Patent Application
20220298578
The present invention relates to a method for predicting the prognosis of a patient suffering from a B-cell lymphoma, through the detection of the status of activation of the RelB protein, in a biological sample of said patient. The inventors indeed identified an associated gene expression signature in the biological sample. Some genes from said signature are over-expressed and other are under-expressed and allow detecting RelB activation and predicting a worse prognosis in B-cell lymphoma.
NF-κB transcription factors family plays a crucial role in the inflammatory and immune response, cell proliferation and survival. In mammals, the NF-κB family is composed of five members, RelA (p65), RelB, cRel (Rel), NF-κB1 (p50 and its precursor p105) and NF-κB2 (p52 and its precursor p100). These proteins form various homo- and heterodimeric complexes, the activity of which is regulated by two main pathways. The first one, known as the canonical NF-κB activation pathway, mainly applies to RelA and/or cRel containing complexes. The second one, the alternative NF-κB activation pathway, leads to the activation of RelB containing dimers.
The B-cell lymphomas are types of lymphoma affecting B cells. Lymphomas are “blood cancers” in the lymph nodes. They develop more frequently in adults and in immunocompromised individuals. B-cell lymphomas include both Hodgkin's lymphomas and most non-Hodgkin lymphomas. They are typically divided into low and high grade, typically corresponding to indolent (slow growing) lymphomas and aggressive lymphomas, respectively. Prognosis and treatment depend on the specific type of lymphoma as well as the stage and grade. Treatment includes radiation and chemotherapy. Early-stage indolent B-cell lymphomas can often be treated with radiation alone, with long-term non-recurrence. Early-stage aggressive disease is treated with chemotherapy and often radiation, with a 70-90% cure rate. Late-stage indolent lymphomas are sometimes left untreated and monitored until they progress. Late-stage aggressive disease is treated with chemotherapy, with cure rates of over 70%.
Diffuse Large B-cell Lymphoma (DLBCL) is the most common of non-Hodgkin lymphoma in adults (Pileri S A, Harris N L, Jaffe E S, Cox J. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues. Revised 4t. IARC publications; 2017). The median age of incidence is the 7th decade of life, although it can occur in young adults and more rarely in children. Its most usual clinical presentation is one or multiple fast growing nodal and/or extra-nodal masses (up to 40% of cases), being the gastrointestinal tract the most frequent site. Even though cure rates have significantly improved since the introduction of anti-CD20 monoclonal antibody rituximab into conventional chemotherapy, refractory/relapse cases can reach up to 40% (De Leval L et al., 2010).
Gene expression profiles (GEP) allowed cases to be separated into two main subgroups that differed in prognosis, based on their cell of origin (COO): germinal center B-cell-like (GCB) and activated B-cell-like (ABC), this latter being associated with worse outcome (Alizadeh A A, et al., 2000). Even though this classification is largely used, heterogeneity within groups is acknowledged and a better stratification for patients is needed. WO2014/047422 discloses p100 and p105 dependent genes.
Surprisingly, the inventors discovered that the activation status of the RelB NF-κB subunit was a prognostic biomarker of patients suffering from a-B-cell Lymphoma, preferably DLBCL.
Accordingly, the inventors surprisingly found that the activation status of the RelB NF-κB subunit was a prognostic biomarker of patients suffering from a-B-cell Lymphoma. In particular, the activation status of the RelB NF-κB subunit can be associated to a gene expression signature.
Thus, inventors identified such genes from said signature as good biomarkers for evaluating the prognosis of B-cell lymphoma patients, preferably Diffuse large B-cell lymphoma (DLBCL), Follicular lymphoma, Marginal zone B-cell lymphoma (MZL) or Mucosa-Associated Lymphatic Tissue lymphoma (MALT), Small lymphocytic lymphoma (also known as chronic lymphocytic leukemia, CLL), Mantle cell lymphoma (MCL), DLBCL variants or sub-types such as Primary mediastinal (thymic) large B cell lymphoma, T cell/histiocyte-rich large B-cell lymphoma, Primary cutaneous diffuse large B-cell lymphoma, leg type (Primary cutaneous DLBCL, leg type), EBV positive diffuse large B-cell lymphoma of the elderly, Diffuse large B-cell lymphoma associated with inflammation, Burkitt's lymphoma, Lymphoplasmacytic lymphoma, which may manifest as Waldenström's macroglobulinemia, Nodal marginal zone B cell lymphoma (NMZL), Splenic marginal zone lymphoma (SMZL), Intravascular large B-cell lymphoma, Primary effusion lymphoma, Lymphomatoid granulomatosis, Primary central nervous system lymphoma, ALK-positive large B-cell lymphoma, Plasmablastic lymphoma, Large B-cell lymphoma arising in HHV8-associated multicentric Castleman's disease, B-cell lymphoma, unclassifiable with features intermediate between diffuse large B-cell lymphoma and Burkitt lymphoma, B-cell lymphoma, unclassifiable with features intermediate between diffuse large B-cell lymphoma and classical Hodgkin lymphoma.
In a more preferred embodiment, subject suffers from diffuse large B-cell lymphoma (DLBCL).
In a first aspect, the invention relates to an in vitro method for predicting the prognosis of a subject suffering from a B-cell lymphoma, said method comprising the step of detecting the status of DNA-binding activation of the RelB protein, in a biological sample of said patient. The method according to the invention relates the identification by the inventor of the genetic signature associated to the DNA-binding activation of the RelB protein, detected by measuring the expression level of activated-RelB dependent genes from an expression signature in the biological sample of said subject. In particular, expression of genes listed in Table 2 was identified as a worse prognosis for DLBCL patients.
In a preferred embodiment, the method comprising determining the expression level of at least one gene selected in the group consisting of SLAMF6 gene, PSMB8-AS1 gene, ZNF621 gene, SARAF gene, SSR1 gene, SLC9A3R1 gene, SQSTM1 gene, RRAGA gene, CCND3 gene, PPP1R2 gene, HDAC5 gene, ITM2A gene, BIN1 gene, LPL gene, FRY gene, GMFG gene, TK2 gene, KIAA0513 gene, ABCG1 gene, HLA-F gene, BTN3A3 gene, IL27RA gene, HCP5 gene, DZIP3 gene, HLA-B gene, HLA-G gene, HLA-C gene, CD59 gene, STAT5B gene, BTN3A2 gene, CSTF2T gene, CLIC5 gene, VAMP4 gene, HLA-A gene, N4BP2L2 gene, HLA-J gene, NDFIP1 gene, ACSL5 gene, FBXO3 gene, ZNF302 gene, ECHDC2 gene, ARHGAP15 gene, NAP1L2 gene, GVINP1 gene, FAM117A gene, ZNF506 gene, DERL1 gene, YPEL3 gene, SENP7 gene, NSD3 gene, PCYT1A gene, TTC14 gene, ARHGAP27 gene, MTMR10 gene, FAM84B gene, TRAPPC5 gene, RDH10 gene, FAM160B1 gene, SLFN5 gene, NHLRC3 gene, SUSD3 gene, FAM171B gene, PLPP6 gene, USF3 gene, POP4 gene, MVB12A gene, LOC100506990 gene, MYLIP gene, KLKB1 gene, RAP1A gene, SNX20 gene, SEMA4D gene, ZNF224 gene, RNASEL gene, ARSD gene, TTC39B gene, ZNF81 gene, ATP5S gene, ZNF818P gene, ZNF829 gene, RGS3 gene, CECR7 gene, ZNF667-AS1 gene, ZNF131 gene, LOC100506730 gene, TMEM67 gene, PHF20 gene, SLC25A3 gene, CCT2 gene, OXCT1 gene, LYPLA1 gene, PAFAH1B3 gene, PMVK gene, PNO1 gene, WRN gene, HOXC4 gene, CBFB gene, HNRNPR gene, ATP2B1 gene, UBE3A gene, RCOR1 gene, SET gene, KNOP1 gene, MRPL42 gene, RSF1 gene, CCNJ gene, SINHCAF gene, PLEKHA8P1 gene, BRIP1 gene, SLC25A33 gene, MFSD14C gene, C12orf73 gene, DNLZ gene, PTEN gene, DENR gene, MTFMT gene, CSKMT gene, ARHGAP30 gene, gene comprising in its Coding DNA Sequence (CDS) SEQ ID NO:3, gene comprising in its CDS, gene comprising in its CDS SEQ ID NO:47, gene comprising in its CDS SEQ ID NO:129, gene comprising in its CDS SEQ ID NO:135, gene comprising in its CDS SEQ ID NO:136 and gene comprising in its CDS SEQ ID NO:139.
In a second aspect, the invention relates to an in vitro method for monitoring the evolution of B-cell lymphoma in a subject being diagnosed for B-cell lymphoma, said method comprising:
In a third aspect, the invention relates to a method for determining or adapting a therapeutic regimen suitable for a subject diagnosed for B-cell lymphoma comprising the step of:
In a preferred embodiment, the status of DNA-binding activation of the RelB protein is determined by the determination of the level of expression of at least one gene of the signature as listed in Table 2, preferably of at least five of said signature, preferably of at least ten of said signature and even more preferably all the genes of said signature.
