[18F]FDG PET/CT Signal Correlates with Neoangiogenesis Markers in Patients with Fibrotic Interstitial Lung Disease Who Underwent Lung Biopsy: Implication for the Use of PET/CT in Diffuse Lung Diseases.

The use of [18F]FDG PET/CT as a biomarker in diffuse lung diseases is increasingly recognized. We investigated the correlation between [18F]FDG uptake with histologic markers on lung biopsy of patients with fibrotic interstitial lung disease (fILD). Methods: We recruited 18 patients with fILD awaiting lung biopsy for [18F]FDG PET/CT. We derived a target-to-background ratio (TBR) of maximum pulmonary uptake of [18F]FDG (SUVmax) divided by the lung background (SUVmin). Consecutive paraffin-embedded lung biopsy sections were immunostained for alveolar and interstitial macrophages (CD68), microvessel density (MVD) (CD31 and CD105/endoglin), and glucose transporter 1. MVD was expressed as vessel area percentage per high-power field (Va%/hpf). Differences in imaging and angiogenesis markers between histologic usual interstitial pneumonia (UIP) and non-UIP were assessed using a nonparametric Mann-Whitney test. Correlation of imaging with angiogenesis markers was assessed using the nonparametric Spearman rank correlation. Univariate Kaplan-Meier survival analysis assessed the difference in the survival curves for each of the angiogenesis markers (separated by their respective optimal cutoff) using the log-rank test. Statistical analysis was performed using SPSS. Results: In total, 18 patients were followed for an average of 41.36 mo (range, 5.69-132.46 mo; median, 30.07 mo). Only CD105 MVD showed a significantly positive correlation with [18F]FDG TBR (Spearman rank correlation, 0.556; P < 0.05, n = 13). There was no correlation between [18F]FDG uptake and macrophage expression of glucose transporter 1. CD105 and CD31 were higher for UIP than for non-UIP, with CD105 reaching statistical significance (P = 0.011). In all patients, MVD assessed with either CD105 or CD31 quantification on biopsy predicted overall survival. Patients with CD105 MVD of less than 12 Va%/hpf or CD31 MVD of less than 35 Va%/hpf had a significantly better prognosis (no deaths during follow-up in the case of CD105) than did patients with higher scores of CD105 MVD (median survival, 35 mo; P = 0.041, n = 13) or CD31 MVD (median survival, 28 mo; P = 0.014, n = 13). Conclusion: Previous work has used [18F]FDG uptake in PET/CT as a biomarker in fILD. Here, we highlight a correlation between angiogenesis and [18F]FDG TBR. We show that MVD is higher for UIP than for non-UIP and is associated with mortality in patients with fILD. These data set the scene to investigate the potential role of vasculature and angiogenesis in fibrosis.

FUME-TCRseq Enables Sensitive and Accurate Sequencing of the T-cell Receptor from Limited Input of Degraded RNA.

Genomic analysis of the T-cell receptor (TCR) reveals the strength, breadth, and clonal dynamics of the adaptive immune response to pathogens or cancer. The diversity of the TCR repertoire, however, means that sequencing is technically challenging, particularly for samples with low-quality, degraded nucleic acids. Here, we developed and validated FUME-TCRseq, a robust and sensitive RNA-based TCR sequencing methodology that is suitable for formalin-fixed paraffin-embedded samples and low amounts of input material. FUME-TCRseq incorporates unique molecular identifiers into each molecule of cDNA, allowing correction for sequencing errors and PCR bias. Using RNA extracted from colorectal and head and neck cancers to benchmark the accuracy and sensitivity of FUME-TCRseq against existing methods demonstrated excellent concordance between the datasets. Furthermore, FUME-TCRseq detected more clonotypes than a commercial RNA-based alternative, with shorter library preparation time and significantly lower cost. The high sensitivity and the ability to sequence RNA of poor quality and limited amount enabled quantitative analysis of small numbers of cells from archival tissue sections, which is not possible with other methods. Spatially resolved FUME-TCRseq analysis of colorectal cancers using macrodissected archival samples revealed the shifting T-cell landscapes at the transition to an invasive phenotype and between tumor subclones containing distinct driver alterations. In summary, FUME-TCRseq represents an accurate, sensitive, and low-cost tool for the characterization of T-cell repertoires, particularly in samples with low-quality RNA that have not been accessible using existing methodology. SIGNIFICANCE: FUME-TCRseq is a TCR sequencing methodology that supports sensitive and spatially resolved detection of TCR clones in archival clinical specimens, which can facilitate longitudinal tracking of immune responses through disease course and treatment.

