Multimodal analysis of cell-free DNA whole-genome sequencing for pediatric cancers with low mutational burden

Ultrasensitive Detection of Tumor Suppressor Gene Methylation by Piezoelectric Sensing Based on Enrichment of Transcription Activator-Like Effectors

The detection of DNA methylation at cytosine/guanine dinucleotide (CpG) islands in promoter regions of tumor suppressor genes has great potential for early cancer screening, diagnosis, and prognosis monitoring. Nevertheless, achieving accurate, sensitive, cost-effective, and quantitative detection of target methylated DNA remains challenging. Herein, we propose a novel piezoelectric sensor (series piezoelectric quartz crystal (SPQC)) based on transcription activator-like effectors (TALEs) for detecting DNA methylation of Ras association domain family 1 isoform A (RASSF1A) tumor suppressor genes (R-5mC). The sensor employs TALEs-Ni magnetic beads to specifically recognize and separate the R-5mC, thereby improving the detection selectivity. The TALEs-Ni magnetic beads-R-5mC complex is sheared by a nucleic acid enzyme (DNAzyme) to release the single-stranded DNA (ST). ST initiates a catalyzed hairpin assembly (CHA) reaction on the surface of the electrode, which in turn triggers the hybridization chain reaction (HCR) and silver staining for enhanced detection sensitivity. The strategy exhibits a linear response in the detection of R-5mC in the range of 1 fM to 1 nM with a detection limit of 0.79 fM. R-5mC as low as 0.01% can be detected, even in the presence of large numbers of unmethylated DNA. The detection of R-5mC in circulating cell-free DNA (cfDNA) derived from clinical plasma specimens of lung cancer patients yielded satisfactory results.

Evaluation of Circulating Tumor DNA as a Liquid Biomarker in Uveal Melanoma

Purpose

Uveal melanoma (UM) has a high propensity to metastasize. Prognosis is associated with specific driver mutations and copy number variations (CNVs), but limited primary tumor tissue is available for molecular characterization due to eye-sparing irradiation treatment. This study aimed to assess the rise in circulating tumor DNA (ctDNA) levels in UM and evaluate its efficacy for CNV-profiling of patients with UM.

Methods

In a pilot study, we assessed ctDNA levels in the blood of patients with UM (n = 18) at various time points, including the time of diagnosis (n = 13), during fractionated stereotactic radiotherapy (fSRT) treatment (n = 6), and upon detection of metastatic disease (n = 13). Shallow whole-genome sequencing (sWGS) combined with in silico size-selection was used to identify prognostically relevant CNVs in patients with UM (n = 26) from peripheral blood retrieved at the time of diagnosis (n = 9), during fSRT (n = 5), during post-treatment follow-up (n = 4), metastasis detection (n = 6), and metastasis follow-up (n = 4).

Results

A total of 34 patients had blood analyzed for ctDNA detection (n = 18) and/or CNV analysis (n = 26) at various time points. At the time of diagnosis, 5 of 13 patients (38%) had detectable ctDNA (median = 0 copies/mL). Upon detection of metastatic disease, ctDNA was detected in 10 of 13 patients (77%) and showed increased ctDNA levels (median = 24 copies/mL, P < 0.01). Among the six patients analyzed during fSRT, three (50%) patients had detectable ctDNA at baseline and three of six (50%) patients had undetectable levels of ctDNA. During the fSRT regimen, ctDNA levels remained unchanged (P > 0.05). The ctDNA fractions were undetectable to low in localized disease, and sWGS did not elucidate chromosome 3 status from blood samples. However, in 7 of 10 (70%) patients with metastases, the detection of chromosome 3 loss corresponded to the high metastatic-risk class.

Conclusions

The rise in ctDNA levels observed in patients with UM harboring metastases suggests its potential utility for CNV profiling. These findings highlight the potential of using ctDNA for metastasis detection and patient inclusion in therapeutic studies targeting metastatic UM.

Circulating cell-free DNA-based methylation pattern in plasma for early diagnosis of esophagus cancer

With the increased awareness of early tumor detection, the importance of detecting and diagnosing esophageal cancer in its early stages has been underscored. Studies have consistently demonstrated the crucial role of methylation levels in circulating cell-free DNA (cfDNA) in identifying and diagnosing early-stage cancer. cfDNA methylation pertains to the methylation state within the genomic scope of cfDNA and is strongly associated with cancer development and progression. Several research teams have delved into the potential application of cfDNA methylation in identifying early-stage esophageal cancer and have achieved promising outcomes. Recent research supports the high sensitivity and specificity of cfDNA methylation in early esophageal cancer diagnosis, providing a more accurate and efficient approach for early detection and improved clinical management. Accordingly, this review aims to present an overview of methylation-based cfDNA research with a focus on the latest developments in the early detection of esophageal cancer. Additionally, this review summarizes advanced analytical technologies for cfDNA methylation that have significantly benefited from recent advancements in separation and detection techniques, such as methylated DNA immunoprecipitation sequencing (MeDIP-seq). Recent findings suggest that biomarkers based on cfDNA methylation may soon find successful applications in the early detection of esophageal cancer. However, large-scale prospective clinical trials are required to identify the potential of these biomarkers.

Multi-modal cell-free DNA genomic and fragmentomic patterns enhance cancer survival and recurrence analysis

The structure of cell-free DNA (cfDNA) is altered in the blood of patients with cancer. From whole-genome sequencing, we retrieve the cfDNA fragment-end composition using a new software (FrEIA [fragment end integrated analysis]), as well as the cfDNA size and tumor fraction in three independent cohorts (n = 925 cancer from >10 types and 321 control samples). At 95% specificity, we detect 72% cancer samples using at least one cfDNA measure, including 64% early-stage cancer (n = 220). cfDNA detection correlates with a shorter overall (p = 0.0086) and recurrence-free (p = 0.017) survival in patients with resectable esophageal adenocarcinoma. Integrating cfDNA measures with machine learning in an independent test set (n = 396 cancer, 90 controls) achieve a detection accuracy of 82% and area under the receiver operating characteristic curve of 0.96. In conclusion, harnessing the biological features of cfDNA can improve, at no extra cost, the diagnostic performance of liquid biopsies.

Early Cancer Detection in Li-Fraumeni Syndrome with Cell-Free DNA

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.

Fragmentstein-facilitating data reuse for cell-free DNA fragment analysis

SUMMARY

Method development for the analysis of cell-free DNA (cfDNA) sequencing data is impeded by limited data sharing due to the strict control of sensitive genomic data. An existing solution for facilitating data sharing removes nucleotide-level information from raw cfDNA sequencing data, keeping alignment coordinates only. This simplified format can be publicly shared and would, theoretically, suffice for common functional analyses of cfDNA data. However, current bioinformatics software requires nucleotide-level information and cannot process the simplified format. We present Fragmentstein, a command-line tool for converting non-sensitive cfDNA-fragmentation data into alignment mapping (BAM) files. Fragmentstein complements fragment coordinates with sequence information from a reference genome to reconstruct BAM files. We demonstrate the utility of Fragmentstein by showing the feasibility of copy number variant (CNV), nucleosome occupancy, and fragment length analyses from non-sensitive fragmentation data.

AVAILABILITY AND IMPLEMENTATION

Implemented in bash, Fragmentstein is available at https://github.com/uzh-dqbm-cmi/fragmentstein, licensed under GNU GPLv3.

ctDNA quantification improves estimation of outcomes in patients with high-grade osteosarcoma: a translational study from the OS2006 trial

BACKGROUND

Osteosarcoma stratification relies on clinical parameters and histological response. We developed a new personalized stratification using less invasive circulating tumor DNA (ctDNA) quantification.

PATIENTS AND METHODS

Plasma from patients homogeneously treated in the prospective protocol OS2006, at diagnosis, before surgery and end of treatment, were sequenced using low-passage whole-genome sequencing (lpWGS) for copy number alteration detection. We developed a prediction tool including ctDNA quantification and known clinical parameters to estimate patients' individual risk of event.

