Classification and Treatment of Gastric Cancer

Protein and mRNA expression based methods for classification of gastric cancer, and methods of treatment based thereon.

TECHNICAL FIELD

Described herein are protein and mRNA expression based methods for classification of gastric cancer, and methods of treatment based thereon.

BACKGROUND

Gastric carcinoma (GC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. Despite better control over known risk factors and development of new chemotherapeutic and targeted agents, in the United States, the 5-year overall survival for GC patients is only 29%.1In regard to patient stratification, although several morphologic classifications have been developed, one broadly used system is the Lauren classification, which divides GC into intestinal and diffuse types.2The simple two-tiered classification gives a general understanding of the histogenesis and biology of GC, and has been particularly helpful in evaluating epidemiologic data. Another widely used system is the WHO classification, which is based on more precise histologic patterns. It is an all-inclusive system, which recognizes all the rare subtypes that were not identified in the Lauren classification.3Nevertheless, its clinical utility is doubtful since there is little difference in outcomes between the distinct histological subgroups.

SUMMARY

The overall survival of gastric carcinoma (GC) patients remains poor despite improved control over known risk factors and surveillance. This highlights the need for new classifications, driven towards identification of potential therapeutic targets. Using sophisticated molecular technologies and analysis, three groups recently provided genetic and epigenetic molecular classifications of gastric cancer (The Cancer Genome Atlas (TCGA), “Singapore-Duke” study, and Asian Cancer Research Group (ACRG)). Herein, the expression of fourteen biomarkers was examined in a cohort of 146 GCs and unsupervised hierarchical clustering analysis was performed using less expensive and widely available immunohistochemistry and in situ hybridization. Ultimately, five groups of GCs were identified based on Epstein Barr virus (EBV)-positivity, microsatellite Instability (MSI), aberrant E-cadherin and p53 expression; the remaining cases constituted a group characterized by normal p53 expression. In addition, the five categories correspond to the reported molecular subgroups by virtue of clinicopathologic features. Furthermore, evaluation between these clusters and survival using the Cox proportional hazards model showed a trend for superior survival in the EBV and MSI-related GCs. Provided herein is a simplified algorithm that is able to identify five subgroups (also referred to herein as clusters) of GC, using immunohistochemical and in situ hybridization techniques, that can be used, e.g., to stratify patients and to select optimal treatments.

Thus, provided herein are methods comprising providing a sample comprising tissue comprising tumor cells from a gastric tumor; and determining in tumor cells in the sample:

(i) a level of Epstein Barr Virus in the tumor cells;

(ii) DNA mismatch repair protein expression in the tumor cells;

(iii) E-cadherin protein expression in the tumor cells;

(iv) p53 protein expression in the tumor cells; and

(v) Mucin protein expression in the tumor cells.

Also provided herein are methods for categorizing gastric tumor in a subject. The methods can include providing a sample comprising tissue comprising tumor cells from a gastric tumor; and determining in tumor cells in the sample:

(i) a level of Epstein Barr Virus (EBV) in the tumor cells, preferably a nuclear level of EBV;

(ii) DNA mismatch repair protein expression in the tumor cells;

(iii) E-cadherin protein expression in the tumor cells;

(iv) p53 protein expression in the tumor cells; and

(v) Mucin protein expression in the tumor cells; and

categorizing a tumor with EBV, preferably nuclear EBV, above a reference level, and optionally with prominent lymphoid infiltrate, in group 1;
categorizing a tumor with a nuclear DNA mismatch repair protein expression below a reference level in group 2;
categorizing a tumor with E-cadherin expression below a reference level, or only cytoplasmic E-cadherin expression, in group 3;
categorizing a tumor with normal p53 expression and cytoplasmic mucin expression above a reference level in group 4; and
categorizing a tumor with aberrant levels of p53 expression and normal levels of mucin expression in group 5.

In the methods described herein, “determining expression” can include determining presence, level, and/or localization of the protein or virus.

In some embodiments, the level of Epstein Barr Virus is determined using EBER in situ hybridization (EBER-ISH), and/or the DNA mismatch repair protein expression; E-cadherin expression; p53 expression; and Mucin expression is detected using immunohistochemistry in intact tumor samples.

In some embodiments, the mismatch repair proteins comprise one, two, three, or all four of MLH1, PMS2, MSH2, or MSH6.

In some embodiments, the mismatch repair proteins comprise MLH1 and PMS2.

In some embodiments, the Mucin is one or more of MUC2, CDX2, CD10, MUC5AC, or MUC6. In some embodiments, the Mucin is MUC6.

In some embodiments, aberrant p53 expression is an absence of expression or strong diffuse expression, and normal p53 expression is weak, patchy expression.