Epigenome and early selection determine the tumour-immune evolutionary trajectory of colorectal cancer.

Immune system control is a major hurdle that cancer evolution must circumvent. The relative timing and evolutionary dynamics of subclones that have escaped immune control remain incompletely characterized, and how immune-mediated selection shapes the epigenome has received little attention. Here, we infer the genome- and epigenome-driven evolutionary dynamics of tumour-immune coevolution within primary colorectal cancers (CRCs). We utilise our existing CRC multi-region multi-omic dataset that we supplement with high-resolution spatially-resolved neoantigen sequencing data and highly multiplexed imaging of the tumour microenvironment (TME). Analysis of somatic chromatin accessibility alterations (SCAAs) reveals frequent somatic loss of accessibility at antigen presenting genes, and that SCAAs contribute to silencing of neoantigens. We observe that strong immune escape and exclusion occur at the outset of CRC formation, and that within tumours, including at the microscopic level of individual tumour glands, additional immune escape alterations have negligible consequences for the immunophenotype of cancer cells. Further minor immuno-editing occurs during local invasion and is associated with TME reorganisation, but that evolutionary bottleneck is relatively weak. Collectively, we show that immune evasion in CRC follows a "Big Bang" evolutionary pattern, whereby genetic, epigenetic and TME-driven immune evasion acquired by the time of transformation defines subsequent cancer-immune evolution.

Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies.

Bone marrow trephine biopsy is crucial for the diagnosis of multiple myeloma. However, the complexity of bone marrow cellular, morphologic, and spatial architecture preserved in trephine samples hinders comprehensive evaluation. To dissect the diverse cellular communities and mosaic tissue habitats, we developed a superpixel-inspired deep learning method (MoSaicNet) that adapts to complex tissue architectures and a cell imbalance aware deep learning pipeline (AwareNet) to enable accurate detection and classification of rare cell types in multiplex immunohistochemistry images. MoSaicNet and AwareNet achieved an AUC of >0.98 for tissue and cellular classification on separate test datasets. Application of MoSaicNet and AwareNet enabled investigation of bone heterogeneity and thickness as well as spatial histology analysis of bone marrow trephine samples from monoclonal gammopathies of undetermined significance (MGUS) and from paired newly diagnosed and posttreatment multiple myeloma. The most significant difference between MGUS and newly diagnosed multiple myeloma (NDMM) samples was not related to cell density but to spatial heterogeneity, with reduced spatial proximity of BLIMP1+ tumor cells to CD8+ cells in MGUS compared with NDMM samples. Following treatment of patients with multiple myeloma, there was a reduction in the density of BLIMP1+ tumor cells, effector CD8+ T cells, and regulatory T cells, indicative of an altered immune microenvironment. Finally, bone heterogeneity decreased following treatment of patients with multiple myeloma. In summary, deep learning-based spatial mapping of bone marrow trephine biopsies can provide insights into the cellular topography of the myeloma marrow microenvironment and complement aspirate-based techniques. SIGNIFICANCE: Spatial analysis of bone marrow trephine biopsies using histology, deep learning, and tailored algorithms reveals the bone marrow architectural heterogeneity and evolution during myeloma progression and treatment.

Poor Diagnostic Reproducibility in the Identification of Nonconventional Dysplasia in Colitis Impacts the Application of Histologic Stratification Tools.

Due to their increased cancer risk, patients with longstanding inflammatory bowel disease are offered endoscopic surveillance with concomitant histopathologic assessments, aimed at identifying dysplasia as a precursor lesion of colitis-associated colorectal cancer. However, this strategy is beset with difficulties and limitations. Recently, a novel classification criterion for colitis-associated low-grade dysplasia has been proposed, and an association between nonconventional dysplasia and progression was reported, suggesting the possibility of histology-based stratification of patients with colitis-associated lesions. Here, a cohort of colitis-associated lesions was assessed by a panel of 6 experienced pathologists to test the applicability of the published classification criteria and try and validate the association between nonconventional dysplasia and progression. While confirming the presence of different morphologic patterns of colitis-associated dysplasia, the study demonstrated difficulties concerning diagnostic reproducibility between pathologists and was unable to validate the association of nonconventional dysplasia with cancer progression. Our study highlights the overall difficulty of using histologic assessment of precursor lesions for cancer risk prediction in inflammatory bowel disease patients and suggests the need for a different diagnostic strategy that can objectively identify high-risk phenotypes.

Self-supervised deep learning for highly efficient spatial immunophenotyping.

