Tertiary lymphoid structures (TLSs) are ectopic lymphoid organs that develop in non-lymphoid tissues at sites of chronic inflammation including tumours. Key common characteristics between secondary ...lymphoid organogenesis and TLS neogenesis have been identified. TLSs exist under different maturation states in tumours, culminating in germinal centre formation. The mechanisms that underlie the role of TLSs in the adaptive antitumour immune response are being deciphered. The description of the correlation between TLS presence and clinical benefit in patients with cancer, suggesting that TLSs could be a prognostic and predictive factor, has drawn strong interest into investigating the role of TLSs in tumours. A current major challenge is to exploit TLSs to promote lymphocyte infiltration, activation by tumour antigens and differentiation to increase the antitumour immune response. Several approaches are being developed using chemokines, cytokines, antibodies, antigen-presenting cells or synthetic scaffolds to induce TLS formation. Strategies aiming to induce TLS neogenesis in immune-low tumours and in immune-high tumours, in this case, in combination with therapeutic agents dampening the inflammatory environment and/or with immune checkpoint inhibitors, represent promising avenues for cancer treatment.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Artificial intelligence (AI) can extract complex information from visual data. Histopathology images of gastrointestinal (GI) and liver cancer contain a very high amount of information which human ...observers can only partially make sense of. Complementing human observers, AI allows an in-depth analysis of digitised histological slides of GI and liver cancer and offers a wide range of clinically relevant applications. First, AI can automatically detect tumour tissue, easing the exponentially increasing workload on pathologists. In addition, and possibly exceeding pathologist's capacities, AI can capture prognostically relevant tissue features and thus predict clinical outcome across GI and liver cancer types. Finally, AI has demonstrated its capacity to infer molecular and genetic alterations of cancer tissues from histological digital slides. These are likely only the first of many AI applications that will have important clinical implications. Thus, pathologists and clinicians alike should be aware of the principles of AI-based pathology and its ability to solve clinically relevant problems, along with its limitations and biases.
Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer, both at the molecular and histological level. High-throughput sequencing and gene expression profiling have identified distinct ...transcriptomic subclasses and numerous recurrent genetic alterations; several HCC subtypes characterised by histological features have also been identified. HCC phenotype appears to be closely related to particular gene mutations, tumour subgroups and/or oncogenic pathways. Non-proliferative tumours display a well-differentiated phenotype. Among this molecular subgroup, CTNNB1-mutated HCCs constitute a homogeneous subtype, exhibiting cholestasis and microtrabecular and pseudoglandular architectural patterns. Another non-proliferative subtype has a gene expression pattern similar to that of mature hepatocytes (G4) and displays a steatohepatitic phenotype. In contrast, proliferative HCCs are most often poorly differentiated, and notably include tumours with progenitor features. A novel morphological variant of proliferative HCC – designated “macrotrabecular-massive” – was recently shown to be associated with angiogenesis activation and poor prognosis. Altogether, these findings may help to translate our knowledge of HCC biology into clinical practice, resulting in improved precision medicine for patients with this highly aggressive malignancy. This manuscript reviews the most recent data in this exciting field, discussing future directions and challenges.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Deep learning methods for digital pathology analysis are an effective way to address multiple clinical questions, from diagnosis to prediction of treatment outcomes. These methods have also been used ...to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides has yet been performed. We show that HE2RNA, a model based on the integration of multiple data modes, can be trained to systematically predict RNA-Seq profiles from whole-slide images alone, without expert annotation. Through its interpretable design, HE2RNA provides virtual spatialization of gene expression, as validated by CD3- and CD20-staining on an independent dataset. The transcriptomic representation learned by HE2RNA can also be transferred on other datasets, even of small size, to increase prediction performance for specific molecular phenotypes. We illustrate the use of this approach in clinical diagnosis purposes such as the identification of tumors with microsatellite instability.
Hepatocellular carcinoma (HCC) currently represents the fifth most common malignancy and the third-leading cause of cancer-related death worldwide, with incidence and mortality rates that are ...increasing. Recently, artificial intelligence (AI) has emerged as a unique opportunity to improve the full spectrum of HCC clinical care, by improving HCC risk prediction, diagnosis, and prognostication. AI approaches include computational search algorithms, machine learning (ML) and deep learning (DL) models. ML consists of a computer running repeated iterations of models, in order to progressively improve performance of a specific task, such as classifying an outcome. DL models are a subtype of ML, based on neural network structures that are inspired by the neuroanatomy of the human brain. A growing body of recent data now apply DL models to diverse data sources – including electronic health record data, imaging modalities, histopathology and molecular biomarkers – to improve the accuracy of HCC risk prediction, detection and prediction of treatment response. Despite the promise of these early results, future research is still needed to standardise AI data, and to improve both the generalisability and interpretability of results. If such challenges can be overcome, AI has the potential to profoundly change the way in which care is provided to patients with or at risk of HCC.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Infection by HBV is the main risk factor for hepatocellular carcinoma (HCC) worldwide. HBV directly drives carcinogenesis through integrations in the human genome. This study aimed to precisely ...characterise HBV integrations, in relation with viral and host genomics and clinical features.