In a preferred embodiment, the biological sample of use in the method of the invention is body effluent such as, for example, urine or blood sample or a tumor sample such as, for example biopsy or surgical/resected specimen.
In another embodiment, the at least one RelB-dependent gene expression signature of the methods according to the invention is determined by RNAseq, microarray, Nanostring or RT-LMPA.
In a preferred embodiment, the subject according to the invention is suffering from Diffuse Large B-cell Lymphoma (DLBCL).
The invention relates also to the use of a kit for predicting a prognosis of patients suffering from B-cell lymphoma, comprising primers or approaches targeting or evaluating specifically the gene sequence and/or expression of SEQ ID NO:1-140.
As used herein, the term “approaches” refers to techniques described below for detecting expression of genes such as, for example RNAseq, DNA microarray, Nanostring or RT-MLPA.
The invention is particularly suited to predict a worse prognosis for patients suffering from DLBCL. As shown in the experimental part, NF-κB signature does not reflect the status of RelB activation and accordingly, the classification of the background art is not enough to correctly stratify the DLBCL patients. It is known that activated B-cell-like (ABC) subtype was associated with worse outcome for DLBCL, but many patients classified in germinal center B-cell-like (GCB) subtype associated with good outcome had finally a worse outcome. Thus, it is admitted that DLBCL patients are really difficult to correctly stratify due to the heterogeneity of the disease. By determining the gene expression profile which is actually linked to the activation status of RelB (i.e. when RelB is found associated with DNA and not only when it is located in the nucleus) inventor has been able to determine a valuable tool to prognose the fate of subjects suffering from B lymphomas, more particularly patients suffering from DLBCL. The signature of the invention is not linked to previously described NF-κB/RelB signature, indicating that said NF-κB/RelB previously described signatures does not reflect the actual DNA binding activity status of RelB which is surprisingly found by the inventor as particularly relevant in regard with prognosis of B cell lymphoma.
As intended herein, the term “comprising” has the meaning of “including” or “containing”, which means that when an object “comprises” one or several elements, other elements than those mentioned may also be included in the object. In contrast, when an object is said to “consist of” one or several elements, the object cannot include other elements than those mentioned.
To our knowledge, no study has ever revealed that the status of activation of the RelB protein was suitable to predict the prognostic of B-cell Lymphoma patients, more particularly DLBCL patients and associated with the detection of a gene expression signature in the biological sample of said subject, which was not known before either.
Yet, inventor has observed that RelB is frequently activated in both GCB and ABC DLBCL subtypes. Inventor has assessed the activation status of classical (RelA and cRel) and alternative (RelB) NF-κB subunits in 66 de novo DLBCL patients from the GHEDI cohort from the Lymphoma Study Association (LYSA) national cooperator group. The characteristics of this group are summarized below on Table 1 below.
For analysis, patients who received R-CHOP 14, R-CHOP 21 and mini-R-CHOP were grouped in “R-CHOP”. Patients who received R-ACVBP+conso, ACVBP+ASCT and ACVBP were grouped in “R-ACVBP”. ECOG, Eastern Cooperative Oncology Group performance status; IPI, international prognostic index; LDH, lactate dehydrogenase; ULN, upper limit of normal; COO, cell of origin; GCB, germinal center B-cell; ABC, activated B-cell; R, rituximab; CHOP, cyclophosphamide, doxorubicin, vincristine, and prednisone; ACVBP, doxorubicin, cyclophosphamide, vindesine, bleomycin, prednisone; Conso, consolidation; ASCT, autologous stem cell transplantation.
As used herein, “R-CHOP” refers to a combination of Rituximab, Cyclophosphamide, Hydroxyadriamicyne, Oncovin (vincristine) and Prednisone. As used herein, “R-ACVBP” refers to a combination of Rituximab, Adriamycine, Cyclophosphamide, Vindesine, Bleomycine and Prednisone.
In particular, RelB subunit presented a DNA binding activity in 44 cases (66.6%), well distributed among ABC and GCB subtype (Table 1). Further, inventors evaluated the effect of this RelB activation in DLBCL patients and have shown that RelB activation correlated with worse overall survival in said 66 de novo DLBCL patients (Table 1).
The status of activation of the RelB protein can be directly assessed by EMSA combined with supershift. As used herein, EMSA combined with supershift refers to a biochemical method allowing the detection of DNA binding of RelB containing complexes (Jacque et al., 2005). Whole cell extract was prepared and analyzed for DNA binding activity using the HIV-LTR tandem κB oligonucleotide as κB probe (Jacque et al., 2005). For supershift assays, whole cell extracts were incubated with specific antibodies for 30 min on ice before incubation with the labeled probe.
NF-κB (Nuclear Factor-KappaB) is a heterodimeric protein composed of different combinations of members of the Rel family of transcription factors, including NF-κB1 (p50), NF-κB2 (p52), RelA (p65), RelB, and c-Rel (Rel). Hetero and homo-dimerization of NF-κB proteins which exhibit differential binding specificities includes p50/RelA, p50/c-Rel, p52/c-Rel, p65/c-Rel, RelA/RelA, p50/p50, p52/p52, RelB/p50 and RelB/p52 and numerous other complexes.
NF-κB is known to be important in regulating a variety of cellular responses. It belongs to the category of “rapid-acting” primary transcription factors, i.e. transcription factors that are present in cells in an inactive state and do not require new protein synthesis to be activated. This allows NF-κB to be a first responder to harmful cellular stimuli. Known inducers of NF-κB activity are highly variable and include reactive oxygen species (ROS), tumor necrosis factor alpha (TNFα), interleukin 1-beta (IL-1β), bacterial lipopolysaccharides (LPS), Toll-like receptors (TLRs), lymphotoxin-α, lymphotoxin-β, BAFF, RANKL, isoproterenol, cocaine, virus, ionizing radiation, and genotoxic agents.
“RelB” or “transcription factor RelB” is a protein that in humans is encoded by the RELB gene and is accessible under the Uniprot number Q01201. “RelB gene” herein designates a protein coding gene. Its DNA sequence is located on chromosome 19 (45,001,449-45,038,194).
In a first aspect, the present invention therefore relates to an in vitro method for predicting the prognosis of a subject suffering from a B-cell lymphoma, said method comprising the step of detecting the status of activation of the RelB protein, in a biological sample of said patient.
As used herein, the term “subject” refers to a mammal, preferably a human. Preferably, it refers to a human patient that may be healthy (without any symptoms of B-cell lymphoma cancer), or that is thought to develop or is suspected of suffering from cancer, preferably B-cell lymphoma. Said subject for example presents at least one of the following symptoms: night sweats, fever, unexplained weight loss, fatigue, appetite loss, trouble breathing, pain or swelling in belly, severe itching. Said subject may also have suffered from a B-cell lymphoma cancer in the past, has been treated, and is monitored for potential disease recurrence. Said subject may also seem to be healthy but is predisposed to develop a B-cell lymphoma, for example genetic, because of, e.g., family history such as a member of his family is suffering or has suffered from the same disease. More preferably, in the methods according to the invention, subject is thought to develop or is suspected of suffering from diffuse large B-cell lymphoma (DLBCL). Diffuse large B-cell lymphoma is the most common subtype of non-Hodgkin lymphoma (NHL) in adults characterized by a median age of presentation in the sixth decade of life (but also rarely occurring in adolescents and children) with the initial presentation being single or multiple rapidly growing masses (that may or may not be painful) in nodal or extranodal sites (such as thyroid, skin, breast, gastrointestinal tract, testes, bone, or brain) and that can be accompanied by symptoms of fever, night sweats and weight loss. DLBCL has an aggressive disease course, with the elderly having a poorer prognosis than younger patients, and with relapses being common.
As used herein, the expression “biological sample” refers to solid tissues such as, for example, a tumor sample such as, for example biopsy or surgical/resected specimen or to fluids, body effluents such as, for example, blood, serum, plasma, urines, feces. Preferably, said biological sample is a fluid sample and most preferably a blood or urines.
The terms “worse prognosis”, “bad outcome”, “bad clinical outcome”, “worse overall survival” when related to a subject, in the context of the present invention, means a subject who is suffering from a B-cell lymphoma, more particularly a DLBCL, which is particularly aggressive or resistant to current therapies thereby resulting in a significant lowering of overall survival of said subjects, when compared to subjects from less aggressive B cell lymphoma. “Worse prognosis”, “bad outcome”, “bad clinical outcome”, is meant to include also worse Overall Survival (OS, defined as period of time for which patient is alive after disease diagnostic), worse Progression Free Survival (PFS, the length of time during and after the treatment of the disease, that a patient lives with the disease but it does not get worse), worse FFS=Failure Free Survival (FFS, defined as period of time with the absence of relapse, non-relapse mortality or addition of another systemic therapy) or worse Event Free Survival (EFS, the length of time after primary treatment for a cancer ends that the patient remains free of certain complications or events that the treatment was intended to prevent or delay).