RESULTS

ctDNA quantification at diagnosis (diagCPA) was evaluated for 183 patients of the protocol OS2006. diagCPA as a continuous variable was a major prognostic factor, independent of other clinical parameters, including metastatic status [diagCPA hazard ratio (HR) = 3.5, P = 0.002 and 3.51, P = 0.012, for progression-free survival (PFS) and overall survival (OS)]. At the time of surgery and until the end of treatment, diagCPA was also a major prognostic factor independent of histological response (diagCPA HR = 9.2, P < 0.001 and 11.6, P < 0.001, for PFS and OS). Therefore, the addition of diagCPA to metastatic status at diagnosis or poor histological response after surgery improved the prognostic stratification of patients with osteosarcoma. We developed the prediction tool PRONOS to generate individual risk estimations, showing great performance ctDNA quantification at the time of surgery and the end of treatment still required improvement to overcome the low sensitivity of lpWGS and to enable the follow-up of disease progression.

CONCLUSIONS

The addition of ctDNA quantification to known risk factors improves the estimation of prognosis calculated by our prediction tool PRONOS. To confirm its value, an external validation in the Sarcoma 13 trial is underway.

Soft Tissue and Bone Tumor Diagnostics: Harnessing the Power of Molecular Techniques

Since the introduction of new molecular techniques, the diagnostic landscape of soft tissue and bone tumors has expanded greatly over the past few years. The use of new molecular techniques has led to the identification of new genetic alterations and, therefore, to a better understanding of tumorigenesis, tumor detection and classification. Furthermore, methylation profiling has emerged as a classification tool for soft tissue and bone tumors. Molecular pathology also plays an important role in the determination of patient prognosis and in the identification of targets that can be used for targeted therapy. As a result, molecular pathology has gained a more prominent role in the daily practice of the surgical pathologist. This review delves into various molecular techniques applied in the surgical pathology of soft tissue and bone tumors. It highlights their applications through the analysis of five specific cases.

GCparagon: evaluating and correcting GC biases in cell-free DNA at the fragment level

Analyses of cell-free DNA (cfDNA) are increasingly being employed for various diagnostic and research applications. Many technologies aim to increase resolution, e.g. for detecting early-stage cancer or minimal residual disease. However, these efforts may be confounded by inherent base composition biases of cfDNA, specifically the over - and underrepresentation of guanine (G) and cytosine (C) sequences. Currently, there is no universally applicable tool to correct these effects on sequencing read-level data. Here, we present GCparagon, a two-stage algorithm for computing and correcting GC biases in cfDNA samples. In the initial step, length and GC base count parameters are determined. Here, our algorithm minimizes the inclusion of known problematic genomic regions, such as low-mappability regions, in its calculations. In the second step, GCparagon computes weights counterbalancing the distortion of cfDNA attributes (correction matrix). These fragment weights are added to a binary alignment map (BAM) file as alignment tags for individual reads. The GC correction matrix or the tagged BAM file can be used for downstream analyses. Parallel computing allows for a GC bias estimation below 1 min. We demonstrate that GCparagon vastly improves the analysis of regulatory regions, which frequently show specific GC composition patterns and will contribute to standardized cfDNA applications.

Single-molecule systems for detection and monitoring of plasma circulating nucleosomes and oncoproteins in Diffuse Midline Glioma

The analysis of cell-free tumor DNA (ctDNA) and proteins in the blood of cancer patients potentiates a new generation of non-invasive diagnostics and treatment monitoring approaches. However, confident detection of these tumor-originating markers is challenging, especially in the context of brain tumors, in which extremely low amounts of these analytes circulate in the patient's plasma. Here, we applied a sensitive single-molecule technology to profile multiple histone modifications on millions of individual nucleosomes from the plasma of Diffuse Midline Glioma (DMG) patients. The system reveals epigenetic patterns that are unique to DMG, significantly differentiating this group of patients from healthy subjects or individuals diagnosed with other cancer types. We further develop a method to directly capture and quantify the tumor-originating oncoproteins, H3-K27M and mutant p53, from the plasma of children diagnosed with DMG. This single-molecule system allows for accurate molecular classification of patients, utilizing less than 1ml of liquid-biopsy material. Furthermore, we show that our simple and rapid detection strategy correlates with MRI measurements and droplet-digital PCR (ddPCR) measurements of ctDNA, highlighting the utility of this approach for non-invasive treatment monitoring of DMG patients. This work underscores the clinical potential of single-molecule-based, multi-parametric assays for DMG diagnosis and treatment monitoring.

Comprehensive characterization of patient-derived xenograft models of pediatric leukemia

Patient-derived xenografts (PDX) remain valuable models for understanding the biology and for developing novel therapeutics. To expand current PDX models of childhood leukemia, we have developed new PDX models from Hispanic patients, a subgroup with a poorer overall outcome. Of 117 primary leukemia samples obtained, successful engraftment and serial passage in mice were achieved in 82 samples (70%). Hispanic patient samples engrafted at a rate (51/73, 70%) that was similar to non-Hispanic patient samples (31/45, 70%). With a new algorithm to remove mouse contamination in multi-omics datasets including methylation data, we found PDX models faithfully reflected somatic mutations, copy-number alterations, RNA expression, gene fusions, whole-genome methylation patterns, and immunophenotypes found in primary tumor (PT) samples in the first 50 reported here. This cohort of characterized PDX childhood leukemias represents a valuable resource in that germline DNA sequencing has allowed the unambiguous determination of somatic mutations in both PT and PDX.

Cancer signature ensemble integrating cfDNA methylation, copy number, and fragmentation facilitates multi-cancer early detection

Cell-free DNA (cfDNA) sequencing has demonstrated great potential for early cancer detection. However, most large-scale studies have focused only on either targeted methylation sites or whole-genome sequencing, limiting comprehensive analysis that integrates both epigenetic and genetic signatures. In this study, we present a platform that enables simultaneous analysis of whole-genome methylation, copy number, and fragmentomic patterns of cfDNA in a single assay. Using a total of 950 plasma (361 healthy and 589 cancer) and 240 tissue samples, we demonstrate that a multifeature cancer signature ensemble (CSE) classifier integrating all features outperforms single-feature classifiers. At 95.2% specificity, the cancer detection sensitivity with methylation, copy number, and fragmentomic models was 77.2%, 61.4%, and 60.5%, respectively, but sensitivity was significantly increased to 88.9% with the CSE classifier (p value < 0.0001). For tissue of origin, the CSE classifier enhanced the accuracy beyond the methylation classifier, from 74.3% to 76.4%. Overall, this work proves the utility of a signature ensemble integrating epigenetic and genetic information for accurate cancer detection.

The landscape of cell-free mitochondrial DNA in liquid biopsy for cancer detection

BACKGROUND

Existing methods to detect tumor signal in liquid biopsy have focused on the analysis of nuclear cell-free DNA (cfDNA). However, non-nuclear cfDNA and in particular mitochondrial DNA (mtDNA) has been understudied. We hypothesize that an increase in mtDNA in plasma could reflect the presence of cancer, and that leveraging cell-free mtDNA could enhance cancer detection.

RESULTS

We survey 203 healthy and 664 cancer plasma samples from three collection centers covering 12 cancer types with whole genome sequencing to catalogue the plasma mtDNA fraction. The mtDNA fraction is increased in individuals with cholangiocarcinoma, colorectal, liver, pancreatic, or prostate cancer, in comparison to that in healthy individuals. We detect almost no increase of mtDNA fraction in individuals with other cancer types. The mtDNA fraction in plasma correlates with the cfDNA tumor fraction as determined by somatic mutations and/or copy number aberrations. However, the mtDNA fraction is also elevated in a fraction of patients without an apparent increase in tumor-derived cfDNA. A predictive model integrating mtDNA and copy number analysis increases the area under the curve (AUC) from 0.73 when using copy number alterations alone to an AUC of 0.81.

CONCLUSIONS

The mtDNA signal retrieved by whole genome sequencing has the potential to boost the detection of cancer when combined with other tumor-derived signals in liquid biopsies.

