Biology subjects

Callari, M.

Publications and source records attributed to Callari, M..

7 recordsLinked to original sources

bioRxiv2025

Prognostic and biological roles of Parkinson's disease genes in cancer

BackgroundIncreasing evidence suggests significant associations between Parkinson disease (PD) and cancer risks, with epidemiological studies revealing a complex relationship. PD patients exhibit lower risks of lung, genitourinary, and gastrointestinal cancers but higher risks of melanoma and brain cancers. Despite these observations, the underlying mechanisms between PD and cancers are poorly understood. ObjectivesWe aimed to identify molecular signatures that could provide insight into this complex connection by assessing the association of PD-related genes with patient survival and the cancer-specific co-expression networks they are involved in. MethodsTo explore this, we analyzed transcriptomic data from 18 cancer types in the TCGA dataset (n=6,088) and 16 cancer types in the DepMap dataset (n=682). We focused on seven genes causally implicated in PD (SNCA, PINK1, LRRK2, PRKN/PARK2, PARK7, GBA1, and ATP13A2) and conducted in silico analyses, to evaluate their associations with survival and correlation with genes, pathways and response to drugs in the context of cancer. ResultsOur findings revealed that the expression levels of the genes correlated with overall survival in a cancer-specific manner, often influenced by the TP53 genetic status. These genes were also associated with key cancer hallmarks such as genomic instability and cell proliferation. Additionally, novel associations were identified linking these genes to drug responses in a context-specific manner. ConclusionsThis study suggests that PD and cancer may be linked by biological pathways, sometimes associated with cancer hallmarks. These findings provide potential insights into druggable targets and the shared molecular mechanisms underlying PD and cancer.

bioinformatics↗

bioRxiv2025

Genome-wide transcriptomic response of whole blood to radiation

The hematological system is impacted in nearly all ionizing radiation exposure scenarios. Whole transcriptome data offer detailed insights into bloods radiation response, crucial for radiotherapy and biodosimetry. We conducted genome-wide RNA-seq analysis on blood from three donors irradiated ex vivo with X-rays and incubated for 2h and 6h. Gene expression was subject to strong inter-donor variation and time post-exposure. After 0.5, 1, 2, and 4 Gy X-rays, 5, 33, 84, and 364 genes (2h) and 72, 99, 274, and 607 genes (6h) were differentially expressed (DEG), compared to 0 Gy. The corresponding number of the inferred transcription factors was 255, 253, 274, and 292 after 2h and 214, 245, 262, and 279 after 6h. In sham-irradiated blood, 924 DEGs and 165 transcription factors were affected by ex vivo incubation alone. We identified 34 radioresponsive DEGs not previously described, 8 and 9 showing significant positive or negative correlations with dose, respectively, including GPN1, MRM2, G0S2, and PTPRS. DNA damage signaling pathways were affected from the lowest dose, with doses [≥] 2 Gy additionally triggering proinflammatory responses. This first genome-wide RNA-seq study of ex vivo X-ray-exposed human blood reveals novel radiosensitive genes, transcription factors, and pathways, enhancing the understanding of the consequences of diagnostic, therapeutic, or accidental exposures on the highly radioresponsive hematological system.

biophysics↗

bioRxiv2024

Development and validation of a gene expression score to account for tumour purity and improve prognostication in breast cancer

The prevalence of malignant cells in clinical specimens, or tumour purity, is affected by both intrinsic biological factors and extrinsic sampling bias. Molecular characterization of large clinical cohorts is typically performed on bulk samples; data analysis and interpretation can be biased by tumour purity variability. Transcription-based strategies to estimate tumour purity have been proposed, but no breast cancer specific method is available yet. We interrogated over 4400 expression profiles from 9 breast cancer datasets to develop and validate a 9-gene Breast Cancer Purity Score (BCPS). BCPS outperformed existing methods for estimating tumour content. Adjusting transcriptomic profiles using the BCPS reduce sampling bias and aid data interpretation. BCPS-estimated tumour purity improved prognostication in luminal breast cancer, correlated with pathologic complete response in on-treatment biopsies from triple-negative breast cancer patients undergoing neoadjuvant treatment and effectively stratified the risk of relapse in HER2+ residual disease post-neoadjuvant treatment.

bioinformatics↗

bioRxiv2023

Cancer-specific association between Tau (MAPT) and cellular pathways, clinical outcome, and drug response