In a preferred embodiment, the activation of the RelB protein is detected by measuring the expression level of at least one RelB-dependent gene from an expression signature associated with the activation status of RelB in a biological sample from said subject.
The terms “gene expression signature”, “gene signature”, “RelB activation signature” or “RelB signature”, when used herein, refer to the gene expression profile that has been identified by the inventor as linked with RelB activation, i.e. the actual RelB DNA binding activity. “RelB activation dependent gene” refers to a gene from said signature.
In a more preferred embodiment, the activation of the RelB protein is detected by determining the expression level of at least one gene selected in the group consisting of the SLAMF6 gene, PSMB8-AS1 gene, ZNF621 gene, SARAF gene, SSR1 gene, SLC9A3R1 gene, SQSTM1 gene, RRAGA gene, CCND3 gene, PPP1R2 gene, PPP1R2 gene, HDAC5 gene, ITM2A gene, BIN1 gene, LPL gene, FRY gene, GMFG gene, TK2 gene, KIAA0513 gene, ABCG1 gene, HLA-F gene, BTN3A3 gene, IL27RA gene, HCP5 gene, DZIP3 gene, HLA-B gene, HLA-B gene, HLA-G gene, ABCG1 gene, HLA-G gene, HLA-C gene, CD59 gene, STAT5B gene, BTN3A2 gene, CSTF2T gene, CLIC5 gene, VAMP4 gene, HLA-A gene, N4BP2L2 gene, HLA-A gene, HLA-J gene, NDFIP1 gene, ACSL5 gene, FBXO3 gene, ZNF302 gene, ECHDC2 gene, ARHGAP15 gene, NAP1L2 gene, GVINP1 gene, FAM117A gene, ZNF506 gene, IL27RA gene, DERL1 gene, YPEL3 gene, SENP7 gene, NSD3 gene, PCYT1A gene, TTC14 gene, TTC14 gene, ARHGAP27 gene, MTMR10 gene, FAM84B gene, TRAPPC5 gene, RDH10 gene, FAM160B1 gene, SLFN5 gene, NHLRC3 gene, SUSD3 gene, FAM171B gene, PLPP6 gene, USF3 gene, USF3 gene, RDH10 gene, POP4 gene, MVB12A gene, LOC100506990 gene, MYLIP gene, KLKB1 gene, ZNF302 gene, ZNF302 gene, RAP1A gene, CD59 gene, SNX20 gene, SEMA4D gene, ZNF224 gene, RNASEL gene, ARSD gene, SLFN5 gene, ABCG1 gene, TTC39B gene, ABCG1 gene, ZNF81 gene, ATP5S gene, ZNF818P gene, ZNF829 gene, RGS3 gene, CECR7 gene, ZNF667-AS1 gene, ZNF131 gene, LOC100506730 gene, TMEM67 gene, PHF20 gene, SLC25A3 gene, CCT2 gene, OXCT1 gene, LYPLA1 gene, PAFAH1B3 gene, PMVK gene, PNO1 gene, WRN gene, HOXC4 gene, CBFB gene, HNRNPR gene, ATP2B1 gene, UBE3A gene, RCOR1 gene, SET gene, KNOP1 gene, MRPL42 gene, RSF1 gene, CCNJ gene, SINHCAF gene, PLEKHA8P1 gene, BRIP1 gene, SINHCAF gene, SLC25A33 gene, MFSD14C gene, C12orf73 gene, DNLZ gene, PTEN gene, DENR gene, MTFMT gene, CSKMT gene and ARHGAP30 gene.
In a another preferred embodiment, the activation of the RelB protein is detected by determining the expression level of at least one gene selected in the group consisting of the SLAMF6 gene, PSMB8-AS1 gene, ZNF621 gene, SARAF gene, SSR1 gene, SLC9A3R1 gene, SQSTM1 gene, RRAGA gene, CCND3 gene, PPP1R2 gene, HDAC5 gene, ITM2A gene, BIN1 gene, LPL gene, FRY gene, GMFG gene, TK2 gene, KIAA0513 gene, ABCG1 gene, HLA-F gene, BTN3A3 gene, IL27RA gene, HCP5 gene, DZIP3 gene, HLA-B gene, HLA-G gene, HLA-C gene, CD59 gene, STAT5B gene, BTN3A2 gene, CSTF2T gene, CLIC5 gene, VAMP4 gene, HLA-A gene, N4BP2L2 gene, HLA-J gene, NDFIP1 gene, ACSL5 gene, FBXO3 gene, ZNF302 gene, ECHDC2 gene, ARHGAP15 gene, NAP1L2 gene, GVINP1 gene, FAM117A gene, ZNF506 gene, DERL1 gene, YPEL3 gene, SENP7 gene, NSD3 gene, PCYT1A gene, TTC14 gene, ARHGAP27 gene, MTMR10 gene, FAM84B gene, TRAPPC5 gene, RDH10 gene, FAM160B1 gene, SLFN5 gene, NHLRC3 gene, SUSD3 gene, FAM171B gene, PLPP6 gene, USF3 gene, POP4 gene, MVB12A gene, LOC100506990 gene, MYLIP gene, KLKB1 gene, RAP1A gene, SNX20 gene, SEMA4D gene, ZNF224 gene, RNASEL gene, ARSD gene, TTC39B gene, ZNF81 gene, ATP5S gene, ZNF818P gene, ZNF829 gene, RGS3 gene, CECR7 gene, ZNF667-AS1 gene, ZNF131 gene, LOC100506730 gene, TMEM67 gene, PHF20 gene, SLC25A3 gene, CCT2 gene, OXCT1 gene, LYPLA1 gene, PAFAH1B3 gene, PMVK gene, PNO1 gene, WRN gene, HOXC4 gene, CBFB gene, HNRNPR gene, ATP2B1 gene, UBE3A gene, RCOR1 gene, SET gene, KNOP1 gene, MRPL42 gene, RSF1 gene, CCNJ gene, SINHCAF gene, PLEKHA8P1 gene, BRIP1 gene, SLC25A33 gene, MFSD14C gene, C12orf73 gene, DNLZ gene, PTEN gene, DENR gene, MTFMT gene, CSKMT gene, ARHGAP30 gene, gene comprising in its Coding DNA Sequence (CDS) SEQ ID NO:3, gene comprising in its CDS, gene comprising in its CDS SEQ ID NO:47, gene comprising in its CDS SEQ ID NO:129, gene comprising in its CDS SEQ ID NO:135, gene comprising in its CDS SEQ ID NO:136 and gene comprising in its CDS SEQ ID NO:139.
In a preferred embodiment, the activation of the RelB protein is detected by determining the expression level of at least ten genes listed in the Table 2, or at least twenty genes listed in the Table 2, or at least thirty genes listed in the Table 2, or at least forty genes listed in the Table 2, or at least fifty genes listed in the Table 2, or at least sixty genes listed in the Table 2, or at least seventy genes listed in the Table 2, or at least eighty genes listed in the Table 2, or at least ninety genes listed in the Table 2, or at least one hundred genes listed in the Table 2, or at least one hundred and ten genes listed in the Table 2, or at least one hundred and twenty genes listed in the Table 2, or at least one hundred and thirty genes listed in the Table 2, more preferably one hundred and forty genes listed in the Table 2.Thus, the invention relates to a method for predicting the prognosis of a patient suffering from a B-cell lymphoma, comprising determining the expression level of at least one of said genes listed in Table 2 and/or any combination thereof, more preferably combination of all the genes, listed in table 2, or of all the genes from the activation RelB signature. In a preferred embodiment, the present invention also relates to a method for predicting the prognosis of a patient suffering from a B-cell lymphoma, consisting in determining the expression level of the genes as listed in Table 2 or described above (SEQ ID NO: 1-140).
In another embodiment of the invention, the activation of the RelB protein is detected by determining over-expression of at least one gene selected in the group consisting in the SLAMF6 gene, PSMB8-AS1 gene, ZNF621 gene, SARAF gene, SSR1 gene, SLC9A3R1 gene, SQSTM1 gene, RRAGA gene, CCND3 gene, PPP1R2 gene, PPP1R2 gene, HDAC5 gene, ITM2A gene, BIN1 gene, LPL gene, FRY gene, GMFG gene, TK2 gene, KIAA0513 gene, ABCG1 gene, HLA-F gene, BTN3A3 gene, IL27RA gene, HCP5 gene, DZIP3 gene, HLA-B gene, HLA-B gene, HLA-G gene, ABCG1 gene, HLA-G gene, HLA-C gene, CD59 gene, STAT5B gene, BTN3A2 gene, CSTF2T gene, CLIC5 gene, VAMP4 gene, HLA-A gene, N4BP2L2 gene, HLA-A gene, HLA-J gene, NDFIP1 gene, ACSL5 gene, FBXO3 gene, ZNF302 gene, ECHDC2 gene, ARHGAP15 gene, NAP1L2 gene, GVINP1 gene, FAM117A gene, ZNF506 gene, IL27RA gene, DERL1 gene, YPEL3 gene, SENP7 gene, NSD3 gene, PCYT1A gene, TTC14 gene, TTC14 gene, ARHGAP27 gene, MTMR10 gene, FAM84B gene, TRAPPC5 gene, RDH10 gene, FAM160B1 gene, SLFN5 gene, NHLRC3 gene, SUSD3 gene, FAM171B gene, PLPP6 gene, USF3 gene, USF3 gene, RDH10 gene, POP4 gene, MVB12A gene, LOC100506990 gene, MYLIP gene, KLKB1 gene, ZNF302 gene, ZNF302 gene, RAP1A gene, CD59 gene, SNX20 gene, SEMA4D gene, ZNF224 gene, RNASEL gene, ARSD gene, SLFN5 gene, ABCG1 gene, TTC39B gene, ABCG1 gene, ZNF81 gene, ATP5S gene, ZNF818P gene, ZNF829 gene, RGS3 gene, CECR7 gene and ZNF667-AS1 gene, wherein over-expression of said genes is prognostic of a bad clinical outcome.
