WO2025221765A1 - Compositions and methods for treating cancer - Google Patents

Abstract

The present disclosure relates to the methods of preventing and/or treating cancer including identifying subjects that are responder or non-responder to an immunotherapy. The present disclosure further provides compositions and kits for performing such methods.

Description

COMPOSITIONS AND METHODS FOR TREATING CANCER

CROSS-REFERENCES TO RELATED APPLICATIONS

The application claims the benefit of priority to U.S. Provisional Patent Application No. 63/634,189, filed April 15, 2024, and U.S. Provisional Patent Application. No. 63/691,694, filed September 6, 2024, the contents of each of which are incorporated by reference in their entireties, and to each of which priority is claimed.

GRANT INFORMATION

This invention was made with government support under EB001026, CA284540, CA251155, DK120531, and CA250227 awarded by the National Institutes of Health. The government has certain rights in the invention.

TECHNICAL FIELD

The present disclosure relates to methods, compositions, and kits for identifying subjects responding or non-responding to immunotherapy. In certain embodiments, the present disclosure further includes treating cancers in a responder or non-responder subject. The present disclosure also relates to biomarkers for predicting and monitoring a subject’s response to a treatment.

BACKGROUND OF THE INVENTION

Liver cancer, of which hepatocellular carcinoma (HCC) is the most common, is the third leading cause of cancer-related death globally (Sung et al., CA Cancer J Clin. May 2021;71(3):209- 249). The global burden is projected to increase as the etiology shifts from viral to nonviral causes, including alcoholic liver disease and metabolic dysfunction associated steatotic liver disease (Toh et al., Gastroenterology. Apr 2023;164(5):766-782). HCC develops in the background of these chronic liver diseases as liver injury and inflammation drive fibrosis, cirrhosis, and eventually cancer. In the advanced disease setting, overall survival is 12-18 months with current systemic therapies (Llovet et al., Nat Rev Dis Primers. Apr 142016;2: 16018). Existing immunotherapeutic combinations have drastically improved the treatment armamentarium for HCC, however, objective response rates remain critically low between 30-35% (Cheng et al., J Hepatol. Apr 2022;76(4):862-873; Abou-Alfa et al., NEJM Evidence . 2022; l(8):EVIDoa2100070). Early post- hoc analysis has indicated that both tumor genetics and tumor microenvironment features likely influence immunotherapy (IO) response (Zhu et al., Nat Med. Aug 2022;28(8):1599-1611).

Thus, there remain needs for the development of novel methods to identify patients responsive to IO and to improve their responsiveness to the same. SUMMARY OF THE INVENTION

The present disclosure relates to methods, compositions, and kits for treating and detecting cancers.

The present disclosure provides methods for identifying a non-responder subject, comprising measuring, in a sample from the subject, the expression level of one or more genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof; wherein an increased expression level of the one or more genes relative to a control indicates that the subject is non-responder. In certain embodiments, the methods further comprise determining a spatial location of a nucleic acid or a protein of the one or more genes. In certain embodiments, presence of the nucleic acid or protein of the one or more genes in an immune excluded location indicates that the subject is non- responder. In certain embodiments, the non-responder subject does not have an antic-cancer effect upon administration of an immunotherapy comprising atezolizumab and bevacizumab.

Additionally or alternatively, the present disclosure provides methods for identifying a responder subject, comprising measuring, in a sample from the subject, the expression level of one or more genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof; wherein a reduced expression level of the one or more genes relative to a control indicates that the subject is responder. In certain embodiments, the methods further comprise determining a spatial location of a nucleic acid or a protein of the one or more genes. In certain embodiments, presence of the nucleic acid or protein of the one or more genes in an immune active location indicates that the subject is responder. In certain embodiments, the responder subject has an anti-cancer effect upon administration of an immunotherapy comprising atezolizumab and bevacizumab.

In certain embodiments, the expression level of three or more genes is measured. In certain embodiments, the expression level of five or more genes is measured. In certain embodiments, the expression level of seven or more genes is measured. In certain embodiments, the expression level of nine or more genes is measured. In certain embodiments, the expression level of ten genes is measured. In certain embodiments, the expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SLC13A3, SP5, TCF7, and TNFRSF19 is measured. In certain embodiments, the expression level of thirteen genes is measured. In certain embodiments, the expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, and TNFRSF19 is measured.

In certain embodiments, an RNA expression level is measured. In certain embodiments, a protein expression level is measured. In certain embodiments, the sample comprises an organ tissue, whole blood, plasma, serum, whole blood cells, erythrocytes, lymphocytes, saliva, urine, stool, tears, sweat, sebum, nipple aspirate, ductal lavage, tumor exudates, synovial fluid, cerebrospinal fluid, lymph, fine needle aspirate, amniotic fluid, any other bodily fluid, exudate or secretory fluid, cell lysates, cellular secretion products, inflammation fluid, semen, or vaginal secretions. In certain embodiments, the organ tissue is a liver tissue. In certain embodiments, the organ tissue comprises a primary tumor tissue or a metastatic tumor tissue. In certain embodiments, the subject is human.

The present disclosure also provides methods for treating a subject having a cancer, comprising:

(a) measuring, in a sample from the subject, the expression level of one or more genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof;

(b) identifying the subject as non-responder when the expression level of the one or more genes is increased relative to a control; and

(c) administering an effective amount of an anti-cancer treatment not including atezolizumab, bevacizumab, tremelimumab, durvalumab, pembrolizumab, nivolumab, or a combination thereof.

In certain embodiments, the anti-cancer treatment comprises chemotherapy, radiation therapy, targeted drug therapy, immunotherapy, immunomodulatory agents, cytokines, monoclonal and polyclonal antibodies, and any combinations thereof. In certain embodiments, the anti-cancer treatment comprises PKF115-584, PNU-74654, PKF118-744, CGP049090, PKF118- 310, ZTM000990, BC21, CCT036477, PKF222-815, CWP232228, PRI-724/C-82, ICG001, MSAB, SAH-BLC9B, ZINC02092166, iCRT3, iCRT5, iCRT14, NLS-StAx-h, Hl-Bl, UU-T01, T02, 4FNPC, Apigenin, Carsonic acid, Curcumin, Esculetin, Magnalol, Resveratrol, Silibinin, T oxoflavin, NRX-252114, rapamycin, everolimus, RM-006 (RM-6272), sapanisertib, or a combination thereof. In certain embodiments, the anti-cancer treatment comprises sorafenib.

In certain embodiments, the methods further comprise determining a spatial location of a nucleic acid or a protein of the one or more genes. In certain embodiments, presence of the nucleic acid or protein of the one or more genes in an immune excluded location indicates that the subject is non-responder. In certain embodiments, the non-responder subject does not have an anti-cancer effect upon administration of an immunotherapy comprising atezolizumab and bevacizumab.

The present disclosure further provides methods for treating a subject having a cancer, comprising:

(a) measuring, in a sample from the subject, the expression level of one or more genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof; (b) identifying the subject as responder when the expression level of the one or more genes is reduced relative to a control; and

(c) administering an effective amount of a cancer therapy.

Moreover, the present disclosure provides methods for treating a subject having a cancer, comprising:

(a) determining, in a sample from the subject, a spatial location of a nucleic acid or a protein of one or more genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof;

(b) measuring the expression level of the one or more genes;

(c) identifying the subject as responder when the expression level of the one or more genes is reduced relative to a control;

(d) administering an effective amount of a cancer therapy.

In certain embodiments, presence of the nucleic acid or protein of the one or more genes in an immune active location indicates that the subject is responder. In certain embodiments, the responder subject has an anti-cancer effect upon administration of an immunotherapy comprising atezolizumab and bevacizumab.

In certain embodiments, the anti-cancer treatment comprises chemotherapy, radiation therapy, targeted drug therapy, immunotherapy, immunomodulatory agents, cytokines, monoclonal and polyclonal antibodies, and any combinations thereof. In certain embodiments, the anti-cancer treatment comprises atezolizumab, bevacizumab, tremelimumab, durvalumab, pembrolizumab, nivolumab, or a combination thereof. In certain embodiments, the anti-cancer treatment comprises atezolizumab and bevacizumab.

In certain embodiments, the anti-cancer treatment comprises PKF 115-584, PNU-74654, PKF118-744, CGP049090, PKF118-310, ZTM000990, BC21, CCT036477, PKF222-815, CWP232228, PRI-724/C-82, ICG001, MSAB, SAH-BLC9B, ZINC02092166, iCRT3, iCRT5, i CRT 14, NLS-StAx-h, Hl -Bl, UU-T01, T02, 4FNPC, Apigenin, Carsonic acid, Curcumin, Esculetin, Magnalol, Resveratrol, Silibinin, Toxoflavin, NRX-252114, rapamycin, everolimus, RM-006 (RM-6272), sapanisertib, or a combination thereof.

In certain embodiments, the expression level of three or more genes is measured. In certain embodiments, the expression level of five or more genes is measured. In certain embodiments, the expression level of seven or more genes is measured. In certain embodiments, the expression level of nine or more genes is measured. In certain embodiments, the expression level of ten genes is measured. In certain embodiments, the expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SLC13A3, SP5, TCF7, and TNFRSF19 is measured. In certain embodiments, the expression level of thirteen genes is measured. In certain embodiments, the expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, and TNFRSF19 is measured.

In certain embodiments, an RNA expression level is measured. In certain embodiments, a protein expression level is measured. In certain embodiments, the sample comprises an organ tissue, whole blood, plasma, serum, whole blood cells, erythrocytes, lymphocytes, saliva, urine, stool, tears, sweat, sebum, nipple aspirate, ductal lavage, tumor exudates, synovial fluid, cerebrospinal fluid, lymph, fine needle aspirate, amniotic fluid, any other bodily fluid, exudate or secretory fluid, cell lysates, cellular secretion products, inflammation fluid, semen, or vaginal secretions. In certain embodiments, the organ tissue is a liver tissue. In certain embodiments, the organ tissue comprises a primary tumor tissue or a metastatic tumor tissue. In certain embodiments, the subject is human.

In certain embodiments, the cancer is associated to CTNNB1. In certain embodiments, the cancer is selected from squamous cell cancer, lung cancer, cancer of the peritoneum, hepatocellular cancer, gastric or stomach cancer, pancreatic cancer, glioma, glioblastoma, cervical cancer, ovarian cancer, liver cancer, bladder cancer, hepatoma, breast cancer, colon cancer, rectal cancer, colorectal cancer, endometrial cancer or uterine carcinoma, salivary gland carcinoma, kidney or renal cancer, prostate cancer, vulvar cancer, thyroid cancer, hepatic carcinoma, anal carcinoma, penile carcinoma, CNS cancer, melanoma, head and neck cancer, bone cancer, bone marrow cancer, duodenum cancer, esophageal cancer, thyroid cancer, or hematological cancer. In certain embodiments, the cancer is selected from endometrial adenocarcinoma, lung adenocarcinoma, colon adenocarcinoma, prostate adenocarcinoma, hepatocellular carcinoma, basal cell carcinoma (BCC), head and neck squamous cell carcinoma (HNSCC), prostate cancer (CaP), pilomatrixoma (PTR), medulloblastoma (MDB), hepatoblastoma (HB), hepatocellular adenomas (HCA), or hepatocellular cancer (HCC). In certain embodiments, the cancer is hepatocellular carcinoma.

In addition, the present disclosure provides methods for identifying a subject having a mutated CTNNB1 gene, comprising measuring, in a sample from the subject, the expression level of one or more genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof; wherein an increased expression level of the one or more genes relative to a control indicates that the subject has the mutated CTNNB1 gene. Alternatively, the present disclosure provides methods for identifying a subject having a mutated CTNNB1 gene, comprising determining, in a sample from the subject, a spatial location of a nucleic acid or a protein of one or more genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof; and measuring the expression level of the one or more genes; wherein an increased expression level of the one or more genes relative to a control indicates that the subject has the mutated CTNNB1 gene.

In certain embodiments, presence of the nucleic acid or protein of the one or more genes in an immune excluded location indicates that the subject has a mutated CTNNB1 gene. In certain embodiments, the expression level of three or more genes is measured. In certain embodiments, the expression level of five or more genes is measured. In certain embodiments, the expression level of seven or more genes is measured. In certain embodiments, the expression level of nine or more genes is measured. In certain embodiments, the expression level of ten genes is measured. In certain embodiments, the expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SLC13A3, SP5, TCF7, and TNFRSF19 is measured. In certain embodiments, the expression level of thirteen genes is measured. In certain embodiments, the expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, and TNFRSF19 is measured.

In certain embodiments, an RNA expression level is measured. In certain embodiments, a protein expression level is measured. In certain embodiments, the sample comprises an organ tissue, whole blood, plasma, serum, whole blood cells, erythrocytes, lymphocytes, saliva, urine, stool, tears, sweat, sebum, nipple aspirate, ductal lavage, tumor exudates, synovial fluid, cerebrospinal fluid, lymph, fine needle aspirate, amniotic fluid, any other bodily fluid, exudate or secretory fluid, cell lysates, cellular secretion products, inflammation fluid, semen, or vaginal secretions. In certain embodiments, the organ tissue is a liver tissue. In certain embodiments, the organ tissue comprises a primary tumor tissue or a metastatic tumor tissue. In certain embodiments, the subject is human.

The present disclosure provides kits for performing a method for identifying a nonresponder subject, identifying a responder subject, treating a non-responder subject, treating a responder subject, or identifying a subject having a mutated CTNNB 1 disclosed herein. In certain embodiments, the kit comprises at least one set of primers comprising a forward primer and a reverse primer that bind to AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof. In certain embodiments, the kit comprises at least one antibody that binds to AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

Figures 1A-1C depict CTNNB 1 mutations occurring in patients with high expression of NRF2 and MET gene signature. Figure 1A shows hierarchical clustering of TCGA-LIHC patients (n=374) for 28-gene NRF2 signature identifies 100 cases (pink cluster) with NRF2-activation (NRF2-high). Figure IB shows hierarchical clustering of TCGA-LIHC patients (n=374) for 18- gene KAPOSI LIVER CANCER MET UP signature identifies 176 cases (pink and green clusters) with MET-activation (MET -high). Figure 1C shows lollipop plot depicting number of CTNNB 1 mutations within each exon of CTNNB 1 gene for the 18 patients with NRF2-/MET-high gene signature overlap and CTNNB 1 mutation.

Figures 2A-2E depict influence of Nrf2 and Met pathway activation on gene expression in HCC with and without CTNNB 1 -mutations. Figure 2A shows Venn diagram of 374 TCGA-LIHC patients categorized as Nrf2-high, Met-high, or CTNNB 1 -mutated, and their patient overlap. 18 (4.8% all HCC) of patients have overlap of CTNNB 1 -mutation, Nrf2-high, Met-high. Figure 2B shows top 10 pathways from Ingenuity pathway analysis (IP A) of differentially expressed genes comparing patients categorized as Nrf2 -/Met-high (n=54) versus normal (n=50). Figure 2C shows top 10 pathways from IP A of differentially expressed genes comparing patients categorized as CTNNB l-mutated/Nrf2 -/Met-high (n=18) versus normal (n=50). For both Figures 2A and 2B ranking of pathways based on -log(p-value) and activation/inhibition of pathway determined by z- score. Figure 2D shows pie chart depicting the distribution of exon mutations and stacked bar plot depicting the frequency of different exon 3 mutations in CTNNB l-mutated/Nrf2 -/Met-high patients. Figure 2E shows Kaplan-Meier curve showing decreased overall survival in CTNNB 1- mutated/Nrf2 -/Met-high (n=18) compared to other CTNNB 1 mutated cases (n=80). Log-rank test p-value: 0.104.

Figures 3A-3F depict the establishment of murine liver cancer models of mutated- CTNNB1 with or without mutated-NFE2L2 and hMET. Figure 3 A shows a schematic of the timeline of SB-HDTVi of S45Y-CTNNB1 with or without G31 A-NFE2L2 and hMET in 6-week- old FVB mice. Figure 3B shows Kaplan-Meier curve showing decreased survival of S45Y- CTNNBl-G31A-NFE2L2-hMET compared to G31A-NFE2L2-hMET mice. Figure 3C shows a bar graph showing significant increase in LW/BW ratio in S45Y-CTNNB1-G31 A-NFE2L2-hMET mice compared to wild-type FVB liver at the same timepoint sacrificed (*p<0.05). Figure 3D shows a bar graph showing significant increase in LW/BW ratio in G31A-NFE2L2-hMET mice compared to wild-type FVB liver at same timepoint sacrificed (**p<0.01). Figure 3E shows macroscopic images of the whole livers from S45Y-CTNNBl-G31A-NFE2L2-hMET and G31A- NFE2L2-hMET at 14-weeks (upper panel) and 5-week (lower panel) post-injection. Gross images indicate the presence of advanced liver tumors in each group. Figure 3F shows IHC of tumor foci positive for P-catenin targets glutamine synthetase (GS) and Cyclin DI in S45Y-CTNNB1-G31 A- NFE2L2-hMET (middle panel) compared to G31A-NFE2L2-hMET (lower panel).

Figures 4A and 4B depict the forced expression of S45Y-CTNNB1 ± G31A- NFE2L2+hMET in mice inducing HCC. Figure 4A shows H&E tiled image of representative mouse liver, and representative tiled images for Myc-tag (present on mutant CTNNB1 plasmid), Nqol (downstream marker of Nqol), and V5-tag (present on hMET plasmid) IHC for S45Y- CTNNB1 ± G31A-NFE2L2+hMET model. Figure 4B shows representative tiled images of H&E staining, Nqol (downstream marker of Nqol), and V5-tag (present on hMET plasmid) IHC for G31A-NFE2L2+hMET model.

Figure 5 shows the characterization of cell proliferative markers in all murine HCC models. Immunohistochemistry for Ki67 for wild-type liver, S45Y-CTNNBl+G31A-NFE2L2+hMET, S45Y-CTNNBl+hMET, S45Y-CTNNB1+G31A-NFE2L2, and G31A-NFE2L2+hMET. 10X objective magnification.

Figures 6A-6F depict transcriptomic analysis of multiple P-catenin-mutated and nonmutated models revealing differences in gene expression. Figure 6A shows a description of the samples used for transcriptomic analysis. Each mouse tumor model had 3 replicates sequenced. Figure 6B shows principal component analysis demonstrates clustering of wild-type distinct from the tumor models, with models of high Met activity clustering similarly and models of high Nrf2 activity clustering similarly. Figure 6C shows top 10 pathways from IPA of differentially expressed genes comparing S45Y-CTNNBl-G31A-NFE2L2-hMET to wild-type. Figure 6D shows top 10 pathways from IPA of differentially expressed genes comparing S45Y-CTNNBl-hMET to wildtype. Figure 6E shows top 10 pathways from IPA of differentially expressed genes comparing S45Y-CTNNB1-G31A-NFE2L2 to wild-type. Figure 6F shows top 10 pathways from IPA of differentially expressed genes comparing G31A-NFE2L2-hMET to wild-type. For Figures 6C-6F ranking of pathways based on -log(p-value) and activation/inhibition of pathway determined by z- score.

Figures 7A-7D depict differential gene expression analysis comparing each tumor model to wild-type normal FVB liver. Figure 7A shows volcano plot illustrating 2627 upregulated and 1950 downregulated genes comparing WT vs 0-N-M. Figure 7B shows volcano plot illustrating 1016 upregulated and 527 downregulated genes comparing WT vs 0-M. Figure 7C shows volcano plot illustrating 2405 upregulated and 1950 downregulated genes comparing WT vs 0-N. Figure 7D shows volcano plot illustrating 1167 upregulated and 697 downregulated genes comparing WT vs N-M.

Figures 8A and 8B depict common differentially expressed genes in mouse and human HCC with similar molecular perturbations. Figure 8A shows heatmap of common 2,377 differentially expressed genes in mouse WT vs 0-N-M and human normal liver (NL) vs CTNNB1- mutant/NRF2-/MET-high. Figure 8B shows heatmap of common 970 differentially expressed genes in mouse WT vs N-M and human NL vs NRF2-/MET-high.

Figures 9A-9D depict a comparison of preclinical HCC to clinical HCC with either CTNNB1 mutations and NRF2/MET activation, orNRF2/MET activation alone. Figure 9A shows differentially expressed genes overlapping in preclinical HCC model (P-N-M) and HCC subset with similar molecular perturbations, with high correlation (0.807 by Pearson correlation). Figure 9B shows differentially expressed genes overlapping in preclinical HCC model (N-M) and HCC subset with similar molecular perturbations, with high correlation (0.758 by Pearson correlation). For Figures 9A-9B, mouse gene expression is plotted on x-axis (MM) and human on y-axis (HG). Figure 9C shows plot of top common IPA pathways between mouse P-N-M and human HCC similar molecular perturbations. Figure 9D shows plot of top common IPA pathways between mouse N-M and human HCC similar molecular perturbations.

Figure 10A-10C depict differential gene expression analysis comparing each P-catenin- mutated tumor model to P-catenin-non-mutated tumor model. Figure 10A shows volcano plot showing differential gene expression and enrichment of mutated P-catenin gene signature (MBGS) in P-N-M vs N-M. Figure 10B shows volcano plot showing differential gene expression and enrichment of MBGS in P-M vs N-M. Figure 10C shows volcano plot showing differential gene expression and enrichment of MBGS in P-N vs N-M.

Figures 11A-11F depict transcriptomic analysis comparing P-catenin-mutated to nonmutated models identifying P-catenin specific gene expression signatures. Figure 11A shows common 95 up genes comparing the three P-catenin-mutated models to the G31 A-NFE2L2-hMET model. Figure 1 IB shows heatmap of 95 up genes showing high expression in each of the three P- catenin-mutated models compared to the G31A-NFE2L2-hMET model. Figure 11C shows the common 53 down genes comparing the three P-catenin-mutated models to the G31A-NFE2L2- hMET model. Figure 1 ID shows heatmap of 53 down genes show low expression in each of the three P-catenin-mutated models compared to the G31 A-NFE2L2-hMET model. Figure 1 IE shows top 20 pathways from IPA of the 95 common up genes. Figure 1 IF shows top 20 pathways from IPA of the 53 common down genes. For Figures 1 IE-1 IF, ranking of pathways is based on -logovalue). Gl : wild-type liver; G2: S45Y-CTNNBl-G31A-NFE2L2-hMET; G3: S45Y-CTNNB1- hMET; G4: S45Y-CTNNB1-G31A-NFE2L2; G5: G31A-NFE2L2-hMET.

Figures 12A-12C depict pathway analysis comparing each P-catenin-mutated tumor model to P-catenin-non-mutated tumor model. Figure 12A shows bar plot showing IPA analysis (top 25 pathways) on differentially expressed genes comparing P-N-M vs N-M. Figure 12B shows bar plot showing IPA analysis (top 25 pathways) on differentially expressed genes comparing P-M vs N-M. Figure 12C shows bar plot showing IPA analysis (top 25 pathways) on differentially expressed genes comparing 0-N vs N-M.

Figure 13 shows visualization in TCGA-LIHC of 85 human ortholog genes of the 95 murine genes that were enriched in P-catenin-mutated tumors. Heatmap of 374 TCGA-LIHC cases for the 85 mapped human orthologs of the 95 differentially expressed mouse genes.

Figures 14A-14C depict transcriptomic analysis of mouse-specific P-catenin activated genes in TCGA identifies mutated-P-catenin gene signature (MBGS). Figure 14A shows volcano plot of differentially expressed genes comparing CTNNB1 -mutated (n=98) vs CTNNB1 -wild-type (n=276) TCG-LIHC cases using the 85 human orthologs of the 95 mouse genes. Figure 14B shows heatmap of the 13 differentially expressed in TCGA-LIHC showing enrichment of the genes in CTNNB1 -mutated cases. Figure 14C shows a boxplot of the expression of each individual gene in the 13 -gene panel showing enrichment in CTNNB1 -mutated compared to CTNNB1 -wild-type and normal tumor liver.

