An improved understanding of cancer genomics through massively parallel sequencing

Pharmacogenomics and Big Data in medical oncology: developments and challenges

Medical oncology, through conventional chemotherapy as well as targeted drugs, remains an important component of cancer patient management, particularly for systemic disease. Despite advances in all areas of medical oncology, certain challenges persist in the form of drug resistance and severe normal tissue toxicity. These unwanted effects can be counteracted through a patient-tailored treatment approach, which in chemotherapy is translated as pharmacogenomics. This research field investigates the way genetic makeup influences a patient's response to various drugs with the aim to minimize trial-and-error associated with drug administration. The paper introduces the role, advances and challenges of pharmacogenomics, highlighting the importance of Big Data mining to reveal the mechanisms behind drug-gene pair interaction for better patient outcomes. International consortiums have prioritized their focus on the clinical implementation of pharmacogenomics while tackling the challenges ahead: data standardization, ethical aspects and the education of physicians and patients alike to comprehend the power of pharmacogenomics to transform medical oncology.

Cancer drug sensitivity estimation using modular deep Graph Neural Networks

Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drugs components that are tailored to the transcriptomic profile of a given primary tumor. The SMILES representation of molecules that is used by state-of-the-art drug-sensitivity models is not conducive for neural networks to generalize to new drugs, in part because the distance between atoms does not generally correspond to the distance between their representation in the SMILES strings. Graph-attention networks, on the other hand, are high-capacity models that require large training-data volumes which are not available for drug-sensitivity estimation. We develop a modular drug-sensitivity graph-attentional neural network. The modular architecture allows us to separately pre-train the graph encoder and graph-attentional pooling layer on related tasks for which more data are available. We observe that this model outperforms reference models for the use cases of precision oncology and drug discovery; in particular, it is better able to predict the specific interaction between drug and cell line that is not explained by the general cytotoxicity of the drug and the overall survivability of the cell line. The complete source code is available at https://zenodo.org/doi/10.5281/zenodo.8020945. All experiments are based on the publicly available GDSC data.

Core Competencies in Cancer Genomics for Healthcare Professionals: Results From a Systematic Literature Review and a Delphi Process

The continuous development and use of genomic sequencing requires healthcare professionals to constantly integrate these advancements into their clinical practice. There is a documented lack of cancer genomics contents in the teaching and learning programs. We aimed to identify the core competencies in cancer genomics for non-genetic healthcare professionals. We performed a literature review in PubMed, SCOPUS, and Web of Science databases to retrieve articles published from 2000 to 2018, in English or Italian language. We included articles that reported the competencies for non-genetic healthcare professionals in cancer genomics. A web-based modified Delphi survey was conducted, aiming to define, through consensus, a set of core competencies that should be covered in the curricula. The international expert panel included specialists in genetics, genomics, oncology, and medical specialists. In the literature review, we retrieved nine articles, from which we identified core competencies for general physicians and nurses. The competencies were organized in three main domains: knowledge, attitudes, and practical abilities. In the second round of Delphi survey, consensus of 83.3% was reached for the definition of the core competencies. Thirty-seven items were defined as the competencies required for physicians and forty-two items for nurses. Through a consensus-based approach, a set of core competencies in cancer genomics for non-genetic healthcare professionals has been identified. Our findings could benchmark standards for curriculum development and future educational strategies.

Exosomal lncRNA PVT1/VEGFA Axis Promotes Colon Cancer Metastasis and Stemness by Downregulation of Tumor Suppressor miR-152-3p

Background

Treating advanced colon cancer remains challenging in clinical settings because of the development of drug resistance and distant metastasis. Mechanisms underlying the metastasis of colon cancer are complex and unclear.

Methods

Computational analysis was performed to determine genes associated with the exosomal long noncoding (lncRNA) plasmacytoma variant translocation 1 (PVT1)/vascular endothelial growth factor A (VEGFA) axis in patients with colon cancer. The biological importance of the exosomal lncRNA PVT1/VEGFA axis was examined in vitro by using HCT116 and LoVo cell lines and in vivo by using a patient-derived xenograft (PDX) mouse model through knockdown (by silencing of PVT1) and overexpression (by adding serum exosomes isolated from patients with distant metastasis (M-exo)).

Results

The in silico analysis demonstrated that PVT1 overexpression was associated with poor prognosis and increased expression of metastatic markers such as VEGFA and epidermal growth factor receptor (EGFR). This finding was further validated in a small cohort of patients with colon cancer in whom increased PVT1 expression was correlated with colon cancer incidence, disease recurrence, and distant metastasis. M-exo were enriched with PVT1 and VEGFA, and both migratory and invasive abilities of colon cancer cell lines increased when they were cocultured with M-exo. The metastasis-promoting effect was accompanied by increased expression of Twist1, vimentin, and MMP2. M-exo promoted metastasis in PDX mice. In vitro silencing of PVT1 reduced colon tumorigenic properties including migratory, invasive, colony forming, and tumorsphere generation abilities. Further analysis revealed that PVT1, VEGFA, and EGFR interact with and are regulated by miR-152-3p. Increased miR-152-3p expression reduced tumorigenesis, where increased tumorigenesis was observed when miR-152-3p expression was downregulated.

Conclusion

Exosomal PVT1 promotes colon cancer metastasis through its association with EGFR and VEGFA expression. miR-152-3p targets both PVT1 and VEGFA, and this regulatory pathway can be explored for drug development and as a prognostic biomarker.

Massively parallel sequencing of cell-free DNA in plasma for detecting gynaecological tumour-associated copy number alteration

The discovery of circulating tumour DNA molecules created a paradigm shift in tumour biomarkers as predictors of recurrence. Non-invasive prenatal testing (NIPT) to detect circulating cell-free foetal DNA in maternal plasma is increasingly recognised as a valuable substitute to perceive foetal copy number variation (CNV). This study aimed to determine whether the copy number detection in plasma samples using NIPT platform could be used as a prognostic biomarker in patients with gynaecological cancer. We conducted a prospective study using samples containing preoperative plasma from 100 women with gynaecological cancers. Samples were randomly rearranged and blindly sequenced using a low-coverage whole-genome sequencing plasma DNA, NIPT platform. The NIPT pipeline identified copy number alterations (CNAs) were counted in plasma as a gain or loss if they exceeded 10 Mb from the expected diploid coverage. Progression-free survival (PFS) and overall survival (OS) were analysed according to the presence of CNA in plasma using Kaplan-Meier analyses. The NIPT pipeline detected 19/100 cases of all gynaecological cancers, including 6/36 ovarian cancers, 3/11 cervical cancers, and 10/53 endometrial cancers. Patients with CNA in plasma had a significantly poorer prognosis in all stages concerning PFS and OS. Therefore, low-coverage sequencing NIPT platform could serve as a predictive marker of patient outcome.