expression perturbation of these lncRNAs in response to treatment and demonstrating its application in prioritizing clinically-related lncRNAs. Furthermore, leveraging 820 epithelial ovarian cancer patients from four public datasets, we applied these lncRNAs identified by DDRLnc to establish and validate a risk stratification model to evaluate the benefits of platinum-based adjuvant chemotherapy for the improvement of clinical treatment outcomes.

CONCLUSIONS

Comprehensive pan-cancer analysis illustrates the utility of computational framework in identifying potentially lncRNAs involved in DDR, thereby offering novel insights into the complex realm of cancer research, and it will become a valuable tool for identifying potential therapeutic targets for cancer.

Molecular portraits of colorectal cancer morphological regions

Heterogeneity of colorectal carcinoma (CRC) represents a major hurdle towards personalized medicine. Efforts based on whole tumor profiling demonstrated that the CRC molecular subtypes were associated with specific tumor morphological patterns representing tumor subregions. We hypothesize that whole-tumor molecular descriptors depend on the morphological heterogeneity with significant impact on current molecular predictors. We investigated intra-tumor heterogeneity by morphology-guided transcriptomics to better understand the links between gene expression and tumor morphology represented by six morphological patterns (morphotypes): complex tubular, desmoplastic, mucinous, papillary, serrated, and solid/trabecular. Whole-transcriptome profiling by microarrays of 202 tumor regions (morphotypes, tumor-adjacent normal tissue, supportive stroma, and matched whole tumors) from 111 stage II-IV CRCs identified morphotype-specific gene expression profiles and molecular programs and differences in their cellular buildup. The proportion of cell types (fibroblasts, epithelial and immune cells) and differentiation of epithelial cells were the main drivers of the observed disparities with activation of EMT and TNF-α signaling in contrast to MYC and E2F targets signaling, defining major gradients of changes at molecular level. Several gene expression-based (including single-cell) classifiers, prognostic and predictive signatures were examined to study their behavior across morphotypes. Most exhibited important morphotype-dependent variability within same tumor sections, with regional predictions often contradicting the whole-tumor classification. The results show that morphotype-based tumor sampling allows the detection of molecular features that would otherwise be distilled in whole tumor profile, while maintaining histopathology context for their interpretation. This represents a practical approach at improving the reproducibility of expression profiling and, by consequence, of gene-based classifiers.

Germline Sequencing Analysis to Inform Clinical Gene Panel Testing for Aggressive Prostate Cancer

Importance

Germline gene panel testing is recommended for men with advanced prostate cancer (PCa) or a family history of cancer. While evidence is limited for some genes currently included in panel testing, gene panels are also likely to be incomplete and missing genes that influence PCa risk and aggressive disease.

Objective

To identify genes associated with aggressive PCa.

Design, Setting, and Participants

A 2-stage exome sequencing case-only genetic association study was conducted including men of European ancestry from 18 international studies. Data analysis was performed from January 2021 to March 2023. Participants were 9185 men with aggressive PCa (including 6033 who died of PCa and 2397 with confirmed metastasis) and 8361 men with nonaggressive PCa.

Exposure

Sequencing data were evaluated exome-wide and in a focused investigation of 29 DNA repair pathway and cancer susceptibility genes, many of which are included on gene panels.

Main Outcomes and Measures

The primary study outcomes were aggressive (category T4 or both T3 and Gleason score ≥8 tumors, metastatic PCa, or PCa death) vs nonaggressive PCa (category T1 or T2 and Gleason score ≤6 tumors without known recurrence), and metastatic vs nonaggressive PCa.

Results

A total of 17 546 men of European ancestry were included in the analyses; mean (SD) age at diagnosis was 65.1 (9.2) years in patients with aggressive PCa and 63.7 (8.0) years in those with nonaggressive disease. The strongest evidence of association with aggressive or metastatic PCa was noted for rare deleterious variants in known PCa risk genes BRCA2 and ATM (P ≤ 1.9 × 10-6), followed by NBN (P = 1.7 × 10-4). This study found nominal evidence (P < .05) of association with rare deleterious variants in MSH2, XRCC2, and MRE11A. Five other genes had evidence of greater risk (OR≥2) but carrier frequency differences between aggressive and nonaggressive PCa were not statistically significant: TP53, RAD51D, BARD1, GEN1, and SLX4. Deleterious variants in these 11 candidate genes were carried by 2.3% of patients with nonaggressive, 5.6% with aggressive, and 7.0% with metastatic PCa.

Conclusions and Relevance

The findings of this study provide further support for DNA repair and cancer susceptibility genes to better inform disease management in men with PCa and for extending testing to men with nonaggressive disease, as men carrying deleterious alleles in these genes are likely to develop more advanced disease.

Resistance Improvement and Sensitivity Enhancement of Cancer Therapy by a Novel Antitumor Candidate onto A2780 CP and A2780 S Cell Lines

Background

To overcome cisplatin resistance, the cytotoxicity of a novel antitumor agent on two ovarian cancer cell lines sensitive and resistant to cisplatin was investigated.

Methods

MTT assay and flow cytometry were performed to assess the cytotoxicity of a novel water-soluble Pd (II) complex, [Pd(bpy)(pyr-dtc)]NO3 (PBPD), on cisplatin-sensitive and cisplatin-resistant ovarian cancer cell lines. Furthermore, variations in the expression of drug resistance gene cluster of differentiation 99 (CD99), signal transducer and activator of transcription 3 (STAT3), octamer-binding transcription factor 4 (OCT4), and multidrug resistance mutation 1 (MDR1) were evaluated using Real-Time PCR.

Results

The IC50 values of PBPD in resistant cells were higher than those in sensitive cells. Furthermore, PBPD has a deadlier effect on sensitive cells compared to resistant cells, and the cell survival rate is reduced over time. Flow cytometry revealed that PBPD enhanced the population of living-resistant cells while driving them to apoptosis. PBPD, on the other hand, has a greater effect on the living cell population and has dramatically shifted the population toward apoptosis and necrosis in the sensitive cells. Furthermore, gene expression analysis showed that when sensitive and resistant cells were treated with cisplatin, all resistance genes increased significantly relative to the control. In contrast to OCT4, MDR1, STAT3, and CD99 resistance genes were not significantly elevated in sensitive cells treated with PBPD compared to the control. Thus, the expression of resistance genes in resistant cells treated with PBPD was lower than cisplatin.

Conclusions

As a result, PBPD is a promising anticancer agent for CDDP-resistant ovarian cancer.

Assessment of Homologous Recombination Deficiency in Ovarian Cancer

Accurately assessing homologous recombination deficiency (HRD) to use as a predictive biomarker is an area of intense research in ovarian cancer. Validated assays have demonstrated utility in determining maintenance therapy following platinum sensitive chemotherapy. Novel functional assays promise the potential to reflect HRD in real time and predict response to PARP inhibitors. See related articles by Pikkusaari et al., p. 3110 and Blanc-Durand et al., p. 3124.

The Scarface Score: Deciphering Response to DNA Damage Agents in High-Grade Serous Ovarian Cancer-A GEICO Study

Genomic Instability (GI) is a transversal phenomenon shared by several tumor types that provide both prognostic and predictive information. In the context of high-grade serous ovarian cancer (HGSOC), response to DNA-damaging agents such as platinum-based and poly(ADP-ribose) polymerase inhibitors (PARPi) has been closely linked to deficiencies in the DNA repair machinery by homologous recombination repair (HRR) and GI. In this study, we have developed the Scarface score, an integrative algorithm based on genomic and transcriptomic data obtained from the NGS analysis of a prospective GEICO cohort of 190 formalin-fixed paraffin-embedded (FFPE) tumor samples from patients diagnosed with HGSOC with a median follow up of 31.03 months (5.87-159.27 months). In the first step, three single-source models, including the SNP-based model (accuracy = 0.8077), analyzing 8 SNPs distributed along the genome; the GI-based model (accuracy = 0.9038) interrogating 28 parameters of GI; and the HTG-based model (accuracy = 0.8077), evaluating the expression of 7 genes related with tumor biology; were proved to predict response. Then, an ensemble model called the Scarface score was found to predict response to DNA-damaging agents with an accuracy of 0.9615 and a kappa index of 0.9128 (p < 0.0001). The Scarface Score approaches the routine establishment of GI in the clinical setting, enabling its incorporation as a predictive and prognostic tool in the management of HGSOC.

signifinder enables the identification of tumor cell states and cancer expression signatures in bulk, single-cell and spatial transcriptomic data

Over the last decade, many studies and some clinical trials have proposed gene expression signatures as a valuable tool for understanding cancer mechanisms, defining subtypes, monitoring patient prognosis, and therapy efficacy. However, technical and biological concerns about reproducibility have been raised. Technical reproducibility is a major concern: we currently lack a computational implementation of the proposed signatures, which would provide detailed signature definition and assure reproducibility, dissemination, and usability of the classifier. Another concern regards intratumor heterogeneity, which has never been addressed when studying these types of biomarkers using bulk transcriptomics. With the aim of providing a tool able to improve the reproducibility and usability of gene expression signatures, we propose signifinder, an R package that provides the infrastructure to collect, implement, and compare expression-based signatures from cancer literature. The included signatures cover a wide range of biological processes from metabolism and programmed cell death, to morphological changes, such as quantification of epithelial or mesenchymal-like status. Collected signatures can score tumor cell characteristics, such as the predicted response to therapy or the survival association, and can quantify microenvironmental information, including hypoxia and immune response activity. signifinder has been used to characterize tumor samples and to investigate intra-tumor heterogeneity, extending its application to single-cell and spatial transcriptomic data. Through these higher-resolution technologies, it has become increasingly apparent that the single-sample score assessment obtained by transcriptional signatures is conditioned by the phenotypic and genetic intratumor heterogeneity of tumor masses. Since the characteristics of the most abundant cell type or clone might not necessarily predict the properties of mixed populations, signature prediction efficacy is lowered, thus impeding effective clinical diagnostics. Through signifinder, we offer general principles for interpreting and comparing transcriptional signatures, as well as suggestions for additional signatures that would allow for more complete and robust data inferences. We consider signifinder a useful tool to pave the way for reproducibility and comparison of transcriptional signatures in oncology.

