Spatially confined sub-tumor microenvironments in pancreatic cancer.

Intratumoral heterogeneity is a critical frontier in understanding how the tumor microenvironment (TME) propels malignant progression. Here, we deconvolute the human pancreatic TME through large-scale integration of histology-guided regional multiOMICs with clinical data and patient-derived preclinical models. We discover "subTMEs," histologically definable tissue states anchored in fibroblast plasticity, with regional relationships to tumor immunity, subtypes, differentiation, and treatment response. "Reactive" subTMEs rich in complex but functionally coordinated fibroblast communities were immune hot and inhabited by aggressive tumor cell phenotypes. The matrix-rich "deserted" subTMEs harbored fewer activated fibroblasts and tumor-suppressive features yet were markedly chemoprotective and enriched upon chemotherapy. SubTMEs originated in fibroblast differentiation trajectories, and transitory states were notable both in single-cell transcriptomics and in situ. The intratumoral co-occurrence of subTMEs produced patient-specific phenotypic and computationally predictable heterogeneity tightly linked to malignant biology. Therefore, heterogeneity within the plentiful, notorious pancreatic TME is not random but marks fundamental tissue organizational units.

Deep spatial profiling of human COVID-19 brains reveals neuroinflammation with distinct microanatomical microglia-T-cell interactions.

COVID-19 can cause severe neurological symptoms, but the underlying pathophysiological mechanisms are unclear. Here, we interrogated the brain stems and olfactory bulbs in postmortem patients who had COVID-19 using imaging mass cytometry to understand the local immune response at a spatially resolved, high-dimensional, single-cell level and compared their immune map to non-COVID respiratory failure, multiple sclerosis, and control patients. We observed substantial immune activation in the central nervous system with pronounced neuropathology (astrocytosis, axonal damage, and blood-brain-barrier leakage) and detected viral antigen in ACE2-receptor-positive cells enriched in the vascular compartment. Microglial nodules and the perivascular compartment represented COVID-19-specific, microanatomic-immune niches with context-specific cellular interactions enriched for activated CD8+ T cells. Altered brain T-cell-microglial interactions were linked to clinical measures of systemic inflammation and disturbed hemostasis. This study identifies profound neuroinflammation with activation of innate and adaptive immune cells as correlates of COVID-19 neuropathology, with implications for potential therapeutic strategies.

MSIreg: an R package for unsupervised coregistration of mass spectrometry and H&E images.

SUMMARY: Joint analysis of mass spectrometry images (MS images) and microscopy images of hematoxylin and eosin (H&E) stained tissues assists pathologists in characterizing the morphological structure of the tissues, and in performing diagnosis. Unfortunately, the analysis is undermined by substantial differences between these modalities in terms of aspect ratios, spatial resolution, number of channels in each image, as well as by large global or small local elastic spatial deformations of one image with respect to the other. Therefore, accurate coregistration of the images is a critical pre-requisite for their joint interpretation. We introduce MSIreg, an open-source R package for coregistration of MSI and H&E images. MSIreg is designed for high-dimensional MSI experiments where each spatial location is represented by thousands of mass features. Unlike most existing coregistration methods, MSIreg implements a landmark free workflow, and quantitative metrics for performance evaluation. We evaluate the performance of MSIreg on six case studies, including coregistration of contiguous tissues with large deformations, as well as simultaneous coregistration of 29 tissue microarray cores. AVAILABILITY: The R package, installation instructions, and fully reproducible vignettes describing methods and Case Studies are are available open-source under the GPL-3.0 license at https://github.com/sslakkimsetty/msireg/.

Neoadjuvant chemo- or chemo-radiation-therapy of pancreatic ductal adenocarcinoma differentially shift ECM composition, complement activation, energy metabolism and ribosomal proteins of the residual tumor mass.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer, often diagnosed at stages that dis-qualify for surgical resection. Neoadjuvant therapies offer potential tumor regression and improved resectability. Although features of the tumor biology (e.g., molecular markers) may guide adjuvant therapy, biological alterations after neoadjuvant therapy remain largely unexplored. We performed mass spectrometry to characterize the proteomes of 67 PDAC resection specimens of patients who received either neoadjuvant chemo (NCT) or chemo-radiation (NCRT) therapy. We employed data-independent acquisition (DIA), yielding a proteome coverage in excess of 3500 proteins. Moreover, we successfully integrated two publicly available proteome datasets of treatment-naïve PDAC to unravel proteome alterations in response to neoadjuvant therapy, highlighting the feasibility of this approach. We found highly distinguishable proteome profiles. Treatment-naïve PDAC was characterized by enrichment of immunoglobulins, complement and extracellular matrix (ECM) proteins. Post-NCT and post-NCRT PDAC presented high abundance of ribosomal and metabolic proteins as compared to treatment-naïve PDAC. Further analyses on patient survival and protein expression identified treatment-specific prognostic candidates. We present the first proteomic characterization of the residual PDAC mass after NCT and NCRT, and potential protein candidate markers associated with overall survival. We conclude that residual PDAC exhibits fundamentally different proteome profiles as compared to treatment-naïve PDAC, influenced by the type of neoadjuvant treatment. These findings may impact adjuvant or targeted therapy options.

