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Development and Validation of a Prognostic and Predictive 32-Gene Signature for Gastric Cancer

ABSTRACT: Objective: Develop a genomic assay to assess prognosis and guide therapy for gastric cancer patients.Design: This was a retrospective study of multiple, independent cohorts of gastric cancer patients. We performed a pan-cancer analysis of somatic mutations found in 6,681 patients with 19 different cancers using our machine learning algorithm NTriPath to identify a gastric cancer-specific gene signature. We applied this signature to a 567-patient cohort to establish genomic-based molecular subtypes and then used a support vector machine to build a molecular subtype-based risk-scoring model. We evaluated the prognostic capability of the model and investigated whether molecular subtypes predicted response to adjuvant chemotherapy and immune checkpoint blockade. Results: We identified a 32-gene signature that categorized gastric cancer patients into four subtypes that were independently associated with overall survival and predicted response to therapy. The 5-year overall survival of Group 1 patients who received adjuvant 5-fluorouracil and platinum was significantly worse than patients who did not receive therapy (hazard ratio (HR), 6.80 (95% CI, 1.46-31.6), P=0.015). Group 3 patients who received therapy did significantly better than those who did not (HR, 0.28 (95% CI, 0.08-0.96), p=0.043). The subtypes also predicted response to immune checkpoint inhibitors in patients with metastatic or recurrent disease. Groups 1 and 3 patients had significantly higher response rates (44-100%) than patients in Groups 2 and 4 (0 to 13%;P < 0.01).Conclusions: The 32-gene signature is a promising prognostic and predictive biomarker to guide the use of adjuvant chemotherapy and immune checkpoint inhibitors in gastric cancer patients

PROVIDER: EGAS00001005588 | EGA |

REPOSITORIES: EGA

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Project description:Purpose Aromatase inhibitors (AIs) have an established role in breast cancer treatment. Response rates are only 50-70% in the neoadjuvant setting and lower in advanced disease. Accurate biomarkers are urgently needed to predict response in these settings and to determine which individuals will benefit from adjuvant AI therapy. Participants and Methods Pre- and on-treatment (after 2 weeks and 3 months) biopsies were obtained from 89 post-menopausal women with ER+ breast cancer receiving neoadjuvant letrozole for transcript profiling. Dynamic clinical response was assessed by three-dimensional ultrasound measurements. Results The molecular response to letrozole was characterised and a four gene classifier of clinical response was established (accuracy of 96%) based upon the level of two genes prior to treatment (one associated with immune signalling, IL6ST and the other with apoptosis, NGFRAP1) and two proliferation genes (ASPM, MCM4) at 2 weeks of therapy. The four gene signature was found to be 91% accurate in a blinded, completely independent validation dataset of patients treated with anastrozole. Matched 2 week on-treatment biopsies improved predictive power over pre-treatment biopsies alone. This signature also significantly predicted recurrence free survival (p=0.029) and breast cancer specific survival (p=0.009). We demonstrate that the test can also be performed using quantitative PCR or immunohistochemistry. Conclusion A four gene predictive model of clinical response to AIs by two weeks has been generated and validated. Deregulated immune and apoptotic responses before treatment and a failure to reduce proliferation by 2 weeks are functional characteristics of breast tumours that do not respond to AIs. 25 Pre treatment, 25 two week and 25 three month on-treatment primary breast tumour samples from the same patients. Data was analysed with two previously generated datasets, GSE55374 which was also processed on Illumina HT-12v4 BeadChips (GPL10558) and GSE20181 on Affymetrix U133A GeneChips (GPL96)

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Development and Validation of a Prognostic and Predictive 32-Gene Signature for Gastric Cancer

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