Figures 15A-15H depict MBGS classifying CTNNB1 -mutated HCC with high accuracy. Figure 15A shows boxplot of 13-gene MBGS stratified by CTNNB1 -mutated (n=98), CTNNB1- wild-type (n=276), and normal tumor liver (n=50) in TGCA-LIHC. Figure 15B shows boxplot of 10-gene MBGS stratified by CTNNB1 -mutated (n=98), CTNNB1 -wild-type (n=276), and normal tumor liver (n=50) in TGCA-LIHC. Figure 15C shows AUC/ROC curve showing high sensitivity and specificity to classify CTNNB1 -mutated cases with 13-gene MBGS of 0.91 and 10-gene MBGS of 0.90 in TCGA-LIHC. Figure 15D shows boxplot of 13-gene MBGS stratified by CTNNB1 -mutated (n=118), CTNNB1 -wild-type (n=280), and normal tumor liver (n=31) in French cohort. Figure 15E shows boxplot of 10-gene MBGS stratified by CTNNB1 -mutated (n=118), CTNNB1 -wild-type (n=280), and normal tumor liver (n=31) in French cohort. Figure 15F shows AUC/ROC curve showing high sensitivity and specificity to classify CTNNB1 -mutated cases with 13-gene MBGS of 0.95 and 10-gene MBGS of0.94 in French cohort. Figure 15G shows stratification of 10-gene MBGS by HCC Hoshida G1-G6 subgroups showing enrichment in G5/G6 groups. Figure 15H shows stratification of 13-gene MBGS by HCC Hoshida G1-G6 subgroups showing enrichment in G5/G6 groups.

Figures 16A and 16B depict MBGS expression across hepatocellular adenoma, hepatoblastoma, and HCC with different exon mutations. Figure 16A shows boxplot of 10-gene MBGS in French cohort of hepatocellular adenoma, hepatoblastoma, and HCC with exon 3, exon 7, and APC biallelic mutations. Figure 16B shows boxplot of 13-gene MBGS in a French cohort of hepatocellular adenoma, hepatoblastoma, and HCC with exon 3, exon 7, and APC biallelic mutations.

Figures 17A and 17B depict MBGS’s predictive ability in pan-cancer atlas and melanoma. Figure 17A shows bar plot of different tumor types in ICGC/TCGA cases across 2,565 patients of multiple tumor types, of which 178 had CTNNB1 mutations. Image from cBioPortal of ICGC/TCGA patient cohort. Figure 17B shows AUC/ROC curve for prediction of CTNNB1 mutation in pan-cancer setting with AUC of 0.703 for 10-gene MBGS.

Figures 18A-18G depict MBGS expression in small HCC immunotherapy cohort. Figure 18A shows UMAP of responders and non-responders in GSE202069 demonstrating separation of responders and non-responders in terms of gene expression (n=8 responders and n=9 non- responders). Figure 18B shows volcano plot of differentially expressed genes comparing responders and non-responders demonstrating enrichment of MBGS in downregulated genes in responders. Figure 18C shows boxplots of all 10 genes in 10-gene MBGS stratified by responders and non-responders in GSE202069. Figure 18D shows boxplot comparing expression of 10-gene MBGS in responders and non-responders. Figure 18E shows AUC/ROC curve demonstrating AUC of 0.78 using 10-gene MBGS to classify immunotherapy resistance in this cohort. Figure 18F shows boxplot comparing expression of gene signature designated as CHIANG LIVER CANCER SUBCLASS CTNNBI UP in responders and non-responders. Figure 18G shows AUC/ROC curve demonstrating AUC of 0.79 using gene signature designated as CHIANG LIVER CANCER SUBCLASS CTNNBI UP to classify immunotherapy resistance in this cohort.

Figures 19A-19C depict prediction of immunotherapy resistance using previously published gene signatures in small HCC immunotherapy cohort. Figure 19A shows T cell- inflamed gene expression profile. Figure 19B shows IFNy response signature. Figure 19C shows tertiary lymphoid structure (TLS) signature Boxplots and AUC/ROC curves for GSE202069 to predict immunotherapy resistance (ROC AUC: 0.68, 0.71, 0.72, respectively).

Figures 20A-20E depict MBGS predicting immunotherapy resistance in IMbravel50 cohort. Figure 20A shows correlation of 10-gene and 13-gene MBGS in IMbravel50 cohort. Figure 20B shows box plot of expression of 10-gene MBGS in CTNNB1 wild-type and mutant cases in IMbravel50 cohort. Figure 20C shows box plot of expression of 13-gene MBGS in CTNNB1 wild-type and mutant cases in IMbravel50 cohort. Figure 20D shows Kaplan-Meier curve for overall survival (left) and progression-free survival (right) demonstrating poor response with high MBGS expression. Figure 20E shows Kaplan-Meier curve for overall survival (left) and progression-free survival (right) demonstrating improved response with low MBGS expression. Log-rank test was used to determine differences in mean survival time. ***p<0.001.

Figures 21A-21C depict NRF2/MET-high expression influences survival in CTNNB1- mutated patients, rather than CTNNB1 -mutation influencing survival outcome. Figure 21 A shows Kaplan-Meier curve comparing CTNNB I -mut/NRF2-high/MET-high (n=18) vs CTNNB1- WT/NRF2-high/MET-high (n=36). Log-rank p-value is p=0.752. Figure 21B shows Kaplan-Meier curve comparing CTNNBl-mut/NRF2-high/MET-high (n=18) vs CTNNB I -WT/NRF2- high/MET-low (n=17). Log-rank p-value is p=0.514. Figure 21C shows Kaplan-Meier curve comparing CTNNBl-mut/NRF2-high/MET-high (n=18) vs CTNNBl-WT/NRF2-low/MET-high (n=23). Log-rank p-value is p=0.216. Additionally, Log-rank p-value is indicated on the Kaplan- Meier curve of 5-year overall survival. Levels of significance: *p<0.05, **p<0.001, ***p<0.0001.

Figure 22 depicts MBGS expression stratified by complete/partial response (CR/PR), stable disease (SD), or progressive disease (PD) defined by mRECIST criteria in each arm. Higher MBGS expression correlated well with sorafenib response. In atezo/bev arm, One-way ANOVA p=0.27. In sorafenib arm, One-way ANOVA p=0.18. Individual values per patient are depicted with bold line in middle representing the median and outside boxes showing inner quartile ranges; no statistical test was used, but depicted this way for visual representation across the different subclasses. Levels of significance: *p<0.05, **p<0.001, ***p<0.0001.

Figures 23A-23D show spatial mapping of molecular gene signatures reveals MBGS-hot tumors are immune excluded. Figure 23 A shows representative H&E and spatial gene expression plots of Boyault molecular subclassification and MBGS on same tissue section for a MBGS-hot and MBGS-low tumor. MBGS overlaps with Boyault G5/G6, but is exclusive to Boyault G1/G2 tumors. Figure 23B shows representative H&E and spatial gene expression plots of Lachenmayer Wnt signatures and MBGS on same tissue section. Spatial mapping of MBGS highlights tumor nodules more clearly than previously published Wnt-CTNNBl signatures. Figure 23C shows representative H&E and spatial gene expression plots of Sia immune signatures and MBGS on same tissue section. MBGS-hot tumors are immune excluded inside tumor nodules, but can have an inflamed stroma. For Figures 23 A-23C, relative expression module scores are depicted with red being higher expression and blue being lower expression. Figure 23D shows diagnostic and therapeutic proposed work-up algorithm using MBGS as a companion diagnostic. Patients which are MBGS-high can benefit from anti-P-catenin therapies + ICIs.

Figures 24A-24H illustrate ability of previously published molecular subclass signatures to predict CTNNB1 mutational status in TCGA-LIHC dataset. ROC AUC and composite average normalized expression value of the gene signature scores for Boyault G5/G6 (Figures 24A-24B), Chiang CTNNB1 (Figures 24C-24D), Hoshida S3 (Figures 24E-24F), and Lachenmayer Wnt- CTNNBl (Figures 24G-24H). The TCGA-LIHC cohort has CTNNB1 -mutated (n=98), CTNNB1- wild-type (n=276), and normal tumor liver (n=50) samples. For Figures 24B, 24D, 24F, and 24H, individual values per patient are depicted with bold line in middle representing the median and outside boxes showing inner quartile ranges. One-way ANOVA p-value for Figure 24B is ***p<2.22e'¹⁶. One-way ANOVA p-value for Figure 24D is ***p<2.22e'¹⁶. One-way ANOVA p- value for Figure 24F is ***p<2.22e'¹⁶. One-way ANOVA p-value for Figure 24H is ***p<2.22e' ¹⁶. Levels of significance: *p<0.05, **p<0.001, ***p<0.0001.

Figures 25A-25F illustrate ability of previously published Wnt signatures to predict CTNNB1 mutational status in TCGA-LIHC dataset. ROC AUC and composite average normalized expression values for each of the different gene signatures specifically for the BIOCARTA WNT PATHWAY (Figures 25A-25B), KEGG WNT SIGNALING PATHWAY (Figures 25C-25D), and REACTOME SIGNALING BY WNT IN CANCER (Figures 25E- 25F) signatures. The TCGA-LIHC cohort has CTNNB1 -mutated (n=98), CTNNB1 -wild-type (n=276), and normal tumor liver (n=50) samples. For Figures 25B, 25D, and 25F, individual values per patient are depicted with bold line in middle representing the median and outside boxes showing inner quartile ranges. One-way ANOVA p-value for Figures 25B is ***p<1.23e'⁵. Oneway ANOVA p-value for Figures 25D is ***p<3.32e'⁷. One-way ANOVA p-value for Figures 25F is ***p<2.49e'⁵. Levels of significance: *p<0.05, **p<0.001, ***p<0.0001.

Figure 26 shows heatmap overlapping all molecular subclasses, CTNNB1 -mutated patients, and MBGS expression indicating MBGS is specific to CTNNB1 mutations. Normalized gene expression scaled based on z-score is shown.

Figures 27A and 27B depict high MBGS expression associated with response to sorafenib. Figure 27A shows MBGS high patients had limited overall (left) and progression-free survival (right) (OS/PFS) benefit comparing treatment groups. Log-rank p-value for OS is p= 0.0542. Logrank p-value for OS is p= 0.404. Figure 27B shows MBGS low patients had improved OS and PFS on atezolizumab/bevacizumab versus sorafenib. Log-rank p-value for OS is *p= 0.0329. Log-rank p-value for OS is *p= 0.0293. MBGS high/low was determined based on median expression value. Log-rank test was used to determine differences in mean survival time. The Kaplan-Meier curves shown here for Figures 27A and 27B are split apart from the Kaplan-Meier curves shown in Figures 20D and 20E to illustrate the specific differences between indicated expression groups and treatment arms. Levels of significance: *p<0.05, **p<0.001, ***p<0.0001.

Figure 28 shows expression of Boyault molecular subclassification onto spatial transcriptomic tissue section compared to MBGS. 11 (12 total slides) individual patient slides with H&E are shown with expression of various subclassification gene signatures shown with each spot. All the slides are normalized to the same expression scale. Relative expression module scores are depicted with red being higher expression and blue being lower expression. Pt 1 and Pt 8 slides are shown in Figure 23 A, but are depicted also here again to show as part of the total cohort analyzed.

Figure 29 shows expression of Chiang molecular subclassification onto spatial transcriptomic tissue section compared to MBGS. 11 (12 total slides) patient slides with H&E are shown with expression of various subclassification gene signatures shown with each spot. All the slides are normalized to the same expression scale. Relative expression module scores are depicted with red being higher expression and blue being lower expression.

Figure 30 shows expression of Hoshida molecular subclassification onto spatial transcriptomic tissue section compared to MBGS. 11 (12 total slides) patient slides with H&E are shown with expression of various subclassification gene signatures shown with each spot. All the slides are normalized to the same expression scale. Relative expression module scores are depicted with red being higher expression and blue being lower expression.

Figure 31 shows expression of Lachenmayer Wnt molecular subclassification onto spatial transcriptomic tissue section compared to MBGS. 11 (12 total slides) patient slides with H&E are shown with expression of various sub classification gene signatures shown with each spot. All the slides are normalized to the same expression scale. Relative expression module scores are depicted with red being higher expression and blue being lower expression. Pt 3 and Pt 11C slides are shown in Figure 23B, but are depicted also here again to show as part of the total cohort analyzed.

Figure 32 shows expression of Sia immune subclass molecular subclassification onto spatial transcriptomic tissue section compared to MBGS. 11 (12 total slides) patient slides with H&E are shown with expression of various sub classification gene signatures shown with each spot. All the slides are normalized to the same expression scale. Relative expression module scores are depicted with red being higher expression and blue being lower expression. Pt 1 and Pt 3 slides are shown in Figure 23 C, but are depicted also here again to show as part of the total cohort analyzed.

DETAILED DESCRIPTION OF THE INVENTION

The present disclosure is based, in part, on the observation of a genetic profile in subjects that do not respond to immunotherapy (e.g., non-responder subjects). The present disclosure shows that non-responder subjects are characterized by a mutated CTNNB 1 gene. The present disclosure provides a diagnostic test for identifying non-responder subjects to thereby guiding clinical decision-making. Further, the present disclosure relates to methods of treating cancer.

Non-limiting embodiments of the present disclosure are described by the present specification and Examples. For purposes of clarity of disclosure and not by way of limitation, the detailed description is divided into the following subsections:

1. Definitions;

2. CTNNB 1 gene;

3. Biomarkers and Diagnostic Methods;

4. Methods of Treatment; and

5. Kits. 1. Definitions

The terms used in this specification generally have their ordinary meanings in the art, within the context of this disclosure and in the specific context where each term is used. Certain terms are discussed below, or elsewhere in the specification, to provide additional guidance to the practitioner in describing the compositions and methods of the disclosure and how to make and use them.

As used herein, the use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims and/or the specification may mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one.” Still further, the terms “having,” “including,” “containing” and “comprising” are interchangeable and one of skill in the art is cognizant that these terms are open-ended terms.

The terms “comprise(s),” “include(s),” “having,” “has,” “can,” “contain(s),” and variants thereof, as used herein, are intended to be open-ended transitional phrases, terms, or words that do not preclude the possibility of additional acts or structures. The present disclosure also contemplates other embodiments “comprising,” “consisting of’, and “consisting essentially of,” the embodiments or elements presented herein, whether explicitly set forth or not.

The term “about” or “approximately” means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, “about” can mean within 3 or more than 3 standard deviations, per the practice in the art. Alternatively, “about” can mean a range of up to 20%, preferably up to 10%, more preferably up to 5%, and more preferably still up to 1% of a given value. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude, preferably within 5-fold, and more preferably within 2-fold, of a value.

An “individual” or “subject” herein is a vertebrate, such as a human or non-human animal, for example, a mammal. Mammals include, but are not limited to, humans, non-human primates, farm animals, sport animals, rodents, and pets. Non-limiting examples of non-human animal subjects include rodents such as mice, rats, hamsters, and guinea pigs; rabbits; dogs; cats; sheep; pigs; goats; cattle; horses; and non-human primates such as apes and monkeys.

As used herein, the term “disease” refers to any condition or disorder that damages or interferes with the normal function of a cell, tissue, or organ.

An “effective amount” or “therapeutically effective amount” is an amount effective, at dosages and for periods of time necessary, that produces a desired effect, e.g., the desired therapeutic or prophylactic result. In certain embodiments, an effective amount can be formulated and/or administered in a single dose. In certain embodiments, an effective amount can be formulated and/or administered in a plurality of doses, for example, as part of a dosing regimen.

As used herein, the term “treating” or “treatment” refers to clinical intervention in an attempt to alter the disease course of the individual or cell being treated, and can be performed either for prophylaxis or during the course of clinical pathology. Therapeutic effects of treatment include, without limitation, preventing occurrence or recurrence of disease, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, preventing cancer, decreasing the rate of disease progression, amelioration or palliation of the disease state, and remission or improved prognosis.

By “preventing” progression of a disease or disorder, a treatment can prevent deterioration due to a disorder (e.g., a cancer) in an affected or diagnosed subject or a subject suspected of having the disorder, but also a treatment can prevent the onset of the disorder or a symptom of the disorder in a subject at risk for the disorder or suspected of having the disorder.

As used herein, the term “anti-cancer effect” refers to one or more of a reduction in aggregate cancer cell mass, a reduction in cancer cell growth rate, a reduction in cancer progression, a reduction in cancer cell proliferation, a reduction in tumor mass, a reduction in tumor volume, a reduction in tumor cell proliferation, a reduction in tumor growth rate and/or a reduction in tumor metastasis. In certain embodiments, an anti-cancer effect can refer to a complete response, a partial response, a stable disease (without progression or relapse), a response with a later relapse, or progression-free survival in a subject diagnosed with cancer.

As used herein, the term “immunotherapy” refers to any treatment that modulates a subject’s immune system including, but not-limited to, use of common immune checkpoint inhibitors such as antibodies against PD1, PD-L1, CTLA4, and the like. In certain embodiments, immunotherapy can elicit or activate an immune response against tumor tissues or cells in order to increase tumor killing. In certain embodiments, immunotherapy can include cell-based immunotherapy or chemical compounds and/or biomolecules (e.g., antibodies, antigens, interleukins, cytokines, or combinations thereof), that modulate a subject’s immune system.

By “increase” is meant to alter positively by at least about 5%. A positive alteration can be an increase of about 5%, about 10%, about 25%, about 30%, about 50%, about 75%, about 100% or more.

By “reduce” is meant to alter negatively by at least about 5%. A negative alteration can be a decrease of about 5%, about 10%, about 25%, about 30%, about 50%, about 75% or more, even by about 100%.

The terms “nucleic acid sequence” and “polynucleotide,” as used herein, refer to a single or double-stranded covalently-linked sequence of nucleotides in which the 3’ and 5’ ends on each nucleotide are joined by phosphodiester bonds. The polynucleotide can include deoxyribonucleotide bases or ribonucleotide bases, and can be manufactured synthetically in vitro or isolated from natural sources.

The terms “polypeptide,” “peptide,” “amino acid sequence” and “protein,” used interchangeably herein, refer to a molecule formed from the linking of at least two amino acids. The link between one amino acid residue and the next is an amide bond and is sometimes referred to as a peptide bond. A polypeptide can be obtained by a suitable method known in the art, including isolation from natural sources, expression in a recombinant expression system, chemical synthesis, or enzymatic synthesis. The terms can apply to amino acid polymers in which one or more amino acid residue is an artificial chemical mimetic of a corresponding naturally occurring amino acid, as well as to naturally occurring amino acid polymers and non-naturally occurring amino acid polymers.

The term “gene” refers to a region of a genomic sequence associated with regulatory regions, transcribed regions, and/or other functional sequence regions. A gene typically includes a coding sequence encoding a gene product, such as an RNA molecule or a polypeptide.

As used herein, the term “mutation” refers to a mutation in an amino acid sequence or in a nucleic acid sequence. In certain embodiments, a mutation in an amino acid sequence can be a substitution (replacement), an insertion (addition), or a deletion (truncation) of at least one amino acid in the amino acid sequence. In certain embodiments, a mutation in a nucleic acid sequence can be a substitution (replacement), an insertion (addition), or a deletion (truncation) of at least nucleotide of the nucleic acid sequence.

As used herein, the term “biological sample” or “sample” refers to any sample of biological material obtained from a subject, e.g., a human subject. In certain non-limiting embodiments, the sample can be organ tissue (e.g., primary or metastatic tumor tissue), whole blood, plasma, serum, whole blood cells, red blood cells, white blood cells (e.g., peripheral blood mononuclear cells), saliva, urine, stool (feces), tears, sweat, sebum, nipple aspirate, ductal lavage, tumor exudates, synovial fluid, cerebrospinal fluid, lymph, fine needle aspirate, amniotic fluid, any other bodily fluid, exudate or secretory fluid, cell lysates, cellular secretion products, inflammation fluid, semen, and vaginal secretions. In certain embodiments, the sample can be obtained from fresh, frozen, or paraffin-embedded surgical samples or biopsies of an organ or tissue. In certain nonlimiting embodiments, the sample is obtained from a tissue or organ having a cancer, a tissue or organ suspected to have a cancer, a tumor microenvironment, or tumor-infiltrating immune cells. In certain embodiments, the sample is obtained from a primary tumor. In certain embodiments, the sample is obtained from a metastasis. By “control” or “reference” is meant a standard of comparison. In certain embodiments, the term control refers to an expression level of a gene detected in a biological sample of a subject having a wild-type CTNNB1 gene. For example, a control can be the level of a biomarker from a healthy individual without cancer. In certain embodiments, the expression levels can also be normalized, for example, to the expression levels of housekeeping genes, such as glyceraldehyde- 3-phosphate-dehydrogenase (GAPDH) and/or P-glucoronidase (GUSB), or to the expression levels of all genes in the sample tested. In certain embodiments, the control value can be a predetermined, average value obtained from a relevant general population (e.g., a population having a wild-type CTNNB1 gene).

As used herein, “a functional fragment” of a molecule or polypeptide includes a fragment of the molecule or polypeptide that retains at least about 80%, at least about 85%, at least about 90%, at least about 95%, or at least about 100% of the primary function of the molecule or polypeptide.

As used herein, the term “substantially identical” or “substantially homologous” refers to a polypeptide or a nucleic acid molecule exhibiting at least about 50% identical or homologous to a reference amino acid sequence (for example, any of the amino acid sequences described herein) or a reference nucleic acid sequence (for example, any of the nucleic acid sequences described herein). In certain embodiments, such a sequence is at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 99%, or at least about 100% identical or homologous to the amino acid sequence or the nucleic acid sequence used for comparison.

As used herein, “conservative” amino acid substitutions are ones in which the amino acid residue is replaced with an amino acid within the same group. For example, amino acids can be classified by charge: positively-charged amino acids include lysine, arginine, histidine, negatively-charged amino acids include aspartic acid, glutamic acid, neutral charge amino acids include alanine, asparagine, cysteine, glutamine, glycine, isoleucine, leucine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, and valine. Amino acids can also be classified by polarity: polar amino acids include arginine (basic polar), asparagine, aspartic acid (acidic polar), glutamic acid (acidic polar), glutamine, histidine (basic polar), lysine (basic polar), serine, threonine, and tyrosine; non-polar amino acids include alanine, cysteine, glycine, isoleucine, leucine, methionine, phenylalanine, proline, tryptophan, and valine. In certain embodiments, no more than one, no more than two, no more than three, no more than four, no more than five residues within a specified sequence are altered. Exemplary conservative amino acid substitutions are shown in Table 1 below.

Table 1

As used herein, the percent homology between two amino acid sequences is equivalent to the percent identity between the two sequences. The percent identity between the two sequences is a function of the number of identical positions shared by the sequences (z.e., % homology = # of identical positions/total # of positions x 100), taking into account the number of gaps, and the length of each gap, which need to be introduced for optimal alignment of the two sequences. The comparison of sequences and determination of percent identity between two sequences can be accomplished using a mathematical algorithm.

The percent homology between two amino acid sequences can be determined using the algorithm of E. Meyers and W. Miller (Comput. Appl. Biosci., 4: 11-17 (1988)) which has been incorporated into the ALIGN program (version 2.0), using a PAM120 weight residue table, a gap length penalty of 12 and a gap penalty of 4. In addition, the percent homology between two amino acid sequences can be determined using the Needleman and Wunsch (J. Mol. Biol. 48:444-453 (1970)) algorithm which has been incorporated into the GAP program in the GCG software package (available at www.gcg.com), using either a Blossum 62 matrix or a PAM250 matrix, and a gap weight of 16, 14, 12, 10, 8, 6, or 4 and a length weight of 1, 2, 3, 4, 5, or 6.

