Single-Cell Quantification of mRNA Expression in The Human Brain

RNA analysis at the cellular resolution in the human brain is challenging. Here, we describe an optimised approach for detecting single RNA transcripts in a cell-type specific manner in frozen human brain tissue using multiplexed fluorescent RNAscope probes. We developed a new robust analytical approach for RNAscope quantification. Our method shows that low RNA integrity does not significantly affect RNAscope signal, recapitulates bulk RNA analysis and provides spatial context to transcriptomic analysis of human post-mortem brain at single-cell resolution. In summary, our optimised method allows the usage of frozen human samples from brain banks to perform quantitative RNAscope analysis

Tumour invasiveness, the local and systemic environment and the basis of staging systems in colorectal cancer

background: The present study aimed to examine the relationship between tumour invasiveness (T stage), the local and systemic environment and cancer-specific survival (CSS) of patients with primary operable colorectal cancer. methods: The tumour microenvironment was examined using measures of the inflammatory infiltrate (Klintrup-Makinen (KM) grade and Immunoscore), tumour stroma percentage (TSP) and tumour budding. The systemic inflammatory environment was examined using modified Glasgow Prognostic Score (mGPS) and neutrophil:lymphocyte ratio (NLR). A 5-year CSS was examined. results: A total of 331 patients were included. Increasing T stage was associated with colonic primary, N stage, poor differentiation, margin involvement and venous invasion (P<0.05). T stage was significantly associated with KM grade (P=0.001), Immunoscore (P=0.016), TSP (P=0.006), tumour budding (P<0.001), and elevated mGPS and NLR (both P<0.05). In patients with T3 cancer, N stage stratified survival from 88 to 64%, whereas Immunoscore and budding stratified survival from 100 to 70% and from 91 to 56%, respectively. The Glasgow Microenvironment Score, a score based on KM grade and TSP, stratified survival from 93 to 58%. conclusions: Although associated with increasing T stage, local and systemic tumour environment characteristics, and in particular Immunoscore, budding, TSP and mGPS, are stage-independent determinants of survival and may be utilised in the staging of patients with primary operable colorectal cancer

In-depth clinical and biological exploration of DNA Damage Immune Response (DDIR) as a biomarker for oxaliplatin use in colorectal cancer

PURPOSE: The DNA Damage Immune Response (DDIR) assay was developed in breast cancer (BC) based on biology associated with deficiencies in homologous recombination and Fanconi Anemia (HR/FA) pathways. A positive DDIR call identifies patients likely to respond to platinum-based chemotherapies in breast and oesophageal cancers. In colorectal cancer (CRC) there is currently no biomarker to predict response to oxaliplatin. We tested the ability of the DDIR assay to predict response to oxaliplatin-based chemotherapy in CRC and characterised the biology in DDIR-positive CRC. METHODS: Samples and clinical data were assessed according to DDIR status from patients who received either 5FU or FOLFOX within the FOCUS trial (n=361, stage 4), or neo-adjuvant FOLFOX in the FOxTROT trial (n=97, stage 2/3). Whole transcriptome, mutation and immunohistochemistry data of these samples were used to interrogate the biology of DDIR in CRC. RESULTS: Contrary to our hypothesis, DDIR negative patients displayed a trend towards improved outcome for oxaliplatin-based chemotherapy compared to DDIR positive patients. DDIR positivity was associated with Microsatellite Instability (MSI) and Colorectal Molecular Subtype 1 (CMS1). Refinement of the DDIR signature, based on overlapping interferon-related chemokine signalling associated with DDIR positivity across CRC and BC cohorts, further confirmed that the DDIR assay did not have predictive value for oxaliplatin-based chemotherapy in CRC. CONCLUSIONS: DDIR positivity does not predict improved response following oxaliplatin treatment in CRC. However, data presented here suggests the potential of the DDIR assay in identifying immune-rich tumours that may benefit from immune checkpoint blockade, beyond current use of MSI status

Tumour budding in oral squamous cell carcinoma : a meta-analysis

Background: Tumour budding has been reported as a promising prognostic marker in many cancers. This meta-analysis assessed the prognostic value of tumour budding in oral squamous cell carcinoma (OSCC). Methods: We searched OvidMedline, PubMed, Scopus and Web of Science for articles that studied tumour budding in OSCC. We used reporting recommendations for tumour marker (REMARK) criteria to evaluate the quality of studies eligible for meta-analysis. Results: A total of 16 studies evaluated the prognostic value of tumour budding in OSCC. The meta-analysis showed that tumour budding was significantly associated with lymph node metastasis (odds ratio = 7.08, 95% CI = 1.75-28.73), disease-free survival (hazard ratio = 1.83, 95% CI = 1.34-2.50) and overall survival (hazard ratio = 1.88, 95% CI = 1.25-2.82). Conclusions: Tumour budding is a simple and reliable prognostic marker for OSCC. Evaluation of tumour budding could facilitate personalised management of OSCC.Peer reviewe

