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Objective: Stroma-rich tumours represent a poor prognostic subtype in stage II/III colon cancer (CC), with high relapse rates and limited response to standard adjuvant chemotherapy., Design: To address the lack of efficacious therapeutic options for patients with stroma-rich CC, we stratified our human tumour cohorts according to stromal content, enabling identification of the biology underpinning relapse and potential therapeutic vulnerabilities specifically within stroma-rich tumours that could be exploited clinically. Following human tumour-based discovery and independent clinical validation, we use a series of in vitro and stroma-rich in vivo models to test and validate the therapeutic potential of elevating the biology associated with reduced relapse in human tumours., Results: By performing our analyses specifically within the stroma-rich/high-fibroblast (HiFi) subtype of CC, we identify and validate the clinical value of a HiFi-specific prognostic signature (HPS), which stratifies tumours based on STAT1-related signalling (High-HPS v Low-HPS=HR 0.093, CI 0.019 to 0.466). Using in silico, in vitro and in vivo models, we demonstrate that the HPS is associated with antigen processing and presentation within discrete immune lineages in stroma-rich CC, downstream of double-stranded RNA and viral response signalling. Treatment with the TLR3 agonist poly(I:C) elevated the HPS signalling and antigen processing phenotype across in vitro and in vivo models. In an in vivo model of stroma-rich CC, poly(I:C) treatment significantly increased systemic cytotoxic T cell activity (p<0.05) and reduced liver metastases (p<0.0002)., Conclusion: This study reveals new biological insight that offers a novel therapeutic option to reduce relapse rates in patients with the worst prognosis CC., Competing Interests: Competing interests: ML has received honoraria from Pfizer, EMF Serono and Roche for presentations unrelated to this work; ML has received an unrestricted educational grant from Pfizer for research unrelated to this work. PT has received honoraria and travel expenses from BMS, Merck, Roche, Lilly and Sanofi-Aventis for contributions that are not related to the present work. The authors declare no other potential conflicts of interest., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Inhibitors of apoptosis proteins (IAPs) are intracellular proteins, with important roles in regulating cell death, inflammation, and immunity. Here, we examined the clinical and therapeutic relevance of IAPs in colorectal cancer. We found that elevated expression of cIAP1 and cIAP2 (but not XIAP) significantly correlated with poor prognosis in patients with microsatellite stable (MSS) stage III colorectal cancer treated with 5-fluorouracil (5FU)-based adjuvant chemotherapy, suggesting their involvement in promoting chemoresistance. A novel IAP antagonist tolinapant (ASTX660) potently and rapidly downregulated cIAP1 in colorectal cancer models, demonstrating its robust on-target efficacy. In cells co-cultured with TNFα to mimic an inflammatory tumor microenvironment, tolinapant induced caspase-8-dependent apoptosis in colorectal cancer cell line models; however, the extent of apoptosis was limited because of inhibition by the caspase-8 paralogs FLIP and, unexpectedly, caspase-10. Importantly, tolinapant-induced apoptosis was augmented by FOLFOX in human colorectal cancer and murine organoid models in vitro and in vivo , due (at least in part) to FOLFOX-induced downregulation of class I histone deacetylases (HDAC), leading to acetylation of the FLIP-binding partner Ku70 and downregulation of FLIP. Moreover, the effects of FOLFOX could be phenocopied using the clinically relevant class I HDAC inhibitor, entinostat, which also induced acetylation of Ku70 and FLIP downregulation. Further analyses revealed that caspase-8 knockout RIPK3-positive colorectal cancer models were sensitive to tolinapant-induced necroptosis, an effect that could be exploited in caspase-8-proficient models using the clinically relevant caspase inhibitor emricasan. Our study provides evidence for immediate clinical exploration of tolinapant in combination with FOLFOX in poor prognosis MSS colorectal cancer with elevated cIAP1/2 expression., (©2021 The Authors; Published by the American Association for Cancer Research.)

The transcription factor p53 is the best-known tumor suppressor, but its sibling p63 is a master regulator of epidermis development and a key oncogenic driver in squamous cell carcinomas (SCC). Despite multiple gene expression studies becoming available, the limited overlap of reported p63-dependent genes has made it difficult to decipher the p63 gene regulatory network. Particularly, analyses of p63 response elements differed substantially among the studies. To address this intricate data situation, we provide an integrated resource that enables assessing the p63-dependent regulation of any human gene of interest. We use a novel iterative de novo motif search approach in conjunction with extensive ChIP-seq data to achieve a precise global distinction between p53-and p63-binding sites, recognition motifs, and potential co-factors. We integrate these data with enhancer:gene associations to predict p63 target genes and identify those that are commonly de-regulated in SCC representing candidates for prognosis and therapeutic interventions., Competing Interests: KR, HK, AS, SM, SH, MF No competing interests declared, (© 2020, Riege et al.)

Modern methods of acquiring molecular data have improved rapidly in recent years, making it easier for researchers to collect large volumes of information. However, this has increased the challenge of recognizing interesting patterns within the data. Atlas Correlation Explorer (ACE) is a user-friendly workbench for seeking associations between attributes in The Cancer Genome Atlas (TCGA) database. It allows any combination of clinical and genomic data streams to be searched using an evolutionary algorithm approach. To showcase ACE, we assessed which RNA sequencing transcripts were associated with estrogen receptor (ESR1) in the TCGA breast cancer cohort. The analysis revealed already well-established associations with XBP1 and FOXA1, but also identified a strong association with CT62, a potential immunotherapeutic target with few previous associations with breast cancer. In conclusion, ACE can produce results for very large searches in a short time and will serve as an increasingly useful tool for biomarker discovery in the big data era. SIGNIFICANCE: ACE uses an evolutionary algorithm approach to perform large searches for associations between any combinations of data in the TCGA database., (©2019 American Association for Cancer Research.)

