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Abstract The study of survival outliers of glioblastoma can provide important clues on gliomagenesis as well as on the ways to alter clinical course of this almost uniformly lethal cancer type. However, there has been little consensus on genetic and epigenetic signatures of the long-term survival outliers of glioblastoma. Although the two classical molecular markers of glioblastoma including isocitrate dehydrogenase 1 or 2 (IDH1/2) mutation and O6-methylguanine DNA methyltransferase (MGMT) promoter methylation are associated with overall survival rate of glioblastoma patients, they are not specific to the survival outliers. In this study, we compared the two groups of survival outliers of glioblastoma with IDH wild-type, consisting of the glioblastoma patients who lived longer than 3 years (n = 17) and the patients who lived less than 1 year (n = 12) in terms of genome-wide DNA methylation profile. Statistical analyses were performed to identify differentially methylated sites between the two groups. Functional implication of DNA methylation patterns specific to long-term survivors of glioblastoma were investigated by comprehensive enrichment analyses with genomic and epigenomic features. We found that the genome of long-term survivors of glioblastoma is differentially methylated relative to short-term survivor patients depending on CpG density: hypermethylation near CpG islands (CGIs) and hypomethylation far from CGIs. Interestingly, these two patterns are associated with distinct oncogenic aspects in gliomagenesis. In the long-term survival glioblastoma-specific sites distant from CGI, somatic mutations of glioblastoma are enriched with higher DNA methylation, suggesting that the hypomethylation in long-term survival glioblastoma can contribute to reduce the rate of somatic mutation. On the other hand, the hypermethylation near CGIs associates with transcriptional downregulation of genes involved in cancer progression pathways. Using independent cohorts of IDH1/2- wild type glioblastoma, we also showed that these two patterns of DNA methylation can be used as molecular markers of long-term survival glioblastoma. Our results provide extended understanding of DNA methylation, especially of DNA hypomethylation, in cancer genome and reveal clinical importance of DNA methylation pattern as prognostic markers of glioblastoma.

Significance: There is an unmet need for effective ovarian cancer screening and diagnostic approaches that enable earlier-stage cancer detection and increased overall survival. We have developed a high-performing accessible approach that evaluates cfDNA fragmentomes and protein biomarkers to detect ovarian cancer., (©2024 The Authors; Published by the American Association for Cancer Research.)

Genetic changes in repetitive sequences are a hallmark of cancer and other diseases, but characterizing these has been challenging using standard sequencing approaches. We developed a de novo kmer finding approach, called ARTEMIS (Analysis of RepeaT EleMents in dISease), to identify repeat elements from whole-genome sequencing. Using this method, we analyzed 1.2 billion kmers in 2837 tissue and plasma samples from 1975 patients, including those with lung, breast, colorectal, ovarian, liver, gastric, head and neck, bladder, cervical, thyroid, or prostate cancer. We identified tumor-specific changes in these patients in 1280 repeat element types from the LINE, SINE, LTR, transposable element, and human satellite families. These included changes to known repeats and 820 elements that were not previously known to be altered in human cancer. Repeat elements were enriched in regions of driver genes, and their representation was altered by structural changes and epigenetic states. Machine learning analyses of genome-wide repeat landscapes and fragmentation profiles in cfDNA detected patients with early-stage lung or liver cancer in cross-validated and externally validated cohorts. In addition, these repeat landscapes could be used to noninvasively identify the tissue of origin of tumors. These analyses reveal widespread changes in repeat landscapes of human cancers and provide an approach for their detection and characterization that could benefit early detection and disease monitoring of patients with cancer.

Non-invasive approaches for cell-free DNA (cfDNA) assessment provide an opportunity for cancer detection and intervention. Here, we use a machine learning model for detecting tumor-derived cfDNA through genome-wide analyses of cfDNA fragmentation in a prospective study of 365 individuals at risk for lung cancer. We validate the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 lung cancer patients. Combining fragmentation features, clinical risk factors, and CEA levels, followed by CT imaging, detected 94% of patients with cancer across stages and subtypes, including 91% of stage I/II and 96% of stage III/IV, at 80% specificity. Genome-wide fragmentation profiles across ~13,000 ASCL1 transcription factor binding sites distinguished individuals with small cell lung cancer from those with non-small cell lung cancer with high accuracy (AUC = 0.98). A higher fragmentation score represented an independent prognostic indicator of survival. This approach provides a facile avenue for non-invasive detection of lung cancer., (© 2021. The Author(s).)

Liquid biopsies are providing new opportunities for detection of residual disease in cell-free DNA (cfDNA) after surgery but may be confounded through identification of alterations arising from clonal hematopoiesis. Here, we identify circulating tumor-derived DNA (ctDNA) alterations through ultrasensitive targeted sequencing analyses of matched cfDNA and white blood cells from the same patient. We apply this approach to analyze samples from patients in the CRITICS trial, a phase III randomized controlled study of perioperative treatment in patients with operable gastric cancer. After filtering alterations from matched white blood cells, the presence of ctDNA predicts recurrence when analyzed within nine weeks after preoperative treatment and after surgery in patients eligible for multimodal treatment. These analyses provide a facile method for distinguishing ctDNA from other cfDNA alterations and highlight the utility of ctDNA as a predictive biomarker of patient outcome to perioperative cancer therapy and surgical resection in patients with gastric cancer.