Optimized detection of homologous recombination deficiency improves the prediction of clinical outcomes in cancer

Homologous recombination DNA-repair deficiency (HRD) is a common driver of genomic instability and confers a therapeutic vulnerability in cancer. The accurate detection of somatic allelic imbalances (AIs) has been limited by methods focused on BRCA1/2 mutations and using mixtures of cancer types. Using pan-cancer data, we revealed distinct patterns of AIs in high-grade serous ovarian cancer (HGSC). We used machine learning and statistics to generate improved criteria to identify HRD in HGSC (ovaHRDscar). ovaHRDscar significantly predicted clinical outcomes in three independent patient cohorts with higher precision than previous methods. Characterization of 98 spatiotemporally distinct metastatic samples revealed low intra-patient variation and indicated the primary tumor as the preferred site for clinical sampling in HGSC. Further, our approach improved the prediction of clinical outcomes in triple-negative breast cancer (tnbcHRDscar), validated in two independent patient cohorts. In conclusion, our tumor-specific, systematic approach has the potential to improve patient selection for HR-targeted therapies.

RNA-Based Classification of Homologous Recombination Deficiency in Racially Diverse Patients with Breast Cancer

BACKGROUND

Aberrant expression of DNA repair pathways such as homologous recombination (HR) can lead to DNA repair imbalance, genomic instability, and altered chemotherapy response. DNA repair imbalance may predict prognosis, but variation in DNA repair in diverse cohorts of breast cancer patients is understudied.

METHODS

To identify RNA-based patterns of DNA repair expression, we performed unsupervised clustering on 51 DNA repair-related genes in the Cancer Genome Atlas Breast Cancer [TCGA BRCA (n = 1,094)] and Carolina Breast Cancer Study [CBCS (n = 1,461)]. Using published DNA-based HR deficiency (HRD) scores (high-HRD ≥ 42) from TCGA, we trained an RNA-based supervised classifier. Unsupervised and supervised HRD classifiers were evaluated in association with demographics, tumor characteristics, and clinical outcomes.

RESULTS

: Unsupervised clustering on DNA repair genes identified four clusters of breast tumors, with one group having high expression of HR genes. Approximately 39.7% of CBCS and 29.3% of TCGA breast tumors had this unsupervised high-HRD (U-HRD) profile. A supervised HRD classifier (S-HRD) trained on TCGA had 84% sensitivity and 73% specificity to detect HRD-high samples. Both U-HRD and S-HRD tumors in CBCS had higher frequency of TP53 mutant-like status (45% and 41% enrichment) and basal-like subtype (63% and 58% enrichment). S-HRD high was more common among black patients. Among chemotherapy-treated participants, recurrence was associated with S-HRD high (HR: 2.38, 95% confidence interval = 1.50-3.78).

CONCLUSIONS

HRD is associated with poor prognosis and enriched in the tumors of black women.

IMPACT

RNA-level indicators of HRD are predictive of breast cancer outcomes in diverse populations.

Noncanonical NF-κB factor p100/p52 regulates homologous recombination and modulates sensitivity to DNA-damaging therapy

Homologous recombination (HR) serves multiple roles in DNA repair that are essential for maintaining genomic stability, including double-strand DNA break (DSB) repair. The central HR protein, RAD51, is frequently overexpressed in human malignancies, thereby elevating HR proficiency and promoting resistance to DNA-damaging therapies. Here, we find that the non-canonical NF-κB factors p100/52, but not RelB, control the expression of RAD51 in various human cancer subtypes. While p100/p52 depletion inhibits HR function in human tumor cells, it does not significantly influence the proficiency of non-homologous end joining, the other key mechanism of DSB repair. Clonogenic survival assays were performed using a pair DLD-1 cell lines that differ only in their expression of the key HR protein BRCA2. Targeted silencing of p100/p52 sensitizes the HR-competent cells to camptothecin, while sensitization is absent in HR-deficient control cells. These results suggest that p100/p52-dependent signaling specifically controls HR activity in cancer cells. Since non-canonical NF-κB signaling is known to be activated after various forms of genomic crisis, compensatory HR upregulation may represent a natural consequence of DNA damage. We propose that p100/p52-dependent signaling represents a promising oncologic target in combination with DNA-damaging treatments.

Identification of an Immune Gene-Based Cisplatin Response Model and CD27 as a Therapeutic Target against Cisplatin Resistance for Ovarian Cancer

Objective. Evidence demonstrates that the immune microenvironment is extensively associated with chemotherapy response of ovarian cancer (OV). Herein, this study is aimed at establishing a cisplatin response prediction model for OV on the basis of immune genes. Methods. The expression profiles of cisplatin-sensitive and cisplatin-resistant OV specimens were integrated from multiple public datasets. The abundance scores of 22 immune cells were estimated with CIBERSORT algorithm. Differentially expressed immune genes (DEGs) were determined between cisplatin-sensitive and cisplatin-resistant groups. Thereafter, a cisplatin response model was constructed based on prognostic DEGs with logistic regression analysis. The prediction performance was validated in independent cohorts. The possible relationships between the model and immunotherapy were then assessed. Results. Treg scores were significantly decreased in cisplatin-resistant than cisplatin-sensitive OV specimens, with the opposite results for naïve B cells and activated dendritic cells. Fourteen prognostic DEGs were identified and used to develop a cisplatin-response model. The response scores, estimated by the model, showed favorable performance in discriminating cisplatin-response and nonresponse samples. The response scores also presented significantly negative correlations with three well-known cisplatin-resistant pathways and a positive correlation with the expression of CD274 (PD-L1). Moreover, the decreased CD27 expression was observed in cisplatin-resistant groups, and OV specimens with higher CD27 expressions were more sensitive to cisplatin treatment. Conclusion. Altogether, our findings proposed a cisplatin response prediction model and identified CD27 that might be involved in cisplatin resistance. Further investigations suggested that CD27 could be a promising immunotherapeutic target for cisplatin-resistant subset of OV.

Homologous Recombination Related Signatures Predict Prognosis and Immunotherapy Response in Metastatic Urothelial Carcinoma

Objective: This study used homologous recombination (HR) related signatures to develop a clinical prediction model for screening immune checkpoint inhibitors (ICIs) advantaged populations and identify hub genes in advanced metastatic urothelial carcinoma.

Methods: The single-sample gene enrichment analysis and weighted gene co-expression network analysis were applied to identify modules associated with immune response and HR in IMvigor210 cohort samples. The principal component analysis was utilized to determine the differences in HR-related module gene signature scores across different tissue subtypes and clinical variables. Risk prediction models and nomograms were developed using differential gene expression analysis associated with HR scores, least absolute shrinkage and selection operator, and multivariate proportional hazards model regression. Additionally, hub genes were identified by analyzing the contribution of HR-related genes to principal components and overall survival analysis. Finally, clinical features from GSE133624, GSE13507, the TCGA, and other data sets were analyzed to validate the relationship between hub genes and tumor growth and mutation.

Results: The HR score was significantly higher in the complete/partial response group than in the stable/progressive disease group. The majority of genes associated with HR were discovered to be involved in the cell cycle and others. Genomically unstable, high tumor level, and high immune level samples all exhibited significantly higher HR score than other sample categories, and higher HR scores were related to improved survival following ICIs treatment. The risk scores for AUNIP, SEPT, FAM72D, CAMKV, CXCL9, and FOXN4 were identified, and the training and verification groups had markedly different survival times. The risk score, tumor neoantigen burden, mismatch repair, and cell cycle regulation were discovered to be independent predictors of survival time following immunotherapy. Patients with a high level of expression of hub genes such as EME1, RAD51AP1, and RAD54L had a greater chance of surviving following immunotherapy. These genes are expressed at significantly higher levels in tumors, high-grade cancer, and invasive cancer than other categories, and are associated with TP53 and RB1 mutations.

Conclusion: HR-related genes are upregulated in genomically unstable samples, the survival time of mUC patients after treatment with ICIs can be predicted using a normogram model based on HR signature.

DNA Repair Protein HELQ and XAB2 as Chemoresponse and Prognosis Biomarkers in Ascites Tumor Cells of High-Grade Serous Ovarian Cancer

Nucleotide excision repair (NER) is an important mediator for responsiveness of platinum-based chemotherapy. Our study is aimed at investigating the NER-related genes expression in ascites tumor cells and its application in the prediction of chemoresponse in high-grade serous ovarian cancer (HGSC) patients. The relationship between 16 NER-related genes and the prognosis of ovarian cancer was analyzed in the TCGA database. NER-related genes including HELQ and XAB2 expressions were determined via immunocytochemistry in ascites cell samples from 92 ovarian cancer patients prior to primary cytoreduction surgery. Kaplan-Meier analysis and Cox model were used to investigate the association between NER-related gene expression and prognosis/chemotherapeutic response. Predicting models were constructed using a training cohort of 60 patients and validated in a validation cohort of 32 patients. We found that high expression of HELQ and XAB2 in the training cohort was associated with poor prognosis (for HELQ, P = 0.001, HR = 2.83, 95% CI: 1.46-5.49; for XAB2, P = 0.008, HR = 2.38, 95% CI: 1.23-4.63) and platinum resistance (for HELQ, P < 0.001; for XAB2, P = 0.006). In the validation cohort, the combination of HELQ and XAB2 (AUC = 0.863) showed the highest AUC. The expression levels of HELQ (RR 5.7, 95% CI 1.7-19.2) and XAB2 (RR 3.2, 95% CI 0.9-10.8) in ascites tumor cells were positively correlated to the risk of platinum resistance. In summary, we revealed that the expression levels of HELQ and XAB2 are candidate predictors for primary chemotherapy responsiveness and prognosis in HGSC. Ascites cytology is applicable as a promising method for chemosensitivity prediction in HGSC.

