公开(公告)号：US20240062898A1

公开(公告)日：2024-02-22

申请号：US18007670

申请日：2021-06-04

Applicant: Cancer Research Technology Limited

Inventor： Geoff Macintyre ,  James Brenton ,  Anna Piskorz ,  Florian Markowetz

IPC: G16H50/20 ,  G16B20/10

CPC classification number: G16H50/20 ,  G16B20/10 ,  G16H10/40

Abstract: The present invention provides methods for predicting the treatment response of a cancer patient, using a tumour copy number profile for the patient. The method comprise analysing the copy number profile to assess whether the characteristics of at least one copy number feature are indicative of the presence of focal amplifications in the tumour genome, wherein the at least one copy number feature is selected from: copy number change-point, segment size and segment copy number. The patient is predicted as being likely to be resistant to treatment with an agent that induces the formation of micronuclei (e.g. doxorubicin) if the characteristics of the at least one copy number features are indicative of the presence of focal amplifications in the tumour genome. Also provided a related methods and systems.

公开(公告)号：US20230014674A1

公开(公告)日：2023-01-19

申请号：US17290924

申请日：2019-11-07

Applicant: Cancer Research Technology Limited

Inventor： Florent Mouliere ,  Dineika Chandrananda ,  Anna Piskorz ,  James Brenton ,  Nitzan Rosenfeld

IPC: G16B40/20 ,  G16B30/00 ,  C12N15/10 ,  G16H10/40 ,  G16H50/20 ,  G16H50/70 ,  G16H70/60

Abstract: The present invention provides a computer-implemented method for detecting variant nucleic acid from a cell-free nucleic acid-containing sample. The method comprises (a) providing data representing fragment sizes of nucleic acid fragments obtained from said sample and/or representing a measure of deviation from copy number neutrality of the nucleic acid fragments obtained from said sample; b) processing the data from step a) according to a classification algorithm, wherein said classification algorithm operates to classify sample data into one of at least a first class containing the variant nucleic acid and a second class not containing the variant nucleic acid, based on a plurality of cell-free nucleic acid fragment size features and/or a deviation from copy number neutrality feature; and c) outputting the classification of the sample from step b, thereby determining whether the sample contains the variant nucleic acid or not, or a probability that the sample contains the variant nucleic acid. Related methods are also provided.