Cell-free DNA in the blood provides a non-invasive diagnostic avenue for patients with cancer 1 . However, characteristics of the origins and molecular features of cell-free DNA are poorly understood. Here we developed an approach to evaluate fragmentation patterns of cell-free DNA across the genome, and found that profiles of healthy individuals reflected nucleosomal patterns of white blood cells, whereas patients with cancer had altered fragmentation profiles. We used this method to analyse the fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric or bile duct cancer and 245 healthy individuals. A machine learning model that incorporated genome-wide fragmentation features had sensitivities of detection ranging from 57% to more than 99% among the seven cancer types at 98% specificity, with an overall area under the curve value of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation-based cell-free DNA analyses detected 91% of patients with cancer. The results of these analyses highlight important properties of cell-free DNA and provide a proof-of-principle approach for the screening, early detection and monitoring of human cancer.

Despite the initial successes of immunotherapy, there is an urgent clinical need for molecular assays that identify patients more likely to respond. Here, we report that ultrasensitive measures of circulating tumor DNA (ctDNA) and T-cell expansion can be used to assess responses to immune checkpoint blockade in metastatic lung cancer patients ( N = 24). Patients with clinical response to therapy had a complete reduction in ctDNA levels after initiation of therapy, whereas nonresponders had no significant changes or an increase in ctDNA levels. Patients with initial response followed by acquired resistance to therapy had an initial drop followed by recrudescence in ctDNA levels. Patients without a molecular response had shorter progression-free and overall survival compared with molecular responders [5.2 vs. 14.5 and 8.4 vs. 18.7 months; HR 5.36; 95% confidence interval (CI), 1.57-18.35; P = 0.007 and HR 6.91; 95% CI, 1.37-34.97; P = 0.02, respectively], which was detected on average 8.7 weeks earlier and was more predictive of clinical benefit than CT imaging. Expansion of T cells, measured through increases of T-cell receptor productive frequencies, mirrored ctDNA reduction in response to therapy. We validated this approach in an independent cohort of patients with early-stage non-small cell lung cancer ( N = 14), where the therapeutic effect was measured by pathologic assessment of residual tumor after anti-PD1 therapy. Consistent with our initial findings, early ctDNA dynamics predicted pathologic response to immune checkpoint blockade. These analyses provide an approach for rapid determination of therapeutic outcomes for patients treated with immune checkpoint inhibitors and have important implications for the development of personalized immune targeted strategies. Significance: Rapid and sensitive detection of circulating tumor DNA dynamic changes and T-cell expansion can be used to guide immune targeted therapy for patients with lung cancer. See related commentary by Zou and Meyerson, p. 1038 ., (©2018 American Association for Cancer Research.)

Early detection and intervention are likely to be the most effective means for reducing morbidity and mortality of human cancer. However, development of methods for noninvasive detection of early-stage tumors has remained a challenge. We have developed an approach called targeted error correction sequencing (TEC-Seq) that allows ultrasensitive direct evaluation of sequence changes in circulating cell-free DNA using massively parallel sequencing. We have used this approach to examine 58 cancer-related genes encompassing 81 kb. Analysis of plasma from 44 healthy individuals identified genomic changes related to clonal hematopoiesis in 16% of asymptomatic individuals but no alterations in driver genes related to solid cancers. Evaluation of 200 patients with colorectal, breast, lung, or ovarian cancer detected somatic mutations in the plasma of 71, 59, 59, and 68%, respectively, of patients with stage I or II disease. Analyses of mutations in the circulation revealed high concordance with alterations in the tumors of these patients. In patients with resectable colorectal cancers, higher amounts of preoperative circulating tumor DNA were associated with disease recurrence and decreased overall survival. These analyses provide a broadly applicable approach for noninvasive detection of early-stage tumors that may be useful for screening and management of patients with cancer., (Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Immune checkpoint inhibitors have shown significant therapeutic responses against tumors containing increased mutation-associated neoantigen load. We have examined the evolving landscape of tumor neoantigens during the emergence of acquired resistance in patients with non-small cell lung cancer after initial response to immune checkpoint blockade with anti-PD-1 or anti-PD-1/anti-CTLA-4 antibodies. Analyses of matched pretreatment and resistant tumors identified genomic changes resulting in loss of 7 to 18 putative mutation-associated neoantigens in resistant clones. Peptides generated from the eliminated neoantigens elicited clonal T-cell expansion in autologous T-cell cultures, suggesting that they generated functional immune responses. Neoantigen loss occurred through elimination of tumor subclones or through deletion of chromosomal regions containing truncal alterations, and was associated with changes in T-cell receptor clonality. These analyses provide insight into the dynamics of mutational landscapes during immune checkpoint blockade and have implications for the development of immune therapies that target tumor neoantigens. Significance: Acquired resistance to immune checkpoint therapy is being recognized more commonly. This work demonstrates for the first time that acquired resistance to immune checkpoint blockade can arise in association with the evolving landscape of mutations, some of which encode tumor neoantigens recognizable by T cells. These observations imply that widening the breadth of neoantigen reactivity may mitigate the development of acquired resistance. Cancer Discov; 7(3); 264-76. ©2017 AACR. See related commentary by Yang, p. 250 This article is highlighted in the In This Issue feature, p. 235 ., (©2017 American Association for Cancer Research.)