Patient-Level DNA Damage Repair Pathway Profiles and Anti-Tumor Immunity for Gastric Cancer

DNA damage repair (DDR) comprises the detection and correction of alterations in the chemical structure of DNA. The dysfunction of the DDR process has been determined to have important implications for tumor carcinogenesis, malignancy progression, treatment resistance, and prognosis assessment. However, the role of the DDR process in gastric cancer (GC) remains to be fully understood. Thus, a total of 2,019 GC samples from our hospital (Harbin Medical University Cancer Hospital in china) and 12 public data sets were included in our study. In this study, single-sample gene set enrichment analysis (ssGSEA) was used to generate the DDR pathway activity profiles of 8 DDR sub-pathways and identify a DDR pathway signature by combining the DDR sub-pathway gene sets. The DDR pathway profiling’s impacts on the clinical outcomes, biological functions, genetic variants, immune heterogeneity, and treatment responses were analyzed through multidimensional genomics and clinical data. The results demonstrate that the DDR pathway profiling was clearly distinguished between tumor and normal tissues. The DDR pathway profiling reveals patient-level variations, which may contribute to explaining the high heterogeneity of human GC for the biological features and treatment outcomes. Thus, tumors with low DDR signature scores were independently correlated with shorter overall survival time and significantly associated with mesenchymal, invasion, and metastasis phenotypes. The statistical model integrating this DDR pathway signature with other clinical predictors outperforms each predictor alone for predicting overall survival in discrimination, calibration, and net clinical benefit. Moreover, low DDR signature scores were tightly associated with genome stability, characterized by low tumor mutational burden (TMB) and low fractions of genome alteration. Furthermore, this study confirms that patients with low DDR pathway signature scores might not benefit from adjuvant chemotherapy and a monoclonal antibody directed against programmed cell death-1 ligand 1 (anti-PD1) therapy. These findings highlighted that the DDR pathway profiling confers important implications for patients with GC and provides insights into the specific clinical and molecular features underlying the DDR process, which may help to facilitate clinical management.

Randomized, open-label, phase 2 study of andecaliximab plus nivolumab versus nivolumab alone in advanced gastric cancer identifies biomarkers associated with survival

Background

Matrix metalloproteinase-9 (MMP9) selectively cleaves extracellular matrix proteins contributing to tumor growth and an immunosuppressive microenvironment. This study evaluated andecaliximab (ADX), an inhibitor of MMP9, in combination with nivolumab (NIVO), for the treatment of advanced gastric cancer.

Methods

Phase 2, open-label, randomized multicenter study evaluating the efficacy, safety, and pharmacodynamics of ADX+NIVO versus NIVO in patients with pretreated metastatic gastric or gastroesophageal junction (GEJ) adenocarcinoma. The primary endpoint was objective response rate (ORR). Secondary endpoints included progression-free survival (PFS), overall survival (OS), and adverse events (AEs). We explored the correlation of efficacy outcomes with biomarkers.

Results

144 patients were randomized; 141 were treated: 81% white, 69% male, median age was 61 years in the ADX+NIVO group and 62 years in the NIVO-alone group. The ORR was 10% (95% CI 4 to 19) in the ADX+NIVO group and 7% (95% CI 2 to 16) in the NIVO-alone group (OR: 1.5 (95% CI 0.4 to 6.1; p=0.8)). There was no response or survival benefit associated with adding ADX. AE rates were comparable in both treatment groups; the most common AEs were fatigue, decreased appetite, nausea, and vomiting. Programmed cell death ligand 1, interferon-γ (IFN), and intratumoral CD8+ cell density were not associated with treatment response or survival. The gene signature most correlated with shorter survival was the epithelial-to-mesenchymal gene signature; high transforming growth factor (TGF)-β fibrosis score was negatively associated with OS (p=0.036). Gene expression analysis of baseline tumors comparing long-(1+ years) and short-term (<1 year) survivors showed that GRB7 was associated with survival beyond 1 year. Human epidermal growth factor receptor 2 (HER2)-positive disease was associated with significantly longer survival (p=0.0077). Median tumor mutation burden (TMB) was 2.01; patients with TMB ≥median had longer survival (p=0.0025) and improved PFS (p=0.016). Based on a model accounting for TMB, TGF-β fibrosis, and HER2, TMB was the main driver of survival in this patient population.

Conclusion

Combination of ADX+NIVO had a favorable safety profile but did not improve efficacy compared with NIVO alone in patients with pretreated metastatic gastric or GEJ adenocarcinoma. HER2 positivity, higher TMB or GRB7, and lower TGF-β were associated with improved outcomes.

Trial registration number

NCT02864381 or GS-US-296–-2013.

DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma

Although osteosarcoma (OS) is a rare cancer, it is the most common primary malignant bone tumor in children and adolescents. BRCAness is a phenotypical trait in tumors with a defect in homologous recombination repair, resembling tumors with inactivation of BRCA1/2, rendering these tumors sensitive to poly (ADP)-ribose polymerase inhibitors (PARPi). Recently, OS was shown to exhibit molecular features of BRCAness. Our goal was to develop a method complementing existing genomic methods to aid clinical decision making on administering PARPi in OS patients. OS samples with DNA-methylation data were divided to BRCAness-positive and negative groups based on the degree of their genomic instability (n = 41). Methylation probes were ranked according to decreasing variance difference between two groups. The top 2000 probes were selected for training and cross-validation of the random forest algorithm. Two-thirds of available OS RNA-Seq samples (n = 17) from the top and bottom of the sample list ranked according to genome instability score were subjected to differential expression and, subsequently, to gene set enrichment analysis (GSEA). The combined accuracy of trained random forest was 85% and the average area under the ROC curve (AUC) was 0.95. There were 449 upregulated and 1,079 downregulated genes in the BRCAness-positive group (fdr < 0.05). GSEA of upregulated genes detected enrichment of DNA replication and mismatch repair and homologous recombination signatures (FWER < 0.05). Validation of the BRCAness classifier with an independent OS set (n = 20) collected later in the course of study showed AUC of 0.87 with an accuracy of 90%. GSEA signatures computed for this test set were matching the ones observed in the training set enrichment analysis. In conclusion, we developed a new classifier based on DNA-methylation patterns that detects BRCAness in OS samples with high accuracy. GSEA identified genome instability signatures. Machine-learning and gene expression approaches add new epigenomic and transcriptomic aspects to already established genomic methods for evaluation of BRCAness in osteosarcoma and can be extended to cancers characterized by genome instability.

Osteosarcoma (OS) is the most common primary malignant tumor of bone in children and young adults with poor prognosis for patients with refractory or metastatic disease. A common feature, so-called BRCAness, exists in multiple cancers including OS and is characterized by homologous recombination deficiency. Tumors exhibiting BRCAness have been shown to respond to therapy with PARP inhibitors. Currently, BRCAness is mostly assessed by the genomic instability score. This method based on the DNA sequencing requires normal tissue DNA as control and is vulnerable to subjective interpretation of "genomic scarring" events. In this study, we implemented a classifier based on DNA methylation patterns. It is capable of detecting BRCAness in OS samples and does not require control tissue DNA. Therefore, it has the potential to support clinical decision making on administering PARPi in OS patients. We further corroborated the presence of BRCAness in OS by detecting homologous recombination signatures through gene expression analysis.

Unsupervised Learning Framework With Multidimensional Scaling in Predicting Epithelial-Mesenchymal Transitions

Clustering tumor metastasis samples from gene expression data at the whole genome level remains an arduous challenge, in particular, when the number of experimental samples is small and the number of genes is huge. We focus on the prediction of the epithelial-mesenchymal transition (EMT), which is an underlying mechanism of tumor metastasis, here, rather than tumor metastasis itself, to avoid confounding effects of uncertainties derived from various factors. In this paper, we propose a novel model in predicting EMT based on multidimensional scaling (MDS) strategies and integrating entropy and random matrix detection strategies to determine the optimal reduced number of dimension in low dimensional space. We verified our proposed model with the gene expression data for EMT samples of breast cancer and the experimental results demonstrated the superiority over state-of-the-art clustering methods. Furthermore, we developed a novel feature extraction method for selecting the significant genes and predicting the tumor metastasis. The source code is available at "https://github.com/yushanqiu/yushan.qiu-szu.edu.cn".

Construction and Validation of a Platinum Sensitivity Predictive Model With Multiple Genomic Variations for Epithelial Ovarian Cancer

Platinum-based chemotherapy is still the standard of care after cytoreductive surgery in the first-line treatment for epithelial ovarian cancer. This study aims to integrate novel biomarkers for predicting platinum sensitivity in EOC after initial cytoreductive surgery precisely. To this end, 60 patients were recruited from September 2014 to October 2019. Based on the duration of progress-free survival, 44 and 16 patients were assigned to platinum-sensitive and platinum-resistant group, respectively. Next generation sequencing was performed to dissect the genomic features of ovarian tumors obtained from surgery. Multiple genomic variations were compared between two groups, including single-nucleotide variant, single base or indel signature, loss of heterozygosity (LOH), whole-genome duplication (WGD), and others. The results demonstrated that patients with characteristics including positive SBS10a signature (p < 0.05), or FAM175A LOH (p < 0.01), or negative WGD (p < 0.01) were significantly enriched in platinum-sensitive group. Consistently, patients with positive SBS10a signature (15.8 vs. 10.1 months, p < 0.05), or FAM175A LOH (16.5 vs. 9.2 months, p < 0.05), or negative WGD (16.5 vs. 9.1 months, p < 0.05) have significantly longer PFS than those without these genetic features. By integrating these three biomarkers, a lasso regression model was employed to train and test for all patients, with the AUC value 0.864 in platinum sensitivity prediction. Notably, 388 ovarian cancer patients from TCGA dataset were leveraged as independent validation cohort with AUC value 0.808, suggesting the favorable performance and reliability of this model.

Beyond the Double-Strand Breaks: The Role of DNA Repair Proteins in Cancer Stem-Cell Regulation

Simple Summary

Cancer stem cells (CSCs) are a tumor cell population maintaining tumor growth and promoting tumor relapse if not wholly eradicated during treatment. CSCs are often equipped with molecular mechanisms making them resistant to conventional anti-cancer therapies whose curative potential depends on DNA damage-induced cell death. An elevated expression of some key DNA repair proteins is one of such defense mechanisms. However, new research reveals that the role of critical DNA repair proteins is extending far beyond the DNA repair mechanisms. This review discusses the diverse biological functions of DNA repair proteins in CSC maintenance and the adaptation to replication and oxidative stress, anti-cancer immune response, epigenetic reprogramming, and intracellular signaling mechanisms. It also provides an overview of their potential therapeutic targeting.