In another embodiment of the invention, the activation of the RelB protein is detected by determining over-expression of at least one gene selected in the group consisting of : SLAMF6 gene, PSMB8-AS1 gene, ZNF621 gene, SARAF gene, SSR1 gene, SLC9A3R1 gene, SQSTM1 gene, RRAGA gene, CCND3 gene, PPP1R2 gene, PPP1R2 gene, HDAC5 gene, ITM2A gene, BIN1 gene, LPL gene, FRY gene, GMFG gene, TK2 gene, KIAA0513 gene, ABCG1 gene, HLA-F gene, BTN3A3 gene, IL27RA gene, HCP5 gene, DZIP3 gene, HLA-B gene, HLA-G gene, HLA-C gene, CD59 gene, STAT5B gene, BTN3A2 gene, CSTF2T gene, CLIC5 gene, VAMP4 gene, HLA-A gene, N4BP2L2 gene, HLA-J gene, NDFIP1 gene, ACSL5 gene, FBXO3 gene, ZNF302 gene, ECHDC2 gene, ARHGAP15 gene, NAP1L2 gene, GVINP1 gene, FAM117A gene, ZNF506 gene, DERL1 gene, YPEL3 gene, SENP7 gene, NSD3 gene, PCYT1A gene, TTC14 gene, ARHGAP27 gene, MTMR10 gene, FAM84B gene, TRAPPC5 gene, RDH10 gene, FAM160B1 gene, SLFN5 gene, NHLRC3 gene, SUSD3 gene, FAM171B gene, PLPP6 gene, USF3 gene, POP4 gene, MVB12A gene, LOC100506990 gene, MYLIP gene, KLKB1 gene, RAP1A gene, SNX20 gene, SEMA4D gene, ZNF224 gene, RNASEL gene, ARSD gene, TTC39B gene, ZNF81 gene, ATP5S gene, ZNF818P gene, ZNF829 gene, RGS3 gene, CECR7 gene, ZNF667-AS1 gene, gene comprising in its Coding DNA Sequence (CDS) SEQ ID NO:3, gene comprising in its CDS, gene comprising in its CDS SEQ ID NO:129, gene comprising in its CDS SEQ ID NO:136 and gene comprising in its CDS SEQ ID NO:139, wherein over-expression of said at least one gene is a prognosis of a bad clinical outcome.
In another embodiment of the invention, the activation of the RelB protein is detected by determining under-expression of at least one gene selected in the group consisting of: ZNF131 gene, LOC100506730 gene, TMEM67 gene, PHF20 gene, SLC25A3 gene, CCT2 gene, OXCT1 gene, LYPLA1 gene, PAFAH1B3 gene, PMVK gene, PNO1 gene, WRN gene, HOXC4 gene, CBFB gene, HNRNPR gene, ATP2B1 gene, UBE3A gene, RCOR1 gene, SET gene, KNOP1 gene, MRPL42 gene, RSF1 gene, CCNJ gene, SINHCAF gene, PLEKHA8P1 gene, BRIP1 gene, SINHCAF gene, SLC25A33 gene, MFSD14C gene, C12orf73 gene, DNLZ gene, PTEN gene, DENR gene, MTFMT gene, CSKMT gene and ARHGAP30 gene, wherein under-expression of said genes is prognostic of a bad clinical outcome.
In another embodiment of the invention, the activation of the RelB protein is detected by determining under-expression of at least one gene selected in the group consisting of : ZNF131 gene, LOC100506730 gene, TMEM67 gene, PHF20 gene, SLC25A3 gene, CCT2 gene, OXCT1 gene, LYPLA1 gene, PAFAH1B3 gene, PMVK gene, PNO1 gene, WRN gene, HOXC4 gene, CBFB gene, HNRNPR gene, ATP2B1 gene, UBE3A gene, RCOR1 gene, SET gene, KNOP1 gene, MRPL42 gene, RSF1 gene, CCNJ gene, SINHCAF gene, PLEKHA8P1 gene, BRIP1 gene, SLC25A33 gene, MFSD14C gene, C12orf73 gene, DNLZ gene, PTEN gene, DENR gene, MTFMT gene, CSKMT gene, ARHGAP30 gene, gene comprising in its CDS SEQ ID NO:47 and gene comprising in its CDS SEQ ID NO:135, wherein under-expression of at least one said gene is a prognosis of a bad clinical outcome.
In a preferred embodiment of the invention, the activation of the RelB protein is detected by determining over-expression of at least one gene selected in the group consisting in the SLAMF6 gene, PSMB8-AS1 gene, ZNF621 gene, SARAF gene, SSR1 gene, SLC9A3R1 gene, SQSTM1 gene, RRAGA gene, CCND3 gene, PPP1R2 gene, PPP1R2 gene, HDAC5 gene, ITM2A gene, BIN1 gene, LPL gene, FRY gene, GMFG gene, TK2 gene, KIAA0513 gene, ABCG1 gene, HLA-F gene, BTN3A3 gene, IL27RA gene, HCP5 gene, DZIP3 gene, HLA-B gene, HLA-B gene, HLA-G gene, ABCG1 gene, HLA-G gene, HLA-C gene, CD59 gene, STAT5B gene, BTN3A2 gene, CSTF2T gene, CLIC5 gene, VAMP4 gene, HLA-A gene, N4BP2L2 gene, HLA-A gene, HLA-J gene, NDFIP1 gene, ACSL5 gene, FBXO3 gene, ZNF302 gene, ECHDC2 gene, ARHGAP15 gene, NAP1L2 gene, GVINP1 gene, FAM117A gene, ZNF506 gene, IL27RA gene, DERL1 gene, YPEL3 gene, SENP7 gene, NSD3 gene, PCYT1A gene, TTC14 gene, TTC14 gene, ARHGAP27 gene, MTMR10 gene, FAM84B gene, TRAPPC5 gene, RDH10 gene, FAM160B1 gene, SLFN5 gene, NHLRC3 gene, SUSD3 gene, FAM171B gene, PLPP6 gene, USF3 gene, USF3 gene, RDH10 gene, POP4 gene, MVB12A gene, LOC100506990 gene, MYLIP gene, KLKB1 gene, ZNF302 gene, ZNF302 gene, RAP1A gene, CD59 gene, SNX20 gene, SEMA4D gene, ZNF224 gene, RNASEL gene, ARSD gene, SLFN5 gene, ABCG1 gene, TTC39B gene, ABCG1 gene, ZNF81 gene, ATP5S gene, ZNF818P gene, ZNF829 gene, RGS3 gene, CECR7 gene and ZNF667-AS1 gene and determining under-expression of at least one gene selected in the group consisting in the ZNF131 gene, LOC100506730 gene, TMEM67 gene, PHF20 gene, SLC25A3 gene, CCT2 gene, OXCT1 gene, LYPLA1 gene, PAFAH1B3 gene, PMVK gene, PNO1 gene, WRN gene, HOXC4 gene, CBFB gene, HNRNPR gene, ATP2B1 gene, UBE3A gene, RCOR1 gene, SET gene, KNOP1 gene, MRPL42 gene, RSF1 gene, CCNJ gene, SINHCAF gene, PLEKHA8P1 gene, BRIP1 gene, SINHCAF gene, SLC25A33 gene, MFSD14C gene, C12orf73 gene, DNLZ gene, PTEN gene, DENR gene, MTFMT gene, CSKMT gene and ARHGAP30 gene, wherein said expression level of said genes is prognostic of a bad clinical outcome.
