Patent Application
20230105645
The present invention relates to the field on oncology, especially to personalized medicine in cancer therapy, especially by an immune checkpoint blockade therapy.
Immunotherapy represents today a major field of interest in the treatment of cancers. Indeed, leverage of the negative immune checkpoint blockade is able to induce durable responses across multiple types of cancers. The most advanced knowledge has been generated around the therapies targeting PD-1/PD-L1 or CTLA-4, and to a less extent anti-LAG3 or anti-TLR4. However, overall, only a fraction of patients has a therapeutic benefit of treatments targeting the immune checkpoint blockade, more specifically less than 25% of them and only some of them has responses with long duration. Indeed, most patients do not respond or, after an initial response, develop resistance. Many patients have also important toxicities problems due to the treatment. Therefore, identifying those patients that would have a clinical benefit remains an important unmet need in the field.
Many biomarkers have been used in the past years aiming to achieve this goal. However, to some extent, biomarkers strategies were biased. Drug developers oriented the research to biomarkers predictive to the response to immunotherapies only to deal with their own therapies. For example, anti-PD-L1 or anti-PD-1 therapies only took into account the level of PD-L1, the tumor mutations burden (an increased number of mutations being supposed to generate an increased amount of neoantigens derived from mutated proteins and recognized as “non-self”, or microsatellite instability reflecting a particular profile of mutations with high number of mutations). Nevertheless, most of them did not meet expectations and failed to correctly predict the efficacy of the immunotherapies treatments.
By consequences, immunotherapies are currently limited to a minority of patient and indications. Only 25% of patients have a response and among them, only a fraction has long durable responses. Therefore, there is a strong need to methods allowing an effective selection of the patients that would have a therapeutic benefit of an immune checkpoint blockade therapy.
The inventors present a novel concept of biomarker assessment that does not consider only the target but the global context of the immune blockade that can be targeted by immune checkpoint therapies. The method proposed by the inventors provide useful information that simultaneously and globally analyzes the key players of the immune-negative blockade. The novel biomarker strategy is made possible by simultaneous analysis of the tumor tissue and the analogous histologically matched normal tissue from the same patient. The method of the present invention enables to analyze individually each key players of immune blockade, but most importantly can assess their complex interactions.
Accordingly, the present disclosure provides a method for determining if a subject suffering from a cancer is susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, comprising
In a particular aspect, the set of genes comprises at least the genes of the group 1) and 2).
In another particular aspect, the set of genes is selected in one of the following sets: a) PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD86, CD28, LAG3, TLR4, CD8, CD16 and FOXP3; b) PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD86, LAG3 and TLR4; c) PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD28, CD8, CD16 and FOXP3; d) PD-1, PD-L1, PD-L2, LAG3, TLR4, CD8, CD16 and FOXP3; and e) CTLA-4, CD80, CD86, CD28, LAG3, TLR4, CD8, CD16 and FOXP3.
In an aspect of the method, mRNA expression level of other genes is studied in the method but the total number of genes is no more than 15 genes.
Optionally, a gene is overexpressed when the fold change between the tumor sample and the normal histologically matched sample is higher than 1.3, a gene is expressed at a similar level when the fold change is between −1.3 and 1.3, and a gene is underexpressed when the fold change is lower than −1.3.
Optionally, the genes classified into the three classes are displayed as a graph, preferably a graph showing the expression intensity in the tumor sample on the ordinate and the expression intensity in the normal histologically matched sample on the abscissa. For instance, the genes of each of the three classes can be shown with a distinct mode between each other's and optionally the genes of each of the groups can also be shown with a distinct mode between each other's.
Optionally, the susceptibility to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy is assessed based on the presence of genes of the groups in one of the three classes and optionally the expression intensity of the genes of the groups in the tumor sample.
Optionally, the immune checkpoint blockade therapy is selected from the group consisting an anti-PD-1 antibody, an anti-PD-L1 antibody, an anti-CTLA-4 antibody, an anti-LAG3 antibody and any combination thereof.
The present disclosure also provides a method for selecting a set of genes for which the differential expression between a tumor sample and a normal histologically matched sample from the same patient is indicative of a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, wherein the method comprises
In an aspect of the method, the set of genes is selected in one of the following sets: a) PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD86, CD28, LAG3, TLR4, CD8, CD16 and FOXP3; b) PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD86, LAG3 and TLR4; c) PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD28, CD8, CD16 and FOXP3; d) PD-1, PD-L1, PD-L2, LAG3, TLR4, CD8, CD16 and FOXP3; and e) CTLA-4, CD80, CD86, CD28, LAG3, TLR4, CD8, CD16 and FOXP3, preferably PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD86, CD28, LAG3, TLR4, CD8, CD16 and FOXP3.
Optionally, mRNA expression level of other genes is studied in the method but the total number of genes is no more than 15 genes.
