Multi-omics characterization of molecular features of gastric cancer correlated with response to neoadjuvant chemotherapy

Abstract

Neoadjuvant chemotherapy is a common treatment for patients with gastric cancer. Although its benefits have been demonstrated, neoadjuvant chemotherapy is underutilized in gastric cancer management, because of the lack of biomarkers for patient selection and a limited understanding of resistance mechanisms. Here, we performed whole-genome, whole-exome, and RNA sequencing on 84 clinical samples (including matched pre- and posttreatment tumors) from 35 patients whose responses to neoadjuvant chemotherapy were rigorously defined. We observed increased microsatellite instability and mutation burden in nonresponse tumors. Through comparisons of response versus nonresponse tumors and pre- versus posttreatment samples, we found that C10orf71 mutations were associated with treatment resistance, which was supported by drug response data and potentially through inhibition of cell cycle, and that MYC amplification correlated with treatment sensitivity, whereas MDM2 amplification showed the opposite pattern. Neoadjuvant chemotherapy also reshapes tumor-immune signaling and microenvironment. Our study provides a critical basis for developing precision neoadjuvant regimens.

“3G” Trial: An RNA Editing Signature to Guide Gastric Cancer Chemotherapy

“…Patient stratification based on molecular information promises a great value for guiding diagnosis and treatment choice, particularly in genetically heterogeneous diseases such as gastric cancer. Till date, a number of studies attempted for the molecular classification of patients for this purpose, most of which are based on gene expression profiling utilizing microarray data (3)(4)(5)(7)(8)(9). With the broader availability and better quality of next-generation sequencing data, now it became possible to reliably study other molecular features than gene expression.…”

“…However, an important question is whether we can improve the selection of patients with advanced GC for chemotherapy in order to achieve greater benefit from the treatment. To address this, different groups have put much effort in stratifying patients with GC into molecular subtypes (3)(4)(5)(6)(7)(8)(9); however, these studies heavily focused on gene expression and/or mutation profiling, often with the limitations of microarray-based platforms or small sample sizes, thus novel molecular data types and approaches are needed for better guiding the chemotherapy treatment for this class of patients. Here, we demonstrate A-to-I RNA editing as a novel epigenetic classifier to reliably identify responders to chemotherapy.…”

Systems Biology of Gastric Cancer: Perspectives on the Omics-Based Diagnosis and Treatment

“…Interestingly, these results showed that the subtypes could be as predictors for survival and response to adjuvant chemotherapy (Sohn et al, 2017). Moreover, a recent study characterized key mutational features, copy number alternations and gene expression changes associated with responses to neoadjuvant chemotherapy with multi-omics data of tumor samples from patients responding to neoadjuvant chemotherapy or not (Li et al, 2020). Compared the responders with non-responders tumors and pre-with post-treatment samples, the C10orf71 mutations were found to be associated with treatment resistance by statistical models (Li et al, 2020).…”

“…Moreover, a recent study characterized key mutational features, copy number alternations and gene expression changes associated with responses to neoadjuvant chemotherapy with multi-omics data of tumor samples from patients responding to neoadjuvant chemotherapy or not (Li et al, 2020). Compared the responders with non-responders tumors and pre-with post-treatment samples, the C10orf71 mutations were found to be associated with treatment resistance by statistical models (Li et al, 2020). Taken together, such machine learning based approach integrates multi-omics data, providing efficient ways to predict the treatment outcome based on the host genetic information.…”

Identification of candidate mediators of chemoresponse in breast cancer through therapy-driven selection of somatic variants

“…A consequence of the increased use of neoadjuvant protocols is the availability for research purposes of matched samples of primary tumour tissue taken before systemic therapy, usually in the form of a diagnostic biopsy, and after therapy from the resection. These matched samples present a powerful resource for study of the molecular response of tumours to therapy, and thereby to identify pathways associated with relative therapy resistance or sensitivity [ 5 – 7 ]. The hypothesis behind such analyses is that tumour cells that remain after therapy include characteristics associated with therapy resistance, while characteristics present before therapy but lost in the matched post-treatment sample include molecular events associated with relative therapy sensitivity.…”