Biology subjects

Santamaria, P. G.

Publications and source records attributed to Santamaria, P. G..

2 recordsLinked to original sources

bioRxiv2025

Tracking ongoing chromosomal instability using single-cell whole-genome sequencing

Chromosomal instability (CIN) generates aneuploid genomes that are characteristic of most cancers. While bulk genome sequencing reveals historical CIN, it lacks the resolution to identify ongoing CIN that actively shapes genome evolution. Here, we present a computational framework that leverages single-cell whole-genome sequencing (scWGS) to identify and quantify ongoing CIN by detecting cell-unique copy number alterations and probabilistically mapping them to known CIN signatures. We validated this framework generating in vitro models with four types of induced CIN, correctly identifying the induced-CIN type in each case. When applied to cell lines and organoids with ongoing homologous recombination deficiency, our method showed improved identification of sensitivity to PARP inhibition and platinum-based chemotherapy. Analysing scWGS data from 8 triple-negative breast cancers, we linked ongoing impaired non-homologous end joining to subclonal diversification, a finding further supported in cohorts of 179 unmatched primary and metastatic TNBCs and 39 matched cases. Collectively, our results demonstrate that distinguishing ongoing from historical chromosomal instability uncovers a distinct dimension of tumour evolution, suggesting that effective precision oncology will require integrating measurements of both past genomic scars and active mutational processes.

genomics↗

bioRxiv2025

Forecasting oncogene amplification and tumour suppressor deletion

Oncogene amplification and tumour suppressor deletion can drive tumour initiation, progression and treatment resistance. Detection at diagnosis often signals poor prognosis, but it can also enable opportunities for treatment with highly effective targeted therapies. Predicting the likelihood that a patient will acquire these driver alterations in the future using a genomic test represents an opportunity to realise the benefits of interventions earlier, potentially with preventative intent. Here, we present a forecasting framework that takes as input a DNA copy number profile and predicts whether the tumour will acquire an oncogene amplification or tumour suppressor deletion in the future. This framework leverages mutation rate estimates from the input tumour, alongside gene-specific selection coefficients derived from a large cohort of 7,880 tumours. We demonstrate feasibility using 7,042 single-time-point samples and longitudinally collected tumour pairs from 44 prostate and 100 lung cancers, identifying tumours that went on to acquire amplifications at a later time point with an average AUC of 0.87. We show potential clinical utility by forecasting poor prognosis in low-grade gliomas via CDK4/PDGFRA amplification or CDKN2A deletion, and osimertinib resistance in lung cancers via MET amplification. This study serves as a proof-of-concept for a new class of biomarker, wherein selective pressures and mutation-generating processes can be harnessed to anticipate future genomic alterations.

genomics↗