Abstract

Cancer stem cells (CSCs) are pluripotent and highly tumorigenic cells that can re-populate a tumor and cause relapses even after initially successful therapy. As with tissue stem cells, CSCs possess enhanced DNA repair mechanisms. An active DNA damage response alleviates the increased oxidative and replicative stress and leads to therapy resistance. On the other hand, mutations in DNA repair genes cause genomic instability, therefore driving tumor evolution and developing highly aggressive CSC phenotypes. However, the role of DNA repair proteins in CSCs extends beyond the level of DNA damage. In recent years, more and more studies have reported the unexpected role of DNA repair proteins in the regulation of transcription, CSC signaling pathways, intracellular levels of reactive oxygen species (ROS), and epithelial–mesenchymal transition (EMT). Moreover, DNA damage signaling plays an essential role in the immune response towards tumor cells. Due to its high importance for the CSC phenotype and treatment resistance, the DNA damage response is a promising target for individualized therapies. Furthermore, understanding the dependence of CSC on DNA repair pathways can be therapeutically exploited to induce synthetic lethality and sensitize CSCs to anti-cancer therapies. This review discusses the different roles of DNA repair proteins in CSC maintenance and their potential as therapeutic targets.

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.

TP53 variant allele frequency correlates with the chemotherapy response score in ovarian/fallopian tube/peritoneal high-grade serous carcinoma

Molecular findings in ovarian, fallopian tube, and peritoneal high-grade serous carcinoma (HGSCa) are emerging as potential prognostic indicators. The chemotherapy response score (CRS) has been proposed as a histologic-based prognostic factor in patients with HGSCa treated with neoadjuvant chemotherapy (NACT). No study details the relationship between the mutational landscape of HGSCa and the CRS. This study addresses this issue using next-generation sequencing (NGS). We retrospectively identified 25 HGSCas treated with NACT and pathology material available to calculate the CRS. All cases had NGS on the primary debulking specimen post-NACT. The three-tier Böhm CRS was applied to the omentum or adnexa and calculated as a combined score. Tumor mutation burden (TMB) and TP53 variant allele frequency (VAF) were calculated and used in correlative analysis. All cases had at least one mutation, most commonly TP53 (25 cases, 100%). Other mutations were BRCA2 (one case, 4%), ARID1A (two cases, 8%), and 1 (4%) of each of the following: ERBB2, NTRK3, STK11, NTRK2, TSC1, PIK3CA, NF1, NOTCH3, CDK2, SMAD4, and PMS2. TMB ranged from 2.58 to 7.75 (median 3.84). There was no statistically significant relationship between the TMB and omental CRS, R-squared = 0.011 (P = 0.62); adnexal CRS, R-squared = 0.005 (P = 0.74); or with the combined CRS, R-squared = 0.009 (P = 0.65). Statistically significant correlation was found between the TP53 VAF and the omental CRS (R-squared = 0.28, P = 0.007), adnexal CRS (R-squared = 0.26, P = 0.01), and the combined CRS (R-squared = 0.33, P = 0.0026). The TP53 VAF was adjusted for percent of tumor present on the slide resulting in an average per cell TP53 mutational load, resulting in similar results with a statistically significant correlation between the average per cell TP53 mutational load and the omental CRS (R-squared = 0.27, P = 0.02), adnexal CRS (R-squared = 0.16, P = 0.05), and the combined CRS (R-squared = 0.23, P = 0.02). In summary, NGS confirmed TP53 mutations in all cases of HGSCa. TMB showed no correlation with the CRS. TP53 VAF and average per cell TP53 mutational load showed significant correlation with the CRS, whether graded on the adnexa or omentum or as a combined score, indicating concordance between molecular and histological findings following NACT.

Artificial Intelligence Based on Blood Biomarkers Including CTCs Predicts Outcomes in Epithelial Ovarian Cancer: A Prospective Study

Objective

We aimed to develop an ovarian cancer-specific predictive framework for clinical use platinum-sensitivity and prognosis using machine learning methods based on multiple biomarkers, including circulating tumor cells (CTCs).

Patients and Methods

We enrolled 156 epithelial ovarian cancer (EOC) patients, randomly assigned into the training and validation cohorts. Eight machine learning classifiers, including Random Forest (RF), Support Vector Machine, Gradient Boosting Machine, Conditional RF, Neural Network, Naive Bayes, Elastic Net, and Logistic Regression, were used to derive predictive information from 11 peripheral blood parameters, including CTCs. Through the advanced CanPatrol CTC-enrichment technique, we detect CTCs and classify them into subpopulations: epithelial, mesenchymal, and hybrids. Survival curves were generated by Kaplan–Meier method and calculated through the Log rank test.

Results

Machine learning techniques, especially the Random Forest classifier, were superior to conventional regression-based analyses in predicting multiple clinical parameters related to EOC. The values for the receiver operating characteristic (ROC) curve for segregating EOC with advanced clinical stages and platinum-sensitivity were 0.796 (95% CI, 0.727–0.866) and 0.809 (95% CI, 0.742–0.876), respectively. Stepwise, we used the unsupervised clustering analysis to identify EOC subgroups with significantly worse overall survival (OS), especially in the advanced-stage group with the p-value of 0.0018 (HR, 2.716; 95% CI, 1.602–4.605) for progression-free survival (PFS) and 0.0037 (HR, 2.359; 95% CI, 1.752–6.390) for overall survival (OS).

Conclusion

Machine learning systems could provide risk stratification for EOC patients before initial intervention through blood variables, including circulating tumor cells. The predictive algorithms could facilitate personalized treatment options through promising pre-treatment stratification of EOC patients.

Trial registration

ChiCTR-DDD-16009601 Registered 25 October 2016.

Rare Variants in the DNA Repair Pathway and the Risk of Colorectal Cancer

BACKGROUND

Inherited susceptibility is an important contributor to colorectal cancer risk, and rare variants in key genes or pathways could account in part for the missing proportion of colorectal cancer heritability.

METHODS

We conducted an exome-wide association study including 2,327 cases and 2,966 controls of European ancestry from three large epidemiologic studies. Single variant associations were tested using logistic regression models, adjusting for appropriate study-specific covariates. In addition, we examined the aggregate effects of rare coding variation at the gene and pathway levels using Bayesian model uncertainty techniques.

RESULTS

In an exome-wide gene-level analysis, we identified ST6GALNAC2 as the top associated gene based on the Bayesian risk index (BRI) method [summary Bayes factor (BF)BRI = 2604.23]. A rare coding variant in this gene, rs139401613, was the top associated variant (P = 1.01 × 10-6) in an exome-wide single variant analysis. Pathway-level association analyses based on the integrative BRI (iBRI) method found extreme evidence of association with the DNA repair pathway (BFiBRI = 17852.4), specifically with the nonhomologous end joining (BFiBRI = 437.95) and nucleotide excision repair (BFiBRI = 36.96) subpathways. The iBRI method also identified RPA2, PRKDC, ERCC5, and ERCC8 as the top associated DNA repair genes (summary BFiBRI ≥ 10), with rs28988897, rs8178232, rs141369732, and rs201642761 being the most likely associated variants in these genes, respectively.

CONCLUSIONS

We identified novel variants and genes associated with colorectal cancer risk and provided additional evidence for a role of DNA repair in colorectal cancer tumorigenesis.

IMPACT

This study provides new insights into the genetic predisposition to colorectal cancer, which has potential for translation into improved risk prediction.

Prognostic Evidence of the miRNA-Based Ovarian Cancer Signature MiROvaR in Independent Datasets

Simple Summary

Epithelial ovarian cancers (EOC) have an unpredictable frequent recurrence often associated with incurable chemo-resistant disease. Basing on the miRNA expression profile of 892 EOC patients, we previously developed a 35 miRNA-based classifier, MiROvaR, able to predict EOC risk of early relapse. Further independent analysis of prediction accuracy represents a crucial step in the test-validation phase. Here we exploited an external and independently collected, handled and profiled EOC cohort, to challenge MirovaR accuracy. Our analysis confirmed the MiROvaR prognostic power, thus opening the way to its prospective validation as a clinical grade assay entering into clinical practice to help in the refinement of therapeutic intervention for high risk EOC patients.

Abstract

Epithelial ovarian cancer (EOC) remains the second most common cause of gynecological cancer deaths. To improve patients’ outcomes, we still need reliable biomarkers of early relapse, of which external independent validation is a crucial process. Our previously established prognostic signature, MiROvaR, including 35 microRNAs (miRNA) able to stratify EOC patients for their risk of relapse, was challenged on a new independent cohort of 197 EOC patients included in the Pelvic Mass Study whose miRNA profile was made publically available, thus resulting in the only accessible database aside from the EOC TCGA collection. Following accurate data matrix adjustment to account for the use of different miRNA platforms, MiROvaR confirmed its ability to discriminate early relapsing patients. The model’s original cutoff separated 156 (79.2%) high- and 41 (20.8%) low-risk patients with median progression free survival (PFS) of 16.3 months and not yet reached (NYR), respectively (hazard ratio (HR): 2.42–95% Confidence Interval (CI) 1.49–3.93; Log-rank p = 0.00024). The MiROvaR predictive accuracy (area under the curve (AUC) = 0.68; 95% Cl 0.57–0.79) confirms its prognostic value. This external validation in a totally independently collected, handled and profiled EOC cohort suggests that MiROvaR is a strong and reliable biomarker of EOC early relapse, warranting prospective validation.

A highly expressed mRNA signature for predicting survival in patients with stage I/II non-small-cell lung cancer after operation

There is an urgent need to identify novel biomarkers that predict the prognosis of patients with NSCLC. In this study,we aim to find out mRNA signature closely related to the prognosis of NSCLC by new algorithm of bioinformatics. Identification of highly expressed mRNA in stage I/II patients with NSCLC was performed with the “Limma” package of R software. Survival analysis of patients with different mRNA expression levels was subsequently calculated by Cox regression analysis, and a multi-RNA signature was obtained by using the training set. Kaplan–Meier estimator, log-rank test and receiver operating characteristic (ROC) curves were used to analyse the predictive ability of the multi-RNA signature. RT-PCR used to verify the expression of the multi-RNA signature, and Westernblot used to verify the expression of proteins related to the multi-RNA signature. We identified fifteen survival-related mRNAs in the training set and classified the patients as high risk or low risk. NSCLC patients with low risk scores had longer disease-free survival than patients with high risk scores. The fifteen-mRNA signature was an independent prognostic factor, as shown by the ROC curve. ROC curve also showed that the combined model of the fifteen-mRNA signature and tumour stage had higher precision than stage alone. The expression of fifteen mRNAs and related proteins were higher in stage II NSCLC than in stage I NSCLC. Multi-gene expression profiles provide a moderate prognostic tool for NSCLC patients with stage I/II disease.