Optionally, the immune checkpoint blockade therapy is selected from the group consisting an anti-PD-1 antibody, an anti-PD-L1 antibody, an anti-CTLA-4 antibody, an anti-LAG3 antibody and any combination thereof.
In addition, the present disclosure provides a method for determining if a subject suffering from a cancer is susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy targeting CTLA-4, especially an anti-CTLA-4 antibody, wherein the expression level of a set of genes comprising or consisting of PD-1, PD-L1, CD80, CD28, LAG3, TLR4, CD8, CD16 and FOXP3 is determined in a tumor sample and a normal histologically matched sample from the patient, and the susceptibility for having a therapeutic benefit of a treatment with the immune checkpoint blockade therapy targeting CTLA-4, especially an anti-CTLA-4 antibody, is inversely correlated with the fold change of the expression for the set of genes.
The present disclosure also provides a method for determining if a subject suffering from a cancer is susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy targeting PD-1/PD-L1, especially an anti-PD-1 or anti-PD-L1 antibody, wherein the expression level of a set of genes comprising or consisting of PD-L2, CTLA-4, LAG3, TLR4 and FOXP3 is determined in a tumor sample and a normal histologically matched sample from the patient, and the susceptibility for having a therapeutic benefit of a treatment with the immune checkpoint blockade therapy targeting PD-1/PD-L1, especially an anti-PD-1 or anti-PD-L1 antibody, is inversely correlated with the fold change of the expression for the set of genes.
Red: Gene over-expressed in tumor compared to expression in normal tissue—Hypothesis: rationale for treating with treatment targeting the product of this gene.
Black: Gene expression levels similar in tumor and normal tissues.—Hypothesis: no rationale for treating with treatment targeting the product of this gene.
Green: Gene under-expression levels similar in tumor and normal tissues.—Hypothesis: no rationale for treatment and major risk of toxicity of the drugs targeting the product of the gene.
The inventors provide a new method for generating information allowing to analyze simultaneously and globally the key players of the immune-negative blockade. More specifically, the method allows to visualize simultaneously, for each individual patient, the steady state of all key genes (e.g.,
Then, the susceptibility to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy for a particular subject suffering from a cancer can be assessed based on the expression of the genes encoding the key players in the tumor and a normal histologically matched sample from the same subject and their expression intensity in the tumor sample.
Accordingly, the present invention relates to a method for determining if a subject suffering from a cancer is susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, comprising
Then, in a first step, the inventors have selected the key players to be taken into consideration in the method of the present invention.
In a first group of genes, they included the genes useful for assessing the responsiveness of a patient to an immune checkpoint blockade therapy, in particular a therapy targeting PD-1/PD-L1. This set of genes includes PD-1, PD-L1 and PD-L2. The role of PD-1, PD-L1 and PD-L2 is well-known. PD-1 negatively regulates T cell activation through interaction with PD-L1 and PD-L2. The therapy targeting PD-1/PD-L1 aims to block the interaction between PD-1 and PD-L1 and/or PD-L2 so as to remove this blockade.
In a second group of genes, they included the genes useful for assessing the responsiveness of a patient to an immune checkpoint blockade therapy, especially a therapy targeting CTLA-4. This set of genes includes CTLA-4, CD80, CD86, and CD28. Briefly, CTLA-4 is immediately upregulated following TCR engagement. CTLA-4 negatively regulates TCR signaling through competition with the costimulating CD28 for the binding of CD80/CD86 for which CTLA-4 has a higher affinity and avidity. The therapy targeting CTLA-4 aims to block the interaction between CTLA-4 and CD80 or CD86 so as to remove this blockade.
As CTLA-4 and PD-1 act at least in part through a similar molecular mechanism of attenuating CD28-mediated costimulation, it seems important to provide in the method information about the expression of genes relating to PD-1 and those relating to CTLA-4 (i.e., groups 1 and 2) in order to provide a global assessment of the immune-negative blockade.
LAG3 is an inhibitory receptor on antigen activated T-cells. It delivers inhibitory signals upon binding to ligands, such as FGL1. Following TCR engagement, LAG3 associates with CD3-TCR in the immunological synapse and directly inhibits T-cell activation. A synergistic effect with PD-1 has been mentioned. LAG3 may act as a coreceptor of PD-1. Then, it could be interesting to provide data on the expression of LAG3. Accordingly, a third group comprises LAG3 and optionally its main ligand FGL1.
The stimulation of TLR4, in particular its overexpression, has also an important role in the stimulation of the immune response. Therefore, the fourth group comprises TLR4.