As used herein, the terms “antibody” and “antigen-binding fragment” refer to a polypeptide comprising at least a light chain or heavy chain immunoglobulin variable region which specifically recognizes and specifically binds an epitope of an antigen (e.g., amphiregulin) or a fragment thereof. Antibodies are composed of a heavy and a light chain, each of which has a variable region, termed the variable heavy (VH) region and the variable light (VL) region. Together, the VH region and the VL region are responsible for binding the antigen recognized by the antibody. Antibodies include intact immunoglobulins and variants thereof. Functional fragments (antigen-binding fragments) of antibodies, that specifically bind an antigen (e.g., amphiregulin) are well known in the art, such as Fab fragments, Fab’ fragments, F(ab)’2 fragments, single chain Fv proteins (“scFv”), and disulfide stabilized Fv proteins (“dsFv”) that specifically bind the target antigen. A scFv protein is a fusion protein in which a light chain variable region of an immunoglobulin and a heavy chain variable region of an immunoglobulin are bound by a linker. In dsFvs, the chains have been mutated to introduce a disulfide bond to stabilize the association of the chains. In certain embodiments, the term also includes genetically engineered forms such as chimeric antibodies (for example, humanized murine antibodies), heteroconjugate antibodies (such as, bispecific antibodies). A naturally occurring immunoglobulin has heavy (H) chains and light (L) chains interconnected by disulfide bonds. There are two types of light chain, lambda (X) and kappa (K). There are five main heavy chain classes (or isotypes) that determine the functional activity of an antibody molecule: IgM, IgD, IgG, IgA and IgE. Each heavy and light chain contains a constant region and a variable region. Light and heavy chain variable regions contain four (4) regions (e.g., FR1, FR2, FR3, and FR4) interrupted by three hypervariable regions, also called “complementarity-determining regions” or “CDR .” The extent of the framework region and CDRs have been defined by designation systems known in the art such as Kabat, Clothia, IMGT, etc. The CDRs are primarily responsible for binding to an epitope of an antigen.

The term “dosage” is intended to encompass a formulation expressed in terms of total amounts for a given timeframe, for example, as pg/kg/hr, pg/kg/day, mg/kg/day, or mg/kg/hr. The dosage is the amount of an ingredient administered in accordance with a particular dosage regimen. A “dose” is an amount of an agent administered to a mammal in a unit volume or mass, e.g., an absolute unit dose expressed in mg of the agent. The dose depends on the concentration of the agent in the formulation, e.g., in moles per liter (M), mass per volume (m/v), or mass per mass (m/m). The two terms are closely related, as a particular dosage results from the regimen of administration of a dose or doses of the formulation. The particular meaning, in any case, will be apparent from the context. Ranges provided herein are understood to be shorthand for all of the values within the range. For example, a range of 1 to 50 is understood to include any number, combination of numbers, or sub-range from the group consisting 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 as well as all intervening decimal values between the aforementioned integers such as, for example, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, and 1.9. Ranges disclosed herein, for example, “between about X and about Y” are, unless specified otherwise, inclusive of range limits about X and about Y as well as X and Y. With respect to sub-ranges, “nested sub-ranges” that extend from either endpoint of the range are specifically contemplated. For example, a nested sub-range of an exemplary range of 1 to 50 can include 1 to 10, 1 to 20, 1 to 30, and 1 to 40 in one direction, or 50 to 40, 50 to 30, 50 to 20, and 50 to 10 in the other direction.

The term “endogenous,” as used herein, refers to a nucleic acid molecule or polypeptide that is normally expressed in a cell or tissue.

The term “exogenous,” as used herein, refers to a nucleic acid molecule or polypeptide that is not endogenously present in a cell. The term “exogenous” would therefore encompass any recombinant nucleic acid molecule or polypeptide expressed in a cell, such as foreign, heterologous, and over-expressed nucleic acid molecules and polypeptides. By “exogenous” nucleic acid is meant a nucleic acid not present in a native wild-type cell; for example, an exogenous nucleic acid can vary from an endogenous counterpart by sequence, by position/location, or both. For clarity, an exogenous nucleic acid can have the same or different sequence relative to its native endogenous counterpart; it can be introduced by genetic engineering into the cell itself or a progenitor thereof, and can optionally be linked to alternative control sequences, such as a non-native promoter or secretory sequence.

2. CTNNB1 gene

The present disclosure is based, in part, on the observation that response to immunotherapy is influenced by the Wnt/p-catenin pathway and, in particular, by mutations of CTNNB1. The CTNNB1 gene (also known as armadillo; beta-catenin; catenin (cadherin-associated protein), beta 1; catenin (cadherin-associated protein), beta 1, 88kDa; catenin beta-1; CTNB1 HUMAN; or CTNNB) encodes for P-catenin, which is present in many types of cells and tissues and is mainly found at junctions that connect neighboring cells (e.g., adherens junctions). P-catenin plays an important role in cell adhesion processes and in communication between cells.

Among its functions, P-catenin is involved in the Wnt signaling pathway. Upon its activation, P-catenin translocates into the nucleus to regulate transcription of multiple genes of the Wnt signaling pathway which promote proliferation and differentiation. P-Catenin is an important proto-oncogene since mutations can be found in cancers including, for example and without any limitation, primary hepatocellular carcinoma, colorectal cancer, ovarian carcinoma, breast cancer, lung cancer and glioblastoma. In pathophysiologic setting, loss-of-function mutations significantly reduce the ubiquitinylation and degradation of P- catenin which can translocate to the nucleus without any external stimulus and continuously drive transcription of its target genes. Notably, said increased nuclear P-catenin levels have been observed in basal cell carcinoma (BCC), head and neck squamous cell carcinoma (HNSCC), prostate cancer (CaP), pilomatrixoma (PTR), medulloblastoma (MDB), hepatoblastoma (HB), hepatocellular adenomas (HCA) and hepatocellular cancer (HCC).

Additional information regarding P-catenin and its role in cancer can be found in Dev et al., Bioengineered 14.1 (2023): 2251696; Yu et al., Signal Transduction and Targeted Therapy 6.1 (2021): 307; and Park and Kim, Cells 12.8 (2023): 1110, the contents of each of which are incorporated by reference in their entireties.

3. Biomarkers and Diagnostic Methods

Current clinical approach for patients having cancer and receiving an immunotherapy cannot determine if the patients would respond or non-response to said immunotherapy.

The present disclosure provides methods that can be used in a new clinical approach. It will be clear to the skilled in the art that the methods disclosed herein allow to a significant improvement of patient’s clinical management and outcome as well as a reduction of costs for analysis of the clinical status.

In certain embodiments, the present disclosure provides methods for identifying a subject having a mutated CTNNB1 gene. In certain embodiments, the methods include measuring an expression level of AXIN2 (Axis Inhibition Protein 2) gene. In certain embodiments, the methods include measuring an expression level of GLUL (Glutamate Ammonia Ligase) gene. In certain embodiments, the methods include measuring an expression level of LGR5 (Leucine-rich repeatcontaining G-protein coupled receptor 5) gene. In certain embodiments, the methods include measuring an expression level of NKD1 (Naked Cuticle 1) gene. In certain embodiments, the methods include measuring an expression level of NOTUM (Palmitoleoyl-protein carboxyl esterase NOTUM) gene. In certain embodiments, the methods include measuring an expression level of RHBG (Ammonium transporter Rh type B) gene. In certain embodiments, the methods include measuring an expression level of SBSPON (Somatomedin-B and thrombospondin type-1 domain-containing protein) gene. In certain embodiments, the methods include measuring an expression level of SLC13A3 (Na(+)/dicarboxylate cotransporter 3) gene. In certain embodiments, the methods include measuring an expression level of SLC1A2 (Excitatory amino acid transporter 2) gene. In certain embodiments, the methods include measuring an expression level of SP5 (Transcription factor Sp5) gene. In certain embodiments, the methods include measuring an expression level of TCF7 (Transcription factor 7) gene. In certain embodiments, the methods include measuring an expression level of TEDDM1 (Transmembrane epidi dymal protein 1) gene. In certain embodiments, the methods include measuring an expression level of TNFRSF19 (Tumor necrosis factor receptor superfamily member 19) gene.

In certain embodiments, the methods include measuring an expression level of two genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of three genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of four genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of five genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of six genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of seven genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of eight genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1 A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of nine genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of ten genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of eleven genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of twelve genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19.

In certain embodiments, the methods include measuring an expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, and TNFRSF19. In certain embodiments, the methods include measuring an expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SLC13A3, SP5, TCF7, and TNFRSF19.

In certain embodiments, an increased expression level of AXIN2 relative to a control indicates that the subject has the mutated CTNNB1 gene. In certain embodiments, an increased expression level of GLUL relative to a control indicates that the subject has the mutated CTNNB1 gene. In certain embodiments, an increased expression level of LGR5 relative to a control indicates that the subject has the mutated CTNNB1 gene. In certain embodiments, an increased expression level of NKD1 relative to a control indicates that the subject has the mutated CTNNB1 gene. In certain embodiments, an increased expression level of NOTUM relative to a control indicates that the subject has the mutated CTNNB 1 gene. In certain embodiments, an increased expression level of RHBG relative to a control indicates that the subject has the mutated CTNNB 1 gene. In certain embodiments, an increased expression level of SBSPON relative to a control indicates that the subject has the mutated CTNNB 1 gene. In certain embodiments, an increased expression level of SLC13 A3 relative to a control indicates that the subject has the mutated CTNNB 1 gene. In certain embodiments, an increased expression level of SLC1A2 relative to a control indicates that the subject has the mutated CTNNB 1 gene. In certain embodiments, an increased expression level of SP5 relative to a control indicates that the subject has the mutated CTNNB 1 gene. In certain embodiments, an increased expression level of TCF7 relative to a control indicates that the subject has the mutated CTNNB 1 gene. In certain embodiments, an increased expression level of TEDDM1 relative to a control indicates that the subj ect has the mutated CTNNB 1 gene. In certain embodiments, an increased expression level of TNFRSF19 relative to a control indicates that the subject has the mutated CTNNB 1 gene.

In certain embodiments, an increased expression level of two genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject has the mutated CTNNB 1 gene. In certain embodiments, the methods include measuring an expression level of three genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject has the mutated CTNNB 1 gene. In certain embodiments, an increased expression level of four genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject has the mutated CTNNB 1 gene. In certain embodiments, an increased expression level of five genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject has the mutated CTNNB 1 gene. In certain embodiments, an increased expression level of six genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject has the mutated CTNNB1 gene. In certain embodiments, an increased expression level of seven genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject has the mutated CTNNB1 gene. In certain embodiments, an increased expression level of eight genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject has the mutated CTNNB1 gene. In certain embodiments, an increased expression level of nine genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject has the mutated CTNNB1 gene. In certain embodiments, an increased expression level of ten genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject has the mutated CTNNB1 gene. In certain embodiments, an increased expression level of eleven genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject has the mutated CTNNB1 gene. In certain embodiments, an increased expression level of twelve genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject has the mutated CTNNB1 gene.

In certain embodiments, an increased expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, and TNFRSF19 relative to a control indicates that the subject has the mutated CTNNB1 gene. In certain embodiments, an increased expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SLC13A3, SP5, TCF7, and TNFRSF19 relative to a control indicates that the subject has the mutated CTNNB1 gene.

In certain embodiments, the present disclosure provides methods for identifying a nonresponder subject. As used herein, the term “non-responder” refers to subjects that do not respond to a treatment or progress/recur after an initial response. Non-responder subjects do not show an anti-cancer effect after receiving a treatment. In certain embodiments, the non-responder subjects have a de novo or primary resistance to an immunotherapy. In certain embodiments, the non- responder subjects have an acquired or secondary resistance to an immunotherapy. In certain embodiments, the non-responder subjects have a de novo or primary resistance to atezolizumab in combination with or without bevacizumab. In certain embodiments, the non-responder subjects have an acquired or secondary resistance to atezolizumab in combination with and without bevacizumab. In certain embodiments, the non-responder subjects have a de novo or primary resistance to durvalumab in combination with or without tremilimumab. In certain embodiments, the non-responder subjects have an acquired or secondary resistance to durvalumab in combination with or without tremilimumab. In certain embodiments, the non-responder subjects have a de novo or primary resistance to nivolumab. In certain embodiments, the non-responder subjects have an acquired or secondary resistance to nivolumab. In certain embodiments, the non-responder subjects have a de novo or primary resistance to pembrolizumab. In certain embodiments, the non-responder subjects have an acquired or secondary resistance to pembrolizumab. As such any of these immune checkpoint inhibitors have significant unintended adverse effects and need to be given to patients with only high probability of response rather than all comers. Use of MBGS would exclude patients to be subjected to these class of immune checkpoint inhibitors and prevent adverse effects in cases not expected to respond to these therapies.

In certain embodiments, the methods include measuring an expression level of AXIN2 gene. In certain embodiments, the methods include measuring an expression level of GLUL gene. In certain embodiments, the methods include measuring an expression level of LGR5 gene. In certain embodiments, the methods include measuring an expression level of NKD1 gene. In certain embodiments, the methods include measuring an expression level of NOTUM gene. In certain embodiments, the methods include measuring an expression level of RHBG gene. In certain embodiments, the methods include measuring an expression level of SBSPON gene. In certain embodiments, the methods include measuring an expression level of SLC13A3 gene. In certain embodiments, the methods include measuring an expression level of SLC1A2 gene. In certain embodiments, the methods include measuring an expression level of SP5 gene. In certain embodiments, the methods include measuring an expression level of TCF7 gene. In certain embodiments, the methods include measuring an expression level of TEDDM1 gene. In certain embodiments, the methods include measuring an expression level of TNFRSF19 gene.

In certain embodiments, the methods include measuring an expression level of two genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of three genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of four genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of five genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of six genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of seven genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of eight genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1 A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of nine genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of ten genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of eleven genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of twelve genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19.

In certain embodiments, the methods include measuring an expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, and TNFRSF19. In certain embodiments, the methods include measuring an expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SLC13A3, SP5, TCF7, and TNFRSF19.

In certain embodiments, an increased expression level of AXIN2 relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of GLUL relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of LGR5 relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of NKD1 relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of NOTUM relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of RHBG relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of SBSPON relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of SLC13A3 relative to a control indicates that the subject is a non- responder. In certain embodiments, an increased expression level of SLC1A2 relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of SP5 relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of TCF7 relative to a control indicates that the subject is a nonresponder. In certain embodiments, an increased expression level of TEDDM1 relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of TNFRSF19 relative to a control indicates that the subject is a non-responder.

In certain embodiments, an increased expression level of two genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a non-responder. In certain embodiments, the methods include measuring an expression level of three genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of four genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of five genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of six genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1 A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of seven genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1 A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of eight genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a non- responder. In certain embodiments, an increased expression level of nine genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of ten genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of eleven genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of twelve genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1 A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a non-responder.

In certain embodiments, an increased expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, and TNFRSF19 relative to a control indicates that the subject is a non-responder. In certain embodiments, an increased expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SLC13A3, SP5, TCF7, and TNFRSF19 relative to a control indicates that the subject is a non-responder.

In certain embodiments, the present disclosure provides methods for identifying a responder subject. As used herein, the term “responder” refers to subjects that respond to a treatment (e.g., an immunotherapy). In certain embodiments, the responder subjects have an anticancer effect upon administration of a treatment. In certain embodiments, the responder subjects have an anti-cancer effect upon administration of atezolizumab in combination with or without bevacizumab. In certain embodiments, the responder subjects have an anti-cancer effect upon administration of atezolizumab and bevacizumab. In certain embodiments, the responder subjects have an anti-cancer effect upon administration of durvalumab in combination with or without tremilimumab. In certain embodiments, the responder subjects have an anti-cancer effect upon administration of nivolumab. In certain embodiments, the responder subjects have an anti-cancer effect upon administration of pembrolizumab.

In certain embodiments, the methods include measuring an expression level of AXIN2 gene. In certain embodiments, the methods include measuring an expression level of GLUL gene. In certain embodiments, the methods include measuring an expression level of LGR5 gene. In certain embodiments, the methods include measuring an expression level of NKD1 gene. In certain embodiments, the methods include measuring an expression level of NOTUM gene. In certain embodiments, the methods include measuring an expression level of RHBG gene. In certain embodiments, the methods include measuring an expression level of SBSPON gene. In certain embodiments, the methods include measuring an expression level of SLC13A3 gene. In certain embodiments, the methods include measuring an expression level of SLC1A2 gene. In certain embodiments, the methods include measuring an expression level of SP5 gene. In certain embodiments, the methods include measuring an expression level of TCF7 gene. In certain embodiments, the methods include measuring an expression level of TEDDM1 gene. In certain embodiments, the methods include measuring an expression level of TNFRSF19 gene.

In certain embodiments, the methods include measuring an expression level of two genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of three genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of four genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of five genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of six genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of seven genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of eight genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1 A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of nine genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of ten genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of eleven genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the methods include measuring an expression level of twelve genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19.

In certain embodiments, the methods include measuring an expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, and TNFRSF19. In certain embodiments, the methods include measuring an expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SLC13A3, SP5, TCF7, and TNFRSF19.

In certain embodiments, a reduced expression level of AXIN2 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of GLUL relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of LGR5 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of NKD1 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of NOTUM relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of RHBG relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of SBSPON relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of SLC13 A3 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of SLC1 A2 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of SP5 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of TCF7 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of TEDDM1 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of TNFRSF19 relative to a control indicates that the subject is a responder.

In certain embodiments, a reduced expression level of two genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a responder. In certain embodiments, the methods include measuring an expression level of three genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of four genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of five genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of six genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1 A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of seven genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of eight genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of nine genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of ten genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1 A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of eleven genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of twelve genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19 relative to a control indicates that the subject is a responder.

In certain embodiments, a reduced expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, and TNFRSF19 relative to a control indicates that the subject is a responder. In certain embodiments, a reduced expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SLC13A3, SP5, TCF7, and TNFRSF19 relative to a control indicates that the subject is a responder.

Any suitable methods known in the art for measuring gene expression levels can be used with the presently disclosed methods.

For example, but without any limitation, methods known in the art for the quantification of messenger ribonucleic acid (mRNA) expression in a sample include northern blotting and in situ hybridization, RNAse protection assays, and reverse transcription polymerase chain reaction (RT- PCR). Alternatively, antibodies can be used to recognize specific duplexes, including deoxyribonucleic acid (DNA) duplexes, RNA duplexes, and DNA-RNA hybrid duplexes or DNA- protein duplexes. Additional exemplary methods include Serial Analysis of Gene Expression (SAGE), and gene expression analysis by massively parallel signature sequencing (MPSS).

In certain embodiments, the methods for measuring gene expression levels are PCR-based methods (e.g., polymerase chain reaction (PCR), quantitative or realtime PCR (qPCR), reverse transcription-PCR (RT-PCR), and/or real-time RT-PCR (rRT-PCR, RT- qPCR, or qRT-PCR)).

PCR is an enzyme-driven process for amplifying short segments of nucleic acid in vitro. This method utilizes partial target nucleic acid sequences to design oligonucleotides (primers) that can hybridize specifically to the target sequences in target nucleic acids. A thermostable polymerase enzyme is used to copy the target sequence in the presence of other necessary components such as nucleotides (e.g., deoxynucleotide triphosphates (dNTPs)) and primers as well as PCR/amplification buffer. The target nucleic acid can be amplified exponentially via multiple amplification cycles including denaturation of target nucleic acid, primer hybridization, and primer extension. This amplification step can be performed in a thermocycler that can run multiple rounds of heating and cooling to provide temperature necessary for each step of the amplification (e.g., denaturation, primer hybridization and extension, etc.). Each step of the cycle can be optimized for different target nucleic acid and primer pair combinations. qPCR is a process where amplification of target nucleic acid and detection of amplified products are coupled in a single reaction vessel. Fluorescent DNA intercalating dyes or fluorescently labeled oligonucleotide probes can be used to visualize the amplified products for real-time monitoring. Examples of fluorescent dyes include, but are not limited to, SYBR-Green I, propidium monoazide (PMA), ethidium monoazide (EMA), SYTOX Orange, SYTO-9, SYTO- 13, SYTO-16, SYTO-60, SYTO-62, SYTO- 64, SYTO-82, BEBO, Y0-PR0-1, LC Green, PO- PRO-3, TO-PRO-3, TOTO-3, POPO-3, and BOBO-3. Examples of oligonucleotide probes include, but are not limited to, TaqMan, fluorescence resonance energy transfer (FRET), molecular beacon probes, scorpion probes, and multiplex probes. The fluorescent signal intensity increases in proportion to the amount of amplified products generated and the amount of starting templates in a sample can be quantified by comparing the exact cycle number at which amplified products accumulate significantly over baseline with a pre-derived quantitative standard. RT-PCR utilizes a reverse transcriptase to generate DNA amplification products from a target RNA by combining the process of reverse transcribing a target RNA into DNA and amplifying specific DNA targets by PCR. RT-PCR can be combined with qPCR to measure the amount of a specific target RNA (rRT-PCR or qRT-PCR).

In certain embodiments, an amplification reaction mixture described herein can include, for example but without any limitation, a target nucleic acid (or a biological sample containing target nucleic acids such as DNA or RNA), a polymerase, deoxynucleotide triphosphates (dNTPs), reaction or amplification buffer, DNAse/RNAse-free water, and magnesium or manganese. In certain embodiments, an amplification reaction mixture can further comprise a pair of oligonucleotide primers. In certain embodiments, a reaction mixture can comprise two or more pairs of oligonucleotide primers. In certain embodiments, a reaction mixture comprises a DNA- dependent DNA polymerase or an RNA-dependent DNA polymerase. In certain embodiments, a reaction mixture comprises a DNA-dependent DNA polymerase and an RNA-dependent DNA polymerase. In certain embodiments, a reaction mixture comprises a reverse transcriptase. Any DNA polymerase useful for PCR can be used in the methods disclosed herein. Non-limiting examples of a DNA-dependent DNA polymerase that can be used in the methods disclosed herein include, but are not limited to, a T4 DNA polymerase, a T7 DNA polymerase, a phi29 DNA polymerase, a Bst DNA polymerase, a E. coli DNA polymerase I, a KI enow DNA polymerase, a Taq polymerase, a Pfu DNA polymerase, a Tfl DNA polymerase, and a Tth DNA polymerase. In certain embodiments, a polymerase is a thermostable polymerase. In certain embodiments, a retroviral reverse transcriptase (RT) can be used for rRT-PCR. Non-limiting examples of retroviral RTs that can be used in the methods disclosed herein include, but are not limited to, Avian myeloblastosis virus (AMV) RT and Moloney murine leukemia virus (MMLV or MuLV) RT. In certain embodiments, a thermostable DNA polymerase that possesses a reverse transcriptase activity (e.g., a Tfl DNA polymerase or a Tth DNA polymerase), can be used. In certain embodiments, a modified version of a DNA polymerase or an RT described herein can be used. For example, an RT with mutations (e.g., point mutations) in RNase H activity domain or deletion of RNase H activity domain can be used to inhibit premature degradation of the RNA strand of an RNA DNA hybrid.

In certain embodiments, gene expression levels can also be determined using microarray techniques. In these methods, polynucleotide sequences of interest (including cDNAs and oligonucleotides) are plated, or arrayed, on a microchip substrate. The arrayed sequences are then hybridized with specific DNA probes from cells or tissues of interest. Just as in the RT-PCR method, the source of mRNA typically is total RNA isolated from human tumors or tumor cell lines and corresponding normal tissues or cell lines. Thus, RNA is isolated from a variety of primary tumors or tumor cell lines. If the source of mRNA is a primary tumor, mRNA is extracted from frozen or archived tissue samples.