Hourglass, a rapid analysis framework for heterogeneous bioimaging data, identifies sex disparity in IL-6/STAT3-associated immune phenotypes in pancreatic cancer.

Integration and mining of bioimaging data remains a challenge and lags behind the rapidly expanding digital pathology field. We introduce Hourglass, an open-access analytical framework that streamlines biology-driven visualization, interrogation, and statistical assessment of multiparametric datasets. Cognizant of tissue and clinical heterogeneity, Hourglass systematically organizes observations across spatial and global levels and within patient subgroups. Applied to an extensive bioimaging dataset, Hourglass promptly consolidated a breadth of known interleukin-6 (IL-6) functions via its downstream effector STAT3 and uncovered a so-far unknown sexual dimorphism in the IL-6/STAT3-linked intratumoral T-cell response in human pancreatic cancer. As an R package and cross-platform application, Hourglass facilitates knowledge extraction from multi-layered bioimaging datasets for users with or without computational proficiency and provides unique and widely accessible analytical means to harness insights hidden within heterogeneous tissues at the sample and patient level. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Proteome alterations in human autopsy tissues in relation to time after death.

Protein expression is a primary area of interest for routine histological diagnostics and tissue-based research projects, but the limitations of its post-mortem applicability remain largely unclear. On the other hand, tissue specimens obtained during autopsies can provide unique insight into advanced disease states, especially in cancer research. Therefore, we aimed to identify the maximum post-mortem interval (PMI) which is still suitable for characterizing protein expression patterns, to explore organ-specific differences in protein degradation, and to investigate whether certain proteins follow specific degradation kinetics. Therefore, the proteome of human tissue samples obtained during routine autopsies of deceased patients with accurate PMI (6, 12, 18, 24, 48, 72, 96 h) and without specific diseases that significantly affect tissue preservation, from lungs, kidneys and livers, was analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). For the kidney and liver, significant protein degradation became apparent at 48 h. For the lung, the proteome composition was rather static for up to 48 h and substantial protein degradation was detected only at 72 h suggesting that degradation kinetics appear to be organ specific. More detailed analyses suggested that proteins with similar post-mortem kinetics are not primarily shared in their biological functions. The overrepresentation of protein families with analogous structural motifs in the kidney indicates that structural features may be a common factor in determining similar postmortem stability. Our study demonstrates that a longer post-mortem period may have a significant impact on proteome composition, but sampling within 24 h may be appropriate, as degradation is within acceptable limits even in organs with faster autolysis.

Morphologic and molecular analysis of liver injury after SARS-CoV-2 vaccination reveals distinct characteristics.