Finally, as discussed above, the inventors consider that the presence of specific immune cells in the tumor is also a key aspect that needs to be taken into consideration. The specific immune cells can be the cytotoxic CD8+ T lymphocytes, the NK cells and/or T T More specifically, the selected specific markers are the following:
1. Level of infiltration of the tumor by Cytotoxic Lymphocytes T CD8 (LyTc). The specific marker of these cells is CD8.
2. Level of the infiltration of the tumor by Natural Killers cells (NK). The specific marker of these cells is CD16; and
3. Level of the infiltration of the tumor by a specific population of Lymphocytes T called T regulatory (T-regs). The specific marker of these cells is FOXP3.
More particularly, it can be CD8A or CD8B. Indeed, the process of immune response starts with activation of the T receptor (TCR) of CD8 cytotoxic lymphocytes. CD8 lymphocyte TCR activated by recognizing neo-antigens from the tumoral cells are recruited and, following the activation, the clone is expanded, proliferate and activated, leading to the antitumor activity in cooperation with NK cells and T-reg cells. However, the activation is controlled by a complex negative blockade mechanism. The presence of activated cytotoxic CD8+ T lymphocytes is necessary for having the antitumor activity when the negative blockade is removed by the immune checkpoint blockade therapy.
Then, a fifth group comprises a marker specific of CD8+ T lymphocytes, a marker specific of NK cells and a marker specific of T reg cells, preferably CD8, CD16 and FOXP3, more preferably CD8A, CD16 and FOXP3.
Additional genes could be taken into consideration in the present method. For instance, TIM3 (T cell membrane protein-3 (Uniprot ID Q8TDQ0), TIGIT and its ligands (e.g., CD113), CD96 and its ligands (e.g., CD111), VISTA, ICOS, 0X40, GITR or 4-IBB can be added in the set of genes to be studied for their expression.
Then, the method takes into consideration a set of genes comprising genes of one or more of the following groups of genes: group 1) PD-1, PD-L1, and PD-L2; group 2) CTLA-4, CD80, CD86, and CD28; group 3) LAG3; group 4) TLR4 and group 5) CD8, CD16 and FOXP3; the set of genes comprising at least the genes of the group 1) or 2).
In one aspect, the method takes into consideration a set of genes comprising the following genes: PD-1, PD-L1, and PD-L2; and/or CTLA-4, CD80, CD86, and CD28. In a preferred aspect, the method takes into consideration a set of genes comprising the following genes: PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD86, and CD28.
Optionally, the set of genes is selected in one of the following sets: a) PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD86, CD28, LAG3, TLR4, CD8, CD16 and FOXP3; b) PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD86, LAG3 and TLR4; c) PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD28, CD8, CD16 and FOXP3; d) PD-1, PD-L1, PD-L2, LAG3, TLR4, CD8, CD16 and FOXP3; and e) CTLA-4, CD80, CD86, CD28, LAG3, TLR4, CD8, CD16 and FOXP3.
In a preferred embodiment, the set of genes comprises, essentially consists in or consists in the following genes PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD86, CD28, LAG3, TLR4, CD8, CD16 and FOXP3.
Optionally, in particular as detailed above, the set of genes may further comprise additional genes. However, in a preferred aspect, the total number of genes in the set of genes is no more than 15, 14, 13, 12, or 11 genes.
The mRNA expression level is determined for the genes of the set in a tumor sample and in a normal histologically matched sample from the same subject or patient. Optionally, the method may comprise a preliminary step of providing a tumor sample and in a normal histologically matched sample from the same subject or patient.
The subject or patient suffers from a cancer or has a cancer. In particular, the cancers or tumors more particularly considered in the present invention are lung cancer, especially NSCLC (non-small cell lung cancer), breast cancer (in particular the triple negative breast cancer), colorectal cancers, kidney cancer, melanomas, rhabdomyosarcomas, brain cancers, liver cancers, head and neck cancers, stomach cancers, ovary cancers, pancreatic cancers, liposarcomas and other types of solid tumors.
Then, two samples are necessary, namely one tumor sample and one normal sample from the same patient. Preferably, the tumour sample and the normal sample provides from the same type of tissue. More particularly, the tumor and normal samples are histologically matched tissues. Tumor tissue is a fragment obtained from the tumor or metastatic lesions, (usually provided in interventional radiology) and containing at least 50% tumoral cells, immune infiltrating cells, stromal cells, vessels. The normal tissue is a fragment from histologically matched normal tissue (usually provided in fibroscopy or endoscopy units) and containing at least 30% normal cells (e.g., epithelial cells). DNA and total RNA preparations are performed and only high quality nucleic acids quality are used for transcriptomics investigations (measure of differential expression of mRNA and optionally miRNA between the tumor and normal tissues.
Typically, the samples can be provided by biopsies. Non-exhaustively, examples of pairs of tumor with corresponding histological normal reference tissue are the followings:
In order to optimize the tumor characterization, the inventors selected parameters that have to be analysed in order to establish the status of the intervention points that can be targeted by a class of drugs.
