Focal Recurrent Copy Number Alterations Characterize Disease Relapse in High Grade Serous Ovarian Cancer Patients with Good Clinical Prognosis: A Pilot Study

Molecular and phenotypic characteristics influencing the degree of cytoreduction in high-grade serous ovarian carcinomas

BACKGROUND

High-grade serous ovarian carcinoma (HGSOC) is the deadliest ovarian cancer subtype, and survival relates to initial cytoreductive surgical treatment. The existing tools for surgical outcome prediction remain inadequate for anticipating the outcomes of the complex relationship between tumour biology, clinical phenotypes, co-morbidity and surgical skills. In this genotype-phenotype association study, we combine phenotypic markers with targeted DNA sequencing to discover novel biomarkers to guide the surgical management of primary HGSOC.

METHODS

Primary tumour tissue samples (n = 97) and matched blood from a phenotypically well-characterised treatment-naïve HGSOC patient cohort were analysed by targeted massive parallel DNA sequencing (next generation sequencing [NGS]) of a panel of 360 cancer-related genes. Association analyses were performed on phenotypic traits related to complete cytoreductive surgery, while logistic regression analysis was applied for the predictive model.

RESULTS

The positive influence of complete cytoreductive surgery (R0) on overall survival was confirmed (p = 0.003). Before surgery, low volumes of ascitic fluid, lower CA125 levels, higher platelet counts and relatively lower clinical stage at diagnosis were all indicators, alone and combined, for complete cytoreduction (R0). Mutations in either the chromatin remodelling SWI_SNF (p = 0.036) pathway or the histone H3K4 methylation pathway (p = 0.034) correlated with R0. The R0 group also demonstrated higher tumour mutational burden levels (p = 0.028). A predictive model was developed by combining two phenotypes and the mutational status of five genes and one genetic pathway, enabling the prediction of surgical outcomes in 87.6% of the cases in this cohort.

CONCLUSION

Inclusion of molecular biomarkers adds value to the pre-operative stratification of HGSOC patients. A potential preoperative risk stratification model combining phenotypic traits and single-gene mutational status is suggested, but the set-up needs to be validated in larger cohorts.

Targeting receptor tyrosine kinases in ovarian cancer: Genomic dysregulation, clinical evaluation of inhibitors, and potential for combinatorial therapies

Epithelial ovarian cancer (EOC) remains one of the leading causes of cancer-related deaths among women worldwide. Receptor tyrosine kinases (RTKs) have long been sought as therapeutic targets for EOC, as they are frequently hyperactivated in primary tumors and drive disease relapse, progression, and metastasis. More recently, these oncogenic drivers have been implicated in EOC response to poly(ADP-ribose) polymerase (PARP) inhibitors and epigenome-interfering agents. This evidence revives RTKs as promising targets for therapeutic intervention of EOC. This review summarizes recent studies on the role of RTKs in EOC malignancy and the use of their inhibitors for clinical treatment. Our focus is on the ERBB family, c-Met, and VEGFR, as they are linked to drug resistance and targetable using commercially available drugs. The importance of these RTKs and their inhibitors is highlighted by their impact on signal transduction and intratumoral heterogeneity in EOC and successful use as maintenance therapy in the clinic through suppression of the VEGF/VEGFR axis. Finally, the therapeutic potential of RTK inhibitors is discussed in the context of combinatorial targeting via co-inhibiting proliferative and anti-apoptotic pathways, epigenomic/transcriptional programs, and harnessing the efficacy of PARP inhibitors and programmed cell death 1/ligand 1 immune checkpoint therapies.

Loss of dystrophin is common in uterine leiomyosarcoma: a potential biomarker for clinical application

Uterine leiomyosarcoma (LMS) is a deadly disease with high rates of recurrence and a poor prognosis. Its tumorigenesis remains largely unknown, and no specific biomarkers can be used for the differential diagnosis of LMS from other mimics. Recent whole-genome studies revealed a loss of dystrophin is common in LMS, especially in uterine LMS. To investigate the expression pattern of dystrophin expression across different types of uterine smooth muscle tumors, immunohistochemistry was performed, including usual-type leiomyoma, fumarate hydratase-deficient leiomyoma, leiomyoma with bizarre nuclei, conventional LMS, and normal myometrium for this study. To further evaluate the genomic change in dystrophin gene region, whole-genome sequencing in 10 LMS cases were analyzed. Dystrophin expression was detected in 94% (45/48) of myometrium, 97% (34/35) of usual-type leiomyoma, 84% (26/31) of fumarate hydratase-deficient leiomyoma, 60% (12/20) of leiomyoma with bizarre nuclei, and 18% (6/34) of LMS. Loss of dystrophin expression was significantly different between benign and malignant tumors (LMS cases counted as malignant only) (p < 0.01). Of note, copy number loss in the dystrophin genomic region was found in all 10 cases of LMS. Additionally, patients with dystrophin-positive LMS tend to have a better overall survival than patients with dystrophin-negative LMS.

A risk model of gene signatures for predicting platinum response and survival in ovarian cancer

Background

Ovarian cancer (OC) is the deadliest tumor in the female reproductive tract. And increased resistance to platinum-based chemotherapy represents the major obstacle in the treatment of OC currently. Robust and accurate gene expression models are crucial tools in distinguishing platinum therapy response and evaluating the prognosis of OC patients.