BACKGROUND & AIMS: Liver injury after COVID-19 vaccination is very rare and shows clinical and histomorphological similarities with autoimmune hepatitis (AIH). Little is known about the pathophysiology of COVID-19 vaccine-induced liver injury (VILI) and its relationship to AIH. Therefore, we compared VILI with AIH. METHODS: Formalin-fixed and paraffin-embedded liver biopsy samples from patients with VILI (n = 6) and from patients with an initial diagnosis of AIH (n = 9) were included. Both cohorts were compared by histomorphological evaluation, whole-transcriptome and spatial transcriptome sequencing, multiplex immunofluorescence, and immune repertoire sequencing. RESULTS: Histomorphology was similar in both cohorts but showed more pronounced centrilobular necrosis in VILI. Gene expression profiling showed that mitochondrial metabolism and oxidative stress-related pathways were more and interferon response pathways were less enriched in VILI. Multiplex analysis revealed that inflammation in VILI was dominated by CD8+ effector T cells, similar to drug-induced autoimmune-like hepatitis. In contrast, AIH showed a dominance of CD4+ effector T cells and CD79a+ B and plasma cells. T-cell receptor (TCR) and B-cell receptor sequencing showed that T and B cell clones were more dominant in VILI than in AIH. In addition, many T cell clones detected in the liver were also found in the blood. Interestingly, analysis of TCR beta chain and Ig heavy chain variable-joining gene usage further showed that TRBV6-1, TRBV5-1, TRBV7-6, and IgHV1-24 genes are used differently in VILI than in AIH. CONCLUSIONS: Our analyses support that SARS-CoV-2 VILI is related to AIH but also shows distinct differences from AIH in histomorphology, pathway activation, cellular immune infiltrates, and TCR usage. Therefore, VILI may be a separate entity, which is distinct from AIH and more closely related to drug-induced autoimmune-like hepatitis. IMPACT AND IMPLICATIONS: Little is known about the pathophysiology of COVID-19 vaccine-induced liver injury (VILI). Our analysis shows that COVID-19 VILI shares some similarities with autoimmune hepatitis, but also has distinct differences such as increased activation of metabolic pathways, a more prominent CD8+ T cell infiltrate, and an oligoclonal T and B cell response. Our findings suggest that VILI is a distinct disease entity. Therefore, there is a good chance that many patients with COVID-19 VILI will recover completely and will not develop long-term autoimmune hepatitis.

Stimulated Raman histology for histological evaluation of oral squamous cell carcinoma.

OBJECTIVES: To investigate whether in patients undergoing surgery for oral squamous cell carcinoma, stimulated Raman histology (SRH), in comparison with H&E-stained frozen sections, can provide accurate diagnoses regarding neoplastic tissue and sub-classification of non-neoplastic tissues. MATERIALS AND METHODS: SRH, a technology based on Raman scattering, was applied to generate digital histopathologic images of 80 tissue samples obtained from 8 oral squamous cell carcinoma (OSCC) patients. Conventional H&E-stained frozen sections were then obtained from all 80 samples. All images/sections (SRH and H&E) were analyzed for squamous cell carcinoma, normal mucosa, connective tissue, muscle tissue, adipose tissue, salivary gland tissue, lymphatic tissue, and inflammatory cells. Agreement between SRH and H&E was evaluated by calculating Cohen's kappa. Accuracy of SRH compared to H&E was quantified by calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) as well as area under the receiver operating characteristic curve (AUC). RESULTS: Thirty-six of 80 samples were classified as OSCC by H&E-based diagnosis. Regarding the differentiation between neoplastic and non-neoplastic tissue, high agreement between H&E and SRH (kappa: 0.880) and high accuracy of SRH (sensitivity: 100%; specificity: 90.91%; PPV: 90.00%, NPV: 100%; AUC: 0.954) were demonstrated. For sub-classification of non-neoplastic tissues, SRH performance was dependent on the type of tissue, with high agreement and accuracy for normal mucosa, muscle tissue, and salivary glands. CONCLUSION: SRH provides high accuracy in discriminating neoplastic and non-neoplastic tissues. Regarding sub-classification of non-neoplastic tissues in OSCC patients, accuracy varies depending on the type of tissue examined. CLINICAL RELEVANCE: This study demonstrates the potential of SRH for intraoperative imaging of fresh, unprocessed tissue specimens from OSCC patients without the need for sectioning or staining.

Krüppel-like factor 7 influences translation and pathways involved in ribosomal biogenesis in breast cancer.

