SUMMARY Concurrent loss-of-function mutations in STK11 and KEAP1 in lung adenocarcinoma (LUAD) are associated with aggressive tumor growth, resistance to available therapies, and early death. We investigated the effects of coordinate STK11 and KEAP1 loss by comparing co-mutant with single mutant and wild-type isogenic counterparts in multiple LUAD models. STK11/KEAP1 co-mutation results in significantly elevated expression of ferroptosis-protective genes, including SCD and AKR1C1/2/3 , and resistance to pharmacologically induced ferroptosis. CRISPR screening further nominates SCD (SCD1) as selectively essential in STK11/KEAP1 co-mutant LUAD. Genetic and pharmacological inhibition of SCD1 confirms the essentiality of this gene and augments the effects of ferroptosis induction by erastin and RSL3. Together these data identify SCD1 as a selective vulnerability and a promising candidate for targeted drug development in STK11/KEAP1 co-mutant LUAD.

IMPORTANCERecommendations for adjuvant therapy after surgical resection of lung adenocarcinoma (LUAD) are based solely on TNM classification but are agnostic to genomic and high-risk clinicopathologic factors. Creation of a prediction model that integrates tumor genomic and clinicopathologic factors may better identify patients at risk for recurrence.OBJECTIVE To identify tumor genomic factors independently associated with recurrence, even in the presence of aggressive, high-risk clinicopathologic variables, in patients with completely resected stages I to III LUAD, and to develop a computational machine-learning prediction model (PRecur) to determine whether the integration of genomic and clinicopathologic features could better predict risk of recurrence, compared with the TNM system. DESIGN, SETTING, AND PARTICIPANTSThis prospective cohort study included 426 patients treated from January 1, 2008, to December 31, 2017, at a single large cancer center and selected in consecutive samples. Eligibility criteria included complete surgical resection of stages I to III LUAD, broad-panel next-generation sequencing data with matched clinicopathologic data, and no neoadjuvant therapy. External validation of the PRecur prediction model was performed using The Cancer Genome Atlas (TCGA). Data were analyzed from 2014 to 2018. MAIN OUTCOMES AND MEASURESThe study end point consisted of relapse-free survival (RFS), estimated using the Kaplan-Meier approach. Associations among clinicopathologic factors, genomic alterations, and RFS were established using Cox proportional hazards regression. The PRecur prediction model integrated genomic and clinicopathologic factors using gradient-boosting survival regression for risk group generation and prediction of RFS. A concordance probability estimate (CPE) was used to assess the predictive ability of the PRecur model. RESULTSOf the 426 patients included in the analysis (286 women [67%]; median age at surgery, 69 [interquartile range, 62-75] years), 318 (75%) had stage I cancer. Association analysis showed that alterations in SMARCA4 (clinicopathologic-adjusted hazard ratio [HR], 2.44; 95% CI, 1.03-5.77; P = .042) and TP53 (clinicopathologic-adjusted HR, 1.73; 95% CI, 1.09-2.73; P = .02) and the fraction of genome altered (clinicopathologic-adjusted HR, 1.03; 95% CI, 1.10-1.04; P = .005) were independently associated with RFS. The PRecur prediction model outperformed the TNM-based model (CPE, 0.73 vs 0.61; difference, 0.12 [95% CI, 0.05-0.19]; P < .001) for prediction of RFS. To validate the prediction model, PRecur was applied to the TCGA LUAD data set (n = 360), and a clear separation of risk groups was noted (log-rank statistic, 7.5; P = .02), confirming external validation. CONCLUSIONS AND RELEVANCEThe findings suggest that integration of tumor genomics and clinicopathologic features improves risk stratification and prediction of recurrence after surgical resection of early-stage LUAD. Improved identification of patients at risk for recurrence could enrich and enhance accrual to adjuv...

Myxofibrosarcoma (MFS) and undifferentiated pleomorphic sarcoma (UPS) are highly genetically complex soft tissue sarcomas. Up to 50% of patients develop distant metastases, but current systemic therapies have limited efficacy. MFS and UPS have recently been shown to commonly harbor copy number alterations or mutations in the tumor suppressor genes RB1 and TP53. As these alterations have been shown to engender dependence on the oncogenic protein Skp2 for survival of transformed cells in mouse models, we sought to examine its function and potential as a therapeutic target in MFS/UPS. Comparative genomic hybridization (CGH) and next-generation sequencing (NGS) confirmed that a significant fraction of MFS and UPS patient samples (n=94) harbor chromosomal deletions and/or loss-of-function mutations in RB1 and TP53 (88% carry alterations in at least one gene; 60% carry alterations in both). Tissue microarray (TMA) analysis identified a correlation between absent Rb and p53 expression and positive expression of Skp2. Downregulation of Skp2 or treatment with the Skp2-specific inhibitor C1 revealed that Skp2 drives proliferation of patient-derived MFS/UPS cell lines deficient in both Rb and p53 by degrading p21 and p27. Inhibition of Skp2 using the neddylation-activating enzyme (NAE) inhibitor pevonedistat decreased growth of Rb/p53negative patient-derived cell lines and mouse xenografts. These results demonstrate that loss of both Rb and p53 renders MFS and UPS dependent on Skp2, which can be therapeutically exploited and could provide the basis for promising novel systemic therapies for MFS and UPS.Significance: Loss of both Rb and p53 renders myxofibrosarcoma and undifferentiated pleomorphic sarcoma dependent on Skp2, which could provide the basis for promising novel systemic therapies.Research.

