Raquel Perez-Lopez

Neoantigen-targeted T-cell therapies require invasive tumor biopsies to identify neoantigens and reactive T cells, limiting their applicability and potentially underestimating tumor heterogeneity. We propose a novel strategy relying solely on peripheral blood to identify nonsynonymous mutations via cell-free DNA whole-exome sequencing and isolate neoantigen-specific CD4+ and CD8+ T cells based on PD-1 and CD39 coexpression. Our method identified most neoantigens and increased the number of T-cell reactivities detected by standard biopsy-based techniques in an initial cohort of eight patients with metastatic cancer. Critically, our approach uncovered additional neoantigens missed by tumor biopsy analysis, highlighting the potential for broader neoantigen targeting using liquid biopsies. Analysis of a cohort of 69 patients with metastatic cancer revealed that approximately half could be eligible for our blood-only approach. This proof-of-concept study demonstrates the potential of using blood as a surrogate or complement for tumor biopsies to develop neoantigen-reactive T-cell therapies against a broader spectrum of neoantigens. Significance: This study establishes a blood-based strategy for identifying neoantigens and reactive T cells. By integrating ctDNA exome sequencing with phenotypic selection of circulating T cells, it captures tumor heterogeneity and the systemic antitumor T-cell response, enabling personalized immunotherapy development without tumor biopsies, expanding the accessibility of neoantigen-targeted treatments.

Purpose: We aim to inform the design of new diffusion MRI (dMRI) approaches for microvasculature quantification that enhance the biological specificity of imaging towards cancer. Methods: We adopted simulation‐informed modelling of the vascular dMRI signal. We synthesised signals from 1500 synthetic vascular networks, for a variety of protocols (flow‐compensated [FC], non‐compensated [NC], hybrid), featuring different samplings and diffusion times. We estimated the number of independent, recoverable signal degrees of freedom in presence of noise (signal‐to‐noise ratio of 5), and ranked 12 microvascular metrics depending on the quality of their estimation. Lastly, we demonstrated the feasibility of estimating the top‐ranking metrics on 3T dMRI of a healthy volunteer and of a metastatic colorectal cancer (CRC) patient. Results: Both NC and FC synthetic vascular signals exhibited complex behaviour as, for example, non‐zero kurtosis and diffusion time dependence. Two independent degrees of freedom appeared recoverable from directionally‐averaged vascular signals (SNR of 5). Mean volumetric flow rate and an Apparent Network Branching (ANB) index maximised correlations between ground truth and estimated values in silico. In the patient, both and detected re‐vascularisation after 3 months of targeted therapy against liver metastases, consistently with Intra‐Voxel Incoherent Motion (IVIM) metrics. Conclusions: Simulation‐based modelling of the vascular dMRI signal suggests and as the most promising metrics for tissue microvasculature characterisation. Their estimation in vivo appears feasible to capture general trends, and demonstrates contrasts that are biologically plausible, encouraging their usage in future studies.

Background: The C-reactive protein (CRP) flare response, an indicator of early immune activation, has emerged as a promising and cost-effective biomarker for predicting response to immune checkpoint inhibitors (ICIs) across various tumor types. This study evaluates the utility of CRP dynamics as a tumor-agnostic biomarker and integrates systemic inflammatory markers with advanced multiparametric MRI metrics to uncover the biological mechanisms underlying the CRP flare phenomenon and its relationship with treatment response. Methods: Patients were stratified into three groups based on CRP kinetics: (1) flare-responders, characterized by an initial doubling of baseline CRP followed by a decrease below baseline; (2) CRP responders, defined as patients with no flare increase but a CRP reduction of at least 30% below baseline and (3) CRP non-responders. Multiparametric MRI was performed at baseline, early (1–3 weeks), and intermediate (6–8 weeks) time points to assess tumor size and microstructural features, including cell density and vascularization. Clinical benefit and survival outcomes, including progression-free survival (PFS) and overall survival (OS), were analyzed using Kaplan-Meier curves and log-rank tests. Cox regression analyses were performed to identify independent predictors of clinical outcomes, while intergroup differences in MRI metrics were assessed using Wilcoxon rank-sum and Kruskal-Wallis tests. Results: Among the 121 evaluable patients with solid tumors enrolled in the PREDICT trial, CRP flare-responders demonstrated significantly longer PFS (5.6 months) and OS (12.1 months) compared with responders (PFS: 3.4 months, OS: 8.0 months) and non-responders (PFS: 3.2 months, OS: 6.7 months; p=0.01 and p<0.01, respectively). Additionally, clinical benefit was achieved in 50% of flare-responders, compared with 13% of responders (p=0.05) and 23% of non-responders (p<0.01). Tumor growth was interrupted early after treatment initiation in CRP flare-responders, whereas non-responders exhibited marked increases in tumor size. In the pilot subset of 33 patients with MRI data, diffusion MRI revealed stable or increased apparent diffusion coefficient values in CRP flare-responders, indicative of reduced tumor cellularity just after 1–3 weeks of treatment. Conclusions: This study highlights the potential of combining early CRP dynamics with non-invasive imaging metrics to identify ICI responders as early as 2 weeks after treatment initiation. By integrating systemic inflammatory biomarkers with MRI-derived insights into tumor size and microstructural changes, these findings optimize therapeutic strategies and advance understanding of immunotherapy-driven tumor dynamics.