In another preferred embodiment of the invention, the activation of the RelB protein is detected by:
In another preferred embodiment of the invention, the activation of the RelB protein is detected by:
In the method of the invention, if the said genes found over expressed in the RelB activation signature (see Table 2) are actually over-expressed in a subject as compared with a reference sample, then said subject is diagnosed as suffering from a B-cell lymphoma with a worse prognosis. In other words, an over-expression of SLAMF6 gene, PSMB8-AS1 gene, ZNF621 gene, SARAF gene, SSR1 gene, SLC9A3R1 gene, SQSTM1 gene, RRAGA gene, CCND3 gene, PPP1R2 gene, PPP1R2 gene, HDAC5 gene, ITM2A gene, BIN1 gene, LPL gene, FRY gene, GMFG gene, TK2 gene, KIAA0513 gene, ABCG1 gene, HLA-F gene, BTN3A3 gene, IL27RA gene, HCP5 gene, DZIP3 gene, HLA-B gene, HLA-G gene, HLA-C gene, CD59 gene, STAT5B gene, BTN3A2 gene, CSTF2T gene, CLIC5 gene, VAM P4 gene, HLA-A gene, N4BP2L2 gene, HLA-J gene, NDFIP1 gene, ACSL5 gene, FBXO3 gene, ZNF302 gene, ECHDC2 gene, ARHGAP15 gene, NAP1L2 gene, GVINP1 gene, FAM117A gene, ZNF506 gene, DERL1 gene, YPEL3 gene, SENP7 gene, NSD3 gene, PCYT1A gene, TTC14 gene, ARHGAP27 gene, MTMR10 gene, FAM84B gene, TRAPPC5 gene, RDH10 gene, FAM160B1 gene, SLFN5 gene, NHLRC3 gene, SUSD3 gene, FAM171B gene, PLPP6 gene, USF3 gene, POP4 gene, MVB12A gene, LOC100506990 gene, MYLIP gene, KLKB1 gene, RAP1A gene, SNX20 gene, SEMA4D gene, ZNF224 gene, RNASEL gene, ARSD gene, TTC39B gene, ZNF81 gene, ATP5S gene, ZNF818P gene, ZNF829 gene, RGS3 gene, CECR7 gene, ZNF667-AS1 gene, gene comprising in its Coding DNA Sequence (CDS) SEQ ID NO:3, gene comprising in its CDS, gene comprising in its CDS SEQ ID NO:129, gene comprising in its CDS SEQ ID NO:136 and gene comprising in its CDS SEQ ID NO:139 is regarded as an indicator of a worse prognosis for B-cell lymphoma. Alternatively, an over-expression of SLAMF6 gene, PSMB8-AS1 gene, ZNF621 gene, SARAF gene, SSR1 gene, SLC9A3R1 gene, SQSTM1 gene, RRAGA gene, CCND3 gene, PPP1R2 gene, PPP1R2 gene, HDAC5 gene, ITM2A gene, BIN1 gene, LPL gene, FRY gene, GMFG gene, TK2 gene, KIAA0513 gene, ABCG1 gene, HLA-F gene, BTN3A3 gene, IL27RA gene, HCP5 gene, DZIP3 gene, HLA-B gene, HLA-B gene, HLA-G gene, ABCG1 gene, HLA-G gene, HLA-C gene, CD59 gene, STAT5B gene, BTN3A2 gene, CSTF2T gene, CLIC5 gene, VAMP4 gene, HLA-A gene, N4BP2L2 gene, HLA-A gene, HLA-J gene, NDFIP1 gene, ACSL5 gene, FBXO3 gene, ZNF302 gene, ECHDC2 gene, ARHGAP15 gene, NAP1L2 gene, GVINP1 gene, FAM117A gene, ZNF506 gene, IL27RA gene, DERL1 gene, YPEL3 gene, SENP7 gene, NSD3 gene, PCYT1A gene, TTC14 gene, TTC14 gene, ARHGAP27 gene, MTMR10 gene, FAM84B gene, TRAPPC5 gene, RDH10 gene, FAM160B1 gene, SLFN5 gene, NHLRC3 gene, SUSD3 gene, FAM171B gene, PLPP6 gene, USF3 gene, USF3 gene, RDH10 gene, POP4 gene, MVB12A gene, LOC100506990 gene, MYLIP gene, KLKB1 gene, ZNF302 gene, ZNF302 gene, RAP1A gene, CD59 gene, SNX20 gene, SEMA4D gene, ZNF224 gene, RNASEL gene, ARSD gene, SLFN5 gene, ABCG1 gene, TTC39B gene, ABCG1 gene, ZNF81 gene, ATP5S gene, ZNF818P gene, ZNF829 gene, RGS3 gene, CECR7 gene and ZNF667-AS1 gene is regarded as an indicator of a worse prognosis for B-cell lymphoma.
In another aspect, expression level of said genes over-expressed in RelB activation signature can be compared with the expression level of the same genes in a previously collected sample of said subject or in reference sample, and then said subject is diagnosed or not as suffering from a B-cell lymphoma with a worse prognosis.
In the method of the invention, if the genes found under-expressed in the RelB activation signature are found in a subject actually under expressed as compared with a reference sample, then said subject is diagnosed as suffering from a B-cell lymphoma with a worse prognosis. In other words, an under-expression of ZNF131 gene, LOC100506730 gene, TMEM67 gene, PHF20 gene, SLC25A3 gene, CCT2 gene, OXCT1 gene, LYPLA1 gene, PAFAH1B3 gene, PMVK gene, PNO1 gene, WRN gene, HOXC4 gene, CBFB gene, HNRNPR gene, ATP2B1 gene, UBE3A gene, RCOR1 gene, SET gene, KNOP1 gene, MRPL42 gene, RSF1 gene, CCNJ gene, SINHCAF gene, PLEKHA8P1 gene, BRIP1 gene, SLC25A33 gene, MFSD14C gene, C12orf73 gene, DNLZ gene, PTEN gene, DENR gene, MTFMT gene, CSKMT gene, ARHGAP30 gene, gene comprising in its CDS SEQ ID NO:47 and gene comprising in its CDS SEQ ID NO:135 as listed in Table 2, is regarded as an indicator of a worse prognostic for B-cell lymphoma. An under-expression of ZNF131 gene, LOC100506730 gene, TMEM67 gene, PHF20 gene, SLC25A3 gene, CCT2 gene, OXCT1 gene, LYPLA1 gene, PAFAH1B3 gene, PMVK gene, PNO1 gene, WRN gene, HOXC4 gene, CBFB gene, HNRNPR gene, ATP2B1 gene, UBE3A gene, RCOR1 gene, SET gene, KNOP1 gene, MRPL42 gene, RSF1 gene, CCNJ gene, SINHCAF gene, PLEKHA8P1 gene, BRIP1 gene, SinHCAF gene, SLC25A33 gene, MFSD14C gene, C12orf73 gene, DNLZ gene, PTEN gene, DENR gene, MTFMT gene, CSKMT gene, and ARHGAP30 gene is also regarded as an indicator of a worse prognosis for B-cell lymphoma.
In another aspect, expression level of said genes under-expressed in the RelB activation signature can be compared with the expression of the same genes of a previously collected sample of said subject or of a reference sample, and then said subject is diagnosed or not as suffering from a B-cell lymphoma with a worse prognosis.
According to any methods of the invention, if a gene is over-expressed and/or under-expressed as compared with a reference sample in accordance with the profile of RelB activation signature as mentioned in Table 2, then the subject is diagnosed as suffering from a B-cell lymphoma with a worse prognosis. In other words, an over-expression and an under-expression of genes of RelB activation signature as detailed in Table 2 below is regarded as an indicator of a worse prognosis for B-cell lymphoma. In another aspect, expression level of said genes of RelB activation signature can be compared with the same genes of a previously collected sample of said subject or of a reference sample, and then said subject is diagnosed or not as suffering from a B-cell lymphoma with a worse prognosis.
“n” in sequence listing represent regions that are not probed by the probe sequences.
In other words, wherein over or under expression of the RelB-dependent genes in said subject is determined by comparison with same genes in a reference sample in accordance with the RelB activation profile of Table 2, then said subject is diagnosed as suffering from a B-cell lymphoma with a worse prognosis.
A number of techniques have been proposed to detect expression of genes such as, for example RNAseq, DNA microarray, Nanostring or RT-MLPA to detect transcription of the gene.
DNA Microarray: A DNA microarray (also commonly known as DNA chip or biochip) is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome.
RNAseq: RNA-Seq is a developed approach to transcriptome profiling that uses deep-sequencing technologies. It provides a precise measurement of levels of transcripts and their isoforms. (Wang Z et al., 2009).
Nanostring: NanoString's nCounter technology is a variation on the DNA microarray and was invented and patented by Krassen Dimitrov and Dwayne Dunaway. It uses molecular “barcodes” and microscopic imaging to detect and count up to several hundred unique transcripts in one hybridization reaction. (Geis, G K et al., 2008).
RT-MLPA: Multiplex ligation-dependent probe amplification (MLPA) is a variation of the multiplex polymerase chain reaction that permits amplification of multiple targets with only a single primer pair. (Schouten J P et al., 2002).
All these methods (RNAseq, microarray, Nanostring or RT-MLPA) can be used for determining the gene expression of the markers of the invention.
Other techniques can be used to detect gene expression of the signature of the invention, at the translational level, when gene is known to encode a protein.
These methods are well known from the skilled in art, they are for example protein microarrays such as RPPA or ELISA.
RPPA (Reverse Phase Protein Assay) is particularly suited for complex samples. After appropriate preparation, sample or lysate or extract thereof is arrayed onto the microarray and probed with antibodies against the target proteins of interest. These antibodies are typically detected with chemiluminescent, fluorescent or colorimetric assays. Reference peptides are printed on the slides to allow for protein quantification of the sample, lysate or extract. Thereby, RPPA allows the quantitative measurement of hundreds of proteins in biological and clinical samples (Boellner & Becker, 2015).
