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Individualised choise of 1.line chemotherapy of metastatic colo-rectal cancer. Can the blood test: TIMP-1 help the doctor to choose the best chemotherapy for the individual patient?

ABSTRACT: Primary objectives: Aim is to investigate whether or not the cancer marker plasma TIMP-1 is related to probability of treatment response on chemotherapy with or without irinotecan Primary endpoints: Progression free survival (PFS)

DISEASE(S): Colorectal Cancer Stage Iv,Metastatisk Tarmkræft Der Udgår Fra Tyk- Eller Endetarm,Metastases To Liver,Metastatic Colon Or Rectal Cancer

PROVIDER: 2530271 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

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Project description:Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive "liquid biopsy" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ideal technology for exploring the plasma proteome for its unbiased and highly specific protein identification and quantification. Despite its potential, plasma proteomics is still a challenge due to the vast dynamic range of protein abundance, hindering the detection of less abundant proteins. Different approaches can help overcome this challenge. Conventional depletion methods face limitations in cost, throughput, accuracy, and off-target depletion. Nanoparticle-based enrichment shows promise in compressing dynamic range, but cost remains a constraint. Enrichment strategies for extracellular vesicles (EVs) can enhance plasma proteome coverage dramatically, but current methods are still too laborious for large series. Neat plasma remains popular for its cost-effectiveness, time efficiency, and low volume requirement. We used a test set of 33 plasma samples for all evaluations. Samples were digested using Strap and analysed on Evosep and nanoElute coupled to a timsTOF Pro using different elution gradients and ion mobility ranges. Data were mainly analyzed using library-free searched using DIANN. This study explores ways to improve proteome coverage in neat plasma both in MS data acquisition and MS data analysis. We demonstrate the value of sampling smaller hydrophilic peptides, increasing chromatographic separation, and using library-free searches. Additionally, we introduce the EV boost approach, that leverages on the extracellular vesicle fraction to enhances protein identification in neat plasma samples. Globally, out optimized analysis workflow allows the quantification of over 1000 proteins in neat plasma with a 24SPD throughput. We believe that these considerations can be of help independently of the LC-MS platform used.

Project description:Abstract: Background: With amyloid-β (Aβ) therapies for Alzheimer’s disease (AD) under active debate, there is a need for diagnostic tools that reflect disease biology beyond Aβ and tau for early detection and for patient stratification. The indicator cell assay platform (iCAP) is a tool for blood-based diagnostics that uses standardized cells as biosensors to transduce complex circulating signals into gene-expression readouts, which are then used to train multivariate disease classifiers for precision medicine. We developed an iCAP for early detection of Alzheimer’s disease (AD-iCAP). Methods: In a retrospective study, AD-iCAP was developed by incubating banked plasma samples from patients with early-stage AD (mild cognitive impairment or mild dementia) and age-matched controls with standardized neurons; whole-transcriptome responses were measured and used train disease classifiers by machine learning. The assay was optimized and analytically validated. Patient AD-iCAP data were separated into a training set, external validation set and an independent test set and used to parameterize and test models. To minimize bias, modeling features were selected from a predefined (a priori) 84-gene panel derived from an independent AD-iCAP dataset generated using plasma from 5xFAD mice. Results: We developed AD-iCAP using 191 banked plasma samples across three cohorts. Performance was assessed in two held-out sets: an external validation set (n=82; AUC 0.64, 95% CI 0.51–0.78) and an independent test set (n=23, AUC 0.77, 95% CI 0.57–0.96). Systems biology analyses of differential response profiles showed concordance with postmortem AD brain transcriptomes and enrichment of AD-relevant pathways beyond amyloid, including cholesterol biosynthesis, synaptic structure/neurotransmission and NGF/TrkA signaling (FDR < 0.05). The final model’s features included AD-linked genes AKT3, GPR50, PALLD and RGMA, related to neuronal signaling and cytoskeletal/axon-guidance processes. Conclusions: AD-iCAP is a blood-based diagnostic for early-stage detection of AD. It outputs cell-based transcriptional profiles with disease-relevance that are induced by circulating factors. In retrospective, multi-cohort testing, it showed modest-to-good discrimination, supporting prospective confirmation in larger cohorts. Because its readout captures biology beyond amyloid and tau, it may provide complementary information for combined testing. The multivariate readout supports further development for patient stratification as Aβ-targeted therapies and alternatives evolve.

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Individualised choise of 1.line chemotherapy of metastatic colo-rectal cancer. Can the blood test: TIMP-1 help the doctor to choose the best chemotherapy for the individual patient?

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