In some embodiments, the methods include selecting, and optionally administering,

a treatment comprising a therapeutically effective amount of an immunotherapy, e.g.,
a checkpoint inhibitor to a subject having a tumor in group 1;
a treatment comprising a therapeutically effective amount of an immunotherapy, topoisomerase-I inhibitors, or platinum-based chemotherapy, e.g., in combination with poly (ADP-ribose) polymerase (PARP) inhibitors, to a subject having a tumor in group 2;
a treatment comprising a therapeutically effective amount of an inhibitor of mammalian target of rapamycin (mTOR) to a subject having a tumor in group 3;
a treatment comprising a therapeutically effective amount of an antimetabolite to a subject having a tumor in group 4; or
a treatment comprising a therapeutically effective amount of an inhibitor of polo-like kinase 1 (PLK1) and/or an inhibitor of Aurora Kinase A to a subject having a tumor in group 5.

Therapeutic agents that can be used in the methods include those known in the art and/or described herein.

In some embodiments, the checkpoint inhibitor is an anti-PD1 or anti-PDL1 antibody, e.g., as known in the art and/or described herein.

In some embodiments, the antimetabolite is a pyrimidine analog, preferably 5-Fluorouracil (5-FU), gemcitabine, fluorouracil, or cytarabine, e.g., as known in the art and/or described herein.

DETAILED DESCRIPTION

Following the successful Trastuzumab for Gastric Cancer (ToGA) trial, the only validated predictive biomarker for personalized therapy of GC is limited to human epidermal growth factor receptor (Her2) protein expression.4Recently, anti-vascular endothelial growth factor receptor 2 (VEGFR2) antibody (ramucirumab) has been FDA approved, and anti-epidermal growth factor receptor (EGFR) and mesenchymal-epithelial transition factor (MET or hepatocyte growth factor receptor therapy) are in clinical development.4

Despite ongoing progress, a certain disconnect persists between the morphologic classification schemes and the biology of GC and, ultimately, the applications of targeted therapies. Recent studies have emphasized the need for new patient stratification strategies that incorporate the emerging molecular classification of GC and identification of potential therapeutic targets (FIG. 8).

The “Singapore-Duke” study identified 3 distinct molecular signatures based on genetic and epigenetic expression of drug responsive clusters, and subclassified GC into (a) a proliferative subtype characterized by high TP53 mutations and activation of oncogenic pathways, (b) a metabolic subtype with low TP53 mutations and expression of genes characteristic of normal gastric mucosa and metabolic pathways, among other features, and (c) a mesenchymal subtype with low expression of CDH1 and TP53, increased stem cell marker, and genes characteristic of the epithelial-mesenchymal transition pathway.5The authors commented that the proliferative and mesenchymal types corresponded to Lauren intestinal and diffuse types, respectively. Metabolic type GCs were highly sensitive to 5-fluorouracil, and the mesenchymal type GCs were more sensitive to phosphatidyl-inositol-3-kinase inhibitors. The Cancer Genome Atlas (TCGA) subdivides GC based on genetic and epigenetic expression in 4 pathogenetic pathways: (a) EBV GCs characterized by hypermethylation of CDKN2A, mutations in PIK3A and PD-L1 expression, (b) MSI-H GCs with hypermethylation and MLH1 silencing, (c) Genomically stable (GS) group with CDH1 and RHOA mutations, and (d) Chromosomal instable (CIN) group corresponding to intestinal histology and a high number of TP53 mutations.6Another recent study from the Asian Cancer Research Group (ACRG) based on gene expression profiling, genome-wide copy number microarrays and targeted gene sequencing proposed classification of GC into (1) MSI GC which present with best overall prognosis and the lowest frequency of recurrence (2) Micro satellite stable (MSS) Epithelial Mesenchymal Transition (EMT) GC with the worst prognosis, (3 & 4) non-MSI and non-EMT TP53 active and inactive GC with intermediate prognosis and recurrence rates.65Although these studies represent a significant progress in defining GC from a biologic point of view, they were limited either by a lack in clinical scope or the lack of immediate clinical applicability. In fact, to define these sub-groups, these studies used multiple advanced molecular techniques including DNA sequencing, RNA sequencing, whole exome sequencing, copy number variation analysis and DNA methylation arrays, which are not currently cost-effective in practice. The present study tested the validity of the three classifications using a series of GCs, focusing on protein and mRNA expression and using techniques available in routine diagnostic practice. In addition, an algorithm was formulated to distinguish the molecular subtypes for easy clinical classification and better patient selection for putative targeted therapy.

Described herein is a biomarker expression-based classification of GCs that parallels the recently recognized genetic classifications of GC.5,6, 65The hierarchical clustering resulted in the determination of five groups of adenocarcinomas.

Group 1: EBV-associated gastric cancers. They represented 5% of the cases. Those were associated with a better survival and a strong association with PD-L1.

Group 2: MSI-H gastric cancers. This group represented 16% overall of the cases. Those were associated with a better survival and a lower frequency of nodal metastases.

Group 3: Gastric cancers with aberrant E-cadherin expression. 21% of the cases fell in this group. Those were associated with diffuse type phenotype.

Group 4: Gastric cancers with aberrant TP53 expression. These represented 51% of the cases. Those were associated with higher lymph node stage and an intestinal phenotype (Lauren classification).

Group 5 grouped gastric cancers with normal TP53 expression and represented 7% of the cases. Those were associated with MUC6 overexpression.

