# US 12,224,043 B2 · Methods for determining treatment for cancer patients

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- Number: US 12,224,043 B2
- Document ID: US12224043B2
- Status: Granted
- Kind: B2
- Application number: 18/367,923
- Filed: 2023-09-13
- Published: 2023-12-28
- Granted: 2025-02-11
- Earliest claimed: 2018-10-18
- Assignees: MedImmune, LLC
- Applicants: Medimmune, LLC
- Inventors: Li Yu; Harry Yang; Mohammed Dar; Lorin Roskos; Jean-Charles Soria; Charles Ferte; Wei Zhao; Aline Gendrin Brokmann; Jolyon Faria; Pralay Mukhopadhyay; Ikbel Achour
- Web page: https://patentgrep.com/patent/US12224043B2

## CPC

- G16B 40/20: Supervised data analysis
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/20: Ensemble learning
- G16B 5/20: Probabilistic models
- G16H 10/20: for electronic clinical trials or questionnaires
- G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G16H 50/50: for simulation or modelling of medical disorders
- A61K 38/00: Medicinal preparations containing peptides (peptides containing beta-lactam rings A61K31/00; cyclic dipeptides not having in their molecule any other peptide link than those which form their ring, e.g. piperazine-2,5-diones, A61K31/00; ergot alkaloids of the cyclic peptide type A61K31/48; containing macromolecular compounds having statistically distributed amino acid units A61K31/74; medicinal preparations containing antigens or antibodies A61K39/00; medicinal preparations characterised by the non-active ingredients, e.g. peptides as drug carriers, A61K47/00)
- C07K 16/2818: {against CD28 or CD152}
- G01N 33/5758: {involving compounds serving as markers for tumours, cancers or neoplasias, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides or metabolites}
- G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- A61K: PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES (devices or methods specially adapted for bringing pharmaceutical products into particular physical or administering forms A61J3/00; chemical aspects of, or use of materials for deodorisation of air, for disinfection or sterilisation, or for bandages, dressings, absorbent pads or surgical articles A61L; soap compositions C11D)
- C07K: PEPTIDES (peptides in foodstuffs A23; obtaining protein compositions for foodstuffs, working-up proteins for foodstuffs A23J; preparations for medicinal purposes A61K; peptides containing beta-lactam rings C07D; cyclic dipeptides not having in their molecule any other peptide link than those which form their ring, e.g. piperazine-2,5-diones, C07D; ergot alkaloids of the cyclic peptide type C07D519/02; macromolecular compounds having statistically distributed amino acid units in their molecules, i.e. when the preparation does not provide for a specific; but for a random sequence of the amino acid units, homopolyamides and block copolyamides derived from amino acids C08G69/00; macromolecular products derived from proteins C08H1/00; preparation of glue or gelatine C09H; single cell proteins, enzymes C12N; genetic engineering processes for obtaining peptides C12N15/00; compositions for measuring or testing processes involving enzymes C12Q; investigation or analysis of biological material G01N33/00)
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)

## Abstract

Methods for determining treatments for cancer patients are disclosed.

## Claims

### Claim 1

A computing system comprising:
computer memory configured to store model training data, wherein the model training data involves a first set of cancer patients that underwent cancer treatments, wherein the model training data associates (i) results from laboratory tests conducted on the first set of cancer patients and tumor types of the first set of cancer patients with (ii) whether individuals from the first set of cancer patients died within a threshold number of weeks from initiation of the cancer treatments; and
one or more processors in communication with the computer memory and configured to execute program instructions to:
generate a plurality of variations of a gradient boosting machine learning model, wherein generating each of the variations comprises:
training, based on the model training data, a sequential series of decision trees until a stopping condition is reached using a respective set of hyperparameters, wherein each subsequent decision tree of the sequential series of decision trees is constructed based on residual values of its preceding decision tree of the sequential series of decision trees, and wherein the respective sets of hyperparameters are different for each of the variations; and
forming an additive function of the sequential series of decision trees, wherein each of the variations of the gradient boosting machine learning model is usable to predict whether a further cancer patient dies within the threshold number of weeks based on the additive function applied to results from the laboratory tests as conducted on the further cancer patient and a tumor type of the further cancer patient;
apply each of the variations of the gradient boosting machine learning model to the model training data; and
select, from among the variations of the gradient boosting machine learning model, a particular variation that provides predictions of whether the first set of cancer patients die within the threshold number of weeks from initiation of the cancer treatments within a threshold degree of accuracy.