Primary adrenal Ewing sarcoma: A systematic review of the literature

BACKGROUND

Ewing sarcoma (ES) is a malignant neoplasm of neuroectodermal origin and is commonly observed in children and young adults. The musculoskeletal system is the main body system impacted and ES is rarely seen in the visceral organs particularly the adrenal gland.

AIM

To present a comprehensive review of primary adrenal ES, with emphasis on diagnosis, therapy and oncological outcomes.

METHODS

A systematic review of the literature was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020. PubMed/ MEDLINE, EMBASE and Google Scholar bibliographic databases were searched to identify articles from 1989 to 2022 and included patients with ES/primitive neuroectodermal tumor (PNET) of the adrenal gland. PubMed, Google Scholar and EMBASE medical databases were searched, combining the terms "adrenal", "ES" and "PNET". Demographic, clinical, pathological and oncological data of patients were analyzed by SPSS version 29.0.

RESULTS

A total of 52 studies were included for review (47 case reports and 5 case series) with 66 patients reported to have primary adrenal ES. Mean age at diagnosis was 26.4 ± 15.4 years (37.9% males, 57.6% females, sex not reported in 3 cases). The most frequent complaint was abdominal/flank pain or discomfort (46.4%) followed by a palpable mass (25.0%), and the average duration of symptoms was 2.6 ± 3.1 mo. The imaging modality of choice was computed tomography scan (81.5%), followed by magnetic resonance imaging (20.4%). Preoperative staging revealed that 17 tumors (27.9%) were metastatic and 14 patients had inferior vena cava or renal vein neoplastic thrombus at initial diagnosis. Open adrenalectomy was performed in the majority of cases (80.0%), of which 27.9% required more extensive resection. Minimally invasive surgery was attempted in 8.2% of tumors. Complete surgical resection was achieved in 89.4% of the patients. Adjuvant therapy was administered to 32 patients, in the form of chemotherapy (62.5%), radiotherapy (3.1%) or combination (34.4%). Median overall survival was 15 mo and 24-mo overall survival was 40.5%. Median disease-free survival was 10 mo and 24-mo disease-free survival was 33.3%.

CONCLUSION

The significant progress in molecular biology and genetics of ES does not reflect on patient outcomes. ES remains an aggressive tumor with a poor prognosis and high mortality.

Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application

Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.

Multimodal analysis of cell-free DNA whole-methylome sequencing for cancer detection and localization

Multimodal epigenetic characterization of cell-free DNA (cfDNA) could improve the performance of blood-based early cancer detection. However, integrative profiling of cfDNA methylome and fragmentome has been technologically challenging. Here, we adapt an enzyme-mediated methylation sequencing method for comprehensive analysis of genome-wide cfDNA methylation, fragmentation, and copy number alteration (CNA) characteristics for enhanced cancer detection. We apply this method to plasma samples of 497 healthy controls and 780 patients of seven cancer types and develop an ensemble classifier by incorporating methylation, fragmentation, and CNA features. In the test cohort, our approach achieves an area under the curve value of 0.966 for overall cancer detection. Detection sensitivity for early-stage patients achieves 73% at 99% specificity. Finally, we demonstrate the feasibility to accurately localize the origin of cancer signals with combined methylation and fragmentation profiling of tissue-specific accessible chromatin regions. Overall, this proof-of-concept study provides a technical platform to utilize multimodal cfDNA features for improved cancer detection.

Sequential genomic analysis using a multisample/multiplatform approach to better define rhabdomyosarcoma progression and relapse

The genomic spectrum of rhabdomyosarcoma (RMS) progression from primary to relapse is not fully understood. In this pilot study, we explore the sensitivity of various targeted and whole-genome NGS platforms in order to assess the best genomic approach of using liquid biopsy in future prospective clinical trials. Moreover, we investigate 35 paired primary/relapsed RMS from two contributing institutions, 18 fusion-positive (FP-RMS) and 17 fusion-negative RMS (FN-RMS) by either targeted DNA or whole exome sequencing (WES). In 10 cases, circulating tumor DNA (ctDNA) from multiple timepoints through clinical care and progression was analyzed for feasibility of liquid biopsy in monitoring treatment response/relapse. ctDNA alterations were evaluated using a targeted 36-gene custom RMS panel at high coverage for single-nucleotide variation and fusion detection, and a shallow whole-genome sequencing for copy number variation. FP-RMS have a stable genome with relapse, with common secondary alterations CDKN2A/B, MYCN, and CDK4 present at diagnosis and impacting survival. FP-RMS lacking major secondary events at baseline acquire recurrent MYCN and AKT1 alterations. FN-RMS acquire a higher number of new alterations, most commonly SMARCA2 missense mutations. ctDNA analyses detect pathognomonic variants in all RMS patients within our collection at diagnosis, regardless of type of alterations, and confirmed at relapse in 86% of FP-RMS and 100% FN-RMS. Moreover, a higher number of fusion reads is detected with increased disease burden and at relapse in patients following a fatal outcome. These results underscore patterns of tumor progression and provide rationale for using liquid biopsy to monitor treatment response.

Multidimensional Analysis of a Cell-Free DNA Whole Methylome Sequencing Assay for Early Detection of Gastric Cancer: Protocol for an Observational Case-Control Study

BACKGROUND

Commonly used noninvasive serological indicators serve as a step before endoscope diagnosis and help identify the high-risk gastric cancer (GC) population. However, they are associated with high false positives and high false negatives. Alternative noninvasive approaches, such as cancer-related features in cell-free DNA (cfDNA) fragments, have been gradually identified and play essential roles in early cancer detection. The integrated analysis of multiple cfDNA features has enhanced detection sensitivity compared to individual features.

OBJECTIVE

This study aimed to develop and validate an assay based on assessing genomic-scale methylation and fragmentation profiles of plasma cfDNA for early cancer detection, thereby facilitating the early diagnosis of GC. The primary objective is to evaluate the overall specificity and sensitivity of the assay in predicting GC within the entire cohort, and subsequently within each clinical stage of GC. The secondary objective involved investigating the specificity and sensitivity of the assay in combination with possible serological indicators.

METHODS

This is an observational case-control study. Blood samples will be prospectively collected before gastroscopy from 180 patients with GC and 180 nonmalignant control subjects (healthy or with benign gastric diseases). Cases and controls will be randomly divided into a training and a testing data set at a ratio of 2:1. Plasma cfDNA will be isolated and extracted, followed by bisulfite-free low-depth whole methylome sequencing. A multidimensional model named Thorough Epigenetic Marker Integration Solution (THEMIS) will be constructed in the training data set. The model includes features such as the methylated fragment ratio, chromosomal aneuploidy of featured fragments, fragment size index, and fragment end motif. The performance of the model in distinguishing between patients with cancer and noncancer controls will then be evaluated in the testing data set. Furthermore, GC-related biomarkers, such as pepsinogen, gastrin-17, and Helicobacter pylori, will be measured for each patient, and their predictive accuracy will be assessed both independently and in combination with the THEMIS model.

RESULTS

Recruitment began in November 2022 and will be ended in April 2024. As of August 2022,250 patients have been enrolled. The final data analysis is anticipated to be completed by September 2024.

CONCLUSIONS

This is the first registered case-control study designed to investigate a stacked ensemble model integrating several cfDNA features generated from a bisulfite-free whole methylome sequencing assay. These features include methylation patterns, fragmentation profiles, and chromosomal copy number changes, with the aim of identifying the GC population. This study will determine whether multidimensional analysis of cfDNA will prove to be an effective strategy for distinguishing patients with GC from nonmalignant individuals within the Chinese population. We anticipate the THEMIS model will complement the standard-of-care screening and aid in identifying high-risk patients for further diagnosis.

TRIAL REGISTRATION

ClinicalTrial.gov NCT05668910; https://www.clinicaltrials.gov/study/NCT05668910.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/48247.

