Machine learning to predict early recurrence after oesophageal cancer surgery

BACKGROUND: Early cancer recurrence after oesophagectomy is a common problem, with an incidence of 20-30 per cent despite the widespread use of neoadjuvant treatment. Quantification of this risk is difficult and existing models perform poorly. This study aimed to develop a predictive model for early recurrence after surgery for oesophageal adenocarcinoma using a large multinational cohort and machine learning approaches. METHODS: Consecutive patients who underwent oesophagectomy for adenocarcinoma and had neoadjuvant treatment in one Dutch and six UK oesophagogastric units were analysed. Using clinical characteristics and postoperative histopathology, models were generated using elastic net regression (ELR) and the machine learning methods random forest (RF) and extreme gradient boosting (XGB). Finally, a combined (ensemble) model of these was generated. The relative importance of factors to outcome was calculated as a percentage contribution to the model. RESULTS: A total of 812 patients were included. The recurrence rate at less than 1 year was 29·1 per cent. All of the models demonstrated good discrimination. Internally validated areas under the receiver operating characteristic (ROC) curve (AUCs) were similar, with the ensemble model performing best (AUC 0·791 for ELR, 0·801 for RF, 0·804 for XGB, 0·805 for ensemble). Performance was similar when internal-external validation was used (validation across sites, AUC 0·804 for ensemble). In the final model, the most important variables were number of positive lymph nodes (25·7 per cent) and lymphovascular invasion (16·9 per cent). CONCLUSION: The model derived using machine learning approaches and an international data set provided excellent performance in quantifying the risk of early recurrence after surgery, and will be useful in prognostication for clinicians and patients

Systematic review and validation of clinical models predicting survival after oesophagectomy for adenocarcinoma

BACKGROUND: Oesophageal adenocarcinoma poses a significant global health burden, yet the staging used to predict survival has limited ability to stratify patients by outcome. This study aimed to identify published clinical models that predict survival in oesophageal adenocarcinoma and to evaluate them using an independent international multicentre dataset. METHODS: A systematic literature search (title and abstract) using the Ovid Embase and MEDLINE databases (from 1947 to 11 July 2020) was performed. Inclusion criteria were studies that developed or validated a clinical prognostication model to predict either overall or disease-specific survival in patients with oesophageal adenocarcinoma undergoing surgical treatment with curative intent. Published models were validated using an independent dataset of 2450 patients who underwent oesophagectomy for oesophageal adenocarcinoma with curative intent. RESULTS: Seventeen articles were eligible for inclusion in the study. Eleven models were suitable for testing in the independent validation dataset and nine of these were able to stratify patients successfully into groups with significantly different survival outcomes. Area under the receiver operating characteristic curves for individual survival prediction models ranged from 0.658 to 0.705, suggesting poor-to-fair accuracy. CONCLUSION: This study highlights the need to concentrate on robust methodologies and improved, independent, validation, to increase the likelihood of clinical adoption of survival predictions models

Mobile element insertions are frequent in oesophageal adenocarcinomas and can mislead paired-end sequencing analysis.