BACKGROUND: Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Label-efficient strategies are urgently needed to reveal diverse cell distribution and spatial interactions in large-scale multiplex datasets. METHODS: This study proposed Self-supervised Learning for Antigen Detection (SANDI) for accurate cell phenotyping while mitigating the annotation burden. The model first learns intrinsic pairwise similarities in unlabelled cell images, followed by a classification step to map learnt features to cell labels using a small set of annotated references. We acquired four multiplex immunohistochemistry datasets and one imaging mass cytometry dataset, comprising 2825 to 15,258 single-cell images to train and test the model. FINDINGS: With 1% annotations (18-114 cells), SANDI achieved weighted F1-scores ranging from 0.82 to 0.98 across the five datasets, which was comparable to the fully supervised classifier trained on 1828-11,459 annotated cells (-0.002 to -0.053 of averaged weighted F1-score, Wilcoxon rank-sum test, P = 0.31). Leveraging the immune checkpoint markers stained in ovarian cancer slides, SANDI-based cell identification reveals spatial expulsion between PD1-expressing T helper cells and T regulatory cells, suggesting an interplay between PD1 expression and T regulatory cell-mediated immunosuppression. INTERPRETATION: By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for histology multiplex imaging data. FUNDING: This study was funded by the Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre.

Limited efficacy of APRIL CAR in patients with multiple myeloma indicate challenges in the use of natural ligands for CAR T-cell therapy.

BACKGROUND: We used a proliferating ligand (APRIL) to construct a ligand-based third generation chimeric antigen receptor (CAR) able to target two myeloma antigens, B-cell maturation antigen (BCMA) and transmembrane activator and CAML interactor. METHODS: The APRIL CAR was evaluated in a Phase 1 clinical trial (NCT03287804, AUTO2) in patients with relapsed, refractory multiple myeloma. Eleven patients received 13 doses, the first 15×106 CARs, and subsequent patients received 75,225,600 and 900×106 CARs in a 3+3 escalation design. RESULTS: The APRIL CAR was well tolerated. Five (45.5%) patients developed Grade 1 cytokine release syndrome and there was no neurotoxicity. However, responses were only observed in 45.5% patients (1×very good partial response, 3×partial response, 1×minimal response). Exploring the mechanistic basis for poor responses, we then compared the APRIL CAR to two other BCMA CARs in a series of in vitro assays, observing reduced interleukin-2 secretion and lack of sustained tumor control by APRIL CAR regardless of transduction method or co-stimulatory domain. There was also impaired interferon signaling of APRIL CAR and no evidence of autoactivation. Thus focusing on APRIL itself, we confirmed similar affinity to BCMA and protein stability in comparison to BCMA CAR binders but reduced binding by cell-expressed APRIL to soluble BCMA and reduced avidity to tumor cells. This indicated either suboptimal folding or stability of membrane-bound APRIL attenuating CAR activation. CONCLUSIONS: The APRIL CAR was well tolerated, but the clinical responses observed in AUTO2 were disappointing. Subsequently, when comparing the APRIL CAR to other BCMA CARs, we observed in vitro functional deficiencies due to reduced target binding by cell-expressed ligand.

Identification of vulnerable carotid plaque with histologically validated CT-derived plaque maps.

OBJECTIVES: Ruptured carotid plaque causes stroke, but differentiating rupture-prone necrotic core from fibrous tissue with CT is limited by overlap of X-ray attenuation. We investigated the ability of CT-derived plaque maps created from ratios of plaque/contrast attenuation to identify histologically proven vulnerable plaques. METHODS: Seventy patients underwent carotid CT angiography and carotid endarterectomy. A derivation cohort of 20 patients had CT images matched with histology and carotid plaque components attenuation defined. In a validation cohort of 50 patients, CT-derived plaque maps were compared in 43 symptomatic vs 40 asymptomatic carotid plaques and accuracy detecting vulnerable plaques calculated. RESULTS: In 250 plaque areas co-registered with histology, the median attenuation (HU) of necrotic core 43(26-63), fibrous plaque 127(110-162) and calcified plaque 964 (816-1207) created significantly different ratios of plaque/contrast attenuation. CT-derived plaque maps revealed symptomatic plaques had larger necrotic core than asymptomatic (13.5%(5.9-33.3) vs 7.4%(2.3-14.3), p = 0.004) with large necrotic core predicting symptoms (area under ROC curve 0.68, p = 0.004). Twenty-four of 47 carotid plaques were histologically classified as most vulnerable (Starry-Type VI). Plaque maps revealed Type VI plaques had a greater necrotic core volume than Type IV/V plaques and a necrotic core/fibrous plaque ratio >0.5 distinguished Type VI plaques with sensitivity 75.0% (55.1-88.0) and specificity of 39.1% (22.2-59.2). CONCLUSIONS: Carotid plaque components can be differentiated by CT using a ratio of plaque/contrast attenuation. CT-derived plaque map volumes of necrotic core help distinguished the most vulnerable plaques. ADVANCES IN KNOWLEDGE: CT-derived plaque maps based on plaque/contrast attenuation may provide new markers of carotid plaque vulnerability.

