Mass Spectrometry-based Proteomics of Epithelial Ovarian Cancers: a Clinical Perspective

Clinical Practice of Poly (ADP-Ribose) Polymerase Inhibitors for Maintenance Treatment of Platinum-Sensitive Recurrent Ovarian Cancer in China

Clinical trials of three poly (ADP-ribose) polymerase (PARP) inhibitors, olaparib, niraparib and fuzuloparib, in platinum-sensitive recurrent ovarian cancer (PSR OC) in China showed that PARP inhibitors improved progression-free survival and achieved an all-comer indication in this population. We reviewed the efficacy and safety of these PARP inhibitors in patient populations studied in clinical trials and highlighted the positive role of PARP inhibitors in improving patient outcomes using clinical trials and real-world studies conducted in China. This article also discusses the issues encountered in clinical practice and how to evaluate the different indications for PSR OC in China and abroad.

AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis

BACKGROUND

Emerging evidence underscores the potential application of artificial intelligence (AI) in discovering noninvasive blood biomarkers. However, the diagnostic value of AI-derived blood biomarkers for ovarian cancer (OC) remains inconsistent.

OBJECTIVE

We aimed to evaluate the research quality and the validity of AI-based blood biomarkers in OC diagnosis.

METHODS

A systematic search was performed in the MEDLINE, Embase, IEEE Xplore, PubMed, Web of Science, and the Cochrane Library databases. Studies examining the diagnostic accuracy of AI in discovering OC blood biomarkers were identified. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-AI tool. Pooled sensitivity, specificity, and area under the curve (AUC) were estimated using a bivariate model for the diagnostic meta-analysis.

RESULTS

A total of 40 studies were ultimately included. Most (n=31, 78%) included studies were evaluated as low risk of bias. Overall, the pooled sensitivity, specificity, and AUC were 85% (95% CI 83%-87%), 91% (95% CI 90%-92%), and 0.95 (95% CI 0.92-0.96), respectively. For contingency tables with the highest accuracy, the pooled sensitivity, specificity, and AUC were 95% (95% CI 90%-97%), 97% (95% CI 95%-98%), and 0.99 (95% CI 0.98-1.00), respectively. Stratification by AI algorithms revealed higher sensitivity and specificity in studies using machine learning (sensitivity=85% and specificity=92%) compared to those using deep learning (sensitivity=77% and specificity=85%). In addition, studies using serum reported substantially higher sensitivity (94%) and specificity (96%) than those using plasma (sensitivity=83% and specificity=91%). Stratification by external validation demonstrated significantly higher specificity in studies with external validation (specificity=94%) compared to those without external validation (specificity=89%), while the reverse was observed for sensitivity (74% vs 90%). No publication bias was detected in this meta-analysis.

CONCLUSIONS

AI algorithms demonstrate satisfactory performance in the diagnosis of OC using blood biomarkers and are anticipated to become an effective diagnostic modality in the future, potentially avoiding unnecessary surgeries. Future research is warranted to incorporate external validation into AI diagnostic models, as well as to prioritize the adoption of deep learning methodologies.

TRIAL REGISTRATION

PROSPERO CRD42023481232; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023481232.

Evaluating the effectiveness of whole blood plasma versus protein precipitates in ovarian cancer detection through infrared spectroscopy

Early diagnosis of ovarian cancer remains challenging due to the absence of effective screening tests. The success of treatment and 5 year survival rates are significantly reliant on identifying the disease at a non-advanced stage, which highlights the urgent need for novel early detection and diagnostic approaches. Blood-based spectroscopic techniques, combined with chemometrics, have the potential to be used as tools for screening and diagnostic purposes in this context. In this study, we utilised attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy to analyse blood plasma samples from benign (n = 15) and ovarian cancer (n = 15) cases. We conducted multivariate discrimination models to compare the results in terms of sensitivity, specificity, and diagnostic accuracy when using either plasmatic protein precipitates or whole plasma to distinguish between benign and ovarian cancer. Notably, diagnostic accuracy values of 96% (sensitivity and specificity of 96%) and 92% (sensitivity and specificity of 88% and 96%, respectively) were achieved for the protein precipitates and whole plasma datasets respectively using genetic algorithms with linear and quadratic discriminant analysis. Furthermore, this methodology demonstrated its capability to categorise samples within the ovarian cancer class, distinguishing between early stage (FIGO I) and advanced stage (FIGO II-III), with excellent accuracy exceeding 97% for protein precipitate dataset. These findings highlight the utilisation of a specific class of biomolecules in a proteomic-like approach based on infrared spectroscopy and chemometrics for detecting ovarian cancer using blood plasma samples.

Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases

Thyroid diseases, including functional and neoplastic diseases, bring a huge burden to people's health. Therefore, a timely and accurate diagnosis is necessary. Mass spectrometry (MS) based multi-omics has become an effective strategy to reveal the complex biological mechanisms of thyroid diseases. The exponential growth of biomedical data has promoted the applications of machine learning (ML) techniques to address new challenges in biology and clinical research. In this review, we presented the detailed review of applications of ML for MS-based multi-omics in thyroid disease. It is primarily divided into two sections. In the first section, MS-based multi-omics, primarily proteomics and metabolomics, and their applications in clinical diseases are briefly discussed. In the second section, several commonly used unsupervised learning and supervised algorithms, such as principal component analysis, hierarchical clustering, random forest, and support vector machines are addressed, and the integration of ML techniques with MS-based multi-omics data and its application in thyroid disease diagnosis is explored.

Development and Validation of a Predictive Model for Resistance to Platinum-Based Chemotherapy in Patients with Ovarian Cancer through Proteomic Analysis

Platinum resistance in ovarian cancer poses a significant challenge, substantially impacting patient outcomes. Developing an accurate predictive model is crucial for improving clinical decision-making and guiding treatment strategies. Proteomic data from 217 high-grade serous ovarian cancer (HGSOC) biospecimens obtained from JHU, PNNL, and PTRC were used to construct a prediction model for identifying individuals who are resistant to platinum-based chemotherapy. A total of 6437 common proteins were detected across all data sets, with 26 proteins overlapping between the development cohorts JHU and PNNL. Using LASSO and logistic regression analysis, a six-protein model (P31323_PRKAR2B, Q13309_SKP2, Q14997_PSME4, Q6ZRP7_QSOX2, Q7LGA3_HS2ST1, and Q7Z2Z2_EFL1) was developed, which accurately predicted platinum resistance, with an AUC of 0.964 (95% CI, 0.929-0.999). Internal validation by resampling resulted in a C-index of 0.972 (95% CI 0.894-0.988). External validation performed on the PTRC cohort achieved an AUC of 0.855 (95% CI 0.748-0.963). Calibration curves showed good consistency, and DCA indicated superior clinical utility. The model also performed well in predicting PFS and OS at various time points. Based on these proteins, our predictive model can precisely predict platinum response and survival outcomes in HGSOC patients, which can assist clinicians in promptly identifying potentially platinum-resistant individuals.

Proteomic landscape of epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is a deadly disease with limited diagnostic biomarkers and therapeutic targets. Here we conduct a comprehensive proteomic profiling of ovarian tissue and plasma samples from 813 patients with different histotypes and therapeutic regimens, covering the expression of 10,715 proteins. We identify eight proteins associated with tumor malignancy in the tissue specimens, which are further validated as potential circulating biomarkers in plasma. Targeted proteomics assays are developed for 12 tissue proteins and 7 blood proteins, and machine learning models are constructed to predict one-year recurrence, which are validated in an independent cohort. These findings contribute to the understanding of EOC pathogenesis and provide potential biomarkers for early detection and monitoring of the disease. Additionally, by integrating mutation analysis with proteomic data, we identify multiple proteins related to DNA damage in recurrent resistant tumors, shedding light on the molecular mechanisms underlying treatment resistance. This study provides a multi-histotype proteomic landscape of EOC, advancing our knowledge for improved diagnosis and treatment strategies.

