Application of ex vivo liver perfusion in hepatoma research

Ex-vivo liver perfusion (EVLP) is an ideal platform to study liver disease, therapeutic interventions, and pharmacokinetic properties of drugs without any patient risk. Rat livers are an ideal model for EVLP due to less organ quality variability, ease of hepatectomy, well-defined molecular pathways, and relatively low costs compared to large animal or human perfusions. However, the major limitation with rat liver normothermic machine perfusion (NMP) is maintaining physiologic liver function on an ex-vivo machine perfusion system. To address this need, our research demonstrates 24-hour EVLP in rats under normothermic conditions. Early (6 hour) perfusate transaminase levels and oxygen consumption of the liver graft are shown to be good markers of perfusion success and correlate with viable 24-hour post-perfusion histology. Finally, we address overcoming challenges in long-term rat liver perfusions such as rising intrahepatic pressures and contamination, and offer future directions necessary to build upon our work.

Recent advances in proteomics technologies provide tremendous opportunities for biomarker-related clinical applications; however, the distinctive characteristics of human biofluids such as the high dynamic range in protein abundances and extreme complexity of the proteomes present tremendous challenges. In this review we summarize recent advances in LC-MS-based proteomics profiling and its applications in clinical proteomics as well as discuss the major challenges associated with implementing these technologies for more effective candidate biomarker discovery. Developments in immunoaffinity depletion and various fractionation approaches in combination with substantial improvements in LC-MS platforms have enabled the plasma proteome to be profiled with considerably greater dynamic range of coverage, allowing many proteins at low ng/ml levels to be confidently identified. Despite these significant advances and efforts, major challenges associated with the dynamic range of measurements and extent of proteome coverage, confidence of peptide/protein identifications, quantitation accuracy, analysis throughput, and the robustness of present instrumentation must be addressed before a proteomics profiling platform suitable for efficient clinical applications can be routinely implemented.

Novel cancer therapeutics have less than a 12% probability of translating from bench to bedside. Unwarranted toxicity and inadequate therapeutic delivery due to uptake by clearance organs, not predicted by current preclinical methods, have contributed towards this high rate of attrition. In the present work, we propose normothermic machine perfusion of human or human-sized organs as a more predictive, closer-to-human model to investigate drug pharmacokinetics and toxicity. Over the past decade, developments in the field of organ preservation for transplantation have enabled prolonged (>12 hours) normothermic machine perfusion (NMP) of isolated porcine or human organs ex vivo, maintaining quasi-physiological haemodynamic, synthetic and metabolic function using a packed red cell perfusate with physiological oxygenation and nutrient levels at normal body temperature. This preserves physiological processes such as metabolism and drug elimination, and enables easy access to tissue, blood and excreted biological fluids, with NMP livers producing bile and NMP kidneys producing urine. We hypothesise that this will provide a physiologically relevant platform to investigate drug pharmacokinetics and toxicity. We selected a widely used small-molecule chemotherapeutic (Irinotecan hydrochloride 2mg/ml, Medac, UK) which has seen decades of clinical use and benefits from extensive clinical pharmacokinetic data. The small-molecule drug was infused into isolated porcine and human livers and kidneys, with quantification of concentration time profiles of the prodrug and its main metabolites in plasma, bile and urine over 16-24 hours of NMP. In addition to irinotecan (CPT11), three of its metabolites (APC, SN38G, SN38) were successfully detected and quantified, demonstrating peak plasma concentrations (Cmax ~ 10,000 ng/mL, 1000 ng/mL, 100 ng/g, 30 ng/g), plasma decay rates and percentages of injected dose in bile (%ID ~20%, 8%, 25%, 1%) and urine (%ID ~20%, 0.06%, 0.5%,0.1%) that are comparable to clinical data. Drug-tissue toxicity could also be adequately replicated in the NMP model. In conclusion, we have demonstrated that human-sized isolated, normothermically perfused livers and kidneys accurately represent the clinically observed pharmacokinetic and toxicity profiles of an established small-molecule therapeutic. Further model validation is ongoing for biologics and other nanomedicines which are susceptible to clearance by the mononuclear phagocytic system or are hepato- or nephrotoxic. If this proves successful, normothermic machine perfusion of isolated porcine and human organs could greatly aid the early screening of candidate therapeutics and significantly enhance the pace and success rate with which they are translated into patients. Citation Format: Tamsyn Clark, Luca Bau, Fungai Dengu, Daniel Voyce, Robert Carlisle, Peter Friend, Constantin Coussios. Predicting clinical pharmacokinetics and toxicity of current and emerging oncology therapeutics by normothermic perfusion of isolated human-sized organs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1369.