Phase I trial of Ganitumab plus Dasatinib to Cotarget the Insulin-Like Growth Factor 1 Receptor and Src Family Kinase YES in Rhabdomyosarcoma

PURPOSE

Antibodies against insulin-like growth factor (IGF) type 1 receptor have shown meaningful but transient tumor responses in patients with rhabdomyosarcoma (RMS). The SRC family member YES has been shown to mediate IGF type 1 receptor (IGF-1R) antibody acquired resistance, and cotargeting IGF-1R and YES resulted in sustained responses in murine RMS models. We conducted a phase I trial of the anti-IGF-1R antibody ganitumab combined with dasatinib, a multi-kinase inhibitor targeting YES, in patients with RMS (NCT03041701).

PATIENTS AND METHODS

Patients with relapsed/refractory alveolar or embryonal RMS and measurable disease were eligible. All patients received ganitumab 18 mg/kg intravenously every 2 weeks. Dasatinib dose was 60 mg/m2/dose (max 100 mg) oral once daily [dose level (DL)1] or 60 mg/m2/dose (max 70 mg) twice daily (DL2). A 3+3 dose escalation design was used, and maximum tolerated dose (MTD) was determined on the basis of cycle 1 dose-limiting toxicities (DLT).

RESULTS

Thirteen eligible patients, median age 18 years (range 8-29) enrolled. Median number of prior systemic therapies was 3; all had received prior radiation. Of 11 toxicity-evaluable patients, 1/6 had a DLT at DL1 (diarrhea) and 2/5 had a DLT at DL2 (pneumonitis, hematuria) confirming DL1 as MTD. Of nine response-evaluable patients, one had a confirmed partial response for four cycles, and one had stable disease for six cycles. Genomic studies from cell-free DNA correlated with disease response.

CONCLUSIONS

The combination of dasatinib 60 mg/m2/dose daily and ganitumab 18 mg/kg every 2 weeks was safe and tolerable. This combination had a disease control rate of 22% at 5 months.

Fragmentomic analysis of circulating tumor DNA-targeted cancer panels

BACKGROUND

The isolation of cell-free DNA (cfDNA) from the bloodstream can be used to detect and analyze somatic alterations in circulating tumor DNA (ctDNA), and multiple cfDNA-targeted sequencing panels are now commercially available for Food and Drug Administration (FDA)-approved biomarker indications to guide treatment. More recently, cfDNA fragmentation patterns have emerged as a tool to infer epigenomic and transcriptomic information. However, most of these analyses used whole-genome sequencing, which is insufficient to identify FDA-approved biomarker indications in a cost-effective manner.

PATIENTS AND METHODS

We used machine learning models of fragmentation patterns at the first coding exon in standard targeted cancer gene cfDNA sequencing panels to distinguish between cancer and non-cancer patients, as well as the specific tumor type and subtype. We assessed this approach in two independent cohorts: a published cohort from GRAIL (breast, lung, and prostate cancers, non-cancer, n = 198) and an institutional cohort from the University of Wisconsin (UW; breast, lung, prostate, bladder cancers, n = 320). Each cohort was split 70%/30% into training and validation sets.

RESULTS

In the UW cohort, training cross-validated accuracy was 82.1%, and accuracy in the independent validation cohort was 86.6% despite a median ctDNA fraction of only 0.06. In the GRAIL cohort, to assess how this approach performs in very low ctDNA fractions, training and independent validation were split based on ctDNA fraction. Training cross-validated accuracy was 80.6%, and accuracy in the independent validation cohort was 76.3%. In the validation cohort where the ctDNA fractions were all <0.05 and as low as 0.0003, the cancer versus non-cancer area under the curve was 0.99.

CONCLUSIONS

To our knowledge, this is the first study to demonstrate that sequencing from targeted cfDNA panels can be utilized to analyze fragmentation patterns to classify cancer types, dramatically expanding the potential capabilities of existing clinically used panels at minimal additional cost.

Case series on clinical applications of liquid biopsy in pediatric solid tumors: towards improved diagnostics and disease monitoring

Background and aims

Solid tumors account for about 30% of all pediatric cancers. The diagnosis is typically based on histological and molecular analysis of a primary tumor biopsy. Liquid biopsies carry several advantages over conventional tissue biopsy. However, their use for genomic analysis and response monitoring of pediatric solid tumors is still in experimental stages and mostly performed retrospectively without direct impact on patient management. In this case series we discuss six clinical cases of children with a solid tumor for whom a liquid biopsy assay was performed and demonstrate the potential of liquid biopsy for future clinical decision making.

Methods

We performed quantitative real-time PCR (RT-qPCR), droplet digital PCR (ddPCR) or reduced representation bisulphite sequencing of cell-free DNA (cfRRBS) on liquid biopsies collected from six pediatric patients with a solid tumor treated between 2017 and 2023 at the Princess Máxima Center for Pediatric Oncology in the Netherlands. Results were used to aid in clinical decision making by contribution to establish a diagnosis, by prognostication and response to therapy monitoring.

Results

In three patients cfRRBS helped to establish the diagnosis of a rhabdomyosarcoma, an Ewing sarcoma and a neuroblastoma (case 1-3). In two patients, liquid biopsies were used for prognostication, by MYCN ddPCR in a patient with neuroblastoma and by RT-qPCR testing rhabdomyosarcoma-specific mRNA in bone marrow of a patient with a rhabdomyosarcoma (case 4 and 5). In case 6, mRNA testing demonstrated disease progression and assisted clinical decision making.

Conclusion

This case series illustrates the value of liquid biopsy. We further demonstrate and recommend the use of liquid biopsies to be used in conjunction with conventional methods for the determination of metastatic status, prognostication and monitoring of treatment response in patients with pediatric solid tumors.

Blood has never been thicker: Cell-free DNA fragmentomics in the liquid biopsy toolbox of B-cell lymphomas

Liquid biopsies utilizing plasma circulating tumor DNA (ctDNA) are anticipated to revolutionize decision-making in cancer care. In the field of lymphomas, ctDNA-based blood tests represent the forefront of clinically applicable tools to harness decades of genomic research for disease profiling, quantification, and detection. More recently, the discovery of nonrandom fragmentation patterns in cell-free DNA (cfDNA) has opened another avenue of liquid biopsy research beyond mutational interrogation of ctDNA. Through examination of structural features, nucleotide content, and genomic distribution of massive numbers of plasma cfDNA molecules, the study of fragmentomics aims at identifying new tools that augment existing ctDNA-based analyses and discover new ways to profile cancer from blood tests. Indeed, the characterization of aberrant lymphoma ctDNA fragment patterns and harnessing them with powerful machine-learning techniques are expected to unleash the potential of nonmutant molecules for liquid biopsy purposes. In this article, we review cfDNA fragmentomics as an emerging approach in the ctDNA research of B-cell lymphomas. We summarize the biology behind the formation of cfDNA fragment patterns and discuss the preanalytical and technical limitations faced with current methodologies. Then we go through the advances in the field of lymphomas and envision what other noninvasive tools based on fragment characteristics could be explored. Last, we place fragmentomics as one of the facets of ctDNA analyses in emerging multiview and multiomics liquid biopsies. We pay attention to the unknowns in the field of cfDNA fragmentation biology that warrant further mechanistic investigation to provide rational background for the development of these precision oncology tools and understanding of their limitations.

A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases

BACKGROUND

The diagnosis of rare genetic diseases is often challenging due to the complexity of the genetic underpinnings of these conditions and the limited availability of diagnostic tools. Machine learning (ML) algorithms have the potential to improve the accuracy and speed of diagnosis by analyzing large amounts of genomic data and identifying complex multiallelic patterns that may be associated with specific diseases. In this systematic review, we aimed to identify the methodological trends and the ML application areas in rare genetic diseases.

METHODS

We performed a systematic review of the literature following the PRISMA guidelines to search studies that used ML approaches to enhance the diagnosis of rare genetic diseases. Studies that used DNA-based sequencing data and a variety of ML algorithms were included, summarized, and analyzed using bibliometric methods, visualization tools, and a feature co-occurrence analysis.