In the microarray technique, PCR-amplified inserts of cDNA clones are applied to a substrate in a dense array. The microarrayed genes, immobilized on the microchip, are suitable for hybridization under stringent conditions. In certain cases, fluorescently labeled cDNA probes are generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest (e.g., cancer tissue). Labeled cDNA probes applied to the chip hybridize with specificity to loci of DNA on the array. After washing to remove non-specifically bound probes, the chip is scanned by confocal laser microscopy or by another detection method, such as a charge-coupled device (CCD) camera. Quantification of hybridization of each arrayed element allows for assessment of corresponding mRNA abundance. In certain configurations, dual color fluorescence is used. With dual color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized pairwise to the array. The relative abundance of the transcripts from the two sources corresponding to each specified gene is thus determined simultaneously. In certain configurations, the miniaturized scale of the hybridization can afford a convenient and rapid evaluation of the expression pattern for large numbers of genes. In various configurations, such methods can have sensitivity required to detect rare transcripts, which are expressed at fewer than 1000, fewer than 100, or fewer than 10 copies per cell. In various configurations, such methods can detect at least approximately two-fold differences in expression levels (Schena et al., Proc. Natl. Acad. Sci. USA 93(2): 106-149 (1996)). In certain configurations, microarray analysis is performed by commercially available equipment, following manufacturer's protocols, such as by using the Affymetrix GenChip technology, or Incyte's microarray technology.

In certain embodiments, gene expression levels can also be determined using RNA-Seq. RNA sequencing (RNA-seq), also called whole transcriptome shotgun sequencing (WTSS), uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment in time. RNA-Seq is used to analyze the continually changing cellular transcriptome. See, e.g., Wang et al., 2009 Nat Rev Genet, 10(1): 57-63, incorporated herein by reference. Specifically, RNA-Seq facilitates the ability to look at alternative gene spliced transcripts, post-transcriptional modifications, gene fusion, mutations/SNPs and changes in gene expression. In addition to mRNA transcripts, RNA-Seq can look at different populations of RNA to include total RNA, small RNA, such as miRNA, tRNA, and ribosomal profiling. RNA-Seq can also be used to determine exon/intron boundaries and verify or amend previously annotated 5' and 3' gene boundaries. Prior to RNA-Seq, gene expression studies were done with hybridizationbased microarrays. Issues with microarrays include cross-hybridization artifacts, poor quantification of lowly and highly expressed genes, and needing to know the sequence of interest. Because of these technical issues, transcriptomics transitioned to sequencing-based methods. These progressed from Sanger sequencing of Expressed Sequence Tag libraries, to chemical tag- based methods (e.g., serial analysis of gene expression), and finally to the current technology, NGS of cDNA (notably RNA-Seq).

Any suitable methods known in the art for measuring protein levels can be used with the presently disclosed methods. These methods include, but are not limited to, mass spectrometry techniques, 1-D or 2-D gel -based analysis systems, chromatography, enzyme linked immunosorbent assays (ELISAs), flow cytometry, radioimmunoassays (RIA), enzyme immunoassays (EIA), Western Blotting, immunoprecipitation, and immunohistochemistry. These methods use antibodies, or antibody equivalents, to detect protein. Antibody arrays or protein chips can also be employed.

ELISA and RIA procedures can be conducted such that a protein standard is labeled (with a radioisotope such as ¹²⁵I or ³⁵S, or an assayable enzyme, such as horseradish peroxidase or alkaline phosphatase), and, together with the unlabeled sample, brought into contact with the corresponding antibody, whereon a second antibody is used to bind the first, and radioactivity or the immobilized enzyme assayed (competitive assay). Alternatively, the protein can react with the corresponding immobilized antibody, radioisotope, or enzyme-labeled anti-marker antibody is allowed to react with the system, and radioactivity or the enzyme assayed (ELISA-sandwich assay). Other conventional methods can also be employed as suitable.

The above techniques can be conducted essentially as a “one-step” or “two-step” assay. A “one-step” assay involves contacting antigen with immobilized antibody and, without washing, contacting the mixture with labeled antibody. A “two-step” assay involves washing before contacting, the mixture with labeled antibody. Other conventional methods can also be employed as suitable. In certain embodiments, the detection of a biomarker from a biological sample includes contacting the sample with an antibody or variant (e.g., fragment) thereof which selectively binds the biomarker, and detecting whether the antibody or variant thereof is bound to the sample. The method can further include contacting the sample with a second antibody, e.g., a labeled antibody. The method can further include one or more washing, e.g., to remove one or more reagents.

It can be desirable to immobilize one component of the assay system on a support, thereby allowing other components of the system to be brought into contact with the component and readily removed without laborious and time-consuming labor. It is possible for a second phase to be immobilized away from the first, but one phase is usually sufficient.

It is possible to immobilize the enzyme itself on a support, but if solid-phase enzyme is required, then this is generally best achieved by binding to antibody and affixing the antibody to a support, models, and systems for which are well-known in the art. Simple polyethylene can provide a suitable support.

Enzymes employable for labeling are not particularly limited but can be selected from the members of the oxidase group, for example. These catalyze the production of hydrogen peroxide by reaction with their substrates, and glucose oxidase is often used for its good stability, ease of availability and cheapness, as well as the ready availability of its substrate (glucose). Activity of the oxidase can be assayed by measuring the concentration of hydrogen peroxide formed after reaction of the enzyme-labeled antibody with the substrate under controlled conditions well-known in the art.

Other techniques can be used to detect a protein marker according to a practitioner’s preference based upon the present disclosure. One such technique is Western blotting (Towbin et al., Proc. Nat. Acad. Sci. 76:4350 (1979)), wherein a suitably treated sample is run on an SDS- PAGE gel before being transferred to a solid support, such as a nitrocellulose filter. Antibodies (unlabeled) are then brought into contact with the support and assayed by a secondary immunological reagent, such as labeled protein A or anti-immunoglobulin (suitable labels including ¹²⁵I, horseradish peroxidase, and alkaline phosphatase). Chromatographic detection can also be used.

Other machine or auto imaging systems can also be used to measure immunostaining results for the marker. As used herein, “quantitative” immunohistochemistry refers to an automated method of scanning and scoring samples that have undergone immunohistochemistry, to identify and quantitate the presence of a specified marker, such as an antigen or other protein. The score given to the sample is a numerical representation of the intensity of the immunohistochemical staining of the sample and represents the amount of target marker present in the sample. As used herein, Optical Density (OD) is a numerical score that represents intensity of staining. As used herein, semi-quantitative immunohistochemistry refers to scoring of immunohistochemical results by human eye, where a trained operator ranks results numerically (e.g., as 1, 2 or 3).

Various automated sample processing, scanning, and analysis systems suitable for use with immunohistochemistry are available in the art. Such systems can include automated staining (see, e.g., the Benchmark system, Ventana Medical Systems, Inc.) and microscopic scanning, computerized image analysis, serial section comparison (to control for variation in the orientation and size of a sample), digital report generation, and archiving and tracking of samples (such as slides on which tissue sections are placed). Cellular imaging systems are commercially available that combine conventional light microscopes with digital image processing systems to perform quantitative analysis on cells and tissues, including immunostained samples.

Antibodies against biomarkers can also be used for imaging purposes, for example, to detect the presence of any of the biomarkers disclosed herein in a sample obtained from a recipient’s blood. Suitable labels include radioisotopes, iodine (¹²⁵1, ¹²¹I), carbon (¹⁴C), sulfur (³⁵S), tritium (³H), indium (¹¹²In), and technetium ("mTc), fluorescent labels, such as fluorescein, rhodamine, and biotin. Immunoenzymatic interactions can be visualized using different enzymes such as peroxidase, alkaline phosphatase, or different chromogens such as DAB, AEC, or Fast Red.

Antibodies for use in the present disclosure include any antibody, whether natural or synthetic, full length or a fragment thereof, monoclonal, or polyclonal, that binds sufficiently strongly and specifically to the marker to be detected. An antibody can have a Ka of at most about 10'⁶M, 10'⁷M, 10'⁸M, 10'⁹M, 1O'^1OM, 10^-11M, 10'¹²M. The phrase “specifically binds” refers to binding of, for example, an antibody to an epitope or antigen or antigenic determinant in such a manner that binding can be displaced or competed with a second preparation of identical or similar epitope, antigen, or antigenic determinant.

Antibodies and derivatives thereof that can be used encompasses polyclonal or monoclonal antibodies, chimeric, human, humanized, primatized (CDR-grafted), veneered or single-chain antibodies, phase produced antibodies (e.g., from phage display libraries), as well as functional binding fragments, of antibodies. For example, antibody fragments capable of binding to a marker, or portions thereof, including, but not limited to Fv, Fab, Fab’ and F(ab’)2 fragments can be used. Such fragments can be produced by enzymatic cleavage or by recombinant techniques. For example, papain or pepsin cleavage can generate Fab or F(ab’)2 fragments, respectively. Other proteases with the requisite substrate specificity can also be used to generate Fab or F(ab’)2 fragments. Antibodies can also be produced in a variety of truncated forms using antibody genes in which one or more stop codons have been introduced upstream of the natural stop site. For example, a chimeric gene encoding a F(ab’)2 heavy chain portion can be designed to include DNA sequences encoding the CH, domain and hinge region of the heavy chain. In certain embodiments, the antibodies can be conjugated to quantum dots.

In addition, a biomarker can be detected using Mass Spectrometry such as MALDI/TOF (time-of-flight), SELDI/TOF, liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography-mass spectrometry (HPLC-MS), capillary electrophoresis-mass spectrometry, nuclear magnetic resonance spectrometry, or tandem mass spectrometry (e.g., MS/MS, MS/MS/MS, ESI-MS/MS, etc.). See, for example, U.S. Patent Application Nos: 2003/0199001, 2003/0134304, 2003/0077616, which are herein incorporated by reference.

Mass spectrometry methods are well known in the art and have been used to detect biomolecules, such as proteins (see, e.g., Li et al. (2000) Tibtech 18: 151-160; Rowley et al. (2000) Methods 20: 383-397; and Kuster and Mann (1998) Curr. Opin. Structural Biol. 8: 393-400). Further, mass spectrometric techniques have been developed that permit at least partial de novo sequencing of isolated proteins. Chait et al., Science 262:89-92 (1993); Keough et al., Proc. Natl. Acad. Sci. USA. 96:7131-6 (1999); reviewed in Bergman, EXS 88: 133-44 (2000).

In certain embodiments, a gas phase ion spectrophotometer can be used. In other embodiments, laser-desorption/ionization mass spectrometry is used to analyze the sample. Modem laser desorption/ionization mass spectrometry (“LDI-MS”) can be practiced in two main variations: matrix assisted laser desorption/ionization (“MALDI”) mass spectrometry and surface- enhanced laser desorption/ionization (“SELDI”). In MALDI, the analyte is mixed with a solution containing a matrix, and a drop of the liquid is placed on the surface of a substrate. The matrix solution then co-crystallizes with the biological molecules. The substrate is inserted into the mass spectrometer. Laser energy is directed to the substrate surface where it desorbs and ionizes the biological molecules without significantly fragmenting them. However, MALDI has limitations as an analytical tool. It does not provide means for fractionating the sample, and the matrix material can interfere with detection, especially for low molecular weight analytes. See, e.g., U.S. Pat. No. 5,118,937 (Hillenkamp et al.), and U.S. Pat. No. 5,045,694 (Beavis & Chait).

For additional information regarding mass spectrometers, see, e.g., Principles of Instrumental Analysis, 3rd edition. Skoog, Saunders College Publishing, Philadelphia, 1985; and Kirk-Othmer Encyclopedia of Chemical Technology, 4th ed. Vol. 15 (John Wiley & Sons, New York 1995), pp. 1071-1094.

Detection of the presence of a marker or other substances can involve detection of signal intensity. This, in turn, can reflect the quantity and character of a polypeptide bound to the substrate. For example, in certain embodiments, the signal strength of peak values from spectra of a first sample and a second sample can be compared (e.g., visually, by computer analysis etc.), to determine the relative amounts of a particular marker. Software programs such as the Biomarker Wizard program (Ciphergen Biosystems, Inc., Fremont, Calif.) can be used to aid in analyzing mass spectra. The mass spectrometers and their techniques are well known to those of skill in the art.

Any person skilled in the art understands the components of a mass spectrometer (e.g., desorption source, mass analyzer, detect, etc.) and varied sample preparations can be combined with other suitable components or preparations described herein, or to those known in the art. For example, in certain embodiments, a control sample can contain heavy atoms (e.g., ¹³C) thereby permitting the test sample to be mixed with the known control sample in the same mass spectrometry run.

In certain embodiments, a laser desorption time-of-flight (TOF) mass spectrometer is used. In laser desorption mass spectrometry, a substrate with a bound marker is introduced into an inlet system. The marker is desorbed and ionized into the gas phase by laser from the ionization source. The ions generated are collected by an ion optic assembly, and then in a time-of-flight mass analyzer, ions are accelerated through a short high voltage field and let drift into a high vacuum chamber. At the far end of the high vacuum chamber, the accelerated ions strike a sensitive detector surface at a different time. Since the time-of-flight is a function of the mass of the ions, the elapsed time between ion formation and ion detector impact can be used to identify the presence or absence of molecules of specific mass to charge ratio.

Additionally or alternatively, the presently disclosed methods include multiplex spatial gene expression and proteomic analysis. In certain embodiments, the presently disclosed methods comprise determining a spatial location of a nucleic acid or protein of one of more genes described above.

Spatial analysis methodologies encompassed by the present disclosure can provide a vast amount of analyte and/or expression data for a variety of analytes within a biological sample at high spatial resolution, while retaining native spatial context. Spatial analysis methods can include, e.g., the use of a capture probe including a spatial barcode and a capture domain that is capable of binding to an analyte produced by and/or present in a cell. Spatial analysis methods can also include the use of a capture probe having a capture domain that captures an intermediate agent for indirect detection of an analyte. For example, the intermediate agent can include a nucleic acid sequence (e.g., a barcode) associated with the intermediate agent. Detection of the intermediate agent is therefore indicative of the analyte in the cell or tissue sample.

Suitable systems for performing spatial analysis can include components such as a chamber (e.g., a flow cell or sealable, fluid-tight chamber) for containing a biological sample. In certain embodiments, the biological sample can be mounted for example, in a biological sample holder. In certain embodiments, one or more fluid chambers can be connected to the chamber and/or the sample holder via fluid conduits, and fluids can be delivered into the chamber and/or sample holder via fluidic pumps, vacuum sources, or other devices coupled to the fluid conduits that create a pressure gradient to drive fluid flow. In certain embodiments, one or more valves can also be connected to fluid conduits to regulate the flow of reagents from reservoirs to the chamber and/or sample holder.

In certain embodiments, the systems can optionally include a control unit that includes one or more electronic processors, an input interface, an output interface (e.g., a display), and a storage unit (e.g., a solid state storage medium such as, but not limited to, a magnetic, optical, or other solid state, persistent, writeable and/or re-writeable storage medium). In certain embodiments, the control unit can optionally be connected to one or more remote devices via a network. In certain embodiments, the control unit (and components thereof) can generally perform any of the steps and functions described herein. In certain embodiments, where the system is connected to a remote device, the remote device (or devices) can perform any of the steps or features described herein. In certain embodiments, the systems can optionally include one or more detectors (e.g., CCD, CMOS) used to capture images. In certain embodiments, the systems can also optionally include one or more light sources (e.g., LED-based, diode-based, lasers) for illuminating a sample, a substrate with features, analytes from a biological sample captured on a substrate, and various control and calibration media.

In certain embodiments, the systems can also include software instructions encoded and/or implemented in one or more of tangible storage media and hardware components such as application specific integrated circuits. In certain embodiments, the software instructions, when executed by a control unit (and in particular, an electronic processor) or an integrated circuit, can cause the control unit, integrated circuit, or other component executing the software instructions to perform any of the method steps or functions described herein.

In certain embodiments, a map of analyte presence and/or level can be aligned to an image of a biological sample using one or more fiducial markers, e.g., objects placed in the field of view of an imaging system which appear in the image produced. Fiducial markers can be used as a point of reference or measurement scale for alignment (e.g., to align a sample and an array, to align two substrates, to determine a location of a sample or array on a substrate relative to a fiducial marker) and/or for quantitative measurements of sizes and/or distances.

In certain embodiments, when a spatial location of a nucleic acid or a protein is determined, presence of the nucleic acid or protein in an immune excluded location indicates that the subject is non-responder. As used herein, the term “immune excluded location” refers to a histological section characterized by a lack of immune cells (e.g., T cells). In certain embodiments, an immune excluded location lacks immune cells in either the tumor parenchyma or the tumor periphery. In certain embodiments, an immune excluded location includes immune cells confined to the stroma of the tumor and lacks immune cells in the parenchyma.

In certain embodiments, when a spatial location of a nucleic acid or a protein is determined, presence of the nucleic acid or protein in an immune active location indicates that the subject is responder. As used herein, the term “immune active location” refers to a histological section characterized by infiltration and presence of immune cells (e.g., T cells). In certain embodiments, an immune active location is characterized by lymphocytic infiltration in the tumor parenchyma, with the immune cells positioned in proximity to the tumor cells.

The methods of the present disclosure can be used with any biological sample (e.g., any biological sample described herein). In certain embodiments, the biological sample is a tissue section. In certain embodiments, the biological sample is a tissue sample. In certain embodiments, the biological sample is a fresh-frozen biological sample. In certain embodiments, the biological sample is a fixed biological sample (e.g., formalin-fixed paraffin embedded (FFPE), paraformaldehyde, acetone, or methanol). In certain embodiments, the biological sample is an FFPE sample. In certain embodiments, the biological sample is an FFPE tissue section. In certain embodiments, the tissue sample is a tumor sample. In certain embodiments, the tissue section is a tumor tissue section. In certain embodiments, the tumor tissue section is a fixed tumor tissue section (e.g., a formal-fixed paraffin-embedded tumor tissue section). In certain embodiments, the tumor sample comprises one or more cancer tumors. In certain embodiments, the tissue sample is derived from a biopsy sample.

In certain embodiments, an FFPE sample is deparaffinized and decrosslinked prior to delivering a plurality of templated ligation probes (e.g., RNA templated ligation probes) and analyte capture agents. For example, the paraffin-embedding material can be removed (e.g., deparaffinization) from the biological sample (e.g., tissue section) by incubating the biological sample in an appropriate solvent (e.g., xylene), followed by a series of rinses (e.g., ethanol of varying concentrations), and rehydration in water. In certain embodiments, the biological sample can be dried following deparaffinization. In certain embodiments, after the step of drying the biological sample, the biological sample can be stained (e.g., H&E stain, any of the variety of stains described herein).

In certain embodiments, the method includes staining the biological sample. In certain embodiments, the staining includes the use of hematoxylin and eosin. In certain embodiments, a biological sample can be stained using any number of biological stains including, but not limited to, acridine orange, Bismarck brown, carmine, coomassie blue, cresyl violet, DAPI, eosin, ethidium bromide, acid fuchsine, hematoxylin, Hoechst stains, iodine, methyl green, methylene blue, neutral red, Nile blue, Nile red, osmium tetroxide, propidium iodide, rhodamine, or safranin.

In certain embodiments, the biological sample can be stained using known staining techniques including Can-Grunwald, Giemsa, hematoxylin and eosin (H&E), Jenner's, Leishman, Masson's trichrome, Papanicolaou, Romanowsky, silver, Sudan, Wright's, and/or Periodic Acid Schiff (PAS) staining techniques. In certain embodiments, the staining includes the use of a detectable label selected from the group consisting of a radioisotope, a fluorophore, a chemiluminescent compound, a bioluminescent compound, or a combination thereof.

In certain embodiments, the biological sample is imaged after staining the biological sample. In certain embodiments, the biological sample is imaged prior to staining the biological sample. In certain embodiments, the biological sample is visualized or imaged using bright field microscopy. In certain embodiments, the biological sample is visualized or imaged using fluorescence microscopy. Additional methods of visualization and imaging are known in the art. Non-limiting examples of visualization and imaging include expansion microscopy, bright field microscopy, dark field microscopy, phase contrast microscopy, electron microscopy, fluorescence microscopy, reflection microscopy, interference microscopy and confocal microscopy. In certain embodiments, the sample is stained and imaged prior to adding the first and/or second primer to the biological sample on the array.

After a fixed (e.g., FFPE, PFA, acetone, methanol) biological sample has undergone deparaffinization, the fixed (e.g., FFPE, PFA) biological sample can be further processed. For example, fixed (e.g., FFPE, PFA) biological samples can be treated to remove crosslinks (e.g., formaldehyde-induced crosslinks (e.g., decrosslinking)). In certain embodiments, decrosslinking the crosslinks (e.g., formaldehyde-induced crosslinks) in the fixed (e.g., FFPE, PFA) biological sample can include treating the sample with heat. In certain embodiments, decrosslinking the formaldehyde-induced crosslinks can include performing a chemical reaction. In certain embodiments, decrosslinking the formaldehyde-induced crosslinks, can include treating the sample with a permeabilization reagent. In certain embodiments, decrosslinking the formaldehyde-induced crosslinks can include heat, a chemical reaction, and/or permeabilization reagents. In certain embodiments, decrosslinking crosslinks (e.g., formaldehyde-induced crosslinks) can be performed in the presence of a buffer. In certain embodiments, the buffer is Tris-EDTA (TE) buffer (e.g., TE buffer for FFPE biological samples). In certain embodiments, the buffer is citrate buffer (e.g., citrate buffer for FFPE biological samples). In certain embodiments, the buffer is Tris-HCl buffer (e.g., Tris-HCl buffer for PFA fixed biological samples). In certain embodiments, the buffer (e.g., TE buffer, Tris-HCl buffer) has a pH of about 5.0 to about 10.0 and a temperature between about 60° C. to about 100° C. In certain embodiments, the biological sample is permeabilized (e.g., permeabilized by any of the methods known in the art). In certain embodiments, the permeabilization is an enzymatic permeabilization. In certain embodiments, the permeabilization is a chemical permeabilization. In certain embodiments, the biological sample is permeabilized before delivering the RNA templated ligation probes and analyte capture agents to the biological sample. In certain embodiments, the biological sample is permeabilized at the same time as the RNA templated ligation probes and analyte capture agents are delivered to the biological sample. In certain embodiments, the biological sample is permeabilized after the RNA templated ligation probes and analyte capture agents are delivered to the biological sample. In certain embodiments, hybridizing the RNA templated ligation products to the second capture domains and the analyte capture sequences of the bound analyte capture agents to the first capture domains further comprises permeabilizing the biological sample.

In certain embodiments, the biological sample is permeabilized from about 30 to about 120 minutes, from about 40 to about 110 minutes, from about 50 to about 100 minutes, from about 60 to about 90 minutes, or from about 70 to 80 minutes. In certain embodiments, the biological sample is permeabilized about 30, about 35, about 40, about 45, about 50, about 55, about 60, about 65, about 70, about 75, about 80, about 90, about 95, about 100, about 105, about 110, about 115, or about 130 minutes.

In certain embodiments, the permeabilization buffer comprises urea. In certain embodiments, the urea is at a concentration of about 0.5M to 3.0M. In certain embodiments, the concentration of the urea is about 0.5, 1.0, 1.5, 2.0, 2.5, or about 3.0M. In certain embodiments, the permeabilization buffer includes a detergent. In certain embodiments, the detergent is sarkosyl. In certain embodiments, the sarkosyl is present at about 2% to about 10% (v/v). In certain embodiments, the sarkosyl is present at about 3%, 4%, 5%, 6%, 7%, 8%, or 9% (v/v). In certain embodiments, the permeabilization buffer comprises polyethylene glycol (PEG). In certain embodiments, the PEG is from about PEG 2K to about PEG 16K. In certain embodiments, the PEG is PEG 2K, 3K, 4K, 5K, 6K, 7K, 8K, 9K, 10K, UK, 12K, 13K, 14K, 15K, or 16K. In certain embodiments, the PEG is present at a concentration from about 2% to 25%, from about 4% to about 23%, from about 6% to about 21%, or from about 8% to about 20% (v/v).