Methods

In this study, 230 samples from The Cancer Genome Atlas (TCGA) OV dataset were subjected to mRNA expression profiling, single nucleotide polymorphism (SNP), and copy number variation (CNV) analysis comprehensively to screen out the differentially expressed genes (DEGs). An SVM classifier and a prognostic model were constructed using the Random Forest algorithm and LASSO Cox regression model respectively via R. The Gene Expression Omnibus (GEO) database was applied as the validation set.

Results

Forty-eight differentially expressed genes (DEGs) were figured out through integrated analysis of gene expression, single nucleotide polymorphism (SNP), and copy number variation (CNV) data. A 10-gene classifier was constructed which could discriminate platinum-sensitive samples precisely with an AUC of 0.971 in the training set and of 0.926 in the GEO dataset (GSE638855). In addition, 8 optimal genes were further selected to construct the prognostic risk model whose predictions were consistent with the actual survival outcomes in the training cohort (p = 9.613e-05) and validated in GSE638855 (p = 0.04862). PNLDC1, SLC5A1, and SYNM were then identified as hub genes that were associated with both platinum response status and prognosis, which was further validated by the Fudan University Shanghai cancer center (FUSCC) cohort.

Conclusion

These findings reveal a specific risk model that could serve as effective biomarkers to identify patients’ platinum response status and predict survival outcomes for OC patients. PNLDC1, SLC5A1, and SYNM are the hub genes that may serve as potential biomarkers in OC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-022-00969-3.

Integrated histologic and molecular analysis of uterine leiomyosarcoma and 2 benign variants with nuclear atypia

Uterine leiomyosarcoma (LMS) is a rare but deadly disease. Due to poor understanding of the molecular and genetic causes of the disease, the diagnosis of LMS has been based primarily on histology. Nuclear atypia is one of hallmarks in LMS, however, it also occurs in 2 clinically benign variants, including smooth muscle tumors with fumarate hydratase alteration (SMT‐FH) and leiomyoma with bizarre nuclei (LM‐BN). In addition to nuclear atypia, many well recognized biomarkers used for LMS are also frequently overexpressed in LM‐BN, and the histogenesis and molecular natures for LM‐BN and LMS remain largely unknown. To characterize the molecular profiling of LMS, SMT‐FH, and LM‐BN, we performed integrated comprehensive genomic profiling including whole‐genome sequencing (WGS) and RNA sequencing and genomic microarray analyses to assess genome‐wide copy number alterations (CNAs) and immunohistochemistry (IHC) in all 3 tumor types. We found that both LM‐BN and LMS showed genomic instability and harbored extensive CNAs throughout the whole genome. By contrast, the SMT‐FH presented its characteristic 1q43‐44 deletions in all cases tested, with minimal CNAs in the rest of genomic regions. Further analyses revealed that LMS and LM‐BN groups showed similar patterns of CNAs that are tended to cluster together and separated from the SMT‐FH group. The integrated molecular profiling enabled the detection of novel and traditional biomarkers and showed excellent discrimination between LM‐BN and LMS. Our study suggests that LM‐BN, despite having similar nuclear atypia to SMT‐FH, showed similar genomic instability but distinct genomic alterations with its malignant counterpart of LMS. The integrated molecular profiling is of clinical importance in characterizing these rare uterine smooth muscle tumors.

Detection of Ovarian Cancer through Exhaled Breath by Electronic Nose: A Prospective Study

BACKGROUND

Diagnostic methods for the early identification of ovarian cancer (OC) represent an unmet clinical need, as no reliable diagnostic tools are available. Here, we tested the feasibility of electronic nose (e-nose), composed of ten metal oxide semiconductor (MOS) sensors, as a diagnostic tool for OC detection.

METHODS

Women with suspected ovarian masses and healthy subjects had volatile organic compounds analysis of the exhaled breath using e-nose.

RESULTS

E-nose analysis was performed on breath samples collected from 251 women divided into three groups: 86 OC cases, 51 benign masses, and 114 controls. Data collected were analyzed by Principal Component Analysis (PCA) and K-Nearest Neighbors' algorithm (K-NN). A first 1-K-NN (cases vs. controls) model has been developed to discriminate between OC cases and controls; the model performance tested in the prediction gave 98% of sensitivity and 95% of specificity, when the strict class prediction was applied; a second 1-K-NN (cases vs. controls + benign) model was built by grouping the non-cancer groups (controls + benign), thus considering two classes, cases and controls + benign; the model performance in the prediction was of 89% for sensitivity and 86% for specificity when the strict class prediction was applied.

CONCLUSIONS

Our preliminary results suggested the potential role of e-nose for the detection of OC. Further studies aiming to test the potential adoption of e-nose in the early diagnosis of OC are needed.

Pharmacogenomics and Personalized Medicine

Pharmacogenomics is one of the emerging approaches to precision medicine, tailoring drug selection and dosing to the patient’s genetic features. In recent years, several pharmacogenetic guidelines have been published by international scientific consortia, but the uptake in clinical practice is still poor. Many coordinated international efforts are ongoing in order to overcome the existing barriers to pharmacogenomic implementation. On the other hand, existing validated pharmacogenomic markers can explain only a minor part of the observed clinical variability in the therapeutic outcome. New investigational approaches are warranted, including the study of the pharmacogenomic role of the immune system genetics and of previously neglected rare genetic variants, reported to account for a large part of the inter-individual variability in drug metabolism. In this Special Issue, we collected a series of articles covering many aspects of pharmacogenomics. These include clinical implementation of pharmacogenomics in clinical practice, development of tools or infrastractures to support this process, research of new pharmacogenomics markers to increase drug efficacy and safety, and the impact of rare genetic variants in pharmacogenomics.