It is unknown whether the human immune system frequently mounts a T cell response against mutations expressed by common epithelial cancers. Using a next-generation sequencing approach combined with ...high-throughput immunologic screening, we demonstrated that tumor-infiltrating lymphocytes (TILs) from 9 out of 10 patients with metastatic gastrointestinal cancers contained CD4⁺ and/or CD8⁺ T cells that recognized one to three neo-epitopes derived from somatic mutations expressed by the patient's own tumor. There were no immunogenic epitopes shared between these patients. However, we identified in one patient a human leukocyte antigen–C*08:02–restricted T cell receptor from CD8⁺ TILs that targeted the KRASG12D hotspot driver mutation found in many human cancers. Thus, a high frequency of patients with common gastrointestinal cancers harbor immunogenic mutations that can potentially be exploited for the development of highly personalized immunotherapies.
Adoptive transfer of tumor-infiltrating lymphocytes (TILs) can mediate regression of metastatic melanoma; however, TILs are a heterogeneous population, and there are no effective markers to ...specifically identify and select the repertoire of tumor-reactive and mutation-specific CD8⁺ lymphocytes. The lack of biomarkers limits the ability to study these cells and develop strategies to enhance clinical efficacy and extend this therapy to other malignancies. Here, we evaluated unique phenotypic traits of CD8⁺ TILs and TCR β chain (TCRβ) clonotypic frequency in melanoma tumors to identify patient-specific repertoires of tumor-reactive CD8⁺ lymphocytes. In all 6 tumors studied, expression of the inhibitory receptors programmed cell death 1 (PD-1; also known as CD279), lymphocyte-activation gene 3 (LAG-3; also known as CD223), and T cell immunoglobulin and mucin domain 3 (TIM-3) on CD8⁺ TILs identified the autologous tumor-reactive repertoire, including mutated neoantigen-specific CD8⁺ lymphocytes, whereas only a fraction of the tumor-reactive population expressed the costimulatory receptor 4-1BB (also known as CD137). TCRβ deep sequencing revealed oligoclonal expansion of specific TCRβ clonotypes in CD8⁺PD-1⁺ compared with CD8⁺PD-1- TIL populations. Furthermore, the most highly expanded TCRβ clonotypes in the CD8⁺ and the CD8⁺PD-1⁺ populations recognized the autologous tumor and included clonotypes targeting mutated antigens. Thus, in addition to the well-documented negative regulatory role of PD-1 in T cells, our findings demonstrate that PD-1 expression on CD8⁺ TILs also accurately identifies the repertoire of clonally expanded tumor-reactive cells and reveal a dual importance of PD-1 expression in the tumor microenvironment.
Deep learning workflow in radiology: a primer Montagnon, Emmanuel; Cerny, Milena; Cadrin-Chênevert, Alexandre ...
Insights into imaging,
02/2020, Letnik:
11, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Interest for deep learning in radiology has increased tremendously in the past decade due to the high achievable performance for various computer vision tasks such as detection, segmentation, ...classification, monitoring, and prediction. This article provides step-by-step practical guidance for conducting a project that involves deep learning in radiology, from defining specifications, to deployment and scaling. Specifically, the objectives of this article are to provide an overview of clinical use cases of deep learning, describe the composition of multi-disciplinary team, and summarize current approaches to patient, data, model, and hardware selection. Key ideas will be illustrated by examples from a prototypical project on imaging of colorectal liver metastasis. This article illustrates the workflow for liver lesion detection, segmentation, classification, monitoring, and prediction of tumor recurrence and patient survival. Challenges are discussed, including ethical considerations, cohorting, data collection, anonymization, and availability of expert annotations. The practical guidance may be adapted to any project that requires automated medical image analysis.