ELISA (enzyme-linked immunosorbent assay) is well known and can be used also to quantitate proteins. This assay uses a solid-phase type of enzyme immunoassay (EIA) to detect the presence of a protein in a liquid sample (lysate of cellular sample, fractionated liquid sample etc. . . . ) using antibodies directed against the protein to be measured.
The method of the invention requires to detect the “gene expression” or “level of expression” or “expression level”. According to the present invention, the terms “gene expression” or “level expression” refer to the phenotypic manifestation of a gene or genes by the processes of genetic transcription and genetic translation. When genes are expressed, the genetic information (base sequence) on DNA is first copied to a molecule of mRNA (transcription). The mRNA molecules then leave the cell nucleus and enter the cytoplasm, where they participate in protein synthesis by specifying the particular amino acids that make up individual proteins (translation). “gene expression” can be quantified according to technologies described above. In an embodiment expression of some of the genes of the signature of the invention can be detected/determined at the transcriptional level (e.g., quantifying mRNA or cDNA) and some other at the translational level (i.e. at the protein level). In a particular embodiment, expression of the gene of the signature of the invention is performed at the transcriptional level.
According to the present invention, the “reference sample” which is used to detect an “gene expression” or “level expression” for carrying out a diagnostic of prognosis of B-cell lymphoma or for following the evolution of prognosis of B-cell lymphoma is a biological sample from a subject that does not suffer from B-cell lymphoma or a biological sample from a subject who has been previously diagnosed as suffering from B-cell lymphoma but, e.g., whom history shown he was in complete remission or with a good clinical outcome. Accordingly, such as a normal or healthy cell or tissue or body fluid, or a data set produced using information from a normal or healthy cell or tissue or body fluid or a biological sample from a subject with a good clinical outcome.
In a preferred embodiment, the reference sample is a blood sample obtained from a healthy subject or from a subject with a good clinical outcome.
In a preferred embodiment, the level expression of the genes of RelB activation signature is determined by Nanostring or by RT-LMPA by using the primers or approaches targeting the 140 sequences of genes of SEQ ID NO:1-140.
In another preferred embodiment, the level expression of each of the genes of RelB activation signature is determined by Nanostring or by RT-MLPA by using the primers or approaches targeting one sequence per gene of the RelB activation signature amongst SEQ ID NO:1-140.
In another aspect, the signature of the invention can be used to predict the outcome of B-cell lymphoma cancer patients. Also, the signature of the invention can be used to aid the skilled oncologist in the selection of appropriate treatments for maximizing the survival of the patients. Appropriate treatments are for example chemotherapeutic treatments, immunotherapeutic treatments, radiotherapeutic treatments and/or surgery. Specifically, said patients have been treated or will be treated with chemotherapeutic drugs. As used herein, “treatments” may include some combination of surgery, chemotherapy, radiation therapy and targeted therapy. In another embodiment “treatments” may include to not chose, to change or to discontinue a treatment because of the diagnosis of worse prognosis of the tested subject.
The present invention also relates to a method for predicting a clinical outcome of a subject afflicted with B-cell lymphoma, said method comprising:
If the following genes: SLAMF6 gene, PSMB8-AS1 gene, ZNF621 gene, SARAF gene, SSR1 gene, SLC9A3R1 gene, SQSTM1 gene, RRAGA gene, CCND3 gene, PPP1R2 gene, PPP1R2 gene, HDAC5 gene, ITM2A gene, BIN1 gene, LPL gene, FRY gene, GMFG gene, TK2 gene, KIAA0513 gene, ABCG1 gene, HLA-F gene, BTN3A3 gene, IL27RA gene, HCP5 gene, DZIP3 gene, HLA-B gene, HLA-B gene, HLA-G gene, ABCG1 gene, HLA-G gene, HLA-C gene, CD59 gene, STAT5B gene, BTN3A2 gene, CSTF2T gene, CLIC5 gene, VAMP4 gene, HLA-A gene, N4BP2L2 gene, HLA-A gene, HLA-J gene, NDFIP1 gene, ACSL5 gene, FBXO3 gene, ZNF302 gene, ECHDC2 gene, ARHGAP15 gene, NAP1L2 gene, GVINP1 gene, FAM117A gene, ZNF506 gene, IL27RA gene, DERL1 gene, YPEL3 gene, SENP7 gene, NSD3 gene, PCYT1A gene, TTC14 gene, TTC14 gene, ARHGAP27 gene, MTMR10 gene, FAM84B gene, TRAPPC5 gene, RDH10 gene, FAM160B1 gene, SLFN5 gene, NHLRC3 gene, SUSD3 gene, FAM171B gene, PLPP6 gene, USF3 gene, USF3 gene, RDH10 gene, POP4 gene, MVB12A gene, LOC100506990 gene, MYLIP gene, KLKB1 gene, ZNF302 gene, ZNF302 gene, RAP1A gene, CD59 gene, SNX20 gene, SEMA4D gene, ZNF224 gene, RNASEL gene, ARSD gene, SLFN5 gene, ABCG1 gene, TTC39B gene, ABCG1 gene, ZNF81 gene, ATP5S gene, ZNF818P gene, ZNF829 gene, RGS3 gene, CECR7 gene and ZNF667-AS1 gene are significantly over-expressed and the 38 genes, ZNF131 gene, LOC100506730 gene, TMEM67 gene, PHF20 gene, SLC25A3 gene, CCT2 gene, OXCT1 gene, LYPLA1 gene, PAFAH1B3 gene, PMVK gene, PNO1 gene, WRN gene, HOXC4 gene, CBFB gene, HNRNPR gene, ATP2B1 gene, UBE3A gene, RCOR1 gene, SET gene, KNOP1 gene, MRPL42 gene, RSF1 gene, CCNJ gene, SINHCAF gene, PLEKHA8P1 gene, BRIP1 gene, SINHCAF gene, SLC25A33 gene, MFSD14C gene, C12orf73 gene, DNLZ gene, PTEN gene, DENR gene, MTFMT gene, CSKMT gene and ARHGAP30 gene are significantly under-expressed in the biological sample of the tested subject as compared to the same signature in a reference sample, then the tested subject is likely to have a bad clinical outcome.
If SLAMF6 gene, PSMB8-AS1 gene, ZNF621 gene, SARAF gene, SSR1 gene, SLC9A3R1 gene, SQSTM1 gene, RRAGA gene, CCND3 gene, PPP1R2 gene, PPP1R2 gene, HDAC5 gene, ITM2A gene, BIN1 gene, LPL gene, FRY gene, GMFG gene, TK2 gene, KIAA0513 gene, ABCG1 gene, HLA-F gene, BTN3A3 gene, IL27RA gene, HCP5 gene, DZIP3 gene, HLA-B gene, HLA-G gene, HLA-C gene, CD59 gene, STAT5B gene, BTN3A2 gene, CSTF2T gene, CLIC5 gene, VAMP4 gene, HLA-A gene, N4BP2L2 gene, HLA-J gene, NDFIP1 gene, ACSL5 gene, FBXO3 gene, ZNF302 gene, ECHDC2 gene, ARHGAP15 gene, NAP1L2 gene, GVINP1 gene, FAM117A gene, ZNF506 gene, DERL1 gene, YPEL3 gene, SENP7 gene, NSD3 gene, PCYT1A gene, TTC14 gene, ARHGAP27 gene, MTMR10 gene, FAM84B gene, TRAPPC5 gene, RDH10 gene, FAM160B1 gene, SLFN5 gene, NHLRC3 gene, SUSD3 gene, FAM171B gene, PLPP6 gene, USF3 gene, POP4 gene, MVB12A gene, LOC100506990 gene, MYLIP gene, KLKB1 gene, RAP1A gene, SNX20 gene, SEMA4D gene, ZNF224 gene, RNASEL gene, ARSD gene, TTC39B gene, ZNF81 gene, ATP5S gene, ZNF818P gene, ZNF829 gene, RGS3 gene, CECR7 gene, ZNF667-AS1 gene, gene comprising in its Coding DNA Sequence (CDS) SEQ ID NO:3, gene comprising in its CDS, gene comprising in its CDS SEQ ID NO:129, gene comprising in its CDS SEQ ID NO:136 and gene comprising in its CDS SEQ ID NO:139 are significantly over-expressed and ZNF131 gene, LOC100506730 gene, TMEM67 gene, PHF20 gene, SLC25A3 gene, CCT2 gene, OXCT1 gene, LYPLA1 gene, PAFAH1B3 gene, PMVK gene, PNO1 gene, WRN gene, HOXC4 gene, CBFB gene, HNRNPR gene, ATP2B1 gene, UBE3A gene, RCOR1 gene, SET gene, KNOP1 gene, MRPL42 gene, RSF1 gene, CCNJ gene, SINHCAF gene, PLEKHA8P1 gene, BRIP1 gene, SLC25A33 gene, MFSD14C gene, C12orf73 gene, DNLZ gene, PTEN gene, DENR gene, MTFMT gene, CSKMT gene, ARHGAP30 gene, gene comprising in its CDS SEQ ID NO:47 and gene comprising in its CDS SEQ ID NO:135 are significantly under-expressed in the biological sample of the tested subject as compared to the same signature in a reference sample, then the tested subject is likely to have a bad clinical outcome.