In certain embodiments, the method includes a step of permeabilizing the biological sample (e.g., a tissue section). For example, the biological sample can be permeabilized to facilitate transfer of the extended products to the capture probes on the array. In certain embodiments, the permeabilizing includes the use of an organic solvent (e.g., acetone, ethanol, and methanol), a detergent (e.g., saponin, Triton X100™, Tween-20™, or sodium dodecyl sulfate (SDS)), and an enzyme (an endopeptidase, an exopeptidase, a protease), or combinations thereof. In certain embodiments, the permeabilizing includes the use of an endopeptidase, a protease, SDS, polyethylene glycol tert-octylphenyl ether, polysorbate 80, and polysorbate 20, N- lauroylsarcosine sodium salt solution, saponin, Triton XI 00™, Tween-20™, or combinations thereof. In certain embodiments, the endopeptidase is pepsin. In certain embodiments, the endopeptidase is Proteinase K. Additional methods for sample permeabilization are described, for example, in Jamur et al., Method Mol. Biol. 588:63-66, 2010, the entire contents of which are incorporated herein by reference.

The methods provided herein can also include antibody staining. In certain embodiments, antibody staining includes the use of an antibody staining buffer. In certain embodiments, the antibody staining buffer (e.g., a PBS-based buffer) includes a detergent (e.g., Tween-20, SDS, sarkosyl). In certain embodiments, the antibody staining buffer includes a serum, such as for example, a goat serum. In some embodiments, the goat serum is from about 1% to about 10% (v/v), from about 2% to about 9% (v/v), from about 3% to about 8% (v/v), or about 4% to about 7% (v/v). In certain embodiments, the antibody staining buffer includes dextran sulfate. In certain embodiments, the dextran sulfate is at a concentration of about 1 mg/ml to about 20 mg/ml, from about 5 mg/ml to about 15 mg/ml, or from about 8 mg/ml to about 12 mg/ml.

The methods provided herein can also utilize blocking probes to block the non-specific binding (e.g., hybridization) of the analyte capture sequence and the capture domain of a capture probe on an array. In certain embodiments, following contact between the biological sample and the array, the biological sample is contacted with a plurality of analyte capture agents, where an analyte capture agent includes an analyte capture sequence that is reversibly blocked with a blocking probe. In certain embodiments, the analyte capture sequence is reversibly blocked with more than one blocking probe (e.g., 2, 3, 4, or more blocking probes). In certain embodiments, the analyte capture agent is blocked prior to binding the target analyte (e.g., a target protein).

In certain embodiments, the oligonucleotide of the analyte capture agent (e.g., analyte capture sequence) is blocked by a blocking probe. In certain embodiments, blocking probes are hybridized to the analyte capture sequence of the analyte capture agents before introducing the analyte capture agents to a biological sample. In certain embodiments, blocking probes are hybridized to the analyte capture sequence of the analyte capture agents after introducing the analyte capture agents to the biological sample. In certain embodiments, the capture domain can also be blocked to prevent non-specific binding, and/or to control the time of binding, between the analyte capture sequence and the capture domain. In certain embodiments, the blocking probes can be alternatively or additionally introduced during staining of the biological sample. In certain embodiments, the analyte capture sequence is blocked prior to binding to the capture domain, where the blocking probe includes a sequence complementary or substantially complementary to the analyte capture sequence.

In certain embodiments, the analyte capture sequence is blocked with one blocking probe. In certain embodiments, the analyte capture sequence is blocked with two blocking probes. In certain embodiments, the analyte capture sequence is blocked with more than two blocking probes (e.g., 3, 4, 5, or more blocking probes). In certain embodiments, a blocking probe is used to block the free 3' end of the analyte capture sequence. In certain embodiments, a blocking probe is used to block the 5' end of the analyte capture sequence. In certain embodiments, two blocking probes are used to block both 5' and 3' ends of the analyte capture sequence. In certain embodiments, both the analyte capture sequence and the capture probe domain are blocked.

In certain embodiments, the blocking probes can differ in length and/or complexity. In certain embodiments, the blocking probe can include a nucleotide sequence of about 8 to about 24 nucleotides in length (e.g., about 8 to about 22, about 8 to about 20, about 8 to about 18, about 8 to about 16, about 8 to about 14, about 8 to about 12, about 8 to about 10, about 10 to about 24, about 10 to about 22, about 10 to about 20, about 10 to about 18, about 10 to about 16, about 10 to about 14, about 10 to about 12, about 12 to about 24, about 12 to about 22, about 12 to about 20, about 12 to about 18, about 12 to about 16, about 12 to about 14, about 14 to about 24, about 14 to about 22, about 14 to about 20, about 14 to about 18, about 14 to about 16, about 16 to about 24, about 16 to about 22, about 16 to about 20, about 16 to about 18, about 18 to about 24, about 18 to about 22, about 18 to about 20, about 20 to about 24, about 20 to about 22, or about 22 to about 24 nucleotides in length).

In certain embodiments, the blocking probe is removed prior to hybridizing the analyte capture sequence of the oligonucleotide of the analyte capture sequence to the first capture domain. For example, once the blocking probe is released from the analyte capture sequence, the analyte capture sequence can bind to the first capture domain on the array. In certain embodiments, blocking the analyte capture sequence reduces non-specific background staining. In certain embodiments, blocking the analyte capture sequence allows for control over when to allow the binding of the analyte capture sequence to the capture domain of a capture probe during a spatial workflow, thereby controlling the time of capture of the analyte capture sequence on the array. In certain embodiments, the blocking probes are reversibly bound, such that the blocking probes can be removed from the analyte capture sequence during or after the time that analyte capture agents are in contact with the biological sample. In certain embodiments, the blocking probe can be removed with RNAse treatment (e.g., RNAse H treatment). In certain embodiments, the blocking probes are removed by increasing the temperature (e.g., heating) the biological sample. In certain embodiments, the blocking probes are removed enzymatically (e.g., cleaved). For example, but without any limitation, the blocking probes are removed by a USER enzyme, an endonuclease, an endonuclease IV, or an endonuclease V.

In certain embodiments, the spatial analysis can include producing a sequencing library of the transcriptomic, and sequencing the library. Producing sequencing libraries are known in the art. For example, the transcripts can be purified and collected for downstream amplification steps including PCR, where primer binding sites flank the spatial barcode and ligation product or analyte binding moiety barcode, or complements thereof, generating a library associated with a particular spatial barcode. In certain embodiments, the library preparation can be quantitated and/or quality controlled to verify the success of the library preparation steps. The library amplicons are sequenced and analyzed to decode spatial information and the ligation product or analyte binding moiety barcode, or complements thereof.

Alternatively or additionally, the amplicons can then be enzymatically fragmented and/or size-selected in order to provide for desired amplicon size. In certain embodiments, when utilizing an Illumina® library preparation methodology, for example, P5 and P7, sequences can be added to the amplicons thereby allowing for capture of the library preparation on a sequencing flowcell (e.g., on Illumina sequencing instruments). Additionally, i7 and i5 can index sequences be added as sample indexes if multiple libraries are to be pooled and sequenced together. Further, Read 1 and Read 2 sequences can be added to the library preparation for sequencing purposes. The aforementioned sequences can be added to a library preparation sample, for example, via End Repair, A-tailing, Adaptor Ligation, and/or PCR. The cDNA fragments can then be sequenced using, for example, paired-end sequencing using TruSeq Read 1 and TruSeq Read 2 as sequencing primer sites, although other methods are known in the art.

4. Methods of Treatment

The present disclosure relates to methods for preventing and/or treating a subject having cancer, including administering an anti-cancer treatment. In certain non-limiting embodiments, anti-cancer treatments include chemotherapy, radiation therapy, targeted drug therapy, immunotherapy, immunomodulatory agents, cytokines, monoclonal and polyclonal antibodies, and any combinations thereof. Non-limiting examples of anti-cancer treatments include chemotherapeutic treatments, radiotherapeutic treatments, anti-angiogenic treatments, apoptosisinducing treatments, anti-cancer antibodies, anti-cyclin-dependent kinase agents, and/or treatments that promote the activity of the immune system including but not limited to cytokines such as but not limited to interleukin 2, interferon, anti-CTLA4 antibody, anti-PD-1 antibody, and/or anti-PD- L1 antibody.

In certain embodiments, the anti-cancer treatment is chemotherapy, which includes administering a chemotherapeutic agent to the subject. Any suitable chemotherapeutic agents known in the art can be used with the presently disclosed methods. Non-limiting examples of chemotherapeutic agents that can be used with the presently disclosed methods include acivicin, aclarubicin, acodazole hydrochloride, acronine, adozelesin, aldesleukin, altretamine, ambomycin, ametantrone acetate, amsacrine, anastrozole, anthramycin, asparaginase, asperlin, azacitidine, azetepa, azotomycin, batimastat, benzodepa, bicalutamide, bisantrene hydrochloride, bisnafide dimesylate, bizelesin, bleomycin sulfate, brequinar sodium, bropirimine, busulfan, cactinomycin, calusterone, caracemide, carbetimer, carboplatin, carmustine, carubicin hydrochloride, carzelesin, cedefingol, celecoxib, chlorambucil, cirolemycin, cisplatin, cladribine, crisnatol mesylate, cyclophosphamide, cytarabine, dacarbazine, dactinomycin, daunorubicin hydrochloride, decitabine, dexormaplatin, dezaguanine, dezaguanine mesylate, diaziquone, docetaxel, doxorubicin, doxorubicin hydrochloride, droloxifene, droloxifene citrate, dromostanolone propionate, duazomycin, edatrexate, efl ornithine hydrochloride, elsamitrucin, enloplatin, enpromate, epipropidine, epirubicin hydrochloride, erbulozole, esorubicin hydrochloride, estramustine, estramustine phosphate sodium, etanidazole, etoposide, etoposide phosphate, etoprine, fadrozole hydrochloride, fazarabine, fenretinide, floxuridine, fludarabine phosphate, fluorouracil, fluorocitabine, fosquidone, fostriecin sodium, gemcitabine, gemcitabine hydrochloride, hydroxyurea, idarubicin hydrochloride, ifosfamide, ilmofosine, iproplatin, irinotecan, irinotecan hydrochloride, lanreotide acetate, letrozole, leuprolide acetate, liarozole hydrochloride, lometrexol sodium, lomustine, losoxantrone hydrochloride, masoprocol, maytansine, mechlorethamine hydrochloride, megestrol acetate, melengestrol acetate, melphalan, menogaril, mercaptopurine, methotrexate, methotrexate sodium, metoprine, meturedepa, mitindomide, mitocarcin, mitocromin, mitogillin, mitomalcin, mitomycin, mitosper, mitotane, mitoxantrone hydrochloride, mycophenolic acid, nocodazole, nogalamycin, ormaplatin, oxisuran, paclitaxel, pegaspargase, peliomycin, pentamustine, peplomycin sulfate, perfosfamide, pipobroman, piposulfan, piroxantrone hydrochloride, plicamycin, plomestane, porfimer sodium, porfiromycin, prednimustine, procarbazine hydrochloride, puromycin, puromycin hydrochloride, pyrazofurin, riboprine, safingol, safingol hydrochloride, semustine, simtrazene, sparfosate sodium, sparsomycin, spirogermanium hydrochloride, spiromustine, spiroplatin, streptonigrin, streptozocin, sulofenur, talisomycin, tecogalan sodium, taxotere, tegafur, teloxantrone hydrochloride, temoporfin, teniposide, teroxirone, testolactone, thiamiprine, thioguanine, thiotepa, tiazofurin, tirapazamine, toremifene citrate, trestolone acetate, triciribine phosphate, trimetrexate, trimetrexate glucuronate, triptorelin, tubulozole hydrochloride, uracil mustard, uredepa, vapreotide, verteporfin, vinblastine sulfate, vincristine sulfate, vindesine, vindesine sulfate, vinepidine sulfate, vinglycinate sulfate, vinleurosine sulfate, vinorelbine tartrate, vinrosidine sulfate, vinzolidine sulfate, vorozole, zeniplatin, zinostatin, zorubicin hydrochloride, analogues and derivative thereof, and combinations thereof. In certain embodiments, the chemotherapeutic agent is sorafenib.

In certain embodiments, the chemotherapeutic agent used with the presently disclosed methods includes one or more agents selected from cisplatin, carboplatin, docetaxel, gemcitabine, paclitaxel, paclitaxel, vinorelbine, pemetrexed, analogs and derivatives thereof, and combinations thereof.

In certain embodiments, the anti-cancer treatment is an immunotherapy (also known as immuno-oncology) that uses components of the immune system. Non-limiting examples of immunotherapies include immune checkpoint inhibitors, adoptive T cell transfer, therapeutic antibodies, cancer vaccines, cytokines, Bacillus Calmette-Guerin (BCG), and any combinations thereof.

In certain embodiments, the anti-cancer treatment includes administering an immune checkpoint inhibitor to the subject. In certain embodiments, the immune checkpoint inhibitor is selected from anti-PDl antibodies, anti-PD-Ll antibodies, anti-CTLA-4 antibodies, and any combinations thereof. Non-limiting examples of anti-PDl antibodies include pembrolizumab (Keytruda®), nivolumab (Opdivo®), cemiplimab (Libtayo®), and combinations thereof. Nonlimiting examples of anti-PD-Ll antibodies include atezolizumab (Tecentriq®), avelumab (Bavencio®), durvalumab (Imfinzi®), and combinations thereof. Non-limiting examples of anti- CTLA-4 antibodies include ipilimumab (Yervoy®).

In certain embodiments, the immune checkpoint inhibitor is directed against one or more immune checkpoint modulators. For example, without limitation, immune checkpoint inhibitors can target AMHRII, B7-H3, B7-H4, BTLA, BTNL2, Butyrophilin family, CD27, CD28, CD30, CD40, CD40L, CD47, CD48, CD70, CD80, CD86, CD155, CD160, CD226, CD244, CEACAM6, CLDN6, CCR2, CTLA4, CXCR4, GD2, GGG (guanylyl cyclase G), GIRT, GIRT ligand, HHLA2, HVEM, ICOS, ICOS ligand, IFN, IL1, IL1 R, IL1 RAP, IL6, IL6R, IL7, IL7R, IL12, IL12R, IL15, IL15R, LAG 3, LIGHT, LIF, MUC16, NKG2A family, 0X40, 0X40 ligand, PD1, PDL1, PDL2, Resokine, SEMA4D, Siglec family, SIRPalpha, STING, TGFbeta family, TIGIT, TIM3, TMIGD2, TNFRSFm VISTA, 4-1BB, and 4-1BB ligand.

In certain embodiments, the anti-cancer treatment does not include administering atezolizumab (Tecentriq®), bevacizumab (Avastin®), and combinations thereof. In certain embodiments, the anti-cancer treatment does not include administering tremelimumab (Imjudo®), durvalumab (Imfinzi®), and combinations thereof. In certain embodiments, the anti-cancer treatment does not include administering pembrolizumab (Keytruda®). In certain embodiments, the anti-cancer treatment does not include administering nivolumab (Opdivo®). In certain embodiments, the anti-cancer treatment includes administering therapies targeting the 0-catenin signaling pathway or its downstream targets. In certain embodiments, the anti-cancer treatment includes PKF115-584, PNU-74654, PKF118-744, CGP049090, PKF118- 310, ZTM000990, BC21, CCT036477, PKF222-815, CWP232228, PRI-724/C-82, ICG001, MSAB, SAH-BLC9_B, ZINC02092166, iCRT3, iCRT5, iCRT14, NLS-StAx-h, Hl-Bl, UU-T01, T02, 4FNPC, Apigenin, Carsonic acid, Curcumin, Esculetin, Magnalol, Resveratrol, Silibinin, T oxoflavin, NRX-252114, or a combination thereof. Additionally or alternatively, in certain embodiments, the anti-cancer treatment includes rapamycin, everolimus, RM-006 (RM-6272), sapanisertib, or a combination thereof. Additional information on therapies targeting the P-catenin signaling pathway or its downstream targets can be found in Park and Kim, Cells 12.8 (2023): 1110; Dev et al., Bioengineered 14.1 (2023): 2251696; and Nalli et al., Molecules 27.22 (2022): 7735, the content of each of which is incorporated by reference in its entirety.

In certain embodiments, the methods disclosed herein can be used for treating any suitable cancers. Non-limiting examples of cancers encompassed by the disclosed subject matter include liver cancers, brain cancers, cervical cancers, colorectal cancers, breast cancers, endometrial carcinomas, gastric cancers, cancers of the head and neck, bladder cancers, lung cancers, ovarian cancers, biliary tree cancers, hepatocellular carcinomas, leukemia, lymphoma, myeloma, and sarcoma. In certain embodiments, the methods disclosed herein can be used for treating a cancer selected from bladder urothelial carcinoma, cervical squamous cell carcinoma, endocervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, prostate adenocarcinoma, rectum adenocarcinoma, stomach adenocarcinoma, and uterine corpus endometrial carcinoma. In certain embodiments, the methods disclosed herein can be used for treating colon cancer, gastric cancer, breast cancer, lung cancer, pancreatic cancer, head and neck cancer, ovarian cancer, melanoma, and combinations thereof.

In certain embodiments, the methods disclosed herein can be used for treating a cancer selected from squamous cell cancer, lung cancer, cancer of the peritoneum, hepatocellular cancer, gastric or stomach cancer, pancreatic cancer, glioma, glioblastoma, cervical cancer, ovarian cancer, liver cancer, bladder cancer, hepatoma, breast cancer, colon cancer, rectal cancer, colorectal cancer, endometrial cancer or uterine carcinoma, salivary gland carcinoma, kidney or renal cancer, prostate cancer, vulvar cancer, thyroid cancer, hepatic carcinoma, anal carcinoma, penile carcinoma, CNS cancer, melanoma, head and neck cancer, bone cancer, bone marrow cancer, duodenum cancer, esophageal cancer, thyroid cancer, or hematological cancer.

In certain embodiments, the methods disclosed herein can be used for treating a cancer selected from endometrial adenocarcinoma, lung adenocarcinoma, colon adenocarcinoma, prostate adenocarcinoma, melanoma, renal cell carcinoma, gastrointestinal tumors (esophageal, gastric, colon and others) hepatocellular carcinoma, basal cell carcinoma (BCC), head and neck squamous cell carcinoma (HNSCC), prostate cancer (CaP), pilomatrixoma (PTR), medulloblastoma (MDB), hepatoblastoma (HB), hepatocellular adenomas (HCA), or hepatocellular cancer (HCC).

In certain embodiments, the methods disclosed herein can be used for treating hepatocellular carcinoma.

In certain embodiments, the subject is a human subject. In certain embodiments, the subject is a non-human subject, such as, but not limited to, a non-primate, a dog, a cat, a horse, a rabbit, a mouse, a rat, a guinea pig, a fowl, a cow, a goat, or a sheep.

It is to be understood that, for any particular subject, specific dosage regimes should be adjusted over time according to the individual need and the professional judgment of the person administering or supervising the administration of the anti-cancer treatment. For example, the dosage of a anti-cancer treatment can be increased if the lower dose does not provide sufficient activity in the treatment of a disease or condition described herein e.g., a cancer). Alternatively, the dosage of the composition can be decreased if the disease (e.g., a cancer) is reduced, no longer detectable, or eliminated.

In certain embodiments, the anti-cancer treatment can be administered once a day, twice a day, once a week, twice a week, three times a week, four times a week, five times a week, six times a week, once every two weeks, once a month, twice a month, once every other month or once every third month. In certain embodiments, the anti-cancer treatment can be administered twice a week. In certain embodiments, the anti-cancer treatment can be administered once a week. In certain embodiments, the anti-cancer treatment can be administered two times a week for about four weeks and then administered once a week for the remaining duration of the treatment.

In certain embodiments, the period of treatment can be at least one day, at least one week, at least one month, at least two months, at least three months, at least four months, at least five months, or at least six months. In certain embodiments, the anti-cancer treatment can be administered until the cancer is no longer detectable.

In certain embodiments, the anti -cancer treatment can be administered to a subject by any route known in the art. In certain embodiments, the anti-cancer treatment can be administered parenterally. In certain embodiments, the anti-cancer treatment can be administered orally, intravenously, intraarterially, intrathecally, intranasally, subcutaneously, intramuscularly, and rectally.

In certain embodiments, one or more anti-cancer treatments can be used alone or in combination with one or more secondary anti-cancer treatments. For example, but not by way of limitation, methods of the present disclosure can include administering one or more anti-cancer treatments. “In combination with,” as used herein, means that the anti-cancer treatment and a secondary anti-cancer treatment are administered to a subject as part of a treatment regimen or plan. In certain embodiments, being used in combination does not require that the anti-cancer treatment and the secondary anti-cancer treatment be physically combined prior to administration, administered by the same route or that they be administered over the same time frame.

In certain non-limiting embodiments, the present disclosure provides a method of treating a subject having a cancer that includes identifying the subject as non-responder and then treating the subject with an effective amount of an anti -cancer treatment not including atezolizumab, bevacizumab, or a combination thereof. In certain embodiments, the methods disclosed herein include measuring the expression level of one or more genes and identifying the subject as non- responder, as disclosed in Section 3 above.

In certain non-limiting embodiments, the present disclosure provides a method of treating a subject having a cancer that includes identifying the subject as non-responder and then treating the subject with an effective amount of sorafenib. In certain embodiments, the methods disclosed herein include measuring the expression level of one or more genes and identifying the subject as non-responder, as disclosed in Section 3 above. In certain non-limiting embodiments, the present disclosure provides a method of treating a subject having a cancer that includes identifying the subject as responder and then treating the subject with an effective amount of an anti-cancer treatment. In certain embodiments, the methods disclosed herein include measuring the expression level of one or more genes and identifying the subject as responder, as disclosed in Section 3 above.

In certain non-limiting embodiments, the present disclosure provides a method of treating a subject having a cancer that includes identifying the subject as responder and then treating the subject with an effective amount of atezolizumab. In certain embodiments, the methods disclosed herein include measuring the expression level of one or more genes and identifying the subject as responder, as disclosed in Section 3 above.

In certain non-limiting embodiments, the present disclosure provides a method of treating a subject having a cancer that includes identifying the subject as responder and then treating the subject with an effective amount of bevacizumab. In certain embodiments, the methods disclosed herein include measuring the expression level of one or more genes and identifying the subject as responder, as disclosed in Section 3 above.

In certain non-limiting embodiments, the present disclosure provides a method of treating a subject having a cancer that includes identifying the subject as responder and then treating the subject with an effective amount of atezolizumab and bevacizumab. In certain embodiments, the methods disclosed herein include measuring the expression level of one or more genes and identifying the subject as responder, as disclosed in Section 3 above.

5. Kits

The present disclosure provides kits for performing any of the methods disclosed herein.

In certain non-limiting embodiments, the present disclosure provides kits for identifying a non-responder subject, a responder subject, or a subject having a mutated CTNNB1. In certain embodiments, the kits are configured for detecting an expression level of at least one gene as described in Section 3. In certain embodiments, the kits are configured to provide treatment guidelines as described in Section 4.

For example, but without any limitation, the presently disclosed kits can include antibodies for immunodetection of the gene to be identified, oligonucleotide primers suitable for polymerase chain reaction (PCR), or nucleic acid sequencing; nucleic acid probes suitable for in situ hybridization or fluorescent in situ hybridization.