Development of a Genomic Signatures-Based Predictor of Initial Platinum-Resistance in Advanced High-Grade Serous Ovarian Cancer Patients

BACKGROUND

High grade serous ovarian cancer (HGSOC) is the most common subtype of ovarian cancer. Although platinum-based chemotherapy has been the cornerstone for HGSOC treatment, nearly 25% of patients would have less than 6 months of interval since the last platinum chemotherapy, referred to as platinum-resistance. Currently, no precise tools to predict platinum resistance have been developed yet.

METHODS

Ninety-nine HGSOC patients, who have finished cytoreductive surgery and platinum-based chemotherapy in Peking University Third Hospital from 2018 to 2019, were enrolled. Whole-genome sequencing (WGS) and whole-exome sequencing (WES) were performed on the collected tumor tissue samples to establish a platinum-resistance predictor in a discovery cohort of 57 patients, and further validated in another 42 HGSOC patients.

RESULTS

A high prevalence of alterations in DNA damage repair (DDR) pathway, including BRCA1/2, was identified both in the platinum-sensitive and resistant HGSOC patients. Compared with the resistant subgroup, there was a trend of higher prevalence of homologous recombination deficiency (HRD) in the platinum-sensitive subgroup (78.95% vs. 47.37%, p=0.0646). Based on the HRD score, microhomology insertions and deletions (MHID), copy number changes load, duplication load of 1-100 kb, single nucleotide variants load, and eight other mutational signatures, a combined predictor of platinum-resistance, named as DRDscore, was established. DRDscore outperformed in predicting the platinum-sensitivity than the previously reported biomarkers with a predictive accuracy of 0.860 at a threshold of 0.7584. The predictive performance of DRDscore was validated in an independent cohort of 42 HGSOC patients with a sensitivity of 90.9%.

CONCLUSIONS

A multi-genomic signature-based analysis enabled the prediction of initial platinum resistance in advanced HGSOC patients, which may serve as a novel assessment of platinum resistance, provide therapeutic guidance, and merit further validation.

Tumor-intrinsic and -extrinsic (immune) gene signatures robustly predict overall survival and treatment response in high grade serous ovarian cancer patients

In the present study, we developed a transcriptomic signature capable of predicting prognosis and response to primary therapy in high grade serous ovarian cancer (HGSOC). Proportional hazard analysis was performed on individual genes in the TCGA RNAseq data set containing 229 HGSOC patients. Ridge regression analysis was performed to select genes and develop multigenic models. Survival analysis identified 120 genes whose expression levels were associated with overall survival (OS) (HR = 1.49-2.46 or HR = 0.48-0.63). Ridge regression modeling selected 38 of the 120 genes for development of the final Ridge regression models. The consensus model based on plurality voting by 68 individual Ridge regression models classified 102 (45%) as low, 23 (10%) as moderate and 104 patients (45%) as high risk. The median OS was 31 months (HR = 7.63, 95% CI = 4.85-12.0, P < 1.0-10) and 77 months (HR = ref) in the high and low risk groups, respectively. The gene signature had two components: intrinsic (proliferation, metastasis, autophagy) and extrinsic (immune evasion). Moderate/high risk patients had more partial and non-responses to primary therapy than low risk patients (odds ratio = 4.54, P < 0.001). We concluded that the overall survival and response to primary therapy in ovarian cancer is best assessed using a combination of gene signatures. A combination of genes which combines both tumor intrinsic and extrinsic functions has the best prediction. Validation studies are warranted in the future.

Single-cell dissection of intratumoral heterogeneity and lineage diversity in metastatic gastric adenocarcinoma

Intratumoral heterogeneity (ITH) is a fundamental property of cancer; however, the origins of ITH remain poorly understood. We performed single-cell transcriptome profiling of peritoneal carcinomatosis (PC) from 15 patients with gastric adenocarcinoma (GAC), constructed a map of 45,048 PC cells, profiled the transcriptome states of tumor cell populations, incisively explored ITH of malignant PC cells and identified significant correlates with patient survival. The links between tumor cell lineage/state compositions and ITH were illustrated at transcriptomic, genotypic, molecular and phenotypic levels. We uncovered the diversity in tumor cell lineage/state compositions in PC specimens and defined it as a key contributor to ITH. Single-cell analysis of ITH classified PC specimens into two subtypes that were prognostically independent of clinical variables, and a 12-gene prognostic signature was derived and validated in multiple large-scale GAC cohorts. The prognostic signature appears fundamental to GAC carcinogenesis and progression and could be practical for patient stratification.

RNA-Seq-Based Breast Cancer Subtypes Classification Using Machine Learning Approaches

Background

Breast invasive carcinoma (BRCA) is not a single disease as each subtype has a distinct morphology structure. Although several computational methods have been proposed to conduct breast cancer subtype identification, the specific interaction mechanisms of genes involved in the subtypes are still incomplete. To identify and explore the corresponding interaction mechanisms of genes for each subtype of breast cancer can impose an important impact on the personalized treatment for different patients.

Methods

We integrate the biological importance of genes from the gene regulatory networks to the differential expression analysis and then obtain the weighted differentially expressed genes (weighted DEGs). A gene with a high weight means it regulates more target genes and thus holds more biological importance. Besides, we constructed gene coexpression networks for control and experiment groups, and the significantly differentially interacting structures encouraged us to design the corresponding Gene Ontology (GO) enrichment based on gene coexpression networks (GOEGCN). The GOEGCN considers the two-side distinction analysis between gene coexpression networks for control and experiment groups. The method allows us to study how the modulated coexpressed gene couples impact biological functions at a GO level.

Results

We modeled the binary classification with weighted DEGs for each subtype. The binary classifier could make a good prediction for an unseen sample, and the experimental results validated the effectiveness of our proposed approaches. The novel enriched GO terms based on GOEGCN for control and experiment groups of each subtype explain the specific biological function changes according to the two-side distinction of coexpression network structures to some extent.

Conclusion

The weighted DEGs contain biological importance derived from the gene regulatory network. Based on the weighted DEGs, five binary classifiers were learned and showed good performance concerning the “Sensitivity,” “Specificity,” “Accuracy,” “F1,” and “AUC” metrics. The GOEGCN with weighted DEGs for control and experiment groups presented a novel GO enrichment analysis results and the novel enriched GO terms would further unveil the changes of specific biological functions among all the BRCA subtypes to some extent. The R code in this research is available at https://github.com/yxchspring/GOEGCN_BRCA_Subtypes.

Integrative network analysis identifies an immune-based prognostic signature as the determinant for the mesenchymal subtype in epithelial ovarian cancer

BACKGROUND

Epithelial ovarian cancer (EOC) has been classified into four molecular subtypes, of which the mesenchymal subtype has the poorest survival. Our goal is to develop an immune-based prognostic signature by incorporating molecular subtypes for EOC patients.

METHODS

The gene expression profiles of EOC samples were collected from seven public datasets as well as an internal retrospective validation cohort, containing 1192 EOC patients. Network analysis was applied to integrate the mesenchymal modalities and immune signature to establish an immune-based prognostic signature for EOC (IPSEOC). The signature was trained and validated in eight independent datasets.

RESULTS

Seven immune genes were identified as key regulators of the mesenchymal subtype and were used to construct the IPSEOC. The IPSEOC significantly divided patients into high- and low-risk groups in discovery (OS: P < .0001), 6 independent public validation sets (OS: P = .04 to P = .002), and an internal retrospective validation cohort (OS: P = .025). Furthermore, pathway analysis revealed that differences between risk groups were mainly activation of mesenchymal-related signalling. Moreover, a significant correlation existed between the IPSEOC values versus clinical phenotypes including late tumor stages, drug resistance.

CONCLUSION

We propose an immune-based signature, which is a promising prognostic biomarker in ovarian cancer. Prospective studies are needed to further validate its analytical accuracy and test the clinical utility.

A 12-immune cell signature to predict relapse and guide chemotherapy for stage II colorectal cancer

The management of stage II colorectal cancer is still difficult. We aimed to construct a new immune cell-associated signature for prognostic evaluation and guiding chemotherapy in stage II colorectal cancer. We used the "Cell Type Identification by Estimating Relative Subsets of RNA Transcripts" (CIBERSORT) method to estimate the fraction of 22 immune cells by analyzing bulk tumor transcriptomes and a LASSO Cox regression model to select the prognostic immune cells. A 12-immune cell prognostic classifier, ISCRC, was built, which could successfully discriminate the high-risk patients in the training cohort (GSE39582: HR = 3.16, 95% CI: 1.85-5.40, P < 0.0001) and another independent cohorts (GSE14333: HR = 3.47, 95% CI: 1.18-10.15, P =0.0167). The receiver operating characteristic analysis revealed that the AUC of the ISCRC model was significantly greater than that of oncotypeDX model (0.7111 versus 0.5647, p=0.0152). We introduced the propensity score matching analysis to eliminate the selection bias; survival analysis showed relatively poor prognosis after chemotherapy in stage II CRC patients. Furthermore, a nomogram was built for clinicians and did well in the calibration plots. In conclusion, this immune cell-based signature could improve prognostic prediction and may help guide chemotherapy in stage II colorectal cancer patients.

Germline Sequencing DNA Repair Genes in 5545 Men With Aggressive and Nonaggressive Prostate Cancer

BACKGROUND

There is an urgent need to identify factors specifically associated with aggressive prostate cancer (PCa) risk. We investigated whether rare pathogenic, likely pathogenic, or deleterious (P/LP/D) germline variants in DNA repair genes are associated with aggressive PCa risk in a case-case study of aggressive vs nonaggressive disease.

