12 results about "Circulating tumor DNA" patented technology

This invention relates to the field of medical technology, specifically to a method for preoperative risk assessment and prediction in liver transplant patients with hepatocellular carcinoma, comprising the following steps: Sample collection: selecting plasma samples and corresponding clinicopathological information from liver transplant recipients of hepatocellular carcinoma, and clarifying the inclusion and exclusion criteria for samples; Plasma cell-free DNA extraction and whole-genome sequencing: extracting and quality-controlling cell-free DNA from the plasma samples collected in step S1, constructing a sequencing library, and performing low-coverage whole-genome sequencing. This invention utilizes plasma-extracted cfDNA for whole-genome sequencing, combined with clinical testing information, to construct a preoperative risk assessment and prediction model for postoperative recurrence in liver transplant recipients of hepatocellular carcinoma based on non-invasive testing. This model can be used to predict the probability of recurrence-free survival before liver transplantation. The model derivation cohort integrates clinical records and circulating tumor DNA data for preoperative recurrence risk prediction.

The invention provides methods for detecting single nucleotide variants in breast cancer, bladder cancer, or colorectal cancer. Additional methods and compositions, such as reaction mixtures and solid supports comprising clonal populations of nucleic acids, are provided. For example, provided here is a method for monitoring and detection of early relapse or metastasis of breast cancer, bladder cancer, or colorectal cancer, comprising generating a set of amplicons by performing a multiplex amplification reaction on nucleic acids isolated from a sample of blood or urine or a fraction thereof from a patient who has been treated for a breast cancer, bladder cancer, or colorectal cancer, wherein each amplicon of the set of amplicons spans at least one single nucleotide variant locus of a set of patient-specific single nucleotide variant loci associated with the breast cancer, bladder cancer, or colorectal cancer; and determining the sequence of at least a segment of each amplicon of the set of amplicons that comprises a patient-specific single nucleotide variant locus, wherein detection of one or more patient-specific single nucleotide variants is indicative of early relapse or metastasis of breast cancer, bladder cancer, or colorectal cancer.

Techniques for predicting expression of cancer cells based on the methylation status of a region of DNA are described. An example method includes identifying data indicative of cell free DNA (cfDNA) from a sample derived from a subject. A methylation status of one or more regions of circulating tumor DNA (ctDNA) among the cfDNA is identified by analyzing the data. The example method further includes inputting input data including the methylation status of the one or more regions into at least one model configured to generate a probability that cancer cells of the subject express a predetermined sequence. In addition, the example method includes generating a report based on the probability that the cancer cells of the subject express the predetermined sequence.

The application relates to the field of biomedical detection technology, in particular to a bladder cancer postoperative recurrence monitoring method based on a ctDNA methylation spectrum, which comprises the following steps: collecting postoperative patient cell-free plasma and extracting circulating tumor DNA; performing bisulfite conversion treatment on the DNA; performing targeted amplification using a multiplex PCR primer group designed for a group of predetermined genomic methylation regions related to recurrence, constructing a sequencing library; performing high-throughput sequencing on the library, obtaining methylation level data of a plurality of CpG sites to form a sample methylation spectrum data matrix; the core of the application is that tumor heterogeneity is overcome through multi-marker combination detection, and a machine learning model is used to integrate multidimensional data to realize precise risk stratification, which has the advantages of non-invasiveness, high sensitivity, high specificity and quantifiable output, and provides an effective tool for individualized follow-up management of bladder cancer postoperation.

Techniques for classifying cancers using fragmentomic features are described. An example method includes identifying data indicative of circulating tumor DNA (ctDNA) from a sample derived from a subject. The example method further includes identifying fragmentomic features based on the data. Input data, including the fragmentomic features, is input into a model configured to generate at least one probability that a tumor is within at least one category. The example method further includes generating a report based on the at least one probability that the tumor is within the at least one category.

A marker group for detecting osteosarcoma circulating tumor cells (CTCs), characterized in that the marker group is COL1A2 isoforms, and the COL1A2 isoforms include at least one of ENST00000620463 and ENST00000297268. Compared with the prior art, the COL1A2 isoforms provided by the present application have a high prevalence in osteosarcoma tissues, a combined detection rate of 98.56%, and a detection rate of 85.71% in metastatic patients; the area under the ROC curve (AUC) of CTCs detection for predicting osteosarcoma metastasis is 0.833, which is better than traditional markers; longitudinal follow-up can detect metastasis 5.11 months earlier than imaging; it can distinguish metastatic patients before operation, and has unique applicability to osteosarcoma with low mutation burden. In addition, the COL1A2 isoforms can complement tumor-aware detection, and can be used for prognosis and monitoring of metastasis and recurrence of osteosarcoma patients who cannot successfully customize circulating tumor DNA detection panel (ctDNA panel).

The application provides a multi-omics joint detection system for prostate cancer recurrence risk assessment, the application synchronously acquires genomic, transcriptomic, epiproteomic and metabolomic data of different regions of a tumor through spatially resolved in situ capture technology, and integrates multi-dimensional information such as circulating tumor DNA epigenetic memory, urological microbiome-host interaction, single-cell clone evolution and tumor microenvironment three-dimensional topology. The system uses a dynamic Bayesian fusion engine to perform probabilistic risk calculation, combines digital twin technology to simulate treatment response, and realizes model adaptive updating through longitudinal follow-up data. The output result has high interpretability and can directly show key driving factors and their clinical interventional properties. The system breaks through the limitations of traditional static and single-omics models, significantly improves the prediction accuracy of recurrence, especially in low-risk populations, and provides intelligent support for individualized auxiliary treatment decisions.

Methods and systems for determining a tumor DNA fraction for a sample from a subject are described. In some instances, the methods comprise receiving sequence read data for a plurality of sequence reads derived from the sample from the subject; determining a variant allele frequency (VAF) for one or more variants detected in the sample based on the sequence read data; generating an empirical distribution of tumor DNA fraction values as a function of the determined VAF for the one or more variants; fitting a model to the empirical distribution of tumor DNA fraction values; and determining a tumor DNA fraction for the sample based on the model.

This invention relates to the field of early cancer diagnostic reagents, specifically to a non-invasive intelligent diagnostic kit for early colorectal cancer based on peripheral blood and its application. The kit targets key gene mutation sites (MLH1 c.113del A, MSH2 c.788del A, MUTYH c.1005 G>C, PMS2 c.288 C>T, and c.780 C>G) screened in the Chinese population and includes a specific primer set, circulating tumor DNA (ctDNA) extraction, and sequencing library preparation reagents. The accompanying detection system analyzes mutation data using a logistic regression model trained on the aforementioned sites to achieve early screening. Clinical trials have shown that this method has good performance in early colorectal cancer screening in the Chinese population.

Methods for determining a prognosis of a patient based on circulating tumor DNA (ctDNA) fraction and circulating free DNA (cfDNA) in liquid biopsy samples are described. The methods may comprise, for example, receiving sequence read data for a plurality of sequence reads obtained for the sample from the subject; determining a ctDNA fraction in the sample based on the one or more nucleic acid molecules; determining if the cfDNA value in the sample is greater than or equal to a threshold; and determining a prognosis of the subject based on the ctDNA fraction if the cfDNA value is greater than or equal to the threshold.

Methods and systems of determining a cancer score, including: receiving a sample collected from a subject, in which the sample includes sequences of circulating tumor DNA (ctDNA); providing the sample to an analysis pipeline, in which the analysis pipeline detects and synthesizes a plurality of features; and determining, with the analysis pipeline, the cancer score based on the synthesized plurality of features.