The expression levels are determined by measuring mRNA level. The determination of the expression level variation for these mRNA is carried out by comparing the expression levels in a tumor tissue and in the corresponding normal tissue. Technologies that can be used are well known in the art and comprise Northern analysis, mRNA or cDNA microarrays, RT-PCT (in particular quantitative RT-PCR) and the like. Alternatively, the level of expression can be determined with a ship comprising a set of primers or probes specific for the set of genes. Expression levels obtained from cancer and normal samples may be normalized by using expression levels of proteins which are known to have stable expression such as RPLPO (acidic ribosomal phosphoprotein PO), TBP (TATA box binding protein), GAPDH (glyceraldehyde 3-phosphate dehydrogenase) or β-actin.
Then, based on the mRNA expression levels of the genes of the set, the genes are classified into three classes: i) a first class in which genes are overexpressed in the tumor sample in comparison to the normal histologically matched sample; ii) a second class in which gene are expressed at a similar level in the tumor sample in comparison to the normal histologically matched sample; and iii) a third class in which genes are underexpressed in the tumor sample in comparison to the normal histologically matched sample.
The classification is based on the expression fold change in the tumor sample in comparison to the normal histologically matched sample.
In a preferred aspect, a gene is overexpressed when the fold change between the tumor sample and the normal histologically matched sample is higher than 1.3, a gene is expressed at a similar level when the fold change is between −1.3 and 1.3, and a gene is underexpressed when the fold change is lower than −1.3. However, different threshold of fold change may also be used, for instance a first class with a fold change higher than x, a second class with a fold change is between −x and x, and a third class with a fold change lower than −x, x being a number between 1 and 5, preferably between 1 and 4, between 1 and 3 or between 1 and 2. For instance, x could be 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9 or 2.
The method may consider a more precise classification including more classes. For instance, the first class may be subdivided into a class with weak overexpression and another with high overexpression. Similarly, the third class may be subdivided into a class with weak underexpression and another with high underexpression.
Optionally, the mRNA fold change of a gene can be corrected by considering the expression of the miRNA of the gene in order to adjust possible miRNA intervention in translation.
More preferably, a mean miRNAs fold change for each gene is calculated as the average of the miRNA fold changes between the tumor sample and the normal histologically matched sample for the gene. Then, a corrected mRNA fold change is calculated by dividing the mRNA fold change between the tumor sample and the normal histologically matched sample of the gene (mRNA TvN fold change) by the mean fold change for the miRNAs of the gene (mean miRNA TvN fold change), and the corrected mRNA fold change of the gene is then used in the method for classifying the genes into the three classes. Levels of miRNAs for the genes are determined in the tumor and normal samples. The miRNAs most likely to be involved in the gene expression regulation can be determined by using Target scan {www.targetscan.org/}. For instance, the top 5 miRNAs can be selected for each gene. Table 2 provides a list of the top 5 miRNAs for the genes. Accordingly, the levels of 5 miRNAs for each gene can be determined in the tumor sample and the normal histologically matched sample. The method for measuring miRNA are well-known in the art. Then, a fold change Tumor versus Normal tissue is determined for the 5 miRNAs and a mean fold change for each gene is calculated as the average of the fold changes of the 5 miRNAs.
The steps of fold change calculation and classification can be computer-implemented steps.
Another information that will be used in the method of the present invention are the intensity of the mRNA expression in tumour and in histological matched normal tissue from the same patients. The intensity can be assessed by measuring the signal that can be detected using of the microarrays technologies that enable to assess the Relative Fluorescent Units, whose value correlates with the steady state level of the mRNA (or microRNA). Detection can be performed also by RNAseq technologies (such as Next generation sequencing) and the intensities are assessed by the counts of the number of reads (tag), which also correlates with the steady state levels of the mRNA studies. Globally, technologies used enable to identify and measure the intensities/expression levels of all the types of mRNA (miRNA). Several technologies are exemplified, Agilent Microarrays, Affymetrix microarrays, Illumina RNAseq, and many others, including but not limited to RT-QPCR, Nanostring etc. The intensities measured in tumour tissues divided by the intensities measured in Normal tissues generates the Fold change of mRNAs and miRNAs.
Then, the method comprises a step of displaying the genes of the set classified into the three classes. This step of display is preferably a computer-implemented step.
In a particular aspect, the genes classified into the three classes are displayed as a graph, especially a point chart, each point representing the expression level of one gene of the set. In a preferred aspect, the graph shows the expression intensity of the genes of the set in the tumor sample on the ordinate and the expression intensity in the normal histologically matched sample on the abscissa.
Preferably, each point is associated with the name of the gene.
In a preferred aspect, each of the three classes is displayed with a distinct mode allowing to differentiate the first class from the second and third classes. In a particular aspect, each of the three classes is displayed with a distinct mode allowing to differentiate the first class, the second class and third class from each other's. The distinct mode can be a different color (e.g., red, black and green points) or a different form (e.g., circle, square and triangle).