METHODS

Participants were 5545 European-ancestry men, including 2775 nonaggressive and 2770 aggressive PCa cases, which included 467 metastatic cases (16.9%). Samples were assembled from 12 international studies and germline sequenced together. Rare (minor allele frequency < 0.01) P/LP/D variants were analyzed for 155 DNA repair genes. We compared single variant, gene-based, and DNA repair pathway-based burdens by disease aggressiveness. All statistical tests are 2-sided.

RESULTS

BRCA2 and PALB2 had the most statistically significant gene-based associations, with 2.5% of aggressive and 0.8% of nonaggressive cases carrying P/LP/D BRCA2 alleles (odds ratio [OR] = 3.19, 95% confidence interval [CI] = 1.94 to 5.25, P = 8.58 × 10-7) and 0.65% of aggressive and 0.11% of nonaggressive cases carrying P/LP/D PALB2 alleles (OR = 6.31, 95% CI = 1.83 to 21.68, P = 4.79 × 10-4). ATM had a nominal association, with 1.6% of aggressive and 0.8% of nonaggressive cases carrying P/LP/D ATM alleles (OR = 1.88, 95% CI = 1.10 to 3.22, P = .02). In aggregate, P/LP/D alleles within 24 literature-curated candidate PCa DNA repair genes were more common in aggressive than nonaggressive cases (carrier frequencies = 14.2% vs 10.6%, respectively; P = 5.56 × 10-5). However, this difference was non-statistically significant (P = .18) on excluding BRCA2, PALB2, and ATM. Among these 24 genes, P/LP/D carriers had a 1.06-year younger diagnosis age (95% CI = -1.65 to 0.48, P = 3.71 × 10-4).

CONCLUSIONS

Risk conveyed by DNA repair genes is largely driven by rare P/LP/D alleles within BRCA2, PALB2, and ATM. These findings support the importance of these genes in both screening and disease management considerations.

Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis

OBJECTIVES

To investigate the association between CT imaging traits and texture metrics with proteomic data in patients with high-grade serous ovarian cancer (HGSOC).

METHODS

This retrospective, hypothesis-generating study included 20 patients with HGSOC prior to primary cytoreductive surgery. Two readers independently assessed the contrast-enhanced computed tomography (CT) images and extracted 33 imaging traits, with a third reader adjudicating in the event of a disagreement. In addition, all sites of suspected HGSOC were manually segmented texture features which were computed from each tumor site. Three texture features that represented intra- and inter-site tumor heterogeneity were used for analysis. An integrated analysis of transcriptomic and proteomic data identified proteins with conserved expression between primary tumor sites and metastasis. Correlations between protein abundance and various CT imaging traits and texture features were assessed using the Kendall tau rank correlation coefficient and the Mann-Whitney U test, whereas the area under the receiver operating characteristic curve (AUC) was reported as a metric of the strength and the direction of the association. P values < 0.05 were considered significant.

RESULTS

Four proteins were associated with CT-based imaging traits, with the strongest correlation observed between the CRIP2 protein and disease in the mesentery (p < 0.001, AUC = 0.05). The abundance of three proteins was associated with texture features that represented intra-and inter-site tumor heterogeneity, with the strongest negative correlation between the CKB protein and cluster dissimilarity (p = 0.047, τ = 0.326).

CONCLUSION

This study provides the first insights into the potential associations between standard-of-care CT imaging traits and texture measures of intra- and inter-site heterogeneity, and the abundance of several proteins.

KEY POINTS

• CT-based texture features of intra- and inter-site tumor heterogeneity correlate with the abundance of several proteins in patients with HGSOC. • CT imaging traits correlate with protein abundance in patients with HGSOC.

Platinum-Based Chemotherapy in Metastatic Prostate Cancer With DNA Repair Gene Alterations

PURPOSE

Alterations in DNA damage repair (DDR) genes occur in up to 25% of patients with metastatic castration-resistant prostate cancer (mCRPC) and may sensitize to platinum chemotherapy. We aimed to evaluate the efficacy of platinum-based chemotherapy in DDR-mutant (DDRmut) mCRPC.

METHODS

We assessed response to platinum chemotherapy based on DDR gene alteration status in men with mCRPC who underwent tumor and germline genomic profiling. Patients with deleterious alterations in a gene panel that included BRCA2, BRCA1, ATM, PALB2, FANCA, and CDK12 were considered DDRmut.

RESULTS

A total of 109 patients with mCRPC received platinum-based chemotherapy between October 2013 and July 2018. Sixty-four of 109 patients were taxane refractory and poly (ADP-ribose) polymerase inhibitor (PARPi) naïve. Within this subset, DDRmut was found in 16/64 patients (25%) and was associated with an increased likelihood of achieving a prostate-specific antigen (PSA) decline of 50% or more from baseline (PSA50; odds ratio, 7.0; 95% CI, 1.9 to 29.2). Time on platinum chemotherapy tended to be longer in the DDRmut group (median, 3.0 v 1.6 months; hazard ratio, 0.55, 95% CI, 0.29 to 1.24). No difference in survival was detected. Of 8 patients with DDRmut disease who received platinum-based therapy after a PARPi, 3/7 evaluable patients had radiographic partial response or stable disease, and 2/7 had a PSA50 response. None of 4 patients with ATM mutations had platinum responses regardless of prior PARPi exposure.

CONCLUSION

Patients with DDRmut disease had better response to platinum-based chemotherapy, suggesting that DDR status warrants prospective validation as a potential biomarker for patient selection. Responses to platinum chemotherapy were observed in BRCA-altered prostate cancer after PARPi progression. Additional studies are needed to determine the predictive role of individual genes on platinum sensitivity in the context of other clinical and genomic factors.

Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing

Chromothripsis is a mutational phenomenon characterized by massive, clustered genomic rearrangements that occurs in cancer and other diseases. Recent studies in selected cancer types have suggested that chromothripsis may be more common than initially inferred from low-resolution copy-number data. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we analyze patterns of chromothripsis across 2,658 tumors from 38 cancer types using whole-genome sequencing data. We find that chromothripsis events are pervasive across cancers, with a frequency of more than 50% in several cancer types. Whereas canonical chromothripsis profiles display oscillations between two copy-number states, a considerable fraction of events involve multiple chromosomes and additional structural alterations. In addition to non-homologous end joining, we detect signatures of replication-associated processes and templated insertions. Chromothripsis contributes to oncogene amplification and to inactivation of genes such as mismatch-repair-related genes. These findings show that chromothripsis is a major process that drives genome evolution in human cancer.

Prediction of Poor Response to Neoadjuvant Chemoradiation in Patients With Rectal Cancer Using a DNA Repair Deregulation Score: Picking the Losers Instead of the Winners

BACKGROUND

Patients with rectal cancer may undergo neoadjuvant chemoradiation even in early stages in an attempt to achieve complete clinical response and undergo organ preservation. However, prediction of tumor response is unavailable. In this setting, accurate identification of poor responders could spare patients with early stage disease from potentially unnecessary chemoradiation.

OBJECTIVE

This study focused on development/test of a score based on DNA repair gene expression to predict response to neoadjuvant chemoradiation in patients with rectal cancer.

DESIGN

Pretreatment biopsy samples from patients with rectal cancer undergoing neoadjuvant chemoradiation were collected and underwent gene expression analysis using RNA-Seq (test cohort). A score was constructed using 8 differentially expressed DNA repair genes between good (complete clinical) and poor responders (incomplete clinical) to treatment. The score was validated in 2 independent cohorts of patients undergoing similar treatment strategies and using quantitative polymerase chain reaction and microarray gene expression data.

SETTINGS

This was a retrospective analysis of gene expression data from 3 independent institutions.

PATIENTS

Patients with rectal cancer undergoing neoadjuvant chemoradiation (50.4-54.0 Gy and 5-fluorouracil-based chemotherapy) were eligible. Patients with complete clinical response, complete pathological response, or ≤10% residual cancer cells were considered good responders. Patients with >10% residual cancer cells were considered poor responders. The test cohort included 25 patients (16 poor responders). Validation cohort 1 included 28 patients (18 poor responders), and validation cohort 2 included 46 patients (22 poor responders).

MAIN OUTCOMES MEASURES

Response was correlated with the DNA repair score calculated using the expression levels of 8 DNA repair genes. DNA repair score sensitivity, specificity, and positive and negative predictive values were determined in test and validation cohorts.

RESULTS

Poor responders had significantly lower DNA repair scores when compared with good responders across all 3 cohorts, regardless of the gene expression platform used. A low score correctly predicted poor response in 93%, 90%, and 71% in test, validation 1, and validation 2 cohorts.

LIMITATIONS

This study was limited by its small sample size, different gene expression platforms, and treatment regimens across different cohorts used.