Purpose: Prior studies have suggested the biomarker potential of plasma-derived ctDNA in patients with cancer treated with immune checkpoint inhibitors. This study investigated the ability of ctDNA to predict progression-free and overall survival in a cohort of patients with advanced cancer treated with immunotherapies. Experimental Design: In order to characterize the potential role of ultrasensitive ctDNA detection in the management of these patients, we have performed tumor whole-genome sequencing–informed, ultrasensitive ctDNA analysis—tracking approximately 1,800 tumor-specific mutations per patient—in a retrospective cohort (n = 136) and a prospective validation cohort (n = 66) across 24 cancer types treated with immune checkpoint inhibitors alone or in combination with bispecific antibodies or immune cell engagers. Results: Analyzing 1,455 longitudinal samples, we found that ctDNA molecular response measured as early as 3 weeks after treatment initiation correlated with improved progression-free and overall survival, whereas ctDNA clearance at any time strongly correlated with radiologic response and prolonged survival. Additionally, ctDNA dynamics distinguished true progression from pseudoprogression and predicted outcomes in patients receiving continued immunotherapy beyond initial progression. Conclusions: This study highlights the broader applicability of ultrasensitive ctDNA as a biomarker across cancer types and immunotherapy modalities.

Diffusion Magnetic Resonance Imaging (dMRI) simulations in geometries mimicking the microscopic complexity of human tissues enable the development of innovative biomarkers with unprecedented fidelity to histology. Simulation-informed dMRI has traditionally focussed on brain imaging, and it has neglected other applications, as for example body cancer imaging, where new non-invasive biomarkers are still sought. This article fills this gap by introducing a Monte Carlo diffusion simulation framework informed by histology, for enhanced body dMR microstructural imaging: the Histo-μSim approach. We generate dictionaries of synthetic dMRI signals with coupled tissue properties from virtual cancer environments, reconstructed from hematoxylin-eosin stains of human liver biopsies. These enable the data-driven estimation of properties such as the intrinsic extra-cellular diffusivity, cell size or cell membrane permeability. We compare Histo-μSim to metrics from well-established analytical multi-compartment models in silico, on fixed mouse tissues scanned ex vivo (kidneys, spleens, and breast tumours) and in cancer patients in vivo. Results suggest that Histo-μSim is feasible in clinical settings, and that it delivers metrics that more accurately reflect histology as compared to analytical models. In conclusion, Histo-μSim offers histologically-meaningful tissue descriptors that may increase the specificity of dMRI towards cancer, and thus play a crucial role in precision oncology.

Background Innovative diffusion Magnetic Resonance Imaging (MRI) models enable the non-invasive measurement of cancer biological properties in vivo. However, while cancers frequently spread to the liver, models tailored for liver application and easy to deploy in the clinic are still sought. We fill this gap by delivering a practical, clinically-viable framework for liver tumour diffusion imaging, informing its design through histology. Methods: We compare MRI and histological data from mice and cancer patients, namely: MRI and hemaotxylin-eosin (HE) stains from N = 7 fixed mouse livers; MRI of N = 38 patients suffering from liver solid tumours, N = 18 of whom with HE biopsies. We study five diffusion models, ranking them according to a total MRI-histology correlation score. Afterwards, we test metrics from the top-ranking model on our cohort, assessing their sensitivity to cell proliferation (Ki-67 staining, N = 10), evaluating their association with tumour volume (N = 140 tumours), and comparing them across primary cancer types. Results: We select a dMRI signal model of restricted intra-cellular diffusion with negligible extra-cellular contributions, which maximises radiological-histological correlations (total score: 0.625). The model provides cell size and density estimates that i) correlate with histology (e.g., for cell size: r = 0.44, p = 0.029), ii) are associated to Ki-67 cell proliferation (for MRI cell density: r = 0.80, p = 0.006) and tumour volume (r = 0.40, p < 10^–5 for tumour volume regression), and iii) that distinguish melanoma (N = 8) from colorectal cancer (N = 13) (p = 0.011 for intra-cellular fraction). Conclusions: Our biologically meaningful approach may complement standard-of-care radiology, and become a new tool for enhanced cancer characterisation in precision oncology.

Purpose: We aim to inform the design of new diffusion MRI (dMRI) approaches for microvasculature mapping that enhance the biological specificity of imaging towards cancer. Methods: We adopted simulation-informed modelling of the vascular dMRI signal. We synthesised signals from 1500 synthetic vascular networks, for a variety of protocols (flow-compensated (FC), non-compensated (NC), hybrid), featuring different b samplings and diffusion times. We estimated the number of independent, recoverable signal degrees of freedom in presence of noise (signal-to-noise ratio of 5), and ranked 12 microvascular metrics depending on the quality of their estimation. Lastly, we demonstrated the feasibility of estimating the top-ranking metrics on 3T dMRI of a healthy volunteer and of a metastatic colorectal cancer (CRC) patient. Results: Both NC and FC synthetic vascular signals exhibit complex behaviour, e.g., non-zero kurtosis and diffusion time dependence. Two independent degrees of freedom appear recoverable from directionally-averaged vascular signals (SNR of 5). Mean volumetric flow rate q _m and an Apparent Network Branching (ANB) index maximise correlations between ground truth and estimated values in silico . Their estimation is proposed for in vivo imaging, and demonstrated herein. In the patient, both q _m and ANB detect re-vascularisation after 3 months of targeted therapy against liver metastases, consistently with Intra-Voxel Incoherent Motion (IVIM) metrics. Conclusions: Simulation-based modelling of the vas-cular dMRI signal informs the design of promising approaches for in vivo microvasculature characterisation.