Physiological normothermic machine perfusion kept kidneys viable for 12 hours with better hemodynamics, urine output, and metabolic stability than clinical normothermic machine perfusion. Physiological normothermic machine perfusion reduced injury (lower urinary neutrophil gelatinase-associated lipocalin), improved histology/appearance, and increased clinical suitability versus clinical normothermic machine perfusion. Prolonged physiological preservation could electivize surgery, improve early function, and expand donor kidney utilization. Hypothermic donor kidney preservation contributes to graft injury, leading to substantial discard rates and peritransplant nephron loss. Normothermic machine perfusion may offer superior preservation, but current clinical protocols are restrictive because of time-dependent injury. On these grounds, we performed a paired preclinical evaluation of a novel physiological NMP (pNMP) protocol versus the current clinical NMP (cNMP) protocol used in the United Kingdom over a 12-hour perfusion period. Paired porcine kidneys underwent standard retrieval and cold storage before randomization to either cNMP or our pNMP. Both groups were perfused for 12 hours while monitoring hemodynamics, biochemistry, and urine output. Kidneys preserved with pNMP exhibited superior perfusion, with improving hemodynamics and metabolic function within physiological ranges. Urine output was stable with a low concentration of the renal injury marker neutrophil gelatinase-associated lipocalin, indicating improved tissue viability. By contrast, cNMP kidneys exhibited evidence of hemodynamic compromise, with polyuria. Histological analyses of cNMP kidneys confirmed AKI. All pNMP kidneys were assessed as suitable for transplant on the basis of clinical assessment score, while several cNMP kidneys were considered marginal. The pNMP protocol improved preservation of donor kidneys compared with the cNMP protocol. Successful prolonged organ preservation could transform clinical kidney transplantation by obviating night implantation and has further potential benefits for improving post-transplant outcomes and expanding the donor pool.

Due to the developments of high-throughput sequencing (HTS) technologies, massive amounts of data are currently available in cancer research which has provided a remarkable opportunity for the identification of biomarkers. However, biomarkers obtained from different studies of the same condition often show lack of agreement with each other due to concerns of inconsistency between laboratories in experimental designs and technical platforms for high throughput measurements and data processing computational methods. In addition, the large number of features and relatively small sample sizes can also lead findings from clinical and biological studies that are often not reproducible and can be biased when tested in independent cohorts. To address this issue, meta-analyses can be performed to reach more general and reliable conclusions along with increasing statistical power. In application, due to the limitations of experimental techniques and cost of HTS experiments, molecular data for some samples is not measured and is duplicated from available datasets in meta-analysis study. Therefore, in this scenario, the conventional meta-analysis procedures can be misleading when key assumptions such as independence of datasets or effect sizes are ignored. We assess the impact of ignoring the assumption of independence in applying the effect-size meta-analysis approaches to estimate heterogeneity across studies along with combined effect sizes using pharmacogenomic datasets. We considered molecular and drug responses data from Breast cancer and Pan-cancer cell lines sensitivity screenings obtained from the PharmacoGx package including CCLE, GDSC, GRAY, gCSI, CTRP and UHNBreast. Under each study, the meta preprocess including missing drug response imputation via multiple imputation by chained equations method using classification and regression trees, gene matching or filtering, and scaling or normalization are considered. For each study, the association between drug sensitivity and expression data is assessed by fitting (adjusted) linear regression models to identify cancer biomarkers. We evaluate the deviation from the assumption of independence by applying the integration analyses using non-independent effect sizes (i.e., standardized regression coefficient) related to the duplicated expression data. In addition, the relationship between increasing in the number of duplicated expression data and violation of non-independent effect-sizes is studied. The results indicate that duplicating gene expression data, therefore violating the independence assumption, can substantially increase the deviation of meta-estimates of effect sizes. Citation Format: Farnoosh Abbas-Aghababazadeh, Benjamin Haibe-Kains. Meta-analysis and lack of independence assumption: Application in biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1356.