In certain non-limiting embodiments, the presently disclosed kit can include a reverse transcriptase, at least one set of primers, a detergent, a carrier nucleic acid, a positive control nucleic acid, a stabilization agent, containers, a DNA polymerase, Uracil-DNA Glycosylase (UDG) enzyme, a protector nucleic acid, a container, or a combination thereof. In certain embodiments, the kit comprises a reverse transcriptase. In certain embodiments, the reverse transcriptase is used to transcribe target RNA into DNA, and to amplify the DNA to a detectable amplification product. In certain embodiments, the reverse transcriptase is selected from a Moloney murine leukemia virus (M- MLV) reverse transcriptase (RT), an avian myeloblastosis virus (AMV) RT, a retrotransposon RT, a telomerase reverse transcriptase, an HIV-1 reverse transcriptase, or a recombinant version thereof. In certain embodiments, the kit comprises a DNA polymerase. In certain embodiments, the DNA polymerase is a Thermus aquaticus (Taq) DNA polymerase or variant thereof.

In certain embodiments, the kit can include at least one set of primers. In certain embodiments, the kit includes a forward primer and a reverse primer that bind to one gene selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19.

In certain embodiments, the kit includes a forward primer and a reverse primer that bind to two genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes a forward primer and a reverse primer that bind to three genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes a forward primer and a reverse primer that bind to four genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes a forward primer and a reverse primer that bind to five genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes a forward primer and a reverse primer that bind to six genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes a forward primer and a reverse primer that bind to seven genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes a forward primer and a reverse primer that bind to eight genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes a forward primer and a reverse primer that bind to nine genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes a forward primer and a reverse primer that bind to ten genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes a forward primer and a reverse primer that bind to eleven genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes a forward primer and a reverse primer that bind to twelve genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19.

In certain embodiments, the kit includes a forward primer and a reverse primer that bind to AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, and TNFRSF19. In certain embodiments, the kit includes a forward primer and a reverse primer that bind to AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SLC13A3, SP5, TCF7, and TNFRSF19.

In certain embodiments, the kit can include carrier nucleic acid, e.g., poly-A60 DNA oligonucleotide and/or E. coli tRNA. In certain embodiments, the kit can include at least one positive control nucleic acid. In certain embodiments, the kit can include a detergent, e.g., Triton- Xi 0. In certain embodiments, the kit can include a stabilization agent selected from an RNase inhibitor, a metal-chelating agent, a reducing agent, an antibiotic, an antimycoctic, a protease inhibitor, or a combination thereof. In certain embodiments, the kit can include Uracil-DNA Glycosylase (UDG) enzyme. In certain embodiments, the UDG enzyme can reduce or inhibit detection of amplification product contaminants.

Additionally or alternatively, the kit can include at least one antibody. In certain embodiments, the kit includes an antibody that binds to one of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19.

In certain embodiments, the kit includes two antibodies that independently bind one of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes three antibodies that independently bind one of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes four antibodies that independently bind one of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes five antibodies that independently bind one of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes six antibodies that independently bind one of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes seven antibodies that independently bind one of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes eight antibodies that independently bind one of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes nine antibodies that independently bind one of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes ten antibodies that independently bind one of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes eleven antibodies that independently bind one of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19. In certain embodiments, the kit includes twelve antibodies that independently bind one of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, or TNFRSF19.

In certain embodiments, the kit includes thirteen antibodies that independently bind one of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, and TNFRSF19. In certain embodiments, the kit includes ten antibodies that independently bind one of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SLC13A3, SP5, TCF7, and TNFRSF19. In certain embodiments, the kit can include a container including, without any limitation, test tube, centrifuge tube, multi-well plate, and the like. In certain embodiments, the kit comprises a reaction buffer including, for example and without any limitation, diluent, water, magnesium acetate (or another magnesium compound such as magnesium chloride), dNTPs, or a combination thereof. As will be appreciated by one of skill in the art, the reagents can be supplied in a lyophilized form or a concentrated form that can diluted or suspended in liquid prior to use. The kit reagents described herein can be supplied in aliquots or in unit doses. In certain embodiments, the components described herein can be provided singularly or in any combination as a kit. Such a kit includes the components described herein and packaging materials thereof. In addition, a kit comprises informational material.

In certain embodiments, the compositions in a kit can be provided in a watertight or gas tight container which in some embodiments is substantially free of other components of the kit. For example, the reagents described herein can be supplied in more than one container, e.g., it can be supplied in a container having sufficient reagent for a predetermined number of applications, e.g., 1, 2, 3 or greater. One or more components as described herein can be provided in any form, e.g., liquid, dried or lyophilized form. Liquids or components for suspension or solution of the reagents can be provided in sterile form and should not contain microorganisms or other contaminants. When the components described herein are provided in a liquid solution, the liquid solution preferably is an aqueous solution.

The informational material can be descriptive, instructional, marketing or other material that relates to the methods described herein. The informational material of the kits is not limited in its form. In certain embodiments, the informational material can include information about production of the reagents, concentration, date of expiration, batch or production site information, and so forth. In certain embodiments, the informational material relates to methods for using or administering the components of the kit.

EXAMPLES

The present disclosure will be better understood by reference to the following Example, which is provided as exemplary of the presently disclosed subject matter, and not by way of limitation.

Example 1 - Mutated fl-catenin Gene Signature to Identify CTNNBl-mutated and Immune Checkpoint Resistant Hepatocellular Cancers

Liver cancer, of which hepatocellular carcinoma (HCC) is the most common, is the third leading cause of cancer-related death globally¹. The global burden is projected to increase as the etiology shifts from viral to nonviral causes, including alcoholic liver disease and metabolic dysfunction associated steatotic liver disease². HCC develops in the background of these chronic liver diseases as liver injury and inflammation drive fibrosis, cirrhosis, and eventually cancer. In the advanced disease setting, overall survival is 12-18 months with current systemic therapies³. Existing immunotherapeutic combinations (e.g., with immune checkpoint inhibitors, e.g., ICIs) have drastically improved the treatment armamentarium for HCC, however, objective response rates remain critically low between 30-35%⁴’⁵. Early post-hoc analysis has indicated that both tumor genetics and tumor microenvironment features likely influence immunotherapy (IO) response⁶.

One such pathway known to influence IO response rates observed in preclinical models and in HCC patients is the Wnt/p-catenin pathway^7,8. In HCC, approximately 26-37% of patients, depending on the geographic region, have mutations in CTNNB 1 (the gene encoding P-catenin)^9,10. These mutations are stabilizing, gain-of-function (GOF) mutations mostly in exon 3 of the CTNNB 1 gene. In adults, P-catenin under normal physiologic conditions (in the absence of Wnt ligand) is phosphorylated, ubiquitinated, and degraded by the proteasomal degradation machinery. However, mutations render P-catenin unable to be phosphorylated or ubiquitinated which leads to its stabilization and nuclear translocation to act as a transcription co-factor with TCF/LEF family members to turn on target gene expression. It has been shown the role of P-catenin in tumor cell proliferation and growth^11,12, tumor metabolism^13,14, and tumor-immune interactions⁸. Identifying patients with P-catenin activation thus can have prognostic and therapeutic implications in HCC as treatment becomes more personalized⁶.

As tissue and/or liquid biopsy continue to augment the diagnostic landscape for HCC, utilizing molecularly targeted therapies in combination with IO are likely to improve response rates¹⁵. Thus, to translate this therapeutic combination, animal models that recapitulate the complex molecular biology driven by specific genetic drivers, rather than random combinations of oncogenes, in an immunocompetent mouse background are needed to improve the understanding of the cellular and molecular basis of this disease. Sleeping beauty transposon/transposase and hydrodynamic tail vein injection (SB-HDTVI) have been used to transfect mouse hepatocytes in vivo with various combinations of oncogenes to mimic human HCC subsets. Using this “inside- out” model, it has been identified that the introduction of P-catenin alone does not initiate tumorigenesis, but rather requires cooperation with other secondary drivers to induce tumors in mice¹². Specifically, it was previously shown that mutated-P-catenin cooperates with hMET and Nuclear-factor-like 2 (NRF2) to induce HCC, with each model representing 9-12% of human HCC subsets^12,14. Thus, further development of these models can provide novel opportunities to understand tumor biology, biomarker discovery, and test therapeutics.

In the present example, around 14% of HCC cases that demonstrate concomitant activation of NRF2 and MET signaling were identified. In addition, a subset of these patients had GOF mutations in CTNNB1. Based on these clinical observations, mutant-CTNNB 1 ± mutant-NRF2 and hMET were co-expressed using SB-HDTVI leading to HCC development in mice. Transcriptomic profiling of multiple tumors demonstrated similarity to respective human HCC subsets. With the availability of a multitude of HCC mouse models with transcriptomic data, the present example was able to derive a common gene signature representing P-catenin activity referred henceforth as the mutant P-catenin-specific gene signature (MBGS) which was verified in its ability to successfully identify CTNNB1 -mutated HCC in multiple patient cohorts. Based on both cell-intrinsic and cell-extrinsic tumor biology driven by mutant P-catenin, the MBGS also predicted lack of optimum response to the standard of care atezolizumab + bevacizumab combination in the IMbravel50 cohort. Overall, the present example derived a transcriptomic signature with a value in patient stratification for personalized medicine in HCC.

Significant subsets of HCC patients have overlap of NRF2 and MET gene signatures and represent a distinct molecular subgroup.

Previously, cooperativity in both patients and in mice was identified between P-catenin and NRF2 activation, and between P-catenin and MET activation, with each subset representing around 9-12% of all HCCs^12,14. Here, it was investigated whether NRF2 and MET co-activation cooperate in the pathogenesis of HCC, and whether this could be modeled in vivo to study liver tumor biology of such a distinct subset. First, to determine whether a subgroup of patient tumors had any overlap in NRF2 and MET activation, TCGA for liver hepatocellular carcinoma (LIHC) patients was analyzed¹⁰. TCGA contained a total of 374 HCC cases including 50 cases for which adjacent normal tissues are also included. To define a population of patients which were NRF2-active (henceforth referred to as NRF2-high), hierarchical clustering was applied to the entire cohort using a previously published 28-gene NRF2 activation gene signature¹⁶, which grouped the cases into 4 distinct clusters as shown in Figure 1A. The pink cluster identified 100 HCC cases with high expression of the 28-gene NRF2 activation gene signature, suggesting -27% of all HCC cases to be NRF2-high, which encompassed the majority of HCC patients with gain of function (GOF)- mutations in NFE2L2 or loss of function (LOF)-mutations in KEAP1, but also captured cases with NRF2 activation independent of these mutations. Additionally, to define a population of patients which were MET-active (henceforth referred to as MET-high), hierarchical clustering was applied to the entire TCGA-LIHC cohort using the previously published KAPOSI LIVER CANCER MET UP 18-gene signature¹⁷ from mSigDB (Figure IB), as also previously shown in a smaller TCGA cohort^12,18. Interestingly, a dichotomous sub-clustering of the 18-gene MET activation signature was observed, where the pink cluster represented patients with high expression of the top 9 genes on the heatmap, and the green cluster represented patients with expression of the bottom 9 genes on the heatmap. Thus, the MET-high patients were classified as those in the pink and green clusters combined, representing 176 HCC patients, or -47% of all HCC cases. From this analysis, it was also observed that many patients comprising the pink and green clusters had GOF-mutations in NFE2L2 or LOF-mutations in KEAP1, suggesting potential cooperativity between NRF2 and MET (Figure IB). Indeed, 54 HCC patients or 14.4% of all HCC cases were identified, which showed an overlap of NRF2-high and MET-high gene signatures in TCGA (Figure 2A).

Differential gene expression analysis, comparing the 54 NRF2/MET-high patients to the 50 normal tissue cases in TCGA-LIHC cohort, yielded 5,238 differentially expressed genes (DEGs) by FDR=0.001 and absolute log fold change greater than 3. Ingenuity pathway analysis (IP A) was performed on the 5,238 DEGs and identified 254 significantly enriched pathways, with the top 10 altered pathways depicted to notably be Cell Cycle Checkpoints, Kinetochore Metaphase Signaling and others (Figure 2B). Thus, concomitant NRF2 and MET activation is apparent in a significant subset of HCC patients.

Distinct subset ofHCCs with overlap of NRF 2 /MET-high gene signature harbor CTNNB1 mutations with unique transcriptome and more aggressive phenotype.

Given the cooperativity of P-catenin and NRF2 and between P-catenin and MET, it was hypothesized that a subset of NRF2/MET-high patients can also harbor CTNNB1 mutations. In TCGA-LIHC cohort, 98 patients have mutations in CTNNB1 : 35 (9.4%) had CTNNB1- mutation/NRF2-high overlap and 41 (10.9%) had CTNNBl-mutation/MET-high overlap, as previously identified^12,14. Also, among the 54 TCGA-LIHC NRF2-high/MET-high patients, 18 patients, or 4.8% of all HCC cases, had mutations in CTNNB1 (Figure 2A). Interestingly of these 18 patients, 12 patients (67%) had mutations in exon 3, 5 (28%) had mutations in exon 8, and 1 patient had a mutation in exon 7 (Figures 1C and 2D). And, of the 12 patients with exon 3 mutations, 8 (67%) were D32-S37 subgroup, 2 (17%) patients were S45 subgroup, and 2 (17%) patients were T41 subgroup (Figure 2D), with D32-S37 subgroup having highest P-catenin activity, T41 moderate, and S45 weakest activity (although with gene duplication) based on previous genotype-phenotype analysis¹⁹. Thus, 18.4% of all CTNNB1 -mutated HCC cases have NRF2- high/MET-high gene signature, and majority of these cases have CTNNB1 point mutations with high P-catenin activity.

Differential gene expression (DGE) analysis, comparing the 18 CTNNB1- mut/NRF2/MET-high patients to the 50 normal tissue cases in TCGA-LIHC cohort, yielded 5,114 DEGs by FDR=0.001 and absolute fold change greater than 3. IPA was performed on the 5,114 DEGs and identified 261 significantly enriched pathways, with the top 10 pathways depicted in Figure 2C. Additionally, given that many patients were identified showing high P-catenin activity (majority in D32-S37 subgroup), it was queried if the tumors in these patients exhibited a more aggressive phenotype. Indeed, the 18 CTNNB1 -mutant/NRF2/MET-high patients trended towards a worse overall survival as compared to all other CTNNB1 -mutated patients (p=0.104) (Figure 2E). However, when comparing CTNNB I -mutant/NRF2/MET-high (n=18) to CTNNBl-wild- type/NRF2/MET-high patients (n=36), there was no difference in overall survival (Figure 21 A), suggesting that within NRF2/MET-high patients, CTNNB1 -mutation is not influencing survival. It appears NRF2 is trending to be a driver of poorer survival in CTNNB1 -mutated cases, although no statistical significance was evident (Figures 21B and 21C). Studies in larger cohorts are needed to extent these findings.

Concomitant expression of mutant-GOF f-catenin with mutant-GOF NFE2L2 and hMET in a subset of murine hepatocytes in vivo induces tumors with early morbidity and mortality.

It has been previously shown that single oncogene induction of either GOF-mutant CTNNB1, GOF-mutant NFE2L2, or hMET does not induce HCC in mice^12,14. Thus, to model in vivo the observations from clinical cohorts of HCC patients, expression of S45Y-CTNNB1 ± G31A-NFE2L2 ± hMET was forced in 6-week-old FVB male mice through hydrodynamic tail vein injection (HDTVI) with sleeping beauty transposon/transpose, as previously described^12,14 (Figure 3 A). Mice injected with S45Y-CTNNB1 + G31A-NFE2L2 + hMET ( -N-M) displayed signs of early morbidity and mortality by -5 weeks post-HDTVI compared to other P-catenin driven models and mice injected with G31A-NFE2L2 + hMET (N-M) (Figure 3B). This aggressive phenotype mirrored survival analysis from the clinical cohort (Figure 2E). At the 5- week timepoint, livers with notable gross HCC and significantly increased liver weight (LW)/body weight (BW) ratio of -15% (p<0.001) compared to 4-5% LW7BW in wild-type FVB were observed (Figures 3C and 3E). Histologically, these nodules were large, well-circumscribed, and well- differentiated HCC foci with trabecular pattern, minimal nuclear atypia, and moderate fatty change (Figure 4A). Microscopically, it was observed that >80% HCC were simultaneously positive for Myc-tag (representing mutant CTNNB1), Nqol (NRF2 target), and V5-tag (representing hMET) (Figure 4A).

The N-M mice showed progressive morbidity by 14 weeks post-HDTVi, with significantly longer survival compared to the P-N-M, P-N, and P-M models (Figure 3B). At the 14-week timepoint, the livers had gross macroscopic tumor nodules with LW7BW ratio of 9-12%, which was significantly greater (p<0.001) than 4-5% in the wild-type FVB mice (Figures 3D and 3E). Histologically, these nodules were moderately-sized, well-circumscribed, and well-differentiated HCC with trabecular pattern, minimal nuclear atypia, and minimal fatty change (Figure 4B). The nodules were dually positive for Nqol and V5-tag (Figure 4B). This indicated that the tumors in the N-M model stemmed from concomitant activation of NRF2 and hMET. Additionally, IHC for Ki67, which stains proliferating cells, demonstrated that models with P-catenin activation tended to be more proliferative (Figure 5).

Lastly, to confirm the activation of P-catenin and its downstream targets in the P-N-M model, IHC for P-catenin was perfomed, demonstrating its nuclear translocation as compared to its membranous localization in the wild-type FVB mice (Figure 3F). It was also observed that P-N- M tumor nodules were glutamine synthetase (GS) and cyclin DI positive (Figure 3F). The tumor nodules in the N-M model stained negative for nuclear P-catenin and lacked intra-tumoral GS staining, with minimal cyclin DI -positive nuclei, suggesting lack of P-catenin activity in this model (Figure 3F). Overall, the P-N-M model demonstrated an aggressive P-catenin-driven model compared to other P-catenin-driven models, and the N-M model lacked any P-catenin activity.

Murine tumors with mutant-GOF P-catenin are transcriptionally distinct from tumors without P-catenin activation.

Next, transcriptional analysis was performed on tumors from mutant-P-catenin models (P- N-M model, n=3; P-M model, n=3; P-N model, n=3) and non-P-catenin model (N-M model, n=3) to compare to normal mouse liver (wild-type or WT FVB livers, n=3) to identify tumor-enriched pathways in each model (Figure 6A). Principal component analysis on the 15 samples showed WT clustered distinctly from all the tumor models (P-N-M, P-M, P-N, and N-M). Liver tumors from mice clustered similarly between P-N-M and P-N, while P-M and N-M clustered similarly (Figure 6B). To identify putative gene signatures in each tumor-bearing model, differential gene expression (DGE) was identified comparing WT to each tumor model (P-N-M, P-M, P-N, and N- M). Briefly, 2627 up-regulated genes and 1950 down-regulated genes were identified comparing WT vs P-N-M selected by FDR=5% and absolute fold change>1.5 (Figure 7A). Pathway analysis on the DEGs identified activation of Aryl Hydrocarbon Receptor Signaling, Kinetochore Metaphase Signaling, and NRF2-mediated oxidative stress response, among others (Figure 6C). DGE analysis identified 1016 up-regulated genes and 527 down-regulated genes comparing WT vs P-M (Figure 7B) and 2405 up-regulated genes and 1950 down-regulated genes comparing WT vs P-N (Figure 7C) with similar post-hoc statistical corrections. Additionally, pathway analysis comparing WT vs P-M (Figure 6D) and WT vs P-N (Figure 6E) identified relevant pathways previously described^12,14. Interestingly, 1167 up-regulated genes and 697 down-regulated genes were also identified comparing WT vs N-M (Figure 7D). Here, pathway analysis on the DEGs identified activation of relevant pathways, including NRF2-mediated oxidative stress response, Xenobiotic Metabolism, Hepatic fibrosis signaling and Glutathione redox reactions, among others (Figure 6F).

Murine tumors with NRF2/MET co-expression ± CTNNB1 mutation show high transcriptional similarity to respective human HCC subsets with similar molecular perturbations. It was previously shown that the T41A-CTNNB1-G31A-NFE2L2 model and the S45Y- CTNNBl-hMET model have 77% and 70% transcriptional similarity, respectively, to human HCC patients with the same molecular alterations^12,14. To determine transcriptional similarity of both the 0-N-M and N-M models to respective human HCCs with similar perturbations, DGE and IPA were determined and compared (see Methods described below). Overlapping the mouse DGE to human orthologs yielded 970 and 2,377 common genes for NRF2/MET-high and CTNNB1- mutant/NRF2/MET-high patient groups, respectively, with the DGE for each depicted on heatmap (Figures 8A and 8B). Next, the transcriptional overlap was compared quantitatively between the mouse models and human HCC and found high correlation of CTNNBl-mutant/NRF2/MET-high (0.807 by Pearson correlation; Figure 9A) and NRF2/MET-high (0.758 by Pearson correlation; Figure 9B). Additionally, there were 261 and 430 significantly enriched pathways in human and mouse CTNNBl-mutant/NRF2/MET-high, respectively, of which 124 were common between mice and humans, with the top enriched pathways shown in Figure 9C. Lastly, there were 254 and 252 significantly enriched pathways in human and mouse NRF2/MET-high respectively, of which 69 were common between the mice and patients, with the top enriched pathways shown in Figure 9D. Overall, the presently disclosed analysis demonstrated the mouse models represent well the human HCC subsets with similar molecular alterations, and thus provide a platform to develop biomarkers and test therapies.

Comparison of mutant-GOF f-catenin models to N-M model identifies a mutated f-catenin gene signature (MGBS).

Since transcriptomic data from multiple clinically relevant models with and without 0- catenin mutation using combinations of the same set of oncogenes were available, it was next derived a mutated 0-catenin specific gene signature (MGBS) specific to 0-catenin activity in HCC. First, DGE analysis was performed comparing the following models: N-M vs 0-N-M (Figure 10A), N-M vs 0-M (Figure 10B), and N-M vs 0-N (Figure 10C), to allow comparison of each 0-catenin mutated model to 0-catenin wild-type model, and then overlapped the DGE which was absolute fold change >3 to identify the common upregulated (95 genes) (Figure 11 A) and downregulated genes (53 genes) (Figure 1 IB), followed by IPA for each of the model comparisons (Figures 12A- 12C). The 95 upregulated and 53 downregulated genes were visualized on heatmaps with the upregulated genes demonstrating high expression (Figure 11C), and the downregulated genes demonstrating low expression (Figure 11D), in all 0-catenin driven models. IPA on the 95 upregulated genes identified pathways enriched for Glutamine Biosynthesis, Wnt/0-catenin Signaling, Glutaminergic Receptor Signaling, and Retinol/Retinoate Biosynthesis (Figure HE). IPA on the 54 downregulated genes identified pathways enriched for SI 00 Family Signaling Pathway, Agranulocyte Adhesion and Diapedesis, and Phagasome Formation (Figure 1 IF). Thus, P-catenin active tumors are enriched in glutamine signaling¹³, as previously shown, and retinol/retinoate signaling, as others have shown to be potential mechanisms of IO response in solid n tumors .

MGBS identifies HCC patients with CTNNB 1 mutations.

Of the 95 enriched mouse genes, 85 mapped to human orthologs in TCGA-LIHC cohort. In the TCGA-LIHC dataset, the z-scores based on normalized expression values of the 85 human genes were visualized on heatmap with three clusters: adjacent normal (n=50), CTNNB1 -wildtype (n=276), and CTNNB1 -mutated (n=98) (Figure 13). DGE analysis was next performed on these 85 human genes comparing CTNNB1 -wild-type to CTNNB1 -mutated cases and identified 13 significantly upregulated genes: AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, which comprised the presently disclosed 13-gene MBGS (Figure 14A). These were also visualized on heatmap comparing expression between normal and CTNNB1 mutated and wild-type cases (Figure 14B). Next, expression of individual genes was compared in normal, CTNNB1 -wild-type, and CTNNB1 -mutated tissues. It was observed that SLC1A2 had high expression in normal tissue; and, TEDDM1 and SBSPON were only expressed in subset of CTNNB1 -mutated HCC cases (Figure 14C). Thus, a reduced 10- gene MBGS was formalized to include AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SLC13A3, SP5, TCF7, TNFRSF19.