A schema for diagnostic distinction and subsequent treatment is provided inFIG. 8. As these groups may not entirely mutually exclusive, in some embodiments, both treatments can be administered, e.g., one can be tried first or they can be administered simultaneously.

In the cohort, there was a lower incidence of EBV GCs (5%) compared to the TCGA6(8.8%) and other publications (2-20%).8However, another study showed a similar frequency of 5.1%.18The mechanism of EBV-carcinogenesis is a genome-wide DNA methylation. The process involves a series of tumor suppressor genes (e.g. p14, p15, p16, APC, CDH, MGMT and PTEN) that result in uncontrolled cell growth.22,23

Of note, although MSI GCs are also associated with hypermethylation and silencing of genes (and their promoters) in a distinctive; pattern. EBV GCs having a more extensive pattern of methylation of promoter and non-promoter CpG islands.24In fact, EBV GCs are almost exclusive of the microsatellite instable phenotype.6,25-27A series of characteristics important to underscore, EBV GCs lack RHOA mutations;6,25,26however, a decreased expression of RHOA (and CDH1) may be seen secondary to hypermethylation of the gene or its promoter.20,25,26,28They also show a lack of TP53 mutations.6,25,26,29,30This trend was also recorded in ACGR data set.65It has been suggested that the profound global DNA effects are responsible for the distinctive clinico-pathologic features of EBV GCs22including male predominance8,31and proximal location.32Notably, the purported younger age has not been confirmed by recent analysis.18,33In the current study, we confirmed the non-antral location. A trend for younger (<70 years) male patients was seen; however, the difference was not statistically significant. A prominent lymphoid infiltrate was seen in all cases, which is consistent with prior studies.34This group also had trend towards better survival, consistent with the reported literature.25,33,35-37

A significant association with the immune-checkpoint pathway protein programmed death ligand (PD-L1) was seen in the EBV GC cluster. Similar findings have been noted at genomic levels with amplification of the 9p24.1 locus which includes the CD274 gene (encoding for PD-L1).6,26The prognostic significance of this finding is unclear currently. Nevertheless, it suggests a potential role for immunotherapy with anti-PD-1/PD-L1 monoclonal antibodies augmenting antitumor immune response.

MSI GCs have been recognized previously as a distinct group based on clinicopathologic38and molecular findings.6,39However, aberrant E-cadherin or p53 expression may be seen in this subgroup, with hypermethylation of CDH128and abnormalities in TP53 (most frequently as a result of loss of heterozygosity).40There is no prognostic significance to these findings, allowing the present segregation strategy. The frequency of MSI GC in the present cohort was 16% versus 21.7% in the TCGA6, 22.7% in the ACRG65and 8%-25.9% in the literature.41-43The reported incidence is higher in western studies44and associated with older age, female gender, larger tumor size, intestinal differentiation, and lower rate of nodal involvement.41,45In the ACRG, the MSI subtype was predominantly associated with antral location, early stage and intestinal phenotype as well.65The association with intestinal differentiation as established by MUC2 positivity (rather than the Lauren intestinal morphotype) and lower frequency of nodal metastasis was confirmed. This observation is also reflective of lesser biologic aggressiveness and a trend towards longer survival in this group. Consistent with prior studies, loss of MLH1 and PMS2 was the predominant pattern.41,46The mechanism is predominantly hypermethylation of MLH1 promoter6,47and, less commonly, mutations in MLH1 and MSH2.47Notably, MMR deficiency and TP53 mutations are not mutually exclusive carcinogenic pathways. Similar observations have been noted in prior studies in GC.40

After exclusion of EBV and MSI clusters, a distinct subgroup of cases characterized by aberrant E-cadherin expression was identified. A few studies have reported TP53 mutations as a late event in the development of poorly cohesive GC, which was our rationale for using cases of “GC with aberrant E-cadherin expression” before “GC with aberrant p53 expression” to assign a group for clinical applicability.49,50The frequency of this subset best correlates with the GC stable group (GS) reported by Bass,6and the MSS/EMT subtype of the ACRG.65This group constituted 21% of the entire cohort similar to the 20% and 15.3% reported in these studies. The histologic Lauren diffuse type, observed in this group (90% diffuse type), is comparable to the so-called mesenchymal group of Lei et al5(seen in 73% diffuse type). An enrichment of mutations in CDH1 has been noted in 37% of the GS group; and an additional 30% of the cases had either RHOA or CLDN18-affecting RHOA or ARHGAP's regulation of RHOA and/or cell motility.6Of note, activated RHOA has been reported to act through effectors, including ROCK1, with resultant activation of STAT3, whose expression has been linked to aberrant E-cadherin expression in diffuse type GCs.51,52Although not confirmed, it is likely that genetic and epigenetic changes in both CDH1 and RHOA would result in decreased expression of E-cadherin protein. Of interest, Lei et al5demonstrated a low level of expression of E-cadherin and p53, similar to our group, in their mesenchymal subgroup. Furthermore, it is worth mentioning that CDH1 and RHOA mutations were not as prevalent in the MSS/EMT subtype of ACRG compared to the TCGA.6, 65