We identify a senescence restriction point (SeRP) as a critical event for cells to commit to senescence. The SeRP integrates the intensity and duration of oncogenic stress, keeps a memory of previous ...stresses, and combines oncogenic signals acting on different pathways by modulating chromatin accessibility. Chromatin regions opened upon commitment to senescence are enriched in nucleolar-associated domains, which are gene-poor regions enriched in repeated sequences. Once committed to senescence, cells no longer depend on the initial stress signal and exhibit a characteristic transcriptome regulated by a transcription factor network that includes ETV4, RUNX1, OCT1, and MAFB. Consistent with a tumor suppressor role for this network, the levels of ETV4 and RUNX1 are very high in benign lesions of the pancreas but decrease dramatically in pancreatic ductal adenocarcinomas. The discovery of senescence commitment and its chromatin-linked regulation suggests potential strategies for reinstating tumor suppression in human cancers.
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•Senescence restriction point integrates stress signals through non-coding chromatin opening•Chromatin opening acts as a memory print of oncogenic threats to trigger senescence•ERK2 mediates chromatin opening through a network of TFs, including ETV4 and RUNX1•ETV4 and RUNX1 are high in human benign lesions of the pancreas and lost in PDAC
Cellular stress sensors are vital for health and homeostasis. Lopes-Paciencia et al. show that chromatin acts as an oncogenic stress sensor, controlling the decision between cell proliferation and senescence. Oncogenic signals trigger transcription factors, which open chromatin and commit cells to senescence, a commitment that persists even after the initial stress subsides.
Finding a noninvasive radiomic surrogate of tumor immune features could help identify patients more likely to respond to novel immune checkpoint inhibitors. Particularly, CD73 is an ectonucleotidase ...that catalyzes the breakdown of extracellular AMP into immunosuppressive adenosine, which can be blocked by therapeutic antibodies. High CD73 expression in colorectal cancer liver metastasis (CRLM) resected with curative intent is associated with early recurrence and shorter patient survival. The aim of this study was hence to evaluate whether machine learning analysis of preoperative liver CT-scan could estimate high vs low CD73 expression in CRLM and whether such radiomic score would have a prognostic significance.
We trained an Attentive Interpretable Tabular Learning (TabNet) model to predict, from preoperative CT images, stratified expression levels of CD73 (CD73
vs. CD73
) assessed by immunofluorescence (IF) on tissue microarrays. Radiomic features were extracted from 160 segmented CRLM of 122 patients with matched IF data, preprocessed and used to train the predictive model. We applied a five-fold cross-validation and validated the performance on a hold-out test set.
TabNet provided areas under the receiver operating characteristic curve of 0.95 (95% CI 0.87 to 1.0) and 0.79 (0.65 to 0.92) on the training and hold-out test sets respectively, and outperformed other machine learning models. The TabNet-derived score, termed rad-CD73, was positively correlated with CD73 histological expression in matched CRLM (Spearman's ρ = 0.6004; P < 0.0001). The median time to recurrence (TTR) and disease-specific survival (DSS) after CRLM resection in rad-CD73
vs rad-CD73
patients was 13.0 vs 23.6 months (P = 0.0098) and 53.4 vs 126.0 months (P = 0.0222), respectively. The prognostic value of rad-CD73 was independent of the standard clinical risk score, for both TTR (HR = 2.11, 95% CI 1.30 to 3.45, P < 0.005) and DSS (HR = 1.88, 95% CI 1.11 to 3.18, P = 0.020).
Our findings reveal promising results for non-invasive CT-scan-based prediction of CD73 expression in CRLM and warrant further validation as to whether rad-CD73 could assist oncologists as a biomarker of prognosis and response to immunotherapies targeting the adenosine pathway.
Purpose
To evaluate the diagnostic performance of Liver Imaging Reporting and Data System (LI-RADS) v2017 major features, the impact of ancillary features, and categories on contrast-enhanced ...computed tomography (CECT) for the diagnosis of hepatocellular carcinoma (HCC).