A novel pipeline was set up to perform viral capture on tumours and non-tumour liver tissues from a French cohort of 177 patients mainly of European and African origins. Clonality of each integration event was determined with the localisation, orientation and content of the integrated sequence. In three selected tumours, complex integrations were reconstructed using long-read sequencing or Bionano whole genome mapping.
Replicating HBV DNA was more frequently detected in non-tumour tissues and associated with a higher number of non-clonal integrations. In HCC, clonal selection of HBV integrations was related to two different mechanisms involved in carcinogenesis. First, integration of viral enhancer nearby a cancer-driver gene may lead to a strong overexpression of oncogenes. Second, we identified frequent chromosome rearrangements at HBV integration sites leading to cancer-driver genes (
) alterations at distance. Moreover, HBV integrations have direct clinical implications as HCC with a high number of insertions develop in young patients and have a poor prognosis.
Deep characterisation of HBV integrations in liver tissues highlights new HBV-associated driver mechanisms involved in hepatocarcinogenesis. HBV integrations have multiple direct oncogenic consequences that remain an important challenge for the follow-up of HBV-infected patients.
Hepatic ischemia/reperfusion injury is a complication of liver surgery that involves mitochondrial dysfunction resulting from mitochondrial permeability transition pore (mPTP) opening. Cyclophilin D ...(PPIF or CypD) is a peptidyl-prolyl cis-trans isomerase that regulates mPTP opening in the inner mitochondrial membrane. We investigated whether and how recently created small-molecule inhibitors of CypD prevent opening of the mPTP in hepatocytes and the resulting effects in cell models and livers of mice undergoing ischemia/reperfusion injury.
We measured the activity of 9 small-molecule inhibitors of cyclophilins in an assay of CypD activity. The effects of the small-molecule CypD inhibitors or vehicle on mPTP opening were assessed by measuring mitochondrial swelling and calcium retention in isolated liver mitochondria from C57BL/6J (wild-type) and Ppif–/– (CypD knockout) mice and in primary mouse and human hepatocytes by fluorescence microscopy. We induced ischemia/reperfusion injury in livers of mice given a small-molecule CypD inhibitor or vehicle before and during reperfusion and collected samples of blood and liver for histologic analysis.
The compounds inhibited peptidyl-prolyl isomerase activity (half maximal inhibitory concentration values, 0.2–16.2 μmol/L) and, as a result, calcium-induced mitochondrial swelling, by preventing mPTP opening (half maximal inhibitory concentration values, 1.4–132 μmol/L) in a concentration-dependent manner. The most potent inhibitor (C31) bound CypD with high affinity and inhibited swelling in mitochondria from livers of wild-type and Ppif–/– mice (indicating an additional, CypD-independent effect on mPTP opening) and in primary human and mouse hepatocytes. Administration of C31 in mice with ischemia/reperfusion injury before and during reperfusion restored hepatic calcium retention capacity and oxidative phosphorylation parameters and reduced liver damage compared with vehicle.
Recently created small-molecule inhibitors of CypD reduced calcium-induced swelling in mitochondria from mouse and human liver tissues. Administration of these compounds to mice during ischemia/reperfusion restored hepatic calcium retention capacity and oxidative phosphorylation parameters and reduced liver damage. These compounds might be developed to protect patients from ischemia/reperfusion injury after liver surgery or for other hepatic or nonhepatic disorders related to abnormal mPTP opening.
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Somatic mutations activating telomerase reverse-trancriptase promoter were recently identified in several tumour types. Here we identify frequent similar mutations in human hepatocellular carcinomas ...(59%), cirrhotic preneoplastic macronodules (25%) and hepatocellular adenomas with malignant transformation in hepatocellular carcinomas (44%). In hepatocellular tumours, telomerase reverse-transcripase- and CTNNB1-activating mutations are significantly associated. Moreover, preliminary data suggest that telomerase reverse-trancriptase promoter mutations can increase the expression of telomerase transcript. In conclusion, telomerase reverse-trancriptase promoter mutation is the earliest recurrent genetic event identified in cirrhotic preneoplastic lesions so far and is also the most frequent genetic alteration in hepatocellular carcinomas, arising from both the cirrhotic or non-cirrhotic liver.
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