Selection bias due to delayed comprehensive genomic profiling in Japan

Comprehensive genomic profiling from C-CAT database unveiled over 80% presence of oncogenic drivers in anaplastic thyroid carcinoma including BRAF, RAS family, NF1, and FGFR1

OBJECTIVE

Anaplastic thyroid carcinoma (ATC) is considered a very aggressive carcinoma and has been difficult to treat with therapeutic strategies. This study examines the landscape of genomic alteration in ATC, including the BRAF V600E mutation, and its clinical implications.

DESIGN, PATIENTS AND MESUREMENT

A retrospective observational study was conducted using collected at the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) in Japan, utilizing comprehensive genomic profiling data from 102 ATC cases. Additionally, AACR-GENIE data from 267 cases were analysed for validation. Statistical methods, including the conditional Kendall tau statistic and χ2 tests, were employed for survival analysis and gene mutation comparisons.

RESULTS

Among 102 ATCs, BRAF, RAS, and other driver mutations were found in 83 cases (81.2%). The prevalence of BRAF V600E mutations was as high as 60%. Co-mutation analysis identified different genomic profiles in the BRAF, RAS, and wild-type groups. Despite the diverse molecular backgrounds, no significant differences in clinical variables and overall survival were observed. The analysis considering left-side amputation suggested that RAS mutations had a poorer prognosis. In the BRAF/RAS wild-type group, FGFR1 and NF1 were identified as driver mutations, with an accumulation of copy number variations and less TERT promoter mutations. This molecular subgrouping was also supported by the AACR-GENIE data.

CONCLUSIONS

Comprehensive genomic analysis of ATC in Japan revealed distinct molecular subgroups, highlighting the importance of BRAF V600E mutations, particularly V600E, as potential therapeutic targets and suggest the relevance of tailor-made therapeutic strategies based on genomic profiling.

Bayesian parametric estimation based on left-truncated competing risks data under bivariate Clayton copula models

In observational/field studies, competing risks and left-truncation may co-exist, yielding 'left-truncated competing risks' settings. Under the assumption of independent competing risks, parametric estimation methods were developed for left-truncated competing risks data. However, competing risks may be dependent in real applications. In this paper, we propose a Bayesian estimator for both independent competing risks and copula-based dependent competing risks models under left-truncation. The simulations show that the Bayesian estimator for the copula-based dependent risks model yields the desired performance when competing risks are dependent. We also comprehensively explore the choice of the prior distributions (Gamma, Inverse-Gamma, Uniform, half Normal and half Cauchy) and hyperparameters via simulations. Finally, two real datasets are analyzed to demonstrate the proposed estimators.

Factors predictive of second-line chemotherapy in soft tissue sarcoma: An analysis of the National Genomic Profiling Database

Of the drugs used in second-line chemotherapy for soft tissue sarcoma (STS), trabectedin is effective for liposarcoma and leiomyosarcoma (L-sarcoma), eribulin for liposarcoma, and pazopanib for non-liposarcoma. The indications for these drugs in STS other than L-sarcoma have not been established. Here we explored the prognosis, mutation profiles, and drug-response factors in STS using real-world big data. Clinicogenomic data on 1761 patients with sarcoma who underwent FoundationOne CDx were obtained from a national database in Japan. Patients with TP53 and KDM2D mutations had a significantly shorter survival period of 253 (95% CI, 99-404) and 330 (95% CI, 20-552) days, respectively, than those without mutations. Non-supervised clustering based on mutation profiles generated 13 tumor clusters. The response rate (RR) to trabectedin was highest in an MDM2-amplification cluster (odds ratio [OR]: 2.2; p = 0.2). The RR was lowest for eribulin in an MDM2-amplification cluster (OR: 0.4; p = 0.03) and highest in a TERT-mutation cluster (OR: 3.0; p = 0.03). The RR was highest for pazopanib in a PIK3CA/PTEN-wild type cluster (OR: 2.1; p = 0.03). In particular, patients harboring mutations in genes regulating the PI3K/Akt/mTOR pathway had a lower RR than patients without mutations (OR: 0.3; p = 0.04). In STS, mutation profiles were more useful in predicting the drug response than histology. The present study demonstrated the potential of tailored therapy guided by mutation profiles established by comprehensive genomic profiling testing in optimizing second-line chemotherapy for STS. The findings of this study will hopefully contribute some valuable insights into enhancing STS treatment strategies and outcomes.