BACKGROUND: Mobile elements are active in the human genome, both in the germline and cancers, where they can mutate driver genes. RESULTS: While analysing whole genome paired-end sequencing of oesophageal adenocarcinomas to find genomic rearrangements, we identified three ways in which new mobile element insertions appear in the data, resembling translocation or insertion junctions: inserts where unique sequence has been transduced by an L1 (Long interspersed element 1) mobile element; novel inserts that are confidently, but often incorrectly, mapped by alignment software to L1s or polyA tracts in the reference sequence; and a combination of these two ways, where different sequences within one insert are mapped to different loci. We identified nine unique sequences that were transduced by neighbouring L1s, both L1s in the reference genome and L1s not present in the reference. Many of the resulting inserts were small fragments that include little or no recognisable mobile element sequence. We found 6 loci in the reference genome to which sequence reads from inserts were frequently mapped, probably erroneously, by alignment software: these were either L1 sequence or particularly long polyA runs. Inserts identified from such apparent rearrangement junctions averaged 16 inserts/tumour, range 0-153 insertions in 43 tumours. However, many inserts would not be detected by mapping the sequences to the reference genome, because they do not include sufficient mappable sequence. To estimate total somatic inserts we searched for polyA sequences that were not present in the matched normal or other normals from the same tumour batch, and were not associated with known polymorphisms. Samples of these candidate inserts were verified by sequencing across them or manual inspection of surrounding reads: at least 85 % were somatic and resembled L1-mediated events, most including L1Hs sequence. Approximately 100 such inserts were detected per tumour on average (range zero to approximately 700). CONCLUSIONS: Somatic mobile elements insertions are abundant in these tumours, with over 75 % of cases having a number of novel inserts detected. The inserts create a variety of problems for the interpretation of paired-end sequencing data.Funding was primarily from Cancer Research UK program grants to RCF and ST (C14478/A15874 and C14303/A17197), with additional support awarded to RCF from UK Medical Research Council, NHS National Institute for Health Research (NIHR), the Experimental Cancer Medicine Centre Network and the NIHR Cambridge Biomedical Research Centre, and Cancer Research UK Project grant C1023/A14545 to PAWE. JMJW was funded by a Wellcome Trust Translational Medicine and Therapeutics grant

Dwarf koa (Desmanthus virgatus)

This is the final version. It was first published by BioMed Central at http://www.biomedcentral.com/1471-2164/16/473.Background: Mobile elements are active in the human genome, both in the germline and cancers, where they can\ud mutate driver genes.\ud Results: While analysing whole genome paired-end sequencing of oesophageal adenocarcinomas to find genomic\ud rearrangements, we identified three ways in which new mobile element insertions appear in the data, resembling\ud translocation or insertion junctions: inserts where unique sequence has been transduced by an L1 (Long interspersed\ud element 1) mobile element; novel inserts that are confidently, but often incorrectly, mapped by alignment software to\ud L1s or polyA tracts in the reference sequence; and a combination of these two ways, where different sequences within\ud one insert are mapped to different loci. We identified nine unique sequences that were transduced by neighbouring\ud L1s, both L1s in the reference genome and L1s not present in the reference. Many of the resulting inserts were small\ud fragments that include little or no recognisable mobile element sequence. We found 6 loci in the reference genome to\ud which sequence reads from inserts were frequently mapped, probably erroneously, by alignment software: these were\ud either L1 sequence or particularly long polyA runs. Inserts identified from such apparent rearrangement junctions\ud averaged 16 inserts/tumour, range 0?153 insertions in 43 tumours. However, many inserts would not be detected by\ud mapping the sequences to the reference genome, because they do not include sufficient mappable sequence. To\ud estimate total somatic inserts we searched for polyA sequences that were not present in the matched normal or other\ud normals from the same tumour batch, and were not associated with known polymorphisms. Samples of these candidate\ud inserts were verified by sequencing across them or manual inspection of surrounding reads: at least 85 % were somatic\ud and resembled L1-mediated events, most including L1Hs sequence. Approximately 100 such inserts were detected per\ud tumour on average (range zero to approximately 700).\ud Conclusions: Somatic mobile elements insertions are abundant in these tumours, with over 75 % of cases having a\ud number of novel inserts detected. The inserts create a variety of problems for the interpretation of paired-end\ud sequencing data.Funding\ud was primarily from Cancer Research UK program grants to RCF and ST\ud (C14478/A15874 and C14303/A17197), with additional support awarded to\ud RCF from UK Medical Research Council, NHS National Institute for Health\ud Research (NIHR), the Experimental Cancer Medicine Centre Network and\ud the NIHR Cambridge Biomedical Research Centre, and Cancer Research UK\ud Project grant C1023/A14545 to PAWE. JMJW was funded by a Wellcome\ud Trust Translational Medicine and Therapeutics grant

The transcriptional landscape of endogenous retroelements delineates esophageal adenocarcinoma subtypes

