Ovarian cancer is the most lethal form of gynaecological malignancy, with almost 314,000 women diagnosed with ovarian cancer in 20201. In addition, ovarian cancer is the 7th most common cancer, and the 8th most common cancer related cause of death for women, worldwide2. The five-year survival rate for late-stage ovarian cancer is estimated to be lower than 41%, and despite the survival rate for all solid tumours improving dramatically within the last 50 years, the five-year survival rate for ovarian cancer has not improved since 19803,4.

In modern times, the prognosis of a patient with ovarian cancer is determined by evaluating the stage of the cancer, along with the age and general health of the patient5. However, this is only an estimate of the prognosis of the patient. Is there any way in which we can improve the prediction of prognoses for ovarian cancer patients?

Fortunately, there is some hope that lies within the integration of DNA microarrays and clinical information.

DNA microarrays are a set of human gene sequences arranged in a grid, where the expression level of each gene is determined by measuring the interaction between the DNA molecules on the array, and the RNA molecules in the sample6. By measuring the expression levels of each gene, we can determine whether there are any statistically significant differences between the expression levels of genes in one sample, and genes in another sample. Using this information, we can determine whether certain genes are indicative of patient prognosis, or a certain type of disease.

On the other hand, clinical information consists of variables such as age of the patient and tumour stage. More specifically, tumour size has been found to be a good indicator for the prognosis of patients with ovarian cancer7.

Therefore, could we combine clinical information with genomic data, to improve prediction of prognosis for patients with ovarian cancer?

Several studies have found that integrating clinical information with genomic data has resulted in an improvement in model prediction. A study published in 2012 observed a synergetic effect exhibited by the integration of clinical information and gene expression data, for predicting outcomes for patients with breast cancer8. More recently, another study published in 2022 found that integrating clinical and genomic data resulted in an improved performance in predicting endometrial cancer recurrence, over models using clinical data alone9.

In conclusion, the aim of this project is to observe whether prediction of prognosis for ovarian cancer patients can be improved, by integrating clinical information with genomic data. Using a dataset from The Cancer Genome Atlas, and the R program for analysis, the results of this project have identified some future directions for research in this topic. Ultimately, this may lead to the development of an analytical tool that decides whether a patient would have a good long term survival, or a poor long term survival, using their clinical information and gene expression data.

  1. Ovarian cancer statistics: World cancer research fund international (2022) WCRF International. World Cancer Research Fund International. Available at: https://www.wcrf.org/cancer-trends/ovarian-cancer-statistics/ (Accessed: February 20, 2023).
  2. Ferlay, J. et al. (2014) “Cancer incidence and mortality worldwide: Sources, methods and major patterns in Globocan 2012,” International Journal of Cancer, 136(5). Available at: https://doi.org/10.1002/ijc.29210.
  3. Ovarian cancer stages, survival rate and prognosis (2023) Ovarian Cancer Research Alliance. Ovarian Cancer Research Alliance Inc. Available at: https://ocrahope.org/get-the-facts/staging/ (Accessed: February 20, 2023).
  4. Vaughan, S. et al. (2011) “Rethinking ovarian cancer: Recommendations for improving outcomes,” Nature Reviews Cancer, 11(10), pp. 719–725. Available at: https://doi.org/10.1038/nrc3144.
  5. Ovarian cancer: Causes, symptoms & treatments (2023) Cancer Council. Cancer Council. Available at: https://www.cancer.org.au/cancer-information/types-of-cancer/ovarian-cancer (Accessed: February 21, 2023).
  6. Embl-Ebi (2023) Microarrays, Microarrays | Functional genomics II. EMBL. Available at: https://www.ebi.ac.uk/training/online/courses/functional-genomics-ii-common-technologies-and-data-analysis-methods/microarrays/ (Accessed: February 21, 2023).
  7. Wu, L. et al. (2022) “Tumor size is an independent prognostic factor for stage I ovarian clear cell carcinoma: A large retrospective cohort study of 1,000 patients,” Frontiers in Oncology, 12. Available at: https://doi.org/10.3389/fonc.2022.862944.
  8. van Vliet, M.H. et al. (2012) “Integration of clinical and gene expression data has a synergetic effect on predicting breast cancer outcome,” PLoS ONE, 7(7). Available at: https://doi.org/10.1371/journal.pone.0040358.
  9. Gonzalez-Bosquet, J., Gabrilovich, S., McDonald, M. E., Smith, B. J., Leslie, K. K., Bender, D. D., Goodheart, M. J., & Devor, E. (2022). “Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence,” International journal of molecular sciences23(24), 16014. Available at: https://doi.org/10.3390/ijms232416014

Jecinta Jaarola
Curtin University

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