BACKGROUND: Ribosomal biogenesis and ribosomal proteins have attracted attention in the context of tumor biology in recent years. Instead of being mere translational machineries, ribosomes might play an active role in tumor initiation and progression. Despite its importance, regulation of ribosomal biogenesis is still not completely understood. METHODS: Using Gene Set Enrichment Analysis of RNA sequencing and proteomical mass spectrometry data in breast cancer cells expressing Krüppel-like factor 7 (KLF7), we identified processes altered by this transcription factor. In silico analyses of a cohort of breast cancer patients in The Cancer Genome Atlas confirmed our finding. We further verified the role of KLF7 the identified ribosomal processes in in vitro assays of mammary carcinoma cell lines and analyses of breast cancer patients' tissue slices. RESULTS: We identified the transcription factor Krüppel-like factor 7 (KLF7) as a regulator of ribosomal biogenesis and translation in breast cancer cells and tissue. Highly significant overlapping processes related to ribosomal biogenesis were identified in proteomics and transcriptomics data and confirmed in patients' breast cancer RNA Seq data. Further, nucleoli, the sites of ribosomal biogenesis, were morphologically altered and quantitatively increased in KLF7-expressing cells. Pre-rRNA processing was identified as one potential process affected by KLF7. In addition, an increase in global translation independent from proliferation and transcription was observed upon exogenous KLF7 expression in vitro. Importantly, in a cohort of breast cancer patients, KLF7-expression levels correlated with aggressiveness of the intrinsic breast cancer subtype and tumor grading. Moreover, KLF7 correlated with nucleolar characteristics in human breast tumor tissue, indicating a role for KLF7 in ribosomal biogenesis. CONCLUSION: In mammary carcinoma, KLF7 is involved in ribosomal biogenesis. Alterations of ribosomal biogenesis has far reaching quantitative and qualitative implications for the proteome of the cancer cells. This might influence the aggressiveness of cancer cells.

SARS-CoV-2 vaccination can elicit a CD8 T-cell dominant hepatitis.

BACKGROUND & AIMS: Autoimmune hepatitis episodes have been described following SARS-CoV-2 infection and vaccination but their pathophysiology remains unclear. Herein, we report the case of a 52-year-old male, presenting with bimodal episodes of acute hepatitis, each occurring 2-3 weeks after BNT162b2 mRNA vaccination. We sought to identify the underlying immune correlates. The patient received oral budesonide, relapsed, but achieved remission under systemic steroids. METHODS: Imaging mass cytometry for spatial immune profiling was performed on liver biopsy tissue. Flow cytometry was performed to dissect CD8 T-cell phenotypes and identify SARS-CoV-2-specific and EBV-specific T cells longitudinally. Vaccine-induced antibodies were determined by ELISA. Data were correlated with clinical laboratory results. RESULTS: Analysis of the hepatic tissue revealed an immune infiltrate quantitatively dominated by activated cytotoxic CD8 T cells with panlobular distribution. An enrichment of CD4 T cells, B cells, plasma cells and myeloid cells was also observed compared to controls. The intrahepatic infiltrate showed enrichment for CD8 T cells with SARS-CoV-2-specificity compared to the peripheral blood. Notably, hepatitis severity correlated longitudinally with an activated cytotoxic phenotype of peripheral SARS-CoV-2-specific, but not EBV-specific, CD8+ T cells or vaccine-induced immunoglobulins. CONCLUSIONS: COVID-19 vaccination can elicit a distinct T cell-dominant immune-mediated hepatitis with a unique pathomechanism associated with vaccination-induced antigen-specific tissue-resident immunity requiring systemic immunosuppression. LAY SUMMARY: Liver inflammation is observed during SARS-CoV-2 infection but can also occur in some individuals after vaccination and shares some typical features with autoimmune liver disease. In this report, we show that highly activated T cells accumulate and are evenly distributed in the different areas of the liver in a patient with liver inflammation following SARS-CoV-2 vaccination. Moreover, within the population of these liver-infiltrating T cells, we observed an enrichment of T cells that are reactive to SARS-CoV-2, suggesting that these vaccine-induced cells can contribute to liver inflammation in this context.

T-cell exhaustion and residency dynamics inform clinical outcomes in hepatocellular carcinoma.