Given the amplification of gastric stem cell markers seen in the mesenchymal subtype, a role for preferential sensitivity to PI3K-AKT-mTOR inhibitors has been suggested.5CDH1 and RHOA signaling pathways dysfunction has been reported in this subgroup; inhibition of RhoA activity by using an inhibitor of Rho associated kinase (ROCK) which is an effector protein of RhoA has been shown to induce apoptosis of GC cells.53A recent study demonstrated a decreased in EMT (increased expression of E-cadherin and decreased expression of N-cadherin) with danusertib (potent pan-Aurora kinase). The drug also inhibits the PI3K/Akt/mTOR-mediated signaling pathway in human GC cells.54

Another interesting finding was the aberrant cytoplasmic expression of E-cadherin. Prior studies refer to this as “heterogeneous aberrant staining”55or “paranuclear staining.”56It has been suggested that mutation of exon 8 of CDH1 expresses an abnormal E-cadherin protein that lacks the appropriate signals for posttranslational modifications which permit the normal transport to the cell membrane and glycosylation of E-cadherin; this results in the arrest of E-cadherin in the Golgi apparatus.55,57In another report, cytoplasmic E-cadherin was present in a GC with a 5-base-pair insertion in exon 9.58The significance of a lower aberrant p53 expression (39% vs 75%) in the two sub-clusters (cytoplasmic vs complete loss of E-cadherin) is unclear. Furthermore, there was no difference in the clinicopathologic features or survival other than a trend towards older age in the subset with cytoplasmic expression.

The cluster with aberrant p53 expression formed the majority of cases (51%), and is concordant with the CIN group (50%) of the TCGA6, the proliferative subtype (45%) of Leis, and the MSS/TP53 type of ACRG (35.7%)65A strong correlation with Lauren intestinal morphotype (81%) was seen, supporting this group's correspondence to CIN (84%)6, proliferative type (75%)5and MSS/TP53 type (85.1%).65A higher lymph node stage was observed, as has been reported previously in GCs with TP53 mutations.59-63A trend towards increased Her2/neu also was noted; however, the overall frequency of Her2/neu positivity was low, which may be attributed to an overall lower percentage of Lauren intestinal type cancers (49%) in our cohort, in comparison to the TOGA trial where intestinal type constituted 91% of tumors.64

For GCs with aberrant p53, potential targets include the blockade of receptor tyrosine kinases (e.g., HER2, EGFR, vascular endothelial growth factor receptor (VEGFR), c-MET, and fibroblast growth factor receptor 2 (FGFR2) and cell cycle mediators (CCNE1, CCND1 and CDK6)), which are amplified in this group.6

The features of the group with normal p53 expression and increased MUC6 expression, likely correspond to the metabolic subtype reported by Lei et al.5This group has been related to the spasmolytic polypeptide-expressing metaplasia (SPEM) pathway, owing to the overexpression of genes characteristic of normal gastric mucosa. Of note, the metabolic subtype is highly sensitive to 5-fluorouracil due to significantly lower expression of both thymidylate synthase (TS) and dihydropyrimidine dehydrogenase (DPD).5

As the prognosis of GC remains dismal despite improving surgical and adjuvant therapies, recent advances in genomic technologies have paved the way toward understanding the molecular underpinning of GCs and the identification of therapeutic biomarkers that influence outcomes and guide management strategies. However, to date, in clinical practice, GCs remain essentially classified histologically, since access and cost of high-throughput genomic technologies may limit universal molecular fingerprinting of all GCs. This study, taking into account the results of recently developed genetic and epigenetic molecular classifications of GC, demonstrated that in situ hybridization and immunohistochemical characterization of tumors can appropriately identify tumor subgroups similar to genomic profiling and could be an alternative in guiding targeted therapies.

Gastric Cancer

Malignancy of the stomach (defined as beginning at the gastroesophageal junction and ending at the duodenum) is often diagnosed at an advanced stage because early cancers typically have no associated symptoms. Diagnosis can be made based on imaging studies such as Esophagogastroduodenoscopy (EGD); double-contrast upper GI series and barium swallows; CT scanning or MRI of the chest, abdomen, and pelvis; and Endoscopic ultrasonography (EUS); and biopsy.

Standard therapies include surgical intervention and chemotherapy. Surgical interventions can include partial (subtotal) or total gastrectomy; esophagogastrectomy (e.g., for tumors of the cardia and gastroesophageal junction). Chemotherapies can include platinum-based combination chemotherapy (e.g., epirubicin/cisplatin/5-fluorouracil (5-FU); docetaxel/cisplatin/5-FU; irinotecan and cisplatin; oxaliplatin and irinotecan); trastuzumab in combination with cisplatin and capecitabine or 5-FU; and Ramucirumab following therapy with a fluoropyrimidine- or platinum-containing regimen. In addition, intraoperative radiotherapy, neoadjuvant chemotherapy, or adjuvant chemotherapy, radiotherapy, or chemoradiotherapy can be used. In cases where palliative treatment only is desired, palliative radiotherapy or surgical resections can be used.

