Phenotypic and molecular investigation of circulating tumor cells (CTCs) isolated from breast cancer patients at single cell resolution

Despite the paramount advances in cancer research, breast cancer (BC) still ranks one of the leading causes of cancer-related death worldwide. Thanks to the screening campaign started in developed countries, BC is often diagnosed at early stages (non-metastatic BC, nmBC), but disease relapse occurrence even after decades and at distant sites is not an uncommon phenomenon. Conversely, metastatic BC (mBC) is considered an incurable disease. The major perpetrators of tumor spread to secondary organs are circulating tumor cells (CTCs), a rare population of cells detectable in the peripheral blood of oncologic patients. In this study, CTCs from patients diagnosed with luminal nmBC and mBC (hormone receptor positive, Human Epidermal Growth Factor Receptor 2 (HER2) negative) were characterized at both phenotypic and molecular levels. To better understand the molecular mechanisms underlying their biology and their metastatic potential, next-generation sequencing (NGS) analyses were performed at single-cell resolution to assess copy number aberrations (CNAs), single nucleotide variants (SNVs) and gene expression profiling. The findings of this study arise hints in CTC detection, and pave the way to new application in CTC research.

The RAS-Lung project: implementing blood and tissue genotyping in KRAS-Positive non-small cell lung cancer treatment

Background: The frontline management of non-oncogene addicted non-small cell lung cancer (NSCLC) involves immunotherapy (ICI) alone or combined with chemotherapy (CT-ICI). As therapeutic options expand, refining NSCLC genotyping gains paramount importance. The dynamic landscape of KRAS-positive NSCLC presents a spectrum of treatment options, including ICI, targeted therapy, and combination strategies currently under investigation. Methods: The two-year RASLUNG project, featuring both retrospective and prospective cohorts, aimed to analyze the predictive and prognostic impact of KRAS mutations on tumor tissue and circulating DNA (ctDNA). Secondary objectives included assessing the roles of co-mutations and longitudinal changes in KRAS mutant copies concerning treatment response and survival outcomes. An external validation study confirmed the prognostic or predictive significance of co-mutations. Results: In the prospective cohort (n=24), patients with liver metastases exhibited significantly elevated ctDNA levels(p=0.01), while those with >3 metastatic sites showed increased Allele Frequency (AF) (P=0.002). Median overall survival (OS) was 7.5 months, progression-free survival (PFS) was 4.0 months, and the objective response rate (ORR) was 33.3%. Higher AF correlated with an increased risk of death (HR 1.04, p = 0.03), though not progression. Notably, a reduction in plasma DNA levels was significantly associated with objective response(p=0.01). In the retrospective cohort, KRAS and STK11 mutations co-occurred in 14/21 patients (p=0.053). STK11 mutations were independently detrimental to OS (HR 1.97, p=0.025) after adjusting for various factors. KRAS tissue AF did not correlate with OS or PFS. Within the validation dataset, STK11 mutations were significantly associated with an increased risk of death in univariate (HR 2.01, p<0.001) and multivariate models (HR 1.66, p=0.001) after adjustments. Conclusion: The RAS-Lung Project, employing innovative genotyping techniques, underscores the significance of comprehensive NSCLC genotyping. Tailored next-generation sequencing (NGS) and ctDNA monitoring may offer potential benefits in navigating the evolving landscape of KRAS-positive NSCLC treatment.

EBV Type II latency relies on PARP1 activity and is essential for gastric epithelial cell transformation in EBV-associated Gastric Cancer (EBVaGC)

Epstein-Barr virus (EBV) establishes a lifelong asymptomatic infection by replicating its chromatinized genome, called episome, together with the host genome. EBV exhibits different latency-associated transcriptional repertoires that mirror its three-dimensional structures of the genome. CTCF, Cohesin and PARP1 are involved in maintaining viral latency and establishing episome architecture. Epstein-Barr virus-associated gastric cancer (EBVaGC) represents almost 10% of all gastric cancers globally. EBVaGC exhibit an intermediate viral transcription profile known as "Latency II", expressing specific viral genes and non-coding RNAs. In this study, we investigated the impact of PARP1 inhibition on CTCF/Cohesin binding in Type II latency. We observed a destabilization of the binding of both factors, leading to a disrupted three-dimensional architecture of the episomes and consequently, an altered viral gene expression. Despite sharing the same CTCF binding profile, Type I, II, and III latencies display different 3D episomal structures that correlate with variations in viral gene expression. Additionally, our analysis of H3K27ac-enriched chromatin interactions revealed differences between Type II latency episomes and a link to cellular transformation through docking of the EBV episomes at specific sites of the Human genome, thus promoting oncogene expression. Overall, this work provides insights into the role of PARP1 in maintaining active latency and novel mechanisms of EBV-induced cellular transformation.

Developing a radiomic based machine learning model to identify patients with resected non-small-cell lung cancer at high risk of relapse

Background There is a wide variation of recurrence risk of Non-small-cell lung cancer (NSCLC) within the same Tumor Node Metastasis (TNM) stage, suggesting that other parameters are involved in determining this probability. Radiomics allows extraction of quantitative information from images that can be used for clinical purposes. The primary objective of this study is to develop a radiomic prognostic model that predicts a 3 year disease free-survival (DFS) of resected Early Stage (ES) NSCLC patients. Material and Methods 56 pre-surgery non contrast Computed Tomography (CT) scans were retrieved from the PACS of our institution and anonymized. Then they were automatically segmented with an open access deep learning pipeline and reviewed by an experienced radiologist to obtain 3D masks of the NSCLC. Images and masks underwent to resampling normalization and discretization. From the masks hundreds Radiomic Features (RF) were extracted using Py-Radiomics. Hence, RF were reduced to select the most representative features. The remaining RF were used in combination with Clinical parameters to build a DFS prediction model using Leave-one-out cross-validation (LOOCV) with Random Forest. Results and Conclusion A poor agreement between the radiologist and the automatic segmentation algorithm (DICE score of 0.37) was found. Therefore, another experienced radiologist manually segmented the lesions and only stable and reproducible RF were kept. 50 RF demonstrated a high correlation with the DFS but only one was confirmed when clinicopathological covariates were added: Busyness a Neighbouring Gray Tone Difference Matrix (HR 9.610). 16 clinical variables (which comprised TNM) were used to build the LOOCV model demonstrating a higher Area Under the Curve (AUC) when RF were included in the analysis (0.67 vs 0.60) but the difference was not statistically significant (p=0,5147).