Deep Learning Applied to Raman Spectroscopy for the Detection of Microsatellite Instability/MMR Deficient Colorectal Cancer.

Defective DNA mismatch repair is one pathogenic pathway to colorectal cancer. It is characterised by microsatellite instability which provides a molecular biomarker for its detection. Clinical guidelines for universal testing of this biomarker are not met due to resource limitations; thus, there is interest in developing novel methods for its detection. Raman spectroscopy (RS) is an analytical tool able to interrogate the molecular vibrations of a sample to provide a unique biochemical fingerprint. The resulting datasets are complex and high-dimensional, making them an ideal candidate for deep learning, though this may be limited by small sample sizes. This study investigates the potential of using RS to distinguish between normal, microsatellite stable (MSS) and microsatellite unstable (MSI-H) adenocarcinoma in human colorectal samples and whether deep learning provides any benefit to this end over traditional machine learning models. A 1D convolutional neural network (CNN) was developed to discriminate between healthy, MSI-H and MSS in human tissue and compared to a principal component analysis-linear discriminant analysis (PCA-LDA) and a support vector machine (SVM) model. A nested cross-validation strategy was used to train 30 samples, 10 from each group, with a total of 1490 Raman spectra. The CNN achieved a sensitivity and specificity of 83% and 45% compared to PCA-LDA, which achieved a sensitivity and specificity of 82% and 51%, respectively. These are competitive with existing guidelines, despite the low sample size, speaking to the molecular discriminative power of RS combined with deep learning. A number of biochemical antecedents responsible for this discrimination are also explored, with Raman peaks associated with nucleic acids and collagen being implicated.

High Interobserver Variability Among Pathologists Using Combined Positive Score to Evaluate PD-L1 Expression in Gastric, Gastroesophageal Junction, and Esophageal Adenocarcinoma.

Reliable, reproducible methods to interpret programmed death ligand-1 (PD-L1) expression on tumor cells (TC) and immune cells (IC) are needed for pathologists to inform decisions associated with checkpoint inhibitor therapies. Our international study compared interpathologist agreement of PD-L1 expression using the combined positive score (CPS) under standardized conditions on samples from patients with gastric/gastroesophageal junction/esophageal adenocarcinoma. Tissue sections from 100 adenocarcinoma pretreatment biopsies were stained in a single laboratory using the PD-L1 immunohistochemistry 28-8 and 22C3 (Agilent) pharmDx immunohistochemical assays. PD-L1 CPS was evaluated by 12 pathologists on scanned whole slide images of these biopsies before and after a 2-hour CPS training session by Agilent. Additionally, pathologists determined PD-L1-positive TC, IC, and total viable TC on a single tissue fragment from 35 of 100 biopsy samples. Scoring agreement among pathologists was assessed using the intraclass correlation coefficient (ICC). Interobserver variability for CPS for 100 biopsies was high, with only fair agreement among pathologists both pre- (range, 0.45-0.55) and posttraining (range, 0.56-0.57) for both assays. For the 35 single biopsy samples, poor/fair agreement was also observed for the total number of viable TC (ICC, 0.09), number of PD-L1-positive IC (ICC, 0.19), number of PD-L1-positive TC (ICC, 0.54), and calculated CPS (ICC, 0.14), whereas calculated TC score (positive TC/total TC) showed excellent agreement (ICC, 0.82). Retrospective histologic review of samples with the poorest interpathologist agreement revealed the following as possible confounding factors: (1) ambiguous identification of positively staining stromal cells, (2) faint or variable intensity of staining, (3) difficulty in distinguishing membranous from cytoplasmic tumor staining, and (4) cautery and crush artifacts. These results emphasize the need for objective techniques to standardize the interpretation of PD-L1 expression when using the CPS methodology on gastric/gastroesophageal junction cancer biopsies to accurately identify patients most likely to benefit from immune checkpoint inhibitor therapy.

