Identification of prostate cancer biomarkers for diagnosis and stratification of disease

  • Ammara Muazzam

Student thesis: Phd


Prostate cancer (PCa) is the second most common type of cancer in men worldwide, with an estimated 1,414,259 new cases in 2020. The commonly used blood test for prostate specific antigen (PSA) has been used for the detection of PCa in its early stages. PSA screening, diagnosis, and prognosis have aided in the clinical management of patients with PCa. On the other hand, the PSA test has been criticised for the possibility of overdiagnosis and hence overtreatment of patients with indolent disease. Regardless of this, the PSA test continues to be the gold standard for PCa early detection. This argument highlights the unmet need for non-invasive PCa biomarkers with increased sensitivity and specificity capable of discriminating benign from malignant disease and predicting therapy response. The goal of this work was to identify prospective biomarkers in the peripheral blood of PCa patients using cutting-edge analytical techniques and machine learning. Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) is a well-established data-independent acquisition (DIA) approach that requires prior knowledge of peptide mass spectrometry characteristics in order to extract relevant data on protein identity and quantification. By merging data from PCa patients with low, moderate, or high-grade prostate cancer and healthy controls, we were able to create a comprehensive PCa serum spectrum library containing thousands of peptides. Using the SWATH-MS method and this broad PCa spectral library, a 12-protein marker signature was identified. This implicated the complement/coagulation pathways and monocyte/macrophage development pathways in disease development. This signature was validated in two different patient cohorts and showed a constant ability in distinguishing between PCa patients and healthy individuals. Further, biomarker expression demonstrated a shift towards the levels seen in healthy individuals following treatment (radiation or prostatectomy). Additionally, nanoscale chromatography, which can take several hours per sample, is commonly used for proteome analysis in clinical research. In order to introduced automation and reduce run-time in proteomics research, we provided a high-throughput LC/MS/MS approach that utilises 1mm scale chromatography and requires around 15 minutes per sample. The reduced run time resulted in a 6-fold increase in productivity and excellent reproducibility when compared to nanoscale LC/MS/MS. Lastly, we took a multi-omics approach, another rapidly growing area of PCa research to develop improved biomarker panels (i.e., enhanced specificity) and contribute to a better understanding of PCa biology. In this study, we used a combination of lipid and protein measures and identified biomarker signatures that were capable of clearly distinguishing malignant from non-cancerous samples in our sample sets. Taken together, we propose a novel PCa blood-based spectral source and an automation approach for speedier proteomics sample processing, and we hope that they will be of use to researchers looking for PCa protein biomarkers in blood samples. Furthermore, to improve diagnosis in patients with localized and locally advanced PCa, our proteomics and multi ‘omics signatures would be helpful to facilitate diagnosis.
Date of Award1 Aug 2022
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorAnthony Whetton (Supervisor), Paul Townsend (Supervisor) & Nophar Geifman (Supervisor)


  • High throughput
  • Library generation
  • Lipidomics
  • Data integration
  • Multi'omics
  • Proteomics
  • Biomarkers
  • Clinical onset
  • Prostate cancer
  • Complement cascade

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