Granted Patent B2

US 12,584,844 · App. 18/513,357 · Granted Mar 24, 2026

Inventors: Michael F. Goldberg (Brookline, MA); Alexander Bagaev (Waltham, MA); Daniiar Dyikanov (Waltham, MA); Aleksandr Zaitsev (Yerevan, AM); Boris Shpak (Yerevan, AM); Evgenii Tikhonov (Yerevan, AM); Polina Turova (Yerevan, AM); Arsenii Sokolov (Yerevan, AM); Anna Gantseva (Yerevan, AM)

Assignee: BostonGene Corporation

G01N15/1429G16B25/10G16B40/20G16B40/30G16H50/20G01N2015/016

Abstract

Claims (31)

1 . A method for determining a leukocyte immunoprofile type of a subject having, suspected of having, or at risk of having cancer, the method comprising:

using at least one computer hardware processor to perform:

obtaining flow cytometry data for white blood cells (WBC) isolated from a biological sample obtained from the subject;

processing the flow cytometry data to determine cell composition percentages for at least 20 of the following cell types:

Naive CD4+ Tregs, Naive CD4+ T cells, Naive CD8+ T cells, Naive B cells, Non-switched memory IgM B cells, Vδ2+ yδ T cells, Class-switched memory B cells, Central memory CD8+ T cells, CD4+ Tregs, Transitional memory CD4+ T cells, Central memory CD4+ T cells, memory CD4+ T cells, CD4+ T cells, CD39+ CD4+ Tregs, Eosinophils, Basophils, Plasmacytoid dendritic cells, Dendritic cells, PD1high CD8+ T cells, Transitional memory CD8+ T cells, Cytotoxic NK cells, Regulatory NK cells, memory CD8+ T cells, CD8+ T cells, Effector memory CD4+ T cells, NKT cells, CD8+ TEMRA, Effector memory CD8+ T cells, CD4+ TEMRA, Neutrophils, Granulocytes, Classical monocytes, Non-classical monocytes, and HLA-DRlow monocytes;

generating a leukocyte signature for the subject using the determined cell composition percentages for the at least 20 cell types, the leukocyte signature comprising the cell composition percentages for the at least 20 cell types;

identifying, using the leukocyte signature and from among a plurality of leukocyte immunoprofile types, a Primed leukocyte immunoprofile type for the subject; and

administering an immune checkpoint inhibitor (ICI) to the subject when the subject is identified as having the Primed leukocyte immunoprofile type.

2 . The method of claim 1 , wherein the WBC consist of granulocyte white blood cells and agranulocyte white blood cells.

3 . The method of claim 1 , wherein processing the flow cytometry data comprises applying one or more machine learning models to the flow cytometry data to obtain the cell composition percentages for the at least 20 cell types.

4 . The method of claim 1 , wherein processing the flow cytometry data comprises determining cell composition percentages for: Naive CD4+ Tregs, Naive CD4+ T cells, Naive CD8+ T cells, Naive B cells, Effector memory CD4+ T cells, Effector memory CD8+ T cells, Classical monocytes, and Non-classical monocytes.

5 . The method of claim 1 , wherein processing the flow cytometry data comprises determining the cell composition percentages for: Naive CD4+ Tregs, Naive CD4+ T cells, Naive CD8+ T cells, Naive B cells, Non-switched memory IgM B cells, Vδ2+ yδ T cells, Class-switched memory B cells, Central memory CD8+ T cells, CD4+ Tregs, Transitional memory CD4+ T cells, Central memory CD4+ T cells, memory CD4+ T cells, CD4+ T cells, CD39+CD4+ Tregs, Eosinophils, Basophils, Plasmacytoid dendritic cells, Dendritic cells, PD1high CD8+ T cells, Transitional memory CD8+ T cells, Cytotoxic NK cells, Regulatory NK cells, memory CD8+ T cells, CD8+ T cells, Effector memory CD4+ T cells, NKT cells, CD8+ TEMRA, Effector memory CD8+ T cells, CD4+ TEMRA, Neutrophils, Granulocytes, Classical monocytes, Non-classical monocytes, and HLA-DRlow monocytes.