Metastasis-associated in colon cancer-1 and aldehyde dehydrogenase 1 are metastatic and prognostic biomarker for non-small cell lung cancer

BACKGROUND: Tumor recurrence and metastasis are the most common reason for treatment failure. Metastasis-associate in colon cancer-1 (MACC1) has been identified as a metastatic and prognostic biomarker for colorectal cancer and other solid tumors. Aldehyde dehydrogenase 1 (ALDH1), a marker of cancer stem cells, is also associated with metastasis and poor prognosis in many tumors. However, the prognostic value of either MACC1 or ALDH1 in non-small cell lung cancer (NSCLC) is unclear. In this study, we explored the relationship between MACC1 and ALDH1 expression, as well as their respective associations with clinicopathological features, to determine if either could be useful for improvement of survival prognosis in NSCLC. METHODS: The expression levels of both MACC1 and ALDH1 in 240 whole tissue sections of NSCLC were examined by immunohistochemistry. Clinical data were also collected. RESULTS: MACC1 and ALDH1 were significantly overexpressed in NSCLC tissues when compared to levels in normal lung tissues. Investigation of associations between MACC1 or ALDH1 protein levels with clinicopathological parameters of NSCLC revealed correlations between the expression of each with tumor grade, lymph node metastasis, and tumor node metastasis. The overall survival of patients with MACC1- or ALDH1-positive NSCLC tumors was significantly lower than that of those who were negative. Importantly, multivariate analysis suggested that positive expression of either MACC1 or ALDH1, as well as TNM stage, could be independent prognostic factors for overall survival in patients with NSCLC. CONCLUSIONS: MACC1 and ALDH1 may represent promising metastatic and prognostic biomarkers, as well as potential therapeutic targets, for NSCLC

Favorable prognosis in colorectal cancer patients with co-expression of c-MYC and ß-catenin

BACKGROUND: The purpose of our research was to determine the prognostic impact and clinicopathological feature of c-MYC and β-catenin overexpression in colorectal cancer (CRC) patients. METHODS: Using immunohistochemistry (IHC), we measured the c-MYC and β-catenin expression in 367 consecutive CRC patients retrospectively (cohort 1). Also, c-MYC expression was measured by mRNA in situ hybridization. Moreover, to analyze regional heterogeneity, three sites of CRC including the primary, distant and lymph node metastasis were evaluated in 176 advanced CRC patients (cohort 2). RESULTS: In cohort 1, c-MYC protein and mRNA overexpression and ß-catenin nuclear expression were found in 201 (54.8 %), 241 (65.7 %) and 221 (60.2 %) of 367 patients, respectively, each of which was associated with improved prognosis (P = 0.011, P = 0.012 and P = 0.033, respectively). Moreover, co-expression of c-MYC and ß-catenin was significantly correlated with longer survival by univariate (P = 0.012) and multivariate (P = 0.048) studies. Overexpression of c-MYC protein was associated with mRNA overexpression (ρ, 0.479; P < 0.001) and nuclear ß-catenin expression (ρ, 0.282; P < 0.001). Expression of c-MYC and ß-catenin was heterogeneous depending on location in advanced CRC patients (cohort 2). Nevertheless, both c-MYC and ß-catenin expression in primary cancer were significantly correlated with improved survival in univariate (P = 0.001) and multivariate (P = 0.002) analyses. c-MYC and ß-catenin expression of lymph node or distant metastatic tumor was not significantly correlated with patients’ prognosis (P > 0.05). CONCLUSIONS: Co-expression of c-MYC and ß-catenin was independently correlated with favorable prognosis in CRC patient. We concluded that the expression of c-MYC and ß-catenin might be useful predicting indicator of CRC patient’s prognosis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12885-016-2770-7) contains supplementary material, which is available to authorized users

Precision immunoprofiling by image analysis and artificial intelligence

Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This requirement can be met by a combination of computational pathology and artificial intelligence. Here, we critically assess computational approaches supporting the development of a standardized methodology in the assessment of immune-oncology biomarkers, such as PD-L1 and immune cell infiltrates. We examine immunoprofiling through spatial analysis of tumor-immune cell interactions and multiplexing technologies as a predictor of patient response to cancer treatment. Further, we discuss how integrated bioinformatics can enable the amalgamation of complex morphological phenotypes with the multiomics datasets that drive precision medicine. We provide an outline to machine learning (ML) and artificial intelligence tools and illustrate fields of application in immune-oncology, such as pattern-recognition in large and complex datasets and deep learning approaches for survival analysis. Synergies of surgical pathology and computational analyses are expected to improve patient stratification in immuno-oncology. We propose that future clinical demands will be best met by (1) dedicated research at the interface of pathology and bioinformatics, supported by professional societies, and (2) the integration of data sciences and digital image analysis in the professional education of pathologists