### Claim 2 (depends on 1)

The computing system of claim 1, wherein the results from the laboratory tests provide one or more of: a neutrophil to lymphocyte ratio, a neutrophil level, an albumin level, a lactate dehydrogenase level, an aspartate amino transferase level, or a gamma-glutamyl transferase level.

### Claim 3 (depends on 2)

The computing system of claim 2, wherein the neutrophil to lymphocyte ratio, the neutrophil level, the albumin level, the lactate dehydrogenase level, the aspartate amino transferase level, and the gamma-glutamyl transferase level were selected from 20 or more laboratory tests conducted on the first set of cancer patients, wherein the selecting comprises:
training the sequential series of decision trees in each of the plurality of variations of the gradient boosting machine learning model on the 20 or more laboratory tests;
for each respective laboratory test, generating an additional variation of the gradient boosting machine learning model, wherein generating the additional variation of the gradient boosting machine learning model comprises training the sequential series of decision trees on the 20 or more laboratory tests with the respective laboratory test removed; and
determining predictive values of each of the 20 or more laboratory tests by comparing accuracies of the additional variations of the gradient boosting machine learning model.

### Claim 4 (depends on 1)

The computing system of claim 1, wherein the tumor type is one of: non-small cell lung cancer, urothelial carcinoma, squamous cell carcinoma of head and neck, gastric cancer, renal cell carcinoma, or Hodgkin's lymphoma.

### Claim 5 (depends on 1)

The computing system of claim 1, wherein predicting whether the further cancer patient dies within the threshold number of weeks comprises:
determining, using the selected variation of the gradient boosting machine learning model, a probability that the further cancer patient dies within the threshold number of weeks.

### Claim 6 (depends on 5)

The computing system of claim 5, wherein predicting whether the further cancer patient dies within the threshold number of weeks further comprises:
based on the probability and a cutoff value, placing the further cancer patient into either a high risk of death category or a low risk of death category.

### Claim 7 (depends on 6)

The computing system of claim 6, wherein the cutoff value is selected so that there is no more than a false positive rate of 5-20% when predicting whether the first set of cancer patients die within the threshold number of weeks.

### Claim 8 (depends on 6)

The computing system of claim 6, wherein cancer patients placed in the high risk of death category are excluded from a clinical trial for treatment of cancer, and wherein cancer patients placed in the low risk of death category are included in the clinical trial for treatment of cancer.

### Claim 9 (depends on 1)

The computing system of claim 1, wherein the computer memory is further configured to store model tuning data, wherein the model tuning data involves a second set of cancer patients that underwent cancer treatments, wherein the model tuning data associates (i) results from the laboratory tests as conducted on the second set of cancer patients and tumor types of the second set of cancer patients with (ii) whether the second set of cancer patients died within the threshold number of weeks from initiation of the cancer treatments, and
wherein the one or more processors are further configured to execute further program instructions to:
apply each of the variations of the gradient boosting machine learning model to the model tuning data; and
select, from among the variations of the gradient boosting machine learning model, a particular variation that provides predictions of whether the second set of cancer patients die within the threshold number of weeks from initiation of the cancer treatments within the threshold degree of accuracy.

### Claim 10 (depends on 1)

The computing system of claim 1, wherein the hyperparameters include one or more of: maximum depth of the decision trees set to 2, 3, 4, 5, or 6, minimum loss reduction required to split a node in the decision trees set to 0, 1, 2, 3, or 4, minimum sum of weights of all observations required in a child node in the decision trees set between 3.0 and 7.0, fraction of input parameters to be randomly sampled for each of the decision trees set between 0.7 and 1.0, fraction of observations to be randomly sampled for each of the decision trees set between 0.4 and 0.8, number of decision trees in the sequential series of decision trees set between 10 and 25.

### Claim 11

A computer-implemented method comprising:
obtaining model training data, wherein the model training data involves a first set of cancer patients that underwent cancer treatments, wherein the model training data associates (i) results from laboratory tests conducted on the first set of cancer patients and tumor types of the first set of cancer patients with (ii) whether individuals from the first set of cancer patients died within a threshold number of weeks from initiation of the cancer treatments;
generate a plurality of variations of a gradient boosting machine learning model, wherein generating each of the variations comprises:
training, based on the model training data, a sequential series of decision trees until a stopping condition is reached using a respective set of hyperparameters, wherein each subsequent decision tree of the sequential series of decision trees is constructed based on residual values of its preceding decision tree of the sequential series of decision trees, and wherein the respective sets of hyperparameters are different for each of the variations; and
forming an additive function of the sequential series of decision trees, wherein each of the variations of the gradient boosting machine learning model is usable to predict whether a further cancer patient dies within the threshold number of weeks based on the additive function applied to results from the laboratory tests as conducted on the further cancer patient and a tumor type of the further cancer patient;
applying each of the variations of the gradient boosting machine learning model to the model training data; and
selecting, from among the variations of the gradient boosting machine learning model, a particular variation that provides predictions of whether the first set of cancer patients die within the threshold number of weeks from initiation of the cancer treatments within a threshold degree of accuracy.