FINDINGS

Our search identified 22 studies that met the inclusion criteria. We found that exome sequencing was the most frequently used sequencing technology (59%), and rare neoplastic diseases were the most prevalent disease scenario (59%). In rare neoplasms, the most frequent applications of ML models were the differential diagnosis or stratification of patients (38.5%) and the identification of somatic mutations (30.8%). In other rare diseases, the most frequent goals were the prioritization of rare variants or genes (55.5%) and the identification of biallelic or digenic inheritance (33.3%). The most employed method was the random forest algorithm (54.5%). In addition, the features of the datasets needed for training these algorithms were distinctive depending on the goal pursued, including the mutational load in each gene for the differential diagnosis of patients, or the combination of genotype features and sequence-derived features (such as GC-content) for the identification of somatic mutations.

CONCLUSIONS

ML algorithms based on sequencing data are mainly used for the diagnosis of rare neoplastic diseases, with random forest being the most common approach. We identified key features in the datasets used for training these ML models according to the objective pursued. These features can support the development of future ML models in the diagnosis of rare genetic diseases.

Advances in Osteosarcoma

PURPOSE OF REVIEW

This article gives a brief overview of the most recent developments in osteosarcoma treatment, including targeting of signaling pathways, immune checkpoint inhibitors, drug delivery strategies as single or combined approaches, and the identification of new therapeutic targets to face this highly heterogeneous disease.

RECENT FINDINGS

Osteosarcoma is one of the most common primary malignant bone tumors in children and young adults, with a high risk of bone and lung metastases and a 5-year survival rate around 70% in the absence of metastases and 30% if metastases are detected at the time of diagnosis. Despite the novel advances in neoadjuvant chemotherapy, the effective treatment for osteosarcoma has not improved in the last 4 decades. The emergence of immunotherapy has transformed the paradigm of treatment, focusing therapeutic strategies on the potential of immune checkpoint inhibitors. However, the most recent clinical trials show a slight improvement over the conventional polychemotherapy scheme. The tumor microenvironment plays a crucial role in the pathogenesis of osteosarcoma by controlling the tumor growth, the metastatic process and the drug resistance and paved the way of new therapeutic options that must be validated by accurate pre-clinical studies and clinical trials.

Detection of circulating tumor-derived material in peripheral blood of pediatric sarcoma patients: A systematic review

BACKGROUND

Detection of circulating tumor-derived material (cTM) in the peripheral blood (PB) of cancer patients has been shown to be useful in early diagnosis, prediction of prognosis, and disease monitoring. However, it has not yet been thoroughly evaluated for pediatric sarcoma patients.

METHODS

We searched the PubMed and EMBASE databases for studies reporting the detection of circulating tumor cells, circulating tumor DNA, and circulating RNA in PB of pediatric sarcoma patients. Data on performance in identifying cTM and its applicability in diagnosis, and evaluation of tumor characteristics, prognostic factors, and treatment response was extracted from publications.

RESULTS

A total of 79 studies were assigned for the present systematic review, including detection of circulating tumor cells (116 patients), circulating tumor DNA (716 patients), and circulating RNA (2887 patients). Circulating tumor cells were detected in 76% of patients. Circulating DNA was detected in 63% by targeted NGS, 66% by shallow WGS, and 79% by digital droplet PCR. Circulating RNA was detected in 37% of patients.

CONCLUSION

Of the cTM from Ewing's sarcoma and rhabdomyosarcoma ctDNA proved to be the best target for clinical application including diagnosis, tumor characterization, prognosis, and monitoring of disease progression and treatment response. For osteosarcoma the most promising targets are copy number alterations or patient specific micro RNAs, however, further investigations are needed to obtain consensus on clinical utility.

Circulating Tumor DNA Is Prognostic in Intermediate-Risk Rhabdomyosarcoma: A Report From the Children's Oncology Group

PURPOSE

Novel biomarkers are needed to differentiate outcomes in intermediate-risk rhabdomyosarcoma (IR RMS). We sought to evaluate strategies for identifying circulating tumor DNA (ctDNA) in IR RMS and to determine whether ctDNA detection before therapy is associated with outcome.

PATIENTS AND METHODS

Pretreatment serum and tumor samples were available from 124 patients with newly diagnosed IR RMS from the Children's Oncology Group biorepository, including 75 patients with fusion-negative rhabdomyosarcoma (FN-RMS) and 49 with fusion-positive rhabdomyosarcoma (FP-RMS) disease. We used ultralow passage whole-genome sequencing to detect copy number alterations and a new custom sequencing assay, Rhabdo-Seq, to detect rearrangements and single-nucleotide variants.

RESULTS

We found that ultralow passage whole-genome sequencing was a method applicable to ctDNA detection in all patients with FN-RMS and that ctDNA was detectable in 13 of 75 serum samples (17%). However, the use of Rhabdo-Seq in FN-RMS samples also identified single-nucleotide variants, such as MYOD1L122R, previously associated with prognosis. Identification of pathognomonic translocations between PAX3 or PAX7 and FOXO1 by Rhabdo-Seq was the best method for measuring ctDNA in FP-RMS and detected ctDNA in 27 of 49 cases (55%). Patients with FN-RMS with detectable ctDNA at diagnosis had significantly worse outcomes than patients without detectable ctDNA (event-free survival, 33.3% v 68.9%; P = .0028; overall survival, 33.3% v 83.2%; P < .0001) as did patients with FP-RMS (event-free survival, 37% v 70%; P = .045; overall survival, 39.2% v 75%; P = .023). In multivariable analysis, ctDNA was independently associated with worse prognosis in FN-RMS but not in the smaller FP-RMS cohort.

CONCLUSION

Our study demonstrates that baseline ctDNA detection is feasible and is prognostic in IR RMS.

Integrative modeling of tumor genomes and epigenomes for enhanced cancer diagnosis by cell-free DNA

Multi-cancer early detection remains a key challenge in cell-free DNA (cfDNA)-based liquid biopsy. Here, we perform cfDNA whole-genome sequencing to generate two test datasets covering 2125 patient samples of 9 cancer types and 1241 normal control samples, and also a reference dataset for background variant filtering based on 20,529 low-depth healthy samples. An external cfDNA dataset consisting of 208 cancer and 214 normal control samples is used for additional evaluation. Accuracy for cancer detection and tissue-of-origin localization is achieved using our algorithm, which incorporates cancer type-specific profiles of mutation distribution and chromatin organization in tumor tissues as model references. Our integrative model detects early-stage cancers, including those of pancreatic origin, with high sensitivity that is comparable to that of late-stage detection. Model interpretation reveals the contribution of cancer type-specific genomic and epigenomic features. Our methodologies may lay the groundwork for accurate cfDNA-based cancer diagnosis, especially at early stages.

The functional and clinical roles of liquid biopsy in patient-derived models

The liquid biopsy includes the detection of circulating tumor cells (CTCs) and CTC clusters in blood, as well as the detection of, cell-free DNA (cfDNA)/circulating tumor DNA (ctDNA) and extracellular vesicles (EVs) in the patient's body fluid. Liquid biopsy has important roles in translational research. But its clinical utility is still under investigation. Newly emerged patient-derived xenograft (PDX) and CTC-derived xenograft (CDX) faithfully recapitulate the genetic and morphological features of the donor patients' tumor and patient-derived organoid (PDO) can mostly mimic tumor growth, tumor microenvironment and its response to drugs. In this review, we describe how the development of these patient-derived models has assisted the studies of CTCs and CTC clusters in terms of tumor biological behavior exploration, genomic analysis, and drug testing, with the help of the latest technology. We then summarize the studies of EVs and cfDNA/ctDNA in PDX and PDO models in early cancer diagnosis, tumor burden monitoring, drug test and response monitoring, and molecular profiling. The challenges faced and future perspectives of research related to liquid biopsy using patient-derived models are also discussed.