Report on the Barbados Workshop

The ongoing organ shortage has forced transplant teams to develop alternate sources of liver grafts. In this setting, ex-situ machine perfusion has rapidly developed as a promising tool to assess viability and improve the function of organs from extended criteria donors, including fatty liver grafts. In particular, normothermic machine perfusion represents a powerful tool to test a liver in full 37 °C metabolism and add pharmacological corrections whenever needed. In this context, many pharmacological agents and therapeutics have been tested to induce liver defatting on normothermic machine perfusion with promising results even on human organs. This systematic review makes a comprehensive synthesis on existing pharmacological therapies for liver defatting, with special focus on normothermic liver machine perfusion as an experimental ex-vivo translational model.

Mass spectrometry (MS) is the method of choice for both qualitative and quantitative high-throughput proteome analysis. In the early years, mass spectrometry was used only for small molecule analysis. However, advances in ionization sources, mass analyzers, and mass detectors made MS the central force in proteomics technologies. Starting from its use in peptide mass fingerprinting (PMF), MS has evolved greatly over the last two decades and now finds use in shotgun proteomics where thousands of proteins can be quantified at once. Currently, MS finds use in targeted proteome analysis and is widely used for biomarker discovery in cancer, diabetes, cardiovascular diseases, and infectious diseases worldwide to diagnose the diseases at the early stage or to unravel the mechanism of pathogenesis. The applications of MS have not been limited to proteomics and have moved to metabolomics, lipidomics, tissue imaging, in understanding posttranslational modifications (PTMs), etc. This chapter provides details of mass spectrometry and its applications in biomarker discovery.

Mass spectrometry (MS) is increasingly employed for the discovery of clinical biomarkers. However, due to sensitivity limitations related to in-source ionization yield, many potential biomarkers are not detected by standard mass spectrometers. Therefore, more efficient ion-source technologies are needed to improve MS applications in biomarker discovery. Among novel ion-source technologies, Surface-Activated Chemical Ionization (SACI), although endowed with high sensitivity linked to its ability to reduce chemical noise in mass spectra, has seen limited application in biomarker discovery to date, due to its selectivity for highly polar compounds. However, in combination with an Electrospray Ionization (ESI) source, SACI selectivity can be enlarged in the range of less polar compounds. To validate the new SACI-ESI approach in biomarker discovery, we applied it to a translational setting in oncology. We performed MS profiles of 101 human serum samples from a male population, aged 40 or older, coming to the clinic for prostate cancer evaluation based on multiple PSA exams, digital rectal examination and echography. The SACI-ESI MS spectra were analyzed and classified with an innovative bioinformatic approach based on the MS-search freeware developed in house. Here we demonstrate that the SACI-ESI combination can produce MS spectra with greater sensitivity and lower noise than those obtained with the common ESI alone. We found that the SACI-ESI combination increased the number of detectable compounds and produced better quality of profiles in liquid chromatography (LC) coupled with MS (LC/MS) analysis of human serum samples, improving disease prediction potential. SACI-ESI can facilitate MS-based discovery of potential biomarkers in human serum. Combined with the proposed bioinformatic approach (based on XCMS and NIST data elaboration) for the analysis of the MS spectra obtained, the potential for developing biomarkers with diagnostic capabilities are demonstrated in a prostate cancer diagnosis clinical setting.