Most cancer types exhibit aberrant transcriptional activity, including derepression of retrotransposable elements (RTEs). However, the degree, specificity and potential consequences of RTE transcriptional activation may differ substantially among cancer types and subtypes. Representing one extreme of the spectrum, we characterize the transcriptional activity of RTEs in cohorts of esophageal adenocarcinoma (EAC) and its precursor Barrett's esophagus (BE) from the OCCAMS (Oesophageal Cancer Clinical and Molecular Stratification) consortium, and from TCGA (The Cancer Genome Atlas). We found exceptionally high RTE inclusion in the EAC transcriptome, driven primarily by transcription of genes incorporating intronic or adjacent RTEs, rather than by autonomous RTE transcription. Nevertheless, numerous chimeric transcripts straddling RTEs and genes, and transcripts from stand-alone RTEs, particularly KLF5- and SOX9-controlled HERVH proviruses, were overexpressed specifically in EAC. Notably, incomplete mRNA splicing and EAC-characteristic intronic RTE inclusion was mirrored by relative loss of the respective fully-spliced, functional mRNA isoforms, consistent with compromised cellular fitness. Defective RNA splicing was linked with strong transcriptional activation of a HERVH provirus on Chr Xp22.32 and defined EAC subtypes with distinct molecular features and prognosis. Our study defines distinguishable RTE transcriptional profiles of EAC, reflecting distinct underlying processes and prognosis, thus providing a framework for targeted studies

Multicentre cohort study to define and validate pathological assessment of response to neoadjuvant therapy in oesophagogastric adenocarcinoma.

BACKGROUND: This multicentre cohort study sought to define a robust pathological indicator of clinically meaningful response to neoadjuvant chemotherapy in oesophageal adenocarcinoma. METHODS: A questionnaire was distributed to 11 UK upper gastrointestinal cancer centres to determine the use of assessment of response to neoadjuvant chemotherapy. Records of consecutive patients undergoing oesophagogastric resection at seven centres between January 2000 and December 2013 were reviewed. Pathological response to neoadjuvant chemotherapy was assessed using the Mandard Tumour Regression Grade (TRG) and lymph node downstaging. RESULTS: TRG (8 of 11 centres) was the most widely used system to assess response to neoadjuvant chemotherapy, but there was discordance on how it was used in practice. Of 1392 patients, 1293 had TRG assessment; data were available for clinical and pathological nodal status (cN and pN) in 981 patients, and TRG, cN and pN in 885. There was a significant difference in survival between responders (TRG 1-2; median overall survival (OS) not reached) and non-responders (TRG 3-5; median OS 2·22 (95 per cent c.i. 1·94 to 2·51) years; P < 0·001); the hazard ratio was 2·46 (95 per cent c.i. 1·22 to 4·95; P = 0·012). Among local non-responders, the presence of lymph node downstaging was associated with significantly improved OS compared with that of patients without lymph node downstaging (median OS not reached versus 1·92 (1·68 to 2·16) years; P < 0·001). CONCLUSION: A clinically meaningful local response to neoadjuvant chemotherapy was restricted to the small minority of patients (14·8 per cent) with TRG 1-2. Among local non-responders, a subset of patients (21·3 per cent) derived benefit from neoadjuvant chemotherapy by lymph node downstaging and their survival mirrored that of local responders

Impact of mutations in Toll-like receptor pathway genes on esophageal carcinogenesis.