PURPOSE Broad-panel sequencing of tumors facilitates routine care of people with cancer as well as clinical trial matching for novel genome-directed therapies. We sought to extend the use of broad-panel sequencing results to survival stratification and clinical outcome prediction. METHODS By using sequencing results from a cohort of 1,054 patients with advanced lung adenocarcinomas, we developed OncoCast, a machine learning tool for survival risk stratification and biomarker identification. RESULTS With OncoCast, we stratified this patient cohort into four risk groups on the basis of tumor genomic profile. Patients whose tumors harbored a high-risk profile had a median survival of 7.3 months (95% CI, 5.5 to 10.9 months) compared with a low-risk group with a median survival of 32.8 months (95% CI, 26.3 to 38.5 months) with a hazard ratio of 4.6 ( P < .001), far superior to any individual gene predictor or standard clinical characteristics. We found that comutations of both STK11 and KEAP1 are strong determinants of unfavorable prognosis with currently available therapies. In patients with targetable oncogenes (eg, EGFR, ALK, ROS1) who received targeted therapies, the tumor genetic background additionally differentiated survival with mutations in TP53 and ARID1A, which contributed to a higher risk score for shorter survival. CONCLUSION A mutational profile derived from broad-panel sequencing presents an effective genomic stratification for patient survival in advanced lung adenocarcinoma. OncoCast is available as a public resource that facilitates the incorporation of mutational data to predict individual patient prognosis and compare risk characteristics of patient populations.

BackgroundMost genetic analyses of ancient and modern dogs have focused on variation in the autosomes or on the mitochondria. Mitochondrial DNA is more easily obtained from ancient samples than nuclear DNA and mitochondrial analyses have revealed important insights into the evolutionary history of canids. Utilizing a recently published dog Y-chromosome reference, we analyzed Y-chromosome sequence across a diverse collection of canids and determined the Y haplogroup of three ancient European dogs.ResultsWe identified 1121 biallelic Y-chromosome SNVs using whole-genome sequences from 118 canids and defined variants diagnostic to distinct dog Y haplogroups. Similar to that of the mitochondria and previous more limited studies of Y diversity, we observe several deep splits in the Y-chromosome tree which may be the result of retained Y-chromosome diversity which predates dog domestication or post-domestication admixture with wolves. We find that Y-chromosomes from three ancient European dogs (4700–7000 years old) belong to distinct clades.ConclusionsWe estimate that the time to the most recent comment ancestor of dog Y haplogroups is 68–151 thousand years ago. Analysis of three Y-chromosomes from the Neolithic confirms long stranding population structure among European dogs.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4749-z) contains supplementary material, which is available to authorized users.

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Background Current risk stratification for patients with malignant pleural mesothelioma based on disease stage and histology is inadequate. For some individuals with early-stage epithelioid tumours, a good prognosis by current guidelines can progress rapidly; for others with advanced sarcomatoid cancers, a poor prognosis can progress slowly. Therefore, we aimed to develop and validate a machine-learning tool-known as OncoCast-MPM-that could create a model for patient prognosis.Methods We did a retrospective study looking at malignant pleural mesothelioma tumours using next-generation sequencing from the Memorial Sloan Kettering Cancer Center-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT). We collected clinical, pathological, and routine next-generation sequencing data from consecutive patients with malignant pleural mesothelioma treated at the Memorial Sloan Kettering Cancer Center (New York, NY, USA), as well as the MSK-IMPACT data. Together, these data comprised the MSK-IMPACT cohort. Using OncoCast-MPM, an open-source, web-accessible, machine-learning risk-prediction model, we integrated available data to create risk scores that stratified patients into low-risk and high-risk groups. Risk stratification of the MSK-IMPACT cohort was then validated using publicly available malignant pleural mesothelioma data from The Cancer Genome Atlas (ie, the TCGA cohort).

Background Patients with brain tumors treated with radiotherapy (RT) and chemotherapy (CT) often experience cognitive dysfunction. We reported that single nucleotide polymorphisms (SNPs) in the APOE, COMT, and BDNF genes may influence cognition in brain tumor patients. In this study, we assessed whether genes associated with late-onset Alzheimer’s disease (LOAD), inflammation, cholesterol transport, dopamine and myelin regulation, and DNA repair may influence cognitive outcome in this population. Methods One hundred and fifty brain tumor patients treated with RT ± CT or CT alone completed a neurocognitive assessment and provided a blood sample for genotyping. We genotyped genes/SNPs in these pathways: (i) LOAD risk/inflammation/cholesterol transport, (ii) dopamine regulation, (iii) myelin regulation, (iv) DNA repair, (v) blood–brain barrier disruption, (vi) cell cycle regulation, and (vii) response to oxidative stress. White matter (WM) abnormalities were rated on brain MRIs. Results Multivariable linear regression analysis with Bayesian shrinkage estimation of SNP effects, adjusting for relevant demographic, disease, and treatment variables, indicated strong associations (posterior association summary [PAS] ≥ 0.95) among tests of attention, executive functions, and memory and 33 SNPs in genes involved in: LOAD/inflammation/cholesterol transport (eg, PDE7A, IL-6), dopamine regulation (eg, DRD1, COMT), myelin repair (eg, TCF4), DNA repair (eg, RAD51), cell cycle regulation (eg, SESN1), and response to oxidative stress (eg, GSTP1). The SNPs were not significantly associated with WM abnormalities. Conclusion This novel study suggests that polymorphisms in genes involved in aging and inflammation, dopamine, myelin and cell cycle regulation, and DNA repair and response to oxidative stress may be associated with cognitive outcome in patients with brain tumors.