Somatic Copy Number Alteration in Circulating Tumor DNA for Monitoring of Pediatric Patients with Cancer

Pediatric tumors share few recurrent mutations and are instead characterized by copy number alterations (CNAs). The cell-free DNA (cfDNA) is a prominent source for the detection of cancer-specific biomarkers in plasma. We profiled CNAs in the tumor tissues for further evaluation of alterations in 1q, MYCN and 17p in the circulating tumor DNA (ctDNA) in the peripheral blood at diagnosis and follow-up using digital PCR. We report that among the different kinds of tumors (neuroblastoma, Wilms tumor, Ewing sarcoma, rhabdomyosarcoma, leiomyosarcoma, osteosarcoma and benign teratoma), neuroblastoma presented the greatest amount of cfDNA, in correlation with tumor volume. Considering all tumors, cfDNA levels correlated with tumor stage, metastasis at diagnosis and metastasis developed during therapy. In the tumor tissue, at least one CNA (at CRABP2, TP53, surrogate markers for 1q and 17p, respectively, and MYCN) was observed in 89% of patients. At diagnosis, CNAs levels were concordant between tumor and ctDNA in 56% of the cases, and for the remaining 44%, 91.4% of the CNAs were present only in cfDNA and 8.6% only in the tumor. Within the cfDNA, we observed that 46% and 23% of the patients had MYCN and 1q gain, respectively. The use of specific CNAs as targets for liquid biopsy in pediatric patients with cancer can improve diagnosis and should be considered for monitoring of the disease response.

Enhancing clinical potential of liquid biopsy through a multi-omic approach: A systematic review

In the last years, liquid biopsy gained increasing clinical relevance for detecting and monitoring several cancer types, being minimally invasive, highly informative and replicable over time. This revolutionary approach can be complementary and may, in the future, replace tissue biopsy, which is still considered the gold standard for cancer diagnosis. “Classical” tissue biopsy is invasive, often cannot provide sufficient bioptic material for advanced screening, and can provide isolated information about disease evolution and heterogeneity. Recent literature highlighted how liquid biopsy is informative of proteomic, genomic, epigenetic, and metabolic alterations. These biomarkers can be detected and investigated using single-omic and, recently, in combination through multi-omic approaches. This review will provide an overview of the most suitable techniques to thoroughly characterize tumor biomarkers and their potential clinical applications, highlighting the importance of an integrated multi-omic, multi-analyte approach. Personalized medical investigations will soon allow patients to receive predictable prognostic evaluations, early disease diagnosis, and subsequent ad hoc treatments.

Bridging biological cfDNA features and machine learning approaches

Liquid biopsies (LBs), particularly using circulating tumor DNA (ctDNA), are expected to revolutionize precision oncology and blood-based cancer screening. Recent technological improvements, in combination with the ever-growing understanding of cell-free DNA (cfDNA) biology, are enabling the detection of tumor-specific changes with extremely high resolution and new analysis concepts beyond genetic alterations, including methylomics, fragmentomics, and nucleosomics. The interrogation of a large number of markers and the high complexity of data render traditional correlation methods insufficient. In this regard, machine learning (ML) algorithms are increasingly being used to decipher disease- and tissue-specific signals from cfDNA. Here, we review recent insights into biological ctDNA features and how these are incorporated into sophisticated ML applications.

From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment

Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniques are applied to medical imaging and to molecular data obtained from liquid and solid tumor biopsies for cancer diagnosis, prognosis, and treatment design. We discuss key considerations in developing ML for the distinct challenges posed by imaging and molecular data. Finally, we examine ML models approved for cancer-related patient usage by regulatory agencies and discuss approaches to improve the clinical usefulness of ML.

Nucleosome Patterns in Circulating Tumor DNA Reveal Transcriptional Regulation of Advanced Prostate Cancer Phenotypes

Advanced prostate cancers comprise distinct phenotypes, but tumor classification remains clinically challenging. Here, we harnessed circulating tumor DNA (ctDNA) to study tumor phenotypes by ascertaining nucleosome positioning patterns associated with transcription regulation. We sequenced plasma ctDNA whole genomes from patient-derived xenografts representing a spectrum of androgen receptor active (ARPC) and neuroendocrine (NEPC) prostate cancers. Nucleosome patterns associated with transcriptional activity were reflected in ctDNA at regions of genes, promoters, histone modifications, transcription factor binding, and accessible chromatin. We identified the activity of key phenotype-defining transcriptional regulators from ctDNA, including AR, ASCL1, HOXB13, HNF4G, and GATA2. To distinguish NEPC and ARPC in patient plasma samples, we developed prediction models that achieved accuracies of 97% for dominant phenotypes and 87% for mixed clinical phenotypes. Although phenotype classification is typically assessed by IHC or transcriptome profiling from tumor biopsies, we demonstrate that ctDNA provides comparable results with diagnostic advantages for precision oncology.

SIGNIFICANCE

This study provides insights into the dynamics of nucleosome positioning and gene regulation associated with cancer phenotypes that can be ascertained from ctDNA. New methods for classification in phenotype mixtures extend the utility of ctDNA beyond assessments of somatic DNA alterations with important implications for molecular classification and precision oncology. This article is highlighted in the In This Issue feature, p. 517.

Integrated, Longitudinal Analysis of Cell-free DNA in Uveal Melanoma

Uveal melanomas are rare tumors arising from melanocytes that reside in the eye. Despite surgical or radiation treatment, approximately 50% of patients with uveal melanoma will progress to metastatic disease, most often to the liver. Cell-free DNA (cfDNA) sequencing is a promising technology due to the minimally invasive sample collection and ability to infer multiple aspects of tumor response. We analyzed 46 serial cfDNA samples from 11 patients with uveal melanoma over a 1-year period following enucleation or brachytherapy (n = ∼4/patient) using targeted panel, shallow whole genome, and cell-free methylated DNA immunoprecipitation sequencing. We found detection of relapse was highly variable using independent analyses (P = 0.06-0.46), whereas a logistic regression model integrating all cfDNA profiles significantly improved relapse detection (P = 0.02), with greatest power derived from fragmentomic profiles. This work provides support for the use of integrated analyses to improve the sensitivity of circulating tumor DNA detection using multi-modal cfDNA sequencing.

Significance

Here, we demonstrate integrated, longitudinal cfDNA sequencing using multi-omic approaches is more effective than unimodal analysis. This approach supports the use of frequent blood testing using comprehensive genomic, fragmentomic, and epigenomic techniques.

An integrated investigation of structural and pathway alteration caused by PIK3CA and TP53 mutations identified in cfDNA of metastatic breast cancer

In peripheral blood, cell-free DNA (cfDNA) contains circulating tumor DNA (ctDNA), which indicates molecular abnormalities in metastatic breast tumor tissue. The sequencing of cfDNA of Metastatic Breast Cancer (MBC) patients allows assessment of therapy response and noninvasive treatment. In the proposed study, clinically significant alterations in PIK3CA and TP53 genes associated with MBC resulting in a missense substitution of His1047Arg and Arg282Trp from an next-generation sequencing-based multi-gene panel were reported in a cfDNA of a patient with MBC. To investigate the impact of the reported mutation, we used molecular docking, molecular dynamics simulation, network analysis, and pathway analysis. Molecular Docking analysis determined the distinct binding pattern revealing H1047R-ATP complex has a higher number of Hydrogen bonds (H-bonds) and binding affinity with a slight difference compared to the PIK3CA-ATP complex. Following, molecular dynamics simulation for 200 ns, of which H1047R-ATP complex resulted in the instability of PIK3CA. Similarly, for TP53 mutant R282W, the zinc-free state (apo) and zinc-bounded (holo) complexes were investigated for conformational change between apo and holo complexes, of which the holo complex mutant R282W was unstable. To validate the conformational change of PIK3CA and TP53, 80% mutation of H1047R in the kinase domain of p110α expressed ubiquitously in PIK3CA protein that alters PI3K pathway, while R282W mutation in DNA binding helix (H2) region of P53 protein inhibits the transcription factor in P53 pathway causing MBC. According to our findings, the extrinsic (hypoxia, oxidative stress, and acidosis); intrinsic factors (MYC amplification) in PIK3CA and TP53 mutations will provide potential insights for developing novel therapeutic methods for MBC therapy.