A Five-Tier Classification of GC

In accordance with the proposed molecular classifications and based on the results of unsupervised hierarchical clustering analysis of EBV-ISH, MMR proteins, E-cadherin, and p53, a five-tier classification schema was generated; a decision tree is shown inFIG. 8.

The methods include using Epstein-Barr encoding region (EBER) in situ hybridization (EBER-ISH) to detect EBV in the tumor cells. Because of the large numbers of copies of EBERs present in latently infected cells, non-isotopic methods can be used. See, e.g., Weiss and Chen, Methods Mol Biol. 2013; 999:223-30.

The methods also include immunohistochemistry to detect DNA mismatch repair protein expression; E-cadherin expression; p53 expression; and Mucin expression in the tumor cells. Sequences of each of these proteins are known in the art; exemplary human protein sequences are provided in the following table.

Included herein are methods for categorizing gastric cancer in subjects, e.g., mammalian subjects, e.g., humans or non-human veterinary or laboratory subjects. The methods rely on detection of the biomarkers described herein, e.g., EBV-ISH, MMR proteins, E-cadherin, and/or p53, to generate a five-tier classification of GC. The methods can include obtaining a sample from a subject, and evaluating the presence and/or level of the biomarkers in the sample, and comparing the presence and/or level with one or more references, e.g., a control reference that represents a normal level or expression localization of the biomarker, e.g., a level in a normal cell from an unaffected subject, and/or a disease reference that represents a level or expression localization of the proteins associated with one of the classification groups described herein. Suitable reference values can include those shown in Table 1.

As used herein the term “sample”, when referring to the material to be tested for the presence of a biological marker using the method of the invention, includes inter alia tissue known or suspected to comprise tumor cells obtained from a biopsy or tumor resection. In some embodiments, the subject has been diagnosed with gastric cancer by a method known in the art.

The presence and/or level of a protein can be evaluated using methods known in the art, e.g., using standard and quantitative immunoassay methods for proteins, including but not limited to, Western blot; enzyme linked immunosorbent assay (ELISA); biotin/avidin type assays; protein array detection; radio-immunoassay; immunohistochemistry (IHC); immune-precipitation assay; FACS (fluorescent activated cell sorting); mass spectrometry (Kim (2010) Am J Clin Pathol 134:157-162; Yasun (2012) Anal Chem 84(14):6008-6015; Brody (2010) Expert Rev Mol Diagn 10(8):1013-1022; Philips (2014) PLOS One 9(3):e90226; Pfaffe (2011) Clin Chem 57(5): 675-687). The methods typically include revealing labels such as fluorescent, chemiluminescent, radioactive, and enzymatic or dye molecules that provide a signal either directly or indirectly. As used herein, the term “label” refers to the coupling (i.e. physically linkage) of a detectable substance, such as a radioactive agent or fluorophore (e.g., phycoerythrin (PE) or indocyanine (Cy5), to an antibody or probe, as well as indirect labeling of the probe or antibody (e.g. horseradish peroxidase, HRP) by reactivity with a detectable substance.

In preferred embodiments, an IHC method may be used. IHC provides a method of detecting a biological marker in situ in structurally intact samples (i.e., wherein the sample preparation preserves the structure of the cells and subcellular compartments such as the nucleus). The presence, level and/or exact cellular location of the biological marker can be detected. Typically, a sample is fixed with formalin or paraformaldehyde, embedded in paraffin, and cut into sections for staining and subsequent inspection by confocal microscopy. Current methods of IHC use either direct or indirect labelling. The sample may also be inspected by fluorescent microscopy when immunofluorescence (IF) is performed, as a variation to IHC.

Measurement of the level of a biomarker can be direct or indirect. For example, the abundance levels of the protein can be directly quantitated, and/or compared to an internal reference, e.g., a housekeeping gene with relatively steady expression levels.

The methods can also be used to select or stratify subjects for a clinical trial, e.g., of a treatment for gastric cancer, to determine whether the treatment being tested is better for subjects with a cancer that falls in group 1, 2, 3, 4, or 5. The methods can include identifying the subjects as having a particular cancer group before administration of the treatment, or afterwards. The subjects can be selected for inclusion, or excluded from inclusion, based on the group into which their cancer call.

Treatment Selection

The method described herein can also be used for selecting, and then optionally administering, an optimal treatment for a subject. Thus the methods described herein include methods for the treatment of gastric cancer. Generally, the methods include administering a therapeutically effective amount of a treatment as described herein, to a subject who is in need of, or who has been determined to be in need of, such treatment.

As used in this context, to “treat” means to ameliorate at least one symptom of the gastric cancer. For example, a treatment can result in a reduction in tumor size, tumor growth, or metastasis or risk of metastasis.