BACKGROUND & AIMS: Despite recent translation of immunotherapies into clinical practice, the immunobiology of hepatocellular carcinoma (HCC), in particular the role and clinical relevance of exhausted and liver-resident T cells remain unclear. We therefore dissected the landscape of exhausted and resident T cell responses in the peripheral blood and tumor microenvironment of patients with HCC. METHODS: Lymphocytes were isolated from the blood, tumor and tumor-surrounding liver tissue of patients with HCC (n = 40, n = 10 treated with anti-PD-1 therapy). Phenotype, function and response to anti-PD-1 were analyzed by mass and flow cytometry ex vivo and in vitro, tissue residence was further assessed by immunohistochemistry and imaging mass cytometry. Gene signatures were analyzed in silico. RESULTS: We identified significant enrichment of heterogeneous populations of exhausted CD8+ T cells (TEX) in the tumor microenvironment. Strong enrichment of severely exhausted CD8 T cells expressing multiple immune checkpoints in addition to PD-1 was linked to poor progression-free and overall survival. In contrast, PD-1 was also expressed on a subset of more functional and metabolically active CD103+ tissue-resident memory T cells (TRM) that expressed few additional immune checkpoints and were associated with better survival. TEX enrichment was independent of BCLC stage, alpha-fetoprotein levels or age as a variable for progression-free survival in our cohort. These findings were in line with in silico gene signature analysis of HCC tumor transcriptomes from The Cancer Genome Atlas. A higher baseline TRM/TEX ratio was associated with disease control in anti-PD-1-treated patients. CONCLUSION: Our data provide information on the role of peripheral and intratumoral TEX-TRM dynamics in determining outcomes in patients with HCC. The dynamics between exhausted and liver-resident T cells have implications for immune-based diagnostics, rational patient selection and monitoring during HCC immunotherapies. LAY SUMMARY: The role of the immune response in hepatocellular carcinoma (HCC) remains unclear. T cells can mediate protection against tumor cells but are frequently dysfunctional and exhausted in cancer. We found that patients with a predominance of exhausted CD8+ T cells (TEX) had poor survival compared to patients with a predominance of tissue-resident memory T cells (TRM). This correlated with the molecular profile, metabolic and functional status of these cell populations. The enrichment of TEX was independently associated with prognosis in addition to disease stage, age and tumor markers. A high TRM proportion was also associated with better outcomes following checkpoint therapy. Thus, these T-cell populations are novel biomarkers with relevance in HCC.

Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: a case study of an urothelial cancer cohort analyzed in the Galaxy framework.

BACKGROUND: Mass spectrometry imaging (MSI) derives spatial molecular distribution maps directly from clinical tissue specimens and thus bears great potential for assisting pathologists with diagnostic decisions or personalized treatments. Unfortunately, progress in translational MSI is often hindered by insufficient quality control and lack of reproducible data analysis. Raw data and analysis scripts are rarely publicly shared. Here, we demonstrate the application of the Galaxy MSI tool set for the reproducible analysis of a urothelial carcinoma dataset. METHODS: Tryptic peptides were imaged in a cohort of 39 formalin-fixed, paraffin-embedded human urothelial cancer tissue cores with a MALDI-TOF/TOF device. The complete data analysis was performed in a fully transparent and reproducible manner on the European Galaxy Server. Annotations of tumor and stroma were performed by a pathologist and transferred to the MSI data to allow for supervised classifications of tumor vs. stroma tissue areas as well as for muscle-infiltrating and non-muscle infiltrating urothelial carcinomas. For putative peptide identifications, m/z features were matched to the MSiMass list. RESULTS: Rigorous quality control in combination with careful pre-processing enabled reduction of m/z shifts and intensity batch effects. High classification accuracy was found for both, tumor vs. stroma and muscle-infiltrating vs. non-muscle infiltrating urothelial tumors. Some of the most discriminative m/z features for each condition could be assigned a putative identity: stromal tissue was characterized by collagen peptides and tumor tissue by histone peptides. Immunohistochemistry confirmed an increased histone H2A abundance in the tumor compared to the stroma tissues. The muscle-infiltration status was distinguished via MSI by peptides from intermediate filaments such as cytokeratin 7 in non-muscle infiltrating carcinomas and vimentin in muscle-infiltrating urothelial carcinomas, which was confirmed by immunohistochemistry. To make the study fully reproducible and to advocate the criteria of FAIR (findability, accessibility, interoperability, and reusability) research data, we share the raw data, spectra annotations as well as all Galaxy histories and workflows. Data are available via ProteomeXchange with identifier PXD026459 and Galaxy results via https://github.com/foellmelanie/Bladder_MSI_Manuscript_Galaxy_links . CONCLUSION: Here, we show that translational MSI data analysis in a fully transparent and reproducible manner is possible and we would like to encourage the community to join our efforts.

Individualized Mini-Panel Sequencing of ctDNA Allows Tumor Monitoring in Complex Karyotype Sarcomas.