Circulating DNA fragmentomics and cancer screening

The high fragmentation of nuclear circulating DNA (cirDNA) relies on chromatin organization and protection or packaging within mononucleosomes, the smallest and the most stabilized structure in the bloodstream. The detection of differing size patterns, termed fragmentomics, exploits information about the nucleosomal packing of DNA. Fragmentomics not only implies size pattern characterization but also considers the positioning and occupancy of nucleosomes, which result in cirDNA fragments being protected and persisting in the circulation. Fragmentomics can determine tissue of origin and distinguish cancer-derived cirDNA. The screening power of fragmentomics has been considerably strengthened in the omics era, as shown in the ongoing development of sophisticated technologies assisted by machine learning. Fragmentomics can thus be regarded as a strategy for characterizing cancer within individuals and offers an alternative or a synergistic supplement to mutation searches, methylation, or nucleosome positioning. As such, it offers potential for improving diagnostics and cancer screening.

Artificial intelligence applications in pediatric oncology diagnosis

Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundamentally capture data, identify underlying patterns, achieve preset endpoints, and provide decisions and predictions about real-world events with working principles of machine learning and deep learning. AI algorithms with sufficient graphic processing unit power have been demonstrated to provide timely diagnostic references based on preliminary training of large amounts of clinical and imaging data. The sample size issue is an inevitable challenge for pediatric oncology considering its low morbidity and individual heterogeneity. However, this problem may be solved in the near future considering the exponential advancements of AI algorithms technically to decrease the dependence of AI operation on the amount of data sets and the efficiency of computing power. For instance, it could be a feasible solution by shifting convolutional neural networks (CNNs) from adults and sharing CNN algorithms across multiple institutions besides original data. The present review provides important insights into emerging AI applications for the diagnosis of pediatric oncology by systematically overviewing of up-to-date literature.

Cellular senescence in cancer: clinical detection and prognostic implications

Cellular senescence is a state of stable cell-cycle arrest with secretory features in response to cellular stress. Historically, it has been considered as an endogenous evolutionary homeostatic mechanism to eliminate damaged cells, including damaged cells which are at risk of malignant transformation, thereby protecting against cancer. However, accumulation of senescent cells can cause long-term detrimental effects, mainly through the senescence-associated secretory phenotype, and paradoxically contribute to age-related diseases including cancer. Besides its role as tumor suppressor, cellular senescence is increasingly being recognized as an in vivo response in cancer patients to various anticancer therapies. Its role in cancer is ambiguous and even controversial, and senescence has recently been promoted as an emerging hallmark of cancer because of its hallmark-promoting capabilities. In addition, the prognostic implications of cellular senescence have been underappreciated due to the challenging detection and sparse in and ex vivo evidence of cellular senescence in cancer patients, which is only now catching up. In this review, we highlight the approaches and current challenges of in and ex vivo detection of cellular senescence in cancer patients, and we discuss the prognostic implications of cellular senescence based on in and ex vivo evidence in cancer patients.

Biomarkers for the Detection and Risk Stratification of Aggressive Prostate Cancer

Current strategies for the clinical management of prostate cancer are inadequate for a precise risk stratification between indolent and aggressive tumors. Recently developed tissue-based molecular biomarkers have refined the risk assessment of the disease. The characterization of tissue biopsy components and subsequent identification of relevant tissue-based molecular alterations have the potential to improve the clinical decision making and patient outcomes. However, tissue biopsies are invasive and spatially restricted due to tumor heterogeneity. Therefore, there is an urgent need for complementary diagnostic and prognostic options. Liquid biopsy approaches are minimally invasive with potential utility for the early detection, risk stratification, and monitoring of tumors. In this review, we focus on tissue and liquid biopsy biomarkers for early diagnosis and risk stratification of prostate cancer, including modifications on the genomic, epigenomic, transcriptomic, and proteomic levels. High-risk molecular alterations combined with orthogonal clinical parameters can improve the identification of aggressive tumors and increase patient survival.

CRAG: de novo characterization of cell-free DNA fragmentation hotspots in plasma whole-genome sequencing

The fine-scale cell-free DNA fragmentation patterns in early-stage cancers are poorly understood. We developed a de novo approach to characterize the cell-free DNA fragmentation hotspots from plasma whole-genome sequencing. Hotspots are enriched in open chromatin regions, and, interestingly, 3'end of transposons. Hotspots showed global hypo-fragmentation in early-stage liver cancers and are associated with genes involved in the initiation of hepatocellular carcinoma and associated with cancer stem cells. The hotspots varied across multiple early-stage cancers and demonstrated high performance for the diagnosis and identification of tissue-of-origin in early-stage cancers. We further validated the performance with a small number of independent case-control-matched early-stage cancer samples.

A framework for clinical cancer subtyping from nucleosome profiling of cell-free DNA

Cell-free DNA (cfDNA) has the potential to inform tumor subtype classification and help guide clinical precision oncology. Here we develop Griffin, a framework for profiling nucleosome protection and accessibility from cfDNA to study the phenotype of tumors using as low as 0.1x coverage whole genome sequencing data. Griffin employs a GC correction procedure tailored to variable cfDNA fragment sizes, which generates a better representation of chromatin accessibility and improves the accuracy of cancer detection and tumor subtype classification. We demonstrate estrogen receptor subtyping from cfDNA in metastatic breast cancer. We predict estrogen receptor subtype in 139 patients with at least 5% detectable circulating tumor DNA with an area under the receive operator characteristic curve (AUC) of 0.89 and validate performance in independent cohorts (AUC = 0.96). In summary, Griffin is a framework for accurate tumor subtyping and can be generalizable to other cancer types for precision oncology applications.

Current Role and Future Potential of CSF ctDNA for the Diagnosis and Clinical Management of Pediatric Central Nervous System Tumors

Most pediatric central nervous system (CNS) tumors are located in eloquent anatomic areas, making surgical resection and, in some cases, even biopsy risky or impossible. This diagnostic predicament coupled with the move toward molecular classification for diagnosis has exposed an urgent need to develop a minimally invasive means to obtain diagnostic information. In non-CNS solid tumors, the detection of circulating tumor DNA (ctDNA) in plasma and other bodily fluids has been incorporated into routine practice and clinical trial design for selection of molecular targeted therapy and longitudinal monitoring. For primary CNS tumors, however, detection of ctDNA in plasma has been challenging. This is likely related at least in part to anatomic factors such as the blood-brain barrier. Due to the proximity of primary CNS tumors to the cerebrospinal fluid (CSF) space, our group and others have turned to CSF as a rich alternative source of ctDNA. Although multiple studies at this time have demonstrated the feasibility of CSF ctDNA detection across multiple types of pediatric CNS tumors, the optimal role and utility of CSF ctDNA in the clinical setting has not been established. This review discusses the work-to-date on CSF ctDNA liquid biopsy in pediatric CNS tumors and the associated technical challenges, and reviews the promising opportunities that lie ahead for integration of CSF ctDNA liquid biopsy into clinical care and clinical trial design.

Liquid biopsy for children with central nervous system tumours: Clinical integration and technical considerations

Circulating cell-free DNA (cfDNA) analysis has the potential to revolutionise the care of patients with cancer and is already moving towards standard of care in some adult malignancies. Evidence for the utility of cfDNA analysis in paediatric cancer patients is also accumulating. In this review we discuss the limitations of blood-based assays in patients with brain tumours and describe the evidence supporting cerebrospinal fluid (CSF) cfDNA analysis. We make recommendations for CSF cfDNA processing to aid the standardisation and technical validation of future assays. We discuss the considerations for interpretation of cfDNA analysis and highlight promising future directions. Overall, cfDNA profiling shows great potential as an adjunct to the analysis of biopsy tissue in paediatric cancer patients, with the potential to provide a genetic molecular profile of the tumour when tissue biopsy is not feasible. However, to fully realise the potential of cfDNA analysis for children with brain tumours larger prospective studies incorporating serial CSF sampling are required.