Transcriptional dysregulation induced by aberrant transcription factors (TF) is a key feature of cancer, but its global influence on drug sensitivity has not been examined. Here, we infer the transcriptional activity of 127 TFs through analysis of RNA-seq gene expression data newly generated for 448 cancer cell lines, combined with publicly available datasets to survey a total of 1,056 cancer cell lines and 9,250 primary tumors. Predicted TF activities are supported by their agreement with independent shRNA essentiality profiles and homozygous gene deletions, and recapitulate mutant-specific mechanisms of transcriptional dysregulation in cancer. By analyzing cell line responses to 265 compounds, we uncovered numerous TFs whose activity interacts with anticancer drugs. Importantly, combining existing pharmacogenomic markers with TF activities often improves the stratification of cell lines in response to drug treatment. Our results, which can be queried freely at dorothea.opentargets.io, offer a broad foundation for discovering opportunities to refine personalized cancer therapies. Significance: Systematic analysis of transcriptional dysregulation in cancer cell lines and patient tumor specimens offers a publicly searchable foundation to discover new opportunities to refine personalized cancer therapies. Cancer Res; 78(3); 769-80. ©2017 AACR ., (©2017 American Association for Cancer Research.)

Background: Approximately 4-25% of patients with early prostate cancer develop disease recurrence following radical prostatectomy., Objective: To identify a molecular subgroup of prostate cancers with metastatic potential at presentation resulting in a high risk of recurrence following radical prostatectomy., Design, Setting, and Participants: Unsupervised hierarchical clustering was performed using gene expression data from 70 primary resections, 31 metastatic lymph nodes, and 25 normal prostate samples. Independent assay validation was performed using 322 radical prostatectomy samples from four sites with a mean follow-up of 50.3 months., Outcome Measurements and Statistical Analysis: Molecular subgroups were identified using unsupervised hierarchical clustering. A partial least squares approach was used to generate a gene expression assay. Relationships with outcome (time to biochemical and metastatic recurrence) were analysed using multivariable Cox regression and log-rank analysis., Results and Limitations: A molecular subgroup of primary prostate cancer with biology similar to metastatic disease was identified. A 70-transcript signature (metastatic assay) was developed and independently validated in the radical prostatectomy samples. Metastatic assay positive patients had increased risk of biochemical recurrence (multivariable hazard ratio [HR] 1.62 [1.13-2.33]; p=0.0092) and metastatic recurrence (multivariable HR=3.20 [1.76-5.80]; p=0.0001). A combined model with Cancer of the Prostate Risk Assessment post surgical (CAPRA-S) identified patients at an increased risk of biochemical and metastatic recurrence superior to either model alone (HR=2.67 [1.90-3.75]; p<0.0001 and HR=7.53 [4.13-13.73]; p<0.0001, respectively). The retrospective nature of the study is acknowledged as a potential limitation., Conclusions: The metastatic assay may identify a molecular subgroup of primary prostate cancers with metastatic potential., Patient Summary: The metastatic assay may improve the ability to detect patients at risk of metastatic recurrence following radical prostatectomy. The impact of adjuvant therapies should be assessed in this higher-risk population., (Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Purpose: A number of independent gene expression profiling studies have identified transcriptional subtypes in colorectal cancer with potential diagnostic utility, culminating in publication of a colorectal cancer Consensus Molecular Subtype classification. The worst prognostic subtype has been defined by genes associated with stem-like biology. Recently, it has been shown that the majority of genes associated with this poor prognostic group are stromal derived. We investigated the potential for tumor misclassification into multiple diagnostic subgroups based on tumoral region sampled., Experimental Design: We performed multiregion tissue RNA extraction/transcriptomic analysis using colorectal-specific arrays on invasive front, central tumor, and lymph node regions selected from tissue samples from 25 colorectal cancer patients., Results: We identified a consensus 30-gene list, which represents the intratumoral heterogeneity within a cohort of primary colorectal cancer tumors. Using a series of online datasets, we showed that this gene list displays prognostic potential HR = 2.914 (confidence interval 0.9286-9.162) in stage II/III colorectal cancer patients, but in addition, we demonstrated that these genes are stromal derived, challenging the assumption that poor prognosis tumors with stem-like biology have undergone a widespread epithelial-mesenchymal transition. Most importantly, we showed that patients can be simultaneously classified into multiple diagnostically relevant subgroups based purely on the tumoral region analyzed., Conclusions: Gene expression profiles derived from the nonmalignant stromal region can influence assignment of colorectal cancer transcriptional subtypes, questioning the current molecular classification dogma and highlighting the need to consider pathology sampling region and degree of stromal infiltration when employing transcription-based classifiers to underpin clinical decision making in colorectal cancer. Clin Cancer Res; 22(16); 4095-104. ©2016 AACRSee related commentary by Morris and Kopetz, p. 3989., (©2016 American Association for Cancer Research.)