Soft tissue sarcomas (STS) are rare tumors of mesenchymal origin with high mortality. After curative resection, about one third of patients suffer from distant metastases. Tumor follow-up only covers a portion of recurrences and is associated with high cost and radiation burden. For metastasized STS, only limited inferences can be drawn from imaging data regarding therapy response. To date there are no established and evidence-based diagnostic biomarkers for STS due to their rarity and diversity. In a proof-of-concept study, circulating tumor DNA (ctDNA) was quantified in (n = 25) plasma samples obtained from (n = 3) patients with complex karyotype STS collected over three years. Genotyping of tumor tissue was performed by exome sequencing. Patient-individual mini-panels for targeted next-generation sequencing were designed encompassing up to 30 mutated regions of interest. Circulating free DNA (cfDNA) was purified from plasma and ctDNA quantified therein. ctDNA values were correlated with clinical parameters. ctDNA concentrations correlated with the tumor burden. In case of full remission, no ctDNA was detectable. Patients with a recurrence at a later stage showed low levels of ctDNA during clinical remission, indicating minimal residual disease. In active disease (primary tumor or metastatic disease), ctDNA was highly elevated. We observed direct response to treatment, with a ctDNA decline after tumor resections, radiotherapy, and chemotherapy. Quantification of ctDNA allows for the early detection of recurrence or metastases and can be used to monitor treatment response in STS. Therapeutic decisions can be made earlier, such as the continuation of a targeted adjuvant therapy or the implementation of extended imaging to detect recurrences. In metastatic disease, therapy can be adjusted promptly in case of no response. These advantages may lead to a survival benefit for patients in the future.

Human Mesenchymal Stromal Cells Do Not Cause Radioprotection of Head-and-Neck Squamous Cell Carcinoma.

Radiotherapy of head-and-neck squamous cell carcinoma (HNSCC) can cause considerable normal tissue injuries, and mesenchymal stromal cells (MSCs) have been shown to aid regeneration of irradiation-damaged normal tissues. However, utilization of MSC-based treatments for HNSCC patients undergoing radiotherapy is hampered by concerns regarding potential radioprotective effects. We therefore investigated the influence of MSCs on the radiosensitivity of HNSCCs. Several human papillomavirus (HPV)-negative and HPV-positive HNSCCs were co-cultured with human bone marrow-derived MSCs using two-dimensional and three-dimensional assays. Clonogenic survival, proliferation, and viability of HNSCCs after radiotherapy were assessed depending on MSC co-culture. Flow cytometry analyses were conducted to examine the influence of MSCs on irradiation-induced cell cycle distribution and apoptosis induction in HNSCCs. Immunofluorescence stainings of γH2AX were conducted to determine the levels of residual irradiation-induced DNA double-strand breaks. Levels of connective tissue growth factor (CTGF), a multifunctional pro-tumorigenic cytokine, were analyzed using enzyme-linked immunosorbent assays. Neither direct MSC co-culture nor MSC-conditioned medium exerted radioprotective effects on HNSCCs as determined by clonogenic survival, proliferation, and viability assays. Consistently, three-dimensional microwell arrays revealed no radioprotective effects of MSCs. Irradiation resulted in a G2/M arrest of HNSCCs at 96 h independently of MSC co-culture. HNSCCs' apoptosis rates were increased by irradiation irrespective of MSCs. Numbers of residual γH2AX foci after irradiation with 2 or 8 Gy were comparable between mono- and co-cultures. MSC mono-cultures and HNSCC-MSC co-cultures exhibited comparable CTGF levels. We did not detect radioprotective effects of human MSCs on HNSCCs. Our results suggest that the usage of MSC-based therapies for radiotherapy-related toxicities in HNSCC patients may be safe in the context of absent radioprotection.

Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations.

MOTIVATION: Mass spectrometry imaging (MSI) characterizes the molecular composition of tissues at spatial resolution, and has a strong potential for distinguishing tissue types, or disease states. This can be achieved by supervised classification, which takes as input MSI spectra, and assigns class labels to subtissue locations. Unfortunately, developing such classifiers is hindered by the limited availability of training sets with subtissue labels as the ground truth. Subtissue labeling is prohibitively expensive, and only rough annotations of the entire tissues are typically available. Classifiers trained on data with approximate labels have sub-optimal performance. RESULTS: To alleviate this challenge, we contribute a semi-supervised approach mi-CNN. mi-CNN implements multiple instance learning with a convolutional neural network (CNN). The multiple instance aspect enables weak supervision from tissue-level annotations when classifying subtissue locations. The convolutional architecture of the CNN captures contextual dependencies between the spectral features. Evaluations on simulated and experimental datasets demonstrated that mi-CNN improved the subtissue classification as compared to traditional classifiers. We propose mi-CNN as an important step toward accurate subtissue classification in MSI, enabling rapid distinction between tissue types and disease states. AVAILABILITY AND IMPLEMENTATION: The data and code are available at https://github.com/Vitek-Lab/mi-CNN_MSI.