Materials and methods
This retrospective study included 59 patients (104 observations including 72 HCCs) with clinical suspicion of HCC undergoing CECT between 2013 and 2016. Two radiologists independently assessed major and ancillary imaging features for each liver observation and assigned a LI-RADS category based on major features only and in combination with ancillary features. The composite reference standard included pathology or imaging. Per-lesion estimates of diagnostic performance of major features, ancillary features, and LI-RADS categories were assessed by generalized estimating equation models.
Results
Major features (arterial phase hyperenhancement, washout, capsule, and threshold growth) respectively had a sensitivity of 86.1%, 81.6%, 20.7%, and 26.1% and specificity of 39.3%, 67.9%, 89.9%, and 85.0% for HCC. Ancillary features (ultrasound visibility as discrete nodule, subthreshold growth, and fat in mass more than adjacent liver) respectively had a sensitivity of 42.6%, 50.8%, and 15.1% and a specificity of 79.2%, 66.9%, and 96.4% for HCC. Ancillary features modified the final category in 4 of 104 observations. For HCC diagnosis, categories LR-3, LR-4, LR-5, and LR-TIV (tumor in vein) had a sensitivity of 5.3%, 29.0%, 53.7%, and 10.7%; and a specificity of 49.1%, 84.4%, 97.3%, and 96.4%, respectively.
Conclusion
On CT, LR-5 category has near-perfect specificity for the diagnosis of HCC and ancillary features modifies the final category in few observations.
Hepatocellular carcinoma (HCC) is a major contributor to cancer-related morbidity and mortality burdens globally. Given the fundamental metabolic activity of hepatocytes within the liver, ...hepatocarcinogenesis is bound to be characterized by alterations in metabolite profiles as a manifestation of metabolic reprogramming.
HCC and adjacent non-tumoral liver specimens were obtained from patients after HCC resection. Global patterns in tissue metabolites were identified using non-targeted
H Nuclear Magnetic Resonance (
H-NMR) spectroscopy whereas specific metabolites were quantified using targeted liquid chromatography-mass spectrometry (LC/MS).
Principal component analysis (PCA) within our
H-NMR dataset identified a principal component (PC) one of 53.3%, along which the two sample groups were distinctively clustered. Univariate analysis of tissue specimens identified more than 150 metabolites significantly altered in HCC compared to non-tumoral liver. For LC/MS, PCA identified a PC1 of 45.2%, along which samples from HCC tissues and non-tumoral tissues were clearly separated. Supervised analysis (PLS-DA) identified decreases in tissue glutathione, succinate, glycerol-3-phosphate, alanine, malate, and AMP as the most important contributors to the metabolomic signature of HCC by LC/MS.
Together,
H-NMR and LC/MS metabolomics have the capacity to distinguish HCC from non-tumoral liver. The characterization of such distinct profiles of metabolite abundances underscores the major metabolic alterations that result from hepatocarcinogenesis.
Immune checkpoint blockade has not yet been effective in patients with mismatch repair proficient metastatic colorectal cancer. Targeting immunosuppressive metabolic pathways is being explored as a ...new immunotherapeutic approach. We assessed whether CD73, the rate limiting enzyme that catalyzes the degradation of extracellular AMP into immunosuppressive adenosine, could be an immunological determinant of colorectal liver metastases (CRLMs). By immunofluorescence on tissue microarrays, intratumoral CD73 expression (tCD73) was analyzed in 391 CRLMs resected in 215 patients, and soluble CD73 (sCD73) was measured by ELISA in the pre-operative serum of 193 patients. High tCD73 was associated with worse pathological features, such as multiple and larger CRLMs, and poorer pathologic response to pre-operative chemotherapy. The median time to recurrence and disease-specific survival after CRLM resection was significantly shorter in patients with high tCD73 (11.0 and 46.4 months, respectively) compared with low tCD73 (19.0 and 61.5 months, respectively). tCD73 was strongly associated with patient outcomes independently of clinicopathological variables. sCD73 did not correlate with tCD73. Patients with high levels of sCD73 also had shorter disease-specific survival. Our results suggested that CD73 in CRLMs may be prognostically informative and may help select patients more likely to respond to adenosine pathway blocking agents.
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