### Claim 12 (depends on 11)

The computer-implemented method of claim 11, wherein the results from the laboratory tests provide one or more of: a neutrophil to lymphocyte ratio, a neutrophil level, an albumin level, a lactate dehydrogenase level, an aspartate amino transferase level, or a gamma-glutamyl transferase level.

### Claim 13 (depends on 11)

The computer-implemented method of claim 11, wherein the tumor type is one of: non-small cell lung cancer, urothelial carcinoma, squamous cell carcinoma of head and neck, gastric cancer, renal cell carcinoma, or Hodgkin's lymphoma.

### Claim 14

A computer-implemented method comprising:
obtaining a gradient boosting machine learning model, wherein the gradient boosting machine learning model was trained on model training data, wherein the model training data involves a first set of cancer patients that underwent cancer treatments, wherein the model training data associates (i) results from laboratory tests conducted on the first set of cancer patients and tumor types of the first set of cancer patients with (ii) whether individuals from the first set of cancer patients died within a threshold number of weeks from initiation of the cancer treatments, wherein training the gradient boosting machine learning model involved:
(i) generating a plurality of variations of a gradient boosting machine learning model, wherein generating each of the variations involved:
training, based on the model training data, a sequential series of decision trees until a stopping condition was reached using a respective set of hyperparameters, wherein each subsequent decision tree of the sequential series of decision trees was constructed based on residual values of its preceding decision tree of the sequential series of decision trees, and wherein the respective sets of hyperparameters are different for each of the variations; and
forming an additive function of the sequential series of decision trees;
(ii) applying each of the variations of the gradient boosting machine learning model to the model training data; and
(iii) selecting, from among the variations of the gradient boosting machine learning model, a particular variation that provides predictions of whether the first set of cancer patients die within the threshold number of weeks from initiation of the cancer treatments within a threshold degree of accuracy;
obtaining results from the laboratory tests as conducted on a further cancer patient;
obtaining a tumor type of the further cancer patient;
applying the gradient boosting machine learning model to results from the laboratory tests as conducted on a further cancer patient and the tumor type of the further cancer patient; and
receiving, from the gradient boosting machine learning model, a prediction of whether the further cancer patient dies within the threshold number of weeks.

### Claim 15 (depends on 14)

The computer-implemented method of claim 14, wherein the results from the laboratory tests provide one or more of: a neutrophil to lymphocyte ratio, a neutrophil level, an albumin level, a lactate dehydrogenase level, an aspartate amino transferase level, or a gamma-glutamyl transferase level.

### Claim 16 (depends on 14)

The computer-implemented method of claim 14, wherein the tumor type is one of: non-small cell lung cancer, urothelial carcinoma, squamous cell carcinoma of head and neck, gastric cancer, renal cell carcinoma, or Hodgkin's lymphoma.

### Claim 17 (depends on 14)

The computer-implemented method of claim 14, wherein predicting whether the further cancer patient dies within the threshold number of weeks comprises:
determining, using the selected variation of the gradient boosting machine learning model, a probability that the further cancer patient dies within the threshold number of weeks.

### Claim 18 (depends on 17)

The computer-implemented method of claim 17, wherein predicting whether the further cancer patient dies within the threshold number of weeks further comprises:
based on the probability and a cutoff value, placing the further cancer patient into either a high risk of death category or a low risk of death category.

### Claim 19 (depends on 18)

The computer-implemented method of claim 18, wherein the cutoff value was selected so that there is no more than a false positive rate of 5-20% when predicting whether the first set of cancer patients die within the threshold number of weeks.

### Claim 20 (depends on 18)

The computer-implemented method of claim 18, wherein cancer patients placed in the high risk of death category are excluded from a clinical trial for treatment of cancer, and wherein cancer patients placed in the low risk of death category are included in the clinical trial for treatment of cancer.