For example, the methods can include selecting and/or administering a treatment comprising a therapeutically effective amount of a checkpoint inhibitor to a subject having a tumor in group 1. In some embodiments, the checkpoint inhibitor is an anti-PD1 or anti-PDL1 antibody, such as nivolumab, pembrolizumab, pidilizumab durvalumab, or atezolizumab. Alternatively or in addition, an immunotherapy can be used.

The methods can also include selecting and/or administering a treatment comprising a therapeutically effective amount of an immunotherapy, topoisomerase-I inhibitors, or platinum-based chemotherapy, e.g., in combination with poly (ADP-ribose) polymerase (PARP) inhibitors, to a subject having a tumor in group 2.

The methods can also include selecting and/or administering a treatment comprising a therapeutically effective amount of an inhibitor of mammalian target of rapamycin (mTOR) to a subject having a tumor in group 3.

The methods can also include selecting and/or administering a treatment comprising a therapeutically effective amount of an antimetabolite to a subject having a tumor in group 4.

The methods can also include selecting and/or administering a treatment comprising a therapeutically effective amount of an inhibitor of polo-like kinase 1 (PLK1) and/or an inhibitor of Aurora Kinase A to a subject having a tumor in group 5.

Any of the methods can also include selecting and/or administering a treatment comprising surgery or radiotherapy for any of groups 1-5.

Immunotherapies include those therapies that target tumor-induced immune suppression; see, e.g., Stewart and Smyth, Cancer Metastasis Rev. 2011 March; 30(1):125-40. In some embodiments, these therapies may primarily target immunoregulatory cell types such as regulatory T cells (Tregs) or M2 polarized macrophages, e.g., by reducing number, altering function, or preventing tumor localization of the immunoregulatory cell types. For example, Treg-targeted therapy includes anti-GITR monoclonal antibody (TRX518), cyclophosphamide (e.g., metronomic doses), arsenic trioxide, paclitaxel, sunitinib, oxaliplatin, PLX4720, anthracycline-based chemotherapy, Daclizumab (anti-CD25); Immunotoxin eg. Ontak (denileukin diftitox); lymphoablation (e.g., chemical or radiation lymphoablation) and agents that selectively target the VEGF-VEGFR signaling axis, such as VEGF blocking antibodies (e.g., bevacizumab), or inhibitors of VEGFR tyrosine kinase activity (e.g., lenvatinib) or ATP hydrolysis (e.g., using ectonucleotidase inhibitors, e.g., ARL67156 (6-N,N-Diethyl-D-β,γ-dibromomethyleneATP trisodium salt), 8-(4-chlorophenylthio) cAMP (pCPT-cAMP) and a related cyclic nucleotide analog (844-chlorophenylthio] cGMP; pCPT-cGMP) and those described in WO 2007135195, as well as mAbs against CD73 or CD39). Docetaxel also has effects on M2 macrophages. See, e.g., Zitvogel et al., Immunity 39:74-88 (2013). In another example, M2 macrophage targeted therapy includes clodronate-liposomes (Zeisberger, et al., Br J Cancer. 95:272-281 (2006)), DNA based vaccines (Luo, et al., J Clin Invest. 116(8): 2132-2141 (2006)), and M2 macrophage targeted pro-apoptotic peptides (Cieslewicz, et al., PNAS. 110(40): 15919-15924 (2013)). Immnotherapies that target Natural Killer T (NKT) cells can also be used, e.g., that support type I NKT over type II NKT (e.g., CD1d type I agonist ligands) or that inhibit the immune-suppressive functions of NKT, e.g., that antagonize TGF-beta or neutralize CD1d.

In some embodiments, an immunotherapy may antagonize the action of cytokines and chemokines such as IL-10, TGF-beta, IL-6, CCL2 and others that are associated with immunosuppression in cancer. For example, TGF-beta neutralizing therapies include anti-TGF-beta antibodies (e.g. fresolimumab, Infliximab, Lerdelimumab, GC-1008), antisense oligodeoxynucleotides (e.g., Trabedersen), and small molecule inhibitors of TGF-beta (e.g. LY2157299), (Wojtowicz-Praga, Invest New Drugs. 21(1): 21-32 (2003)). Another example of therapies that antagonize immunosuppression cytokines can include anti-IL-6 antibodies (e.g. siltuximab) (Guo, et al., Cancer Treat Rev. 38(7):904-910 (2012). mAbs against IL-10 or its receptor can also be used, e.g., humanized versions of those described in Llorente et al., Arthritis & Rheumatism, 43(8): 1790-1800, 2000 (anti-IL-10 mAb), or Newton et al., Clin Exp Immunol. 2014 July; 177(1):261-8 (Anti-interleukin-10R1 monoclonal antibody). mAbs against CCL2 or its receptors can also be used. In some embodiments, the cytokine immunotherapy is combined with a commonly used chemotherapeutic agent (e.g., gemcitabine, docetaxel, cisplatin, tamoxifen) as described in U.S. Pat. No. 8,476,246.