FAK inhibition radiosensitizes pancreatic ductal adenocarcinoma cells in vitro.

INTRODUCTION: Focal adhesion kinase (FAK) is a nonreceptor tyrosine kinase protein frequently overexpressed in cancer and has been linked to an increase in the stem cell population of tumors, resistance to therapy, and metastatic spread. Pharmacological FAK inhibition in pancreatic cancer has received increased attention over the last few years, either alone or in combination with other therapeutics including chemotherapy and immunotherapy. However, its prognostic value and its role in radioresistance of pancreatic ducal adenocarcinoma (PDAC) is unknown. METHODS AND MATERIALS: Using the TCGA and GTEx databases, we investigated the genetic alterations and mRNA expression levels of PTK2 (the encoding-gene for FAK) in normal pancreatic tissue and pancreatic cancer and its impact on patient survival. Furthermore, we evaluated the expression of FAK and its tyrosine domain Ty-397 in three pancreatic cancer cell lines. We went further and evaluated the role of a commercial FAK tyrosine kinase inhibitor VS-4718 on the viability and radiosensitization of the pancreatic cell lines as well as its effect on the extracellular matrix (ECM) production from the pancreatic stellate cells. Furthermore, we tested the effect of combining radiation with VS-4718 in a three-dimensional (3D) multicellular pancreatic tumor spheroid model. RESULTS: A database analysis revealed a relevant increase in genetic alterations and mRNA expression of the PTK2 in PDAC, which were associated with lower progression-free survival. In vitro, there was only variation in the basal phosphorylation level of FAK in cell lines. VS-4718 radiosensitized pancreatic cell lines only in the presence of ECM-producing pancreatic stellate cells and markedly reduced the ECM production in the stromal cells. Finally, using a 3D multicellular tumor model, the combination of VS-4718 and radiotherapy significantly reduced the growth of tumor aggregates. CONCLUSION: Pharmacological inhibition of FAK in pancreatic cancer could be a novel therapeutic strategy as our results show a radiosensitization effect of VS-4718 in vitro in a multicellular 2D- and in a 3D-model of pancreatic cancer.

Uncovering the invisible-prevalence, characteristics, and radiomics feature-based detection of visually undetectable intraprostatic tumor lesions in 68GaPSMA-11 PET images of patients with primary prostate cancer.

INTRODUCTION: Primary prostate cancer (PCa) can be visualized on prostate-specific membrane antigen positron emission tomography (PSMA-PET) with high accuracy. However, intraprostatic lesions may be missed by visual PSMA-PET interpretation. In this work, we quantified and characterized the intraprostatic lesions which have been missed by visual PSMA-PET image interpretation. In addition, we investigated whether PSMA-PET-derived radiomics features (RFs) could detect these lesions. METHODOLOGY: This study consists of two cohorts of primary PCa patients: a prospective training cohort (n = 20) and an external validation cohort (n = 52). All patients underwent 68Ga-PSMA-11 PET/CT and histology sections were obtained after surgery. PCa lesions missed by visual PET image interpretation were counted and their International Society of Urological Pathology score (ISUP) was obtained. Finally, 154 RFs were derived from the PET images and the discriminative power to differentiate between prostates with or without visually undetectable lesions was assessed and areas under the receiver-operating curve (ROC-AUC) as well as sensitivities/specificities were calculated. RESULTS: In the training cohort, visual PET image interpretation missed 134 tumor lesions in 60% (12/20) of the patients, and of these patients, 75% had clinically significant (ISUP > 1) PCa. The median diameter of the missed lesions was 2.2 mm (range: 1-6). Standard clinical parameters like the NCCN risk group were equally distributed between patients with and without visually missed lesions (p < 0.05). Two RFs (local binary pattern (LBP) size-zone non-uniformality normalized and LBP small-area emphasis) were found to perform excellently in visually unknown PCa detection (Mann-Whitney U: p < 0.01, ROC-AUC: ≥ 0.93). In the validation cohort, PCa was missed in 50% (26/52) of the patients and 77% of these patients possessed clinically significant PCa. The sensitivities of both RFs in the validation cohort were ≥ 0.8. CONCLUSION: Visual PSMA-PET image interpretation may miss small but clinically significant PCa in a relevant number of patients and RFs can be implemented to uncover them. This could be used for guiding personalized treatments.