Conversely, if gene expression profile of said genes described above are not significantly expressed in the biological sample of the tested subject as compared to the same signature in the reference sample, in accordance with expression profile of the RelB signature of the invention, then the tested subject is likely to have a good clinical outcome
In another aspect, the signature of the invention can be used for monitoring the evolution of B-cell lymphoma in a subject being diagnosed for B-cell lymphoma, said method comprising:
In a preferred embodiment, the biological sample in step a) is obtained prior to the treatment for B-cell lymphoma and the sample in step b) is obtained after said subject has been treated for B-cell lymphoma.
It can be concluded that the malignancy of the B-cell lymphoma is worsening if there is a DNA-binding activation of the RelB protein. In a preferred embodiment, the status of DNA-binding activation of the RelB protein is determined through the determination of the level of expression of the genes of the RelB signature according to the invention.
It can be concluded that the malignancy of the B-cell lymphoma is worsening if the expression level of the genes shown to be overexpressed in the RelB signature of Table 2 determined in step b) is found significantly higher than the expression level of same genes as determined in step a) and the expression level of the genes shown to be under expressed in the RelB signature of Table 2 determined in step b) is found significantly lower than the expression level of same gene in step a). In other words, the tested subject has a disease that evolves badly, even though he/she may be treated already, by conventional treatment (R-CHOP, R-ACVBP, . . . ), or more innovative immunotherapy such as, for example, CAR-T-cells treatment.
In another aspect, the signature of the invention can be used for determining or adapting a therapeutic regimen suitable for a subject diagnosed for B-cell lymphoma comprising the step of:
In particular, said surgery or therapeutic regimen is efficient or suitable if there is no DNA-binding activation of the RelB protein. Conversely, said therapeutic regimen should have to be changed if there is a DNA-binding activation of the RelB protein.
In a preferred embodiment, the status of DNA-binding activation of the RelB protein is determined by determining the level of expression of the genes of RelB signature according to the invention.
In particular, said surgery or therapeutic regimen is efficient or suitable if, in the subject, the expression level of the genes of the signature does not match with the expression level of same genes consisting the signature in the reference sample.
More particularly, said surgery or therapeutic regimen is efficient or suitable if, in the subject:
In particular, said surgery or therapeutic regimen is efficient or suitable if, in the subject, the expression level of the genes of the signature matches with the expression level of same genes consisting the signature in the reference sample.
In other words, said therapeutic regimen should have to be changed if, in the subject:
This aspect of treatment strategy is a crucial goal in a context of personalized medicine in order to improve survival while maintaining the quality of life and avoiding needless toxic effects of an ineffective treatment.
The present invention furthermore provides prognostic tools for determining the gene expression of the signature of the invention in order to prognose the outcome of B-cell lymphoma.
The present invention also relates to the use a kit for predicting a prognosis of subject suffering from B-cell lymphoma, comprising primers and/or nucleic acid probes targeting specifically the gene sequence of SEQ ID NO:1-140.
The present invention also relates to the use a kit for predicting a prognosis of subject suffering from B-cell lymphoma, comprising primers targeting specifically one sequence per gene of the RelB activation signature amongst SEQ ID NO:1-140.
The present invention also relates to the use a kit for predicting a prognosis of subject suffering from B-cell lymphoma, comprising primers targeting ZNF131 gene, LOC100506730 gene, TMEM67 gene, PHF20 gene, SLC25A3 gene, CCT2 gene, OXCT1 gene, LYPLA1 gene, PAFAH1B3 gene, PMVK gene, PNO1 gene, WRN gene, HOXC4 gene, CBFB gene, HNRNPR gene, ATP2B1 gene, UBE3A gene, RCOR1 gene, SET gene, KNOP1 gene, MRPL42 gene, RSF1 gene, CCNJ gene, SINHCAF gene, PLEKHA8P1 gene, BRIP1 gene, SLC25A33 gene, MFSD14C gene, C12orf73 gene, DNLZ gene, PTEN gene, DENR gene, MTFMT gene, CSKMT gene, ARHGAP30 gene, gene comprising in its CDS SEQ ID NO:47, gene comprising in its CDS SEQ ID NO:135, SLAMF6 gene, PSMB8-AS1 gene, ZNF621 gene, SARAF gene, SSR1 gene, SLC9A3R1 gene, SQSTM1 gene, RRAGA gene, CCND3 gene, PPP1R2 gene, PPP1R2 gene, HDAC5 gene, ITM2A gene, BIN1 gene, LPL gene, FRY gene, GMFG gene, TK2 gene, KIAA0513 gene, ABCG1 gene, HLA-F gene, BTN3A3 gene, IL27RA gene, HCP5 gene, DZIP3 gene, HLA-B gene, HLA-G gene, HLA-C gene, CD59 gene, STAT5B gene, BTN3A2 gene, CSTF2T gene, CLIC5 gene, VAMP4 gene, HLA-A gene, N4BP2L2 gene, HLA-J gene, NDFIP1 gene, ACSL5 gene, FBXO3 gene, ZNF302 gene, ECHDC2 gene, ARHGAP15 gene, NAP1L2 gene, GVINP1 gene, FAM117A gene, ZNF506 gene, DERL1 gene, YPEL3 gene, SENP7 gene, NSD3 gene, PCYT1A gene, TTC14 gene, ARHGAP27 gene, MTMR10 gene, FAM84B gene, TRAPPC5 gene, RDH10 gene, FAM160B1 gene, SLFN5 gene, NHLRC3 gene, SUSD3 gene, FAM171B gene, PLPP6 gene, USF3 gene, POP4 gene, MVB12A gene, LOC100506990 gene, MYLIP gene, KLKB1 gene, RAP1A gene, SNX20 gene, SEMA4D gene, ZNF224 gene, RNASEL gene, ARSD gene, TTC39B gene, ZNF81 gene, ATP5S gene, ZNF818P gene, ZNF829 gene, RGS3 gene, CECR7 gene, ZNF667-AS1 gene, gene comprising in its Coding DNA Sequence (CDS) SEQ ID NO:3, gene comprising in its CDS, gene comprising in its CDS SEQ ID NO:129, gene comprising in its CDS SEQ ID NO:136 and gene comprising in its CDS SEQ ID NO:139.
As used herein, the term “kit” refers to any system for delivering materials. In the context of the invention, it includes systems that allow the storage, transport, or delivery of reaction reagents (e.g., oligonucleotides, enzymes, etc. in the appropriate containers) and/or supporting materials (e.g., buffers, written instructions for performing the assay etc.) from one location to another. For example, kits include one or more enclosures (e.g., boxes) containing the relevant reaction reagents and/or supporting materials. The present kit can also include one or more reagents, buffers, hybridization media, nucleic acids, primers, nucleotides, probes, molecular weight markers, enzymes, solid supports, databases, computer programs for calculating dispensation orders and/or disposable lab equipment, such as multi-well plates, in order to readily facilitate implementation of the present methods. Enzymes that can be included in the present kits include nucleotide polymerases and the like. Solid supports can include beads and the like whereas molecular weight markers can include conjugatable markers, for example biotin and streptavidin or the like.
Further aspects and advantages of the invention will be disclosed in the following examples, which should be considered illustrative.
Patients were selected from the GHEDI (Deciphering the Genetic Heterogeneity of Diffuse large B-cell lymphoma in the rituximab era) study program of the LYSA group, previously published and described (Dubois et al. 2016). Patients were enrolled in previous trials, with available frozen tumor samples, centralized histopathological review, adequate DNA/RNA quality and complete clinical information (Jais et al., 2017). COO molecular classification was obtained with HGU133+2.0 Affymetrix GeneChip arrays (Affymetrix), grouping patients into ABC, GCB and “unclassified”. We had access to 70 frozen samples from de novo DLBCL patients, as well as complete clinical and transcriptomic data of 202 patients. Patients included in each part of the study are listed on Table 1.
For all the analyses patients who received R-CHOP 14, R-CHOP 21 and mini-R-CHOP were grouped as “R-CHOP”. Patients who received R-ACVBP+conso, ACVBP+ASCT and ACVBP were grouped as “R-ACVBP”.
DLBCL cell lines were obtained from José Angel Climent. Cells were grown in RPMI-1640 medium (Gibco glutamax) supplemented with 10% heat-inactivated fetal bovine serum (HyClone), 2 mM L-glutamine, 100 U/mL penicillin, and 100 mg/mL streptomycin (Invitrogen).
The antibodies were purchased from Santa Cruz (RelA, RelB, p105/p50, p100/p52, c-Rel, Bcl-2), Sigma (β-actin) and Cell Signaling (cleaved caspase 3 (Asp175), Bcl-xL and clAP2).