6 . The method of claim 1 , wherein the leukocyte signature comprises the cell composition percentages for: Naive CD4+ Tregs, Naive CD4+ T cells, Naive CD8+ T cells, Naive B cells, Effector memory CD4+ T cells, Effector memory CD8+ T cells, Classical monocytes, and Non-classical monocytes.

7 . The method of claim 1 , wherein the leukocyte signature comprises the cell composition percentages for: Naive CD4+ Tregs, Naive CD4+ T cells, Naive CD8+ T cells, Naive B cells, Non-switched memory IgM B cells, Vδ2+ yδ T cells, Class-switched memory B cells, Central memory CD8+ T cells, CD4+ Tregs, Transitional memory CD4+ T cells, Central memory CD4+ T cells, memory CD4+ T cells, CD4+ T cells, CD39+ CD4+ Tregs, Eosinophils, Basophils, Plasmacytoid dendritic cells, Dendritic cells, PD1high CD8+ T cells, Transitional memory CD8+ T cells, Cytotoxic NK cells, Regulatory NK cells, memory CD8+ T cells, CD8+ T cells, Effector memory CD4+ T cells, NKT cells, CD8+ TEMRA, Effector memory CD8+ T cells, CD4+ TEMRA, Neutrophils, Granulocytes, Classical monocytes, Non-classical monocytes, and HLA-DRlow monocytes.

8 . The method of claim 1 ,

wherein identifying, using the leukocyte signature and from among a plurality of leukocyte immunoprofile types, the Primed leukocyte immunoprofile type for the subject comprises:

associating the leukocyte signature of the subject with a particular one of the plurality of leukocyte immunoprofile types, and

identifying the Primed leukocyte immunoprofile type for the subject as the leukocyte immunoprofile type corresponding to the particular one of the plurality of leukocyte immunoprofile types to which the leukocyte signature of the subject is associated.

9 . The method of claim 8 , wherein associating the leukocyte signature of the subject with a particular one of the plurality of leukocyte immunoprofile types comprises:

processing the leukocyte signature with a trained classifier to obtain an output indicative of the particular one of the plurality of leukocyte immunoprofile types.

10 . The method of claim 9 , wherein the trained classifier comprises a trained neural network classifier, optionally, a tabular prior-data fitted network transformer (TabPFN) classifier.

11 . The method of claim 8 , wherein the associating the leukocyte signature of the subject with a particular one of the plurality of leukocyte immunoprofile types comprises:

determining, for each particular one of the plurality of leukocyte immunoprofile types, a score indicating whether the leukocyte signature of the subject is associated with that particular leukocyte immunoprofile type,

wherein determining the score for a particular leukocyte immunoprofile type comprises applying a linear regression model associated with the particular leukocyte immunoprofile type, to the cell composition percentages in the leukocyte signature.

12 . The method of claim 1 , further comprising generating the plurality of leukocyte immunoprofile types, the generating comprising:

obtaining multiple sets of flow cytometry data from white blood cells (WBC) isolated from biological samples obtained from multiple respective subjects, each of the multiple sets of flow cytometry data indicating cell composition percentages for the at least 20 cell types;

generating multiple leukocyte signatures from the multiple sets of flow cytometry data, each of the multiple leukocyte signatures comprising cell composition percentages for the at least 20 cell types, the generating comprising, for each particular one of the multiple leukocyte signatures:

determining the leukocyte signature by determining the cell composition percentages using the flow cytometry data in the particular set of flow cytometry data for which the particular one leukocyte signature is being generated; and

clustering the multiple leukocyte signatures to obtain the plurality of leukocyte immunoprofile types.

13 . The method of claim 1 , wherein the plurality of leukocyte immunoprofile types comprises: a Naïve leukocyte immunoprofile type, the Primed leukocyte immunoprofile type, a Progressive leukocyte immunoprofile type, a Chronic leukocyte immunoprofile type, and a Suppressive leukocyte immunoprofile type.

14 . The method of claim 1 , wherein the subject has head and neck squamous cell carcinoma (HNSCC).

Assignments (3)

Continuity (3)

References Cited (160)