Methods for characterizing and treating a cancer type using cancer images
AssigneeTesaro, Inc.
InventorsBin Feng, Hailei Zhang, Jaegil Kim
Life of this patent
Estimating the term…
Abstract
Described herein are methods, systems, devices and computer program products for characterizing or identifying a type of cancer. Also described are methods of treating a characterized or identified chancer. For example, certain methods may be used to characterize a homologous recombination deficiency status of a cancer.
Claims18
A computer-implemented method of identifying a cancer as homologous recombination deficient or homologous recombination proficient, comprising:
- inputting image data comprising tiles or pixels corresponding to a digitized image of the cancer into a trained machine learning model, trained on training image data comprising a plurality of digitized images associated with a homologous recombination status;
- processing, using one or more processors, the inputted image data by the trained machine learning model that associates the inputted image data with a homologous recombination deficiency status, wherein the trained machine learning model comprises (i) a deep learning model (ii) a convolutional neural network (CNN) learning model, (iii) an ensemble model or (iv) an image classification model, and wherein the trained machine learning model comprises one or more weighted features including a first feature comprising a mutational signature score associated with the cancer and a second feature comprising a large-scale state transition (LST) score associated with the cancer;
- outputting, from the trained machine learning model, an output indicating the homologous recombination deficiency status of the cancer;
- and
- identifying the cancer as homologous recombination deficient (HRD) or homologous recombination proficient (HRP) based on the output indicating the homologous recombination deficiency status of the cancer.
A computer-implemented method for identifying whether a cancer in a subject is homologous recombination deficient (HRD) comprising the steps of:
- i. receiving an image of the subject's cancer, wherein the image comprises tiles or pixels corresponding to a digitized image of the cancer, and
- ii. processing said image, using one or more processors, with an image analysis system and using a trained image classification model to determine a homologous recombination deficiency status to identify therefrom if the cancer is HRD, wherein the trained image classification model comprises one or more weighted features including a first feature comprising a mutational signature score associated with the cancer and a second feature comprising a large-scale state transition (LST) score associated with the cancer, and
- iii. outputting a result including the HRD status from the image processing step.
A system to identify a homologous recombination (HR) status of a cancer of a subject, preferably whether the cancer of the subject is HR deficient (HRD) or whether the cancer is HRD or other than HRD, the system comprising an image analysis system and one or more processors configured to:
- i) receive as input an image of the cancer of the subject, wherein the image comprises tiles or pixels corresponding to a digitized image of the cancer,
- ii) process the cancer image with a trained machine learning (ML) model to determine the HR status of the cancer of the subject from the image of the cancer, wherein the trained ML model is (a) trained by supervised learning, (b) a deep learning (DL) model, (c) configured by at least one artificial neural network, (d) a convolutional neural network learning model, (e) an ensemble model, or (f) any combination of the foregoing options (a) to (e), wherein the trained machine learning model comprises an image classification model and one or more weighted features including a first feature comprising a mutational signature score associated with the cancer and a second feature comprising a large-scale state transition (LST) score associated with the cancer, and
- iii) provide an output to indicate the HR status for the cancer based on the processing of the image of said cancer by the trained ML model.
A computer-implemented method performed by a data processing system comprising one or more processors for identifying a cancer as homologous recombination deficient (HRD) or homologous recombination proficient (HRP), comprising:
- inputting image data comprising tiles or pixels corresponding to a digitized image of the cancer into the data processing system;
- accessing a computer program that includes a trained machine learning model that associates the inputted image data with a homologous recombination deficiency status, wherein the trained machine learning model comprises one or more weighted features including a first feature comprising a mutational signature score associated with the cancer and a second feature comprising a large-scale state transition (LST) score associated with the cancer;
- executing the computer program with the trained machine learning model to process the inputted image data to identify image data features;
- determining, based on the identified image data features, the homologous recombination deficiency status for the inputted image data;
- and
- storing, in memory, a data structure with fields representing the inputted image data and its homologous recombination deficiency status.
A computer program product comprising a computer readable storage medium comprising instructions which, when the program is executed by a computer or data processor(s), cause the computer or data processor(s) to process a digitized image of a cancer to determine or predict a homologous recombination deficient (HRD) status of the cancer, wherein the instructions when executed implement (a) a trained machine learning model, wherein the trained machine learning model comprises an image classification model and one or more weighted features including a first feature comprising a mutational signature score associated with the cancer and a second feature comprising a large-scale state transition (LST) score associated with the cancer, and wherein the instructions when executed provide an output with an indication of the HRD status.
Dependencies are inferred from the claim text.