PTPN11 variant may be a prognostic indicator of IDH-wildtype glioblastoma in a comprehensive genomic profiling cohort

PURPOSE

Glioblastoma (GBM) is the most common type of primary malignant brain tumor and has a poor prognosis. Identifying novel targets and stratification strategies is urgently needed to improve patient survival. The present study aimed to identify clinically relevant genomic alterations in IDH-wildtype GBM using data from comprehensive genomic profiling (CGP) assays performed nationwide in Japan.

METHODS

The CGP assay results of 392 IDH-wildtype GBM cases performed between October 2019 and February 2023 obtained from the Center for Cancer Genomics and Advanced Therapeutics were retrospectively analyzed.

RESULTS

The median patient age was 52.5 years, and 207 patients (53%) were male. In the 286 patients for whom survival information was available, a protein-tyrosine phosphatase non-receptor type 11 (PTPN11) variant detected in 20 patients (6.8%) was extracted as the gene associated with significantly shorter overall survival (p = 0.002). Multivariate analysis demonstrated that the PTPN11 variant and poor performance status were independent prognostic indicators. In contrast, no prognostic impact was observed in the cohort in The Cancer Genome Atlas data. The discrepancy in the prognostic impact of the PTPN11 variant from these two pools might have resulted from differences in the biases affecting the survival of patients who underwent a CGP assay, including left-truncation and right-censored bias. However, survival simulation done to adjust for these biases showed that the prognostic impact of the PTPN11 variant was also significant.

CONCLUSIONS

The PTPN11 variant was a negative prognostic indicator of IDH-wildtype GBM in the patient cohort with the CGP assay.

FoundationOne CDx detected an uncovered variant of epidermal growth factor receptor exon 19 deletion by Oncomine Dx target test in a patient with lung adenocarcinoma

A non-smoker woman with advanced lung adenocarcinoma was referred to us. The Oncomine Dx target test (ODxTT), a next-generation sequencing (NGS)-based hot spots panel test, did not detect any driver mutations, so we treated her with chemo-immunotherapy. After second-line chemotherapy, we performed FoundationOne CDx, a NGS-based comprehensive genomic profiling (CGP) test, and identified a rare variant of epidermal growth factor receptor exon 19 deletion that had not been covered by ODxTT. This case highlights the importance of considering the indication of a CGP test for patients who are likely to harbor driver mutations, even when ODxTT fails to detect any.

Construction and validation of a prognostic prediction model for gastric cancer using a series of genes related to lactate metabolism

Background

Gastric cancer (GC) is one of the most common clinical malignant tumors worldwide, with high morbidity and mortality. The commonly used tumor-node-metastasis (TNM) staging and some common biomarkers have a certain value in predicting the prognosis of GC patients, but they gradually fail to meet the clinical demands. Therefore, we aim to construct a prognostic prediction model for GC patients.

Methods

A total of 350 cases were included in the STAD (Stomach adenocarcinoma) entire cohort of TCGA (The Cancer Genome Atlas), including the STAD training cohort of TCGA (n = 176) and the STAD testing cohort of TCGA (n = 174). GSE15459 (n = 191), and GSE62254 (n = 300) were for external validation.

Results

Through differential expression analysis and univariate Cox regression analysis in the STAD training cohort of TCGA, we screened out five genes among 600 genes related to lactate metabolism for the construction of our prognostic prediction model. The internal and external validations showed the same result, that is, patients with higher risk score were associated with poor prognosis (all p < 0.05), and our model works well without regard of patients' age, gender, tumor grade, clinical stage or TNM stage, which supports the availability, validity and stability of our model. Gene function analysis, tumor-infiltrating immune cells analysis, tumor microenvironment analysis and clinical treatment exploration were performed to improve the practicability of the model, and hope to provide a new basis for more in-depth study of the molecular mechanism for GC and for clinicians to formulate more reasonable and individualized treatment plans.

Conclusions

We screened out and used five genes related to lactate metabolism to develop a prognostic prediction model for GC patients. The prediction performance of the model is confirmed by a series of bioinformatics and statistical analysis.

Prognostic benefit of comprehensive genomic profiling in clinical practice remains uncertain

The overall survival of patients who received genomically matched therapy was not significantly longer than that of patients receiving treatment only other than genomically matched therapy in the breast invasive ductal carcinoma (A), colorectal adenocarcinoma (B), and pancreatic adenocarcinoma (C) cohorts.