Biology subjects

Lambin, P.

Publications and source records attributed to Lambin, P..

5 recordsLinked to original sources

bioRxiv2025

Deficiencies in the Fanconi Anemia or the Homologous Recombination Pathway enhance the antitumor effects of the novel hypoxia-activated prodrug CP-506

The novel hypoxia-activated prodrug CP-506 has been shown to selectively target hypoxic tumor cells, which are associated with disease progression and resistance to conventional anti-cancer therapies. Given the alkylating effector metabolites, we hypothesize that defects in interstrand crosslink (ICL) and double strand break (DSB) DNA repair may serve as a predictive biomarker of sensitivity to CP-506. Here, we evaluated the role of DNA damage repair pathways in the antitumor response to CP-506. Isogenic cancer cell lines proficient or deficient in the Fanconi Anemia (FA), homologous recombination (HR), or non-homologous end joining (NHEJ) pathway were cultured as 2D monolayers and 3D spheroids. Cell viability, clonogenic cell survival, and spheroid growth inhibition were assessed following CP-506 exposure. Mice bearing subcutaneous isogenic xenografts received CP-506 (600 mg/kg; QD5) or vehicle treatment upon reaching a tumor starting volume (SV) of 244.9 {+/-} 72.0 mm3. Treatment response was quantified as time to reach 4xSV (T4xSV) and respective enhancement ratios (ER), defined as T4xSVCP-506/T4xSVvehicle. DNA damage and repair capacity were evaluated by {gamma}H2AX and alkaline comet assays. In vitro, cell lines deficient in FA or HR, but not NHEJ, showed enhanced sensitivity to CP-506 compared to parental cells in viability and clonogenic assays. This was confirmed in spheroid growth inhibition studies. In vivo, the antitumor response to CP-506 was more pronounced (P<0.0001) in LNCaP AR FANCA-/- (ER 4.0{+/-}1.1) and LNCaP AR FANCD2-/- (ER 3.4{+/-}0.8) xenografts compared to parental LNCaP AR (ER 1.5{+/-}0.5) xenografts. CP-506 treatment of DLD-1 BRCA2-/- (2.9{+/-}0.7; P<0.0001) xenografts resulted in significantly enhanced ER compared to parental (1.3{+/-}0.2) xenografts. Similar results were obtained in HCT116 BRCA2-/- (ER 4.0{+/-}0.6; P<0.0001) versus parental (ER 1.7{+/-}0.6) xenografts. In contrast, the ER of HCT116 DNA-PKcs-/- (1.4{+/-}0.3; P=0.18) xenografts was not different from HCT116 parental xenografts. Under anoxic conditions, CP-506 caused elevated {gamma}H2AX foci counts in FANCA-(2.0-fold increase at 48 hours) and FANCD2-deficient cells (1.4-fold increase at 72 hours) compared to LNCaP AR parental cells (P<0.0001). Similarly, {gamma}H2AX expression was increased in DLD-1 BRCA2-/- cells, but not in HCT116 BRCA2-/-cells, compared to their respective parental cells. In xenografts, FA- and HR-deficiency caused elevated {gamma}H2AX expression compared to respective parental tumors (1.6-9.3-fold increase). HCT116 DNA-PKcs-/- cells and xenografts displayed reduced {gamma}H2AX expression compared to their parental counterpart, with an 0.5-fold reduction. The comet assay confirmed CP-506-induced ICLs and DNA strand breaks but was unable to explain the differential therapeutic responses of CP-506 among isogenic tumor cells. Deficiencies within FA or HR, but not NHEJ, enhanced the antitumor effects of CP-506 through a mechanism consistent with the concept of synthetic lethality. Therefore, both DNA repair status and the presence of tumor hypoxia represent key biomarkers for patient stratification in clinical trials of CP-506.

cancer biology↗

bioRxiv2025

The fractionation dependence of tumor control in proton therapy for early-stage non-small cell lung cancer

PurposeThe relative biological effectiveness (RBE) of tumor control for proton beam therapy (PBT) compared to photon radiotherapy (RT) is typically assumed to be independent of fractionation. To test this, we modeled published PBT outcome results for early-stage non-small cell lung cancer (NSCLC) treatments across a range of fractionation schedules. Materials and MethodsAll published and analyzable cohorts were included (399 patients, 413 treated lesions). Two models were used to fit the data: a previously published tumor simulation model that fits photon RT results of NSCLC across all fractionation regimes and the Fowler LQ model with a kick-off time term. The treatment effect of each cohort was referenced to the photon equivalent dose through mechanistic model simulations in a 2 Gy/weekday scenario, with radiobiological parameters determined to simultaneously best-fit all fractionation results. The tumor control RBE of each published treatment schedule, compared to the modeled photon RT effect of the same schedule, was then estimated. ResultsFor cohorts whose treatments lasted less than three weeks (i.e., 12 fractions or less), the RBE of PBT was in the range of 1.08 to 1.11. However, for fractionated treatments stretching over four weeks or more (20-25 fractions), the relative effectiveness was much lower, with RBEs in the range of 0.82-0.89. This conclusion was unchanged using the simpler Fowler LQ + time model. ConclusionsThe proton RBE for hypo-fractionated schedules was 20-30% higher than for conventional schedules. The derived radiobiological parameters of PBT differ significantly from those of photon RT, indicating that PBT is influenced differentially by radiobiological mechanisms which require further investigation.