Optionally, the display can make apparent the different group of genes, e.g., groups 1, 2, 3, 4 and/or 5. For instance, a color can be associated to each class and a form to each group.
The main goal of the display is to make available which gene is in which class and the intensity of expression in the tumor sample for the set of genes.
Based on this display, the person skilled in art has at his/her disposal the information allowing to determining if a subject suffering from a cancer is susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy or not. Indeed, this susceptibility is based on the global information provided by the display
The susceptibility to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy is assessed based on the presence of the genes of groups in one of the three classes and the expression intensity of the genes of groups in the tumor sample. The determination of the susceptibility can be part of the method of the present invention.
The method may further comprise a step of selecting a patient susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy. It can also comprise a step of administering a therapeutic amount of the immune checkpoint blockade therapy to the selected patient.
The method may also or alternatively comprise a step of selecting a patient who is not susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy or is a non-responder. Then, the selected patient will not be suitable to receive a therapeutic benefit of a treatment with an immune checkpoint blockade therapy because he/she would be a non-responder or because the treatment will likely be associated with adverse side effects.
Generally, the more of genes from the set belonging to the first class and the higher is the expression intensity, the higher is the likelihood that the patient will have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy. On the opposite, the presence of genes of the set in the third class would be indicative of the lack of response and/or the occurrence of adverse side effects.
Within the context of this invention, “responder”, “responsive” or “have a therapeutic benefit” refers to a patient who responds to a treatment of cancer, i.e. the volume of the tumor is decreased, at least one of his symptoms is alleviated, or the development of the cancer is stopped, or slowed down. Typically, a subject who responds to a cancer treatment is a subject who will be completely treated (cured), i.e., a subject who will survive to the cancer. A subject who responds to a cancer treatment is also, in the sense of the present invention, a subject who have an overall survival higher than the mean overall survival known for the particular cancer. By “good responder” or “susceptible to have a therapeutic benefit” is intended a patient who shows a good therapeutic benefit of the treatment, that is to say a longer disease-free survival, a longer overall survival, a decreased metastasis occurrence, a decreased tumor growth and/or a tumor regression in comparison to a population of patients suffering from the same cancer and having the same treatment.
Within the context of this invention, “non-responder” refers to a subject who does not respond to a treatment of cancer, i.e. the volume of the tumor does not substantially decrease, or the symptoms of the cancer in the subject are not alleviated, or the cancer progresses, for example the volume of the tumor increases and/or the tumor generates local or distant metastasis. The terms “non-responder” also refer to a subject who will die from the cancer, or will have an overall survival lower than the mean overall survival known for the particular cancer. By “poor responder” or “non-responder” is intended a patient who shows a weak therapeutic benefit of the treatment, that is to say a shorter disease-free survival, a shorter overall survival, an increased metastasis occurrence and/or an increased tumor growth in comparison to a population of patients suffering from the same cancer and having the same treatment.
In a particular aspect, when some genes of the group 1) are ranked in the first class (e.g., at least two genes of the group 1), in particular at least PD-1 and PD-L1) and optionally their expression intensity in the tumor sample is high, then the subject is susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, in particular an immune checkpoint blockade therapy targeting PD-1/PD-L1. Optionally, the genes of the group 1) are all ranked in the first class (i.e., PD-1, PD-L1 and PD-L2) and their expression intensity in the tumor sample is high.
On the opposite, when some genes of the group 1) are ranked in the second or third class (e.g., in particular at least PD-1 or PD-L1), then the subject has a low susceptibility to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, in particular an immune checkpoint blockade therapy targeting PD-1/PD-L1, or the subject is a non-responder. In other word, the patient has a high likelihood to be non-responder or that adverse side effect occurs. This low susceptibility to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy could be even lower when their expression intensity in the tumor sample is low. Optionally, the genes of the group 1) are all ranked in the second or third class (i.e., PD-1, PD-L1 and PD-L2).
In another particular aspect, when some genes of the group 2) are ranked in the first class (e.g., at least two genes of the group 2), in particular at least CTLA-4 and CD28) and optionally their expression intensity in the tumor sample is high, then the subject is susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, in particular an immune checkpoint blockade therapy targeting CTLA-4. Optionally, at least 2, 3 or all genes of the group 2) are ranked in the first class and their expression intensity in the tumor sample is high. For instance, the genes ranked in the first class could be CTLA-4 and CD28; CTLA-4 and; CTLA-4 and CD86; CTLA-4, CD80 and CD86; CTLA-4, CD28 and CD86; CTLA-4, CD80 and CD28; or CTLA-4, CD80, CD86 and CD28.
