Objective To develop a novel machine learning‐based algorithm called the Genomic Scar Score (GSS) for predicting homologous recombination deficiency (HRD) events. Design Method development study. Setting AmoyDx Medical Laboratory and Jiangsu Cancer Hospital. Population or sample A cohort of individuals with ovarian or breast cancer (n = 377) were collected from the AmoyDx Medical Laboratory. Another cohort of patients with ovarian cancer treated with PARP inhibitors (n = 58) was enrolled in the Jiangsu Cancer Hospital. Methods We used linear support vector machines to build a Genomic Scar (GS) model to predict HRD events, and Kaplan–Meier analyses were performed by comparing the progression‐free survival (PFS) of patients in different groups using a two‐sided log‐rank test. Main outcome measures The performance of the GS model and the result of clinical validation. Results The GS model displayed more than 97.0% sensitivity to detect BRCA‐deficient events, and the GS model identified patients that could benefit from poly(ADP‐ribose) polymerase inhibitors (PARPi), as the GS score (GSS)‐positive group had a longer progression‐free survival (PFS) (9.4 versus 4.4 months; hazard ratio [HR] = 0.54, P < 0.001) than the GSS‐negative group after PARPi treatment. Meanwhile, the GSS showed high concordance among different NGS panels, which implied the robustness of the GS model. Conclusions The GS was a robust model to predict HRD and had broad clinical applications in predicting which patients will respond favourably to PARPi treatment.
Background: The prognosis for patients with stage II/III non-small cell lung cancer (NSCLC) is unsatisfactory, even after complete tumor resection and adjuvant chemotherapy. Here, we assessed the prognostic and predictive value of immunogenomic signatures for stage II/III NSCLC in Chinese patients. Methods: A total of 91 paired resected stage II/III NSCLC and normal tissues, including 47 squamous cell lung carcinomas (SCC) and 44 lung adenocarcinomas (ADC), were collected and analyzed using whole exome sequencing (WES) to identify immunogenomic signatures for association with clinicopathological variables and disease-free survival (DFS). Results: Higher neoantigen load (NAL, >2 neoantigens/Mb) exhibited better DFS for SCC patients (p = 0.021) but not ADC patients. A benefit from adjuvant chemotherapy was correlated with lower NAL (≤2 neoantigens/Mb) (p = 0.009). However, tumor mutation burden (TMB), mutations of individual gene, oncogene pathways, and antigen presentation machinery genes, and human leukocyte antigen (HLA)-I number and HLA-I loss of heterozygosity (LOH) had no prognostic or predictive value for DFS of SCC or ADC patients. Conclusions: NAL is a useful biomarker for lung SCC prognosis and prediction of chemotherapy responses in Chinese patients. The predictive value of NAL for adjuvant immunotherapy should be further explored in patients with resected NSCLC. K E Y W O R D S biomarker, neoantigen load, NSCLC, prognosis, whole exome sequencing Lei Gong and Ronghui He contributed equally to this work.
3065 Background: In clinics, it can be challenging to make correct diagnosis of LCNEC, Small cell lung cancer (SCLC), if tissues, like needle biopsies, are insufficient or morphology was poorly preserved. In this study, a reliable classifier was constructed based on transcriptome data and machine learning (Ridge regression) to improve the diagnostic accuracy for LCNEC and SCLC. Methods: RNA-Seq data obtained from 3 public cohorts were collected as training set, including 60 NSCLC cases from The Cancer Genome Atlas (TCGA), 66 LCNEC cases from Julie George et al., Nature Communications 2018, and 33 SCLC cases from Julie George et al., Nature 2015. Another 80 NSCLC, 30 LCNEC and 15 SCLC cases published by Martin Peifer et al., Nature Genetics 2012 were used as validation set. Additionally, RNA-Seq data of 27 borderline samples which were hard to make diagnosis based on histology and Immunohistochemistry were used to test the accuracy of the prediction model. Results: 13,959 genes mapped to 186 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were included. Gene Set Variation Analysis (GSVA) algorithm was used to enrich and score each KEGG pathway. A prediction model based on GSVA score of each pathway was constructed via Ridge regression. This GSVA Score Model achieved ROC-AUC 0.949 and concordant rate of 0.75 for the entire prediction efficiency. Of the 27 borderline samples which were hard to make confirmed diagnosis, 17/27 (63.0%) were predicted as LCNEC, 7/27 were predicted as SCLC, and the remainder were predicted as NSCLC. While only 8 (29.6%) cases with LCNEC were diagnosed by pathologists, which was significantly lower than the results predicted by the model. Furthermore, cases with model predicted LCNEC had a significant longer disease-free survival than that with model predicted SCLC (median DFS,59 months for LCNEC vs 5 months for SCLC, p = 0.0043), which was in parallel with currently known prognostic difference of these two types of neuroendocrine tumors. Conclusions: This GSVA algorithm-based prediction model was able to make accurate diagnosis of LCNEC and SCLC. And it may provide valuable information for clinics to choose optimal therapeutic approach for patients with pulmonary neuroendocrine tumors.[Table: see text]
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