biophysics↗

bioRxiv2021

Covid19Risk.ai: An open source repository and online calculator of prediction models for early diagnosis and prognosis of Covid-19

ObjectiveThe current pandemic has led to a proliferation of predictive models being developed to address various aspects of COVID-19 patient care. We aimed to develop an online platform that would serve as an open source repository for a curated subset of such models, and provide a simple interface for included models to allow for online calculation. This platform would support doctors during decision-making regarding diagnoses, prognoses, and follow-up of COVID-19 patients, expediting the models transition from research to clinical practice. MethodsIn this proof-of-principle study, we performed a literature search in PubMed and WHO database to find suitable models for implementation on our platform. All selected models were publicly available (peer reviewed publications or open source repository) and had been validated (TRIPOD type 3 or 2b). We created a method for obtaining the regression coefficients if only the nomogram was available in the original publication. All predictive models were transcribed on a practical graphical user interface using PHP 8.0.0, and published online together with supporting documentation and links to the associated articles. ResultsThe open source website https://covid19risk.ai/ currently incorporates nine models from six different research groups, evaluated on datasets from different countries. The website will continue to be populated with other models related to COVID-19 prediction as these become available. This dynamic platform allows COVID-19 researchers to contact us to have their model curated and included on our website, thereby increasing the reach and real-world impact of their work. ConclusionWe have successfully demonstrated in this proof-of-principle study that our website provides an inclusive platform for predictive models related to COVID-19. It enables doctors to supplement their judgment with patient-specific predictions from externally-validated models in a user-friendly format. Additionally, this platform supports researchers in showcasing their work, which will increase the visibility and use of their models.

bioinformatics↗

bioRxiv2019

Combining Hypoxia-Activated Prodrugs and Radiotherapy in silico: Impact of Treatment Scheduling and the Intra-Tumoural Oxygen Landscape

Hypoxia-activated prodrugs (HAPs) present a conceptually elegant approach to not only overcome, but better yet, exploit intra-tumoural hypoxia. Despite being successful in vitro and in vivo, HAPs are yet to achieve successful results in clinical settings. It has been hypothesised that this lack of clinical success can, in part, be explained by the insufficiently stringent clinical screening selection of determining which tumours are suitable for HAP treatments. Taking a mathematical modelling approach, we investigate how tumour properties and HAP-radiation scheduling influence treatment outcomes in simulated tumours. The following key results are demonstrated in silico: (i) HAP and ionising radiation (IR) monotherapies may attack tumours in dissimilar, and complementary, ways. (ii) HAP-IR scheduling may impact treatment efficacy. (iii) HAPs may function as IR treatment intensifiers. (iv) The spatio-temporal intra-tumoural oxygen landscape may impact HAP efficacy. Our in silico framework is based on an on-lattice, hybrid, multiscale cellular automaton spanning three spatial dimensions. The mathematical model for tumour spheroid growth is parameterised by multicellular tumour spheroid (MCTS) data. Author SummaryWhen cancer patients present with solid tumours, the tumours often contain regions that are oxygen-deprived or, in other words, hypoxic. Hypoxic cancer cells are more resistant to conventional anti-cancer therapies, such as chemotherapy and radiotherapy, and therefore tumour hypoxia may complicate treatments. Hypoxia-activated prodrugs constitute a conceptually elegant approach to not only overcome, but better yet, exploit tumour hypoxia. Hypoxia-activated prodrugs are drugs that act as Trojan horses, they are theoretically harmless vehicles that are converted into warheads when they reach their targets: hypoxic tumour cells. Despite being conceptually clever and successful in experimental settings, hypoxia-activated prodrugs are yet to achieve successful results in clinical trials. It has been hypothesised that this lack of clinical success can, in part, be explained by an insufficiently stringent clinical screening selection of determining which tumours are suitable for hypoxia-activated prodrug treatments. In this article, we investigate how simulated tumours with different oxygen landscapes respond to anti-cancer treatments that include hypoxia-activated prodrugs, either alone or in combination with radiotherapy. Our simulation framework is based on a mathematical model that describes how individual cancer cells in a tumour divide and respond to treatments. We demonstrate that the efficacy of hypoxia-activated prodrugs depends on both the treatment scheduling, and on the oxygen landscape of the specific, simulated tumour.

cancer biology↗

bioRxiv2018

Network-Based Biomarkers Enable Cross-Disease Biomarker Discovery

Biomarkers lie at the heart of precision medicine, biodiversity monitoring, agricultural pathogen detection, amongst others. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers almost always involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. We therefore created SIMMS: an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We applied SIMMS to multiple data-types across four diseases, and in each it reproducibly identified subtypes, made superior predictions to the best bespoke approaches, and identified known and novel signaling nodes. To demonstrate its ability on a new dataset, we measured 33 genes/nodes of the PIK3CA pathway in 1,734 FFPE breast tumours and created a four-subnetwork prediction model. This model significantly out-performed existing clinically-used molecular tests in an independent 1,742-patient validation cohort. SIMMS is generic and can work with any molecular data or biological network, and is freely available at: https://cran.r-project.org/web/packages/SIMMS.

bioinformatics↗