Optionally, when some genes of the group 2) are ranked in the first class (e.g., at least two genes of the group 2), in particular at least CTLA-4 and CD28) and optionally their expression intensity in the tumor sample is low, then the subject has a low susceptibility to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, in particular an immune checkpoint blockade therapy targeting CTLA-4, or the subject is a non-responder. Optionally, at least the expression intensity of CTLA-4 or CD28 is low in the tumor sample. Optionally, at least the expression intensity of CTLA-4 and CD28; CTLA-4 and; CTLA-4 and CD86; CTLA-4, CD80 and CD86; CTLA-4, CD28 and CD86; CTLA-4, CD80 and CD28; or CTLA-4, CD80, CD86 and CD28, is low in the tumor sample.
On the opposite, when some genes of the group 2) are ranked in the second or third class (e.g., at least two genes of the group 2), in particular at least CTLA-4 and CD28), then the subject has a low susceptibility to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, in particular an immune checkpoint blockade therapy targeting CTLA-4, or the subject is a non-responder. In other word, the patient has a high likelihood to be non-responder or that adverse side effect occurs. This low susceptibility to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy could be even lower when their expression intensity in the tumor sample is low. Optionally, at least 2, 3 or all genes of the group 2) are ranked in the second or third class. For instance, the genes ranked in the first class could be CTLA-4 and CD28; CTLA-4 and; CTLA-4 and CD86; CTLA-4, CD80 and CD86; CTLA-4, CD28 and CD86; CTLA-4, CD80 and CD28; or CTLA-4, CD80, CD86 and CD28.
In a third particular aspect, when some genes of the group 1) are ranked in the first class (e.g., at least two genes of the group 1), in particular at least PD-1 and PD-L1) and optionally their expression intensity in the tumor sample is high; and when some genes of the group 2) are ranked in the first class (e.g., at least two genes of the group 2), in particular at least CTLA-4 and CD28) and optionally their expression intensity in the tumor sample is high; then the subject is susceptible to have a therapeutic benefit of a treatment with a combined immune checkpoint blockade therapy targeting PD-1/PD-L1 and CTLA-4. Optionally, the genes of the group 1) are all ranked in the first class (i.e., PD-1, PD-L1 and PD-L2). Optionally, at least 2, 3 or all genes of the group 2) are ranked in the first class. For instance, the genes ranked in the first class could be CTLA-4 and CD28; CTLA-4 and; CTLA-4 and CD86; CTLA-4, CD80 and CD86; CTLA-4, CD28 and CD86; CTLA-4, CD80 and CD28; or CTLA-4, CD80, CD86 and CD28.
In a fourth particular aspect, when some genes of the group 3) are ranked in the first class (e.g., at least the gene LAG3) and optionally their expression intensity in the tumor sample is high, then the subject is susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, in particular an immune checkpoint blockade therapy targeting LAG3.
On the opposite, when some genes of the group 3) are ranked in the second or third class (e.g., at least the gene LAG3), then the subject has a low susceptibility to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, in particular an immune checkpoint blockade therapy targeting LAG3, or the subject is a non-responder. In other word, the patient has a high likelihood to be non-responder or that adverse side effect occurs. This low susceptibility to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy could be even lower when their expression intensity in the tumor sample is low. Optionally, the genes of the group 3) are all ranked in the second or third class (i.e., LAG3 and FLG1).
The genes of the groups 4) and 5) are additional elements in order to predict the susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy. In other words, they could be used in order to refine the prediction based on the groups 1) and 2). Alternatively, the prediction can also be based on the groups 4) and 5).
Accordingly, when some genes of the group 4) and 5) are ranked in the first class (e.g., TLR4, CD8, CD16 and/or FOXP3), and optionally their expression intensity in the tumor sample is high, then this is indicative of the subject susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy. On the opposite, wherein when some genes of the group 4) and 5) are ranked in the second or third class (e.g., TLR4, CD8, CD16 and FOXP3), then this is indicative of the subject having low susceptibility to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy or who is a non-responder.
In a particular aspect, when CD8, CD16 and/or FOXP3 is ranked in the first class, and optionally its expression intensity in the tumor sample is high, then this is indicative of the subject susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy. On the opposite, wherein when CD8, CD16 and/or FOXP3 is ranked in the second or third class, then this is indicative of the subject having low susceptibility to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy or who is a non-responder.
In another particular aspect, when TLR4 is ranked in the first class, and optionally its expression intensity in the tumor sample is high, then this is indicative of the subject susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy. On the opposite, wherein when TLR4 is ranked in the second or third class, then this is indicative of the subject having low susceptibility to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy or who is a non-responder.
The immune checkpoint blockade therapies are well-known in the art. In particular, it could be an immune checkpoint blockade therapy targeting PD-1/PD-L1. Alternatively, it could be an immune checkpoint blockade therapy targeting CTLA-4. It could also be an immune checkpoint blockade therapy targeting both PD-1/PD-L1 and CTLA-4.
