High-grade serous ovarian cancer (HGSOC) is the deadliest gynecological cancer, accounting for nearly 80% of ovarian cancer deaths. Marked by TP53 gene mutations, HGSOC leads to extensive copy number variations (CNVs) and significant genomic instability. Despite initial positive treatment responses, HGSOC tumors frequently relapse as drug-resistant cell populations expand, complicating the development of targeted therapies due to the absence of common oncogenic mutations. Understanding the mechanisms behind HGSOC chemo-resistance is challenging due to the complexity of genetic aberrations and tumor heterogeneity.
The main goal of this project was to analyze the impact of CNVs on the phenotype of patients with HGSOC using machine learning (ML) approaches on single-cell RNA sequencing (scRNAseq) data. Achieving high resolution was crucial, and for this purpose, scRNAseq data was utilized, enabling detailed characterization of heterogeneous cancer cell populations and allowing the inference of CNV profiles from transcriptomic data.
To begin, given the complexity of deep learning models, the first objective consisted on the development of a baseline method , such as a multivariate linear model, necessary to procure a controlled reference set of relationships between individual CNVs and transcriptomic changes. Once a reference framework was established for understanding how CNVs influence gene expression, the next objective was to detect which combinatorial CNV patterns were relevant to predict a cell’s complex phenotype using a more complex ML model. This involved developing models that could reconstruct CNV profiles from gene expression data and vice versa, ensuring reliable predictions and interpretations. The use of variational autoencoder (VAE) models played a key role here, as they effectively captured the complex relationships between CNVs and gene expression, enabling the identification of CNV patterns that were predictive of specific phenotypes. Another critical aspect was interpreting the latent space of the models to link CNV patterns with phenotypic traits of cells, enhancing the understanding of resistance mechanisms. By analyzing the latent representations generated by the VAE models, the project aimed to uncover secondary targets crucial in driving resistance to chemotherapy in HGSOC tumors.
Validation of the associations found in the VAE models was also crucial. For this purpose, organoids derived from patient tumors were analyzed to ensure that the findings were applicable in real-world settings. By integrating data from various sources and validating the results through multiple methods, the project aspired to translate its findings into practical therapeutic strategies.
Through these comprehensive objectives, the project sought to address the critical challenge of chemo-resistance in HGSOC, offering new paths for treatment and improving outcomes for patients affected by this aggressive cancer.