Next, it was determined whether the 13- and 10-gene MBGS had predictive ability to classify TCGA-LIHC patients with CTNNB 1 mutations. The composite average expression of 13- gene (Figure 15 A) and 10-gene (Figure 15B) MBGS panels was assessed in normal, CTNNB 1- wild-type, and CTNNB 1 -mutated tissues. In TCGA-LIHC, the 13-gene and 10-gene MBGS had predictive ability to classify CTNNB 1 -mutated cases with AUC of 0.91 and 0.90, respectively, and the sensitivity and specificity of the 13-gene MBGS were 0.857 and 0.877, respectively (Figure 15C).

The predictive ability of CTNNB 1 -mutation status with composite average expression of other molecular subclasses gene signatures known to overlap with patients harboring CTNNB 1 mutations was also assessed, along with previously published Wnt gene signatures using TCGA- LIHC dataset. Boyault G5/G6²², Chiang CTNNB 1 subclass²⁵, Hoshida S3⁶⁸, and Lachenmayer Wnt-CTNNBl⁶⁹ gene signatures predicted CTNNB1 mutational status with AUC of 0.9013 (sensitivity: 0.837; specificity: 0.891), 0.8983 (sensitivity: 0.867; specificity: 0.837), 0.5898 (sensitivity: 0.969; specificity: 0.203), and 0.8892 (sensitivity: 0.776; specificity: 0.931), respectively (Figures 24A-24H). Additionally, BIOCARTA WNT PATHWAY, KEGG WNT SIGNALING PATHWAY, and

REACTOME SIGNALING BY WNT IN CANCER predicted CTNNB 1 mutational status with AUC of 0.6299 (sensitivity: 0.489; specificity: 0.743), 0.5877 (sensitivity: 0.918; specificity: 0.301), and 0.6008 (sensitivity: 0.439; specificity: 0.743), respectively (Figure 25A-25F).

Expression of this signature in another HCC cohort of 398 cases from France²¹ was also tested. Expression of the 13-gene and 10-gene MGBS was assessed in normal (n=31), CTNNB1- wild-type (n=280), and CTNNB1 -mutated (n=l 18) cases and showed significant enrichment of the signature in CTNNB1 -mutated cases (Figures 15D and 15E). In the French cohort, the 13-gene and 10-gene MBGS had predictive ability to classify CTNNB1 -mutated cases with AUC of 0.95 and 0.94, respectively (Figure 15F). Additionally, the average expression of the signature was assessed in patient groups stratified by G1-G6 subgroup status²², and demonstrated enrichment in G5/G6 subgroups, as this is the subgroup known for CTNNB1 -mutated and active tumors (Figures 15G and 15H). It was also identified that MBGS is specific to predicting CTNNB1 -mutated patients compared to other molecular subclass predictions (Boyault, Hoshida, Chiang), as can be seen in heatmap overlapping all subclasses, CTNNB1 -mutated patients, and MBGS expression by all 13 -genes (Figure 26).

Thus, the present disclosure successfully developed a 13-gene panel to identify CTNNB1- mutated HCCs across multiple patient cohorts with superior or comparable performance to previously reported molecular subclasses or Wnt-CTNNBl gene signatures.

MBGS classifies tumors with f-catenin mutations in pan-cancer atlas.

Next, it was determined whether MBGS would be able to classify non-HCC liver tumors with Wnt/p-catenin pathway activity, including hepatocellular adenoma and hepatoblastoma. It was first observed that MBGS is mostly enriched in HCC patients with exon 3 mutations (Figures 16A and 16B). Additionally, it was observed enrichment of MBGS in both hepatocellular adenomas (n=6) and hepatoblastomas with CTNNB1 alterations, accounting for patients with either mutations in CTNNB1 (n=92) or biallelic APC mutations (n=4) (Figures 16A and 16B). Next, the pan-cancer atlas which integrates transcriptomic-exome data from ICGC/TCGA cases was utilized with 2,565 patients across 2,683 samples of multiple tumor types (Figure 17A), of which 178 harbored mutations in CTNNB1. The presently disclosed 10-gene signature was able to classify CTNNB1 -mutated tumors with an ROC AUC of 0.703 (Figure 17B). Overall, MBGS has more liver specificity and additional targets that are tumor- or tissue-type specific can be needed to further improve its performance.

MBGS predicts fewer immunotherapy related treatment effects in HCC patients.

Given the known role of Wnt/p-catenin pathway activation influencing IO response in HCC^6,23, there was interest in whether MBGS would also prognosticate IO response in immunotherapy treated cohorts. First, it was analyzed a previously reported smaller HCC dataset including 17 patients with 8 responders and 9 non-responders who received IO²⁴. Uniform Manifold Approximation and Projection (UMAP) demonstrated gene expression separation of responders and non-responders (Figure 18 A). DGE analysis showed many of the MBGS genes downregulated in responders vs. non-responders (Figure 18B). These genes were enriched in non- responders (Figure 18C). Comparison of MBGS to previously published 175-gene CHIANG_LIVER_CANCER_SUBCLASS_CTNNB1_UP25 to predict IO response resulted in similar ROC AUC of 0.78 and 0.79, respectively (Figures 18D-18G). It was also compared MBGS to other previously reported IO response gene signatures, including the T cell-inflamed gene expression profile²⁶, IFNg response signature²⁷, and tertiary lymphoid structure (TLS) signature²⁸, with each demonstrating ROC AUC of 0.68, 0.71, and 0.72, respectively (Figures 19A-19C).

It was also assessed whether MBGS expression levels mirrored the results observed in CTNNB1 -mutated patients in IMbravel50 cohort⁶, in which fewer atezolizumab/bevacizumab (atezo/bev) specific treatment effects were observed in CTNNB1 -mutated compared to wild-type patients. In IMbravel50 cohort, both the 13- and 10-gene MBGS correlated with each other and were enriched in CTNNB1 -mutated cases (Figures 20A-20C). Overall survival (OS) and progression-free survival (PFS) were stratified by high and low expression of 13-gene MBGS and demonstrated that patients with high expression of MBGS showed no clinical benefit with atezolizumab + bevacizumab compared to sorafenib in terms of OS (p=0.0542) and PFS (p=0.404) (Figure 20D). Meanwhile, patients with low expression of MBGS derived clinical improvement with atezolizumab + bevacizumab compared to sorafenib in terms of OS (p=0.039) and PFS (p=0.0293) (Figure 20E).

Interestingly, in patients receiving atezo/bev, expression of MBGS did not prognosticate OS or PFS (Figures 20D and 20E). Also, in patients with higher MBGS expression, improved OS/PFS was observed in the sorafenib arm, illustrating the rationale for observed fewer treatment effects in MBGS high vs low cohorts comparing atezo/bev versus sorafenib arms. Specifically, patients with low expression of MBGS showed clinical improvement with atezo/bev compared to sorafenib in terms of OS (p=0.0329) and PFS (p=0.0293) (Figures 27A and 27B), likely due to fewer treatment effects of sorafenib in MBGS low compared to high patients. Clinical response was further stratified using mRECIST criteria for complete/partial response (CR/PR), stable disease (SD), and progressive disease (PD) in each treatment arm and by MBGS expression. Higher MBGS expression was associated with CR/PR or SD in sorafenib arm compared to lack of association in atezo/bev arm (Figure 22), illustrating that patients with CTNNB 1 activity derive significant clinical benefit from sorafenib but are not associated with primary resistance to combination ICI. Thus, using MBGS expression levels as a surrogate readout mirrors the results observed in IMbavel50 cohort when profiling CTNNB 1 mutational status. In conclusion, MBGS can be used as a direct predictor of IO resistance in HCC. Spatial Mapping of Molecular Subclass Signatures Identifies Tumor Intrinsic and Extrinsic Features withMBGS Depicting Immune Excluded Tumors

Lastly, given that CTNNB1 -mutated patients have previously been reported to have an immune excluded phenotype, yet not necessarily correlated with 10 resistance (e.g., immune checkpoint resistance) as demonstrated here, spatial transcriptomic datasets were used to map MBGS (and other molecular subclass signatures) onto the tissue section to observe the immune profile in tumors which were MBGS-hot. Previously published HCC spatial transcriptomic datasets which used the 10X Visium platform of 7⁷⁰ and 5⁷¹ HCC cases each were integrated. Following spot integration, normalization, and quality control metrics, analysis was restricted to 12 (from 11 patients) slides as 1 slide from Zhang et al ⁷⁰ did not meet quality control standards. First, normalized module score expression values were computed for each 10X Visium spot for the Boyault²², Chiang²³, and Hoshida⁶⁸ molecular subclassification schemes. Interestingly, across the slides, Boyault G5/G6 signature highlighted tumor nodules which were also MBGS-hot (Figures 23 A-23D and 28). Additionally, nodules which were G1/G2 subclass were exclusive from G3 or G5/G6 or MBGS-hot nodules (Figure 28), demonstrating that Boyault classification is specific to tumor intrinsic signaling. However, Chiang molecular subclassification demonstrated MBGS-hot tumors overlapped well with tumors which were Chiang CTNNB 1 and Chiang lFN subclasses (Figure 29), with regions in both the tumor nodules and the tissue stroma demonstrating high expression for Chiang lFN subclass. Hoshida S3 tumors captured nodules which were MBGS-hot, and mutually exclusive to nodules which where Hoshida SI (Figure 30). Moreover, MBGS were compared to previously reported Wnt-CTNNBl signature⁶⁹ and it was demonstrated that genes in MBGS have higher overall expression to detect tumor nodules compared to other mutant-Wnt classifiers (Figure 23B and 31). Lastly, the immune microenvironment within tumors which were MBGS-hot was analyzed. Thus, expression of Sia et al. “Immune Class” gene signature⁷² was spatially mapped to the different tissue sections and observed that tumors which were MBGS-hot are immune excluded within the tumor parenchyma, but can have an inflamed stroma in some cases (Figure 23C and 32), which likely contributes to the reported “Immune-like” subclass⁷³ in a subset of CTNNB1 -mutated patients which can show response to ICIs.

Discussion

In the present example, MBGS was developed using multiple mouse models either dependent or non-dependent on P-catenin activation and validated it in multiple large human HCC integrated genomic-transcriptomic datasets. Aberrant tumor-intrinsic Wnt/ P-catenin pathway activation, either through mutations in CTNNB1, AXIN1, or APC, has been identified in several solid tumor types, including HCC, melanoma, colorectal cancer, and endometrial cancer²⁹. Activation of this signaling pathway can hold prognostic value in terms of therapy response^30,31. Most importantly, in the maj ority of these tumor types, mutated CTNNB 1 has been associated with immune exclusion in the tumor microenvironment⁷’³², which has been categorized as part of the immune excluded subclass in HCC^{72, 73} and associated with lack of IO response in both HCC^23,22 and melanoma patients^34,35. Thus, defining biomarkers of Wnt/p-catenin activity holds diagnostic utility and prognostic implications for treatment selection and stratification.

The present disclosure also developed and characterized two additional SB-HDTVI mouse HCC models to understand tumor biology in the representative patient subsets. It was demonstrated that P-M, P-N, P-N-M, and N-M models show high molecular similarity to respective human HCC subsets with similar perturbations at both the transcriptomic and pathway level^12,14. Additionally, these models closely mimic the pathophysiology in humans, as demonstrated by the P-N-M model mice requiring euthanasia by 4-5 weeks with greater Ki67-positive cells compared to P-M and P-N model, mirroring the shorter survival seen in patients with concomitant activation of P-catenin, NRF2, and MET signaling. Notably, an interesting link between P-catenin activation and retinol/retinoate biosynthesis was identified across all models. Retinoic acid is known to modulate expression of NKG2D ligands, which upon binding to NK cells induces cytotoxicity and cytokine secretion^36,37. In fact, expression ofNKG2D ligands (MICA, MICB, ULBP1 and ULBP2) has been reported to be inhibited by P-catenin signaling in HCC³⁸. Further, vitamin A, or all-trans retinoic acid (ATRA), used in conjunction with IO can be efficacious in tumors with reduced expression of NKG2D ligands³⁹, as is the case in P-catenin-mutated HCC.

Another gene identified in MBGS involved in immune exclusion in P-catenin-mutated HCC is Tumor necrosis factor receptor superfamily, member 19 (TNFRSF19). TNFRSF19 is part of the TNF-receptor superfamily, a target gene of the Wnt/p-catenin pathway, and leads to NF-kB activation in Wnt active cells^40,41. Additionally, TNFRSF19 has been shown to play a role in inhibiting the p38/mitogen-activated protein kinase (MAPK) signaling pathway in the liver⁴². Interestingly, in a recent study, Wong et al. demonstrated that NAFLD-associated HCC has an enrichment for CTNNB 1 -mutated HCC with TNFRSF 19 reshaping the immune microenvironment through repression of immunostimulatory cytokines, such as IL6, IL8, CXCL8, CXCL9, and CXCL5.⁴³ Moreover, IO resistance and/or response could be overcome/induced through inhibiting both Wnt signaling (via ICG001) and TNFRSF 19 in a mouse model of NAFLD-HCC via orthotopic injection of murine Hepal-6 cells overexpressing S45P-CTNNB1 on a choline-deficient high fat diet⁴³. Whether this signaling axis is sufficient to drive IO resistance in non-NAFLD CTNNB 1-mutatd HCC remains to be studied. Future work aimed at testing the direct role of TNFRSF 19 in immunocompetent genetic mouse models are needed to further understanding of this axis as a main driver of immune exclusion in CTNNB 1 -mutated HCC.

It is important to note the discrepancies in association of CTNNB 1 mutations with IO response. Harding et al. first described that HCC patients with Wnt/p-catenin pathway alterations (with majority receiving ICI as monotherapy) had worse OS and PFS than those without. This was also observed in a study by Ruiz de Galaretta et al. through in vivo studies and in a small cohort of patients receiving anti-PD-1 therapy. Additionally, Morita et al. described that patients with Wnt activation defined by GS+ IHC had worse OS/PFS on anti-PD-1 therapy. However, work from other groups, albeit in small cohorts as well, have challenged this notion and observed no significant differences in response rates or OS/PFS in patients with or without CTNNB1 mutations receiving ICI through either profiling pre-treatment biopsy specimens or cell-free DNA. Reanalysis of the IMbravel50 study results additionally illustrates how patients with and without CTNNB1 mutatations exhibit comparable responses and OS/PFS in atezo/bev cohort, yet patients with CTNNB1 mutations have unique responses to sorafenib, which has been illustrated previously. CTNNB1 -mutated patients could be deriving benefit from bevacizumab addition and/or susbets of patients who demonstrate response to ICIs have upregulation of inflammatory gene profiles involved in cytolytic immune activity. This aligns with other work describing that patients with CTNNB1 mutations can be categorized as “immune-like” (15% of HCC) or immune excluded (20% of HCC) subclass under the revised immune subclass algorithm, with patients in the “immune-like” class demonstrating enrichment of antigen type I presentation through hypermethylation. Through spatial mapping of MBGS and inflamed class gene signatures, this “immune-like” subclass can in fact be driven by yet unknown microenvironmental features. To reconcile these findings based on previous literature and the findings here, Wnt/p-catenin signaling can likely be driving immune exclusion within tumors. Yet, unknown mechanisms can influence the development of an inflamed stroma, and inherent tumor-intrinsic IO resistance mechanisms influenced by oncogenic Wnt signaling can be counterbalanced by tumors harboring engaged interferon signaling or antigen presentation machinery. Thus, further clinical studies in larger cohorts and mechanistic studies are needed to dissect the exact tumor intrinsic and extrinsic features driving different immune phenotypes in CTNNB1 -mutated HCC leading to the observed heterogenous ICI responses.

Another common pathway identified in the presently disclosed gene signature was Glutamine Biosynthesis. It was previously shown addiction to mutated-P-catenin-GLUL- glutamine-mTORCl axis in multiple preclinical models of P-catenin-mutated HCC^{13 14}. Indeed, these models are sensitive to mTOR inhibitors, such as everolimus or rapamycin¹³’¹⁴ The Everolimus for Liver Cancer Evaluation (EVOLVE-1) trial, which tested everolimus to placebo in HCC patients in second-line setting, failed to demonstrate any significant survival difference. However, treatment was not restricted to mTOR-addicted tumors as this can have led to more favorable outcomes through screening for patients with either tissue or liquid biopsy-proven P- catenin mutated HCC or tumors with loss of tuberous sclerosis complex 2 (TSC2), which both lead to increased mTOR signaling^44,45. Thus, subsets of patients with mTOR-addicted tumors can benefit from mTOR inhibitors in neoadjuvant or adjuvant setting. With NIH Cancer Therapy Evaluation Program (CTEP) designation of a novel mTORCl/2 inhibitor, sapanisertib (MLN0128/TAK228), this drug can prove to be efficacious in treating mTOR-addicted HCC tumors given its broad mechanism of action, as has been shown in other solid tumors, including renal, endometrial, and bladder cancer⁴⁶'⁴⁸. In mouse models of liver cancer, sapanisertib has shown efficacy in HCC with P-catenin activity⁴⁹'⁵¹. In fact, in a HCC preclinical model of P-M, combination of sapanisertib with MET inhibitor (cabozantinib) led to tumor regression over three treatment weeks⁴⁹. Thus, future studies testing sapanisertib as monotherapy or in combination with other targeted therapies can provide preclinical rationale for a rational clinical trial testing sapanisertib in HCC patients with biopsy-proven P-catenin-mutated HCC, such as using MBGS as a companion diagnostic.

Lastly, these SB-HDTVI HCC models are useful systems to identify treatment response biomarkers. Given the unique “inside-out” approach of these models, through dual oncogene induction, and use of immunocompetent mice, these tumor mouse models are useful to test targeted therapies and systemic agents, including sorafenib, mTOR inhibitors, and IO, and monitor their biological responses^8,13,14,52. In fact, mechanisms of response to various c-MET inhibitors in the P-M model^49,53,54, along with studying mechanisms following directed P-catenin inhibition via siRNA therapeutics in multiple P-catenin-driven models, including mutant-P-catenin/KRAS model were demonstrated^11,55. As liver tumor biopsies (both tissue and/or liquid) are increasingly becoming more frequent to identify oncologic actionable targets for patients¹⁵, along with molecular pathology laboratories expanding their capacity to perform whole transcriptome testing on patient tissues, developing biomarkers of response becomes ever more crucial in patient molecular stratification for selection of first-line IO-based treatment regimens. In advanced stage HCC setting, updated results from IMbravel50 trial demonstrated an overall response rate (ORR) of 30% and grade 3-4 treatment-related adverse events (TRAEs) in 43% of patients receiving atezolizumab + bevacizumab⁴ Therefore, proper selection of patients prior to therapy initiation can prevent TRAEs and improve ORR⁵⁶. However, there is currently no clinically approved biomarker to predict IO response in HCC patients. Immunohistochemical staining for PD-L1 protein expression in HCC has not translated well for predicting IO response compared to its use in other tumor types⁵⁷. RNA based assays, including transcriptomic profiling, has already yielded promising results to predict response⁵⁸, including the present example. Thus, gene signatures can prove crucial to aid in patient molecular stratification in both the neoadjuvant and adjuvant settings post-resection or transplantation^59,60. In summary, the presently disclosed MBGS panel can assist in diagnosing an important HCC molecular subset, which demonstrates heterogenous responses to first-line IO combinations. Ultimately, it would aid in patient selection for precision therapies using whole or spatial transcriptomics and data from additional innovative technological platforms as molecular testing becomes more desirable and routine in HCC. Specifically, applications of MBGS would be most desirable where transcriptomic platforms are utilizing fewer genes in their panels (e.g., NanoString, Molecular Cartography™). Furthermore, as digital pathology and Al-based machine learning becomes integrated into molecular diagnostics laboratories, there will be opportuinities for MBGS like panels to be instructive. Therapies employing TKIs, mTOR inhibitors, and anti-b-catenin therapies alone or in combination with ICIs have already shown to benefit CTNNB 1 -mutated HCC in preclinical models and are awaiting clinical validation (Figure 23D). Having tools such as MBGS to serve as a companion diagnostic will expedite precision trials and successful translation.

Overall, MBGS fulfills an unmet clinical need to diagnose an important HCC molecular subset, which lacks a response to first-line IO combination, and hence would help pave the path towards precision medicine in HCC.

Materials and Methods

Plasmids. The S45Y-CTNNBl-Myc-tag plasmid was previously described⁶¹. Briefly, using PCR-based site-directed mutagenesis, the S45Y substitution is introduced into human WT- CTNNBl-Myc-tag-bearing plasmid and subcloned into pT3-EFla plasmid using Gateway PCR cloning technology (Invitrogen, Carlsbad, CA) (pT3-EFla-S45Y-CTNNBl-Myc-tag). G31A- mutated human NFE2L2 was previously purchased from Addgene (catalog #81524) as a Gateway donor vector and subcloned into pT3-EFla destination vector (pT3-EFla-G31A-NFE2L2) as previously described⁶². The pT3-EF5a-hMet-V5-tag and pCMV/SB transposase plasmid have been described previously^61,63. All these plasmid constructs were purified using Endotoxin-Free Maxiprep kit (NA 0410, Sigma-Aldrich, St. Louis, MO) for hydrodynamic delivery. For hydrodynamic delivery, plasmids were diluted in 0.9% normal saline (NaCl) purchased from TEKNOVA (#S5815).

Mice for Tumor Study. All FVB/N mice used for tumor study were purchased from the Jackson Laboratory (Bar Harbor, ME). All procedures were performed in accordance with and approved by University of Pittsburgh School of Medicine Institutional Animal Use and Care Committee. All mice were fed a standard chow diet ad libitum, water, had access to enrichment, and exposed to 12h light/dark cycles in ventilated cages. Mice were monitored for signs of abdominal girth, morbidity, and were euthanized appropriately. All mice were euthanized at the indicated timepoints. Prior to sacrifice, mice were fasted for 4-6 hours. Body and liver weights were measured, along with documenting the gross morphology of the mouse livers at time of tissue harvesting. Kaplan Meier survival curve was generated using Prism 8 software (GraphPad Software Inc., La Jolla, CA).

Hydrodynamic Tail Vein Gene Delivery. The SB-HDTVI model has been described previously⁶¹'⁶⁴. For the CTNNB I -mutated/NRF2/hMET model (0-N-M), 20pg of pT3-EFla- S45Y-CTNNBl-Myc-tag, 20pg of NFE2L2-plasmid (pT3-EFla-G31A-NFE2L2), and 20pg of hMET-plasmid (pT3-EF5a-hMet-V5-tag) were mixed. For the CTNNBl-mutated/hMET model (P-M), 20pg of pT3-EFla-S45Y-CTNNBl-Myc-tag and 20pg of hMET-plasmid (pT3-EF5a- hMet-V5-tag) were mixed. For the CTNNBl-mutated/NRF2 model (0-N), 20pg of pT3-EFla- S45Y-CTNNBl-Myc-tag and 20pg of NFE2L2 -plasmid (pT3-EFla-G31 A-NFE2L2) were mixed. For the NRF2/hMET model (N-M), 20pg of NFE2L2-plasmid (pT3-EFla-G31A-NFE2L2) and 20pg of hMET-plasmid (pT3-EF5a-hMet-V5-tag) were mixed. Each of these plasmid combinations were additionally mixed with pCMV/SB transposase plasmid at a concentration of 25: 1 in 2ml normal saline (0.9% NaCl) and filtered through 0.22 um filter (Millipore) for injection. For hydrodynamic delivery, 6-8-week-old FVB/N male mice were injected in the lateral tail vein in 5-7 seconds.