The co-evolution of the genome and epigenome in colorectal cancer.

Colorectal malignancies are a leading cause of cancer-related death1 and have undergone extensive genomic study2,3. However, DNA mutations alone do not fully explain malignant transformation4-7. Here we investigate the co-evolution of the genome and epigenome of colorectal tumours at single-clone resolution using spatial multi-omic profiling of individual glands. We collected 1,370 samples from 30 primary cancers and 8 concomitant adenomas and generated 1,207 chromatin accessibility profiles, 527 whole genomes and 297 whole transcriptomes. We found positive selection for DNA mutations in chromatin modifier genes and recurrent somatic chromatin accessibility alterations, including in regulatory regions of cancer driver genes that were otherwise devoid of genetic mutations. Genome-wide alterations in accessibility for transcription factor binding involved CTCF, downregulation of interferon and increased accessibility for SOX and HOX transcription factor families, suggesting the involvement of developmental genes during tumourigenesis. Somatic chromatin accessibility alterations were heritable and distinguished adenomas from cancers. Mutational signature analysis showed that the epigenome in turn influences the accumulation of DNA mutations. This study provides a map of genetic and epigenetic tumour heterogeneity, with fundamental implications for understanding colorectal cancer biology.

Phenotypic plasticity and genetic control in colorectal cancer evolution.

Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity1. The interplay of these biological processes and their respective contributions to tumour evolution remain unknown. Here we show that intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer (CRC). Using spatially resolved paired whole-genome and transcriptome sequencing, we find that the majority of intratumour variation in gene expression is not strongly heritable but rather 'plastic'. Somatic expression quantitative trait loci analysis identified a number of putative genetic controls of expression by cis-acting coding and non-coding mutations, the majority of which were clonal within a tumour, alongside frequent structural alterations. Consistently, computational inference on the spatial patterning of tumour phylogenies finds that a considerable proportion of CRCs did not show evidence of subclonal selection, with only a subset of putative genetic drivers associated with subclone expansions. Spatial intermixing of clones is common, with some tumours growing exponentially and others only at the periphery. Together, our data suggest that most genetic intratumour variation in CRC has no major phenotypic consequence and that transcriptional plasticity is, instead, widespread within a tumour.

Biobank and Pathology Facility: A Successful Combination.

Financial sustainability in biobanks has recently become a key issue globally, as biorepositories struggle to balance limited external funding and high operating costs. To maximize governance and operational efficiency, the Pathology Facility and the University College London (UCL)/UCL Hospitals Biobank for Studying Health and Disease ("the Biobank") have been grouped together under the same management at the UCL Cancer Institute. This paper explores the operational and financial interaction between the Pathology Facility and the Biobank over a period of 3 years (2017-2019). Since 2017, only a minority of the requests included collection of samples from the archive or molecular biology services, and most of the requests included histology services. Our data confirmed the difficulty for a biobank to achieve financial sustainability. The integration of the Pathology Facility with the Biobank within a single laboratory management and delivery infrastructure was shown to be an effective management option and presented a unique opportunity to overcome financial and operational challenges, thus improving efficiency and lowering costs for both parties.

CD229 (Ly9) a Novel Biomarker for B-Cell Malignancies and Multiple Myeloma.

CD229 (Ly9) homophilic receptor, which belongs to the SLAM family of cell-surface molecules, is predominantly expressed on B and T cells. It acts as a signaling molecule, regulating lymphocyte homoeostasis and activation. Studies of CD229 function indicate that this receptor functions as a regulator of the development of marginal-zone B cells and other innate-like T and B lymphocytes. The expression on leukemias and lymphomas remains poorly understood due to the lack of CD229 monoclonal antibodies (mAb) for immunohistochemistry application (IHC). In this study, we used a new mAb against the cytoplasmic region of CD229 to study the expression of CD229 on normal tissues and B-cell malignancies, including multiple myeloma (MM), using tissue microarrays. We showed CD229 to be restricted to hematopoietic cells. It was strongly expressed in all cases of MM and in most marginal-zone lymphomas (MZL). Moderate CD229 expression was also found in chronic lymphocyte leukemia (CLL), follicular (FL), classic mantle-cell (MCL) and diffuse large B-cell lymphoma. Given the high expression on myeloma cells, we also analyzed for the presence of soluble CD229 in the sera of these patients. Serum levels of soluble CD229 (sCD229) at the time of diagnosis in MM patients could be useful as a prognostic biomarker. In conclusion, our results indicate that CD229 represents not only a useful biomarker but also an attractive therapeutic target.