Genomic characterization and detection of potential therapeutic targets for peritoneal mesothelioma in current practice

Peritoneal mesothelioma (PeM) is an aggressive tumor with limited treatment options. The current study aimed to evaluate the value of next generation sequencing (NGS) of PeM samples in current practice. Foundation Medicine F1CDx NGS was performed on 20 tumor samples. This platform assesses 360 commonly somatically mutated genes in solid tumors and provides a genomic signature. Based on the detected mutations, potentially effective targeted therapies were identified. NGS was successful in 19 cases. Tumor mutational burden (TMB) was low in 10 cases, and 11 cases were microsatellite stable. In the other cases, TMB and microsatellite status could not be determined. BRCA1 associated protein 1 (BAP1) mutations were found in 32% of cases, cyclin dependent kinase inhibitor 2A/B (CDKN2A/B) and neurofibromin 2 (NF2) mutations in 16%, and ataxia-telangiectasia mutated serine/threonine kinase (ATM) in 11%. Based on mutations in the latter two genes, potential targeted therapies are available for approximately a quarter of cases (i.e., protein kinase inhibitors for three NF2 mutated tumors, and polyADP-ribose polymerase inhibitors for two ATM mutated tumors). Extensive NGS analysis of PeM samples resulted in the identification of potentially effective targeted therapies for about one in four patients. Although these therapies are currently not available for patients with PeM, ongoing developments might result in new treatment options in the future.</p

Genomic characterization and detection of potential therapeutic targets for peritoneal mesothelioma in current practice

Peritoneal mesothelioma (PeM) is an aggressive tumor with limited treatment options. The current study aimed to evaluate the value of next generation sequencing (NGS) of PeM samples in current practice. Foundation Medicine F1CDx NGS was performed on 20 tumor samples. This platform assesses 360 commonly somatically mutated genes in solid tumors and provides a genomic signature. Based on the detected mutations, potentially effective targeted therapies were identified. NGS was successful in 19 cases. Tumor mutational burden (TMB) was low in 10 cases, and 11 cases were microsatellite stable. In the other cases, TMB and microsatellite status could not be determined. BRCA1 associated protein 1 (BAP1) mutations were found in 32% of cases, cyclin dependent kinase inhibitor 2A/B (CDKN2A/B) and neurofibromin 2 (NF2) mutations in 16%, and ataxia-telangiectasia mutated serine/threonine kinase (ATM) in 11%. Based on mutations in the latter two genes, potential targeted therapies are available for approximately a quarter of cases (i.e., protein kinase inhibitors for three NF2 mutated tumors, and polyADP-ribose polymerase inhibitors for two ATM mutated tumors). Extensive NGS analysis of PeM samples resulted in the identification of potentially effective targeted therapies for about one in four patients. Although these therapies are currently not available for patients with PeM, ongoing developments might result in new treatment options in the future.</p

Clinical Utility of Circulating Tumor DNA in Patients With Advanced KRAS^G12C-Mutated NSCLC Treated With Sotorasib

Introduction: For patients with KRASG12C-mutated NSCLC who are treated with sotorasib, there is a lack of biomarkers to guide treatment decisions. We therefore investigated the clinical utility of pretreatment and on-treatment circulating tumor DNA (ctDNA) and treatment-emergent alterations on disease progression. Methods: Patients with KRASG12C-mutated NSCLC treated with sotorasib were prospectively enrolled in our biomarker study (NCT05221372). Plasma samples were collected before sotorasib treatment, at first-response evaluation and at disease progression. The TruSight Oncology 500 panel was used for ctDNA and variant allele frequency analysis. Tumor response and progression-free survival were assessed per Response Evaluation Criteria in Solid Tumors version 1.1. Results: Pretreatment KRASG12C ctDNA was detected in 50 of 66 patients (76%). Patients with detectable KRASG12C had inferior progression-free survival (hazard ratio [HR] 2.13 [95% confidence interval [CI]: 1.06–4.30], p = 0.031) and overall survival (HR 2.61 [95% CI: 1.16–5.91], p = 0.017). At first-response evaluation (n = 40), 29 patients (73%) had a molecular response. Molecular nonresponders had inferior overall survival (HR 3.58 [95% CI: 1.65–7.74], p = 0.00059). The disease control rate was significantly higher in those with a molecular response (97% versus 64%, p = 0.015). KRAS amplifications were identified as recurrent treatment-emergent alterations. Conclusions: Our data suggest detectable pretreatment KRASG12C ctDNA as a marker for poor prognosis and on-treatment ctDNA clearance as a marker for treatment response. We identified KRAS amplifications as a potential recurring resistance mechanism to sotorasib. Identifying patients with superior prognosis could aid in optimizing time of treatment initiation, and identifying patients at risk of early progression could allow for earlier treatment decisions.</p