Immunoblotting were performed as typically known in the art.
Production of infectious recombinant lentiviruses was performed by transient transfection of 293T cells. For infections, cells were incubated overnight with recombinant lentiviruses. An equal amount of fresh culture medium was added 24 hours later and after 48 hours, cells were washed and seeded in fresh culture medium. GFP positive cells were sorted with FACSAria™ sorter (Becton Dickinson). Only cells with a high expression of GFP, above 40000 of mean fluorescence intensity (MFI), were selected and then amplified.
Cells were harvested and washed twice with cold PBS. They were resuspended in 1× binding buffer containing Annexin V-APC (BD Biosciences Pharmingen) and 4′,6-diaminidino-2-phenylindole (DAPI, Molecular Probes) following the manufacturer's instructions. The samples were subjected to cytometric analysis with a MACSQuant cytometer (Miltenyi Biotec) and the data was statistically evaluated using the Flowjo v10.2 software.
Cells incubated with or without doxorubicin were fixated in formalin 4%, permeabilized in SDS 1%, and then stained with an anti-γH2AX antibody. Samples were incubated with secondary antibody Alexa Fluor 647® following analysis using an ImageStream X Mark II Imaging Flow Cytometer. Data were acquired at a 60× magnification with EDF using the 642 nm laser at 150 mW and INSPIRE software. At least 8000 events of cells per sample were analyzed. Acquired data were analyzed using the IDEAS analysis software (v6.1; Merck-Millipore). Cells were gated for focused cells using the Gradient RMS feature. Cells were gated for single cells using the aspect ratio and area features. The spot counting analysis wizard was used.
For electrophoretic mobility shift assay (EMSA) we used the human immunodeficiency virus long terminal repeat tandem κB oligonucleotide as κB probe. For supershift assays, total protein extracts were incubated with specific antibodies (Santa Cruz—RelA C-20X; RelB C-19X; c-Rel (C)X; p50 H-119X; p52 K-27X). Cases were analyzed and classified by 3 different researchers independently. A consensus was achieved for the discordant cases.
Total RNA extraction and reverse transcription were performed. Real-time PCR analysis was carried out with LightCycler FastStart DNA Master plus SYBR Green I on a Light Cycler 1.5 (Roche Applied Science). All values were normalized to the level of HPRT mRNA.
p53 gene functional status is determined by the functional analysis of separated alleles in yeast (FASAY) method. p53 status was considered mutated when: (a) >10% of the yeast colonies are red, (b) analysis using the split versions of the test could identify the defect in the 5′ or 3′ part of the gene, and (c) sequence analysis from mutant yeast colonies (Sanger) could identify an unambiguous genetic defect.
Immunoperoxidase staining was centrally performed on an Ultra auto- mated system (Roche Ventana, Tucson, Ariz.) using UltraVIEW detection Original kits and optimized protocols for BCL-2, and MYC staining. In the absence of an internal positive control, immunostains were considered non-evaluable. The tissue core with the highest percentage of tumor cell staining was considered for analysis. The thresholds employed were 40% for MYC and 50% or 70% for BCL2.
Statistical significance was assessed using unpaired t tests (Prism 5.0c, GraphPad Software). A value of p=0.05 was considered as statistically significant with the following degrees: *p<0.05; **p<0.01; ***p<0.001.
Statistical analyses were performed using the software Statistical Package for the Social Sciences (SPSS, version 24), including descriptive statistics and statistical tests (chi-square for categorical variables or Fisher's exact test when Chi-square was not appropriate; T test for comparison of means; Kaplan-Meier method for evaluating prognostic impact of predictors; Cox proportional hazard model for survival-time outcomes on one or more predictors). Survival curves were created for overall survival based on Kaplan-Meier method, according to clinical data obtained from GHEDI cohort, previously described elsewhere (39)Cox proportional hazard regression analyses for univariate and multivariate were based on forward stepwise regression, with a cutoff for p-values≤0.2. Two-side P-values<0.05 were considered statistically significant (p=0.05).
Microarray experiments were performed on Affymetrix Human Genome HGU133plus2.0 GeneChips (a genome wide array with 54674 probe sets targeting 19418 transcripts). Gene expression levels were normalized using the GC-RMA algorithm and flags were computed using MAS5. Quality assessment of the chips has been performed with affyQCReport R package. MAS5 algorithm produces a flag “P” for “Present”, “M” for “Marginal” or “A” for “Absent” associated to each intensity measure. This flag is an estimation of the statistical difference between PM (Perfect Match) and MM (Mismatch). Three probe lists have been used for each comparison according to flagged measurement in the relevant chips. The “PP” list is made of probes only flagged as “Present” for all chips involved in the comparison. The “P50” list has been created filtering probes flagged as “Present” for at least half of the chips. The “All” list is made of all probes without any filter. Three groups of two biologically independent samples were compared. The group comparisons were done using Student's t test. To estimate the false discovery rate we filtered the resulting p values at 5% and used the Benjamini and Hochberg (BH), Bonferroni (B) or without correction (SC). Cluster analysis was performed by hierarchical clustering using the Spearman correlation similarity measure and average linkage algorithm. Data were subsequently submitted to Ingenuity Pathway Analysis (IPA) to model relationships among genes and proteins and to construct putative pathways and relevant biological processes.
RelB activation correlated with worse overall survival (OS) (p=0.037) in the 66 patients analyzed. Multivariate Cox regression analysis showed similar tendency as in Kaplan-Meier (KM) survival curves when adjusted by grouped IPI (p=0.071) (
Further, we established a gene expression profile (GEP) of genes associated with RelB activation in the EMSA-tested cohort. RelB-specific GEP was then projected on EMSA tested cases. The RelB-specific GEP was able to reproduce the EMSA established RelB activation status with 85% of sensitivity, 100% of specificity and a positive predictive value of 100%. RelB positive group defined by GEP revealed a tendency to a worse outcome in Kaplan-Meier survival curves (p=0.057). When adjusted by grouped IPI, RelB determined a worse outcome (p=0.034) (
In a second step, RelB transcriptomic signature was extended to a larger cohort (98), including the patients from the training cohort. We have excluded patients not treated by R-CHOP, since it is the main first line therapy for DLBCL, in which the main drug is doxorubicin further tested in DLBCL cell lines in our study. We also excluded patients unclassifiable as either GCB or ABC, because the signature was established in a training cohort containing only one of unclassified case. We observed a tendency of worse outcome in RelB positive group (p=0.129). When adjusted also by COO classification and grouped IPI in multivariate Cox regression analysis, RelB positivity defined a group of poorer prognosis (p=0.016) (
Next, we have evaluated in our training cohort of 66 DLBCL patients whether RelB activation is linked to the published NF-κB transcriptional signature composed of six genes (Davis et al., 2001). Remarkably, RelB activation status was not linked to the expression of any of these genes, indicating that the actual NF-κB signature does not reflect the status of RelB activation. As described, this so-called NF-κB signature marked preferentially the ABC DLBLC with an overexpression of four genes out of six (p<0.05). When comparing patients with strong RelA vs cRel activity, four genes showed significant overexpression in the RelA group (p<0.05) which is in line with RelA activation correlating with the ABC subtype (Table 4). Importantly, RelB activation did not correlate with any of the mutations commonly associated with NF-κB in DLBCL (e.g. MYD88, CARD11, CD79A/B, TNFAIP3). Altogether, these observations indicate that the current tools to evaluate NF-κB activity only apply to the classical NF-κB pathway and do not provide valuable information on RelB activation status.
Expression of Bcl-2 and c-Myc was assessed by IHC in 47 and 45 cases, respectively. There was no association between Bcl-2, c-Myc or double expression with RelB activation status (p=1.0, p=0.375, p=0.096 respectively). Double expression of c-Myc and Bcl-2 by IHC did not affect the overall survival of patients (p=0.652).
Altogether, our study shed light for the first time on the frequent engagement of RelB DNA binding activity in a cohort of DLBCL patients which is associated with a worse prognosis independently from COO classification and grouped IPI. Further, we identified a RelB transcriptomic signature associated with a prognosis value on a larger validation cohort.
Inventor has demonstrated for the first time by direct DNA binding that RelB is frequently activated in DLBCL patients. RelB activation defined by EMSA was associated with poorer outcome. The direct assessment of RelB activation status allowed the definition of a RelB signature that was able to predict RelB activation status and define a group of worse prognosis in a larger validation cohort. RelB prognostic value was statistically significant when adjusted by grouped IPI and COO classification. Finally, RelB is a new prognostic marker for DLBCL patients and might open the road for improvement in patient stratification and new targeted therapy.
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Reference to a “Sequence Listing,” a Table, or a Computer Program Listing Appendix submitted as an ASCII Text File
The material in the ASCII text file, name “APIC-65499-Sequence-Listing_ST25.txt”, created December 17, 2021, file size 122,880 bytes, is hereby incorporated by reference.
| Number | Date | Country | Kind |
|---|---|---|---|
| 19305847.6 | Jun 2019 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2020/067814 | 6/25/2020 | WO |