Human Primary Breast Cancer Stem Cells Are Characterized by Epithelial-Mesenchymal Plasticity.

Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer, with only limited treatment options available. Recently, cancer stem cells (CSCs) have emerged as the potential drivers of tumor progression due to their ability to both self-renew and give rise to differentiated progeny. The CSC state has been linked to the process of epithelial-mesenchymal transition (EMT) and to the highly flexible state of epithelial-mesenchymal plasticity (EMP). We aimed to establish primary breast cancer stem cell (BCSC) cultures isolated from TNBC specimens. These cells grow as tumor spheres under anchorage-independent culture conditions in vitro and reliably form tumors in mice when transplanted in limiting dilutions in vivo. The BCSC xenograft tumors phenocopy the original patient tumor in architecture and gene expression. Analysis of an EMT-related marker profile revealed the concomitant expression of epithelial and mesenchymal markers suggesting an EMP state for BCSCs of TNBC. Furthermore, BCSCs were susceptible to stimulation with the EMT inducer TGF-ß1, resulting in upregulation of mesenchymal genes and enhanced migratory abilities. Overall, primary BCSC cultures are a promising model close to the patient that can be used both in vitro and in vivo to address questions of BCSC biology and evaluate new treatment options for TNBC.

Multiple Immunostainings with Different Epitope Retrievals-The FOLGAS Protocol.

We describe a sequential multistaining protocol for immunohistochemistry, immunofluorescence and CyTOF imaging for formalin-fixed, paraffin-embedded specimens (FFPE) in the formalin gas-phase (FOLGAS), enabling sequential multistaining, independent from the primary and secondary antibodies and retrieval. Histomorphologic details are preserved, and crossreactivity and loss of signal intensity are not detectable. Combined with a DAB-based hydrophobic masking of metal-labeled primary antibodies, FOLGAS allows the extended use of CyTOF imaging in FFPE sections.

Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study.

BACKGROUND: Bayesian networks (BNs) are machine-learning-based computational models that visualize causal relationships and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. Preoperative identification of patients at risk for lymph node metastasis (LNM) is challenging in endometrial cancer, and although several biomarkers are related to LNM, none of them are incorporated in clinical practice. The aim of this study was to develop and externally validate a preoperative BN to predict LNM and outcome in endometrial cancer patients. METHODS AND FINDINGS: Within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), we performed a retrospective multicenter cohort study including 763 patients, median age 65 years (interquartile range [IQR] 58-71), surgically treated for endometrial cancer between February 1995 and August 2013 at one of the 10 participating European hospitals. A BN was developed using score-based machine learning in addition to expert knowledge. Our main outcome measures were LNM and 5-year disease-specific survival (DSS). Preoperative clinical, histopathological, and molecular biomarkers were included in the network. External validation was performed using 2 prospective study cohorts: the Molecular Markers in Treatment in Endometrial Cancer (MoMaTEC) study cohort, including 446 Norwegian patients, median age 64 years (IQR 59-74), treated between May 2001 and 2010; and the PIpelle Prospective ENDOmetrial carcinoma (PIPENDO) study cohort, including 384 Dutch patients, median age 66 years (IQR 60-73), treated between September 2011 and December 2013. A BN called ENDORISK (preoperative risk stratification in endometrial cancer) was developed including the following predictors: preoperative tumor grade; immunohistochemical expression of estrogen receptor (ER), progesterone receptor (PR), p53, and L1 cell adhesion molecule (L1CAM); cancer antigen 125 serum level; thrombocyte count; imaging results on lymphadenopathy; and cervical cytology. In the MoMaTEC cohort, the area under the curve (AUC) was 0.82 (95% confidence interval [CI] 0.76-0.88) for LNM and 0.82 (95% CI 0.77-0.87) for 5-year DSS. In the PIPENDO cohort, the AUC for 5-year DSS was 0.84 (95% CI 0.78-0.90). The network was well-calibrated. In the MoMaTEC cohort, 249 patients (55.8%) were classified with <5% risk of LNM, with a false-negative rate of 1.6%. A limitation of the study is the use of imputation to correct for missing predictor variables in the development cohort and the retrospective study design. CONCLUSIONS: In this study, we illustrated how BNs can be used for individualizing clinical decision-making in oncology by incorporating easily accessible and multimodal biomarkers. The network shows the complex interactions underlying the carcinogenetic process of endometrial cancer by its graphical representation. A prospective feasibility study will be needed prior to implementation in the clinic.