A hitchhiker's guide to cell-free DNA biology

Liquid biopsy provides a noninvasive window to the cancer genome and physiology. In particular, cell-free DNA (cfDNA) is a versatile analyte for guiding treatment, monitoring treatment response and resistance, tracking minimal residual disease, and detecting cancer earlier. Despite certain successes, brain cancer diagnosis is amongst those applications that has so far resisted clinical implementation. Recent approaches have highlighted the clinical gain achievable by exploiting cfDNA biological signatures to boost liquid biopsy or unlock new applications. However, the biology of cfDNA is complex, still partially understood, and affected by a range of intrinsic and extrinsic factors. This guide will provide the keys to read, decode, and harness cfDNA biology: the diverse sources of cfDNA in the bloodstream, the mechanism of cfDNA release from cells, the cfDNA structure, topology, and why accounting for cfDNA biology matters for clinical applications of liquid biopsy.

Bioplatforms in liquid biopsy: advances in the techniques for isolation, characterization and clinical applications

Tissue biopsy analysis has conventionally been the gold standard for cancer prognosis, diagnosis and prediction of responses/resistances to treatments. The existing biopsy procedures used in clinical practice are, however, invasive, painful and often associated with pitfalls like poor recovery of tumor cells and infeasibility for repetition in single patients. To circumvent these limitations, alternative non-invasive, rapid and economical, yet sturdy, consistent and dependable, biopsy techniques are required. Liquid biopsy is an emerging technology that fulfills these criteria and potentially much more in terms of subject-specific real-time monitoring of cancer progression, determination of tumor heterogeneity and treatment responses, and specific identification of the type and stages of cancers. The present review first briefly revisits the state-of-the-art technique of liquid biopsy and then proceeds to address in detail, the advances in the potential clinical applications of four major biological agencies present in liquid biopsy samples (circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes and tumor-educated platelets (TEPs)). Finally, the authors conclude with the limitations that need to be addressed in order for liquid biopsy to effectively replace the conventional invasive biopsy methods in the clinical settings.

An international working group consensus report for the prioritization of molecular biomarkers for Ewing sarcoma

The advent of dose intensified interval compressed therapy has improved event-free survival for patients with localized Ewing sarcoma (EwS) to 78% at 5 years. However, nearly a quarter of patients with localized tumors and 60–80% of patients with metastatic tumors suffer relapse and die of disease. In addition, those who survive are often left with debilitating late effects. Clinical features aside from stage have proven inadequate to meaningfully classify patients for risk-stratified therapy. Therefore, there is a critical need to develop approaches to risk stratify patients with EwS based on molecular features. Over the past decade, new technology has enabled the study of multiple molecular biomarkers in EwS. Preliminary evidence requiring validation supports copy number changes, and loss of function mutations in tumor suppressor genes as biomarkers of outcome in EwS. Initial studies of circulating tumor DNA demonstrated that diagnostic ctDNA burden and ctDNA clearance during induction are also associated with outcome. In addition, fusion partner should be a pre-requisite for enrollment on EwS clinical trials, and the fusion type and structure require further study to determine prognostic impact. These emerging biomarkers represent a new horizon in our understanding of disease risk and will enable future efforts to develop risk-adapted treatment.

Somatic copy number alteration and fragmentation analysis in circulating tumor DNA for cancer screening and treatment monitoring in colorectal cancer patients

Background

Analysis of circulating free DNA (cfDNA) is a promising tool for personalized management of colorectal cancer (CRC) patients. Untargeted cfDNA analysis using whole-genome sequencing (WGS) does not need a priori knowledge of the patient´s mutation profile.

Methods

Here we established LIquid biopsy Fragmentation, Epigenetic signature and Copy Number Alteration analysis (LIFE-CNA) using WGS with ~ 6× coverage for detection of circulating tumor DNA (ctDNA) in CRC patients as a marker for CRC detection and monitoring.

Results

We describe the analytical validity and a clinical proof-of-concept of LIFE-CNA using a total of 259 plasma samples collected from 50 patients with stage I-IV CRC and 61 healthy controls. To reliably distinguish CRC patients from healthy controls, we determined cutoffs for the detection of ctDNA based on global and regional cfDNA fragmentation patterns, transcriptionally active chromatin sites, and somatic copy number alterations. We further combined global and regional fragmentation pattern into a machine learning (ML) classifier to accurately predict ctDNA for cancer detection. By following individual patients throughout their course of disease, we show that LIFE-CNA enables the reliable prediction of response or resistance to treatment up to 3.5 months before commonly used CEA.

Conclusion

In summary, we developed and validated a sensitive and cost-effective method for untargeted ctDNA detection at diagnosis as well as for treatment monitoring of all CRC patients based on genetic as well as non-genetic tumor-specific cfDNA features. Thus, once sensitivity and specificity have been externally validated, LIFE-CNA has the potential to be implemented into clinical practice. To the best of our knowledge, this is the first study to consider multiple genetic and non-genetic cfDNA features in combination with ML classifiers and to evaluate their potential in both cancer detection and treatment monitoring.

Trial registration DRKS00012890.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13045-022-01342-z.

Deep whole-genome ctDNA chronology of treatment-resistant prostate cancer

Circulating tumour DNA (ctDNA) in blood plasma is an emerging tool for clinical cancer genotyping and longitudinal disease monitoring¹. However, owing to past emphasis on targeted and low-resolution profiling approaches, our understanding of the distinct populations that comprise bulk ctDNA is incomplete^2-12. Here we perform deep whole-genome sequencing of serial plasma and synchronous metastases in patients with aggressive prostate cancer. We comprehensively assess all classes of genomic alterations and show that ctDNA contains multiple dominant populations, the evolutionary histories of which frequently indicate whole-genome doubling and shifts in mutational processes. Although tissue and ctDNA showed concordant clonally expanded cancer driver alterations, most individual metastases contributed only a minor share of total ctDNA. By comparing serial ctDNA before and after clinical progression on potent inhibitors of the androgen receptor (AR) pathway, we reveal population restructuring converging solely on AR augmentation as the dominant genomic driver of acquired treatment resistance. Finally, we leverage nucleosome footprints in ctDNA to infer mRNA expression in synchronously biopsied metastases, including treatment-induced changes in AR transcription factor signalling activity. Our results provide insights into cancer biology and show that liquid biopsy can be used as a tool for comprehensive multi-omic discovery.

The applications of plasma cell-free DNA in cancer detection: Implications in the management of breast cancer patients

Liquid biopsy probes DNA, RNA, and proteins in body fluids for cancer detection and is one of the most rapidly developing areas in oncology. Tumor-derived DNA (circulating tumor DNA, ctDNA) in the context of cell-free DNA (cfDNA) in blood has been the main target for its potential utilities in cancer detection. Liquid biopsy can report tumor burden in real-time without invasive interventions, and would be feasible for screening tumor types that lack standard-of-care screening approaches. Two major approaches to interrogating ctDNA are genetic mutation and DNA methylation profiling. Mutation profiling can identify tumor driver mutations and guide precision therapy. Targeted genomic profiling of DNA methylation has become the main approach for cancer screening in the general population. Here we review the recent technological development and ongoing efforts in clinical applications. For clinical applications, we focus on breast cancer, in which subtype-specific biology demarcates the applications of ctDNA.

The interactions between DNA nanostructures and cells: A critical overview from a cell biology perspective

DNA nanotechnology has yielded remarkable advances in  composite materials with diverse applications in biomedicine. The specificity and predictability of building 3D structures at the nanometer scale make DNA nanotechnology a promising tool for uses in biosensing, drug delivery, cell modulation, and bioimaging. However, for successful translation of DNA nanostructures to real-world applications, it is crucial to understand how they interact with living cells, and the consequences of such interactions. In this review, we summarize the current state of knowledge on the interactions of DNA nanostructures with cells. We identify key challenges, from a cell biology perspective, that influence progress towards the clinical translation of DNA nanostructures. We close by providing an outlook on what questions must be addressed to accelerate the clinical translation of DNA nanostructures. STATEMENT OF SIGNIFICANCE: Self-assembled DNA nanostructures (DNs) offers unique opportunities to overcome persistent challenges in the nanobiotechnology field. However, the interactions between engineered DNs and living cells are still not well defined. Critical systematization of current cellular models and biological responses triggered by DNs is a crucial foundation for the successful clinical translation of DNA nanostructures. Moreover, such an analysis will identify the pitfalls and challenges that are present in the field, and provide a basis for overcoming those challenges.