An immune checkpoint blockade therapy targeting PD-1/PD-L1 can be for instance an inhibiting anti-PD-1 antibody, an inhibiting anti-PD-L1 antibody or an inhibiting anti-PD-L2 antibody, preferably an inhibiting anti-PD-1 antibody or an inhibiting anti-PD-L1 antibody. Such antibodies are well-known in the art. Several antibodies have already been accepted as drug. Others are still in clinical development.
The inhibiting anti-PD-1 antibody can be for instance selected from PDR001 (Novartis), Nivolumab (Bristol-Myers Squibb), Pembrolizumab (Merck & Co), Pidilizumab (CureTech), MEDI0680 (Medimmune), REGN2810 (Regeneron), TSR-042 (Tesaro), PF-06801591 (Pfizer), BGB-A317 (Beigene), BGB-108 (Beigene), INCSHR1210 (Incyte), or AMP-224 (Amplimmune).
The inhibiting anti-PD-L1 antibody can be for instance selected from FAZ053 (Novartis), Atezolizumab (Genentech/Roche), Avelumab (Merck Serono and Pfizer), Durvalumab (MedImmune/AstraZeneca), or BMS-936559 (Bristol-Myers Squibb).
An immune checkpoint blockade therapy targeting CTLA-4 can be for instance an inhibiting anti-CTLA-4 antibody. Such antibodies are well-known in the art. Several antibodies have already been accepted as drug. Others are still in clinical development. For instance, the CTLA-4 inhibitor can be selected from ipilimumab, tremelimumab, and AGEN-1884.
An immune checkpoint blockade therapy targeting LAG3 can be for instance an inhibiting anti-LAG3 antibody. Such antibodies are well-known in the art. Several antibodies have already been accepted as drug. Others are still in clinical development. For instance, the LAG-3 inhibitor can be selected from LAG525 (Novartis), BMS-986016 (Bristol-Myers Squibb), or TSR-033 (Tesaro).
In addition, the present method can be used in a clinical trial analysis. Indeed, when applied to a population of patients, the analysis of the genes classified into the three classes and the therapeutic response to an immune checkpoint therapy may permit to define specific patterns of the genes classified into the three classes suitable for predicting a response to the treatment and for selecting the patients to be treated and/or those for whom the treatment is useless or to be avoided.
The present invention also relates to a method for selecting a set of genes for which the differential expression between a tumor sample and a normal histologically matched sample from the same patient is indicative of a therapeutic benefit of a treatment with an immune checkpoint blockade therapy. More particularly, as disclosed above, the method provides the mRNA expression level, and optionally miRNA expression levels, in a tumor sample and a normal histologically matched sample from the same patient.
This information is provided for a group of patients having a cancer and receiving, having received and planed to receive the same immune checkpoint blockade therapy. The group of patients may include 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, or 50 patients or more. By the same immune checkpoint blockade therapy, it is intended that the immune checkpoint blockade therapy has the same target, i.e., PD-1/PD-L1, PD-L2 or CTLA-4/CD80,CD86,CD28. More specifically, the same immune checkpoint blockade therapy can be an antibody directed against the same protein, e.g., PD-1, PD-L1, CTLA-4, or LAG3. Still more specifically, the same immune checkpoint blockade therapy can be the same antibody. Optionally, the immune checkpoint blockade therapy is selected from the group consisting an anti-PD-1 antibody, an anti-PD-L1 antibody, an anti-CTLA-4 antibody, an anti-LAG3 antibody and any combination thereof.
Optionally, the patients may have any type of cancer or any type of solid tumors. Optionally, the patients may have the same type of cancers. Optionally, the patients may have the same cancer. Optionally, the patients may have various therapeutic history. Optionally, the patients may have received the same number of therapeutic lines, or even the same therapeutic lines.
The set of genes are as defined previously in the present disclosure. Then, the set of genes comprising genes of one or more of the following groups of genes: 1) PD-1, PD-L1, and PD-L2; 2) CTLA-4, CD80, CD86, and CD28; 3) LAG3; 4) TLR4 and 5) CD8, CD16 and FOXP3; the set of genes comprising at least the genes of the group 1) or 2). More specifically, the set of genes can be selected in one of the following sets: a) PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD86, CD28, LAG3, TLR4, CD8, CD16 and FOXP3; b) PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD86, LAG3 and TLR4; c) PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD28, CD8, CD16 and FOXP3; d) PD-1, PD-L1, PD-L2, LAG3, TLR4, CD8, CD16 and FOXP3; and e) CTLA-4, CD80, CD86, CD28, LAG3, TLR4, CD8, CD16 and FOXP3. In a preferred aspect, the set of genes comprises, essentially consists in or consists in the genes PD-1, PD-L1, PD-L2, CTLA-4, CD80, CD86, CD28, LAG3, TLR4, CD8, CD16 and FOXP3. Optionally, the total number of genes is no more than 15 genes.