In some embodiments, immunotherapies can antagonize cell surface receptors to enhance the anti-cancer immune response. For example, antagonistic monoclonal antibodies that boost the anti-cancer immune response can include antibodies that target CTLA-4 (ipilimumab, see Tarhini and Iqbal, Onco Targets Ther. 3:15-25 (2010) and U.S. Pat. No. 7,741,345 or Tremelimumab) or antibodies that target PD-1 (nivolumab, see Topalian, et al., NEJM. 366(26): 2443-2454 (2012) and WO2013/173223A1, pembrolizumab/MK-3475, Pidilizumab (CT-011)).

An “effective amount” is an amount sufficient to effect beneficial or desired results. For example, a therapeutic amount is one that achieves the desired therapeutic effect. This amount can be the same or different from a prophylactically effective amount, which is an amount necessary to prevent onset of disease or disease symptoms. An effective amount can be administered in one or more administrations, applications or dosages. A therapeutically effective amount of a therapeutic compound (i.e., an effective dosage) depends on the therapeutic compounds selected. The compositions can be administered one from one or more times per day to one or more times per week; including once every other day. The skilled artisan will appreciate that certain factors may influence the dosage and timing required to effectively treat a subject, including but not limited to the severity of the disease or disorder, previous treatments, the general health and/or age of the subject, and other diseases present. Moreover, treatment of a subject with a therapeutically effective amount of the therapeutic compounds described herein can include a single treatment or a series of treatments.

EXAMPLES

Materials and Methods

The following materials and methods were used in the Examples set forth below.

Tissue samples. The tissue microarrays (TMA) (24 cores per slide, 3 mm each, 1-2 cores/case with 2 controls per slide) were constructed from 146 primary GC resections performed at Massachusetts General Hospital from 1988 to 2007. The review board at Massachusetts General Hospital approved this study. Collected clinical and pathologic features include age ((greater than or less than 70 years of age, using 70 years (median age)), gender, maximal size ((greater than or less than 5 cm, using 5 cm (median)), Lauren type (diffuse, intestinal), TNM stage, tumor grade (grades 1/2/3/4),7tumor site (cardia, body/fundus, antrum, multifocal), lymphovascular and/or venous invasion, perineural invasion and overall survival (months). At least two gastrointestinal pathologists reviewed all H&E slides.

Biomarker panel. Expression for 14 different biomarkers was performed and included EBER in situ hybridization (ISH), p53, MMR proteins (MLH1, PMS2, MSH2, and MSH6), E-Cadherin, PD-L1, MUC2, CDX2, CD10, MUCSAC, MUC6, and HER2. The specifications and dilution, site (membranous, nuclear, and cytoplasmic), scoring criteria, and pattern of staining for all biomarkers are listed in Table 1. Appropriate RNA controls, to assure RNA preservation, were performed on all TMA slides where required. Two gastrointestinal pathologists reviewed all immunohistochemical stains and in situ hybridization (GYL and NS).

Rationale for biomarker evaluation. EBER-ISH is the gold standard to detect EBV status, and localizes the abundantly expressed long noncoding RNAs EBER1 or EBER2 in malignant cells.8,9Use of immunohistochemistry for MMR proteins has a high sensitivity, specificity, and positive and negative predictive value for deficiency of the MMR system.10Additionally, the predominant mechanism of MSI in GC is promoter hypermethylation of MLH1 rather than mutations.6,11Several studies have shown an association between aberrant E-cadherin expression and diffuse-type adenocarcinomas,12,13and it has been suggested that loss of E-cadherin is a phenotypic expression of the genetic alteration noted in diffuse type GC (CDH1 mutations).14Immunohistochemical staining of E-cadherin has been shown to mirror the mRNA expression also.15A disconcordance between the immunostaining of p53 and its mutational status has been reported by several studies;16however, the difference has been reported to decrease if the criteria for overexpression are stringently applied,17as in the current study (Table 1).

Statistics. The statistical analysis was performed using R software for statistical computing v3.0.2 (r-project.org). Categorical variables were compared with the Fisher Exact Test, and P value of <0.05 (two-sided) was considered statistically significant. The overall survival was measured from the date of resection of the GC to the date of death from any cause (recorded from the medical chart and/or the Social Security death index, genealogybank.com/gbnk/ssdi/). The survival data was analyzed by Cox Proportional Hazards analysis. Unsupervised hierarchical clustering analysis with average linkage algorithms was applied to the dataset with the chosen 5 biomarkers (EBER-ISH, MLH1, PMS2, E-cadherin, and p53), followed by comparison of clinical phenotype and outcome analysis. This was performed using GeneCluster 3.0 (eisenlab.org/eisen/?page_id=42); TreeView (rana.lbl.gov/EisenSoftware.htm) was used for graphical representation of the results.