Colorectal cancer liver metastasis (CRLM) is associated with poor survival, primarily due to acquired therapy resistance. While novel therapies arise, translation is limited by the lack of tumor models accurately representing dynamic microenvironmental interplay. Here, we show that ex vivo normothermic machine perfusion (NMP) offers a novel preclinical framework to study the intratumoral dynamics of CRLM biology. Six resected metastatic human livers were preserved for two days and subjected to multi-omic profiling of serially sampled adjacent liver and metastatic tissue using single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST). Tissue integrity was assessed and cross-validated by immunofluorescence (IF), high-resolution respirometry (HRR) and flow-cytometry. NMP was successfuly applied to metastatic livers with minimal surgical adaptations, preserving both intrinsic hepatic properties and tissue viability over an extended duration. Single-cell and spatial mapping confirmed preservation of CRLM phenotypic properties and demonstrated high clinical translatability by applicability of the intrinsic epithelial consensus molecular subtypes to metastasis. Spatially deconvoluted pathway activities reflected functional tissue-microenvironments. Transcriptomic profiles - including those of tumor-associated myeloid cells - were preserved during NMP. Finally, we demonstrate tumor-associated myeloid cell persistence as a driver of disease progression and poor survival in colorectal cancer. Our findings represent the basis for future innovative applications adopting NMP in the context of CRLM, providing a new preclinical tumor model avenue.

Ex-vivo perfusion describes the extra-corporeal delivery of fluid to an organ or tissue. Although it has been widely studied in the context of organ preservation and transplantation, it has also proven to be an invaluable tool in the development of novel models for translational pre-clinical research. Here, we review the literature reporting ex-vivo human liver perfusion experiments to further understand current perfusion techniques and protocols together with their applications. A computerised search was made of Ovid, MEDLINE, and Embase using the search words “ex-vivo liver or hepatic perfusion”. All relevant studies in English describing experiments using ex-vivo perfusion of human livers between 2016 and 2021, inclusive, were included. Of 21 reviewed studies, 19 used ex-vivo human liver perfusion in the context of allogeneic liver transplantation. The quality and size of the studies varied considerably. Human liver perfusion was almost exclusively limited to whole organs and “split” livers, although one study did describe the successful perfusion of tissue sections following a partial hepatectomy. This review of recent literature involving ex-vivo human liver perfusion demonstrates that the technique is not limited to whole liver perfusion. Split-liver perfusion is extremely valuable allowing one lobe to act as a control and increasing the number available for research. This review also highlights the present lack of any reports of segmental liver perfusion. The discarded donor liver is a scarce resource, and the successful use of segmental perfusion has the potential to expand the available experimental models to facilitate pre-clinical experimentation.

With the Cellular Thermal Shift Assay (CETSA) celebrating its first decade since the PoC publication, the method has gained much interest in the basic science field as well as become an industry standard in applied drug discovery setting. In the last few years it has become apparent that in addition to providing information about drug-protein interaction, CETSA based thermal profiling of cells can help describe additional aspects of cell biology as it gives insight into the activating and rewiring of protein-protein interaction networks inside the cell upon stimuli. In the work presented here, we have used CETSA coupled to high resolution mass spectrometric readout to profile the response patterns of about 8000 proteins to several hundred molecular probes and marketed drugs. The goal has been to provide a map of wanted and unwanted effects following treatment and to correlate it to alternative readouts, by for example cellular imaging and cell death assays. We have applied the CETSA protocol for close to five hundred compounds, many in several cell-lines (HepG2, U87MG and K562). The experimental setup includes the step of treating the cells with a certain compound at a fixed concentration. After incubation for an hour, we heated the cells to a range of temperatures, followed by a lysis protocol and pooling of the samples. After this, the samples have been centrifuged to separate the soluble from insoluble fraction. The soluble fraction has then been prepared for MS acquisition using TMT based multiplexing. Typically, this allowed us to monitor changes in thermal stability among more than 8,000 proteins, as a consequence of compound treatment. This data reveals specific compound “fingerprints” that can be used to decipher mode of action and identify biomarkers. Despite the short incubation time, proteins with compound-induced thermal stability shifts are not only targets/off-targets (direct binders), but also downstream, and sometimes upstream, pathway members and general cellular responses. All data combined makes the basis of the Target Engagement Atlas. Here we can see clusters of compounds, sharing similar protein binding profiles. Among the most prominent ones are mTOR inhibitors, NSAIDs, as well as tubulin binders. Alternatively, one can use this resource to reveal clusters of “pharmacologically” associated protein networks. We show that pharmacological perturbation allows identification of clusters representing tight protein complexes (for example ribosomes), known metabolic pathways (folate biosynthesis), molecular functions (kinases), as well as networks of proteins linked only by ligand binding specificity. CETSA MS allows for unbiased, cellular, and molecular profiling of compound effects. Together with data from clinical settings, as well as in vivo/in vitro end point assays it is possible to correlate the CETSA patterns to wanted but also unwanted responses. Citation Format: Tomas Friman, Alexey Chernobrovkin, Tuomas Tolvanen, Erin Gilson, Stina Lundgren, Victoria Brehmer, Daniel Martinez Molina. The Cellular Thermal Shift assay and its applications in Target ID, MoA determination and biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1848.