CONCLUSIONS

A DNA repair gene score may predict patients likely to have poor response to chemoradiation. This score may be a relevant tool to be investigated in future studies focused on chemoradiation used in the context of organ preservation. See Video Abstract at http://links.lww.com/DCR/B104. PREDICCIÓN DE RESPUESTA DEFICIENTE A LA RADIO-QUIMIOTERAPIA NEOADYUVANTE EN PACIENTES CON CÁNCER RECTAL UTILIZANDO UNA PUNTUACIÓN DE DESREGULACIÓN DE REPARACIÓN DE ADN: ESCOGER LOS PERDEDORES EN LUGAR DE LOS GANADORES: Los pacientes con cáncer rectal pueden someterse a radio-quimioterapia neoadyuvante incluso en estadios tempranos en el intento por lograr una respuesta clínica completa y permitir una preservación de órgano. Sin embargo, aun no existen herramientas disponible para la prediccion de la respuesta tumoral al tratamiento. En este contexto, la identificación precisa de los tumores con mala respuesta al tratamiento podría evitar que los pacientes con enfermedad en estadio temprano sean sometidos a radio-quimioterapia potencialmente innecesaria.Desarrollo / testeo de una puntuación basada en la expresión genes reparadores del ADN para predecir la respuesta a la nCRT en pacientes con cáncer rectal.Se recogieron muestras de biopsia de pre-tratamiento de pacientes con cáncer rectal sometidos a radio-quimioterapia neoadyuvante y se les realizó un análisis de expresión génica utilizando RNAseq (cohorte de prueba). Se construyó una puntuación utilizando 8 genes de reparación de ADN expresados diferencialmente entre buenos (respuesta clinica completa) y pobres respondedores (respuesta clinica incompleta) al tratamiento. La puntuación se validó en 2 cohortes independientes de pacientes sometidos a estrategias de tratamiento similares y utilizando qPCR y datos de expresión de genes en chips ADN (biotecnología -microarrays).Análisis retrospectivo de los datos de expresión génica de 3 instituciones independientes.Fueron incluidos aquellos pacientes con cáncer rectal sometidos a radio-quimioterapia neoadyuvante (50,4-54 Gy y quimioterapia basada en 5FU). Los pacientes con respuesta clínica completa, respuesta patológica completa o ≤10% de células cancerosas residuales se consideraron buenos respondedores. Los pacientes con> 10% de células cancerosas residuales se consideraron de respuesta deficiente. La cohorte de prueba incluyó a 25 pacientes (16 respondedores pobres). La cohorte de validación n. ° 1 incluyó a 28 pacientes (18 respondedores pobres) y la cohorte de validación n. ° 2 incluyó a 46 pacientes (22 respondedores pobres).La respuesta se correlacionó con la puntuación de reparación de ADN calculada utilizando los niveles de expresión de 8 genes de reparación de ADN. La sensibilidad del puntaje de reparación del ADN, la especificidad, los valores predictivos positivos y negativos se determinaron en las cohortes de prueba y validación.Los malos respondedores tuvieron puntuaciones de reparación de ADN significativamente más bajas en comparación con los buenos respondedores en las 3 cohortes, independientemente de la plataforma de expresión génica utilizada. Una puntuación baja predijo correctamente una respuesta pobre en el 93%, 90% y 71% en las cohortes de prueba, validación n. ° 1 y validación n. ° 2, respectivamente.Pequeño tamaño de la muestra, diferentes plataformas de expresión génica y regímenes de tratamiento en diferentes cohortes utilizadas.La puntuacion basada en genes de reparación del ADN puede predecir los pacientes con respuesta pobre a la radio-quimioterapia. Esta puntuación puede ser una herramienta relevante para investigar en futuros estudios centrados en la radio-quimioterapia utilizada en el contexto de la preservación de órganos. Consulte Video Resumen en http://links.lww.com/DCR/B104. (Traducción-Dr. Xavier Delgadillo and Dr. Laura Melina Fernandez).

A Qualitative Transcriptional Signature for Predicting Recurrence Risk for High-Grade Serous Ovarian Cancer Patients Treated With Platinum-Taxane Adjuvant Chemotherapy

Resistance to platinum and taxane adjuvant chemotherapy (ACT) is the main cause of the recurrence and poor prognosis of high-grade serous ovarian cancer (HGS-OvCa) patients receiving platinum-taxane ACT after surgery. However, currently reported quantitative transcriptional signatures, which are commonly based on risk scores summarized from gene expression, are unsuitable for clinical application because of their high sensitivity to experimental batch effects and quality uncertainties of clinical samples. Using 226 samples of HGS-OvCa patients receiving platinum-taxane ACT in TCGA, we developed a qualitative transcriptional signature, consisting of four gene pairs whose within-samples relative expression orderings could robustly predict patient recurrence-free survival (RFS). In two independent test datasets, the predicted non-responders had significantly shorter RFS than the predicted responders (log-rank p < 0.05). In a test dataset containing data for patient pathological response state, the signature reclassified 12 out of 22 pathological complete response patients as non-responders and two out of 16 pathological non-complete response patients as responders. Notably, the 12 predicted non-responders in the pathological complete response group had significantly shorter RFS than the predicted responders (log-rank p = 0.0122). This qualitative transcriptional signature, which is insensitive to experimental batch effects and quality uncertainties of clinical samples, can individually identify HGS-OvCa patients who are more likely to benefit from platinum-taxane adjuvant chemotherapy.

Novel Model for Comprehensive Assessment of Robust Prognostic Gene Signature in Ovarian Cancer Across Different Independent Datasets

Different analytical methods or models can often find completely different prognostic biomarkers for the same cancer. In the study of prognostic molecular biomarkers of ovarian cancer (OvCa), different studies have reported a variety of prognostic gene signatures. In the current study, based on geometric concepts, the linearity-clustering phase diagram with integrated P-value (LCP) method was used to comprehensively consider three indicators that are commonly employed to estimate the quality of a prognostic gene signature model. The three indicators, namely, concordance index, area under the curve, and level of the hazard ratio were determined via calculation of the prognostic index of various gene signatures from different datasets. As evaluation objects, we selected 13 gene signature models (Cox regression model) and 16 OvCa genomic datasets (including gene expression information and follow-up data) from published studies. The results of LCP showed that three models were universal and better than other models. In addition, combining the three models into one model showed the best performance in all datasets by LCP calculation. The combination gene signature model provides a more reliable model and could be validated in various datasets of OvCa. Thus, our method and findings can provide more accurate prognostic biomarkers and effective reference for the precise clinical treatment of OvCa.

Reannotation and Analysis of Clinical and Chemotherapy Outcomes in the Ovarian Data Set From The Cancer Genome Atlas

PURPOSE

The ovarian cancer data set from The Cancer Genome Atlas integrates genomic and proteomic data with clinical annotations based on chart abstractions. We aimed to develop an algorithm to create a matching, more accessible clinical data set cataloging time to treatment failure (TTF) of sequential lines of treatment in patients with serous ovarian cancers.

MATERIALS AND METHODS

The master data set of 587 patients with serous ovarian cancer was condensed into a more homogeneous and clinically relevant population comprised of high-risk patients with both grade 3 cancers and stage III or IV disease, resulting in a subgroup of 450 patients. We quantified the TTF of different lines of therapy as well as different therapeutic combinations by extrapolating from the time of starting one therapy to the time of starting a subsequent therapy.

RESULTS

The overall survival (OS) of patients was highly related to platinum sensitivity status, with median OS times of 56.6, 27.0, and 11.6 months in patients who had platinum-sensitive, -resistant, or -refractory disease, respectively. In high-risk patients, the median TTFs were 14.8, 10.2, 5.7, and 4.1 months with the first, second, third, and fourth lines of chemotherapy, respectively. Patients with stable disease after first-line therapy had similar OS outcomes as patients with partial remissions (34.4 v 33.7 months, respectively).

CONCLUSION

This new data set enhances the clinical annotation by providing exploitable chemotherapy benefit data that can now be paired with genomic and proteomic data within The Cancer Genome Atlas data. The major determinant of OS in this study was platinum sensitivity status. TTF decreased with each successive line of therapy. However, patients who achieved only stable disease with first-line therapy had OS similar to those with partial remission.

A novel gene signature combination improves the prediction of overall survival in urinary bladder cancer

Objectives: Bladder carcinoma is a clinical heterogeneous disease, which is with significant variability of the prognosis and high risk of death. This revealed prominently the need to identify high-efficiency cancer characteristics to predict clinical prognosis.

Methods: Gene expression profiles of 93 bladder tumor patients from Gene Expression Omnibus data sets was performed in this study, along with 408 bladder tumor patients retrieved from The Cancer Genome Atlas database. The relationship of gene signature and overall survival was analyzed in the training cohort (n = 46). The validation for that was performed in an internal validation cohort (n = 47) and an external validation cohort (n = 408).

Results: Four genes (TMPRSS11E, SCEL, KRT78, TMEM185A) were identified by univariable and multivariable Cox regression analysis. According to a risk score on the bases on the four-gene signature, we grouped these patients in high-risk group and low-risk group with significantly different overall survival in the training series and successfully validated it in both the internal and external validation cohorts. Subsequent studies demonstrated that the four-gene expression risk score was independent of radical cystectomy stage, chemotherapy and lymph node status. Higher rates of FAT4 mutation and MACF1 mutation in bladder tumors with high risk score were found compared with tumors with low risk score. Gene set enrichment analysis revealed high-risk score was associated with some tumor progression and recurrence associated pathways.

Conclusions: This four-gene risk score might have potential clinical implications in the selection of high-risk urinary bladder cancer patients for aggressive therapy. The selected four genes might become potential therapeutic targets and diagnostic markers for urinary bladder cancer.

A Novel Scoring System for Pivotal Autophagy-Related Genes Predicts Outcomes after Chemotherapy in Advanced Ovarian Cancer Patients

BACKGROUND

In the clinical practice of ovarian cancer, the application of autophagy, an important regulator of carcinogenesis and chemoresistance, is still limited. This study aimed to establish a scoring system based on expression profiles of pivotal autophagy-related (ATG) genes in patients with stage III/IV ovarian cancer who received chemotherapy.

METHODS

Data of ovarian serous cystadenocarcinoma in The Cancer Genome Atlas (TCGA-OV) were used as training dataset. Two validation datasets comprised patients in a Chinese local database and a dataset from the Gene Expression Omnibus (GEO). ATG genes significantly (P < 0.1) associated with overall survival (OS) were selected and aggregated into an ATG scoring scale, of which the abilities to predict OS and recurrence-free survival (RFS) were examined.

RESULTS

Forty-three ATG genes were selected to develop the ATG score. In TCGA-OV, patients with lower ATG scores had better OS [HR = 0.41; 95% confidence interval (CI), 0.26-0.65; P < 0.001] and RFS [HR = 0.47; 95% CI, 0.27-0.82; P = 0.007]. After complete or partial remission to primary therapy, the rate of recurrence was 47.2% in the low-score group and 68.3% in the high-score group (odds ratio = 0.42; 95% CI, 0.18-0.92; P = 0.03). Such findings were verified in the two validation datasets.

CONCLUSIONS

We established a novel scoring system based on pivotal ATG genes, which accurately predicts the outcomes of patients with advanced ovarian cancer after chemotherapy.

IMPACT

The present ATG scoring system may provide a novel perspective and a promising tool for the development of personalized therapy in the future.

