People with Li–Fraumeni syndrome (LFS) harbor a germline pathogenic variant in the TP53 tumor suppressor gene, face a near 100% lifetime risk of cancer, and routinely undergo intensive surveillance protocols. Liquid biopsy has become an attractive tool for a range of clinical applications, including early cancer detection. Here, we provide a proof-of-principle for a multimodal liquid biopsy assay that integrates a targeted gene panel, shallow whole-genome, and cell-free methylated DNA immunoprecipitation sequencing for the early detection of cancer in a longitudinal cohort of 89 LFS patients. Multimodal analysis increased our detection rate in patients with an active cancer diagnosis over uni-modal analysis and was able to detect cancer-associated signal(s) in carriers prior to diagnosis with conventional screening (positive predictive value = 67.6%, negative predictive value = 96.5%). Although adoption of liquid biopsy into current surveillance will require further clinical validation, this study provides a framework for individuals with LFS. Significance: By utilizing an integrated cell-free DNA approach, liquid biopsy shows earlier detection of cancer in patients with LFS compared with current clinical surveillance methods such as imaging. Liquid biopsy provides improved accessibility and sensitivity, complementing current clinical surveillance methods to provide better care for these patients. See related commentary by Latham et al., p. 23. This article is featured in Selected Articles from This Issue, p. 5

Disease gene prediction is a challenging task that has a variety of applications such as early diagnosis and drug development. The existing machine learning methods suffer from the imbalanced sample issue because the number of known disease genes (positive samples) is much less than that of unknown genes which are typically considered to be negative samples. In addition, most methods have not utilized clinical data from patients with a specific disease to predict disease genes. In this study, we propose a disease gene prediction algorithm (called dgSeq) by combining protein-protein interaction (PPI) network, clinical RNA-Seq data, and Online Mendelian Inheritance in Man (OMIN) data. Our dgSeq constructs differential networks based on rewiring information calculated from clinical RNA-Seq data. To select balanced sets of non-disease genes (negative samples), a disease-gene network is also constructed from OMIM data. After features are extracted from the PPI networks and differential networks, the logistic regression classifiers are trained. Our dgSeq obtains AUC values of 0.88, 0.83 and 0.80 for identifying breast cancer genes, thyroid cancer genes and Alzheimer's disease genes, respectively, which indicates its superiority to other three competing methods. Both gene set enrichment analysis and predicted results demonstrate that dgSeq can effectively predict new disease genes.

The role of the immune microenvironment in maintaining disease remission in patients with multiple myeloma (MM) is not well understood. In this study, we comprehensively profile the immune system in patients with newly diagnosed MM receiving continuous lenalidomide maintenance therapy with the aim of discovering correlates of long-term treatment response. Leveraging single-cell RNA sequencing and T cell receptor β sequencing of the peripheral blood and CyTOF mass cytometry of the bone marrow, we longitudinally characterize the immune landscape in 23 patients before and one year after lenalidomide exposure. We compare patients achieving sustained minimal residual disease (MRD) negativity to patients who never achieved or were unable to maintain MRD negativity. We observe that the composition of the immune microenvironment in both the blood and the marrow varied substantially according to both MRD negative status and history of autologous stem cell transplant, supporting the hypothesis that the immune microenvironment influences the depth and duration of treatment response.

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. Significance: 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. This article is featured in Selected Articles from This Issue, p. 897

Germline pathogenic TP53 variants predispose individuals to a high lifetime risk of developing multiple cancers and are the hallmark feature of Li-Fraumeni syndrome (LFS). Our group has previously shown that LFS patients harbor shorter plasma cell-free DNA fragmentation; independent of cancer status. To understand the functional underpinning of cfDNA fragmentation in LFS, we conducted a fragmentomic analysis of 199 cfDNA samples from 82 TP53 mutation carriers and 30 healthy TP53-wildtype controls. We find that LFS individuals exhibit an increased prevalence of A/T nucleotides at fragment ends, dysregulated nucleosome positioning at p53 binding sites, and loci-specific changes in chromatin accessibility at development-associated transcription factor binding sites and at cancer-associated open chromatin regions. Machine learning classification resulted in robust differentiation between TP53 mutant versus wildtype cfDNA samples (AUC-ROC = 0.710–1.000) and intra-patient longitudinal analysis of ctDNA fragmentation signal enabled early cancer detection. These results suggest that cfDNA fragmentation may be a useful diagnostic tool in LFS patients and provides an important baseline for cancer early detection.

Li-Fraumeni syndrome (LFS) is an autosomal dominant cancer-predisposition disorder. Approximately 70% of individuals who fit the clinical definition of LFS harbor a pathogenic germline variant in the TP53 tumor suppressor gene. However, the remaining 30% of patients lack a TP53 variant and even among variant TP53 carriers, approximately 20% remain cancer-free. Understanding the variable cancer penetrance and phenotypic variability in LFS is critical to developing rational approaches to accurate, early tumor detection and risk-reduction strategies. We leveraged family-based whole-genome sequencing and DNA methylation to evaluate the germline genomes of a large, multi-institutional cohort of patients with LFS (n = 396) with variant (n = 374) or wildtype TP53 (n = 22). We identified alternative cancer-associated genetic aberrations in 8/14 wildtype TP53 carriers who developed cancer. Among variant TP53 carriers, 19/49 who developed cancer harbored a pathogenic variant in another cancer gene. Modifier variants in the WNT signaling pathway were associated with decreased cancer incidence. Furthermore, we leveraged the noncoding genome and methylome to identify inherited epimutations in genes including ASXL1, ETV6, and LEF1 that confer increased cancer risk. Using these epimutations, we built a machine learning model that can predict cancer risk in patients with LFS with an area under the receiver operator characteristic curve (AUROC) of 0.725 (0.633–0.810). Significance: Our study clarifies the genomic basis for the phenotypic variability in LFS and highlights the immense benefits of expanding genetic and epigenetic testing of patients with LFS beyond TP53. More broadly, it necessitates the dissociation of hereditary cancer syndromes as single gene disorders and emphasizes the importance of understanding these diseases in a holistic manner as opposed to through the lens of a single gene.