Mass spectrometry-based spatial omics is a powerful approach for visualizing the spatial organization of proteins, metabolites, lipids, and other biomolecules in situ, combining the molecular depth of mass spectrometry with spatially resolved imaging. This systematic review traces the rapid technological and computational evolution of this field, including innovations in mass spectrometry imaging (MSI), labeling-based approaches, and proximity labeling techniques. It also highlights recent advances that enhance spatial resolution, expand molecular coverage, and enable deep molecular characterization and review analytical pipelines that integrate deep learning, cross-modality registration, and cloud-optimized data formats. From the multimodal and practical perspective, the integration of MSI with other spatial omics platforms and its transformative applications in tumor microenvironment profiling, neurodegenerative disease, developmental biology, biomarker discovery, and precision medicine are discussed. Finally, this review outlines challenges and opportunities, emphasizing the need for standardization, clinical validation, and interpretable artificial intelligence to enable broader adoption. These advances position MS-based spatial omics as a foundational pillar for multimodal spatial biology and personalized healthcare.

Advancing in genome sequencing has greatly propelled the understanding of the living world; however, it is insufficient for full description of a biological system. Focusing on proteomics has emerged as another large-scale platform for improving the understanding of biology. Proteomic experiments can be used for different aspects of clinical and health sciences such as food technology, biomarker discovery and drug target identification. Since proteins are main constituents of foods, proteomic technology can monitor and characterize protein content of foods and their change during production process. The proteomic biomarker discovery is advanced in various diseases such as cancer, cardiovascular diseases, AIDS, and renal diseases which provide non-invasive methods by the use of body fluids such as urine and serum. Proteomics is also used in drug target identification using different approaches such as chemical proteomics and protein interaction networks. The development and application of proteomics has increased tremendously over the last decade. Advances in proteomics methods offer many promising new directions of studying in clinical fields. In this regard, we want to discuss proteomics technology application in food investigations, drug, and biomarker discovery.

The global, integrated analysis of large-scale data sets encoding different levels of biological information opens up new possibilities to discover new biomarkers and elucidate complex mechanisms driving health and disease. This article reviews fundamental systems approaches and applications for biomarker discovery in different biomedical domains. It introduces key challenges and requirements for the development of advanced computational techniques, resources and applications. It discusses how these approaches can fill in some of the current gaps in traditional biomarker discovery and disease classification. The reader will be introduced to recent advances, techniques and applications of systems approaches to biomarker discovery and disease classification. The reader will learn fundamental research principles and tasks required in the implementation of these approaches and applications. The reader will gain a better understanding of the role of systems biology, as well as of potential opportunities and advances. Systems approaches to biomarker discovery may contribute to the discovery of more accurate and robust predictors of disease and clinical responses. Moreover, they can provide new and deeper clues of potential causal mechanisms underpinning physiological and pathological conditions.