Example 1. Clinicopathological Features of the Five Subtypes of Gastric Cancers

In accordance with the proposed molecular classifications and based on the results of unsupervised hierarchical clustering analysis of the expression EBV-ISH, MMR proteins, E-cadherin, and p53, a five-tier classification algorithm of GC was generated. Out of the cohort of 146 cases, EBER-positive-GC constituted 5% of the cases (n=7) (Cluster 1); MMR deficient-GC represented 16% of the cases (n=24) (Cluster 2); aberrant expression of E-cadherin (Cluster 3) was noted in 21% of the GCs (n=30); and GC with aberrant p53 expression (Cluster 4) represented 51% of the cases (n=71). Cluster 4 with aberrant p53 expression was further subclassified into intestinal (33%, 25/75, MUC2 and/or CD10 positive), gastric (32%, 24/75, MUCSAC and/or MUC6 positive), mixed (15%, 11/75, both MUC2 and/or CD10 positive and MUCSAC and/or MUC6 positive), and null (20%, 15/75, MUC2, CD10, MUCSAC and MUC6 negative) sub-clusters. The remaining cases with normal p53 expression were designated as Cluster 5, comprising 7% of GCs (n=10).FIG. 1depicts the biomarker expression-based clusters along with key clinicopathologic features.FIG. 8presents the protein based expression.

See clinical characteristics in Table 2. The lesions were characterized by prominent lymphoid infiltrate in all cases. (FIG. 2) EBV-positive GCs trended towards a better survival (p 0.15, CI 0.14-1.36, HR 0.43 median survival 263.51 months vs. 29.31 months for non-EBV GC,FIG. 3); however, the difference was not statistically significant. A strong correlation with membranous expression of PD-L1 in tumor cells was seen (57% vs 0%, p-0.001) (FIG. 2).

See clinical characteristics in Table 2. There was a significant association with MUC2 expression (8/24 (33%) in this cluster compared to the rest of the cohort 18/122 (15%), p 0.04). This cluster had a lower frequency of lymph node (LN) metastasis (LN stage category >N1, 27% MSI GC vs. 55% non-MSI GC, p 0.02, CI 0.09-0.91), and the patients trended toward better survival (p 0.09, CI 0.32-1.09, HR 0.59, median survival-56.05 months vs 27.12 months for non-MSI GC,FIG. 3). The loss of MLH1 and PMS2 was the predominant pattern (95.8%); an additional case showed loss of PMS2 only (4.16%) (FIG. 4)

See clinical characteristics in Table 2. The GCs were predominantly of the poorly cohesive (i.e., diffuse) type (27/30, 90%). This group was subdivided into Cluster 3A showing complete loss of E-cadherin (40%) and Cluster 3B with cytoplasmic granular E-cadherin (60%) staining (FIG. 5). The patients of Cluster 3 had significantly lower aberrant p53 expression (16/30, 53%) versus 91/114 (80%) in the remainder (p 0.004). The difference was more significant in subcluster 3B versus the remainder ((7/18, 39%) in 3B versus 100/126 (79%) in the remainder, p 0.0007)). The patients in 3B also were older than 3A (3A: 62.4 yrs±16.69, 30%>70 yrs; 3B: 71.4 yrs±11.86, 65%>70 yrs). There was no survival difference (p 0.57, CI 0.71-1.88, HR 1.15, median survival—14.86 months vs 35.79 months for the remaining GC cases,FIG. 3) in this group.

See clinical characteristics in Table 2. The tumors were predominantly of intestinal type (61/76, 81%). The lesions were associated with higher lymph node stage >N0 (p 0.03, CI 1.06-6.24, 81% vs. 63% in the remaining GCs). This cluster trended towards increased Her2 staining (Her2 >0, p 0.05, CI 0.97-11.34, 20% vs. 8% in the remaining GC cases). No significant survival difference was noted in this group (p 0.13 CI 0.92-2.03, HR 1.36, median survival 26.84 months vs 37.55 months in the remaining GC cases,FIG. 3).

Based on the pattern of MUC and CD10 expression, this cluster was subdivided into 4 subgroups (intestinal (MUC2±CD10); gastric (MUCSAC MUC6); mixed (MUC2±CD10 AND MUCSAC±MUC6); and null (MUC2-CD10-, MUCSAC-, and MUC6-) subgroups) (FIGS. 6 and 7). Intestinal and mixed GCs trended towards increased Her2/neu expression ((Intestinal GC: Her2 >0, p 0.10, CI 0.60-13.40, 17% vs. 6%; mixed GC: Her2 >0, p 0.05, CI 0.76-17.70, 36% vs. 13%)). Null GC were associated with higher frequency of nodal metastasis >N0 (p 0.03, CI 1.03->103, 100% vs. 69% in the remaining cases). Those were also associated with a significantly higher percentage of lymphovascular invasion (p 0.01, CI 1.35->103, 100% vs. 61%). There was no survival difference between the sub-categories.

The remaining cancers (7% of cases with normal p53 expression) constituted Cluster 5. Observed in this group were a lack of EBV-ISH, MMR deficiency and aberrant E-cadherin expression. See clinical characteristics in Table 2. Morphologically, all 10 cases presented a gland-forming (i.e., intestinal) morphotype. This cluster was associated with an increased expression of MUC6 (p 0.01, CI 1.23-28.88, 60% vs. 21%). There was no survival difference (p 0.84, CI 0.52-2.22, HR 1.08, median survival 34.85 months vs 29.23 months for the remaining GC cases. (FIG. 3).

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