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

Abstract: 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

A comparative analysis of whole genome sequencing of esophageal adenocarcinoma pre- and post-chemotherapy

The scientific community has avoided using tissue samples from patients that have been exposed to systemic chemotherapy to infer the genomic landscape of a given cancer. Esophageal adenocarcinoma is a heterogeneous, chemoresistant tumor for which the availability and size of pretreatment endoscopic samples are limiting. This study compares whole-genome sequencing data obtained from chemo-naive and chemo-treated samples. The quality of whole-genomic sequencing data is comparable across all samples regardless of chemotherapy status. Inclusion of samples collected post-chemotherapy increased the proportion of late-stage tumors. When comparing matched pre- and post-chemotherapy samples from 10 cases, the mutational signatures, copy number, and SNV mutational profiles reflect the expected heterogeneity in this disease. Analysis of SNVs in relation to allele-specific copy-number changes pinpoints the common ancestor to a point prior to chemotherapy. For cases in which pre- and post-chemotherapy samples do show substantial differences, the timing of the divergence is near-synchronous with endoreduplication. Comparison across a large prospective cohort (62 treatment-naive, 58 chemotherapy-treated samples) reveals no significant differences in the overall mutation rate, mutation signatures, specific recurrent point mutations, or copy-number events in respect to chemotherapy status. In conclusion, whole-genome sequencing of samples obtained following neoadjuvant chemotherapy is representative of the genomic landscape of esophageal adenocarcinoma. Excluding these samples reduces the material available for cataloging and introduces a bias toward the earlier stages of cancer.This study was partly funded by a project grant from Cancer Research UK. R.C.F. is funded by an NIHR Professorship and receives core funding from the Medical Research Council and infrastructure support from the Biomedical Research Centre and the Experimental Cancer Medicine Centre. We acknowledge the support of The University of Cambridge, Cancer Research UK (C14303/A17197) and Hutchison Whampoa Limited

Genomic copy number predicts esophageal cancer years before transformation

Summary Recent studies show that aneuploidies and driver gene mutations precede cancer diagnosis by many years1–4. We assess whether these genomic signals can be used for early detection and pre-emptive cancer treatment using the neoplastic precursor lesion Barrett’s esophagus, as an exemplar5. Shallow whole genome sequencing of 777 biopsies, sampled from 88 patients in Barrett’s surveillance over a period of up to 15 years shows that genomic signals can distinguish progressive from stable disease even ten years prior to histopathological transformation. These findings are validated on two independent cohorts of 76 and 248 patients. These methods are low cost and applicable to standard clinical biopsy samples. Compared with current management guidelines based on histopathology and clinical presentation, genomic classification enables earlier treatment for high risk patients as well as reduction of unnecessary treatment and monitoring for patients who are unlikely to develop cancer.The laboratory of R.C.F. is funded by a Core Programme Grant from the Medical Research Council (RG84369). This work was also funded by a United European Gastroenterology Research Prize (RG76026)

Genomic copy number predicts esophageal cancer years before transformation

Summary Recent studies show that aneuploidies and driver gene mutations precede cancer diagnosis by many years1–4. We assess whether these genomic signals can be used for early detection and pre-emptive cancer treatment using the neoplastic precursor lesion Barrett’s esophagus, as an exemplar5. Shallow whole genome sequencing of 777 biopsies, sampled from 88 patients in Barrett’s surveillance over a period of up to 15 years shows that genomic signals can distinguish progressive from stable disease even ten years prior to histopathological transformation. These findings are validated on two independent cohorts of 76 and 248 patients. These methods are low cost and applicable to standard clinical biopsy samples. Compared with current management guidelines based on histopathology and clinical presentation, genomic classification enables earlier treatment for high risk patients as well as reduction of unnecessary treatment and monitoring for patients who are unlikely to develop cancer.The laboratory of R.C.F. is funded by a Core Programme Grant from the Medical Research Council (RG84369). This work was also funded by a United European Gastroenterology Research Prize (RG76026)

Author Correction: Authentication and characterisation of a new oesophageal adenocarcinoma cell line: MFD-1 (Scientific Reports, (2016), 6, 1, (32417), 10.1038/srep32417)

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