Esophageal adenocarcinoma (EAC) develops in an inflammatory microenvironment with reduced microbial diversity, but mechanisms for these influences remain poorly characterized. We hypothesized that mutations targeting the Toll-like receptor (TLR) pathway could disrupt innate immune signaling and promote a microenvironment that favors tumorigenesis. Through interrogating whole genome sequencing data from 171 EAC patients, we showed that non-synonymous mutations collectively affect the TLR pathway in 25/171 (14.6%, PathScan p = 8.7x10-5) tumors. TLR mutant cases were associated with more proximal tumors and metastatic disease, indicating possible clinical significance of these mutations. Only rare mutations were identified in adjacent Barrett's esophagus samples. We validated our findings in an external EAC dataset with non-synonymous TLR pathway mutations in 33/149 (22.1%, PathScan p = 0.05) tumors, and in other solid tumor types exposed to microbiomes in the COSMIC database (10,318 samples), including uterine endometrioid carcinoma (188/320, 58.8%), cutaneous melanoma (377/988, 38.2%), colorectal adenocarcinoma (402/1519, 26.5%), and stomach adenocarcinoma (151/579, 26.1%). TLR4 was the most frequently mutated gene with eleven mutations in 10/171 (5.8%) of EAC tumors. The TLR4 mutants E439G, S570I, F703C and R787H were confirmed to have impaired reactivity to bacterial lipopolysaccharide with marked reductions in signaling by luciferase reporter assays. Overall, our findings show that TLR pathway genes are recurrently mutated in EAC, and TLR4 mutations have decreased responsiveness to bacterial lipopolysaccharide and may play a role in disease pathogenesis in a subset of patients

Mutational signature dynamics shaping the evolution of oesophageal adenocarcinoma

A variety of mutational processes drive cancer development, but their dynamics across the entire disease spectrum from pre-cancerous to advanced neoplasia are poorly understood. We explore the mutagenic processes shaping oesophageal adenocarcinoma tumorigenesis in 997 instances comprising distinct stages of this malignancy, from Barrett Oesophagus to primary tumours and advanced metastatic disease. The mutational landscape is dominated by the C[T > C/G]T substitution enriched signatures SBS17a/b, which are linked with TP53 mutations, increased proliferation, genomic instability and disease progression. The APOBEC mutagenesis signature is a weak but persistent signal amplified in primary tumours. We also identify prevalent alterations in DNA damage repair pathways, with homologous recombination, base and nucleotide excision repair and translesion synthesis mutated in up to 50% of the cohort, and surprisingly uncoupled from transcriptional activity. Among these, the presence of base excision repair deficiencies show remarkably poor prognosis in the cohort. In this work, we provide insights on the mutational aetiology and changes enabling the transition from pre-neoplastic to advanced oesophageal adenocarcinoma

Open chromatin profiling identifies AP1 as a transcriptional regulator in oesophageal adenocarcinoma.

Oesophageal adenocarcinoma (OAC) is one of the ten most prevalent forms of cancer and is showing a rapid increase in incidence and yet exhibits poor survival rates. Compared to many other common cancers, the molecular changes that occur in this disease are relatively poorly understood. However, genes encoding chromatin remodeling enzymes are frequently mutated in OAC. This is consistent with the emerging concept that cancer cells exhibit reprogramming of their chromatin environment which leads to subsequent changes in their transcriptional profile. Here, we have used ATAC-seq to interrogate the chromatin changes that occur in OAC using both cell lines and patient-derived material. We demonstrate that there are substantial changes in the regulatory chromatin environment in the cancer cells and using this data we have uncovered an important role for ETS and AP1 transcription factors in driving the changes in gene expression found in OAC cells.Our work received funding from the Wellcome Trust (https://wellcome.ac.uk/) the National Institute for Health Research (https://www.nihr.ac.uk/) and Cancer Research UK (http:// www.cancerresearchuk.org/)

Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma.

The identification of cancer-promoting genetic alterations is challenging particularly in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we describe a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously. Analysing 261 EACs from the OCCAMS Consortium, we discover helper genes that, alongside well-known drivers, promote cancer. We confirm the robustness of our approach in 107 additional EACs. Unlike recurrent alterations of known drivers, these cancer helper genes are rare or patient-specific. However, they converge towards perturbations of well-known cancer processes. Recurrence of the same process perturbations, rather than individual genes, divides EACs into six clusters differing in their molecular and clinical features. Experimentally mimicking the alterations of predicted helper genes in cancer and pre-cancer cells validates their contribution to disease progression, while reverting their alterations reveals EAC acquired dependencies that can be exploited in therapy