Drug Sensitivity Prediction Models Reveal a Link between DNA Repair Defects and Poor Prognosis in HNSCC

Head and neck squamous cell carcinoma (HNSCC) is characterized by the frequent manifestation of DNA crosslink repair defects. We established novel expression-based DNA repair defect markers to determine the clinical impact of such repair defects. Using hypersensitivity to the DNA crosslinking agents, mitomycin C and olaparib, as proxies for functional DNA repair defects in a panel of 25 HNSCC cell lines, we applied machine learning to define gene expression models that predict repair defects. The expression profiles established predicted hypersensitivity to DNA-damaging agents and were associated with mutations in crosslink repair genes, as well as downregulation of DNA damage response and repair genes, in two independent datasets. The prognostic value of the repair defect prediction profiles was assessed in two retrospective cohorts with a total of 180 patients with advanced HPV-negative HNSCC, who were treated with cisplatin-based chemoradiotherapy. DNA repair defects, as predicted by the profiles, were associated with poor outcome in both patient cohorts. The poor prognosis association was particularly strong in normoxic tumor samples and was linked to an increased risk of distant metastasis. In vitro, only crosslink repair-defective HNSCC cell lines are highly migratory and invasive. This phenotype could also be induced in cells by inhibiting rad51 in repair competent and reduced by DNA-PK inhibition. In conclusion, DNA crosslink repair prediction expression profiles reveal a poor prognosis association in HNSCC. SIGNIFICANCE: This study uses innovative machine learning-based approaches to derive models that predict the effect of DNA repair defects on treatment outcome in HNSCC.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/21/5597/F1.large.jpg.

Identification of a Prognostic Signature Associated With DNA Repair Genes in Ovarian Cancer

Introduction: Ovarian cancer is a highly malignant cancer with a poor prognosis. At present, there is no accurate strategy for predicting the prognosis of ovarian cancer. A prognosis prediction signature associated with DNA repair genes in ovarian cancer was explored in this study.

Methods: Gene expression profiles of ovarian cancer were downloaded from the GEO, UCSC, and TCGA databases. Cluster analysis, univariate analysis, and stepwise regression were used to identify DNA repair genes as potential targets and a prognostic signature for ovarian cancer survival prediction. The top genes were evaluated by immunohistochemical staining of ovarian cancer tissues, and external data were used to assess the signature.

Results: A total of 28 DNA repair genes were identified as being significantly associated with overall survival (OS) among patients with ovarian cancer. The results showed that high expression of XPC and RECQL and low expression of DMC1 were associated with poor prognosis in ovarian cancer patients. The prognostic signature combining 14 DNA repair genes was able to separate ovarian cancer samples associated with different OS times and showed robust performance for predicting survival (Training set: p < 0.0001, AUC = 0.759; Testing set: p < 0.0001, AUC = 0.76).

Conclusion: Our study identified 28 DNA repair genes related to the prognosis of ovarian cancer. Using some of these potential biomarkers, we constructed a prognostic signature to effectively stratify ovarian cancer patients with different OS rates, which may also serve as a potential therapeutic target in ovarian cancer.

Large-scale integrated analysis of ovarian cancer tumors and cell lines identifies an individualized gene expression signature for predicting response to platinum-based chemotherapy

Heterogeneity in chemotherapeutic response is directly associated with prognosis and disease recurrence in patients with ovarian cancer (OvCa). Despite the significant clinical need, a credible gene signature for predicting response to platinum-based chemotherapy and for guiding the selection of personalized chemotherapy regimens has not yet been identified. The present study used an integrated approach involving both OvCa tumors and cell lines to identify an individualized gene expression signature, denoted as IndividCRS, consisting of 16 robust chemotherapy-responsive genes for predicting intrinsic or acquired chemotherapy response in the meta-discovery dataset. The robust performance of this signature was subsequently validated in 25 independent tumor datasets comprising 2215 patients and one independent cell line dataset, across different technical platforms. The IndividCRS was significantly correlated with the response to platinum therapy and predicted the improved outcome. Moreover, the IndividCRS correlated with homologous recombination deficiency (HRD) and was also capable of discriminating HR-deficient tumors with or without platinum-sensitivity for guiding HRD-targeted clinical trials. Our results reveal the universality and simplicity of the IndividCRS as a promising individualized genomic tool to rapidly monitor response to chemotherapy and predict the outcome of patients with OvCa.

Biomarkers for Homologous Recombination Deficiency in Cancer

Defective DNA repair is a common hallmark of cancer. Homologous recombination is a DNA repair pathway of clinical interest due to the sensitivity of homologous recombination-deficient cells to poly-ADP ribose polymerase (PARP) inhibitors. The measurement of homologous recombination deficiency (HRD) in cancer is therefore vital to the appropriate design of clinical trials incorporating PARP inhibitors. However, methods to identify HRD in tumors are varied and controversial. Understanding existing and new methods to measure HRD is important to their appropriate use in clinical trials and practice. The aim of this review is to summarize the biology and clinical validation of current methods to measure HRD, to aid decision-making for patient stratification and translational research in PARP inhibitor trials. We discuss the current clinical development of PARP inhibitors, along with established indicators for HRD such as germline BRCA1/2 mutation status and clinical response to platinum-based therapy. We then examine newer assays undergoing clinical validation, including 1) somatic mutations in homologous recombination genes, 2) "genomic scar" assays using array-based comparative genomic hybridization (aCGH), single nucleotide polymorphism (SNP) analysis or mutational signatures derived from next-generation sequencing, 3) transcriptional profiles of HRD, and 4) phenotypic or functional assays of protein expression and localization. We highlight the strengths and weaknesses of each of these assays, for consideration during the design of studies involving PARP inhibitors.

miR-211 facilitates platinum chemosensitivity by blocking the DNA damage response (DDR) in ovarian cancer

The DNA damage response (DDR) is one of the most important mechanisms of platinum resistance in ovarian cancer. Some miRNAs have been identified to be involved in the regulatory network of DDR, thus the abnormal expression of miRNAs might affect platinum chemosensitivity in ovarian cancer. In this study, by assessing miRNAs simultaneously targeting a set of DDR genes that exhibited response to platinum, we found that miR-211 inhibited most of those genes, and proposed that miR-211 might affect the sensitivity of ovarian cancer cells to platinum by targeting multiple DDR genes and thereby determine the prognosis of ovarian cancer. To verify the hypothesis, we analyzed the association between miR-211 level and clinical prognosis, assessed the effect of miR-211 on DDR and platinum chemosensitivity, and explored the possible molecular mechanism. We revealed that miR-211 enhanced platinum chemosensitivity and was positively correlated with favorable outcomes in ovarian cancer patients. Many DDR genes including TDP1 were identified as targets of miR-211. In contrast, TDP1 suppressed DNA damage and platinum chemosensitivity. Moreover, the miR-211 level in tissues was shown to be associated with the good outcome of neoadjuvant chemotherapy and negatively correlated with the expression of TDP1. Conclusively, we demonstrated that miR-211 improves the prognosis of ovarian cancer patients by enhancing the chemosensitivity of cancer cells to platinum via inhibiting DDR gene expression, which provides an essential basis to identify novel treatment targets to block DDR effectively and improve chemosensitivity in ovarian cancer.

An Inflammation-Immunity Classifier of 11 Chemokines for Prediction of Overall Survival in Head and Neck Squamous Cell Carcinoma

BACKGROUND Chemokines are important in inflammation, immunity, tumor progression, and metastasis. The purpose of this research was to find an integrated-RNA signature of chemokine family genes to predict the survival prognosis in head and neck squamous carcinoma (HNSC) patients. MATERIAL AND METHODS Relevant data of 504 HNSC patients were extracted from The Cancer Genome Atlas (TCGA) database. Through analyzing RNA sequencing data, the univariate Cox model was used to identify chemokine family genes associated with survival and then to develop a multiple-RNA signature in the training set. The prediction value of this multiple-RNA signature was further verified in the validation and entire sets. The receiver operating characteristic curves were used to assess the predictive value of this multiple-RNA signature. RESULTS Eleven chemokines were included in this prognostic signature. Based on this 11-chemokine signature, we further categorized patients as high or low risk. Compared with low-risk patients, high-risk patients had shorter overall survival (OS) time in the training set [hazard ratio (HR)=3.497, 95% confidence interval (CI)=2.142-5.711, p<0.001], validation set (HR=3.575, 95% CI=1.988-6.390, p<0.001), and entire set (HR=3.416, 95% CI=2.363-4.939, p<0.001). This 11-chemokine signature was an independent prognostic factor for OS in these datasets (p<0.05). The AUC values for predicting overall survival within 48 months in the training, validation, and entire sets were 0.71, 0.69, and 0.69, respectively. CONCLUSIONS This 11-chemokine signature could serve as a reliable prognostic tool for HNSC patients and might be useful to guide individualized treatment or even gene target therapy for high-risk patients.

Integrative Network Analysis Reveals a MicroRNA-Based Signature for Prognosis Prediction of Epithelial Ovarian Cancer

Background

Epithelial ovarian cancer (EOC) is a heterogeneous disease, which has been recently classified into four molecular subtypes, of which the mesenchymal subtype exhibited the worst prognosis. We aimed to identify a microRNA- (miRNA-) based signature by incorporating the molecular modalities involved in the mesenchymal subtype for risk stratification, which would allow the identification of patients who might benefit from more rigorous treatments.

Method

We characterized the regulatory mechanisms underlying the mesenchymal subtype using network analyses integrating gene and miRNA expression profiles from The Cancer Genome Atlas (TCGA) cohort to identify a miRNA signature for prognosis prediction.

Results

We identified four miRNAs as the master regulators of the mesenchymal subtype and developed a risk score model. The 4-miRNA signature significantly predicted overall survival (OS) and progression-free survival (PFS) in discovery (p=0.004 and p=0.04) and two independent public datasets (GSE73582: OS, HR: 2.26 (1.26-4.05), p=0.005, PFS, HR: 2.03 (1.34-3.09), p<0.001; GSE25204: OS, HR: 3.07 (1.73-5.46), p<0.001, PFS, HR: 2.59 (1.72-3.88), p<0.001). Moreover, in multivariate analyses, the miRNA signature maintained as an independent prognostic predictor and achieved superior efficiency compared to the currently used clinical factors.

Conclusions

In conclusion, our network analysis identified a 4-miRNA signature which has prognostic value superior to currently reported clinical covariates. This signature warrants further testing and validation for use in clinical practice.