Then, based on the level of expression, the fold change of the mRNA expression between the tumor sample and the normal histologically matched sample (TvN fold change) of each gene and for each patient is determined. Optionally, the fold change of the mRNA expression can be corrected by taking into account the miRNA expression, especially of the miRNA as listed in Table 2.
The fold changes of each gene, optionally with the expression of the gene, respectively, can be used to determine the correlation between the fold change (with or without the expression) and the therapeutic benefit of the patient to the immune checkpoint blockade. The therapeutic benefit of the patient to the immune checkpoint blockade can be assessed based on any parameter usually used in clinical trials, such as OS (overall survival), tumor growth or regression, the disease-free survival, the relapse, the duration of the response, the disease progression, etc . . .
For instance, the correlation between the TvN fold changes for several combinations of genes and the therapeutic benefit can be determined and the combination(s) associated with the best correlation is/are selected. The step of correlation calculation can be computer-implemented step. The combination may include some or all combinations of 4, 5, 6, 7, 8, 9, 10, 11 and/or 12 genes of the set of genes.
On the basis of the selected combination, it is then provided methods for predicting the therapeutic benefit to the immune checkpoint blockade therapy of a subject, methods for selecting the subject who will have a therapeutic benefit of a treatment with the immune checkpoint blockade therapy, methods for determining that a subject is not a responder, methods for defining a sub-group of patients that is suitable for receiving a treatment with the immune checkpoint blockade therapy, etc . . .
Accordingly, the present disclosure provides a method for selecting a set of genes for which the differential expression between a tumor sample and a normal histologically matched sample from the same patient is indicative of a therapeutic benefit of a treatment with an immune checkpoint blockade therapy, wherein the method comprises
By applying the following method, the inventors identified correlation of interest.
Accordingly, the present disclosure provides a method for determining if a subject suffering from a cancer is susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy targeting CTLA-4, especially an anti-CTLA-4 antibody, wherein the expression level of a set of genes comprising or consisting of PD-1, PD-L1, CD80, CD28, LAG3, TLR4, CD8, CD16 and FOXP3 is determined in a tumor sample and a normal histologically matched sample from the patient, and the susceptibility for having a therapeutic benefit of a treatment with the immune checkpoint blockade therapy targeting CTLA-4, especially an anti-CTLA-4 antibody, is inversely correlated with the fold change of the expression for the set of genes.
In addition, the present disclosure provides a method for determining if a subject suffering from a cancer is susceptible to have a therapeutic benefit of a treatment with an immune checkpoint blockade therapy targeting PD-1/PD-L1, especially an anti-PD-1 or anti-PD-L1 antibody, wherein the expression level of a set of genes comprising or consisting of PD-L2, CTLA-4, LAG3, TLR4 and FOXP3 is determined in a tumor sample and a normal histologically matched sample from the patient, and the susceptibility for having a therapeutic benefit of a treatment with the immune checkpoint blockade therapy targeting PD-1/PD-L1, especially an anti-PD-1 or anti-PD-L1 antibody, is inversely correlated with the fold change of the expression for the set of genes.
To provide consistent data supporting the new biomarker concept described in the invention, the inventors used the data obtained from patients treated in the clinical trial WINTHER (NCT01856296). The results of Winther trial were published in Nature Medicine volume25, pages 751-758 (2019). The inventors only used from this work the transcriptomics data. The methodology used to obtain transcriptomic data is provided in the Nature Medicine article, which incorporated herein by reference.
This is the first and as of today remains the only clinical trial that used transcriptomics in a clinical setting in addition to classic sequencing of DNA. It is also the first trial and the only that used the dual biopsies strategy investigating both tumor and normal matched tissue from the same patients, harboring a variety of solid tumors: NSCLC, Head & Neck, Colon, breast, bladder, liver, kidney, liposarcoma, rhabdomiosarcoma, neuroendocrine tumors, stomach, and oesophagus tumors. Based on DNA and RNA investigations, 107 patients could be treated in personalized manner with drugs selected among 159 different medicines. Patients were treated, and followed, and the clinical outcome (Progression free survival and overall survival could be recorded). The results of Winther trial, published in Nature medicine, outline the benefit of using transcriptomics in a clinical setting.
Among the patients of Winther trial, 6 were treated with immunotherapies: 3 of them received ipilimumab (anti-CTLA-4) and 3 patients received anti-PD-L1/PD-1 based therapies: one patient received pembrolizumab, one patient received nivolumab, and one patient received atezolizumab. The overall survival under treatment was recorded for all of the 6 patients, and availability of transcriptomics data enabled to perform the novel digital display investigations described in this invention. These data as illustrated by
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| Number | Date | Country | Kind |
|---|---|---|---|
| 20305161.0 | Feb 2020 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2021/053970 | 2/18/2021 | WO |