Integration of proteomics in the molecular tumor board

Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in-depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor-driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor-driving molecular characteristics of the tissue. Technological advancements in mass spectrometry-based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi-omic data integration.

The Mechanism of Tigecycline Resistance in Acinetobacter baumannii under Sub-Minimal Inhibitory Concentrations of Tigecycline

The presence of sub-minimal inhibitory concentration (sub-MIC) antibiotics in our environment is widespread, and their ability to induce antibiotic resistance is inevitable. Acinetobacter baumannii, a pathogen known for its strong ability to acquire antibiotic resistance, has recently shown clinical resistance to the last-line antibiotic tigecycline. To unravel the complex mechanism of A. baumannii drug resistance, we subjected tigecycline-susceptible, -intermediate, and -mildly-resistant strains to successive increases in sub-MIC tigecycline and ultimately obtained tigecycline-resistant strains. The proteome of both key intermediate and final strains during the selection process was analyzed using nanoLC-MS/MS. Among the more than 2600 proteins detected in all strains, we found that RND efflux pump AdeABC was associated with the adaptability of A. baumannii to tigecycline under sub-MIC pressure. qRT-PCR analysis also revealed higher expression of AdeAB in strains that can quickly acquire tigecycline resistance compared with strains that displayed lower adaptability. To validate our findings, we added an efflux pump inhibitor, carbonyl cyanide m-chlorophenyl hydrazine (CCCP), to the medium and observed its ability to inhibit tigecycline resistance in A. baumannii strains with quick adaptability. This study contributes to a better understanding of the mechanisms underlying tigecycline resistance in A. baumannii under sub-MIC pressure.

Quantification of putative ovarian cancer serum protein biomarkers using a multiplexed targeted mass spectrometry assay

BACKGROUND

Ovarian cancer is the most lethal gynecologic malignancy in women, and high-grade serous ovarian cancer (HGSOC) is the most common subtype. Currently, no clinical test has been approved by the FDA to screen the general population for ovarian cancer. This underscores the critical need for the development of a robust methodology combined with novel technology to detect diagnostic biomarkers for HGSOC in the sera of women. Targeted mass spectrometry (MS) can be used to identify and quantify specific peptides/proteins in complex biological samples with high accuracy, sensitivity, and reproducibility. In this study, we sought to develop and conduct analytical validation of a multiplexed Tier 2 targeted MS parallel reaction monitoring (PRM) assay for the relative quantification of 23 putative ovarian cancer protein biomarkers in sera.

METHODS

To develop a PRM method for our target peptides in sera, we followed nationally recognized consensus guidelines for validating fit-for-purpose Tier 2 targeted MS assays. The endogenous target peptide concentrations were calculated using the calibration curves in serum for each target peptide. Receiver operating characteristic (ROC) curves were analyzed to evaluate the diagnostic performance of the biomarker candidates.

RESULTS

We describe an effort to develop and analytically validate a multiplexed Tier 2 targeted PRM MS assay to quantify candidate ovarian cancer protein biomarkers in sera. Among the 64 peptides corresponding to 23 proteins in our PRM assay, 24 peptides corresponding to 16 proteins passed the assay validation acceptability criteria. A total of 6 of these peptides from insulin-like growth factor-binding protein 2 (IBP2), sex hormone-binding globulin (SHBG), and TIMP metalloproteinase inhibitor 1 (TIMP1) were quantified in sera from a cohort of 69 patients with early-stage HGSOC, late-stage HGSOC, benign ovarian conditions, and healthy (non-cancer) controls. Confirming the results from previously published studies using orthogonal analytical approaches, IBP2 was identified as a diagnostic biomarker candidate based on its significantly increased abundance in the late-stage HGSOC patient sera compared to the healthy controls and patients with benign ovarian conditions.

CONCLUSIONS

A multiplexed targeted PRM MS assay was applied to detect candidate diagnostic biomarkers in HGSOC sera. To evaluate the clinical utility of the IBP2 PRM assay for HGSOC detection, further studies need to be performed using a larger patient cohort.