The Hematoxylin and eosin (H&E) staining. Liver tissue chunks were fixed with 10% buffered formalin (Fisher Chemicals) at room temperature for 48-72h. Liver tissue is then transferred to 70% ethanol for tissue dehydration and paraffin embedding (FFPE) in blocks. The FFPE blocks are cut to 4pm sections for tissue staining. Standard workflow was used for hematoxylin and eosin (H&E) stain (Fisher Chemical Harris Modified Method Hematoxylin Stains, #SH26-500D; Eosin Y, # 23-314-630; ThermoFisher Scientific, Waltham, MA). This allowed identification and characterization of neoplastic foci in liver tissue sections.

Histology and Immunohistochemistry (IHC). For IHC, FFPE sections underwent deparaffinization in xylene, followed by serial deparaffinization in stepwise decreases in ethanol (100%, 95%, 90%) and rinsed in water. Antigen retrieval consisted of either Citrate Buffer (0.01 M, pH 6.0), or Tris-EDTA (IX Tris-EDTA Buffer, pH 9.0), or DAKO reagent (Agilent, Santa Clara, CA). Slides were then heated by either microwave for total of 18 minutes or under high pressure and temperature (via pressure cooker) for 20 minutes. Slides were then cooled on ice for 30-45mins. Slides were then incubated in 3% H2O2 dissolved in IX phosphate-buffered saline (PBS) for 10 minutes to quench endogenous liver peroxidases. Slides were then washed in PBS 3x. Next, sections were blocked with Super Block (ScyTek Laboratories) for lOmin to prevent non-specific binding. Slides were then incubated with the following antibodies at room temperature for Ih at indicated dilutions: glutamine synthetase (#G2781, Sigma-Aldrich; 1 : 1500), Cyclin-Dl (#134175, Abeam; 1 : 100), Ki67 (#csl2202; Cell Signaling; 1 :500), or P-catenin (#BD610154; BD BioSciences; 1 : 100); Or, at cold temperature overnight: NQO1 (#sc-376023, Santa Cruz; 1 : 100), Myc-tag (#cs-2278; Cell Signaling; 1 : 100), or V5-tag (#eBioSci-14-6796-82; eBioSciences; 1 : 100). Next, slides were then washed with lx PBS 3x and then incubated with species-specific biotinylated secondary antibodies (EMD Millipore) for 30 mins at room temperature. Next, slides were then washed with lx PBS 3x and then incubated with ABC reagent (Vectastain ABC Elite kit, Vector Laboratories) for 15 minutes. Then, slides were washed with lx PBS 3x and then brown stain signal was observed with incubation with DAB Peroxidase Substrate Kit (Vector Laboratories) for 30 seconds to 2mins. Last, slides were counterstained with hematoxylin (ThermoFisher Scientific), and rinsed, then dehydrated, mounted, and cover slipped. Slides were imaged on Zeiss Axioskop microscope and analyzed in Adobe Photoshop CS6 (Version 13.0 x64).

RNA-Sequencing and Analysis. Using fresh frozen liver tissue, RNA was isolated using the RNeasy Mini kit (Qiagen) according to standard manufacturer protocols for tissue RNA isolation and as previously described^61,64. RNA sequencing was performed on 15 mice for this study: 3 mice wild-type, 3 mice from S45Y-CTNNBl/G31A-NFE2L2/hMET (0-N-M), 3 mice from S45Y-CTNNBl/hMET (p-M), 3 mice from S45Y-CTNNB1/G31A-NFE2L2 (p-N), and 3 mice from G31A-NFE2L2/hMET (N-M). Transcriptome sequencing, quality control, and data preprocessing was performed as previously described⁶². The RNA-seq data is deposited to Gene Expression Omnibus (GEO) under accession number: GSE261316. To identify differentially expressed genes (DEGs) between each of the models and wild-type liver and between different models, differential expression analysis was performed in R using the R package ‘DEseq2’ using total gene counts. DEGs were selected based on absolute fold-change greater than 1.5 and FDR=0.05. These DEGs were then further used for input to Ingenuity Pathway Analysis (IP A)® (Qiagen) to enrich for pathways with biological meaning (FDR=0.1).

Human HCC Data Mining. For The Cancer Genome Atlas (TCGA) Liver Hepatocellular Carcinoma (TCGA-LIHC) analysis, RNA-seq transcriptomic and whole exome sequencing data were downloaded from Genomic Data Commons (GDC) through the R Bioconductor package ‘GenomicDataCommons’. Gene counts were normalized and the R package ‘DEseq2’ was used to determine differentially expressed genes (DEGs). DEGs were defined based on FDR and absolute fold change thresholds and used for Ingenuity Pathway Analysis (IP A)® (Qiagen) for inferred biological meaning. For patient stratification by gene signature overlap, it was used the previously published NRF2 activation gene signature⁶⁵ and the KAPOSI LIVER CANCER MET UP gene signature from mSigDB.⁶ Patients were hierarchically clustered based on high/low expression of the gene signature and patients with high expression of each were defined as NRF2/MET-high patients. Those patients that were also CTNNB1 -mutated based on exome sequencing, were defined as CTNNBl-mutated/NRF2/MET- high. Lollipop plots for CTNNB1 gene were generated using cBioPortal MutationMapper online tool (www.cbioportal.org/mutation_mapper). Additionally, analysis was performed in a separate

French cohort which contained genomic data (Whole-Genome Sequencing, Whole Exome Sequencing and RNAseq) from 398 adult HCC, 100 hepatoblastomas, 34 hepatocellular adenomas and 31 non-tumor liver samples previously sequenced (EGA accession numbers EGAS00001001284, EGAS00001002091, EGAS00001002879, EGAS00001003025,

EGAS00001003310, EGAS00001003685, EGAS00001003837, EGAS00001004629,

EGAS00001005108, EGAS00001005986, EGAS00001006692, EGAS00001001002,

EGAS00001000217, EGAS00001005629, EGAS00001003063, EGAS00001000706,

EGAS00001003130, EGAS00001002408, EGAS00001002888, EGAS00001000679 and

EGAS00001003686) and all CTNNB1 activating mutations or deletions were annotated as well as APC biallelic inactivation. Moreover, MBGS predictive ability was tested in a small immunotherapy HCC cohort (n=8 responders; n=9 non-responders) (GSE202069). Following differential gene expression analysis, average normalized expression values were calculated for each of the genes in 10-gene MBGS and composite score, along with calculation of ROC AUC values for each. Additionally, MBGS was compared against Chiang CTNNB1 subclass gene signature for ICI response, and other ICI response gene signatures, including T cell-inflamed gene expression profile ("CCL5", "CD27", "CD274", "CD276", "CD8A", "CMKLR1", "CXCL9", "CXCR6", "HLA- DQA1", "HLA-DRB1", "HLA-E", "IDO1", "LAG3", "NKG7", "PDCD1LG2", "PSMB10", "STAT1"), IFNg response signature ("CXCL10", "CXCL9", "HLA-DRA", "IDOl", "IFNG", "STAT1"), and tertiary lymphoid structure (TLS) signature ("CCL19", "CCL21", "CXCL13", "CCR7", "SELL", "LAMP3", "CXCR4", "CD86", "BCL6").

Lastly, clinical and genomic data (Whole Exome Sequencing and RNAseq) from IMbravel50 trial⁶⁷ were retrospectively analyzed for expression of the presently disclosed 10- and 13-gene signatures and association with clinical parameters (overall and progression-free survival).

To assess performance of MBGS in the pan-cancer atlas, genomic and transcriptomic data was accessed from cBioPortal.org using the “Pan-cancer analysis of whole genomes (ICGC/TCGA, Nature 2020)” dataset. ROC AUC value was calculated to predict CTNNB1 mutational status using 10-gene MBGS in this cohort. Additionally, performance of MBGS was compared to other molecular subclass gene signatures and Wnt gene signatures (accessed from MSigDB or the publications themselves), composite average expression of the different genes of the signature were computed and a logistic regression model was used to predict gene signature score with CTNNB1 -mutation status. AUC and ROC curves were computed R package ‘pROC’. Sensitivity (True Positive Rate) and Specificity (True Negative Rate) values were determined using Youden's J statistic (sensitivity + specificity - 1) to define the best fit threshold for these values on the ROC curve. Boxplots were used to compare composite average expression across the normal liver, CTNNB1 -mutated, and CTNNB1 -wild-type cases.

Human HCC Molecular Subclassification of TCGA data. To define TCGA-LIHC patients according to Hoshida⁶⁸, Boyault²², and Chiang²³ molecular subclasses for heatmap representation, the ‘MS.liverK’ R package⁷⁴ downloaded from github.com/cit-bioinfo/MS.liverK was used. Following data conversion step since the package algorithm was meant to be used on microarray dataset, it was followed the package vignette to categorize all the TCGA-LIHC cases (including adjacent normal) into the different molecular subclasses using normalized data. Data was exported as .csv file and used to generate heatmap.

Human HCC Spatial Transcriptomic Data Mining. Two publicly available human HCC spatial transcriptomic (10X Visium) datasets^70,71 were used to visualize expression of molecular subclass gene signatures and Wnt gene signatures on the H&E tissue section. The Zhang et al. study data was accessed from gene expression omnibus (GSE238264) and the Wu et al. study data was accessed directly from lifeome.net/supp/livercancer-st/data.htm. Raw data was downloaded and all 12 patient 10X Visium slides were processed using the R package ‘Seurat’.⁷⁴ Sequenced 55 pm spatial regions (spots) were filtered to exclude regions of low sequencing quality, using a threshold of 2000 reads per spot. Spots were subsequently normalized and integrated using Seurat. Additionally, as part of this quality control step, analysis of slide ‘HCC 2R’ from Zhang et al. study was not conducted since it did not have sufficient spots for analysis following this preprocessing step. Thus, analysis was limited to 11 individual patient slides (across 12 total slides). The Wu et al. study contained typically 3 slides per patient (1 normal liver, 1 leading edge [tumor + normal], and 1 tumor region]. Analysis was limited to just the tumor region slide, although all these slides were ultimately integrated in Seurat object. Additionally, slide 5 in the Wu et al. study had only tumor regions, but there were 4 regions [labeled A-D], The best quality data were from regions BC, which was ultimately what the analysis was performed on.

Each of the molecular subclass signatures or Wnt gene signatures were spatially plotted on the tissue section using the ‘addGeneSig’ function within the ‘SpatialPlof function of Seurat. Genes from the ‘addGeneSig’ function that were expressed with fewer than 1 count in an individual spot were filtered out. Due to sequencing depth, some genes in the signature can not have been analyzed. Lastly, all the module scores for a given molecular subclass or gene signature were normalized within each HCC patient slide.

Statistical Analysis. All data presented in the manuscript is depicted as mean ± standard deviation (SD) for each group. The indicated statistical tests were performed in Prism 9 software (GraphPad Software Inc., La Jolla, CA). For the presently disclosed example, P < 0.05 was considered statistically significant (*p<0.05, **p<0.01, ***p<0.001). References

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* * *

Although the presently disclosed subject matter and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means, methods and steps described in the specification. As one of ordinary skill in the art will readily appreciate from the disclosure of the presently disclosed subject matter, processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein can be utilized according to the presently disclosed subject matter. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.

Patents, patent applications, publications, product descriptions and protocols are cited throughout this application the disclosures of which are incorporated herein by reference in their entireties for all purposes.

Claims

WHAT IS CLAIMED IS:

1. A method for identifying a non-responder subject, comprising: measuring, in a sample from the subject, the expression level of one or more genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof; wherein an increased expression level of the one or more genes relative to a control indicates that the subject is non-responder.

2. The method of claim 1, further comprising determining a spatial location of a nucleic acid or a protein of the one or more genes.

3. The method of claim 2, wherein presence of the nucleic acid or protein of the one or more genes in an immune excluded location indicates that the subject is non-responder.

4. The method of any one of claims 1-3, wherein the non-responder subject does not have an antic-cancer effect upon administration of an immunotherapy comprising atezolizumab and bevacizumab.

5. A method for identifying a responder subject, comprising: measuring, in a sample from the subject, the expression level of one or more genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof; wherein a reduced expression level of the one or more genes relative to a control indicates that the subject is responder.

6. The method of claim 5, further comprising determining a spatial location of a nucleic acid or a protein of the one or more genes.

7. The method of claim 6, wherein presence of the nucleic acid or protein of the one or more genes in an immune active location indicates that the subject is responder.

8. The method of any one of claims 5-7, wherein the responder subject has an anti-cancer effect upon administration of an immunotherapy comprising atezolizumab and bevacizumab.

9. The method of any one of claims 1-8, wherein the expression level of three or more genes is measured.

10. The method of any one of claims 1-9, wherein the expression level of five or more genes is measured.

11. The method of any one of claims 1-10, wherein the expression level of seven or more genes is measured.

12. The method of any one of claims 1-11, wherein the expression level of nine or more genes is measured.

13. The method of any one of claims 1-12, wherein the expression level of ten genes is measured.

14. The method of claim 13, wherein the expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SLC13A3, SP5, TCF7, and TNFRSF19 is measured.

15. The method of any one of claims 1-14, wherein the expression level of thirteen genes is measured.

16. The method of claim 15, wherein the expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, and TNFRSF19 is measured.

17. The method of any one of claims 1-16, wherein an RNA expression level is measured.

18. The method of any one of claims 1-17, wherein a protein expression level is measured.

19. The method of any one of claims 1-18, wherein the sample comprises an organ tissue, whole blood, plasma, serum, whole blood cells, erythrocytes, lymphocytes, saliva, urine, stool, tears, sweat, sebum, nipple aspirate, ductal lavage, tumor exudates, synovial fluid, cerebrospinal fluid, lymph, fine needle aspirate, amniotic fluid, any other bodily fluid, exudate or secretory fluid, cell lysates, cellular secretion products, inflammation fluid, semen, or vaginal secretions.

20. The method of claim 19, wherein the organ tissue is a liver tissue.

21. The method of claim 19 or 20, wherein the organ tissue comprises a primary tumor tissue or a metastatic tumor tissue.

22. The method of any one of claims 1-21, wherein the subject is human.

23. A method for treating a subject having a cancer, comprising: (a) measuring, in a sample from the subject, the expression level of one or more genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1 A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof;

(b) identifying the subject as non-responder when the expression level of the one or more genes is increased relative to a control; and

(c) administering an effective amount of an anti-cancer treatment not including atezolizumab, bevacizumab, tremelimumab, durvalumab, pembrolizumab, nivolumab, or a combination thereof.

24. The method of claim 23, wherein the anti-cancer treatment comprises chemotherapy, radiation therapy, targeted drug therapy, immunotherapy, immunomodulatory agents, cytokines, monoclonal and polyclonal antibodies, and any combinations thereof.

25. The method of claim 23 or 24, wherein the anti-cancer treatment comprises sorafenib, PKF115-584, PNU-74654, PKF118-744, CGP049090, PKF118-310, ZTM000990, BC21, CCT036477, PKF222-815, CWP232228, PRI-724/C-82, ICG001, MSAB, SAH-BLC9_B, ZINC02092166, iCRT3, iCRT5, iCRT14, NLS-StAx-h, Hl-Bl, UU-T01, T02, 4FNPC, Apigenin, Carsonic acid, Curcumin, Esculetin, Magnalol, Resveratrol, Silibinin, Toxoflavin, NRX-252114, rapamycin, everolimus, RM-006 (RM-6272), sapanisertib, or a combination thereof.

26. The method of any one of claims 23-25, wherein the anti-cancer treatment comprises sorafenib.

27. The method of any one of claims 23-26, further comprising determining a spatial location of a nucleic acid or a protein of the one or more genes.

28. The method of claim 27, wherein presence of the nucleic acid or protein of the one or more genes in an immune excluded location indicates that the subject is non-responder.

29. The method of any one of claims 23-28, wherein the non-responder subject does not have an anti-cancer effect upon administration of an immunotherapy comprising atezolizumab and bevacizumab.

30. A method for treating a subject having a cancer, comprising:

(a) measuring, in a sample from the subject, the expression level of one or more genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof; (b) identifying the subject as responder when the expression level of the one or more genes is reduced relative to a control;

(c) administering an effective amount of a cancer therapy.

31. A method for treating a subject having a cancer, comprising:

(a) determining, in a sample from the subject, a spatial location of a nucleic acid or a protein of one or more genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof;

(b) measuring the expression level of the one or more genes;

(c) identifying the subject as responder when the expression level of the one or more genes is reduced relative to a control;

(d) administering an effective amount of a cancer therapy.

32. The method of claim 31, wherein presence of the nucleic acid or protein of the one or more genes in an immune active location indicates that the subject is responder.

33. The method of any one of claims 30-32, wherein the responder subject has an anti-cancer effect upon administration of an immunotherapy comprising atezolizumab and bevacizumab.

34. The method of any one of claims 30-33, wherein the anti-cancer treatment comprises chemotherapy, radiation therapy, targeted drug therapy, immunotherapy, immunomodulatory agents, cytokines, monoclonal and polyclonal antibodies, and any combinations thereof.

35. The method of any one of claims 30-34, wherein the anti-cancer treatment comprises atezolizumab, bevacizumab, tremelimumab, durvalumab, pembrolizumab, nivolumab, or a combination thereof.

36. The method of any one of claims 30-34, wherein the anti-cancer treatment comprises atezolizumab and bevacizumab.

37. The method of any one of claims 30-35, wherein the anti-cancer treatment comprises sorafenib, PKF115-584, PNU-74654, PKF118-744, CGP049090, PKF118-310, ZTM000990, BC21, CCT036477, PKF222-815, CWP232228, PRI-724/C-82, ICG001, MSAB, SAH-BLC9_B, ZINC02092166, iCRT3, iCRT5, iCRT14, NLS-StAx-h, Hl-Bl, UU-T01, T02, 4FNPC, Apigenin, Carsonic acid, Curcumin, Esculetin, Magnalol, Resveratrol, Silibinin, Toxoflavin, NRX-252114, rapamycin, everolimus, RM-006 (RM-6272), sapanisertib, or a combination thereof.

38. The method of any one of claims 23-37, wherein the expression level of three or more genes is measured.

39. The method of any one of claims 23-38, wherein the expression level of five or more genes is measured.

40. The method of any one of claims 23-39, wherein the expression level of seven or more genes is measured.

41. The method of any one of claims 23-40, wherein the expression level of nine or more genes is measured.

42. The method of any one of claims 23-41, wherein the expression level of ten genes is measured.

43. The method of claim 42, wherein the expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SLC13A3, SP5, TCF7, and TNFRSF19 is measured.

44. The method of any one of claims 23-43, wherein the expression level of thirteen genes is measured.

45. The method of claim 44, wherein the expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, and TNFRSF19 is measured.

46. The method of any one of claims 23-45, wherein an RNA expression level is measured.

47. The method of any one of claims 23-46, wherein a protein expression level is measured.

48. The method of any one of claims 23-47, wherein the sample comprises an organ tissue, whole blood, plasma, serum, whole blood cells, erythrocytes, lymphocytes, saliva, urine, stool, tears, sweat, sebum, nipple aspirate, ductal lavage, tumor exudates, synovial fluid, cerebrospinal fluid, lymph, fine needle aspirate, amniotic fluid, any other bodily fluid, exudate or secretory fluid, cell lysates, cellular secretion products, inflammation fluid, semen, or vaginal secretions.

49. The method of claim 48, wherein the organ tissue is a liver tissue.

50. The method of claim 48 or 49, wherein the organ tissue comprises a primary tumor tissue or a metastatic tumor tissue.

51. The method of any one of claims 23-50, wherein the subject is human.

52. The method of any one of claims 23-51, wherein the cancer is associated to CTNNB 1.

53. The method of any one of claims 23-52, wherein the cancer is selected from squamous cell cancer, lung cancer, cancer of the peritoneum, hepatocellular cancer, gastric or stomach cancer, pancreatic cancer, glioma, glioblastoma, cervical cancer, ovarian cancer, liver cancer, bladder cancer, hepatoma, breast cancer, colon cancer, rectal cancer, colorectal cancer, endometrial cancer or uterine carcinoma, salivary gland carcinoma, kidney or renal cancer, prostate cancer, vulvar cancer, thyroid cancer, hepatic carcinoma, anal carcinoma, penile carcinoma, CNS cancer, melanoma, head and neck cancer, bone cancer, bone marrow cancer, duodenum cancer, esophageal cancer, thyroid cancer, or hematological cancer.

54. The method of any one of claims 23-53, wherein the cancer is selected from endometrial adenocarcinoma, lung adenocarcinoma, colon adenocarcinoma, prostate adenocarcinoma, hepatocellular carcinoma, basal cell carcinoma (BCC), head and neck squamous cell carcinoma (HNSCC), prostate cancer (CaP), pilomatrixoma (PTR), medulloblastoma (MDB), hepatoblastoma (HB), hepatocellular adenomas (HCA), or hepatocellular cancer (HCC).

55. The method of any one of claims 23-54, wherein the cancer is hepatocellular carcinoma.

56. A method for identifying a subject having a mutated CTNNB1 gene, comprising: measuring, in a sample from the subject, the expression level of one or more genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof; wherein an increased expression level of the one or more genes relative to a control indicates that the subject has the mutated CTNNB 1 gene.

57. A method for identifying a subject having a mutated CTNNB1 gene, comprising: determining, in a sample from the subject, a spatial location of a nucleic acid or a protein of one or more genes selected from AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof; and measuring the expression level of the one or more genes; wherein an increased expression level of the one or more genes relative to a control indicates that the subject has the mutated CTNNB 1 gene.

58. The method of claim 57, wherein presence of the nucleic acid or protein of the one or more genes in an immune excluded location indicates that the subject has a mutated CTNNB 1 gene.

59. The method of any one of claims 56-58, wherein the expression level of three or more genes is measured.

60. The method of any one of claims 56-59, wherein the expression level of five or more genes is measured.

61. The method of any one of claims 56-60, wherein the expression level of seven or more genes is measured.

62. The method of any one of claims 56-61, wherein the expression level of nine or more genes is measured.

63. The method of any one of claims 56-62, wherein the expression level of ten genes is measured.

64. The method of claim 63, wherein the expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SLC13A3, SP5, TCF7, and TNFRSF19 is measured.

65. The method of any one of claims 56-64, wherein the expression level of thirteen genes is measured.

66. The method of claim 65, wherein the expression level of AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, and TNFRSF19 is measured.

67. The method of any one of claims 56-66, wherein an RNA expression level is measured.

68. The method of any one of claims 56-67, wherein a protein expression level is measured.

69. The method of any one of claims 56-68, wherein the sample comprises an organ tissue, whole blood, plasma, serum, whole blood cells, erythrocytes, lymphocytes, saliva, urine, stool, tears, sweat, sebum, nipple aspirate, ductal lavage, tumor exudates, synovial fluid, cerebrospinal fluid, lymph, fine needle aspirate, amniotic fluid, any other bodily fluid, exudate or secretory fluid, cell lysates, cellular secretion products, inflammation fluid, semen, or vaginal secretions.

70. The method of claim 69, wherein the organ tissue is a liver tissue.

71. The method of claim 69 or 70, wherein the organ tissue comprises a primary tumor tissue or a metastatic tumor tissue.

72. The method of any one of claims 56-71, wherein the subject is human.

73. A kit for performing a method for identifying a non-responder subject, identifying a responder subject, treating a non-responder subject, treating a responder subject, or identifying a subject having a mutated CTNNB1 according to any one of claims 1-72.

74. The kit of claim 73, wherein the kit comprises at least one set of primers comprising a forward primer and a reverse primer that bind to AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof.

75. The kit of claim 73 or 74, wherein the kit comprises at least one antibody that binds to AXIN2, GLUL, LGR5, NKD1, NOTUM, RHBG, SBSPON, SLC13A3, SLC1A2, SP5, TCF7, TEDDM1, TNFRSF19, or a combination thereof.