<p>Supplementary Figures S1-S5. Supplementary Tables S1-S5. Figure S1. Comparison of SNV mutation signatures across histological and molecular subtypes. Figure S2. Treatment timelines and radiographic outcomes. Figure S3. Mutation signature stability metrics and comparisons to known signatures. Figure S4. Structural variant mutation sigantures. Figure S5. Comparison of mutation burden between aging-driven and non-aging-driven breast tumours. Table S1. Kruskal-Wallis tests. Table S2. HRDetect prediction test metrics. Table S3. Area under the curve of HRD signatures in platinum response prediction. Table S4. Logistic regression model odds ratios of clinical improvement on platinum. Table S5. Accession IDs of genome datasets submitted to the European Genome-Phenome Archive.</p>
<p>Table S5: Data from this study have been submitted to the European Genome-Phenome Archive (EGA) (www.ebi.ac.uk/ega/home) under the study accession number EGAS00001001159. This table provides the accession numbers for each dataset included in this study.</p>
Abstract Patients with Li-Fraumeni syndrome (LFS) who develop medulloblastoma (MB) have a very poor prognosis. The development of novel therapeutic strategies is challenged by the lack of clinical data for this patient group. We here present clinical and molecular data on a retrospective cohort of pediatric patients with LFS-associated MB. This is an international, retrospective, multicenter cohort study. Patients with LFS-associated MB under 21 years and class 5 (pathogenic) or class 4 (likely pathogenic) constitutional TP53 variants were included. We evaluated TP53 mutation status (constitutional and somatic), DNA methylation subgroup, treatment modalities, event-free (EFS) and overall survival (OS), patterns of recurrence, as well as occurrence of secondary neoplasms. Forty-seven individuals with LFS-associated MB were included. MBs were classified mainly as Sonic Hedgehog (SHH) group (87%). TP53 variants were classified as class 5 (70%) and class 4 (30%). The majority (74%) of TP53 variants represented missense variants. The 2-year (y-) EFS and -OS were 33% and 53%, respectively. A significantly better outcome was seen in patients who received post-operative radiotherapy (RT) (2y-EFS: 44%, 2y-OS: 60%) or chemotherapy before RT (2y-EFS: 24%, 2y-OS: 48%) compared with patients who received no RT (2y-EFS: n.a., 2y-OS: 25%). No significantly different outcomes were seen between patients treated either according to protocols including high-intensity chemotherapy or receiving only maintenance-type chemotherapy (2y-EFS: 42% and 31%, 2y-OS: 68% and 53%, respectively). Patients with LFS-associated MB have a dismal prognosis. Use of RT in LFS-associated MB significantly increased survival rates in the presented cohort, but choice of chemotherapy regimen did not influence their clinical outcome. To improve the outcome of patients with LFS-associated MB, prospective collection of clinical data and development of novel treatments are required.
We study the problem of training a principal in a multi-agent general-sum game using reinforcement learning (RL). Learning a robust principal policy requires anticipating the worst possible strategic responses of other agents, which is generally NP-hard. However, we show that no-regret dynamics can identify these worst-case responses in poly-time in smooth games. We propose a framework that uses this policy evaluation method for efficiently learning a robust principal policy using RL. This framework can be extended to provide robustness to boundedly rational agents too. Our motivating application is automated mechanism design: we empirically demonstrate our framework learns robust mechanisms in both matrix games and complex spatiotemporal games. In particular, we learn a dynamic tax policy that improves the welfare of a simulated trade-and-barter economy by 15%, even when facing previously unseen boundedly rational RL taxpayers.
<p>Cancer-specific methylation (CSM) and fragment length scores (FLS) are moderately correlated. CSM and FLS scores were computed for each sample. Correlation testing between FLS and log-adjusted CSM was performed using Spearman's method.</p>
<div>Abstract<p>Early kinetics of circulating tumor DNA (ctDNA) in plasma predict response to pembrolizumab but typically requires sequencing of matched tumor tissue or fixed gene panels. We analyzed genome-wide methylation and fragment-length profiles using cell-free methylated DNA immunoprecipitation and sequencing (cfMeDIP-seq) in 204 plasma samples from 87 patients before and during treatment with pembrolizumab from a pan-cancer phase II investigator-initiated trial (INSPIRE). We trained a pan-cancer methylation signature using independent methylation array data from The Cancer Genome Atlas to quantify cancer-specific methylation (CSM) and fragment-length score (FLS) for each sample. CSM and FLS are strongly correlated with tumor-informed ctDNA levels. Early kinetics of CSM predict overall survival and progression-free survival, independently of tumor type, PD-L1, and tumor mutation burden. Early kinetics of FLS are associated with overall survival independently of CSM. Our tumor-naïve mutation-agnostic ctDNA approach integrating methylomics and fragmentomics could predict outcomes in patients treated with pembrolizumab.</p>Significance:<p>Analysis of methylation and fragment length in plasma using cfMeDIP-seq provides a tumor-naive approach to measure ctDNA with results comparable with a tumor-informed bespoke ctDNA. Early kinetics within the first weeks of treatment in methylation and fragment quantity can predict outcomes with pembrolizumab in patients with various advanced solid tumors.</p><p><i><a href="https://aacrjournals.org/cancerdiscovery/article/doi/10.1158/2159-8290.CD-14-6-ITI" target="_blank">This article is featured in Selected Articles from This Issue, p. 897</a></i></p></div>
<p>Multivariable analysis of OS and PFS using cancer mutation concentration (CMC) at baseline and cycle 3 of pembrolizumab. CMC was determined using a bespoke targeted approach across the trial cohort. At both baseline and cycle 3, patients were split into above- or below-median groups. Survival outcomes are shown in a multivariable analysis including cohort, PD-L1 expression, and tumor mutation burden (TMB).</p>