Tau (MAPT) is a microtubule-associated protein causing common neurodegenerative diseases or inherited frontotemporal lobar degenerations. Emerging evidence for non-canonical functions of Tau in DNA repair and P53 regulation suggests its involvement in cancer. Indeed, preliminary studies have correlated Tau expression with cancer survival or response to therapies. To bring new evidence for a relevant role of Tau in cancer, we carried out an in silico pan-cancer analysis of MAPT transcriptomic profile in over 10000 clinical samples from 32 cancer types and over 1300 pre-clinical samples from 28 cancer types provided by the TCGA and the DEPMAP datasets respectively. MAPT expression associated with key cancer hallmarks including inflammation, proliferation, and epithelial to mesenchymal transition, showing cancer-specific patterns. In some cancer types, MAPT functional networks were affected by P53 mutational status. We identified new associations of MAPT with clinical outcomes and drug response in a context-specific manner. Overall, our findings indicate that the MAPT gene is a potential major player in multiple types of cancer. Importantly, the impact of Tau on cancer seems to be heavily influenced by the specific cellular environment.

cancer biology↗

bioRxiv2023

A large-scale retrospective study in metastatic breast cancer patients using circulating tumor DNA and machine learning to predict treatment outcome and progression-free survival

PurposeMonitoring levels of circulating tumor-derived DNA (ctDNA) represents a non-invasive snapshot of tumor burden and potentially clonal evolution. Here we describe how a novel statistical model that uses serial ctDNA measurements from shallow whole genome sequencing (sWGS) in metastatic breast cancer patients produces a rapid and inexpensive assessment that is predictive of treatment response and progression-free survival. Patients and MethodsA cohort of 188 metastatic breast cancer patients had DNA extracted from serial plasma samples (total 1098, median=4, mean=5.87). Plasma DNA was assessed using sWGS and the tumor fraction in total cell free DNA estimated using ichorCNA. This approach was compared with ctDNA targeted sequencing and serial CA 15-3 measurements. The longitudinal ichorCNA values were used to develop a Bayesian learning model to predict subsequent treatment response. ResultsWe identified a transition point of 7% estimated tumor fraction to stratify patients into different categories of progression risk using ichorCNA estimates and a time-dependent Cox model, validated across different breast cancer subtypes and treatments, outperforming the alternative methods. We then developed a Bayesian learning model to predict subsequent treatment response with a sensitivity of 0.75 and a specificity of 0.66. ConclusionIn patients with metastatic breast cancer, sWGS of ctDNA and ichorCNA provide prognostic and predictive real-time valuable information on treatment response across subtypes and therapies. A prospective large-scale clinical trial to evaluate clinical benefit of early treatment changes based on ctDNA levels is now warranted.

genomics↗

bioRxiv2023

Modelling drug responses and evolutionary dynamics using triple negative breast cancer patient-derived xenografts

Triple negative breast cancers (TNBC) exhibit inter- and intra-tumour heterogeneity, which is reflected in diverse drug responses and interplays with tumour evolution. Here, we use TNBC patient-derived tumour xenografts (PDTX) as a platform for co-clinical trials to test their predictive value and explore the molecular features of drug response and resistance. Patients and their matched PDTX exhibited mirrored drug responses to neoadjuvant therapy in a clinical trial. In parallel, additional clinically-relevant treatments were tested in PDTXs in vivo to identify alternative effective therapies for each PDTX model. This framework establishes the foundation for anticipatory personalised therapies for those patients with resistant or relapsed tumours. The PDTXs were further explored to model PDTX- and treatment-specific behaviours. The dynamics of drug response were characterised at single-cell resolution revealing a novel mechanism of response to olaparib. Upon olaparib treatment PDTXs showed phenotypic plasticity, including transient activation of the immediate-early response and irreversible sequential phenotypic switches: from epithelial to epithelial-mesenchymal-hybrid states, and then to mesenchymal states. This molecular mechanism was exploited ex vivo by combining olaparib and salinomycin (an inhibitor of mesenchymal-transduced cells) to reveal synergistic effects. In summary, TNBC PDTXs have the potential to help design individualised treatment strategies derived from model-specific evolutionary insights.

cancer biology↗

bioRxiv2018

Next Generation-Targeted Amplicon Sequencing (NG-TAS): An optimised protocol and computational pipeline for cost-effective profiling of circulating tumour DNA

Circulating tumour DNA (ctDNA) detection and monitoring has enormous potential clinical utility in oncology. We describe here a fast, flexible and cost-effective method to profile multiple genes simultaneously in low input cell-free DNA (cfDNA): Next Generation-Targeted Amplicon Sequencing (NG-TAS). We designed a panel of 377 amplicons spanning 20 cancer genes and tested the NG-TAS pipeline using cell-free DNA from two hapmap lymphoblastoid cell lines. NG-TAS consistently detected mutations in cfDNA when mutation allele fraction was >1%. We applied NG-TAS to a clinical cohort of metastatic breast cancer patients, demonstrating its potential in monitoring the disease. The computational pipeline is available at: https://github.com/cclab-brca/NGTAS_pipeline.

genomics↗