In-depth molecular analysis of combined and co-primary pulmonary large cell neuroendocrine carcinoma and adenocarcinoma

Up to 14% of large cell neuroendocrine carcinomas (LCNECs) are diagnosed in continuity with nonsmall cell lung carcinoma. In addition to these combined lesions, 1% to 7% of lung tumors present as co-primary tumors with multiple synchronous lesions. We evaluated molecular and clinicopathological characteristics of combined and co-primary LCNEC-adenocarcinoma (ADC) tumors. Ten patients with LCNEC-ADC (combined) and five patients with multiple synchronous ipsilateral LCNEC and ADC tumors (co-primary) were included. DNA was isolated from distinct tumor parts, and 65 cancer genes were analyzed by next generation sequencing. Immunohistochemistry was performed including neuroendocrine markers, pRb, Ascl1 and Rest. Pure ADC (N = 37) and LCNEC (N = 17) cases were used for reference. At least 1 shared mutation, indicating tumor clonality, was found in LCNEC- and ADC-parts of 10/10 combined tumors but only in 1/5 co-primary tumors. A range of identical mutations was observed in both parts of combined tumors: 8/10 contained ADC-related (EGFR/KRAS/STK11 and/or KEAP1), 4/10 RB1 and 9/10 TP53 mutations. Loss of pRb IHC was observed in 6/10 LCNEC- and 4/10 ADC-parts. The number and intensity of expression of Ascl1 and neuroendocrine markers increased from pure ADC (low) to combined ADC (intermediate) and combined and pure LCNEC (high). The opposite was true for Rest expression. In conclusion, all combined LCNEC-ADC tumors were clonally related indicating a common origin. A relatively high frequency of pRb inactivation was observed in both LCNEC- and ADC-parts, suggesting an underlying role in LCNEC-ADC development. Furthermore, neuroendocrine differentiation might be modulated by Ascl1(+) and Rest(-) expression

TP53 Mutations in Serum Circulating Cell-Free Tumor DNA As Longitudinal Biomarker for High-Grade Serous Ovarian Cancer

The aim of this study was to determine an optimal workflow to detect TP53 mutations in baseline and longitudinal serum cell free DNA (cfDNA) from high-grade serous ovarian carcinomas (HGSOC) patients and to define whether TP53 mutations are suitable as biomarker for disease. TP53 was investigated in tissue and archived serum from 20 HGSOC patients by a next-generation sequencing (NGS) workflow alone or combined with digital PCR (dPCR). AmpliSeq™-focused NGS panels and customized dPCR assays were used for tissue DNA and longitudinal cfDNAs, and Oncomine NGS panel with molecular barcoding was used for baseline cfDNAs. TP53 missense mutations were observed in 17 tissue specimens and in baseline cfDNA for 4/8 patients by AmpliSeq, 6/9 patients by Oncomine, and 4/6 patients by dPCR. Mutations in cfDNA were detected in 4/6 patients with residual disease and 3/4 patients with disease progression within six months, compared to 5/11 patients with no residual disease and 6/13 patients with progression after six months. Finally, mutations were detected at progression in 5/6 patients, but not during chemotherapy. NGS with molecular barcoding and dPCR were most optimal workflows to detect TP53 mutations in baseline and longitudinal serum cfDNA, respectively. TP53 mutations were undetectable in cfDNA during treatment but re-appeared at disease progression, illustrating its promise as a biomarker for disease monitoring

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing