News Column

Patent Application Titled "Methods of Using Biomarkers for Predicting the Outcome of an Immunotherapy against Cancer" Published Online

September 8, 2014



By a News Reporter-Staff News Editor at Cancer Vaccine Week -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventors WEINSCHENK, Toni (Aichwald, DE); Singh, Harpreet (Tuebingen, DE); Mahr, Andrea (Tuebingen, DE); Fritsche, Jens (Tuebingen, DE), filed on January 17, 2014, was made available online on August 28, 2014 (see also Immatics Biotechnologies GmbH).

The assignee for this patent application is Immatics Biotechnologies GmbH.

Reporters obtained the following quote from the background information supplied by the inventors: "A. Field of the Invention

"The present invention relates to methods for predicting the effect of an immunotherapy against cancer in a patient based on new biomarkers. The present invention furthermore relates to a prognosis regarding the outcome based on said biomarkers. The present invention furthermore relates to panels of biomarkers for use in the above methods.

"B. Brief Description of Related Art

"Stimulation of an immune response is dependent upon the presence of antigens recognized as foreign by the host immune system. The discovery of the existence of tumor-associated and tumor-specific antigens has raised the possibility of using a host's immune system to intervene in tumor growth. Various mechanisms of harnessing both the humoral and cellular arms of the immune system are currently being explored for cancer immunotherapy.

"Certain elements of the cellular immune response are capable of specifically recognizing and destroying tumor cells. The isolation of cytotoxic T-cells (CTLs) from tumor-infiltrating cell populations or from peripheral blood suggests that these cells play an important role in the natural immune defense against cancer (Cheever et al., Annals N.Y. Acad. Sci. 1993 690:101-112; Zeh H J, Perry-Lalley D, Dudley M E, Rosenberg S A, Yang J C; J. Immunol. 1999, 162(2):989-94). CD8-positive (CD8+) T-cells in particular, which recognize complexes of major histocompatibility complex (MHC) class I molecules and peptides of usually 8 to 10 amino acid residues derived from cytosolic proteins or defective ribosomal products (DRiPs) (Schubert U, Anton L C, Gibbs J, Norbury C C, Yewdell J W, Bennink J R. Nature 2000; 404(6779):770-774), play an important role in this response. Human MHC-molecules are also designated as human leukocyte antigens (HLA).

"There are two classes of MHC-molecules: MHC class I molecules can be found on most cells having a nucleus and present peptides that result from proteolytic cleavage of endogenous proteins, DRiPs, and larger peptides. MHC class II molecules can be found predominantly on professional antigen presenting cells (APCs) and present peptides of exogenous proteins that are taken up by APCs, and are subsequently processed (Cresswell P. Annu. Rev. Immunol. 1994; 12:259-93). Complexes of peptide and MHC class I molecules are recognized by CD8+ CTLs bearing the appropriate T cell receptor (TCR), while complexes of peptide and MHC class II molecules are recognized by CD4+ helper-T-cells bearing the appropriate TCR. It is well known that the TCR, the peptide and the MHC are thereby present in a stoichiometric amount of 1:1:1.

"For a peptide to elicit a cellular immune response, it must bind to an MHC-molecule. This process depends on the allele of the MHC-molecule and on the amino acid sequence of the peptide. MHC class I-binding peptides are usually 8, 9 or 10 amino acid residues in length and contain conserved residues ('anchors') in their sequences that interact with the corresponding binding groove of the MHC-molecule. Thus, each MHC allele has a 'binding motif' determining which peptides can bind specifically to the binding groove (Rammensee H. G., Bachmann J. and Stevanovic, S; MHC Ligands and Peptide Motifs, Chapman & Hall 1998). To elicit an immune reaction, peptides not only have to be able to bind to certain MHC-molecules, they also have to be recognized by T-cells bearing specific TCRs. A further prerequisite for efficient immune reactions is the absence of immunological tolerance against the antigen.

"Tumor-associated antigens (TAAs) from which epitopes recognized by CTLs are derived, can be molecules from all protein classes, such as enzymes, receptors, transcription factors, etc., which are upregulated in cells of the respective tumor. Furthermore, antigens can be tumor-specific, i.e. unique to tumor cells, for example as products of mutated genes or from alternative open reading frames (ORFs), or from protein splicing (Vigneron N, Stroobant V, Chapiro J, Ooms A, Degiovanni G, Morel S, van der Bruggen P, Boon T, Van den Eynde B J. Science 2004 Apr. 23; 304 (5670):587-90). Another important class of antigens are tissue-specific antigens, such as 'cancer-testis-' (CT)-antigens that are expressed in different kinds of tumors and in healthy tissue of the testis.

"Therefore, TAAs are a starting point for the development of a tumor vaccine. The methods for identifying and characterizing the TAAs are based e.g. on the use of CTLs that can be isolated from patients or healthy subjects, or on the generation of differential transcription profiles or differential peptide expression patterns between tumors and normal tissues (Lemmel C., Weik S., Eberle U., Dengjel J., Kratt T., Becker H. D., Rammensee H. G., Stevanovic S. Nat. Biotechnol. 2004 April; 22(4):450-4, T. Weinschenk, C. Gouttefangeas, M. Schirle, F. Obermayr, S. Walter, O. Schoor, R. Kurek, W. Loeser, K. H. Bichler, D. Wernet, S. Stevanovic, and H. G. Rammensee. Cancer Res. 62 (20):5818-5827, 2002).

"However, the identification of genes overexpressed or selectively expressed in tumor tissues or human tumor cell lines does not provide sufficient information if the corresponding antigen is a useful target for a T-cell based immunotherapy. This is because only an individual subpopulation of epitopes of these antigens are a) presented and b) recognized by T-cells with corresponding TCRs. In addition, immunological tolerance for this particular epitope needs to be absent or negligible. It is therefore important to select only those peptides from overexpressed or selectively expressed proteins that are presented in connection with MHC molecules and are targets of functional T-cells. A functional T-cell is defined as a T-cell that upon stimulation with a specific antigen can be clonally expanded and is able to execute effector functions ('effector T-cell').

"T-helper cells play an important role in orchestrating the effector functions of CTLs in anti-tumor immunity. T-helper cell epitopes that trigger a T-helper cell response of the T.sub.H1 type support effector functions of CD8+ CTLs, which include cytotoxic functions directed against tumor cells displaying tumor-associated peptide/MHC complexes on their cell surfaces. In this way tumor-associated T-helper cell epitopes, alone or in combination with other tumor-associated peptides, can serve as active pharmaceutical ingredients of vaccine compositions which stimulate anti-tumor immune responses.

"Since both types of response, CD8- and CD4-dependent, contribute jointly and synergistically to the anti-tumor effect, the identification and characterization of TAAs recognized by CD8+ CTLs (ligand: MHC class I molecule+peptide epitope) and of TAAs recognized by CD4+ T-helper cells (ligand: MHC class II molecule+peptide epitope) are both important in the development of effective tumor vaccines and an effective treatment based on these vaccines.

"In Europe, renal cell carcinoma (RCC) ranks as the seventh most common malignancy in men, amongst whom there are 29,600 new cases each year (3.5% of all cancers). Among women, RCC ranks twelfth, with 16,700 cases a year (2.3% of all cancers). RCC is rare before the age of 40, and above this age it is twice as common in men as in women. Incidence by age rises rapidly from less than 2 per 100,000/year in patients under 40 years old to 38 per 100,000/year in the age group 65-69 years. Thereafter, it increases to 46 per 100,000/year in those older than 75 years.

"A total of 25-30% of patients with RCC display overt metastases at initial presentation. About one third of patients with RCC will develop metastatic disease over time. Thus, nearly 50-60% of all patients with RCC will eventually present with metastatic disease. Among those with metastatic disease, approximately 75% have lung metastases, 36% lymph node and/or soft tissue involvement, 20% bone involvement, and 18% liver involvement.

"RCC is the most lethal carcinoma of the genitourinary tumors with a 65% five-year survival rate compared to the 82% and 100% five-year survival rate for bladder or prostate cancer, respectively (US 1972-2001 data). Average survival rates at 5 years (up to 1999) after diagnosis (1990-1994) for kidney cancer were about 58% in Europe, and RCC was classified by several authors as a cancer with only moderate prognosis. Overall, RCC is fatal in nearly 80% of patients. This figure indicates a strong medical need for effective and early clinical follow-up and treatment for recurrences.

"Survival strongly depends on the stage at which the tumor is diagnosed: 5-year survival is only 12% for patients bearing lesions with distant metastases, but 80% for those with localized malignancies.

"Globally, colorectal carcinoma (CRC) is the third most common cancer. Colon and rectum cancer account for about 1 million new cases per year, and unlike for most other tumors, numbers are similar in men and women (ratio 1.2:1). In Europe, CRC is the second most common cancer and the second most common cancer-related cause of death in both men and women with approximately 380,000 new cases and about 200,000 disease-related deaths per year. The raw incidence rate in 2002 for men and women was 88.3 and 84.0/100,000, respectively; the raw mortality was 34.8 and 35.2/100,000, respectively. These data clearly reflect the significance of CRC as an enormous source of both individual and societal burden. CRC is a cancer of the elderly population, as the mean age at the time of disease manifestation in men and women is 69 and 75 years, respectively. Besides dietary and lifestyle factors (e.g. obesity, lack of physical exercise, smoking, regular alcohol consumption), other risk factors are familial occurrence of CRC, hereditary types of CRC (familial adenomatous polyposis [FAP], attenuated FAP [attenuated adenomatous polyposis coli; AAPC], hereditary non-polyposis colorectal carcinoma [HNPCC], hamartomatous polyposis syndromes) and inflammatory bowel diseases such as ulcerative colitis or Crohn's disease.

"CRC mostly occurs as adenocarcinoma of the mucous membranes in rectum, sigma, colon transversum/descendens, and colon ascendens/caecum. Early colorectal carcinoma may be cured by primary surgery. Distant metastases, however, spread to regional lymph nodes and to liver, lung, and other organs (such as CNS). Due to unspecific symptoms, CRC is often diagnosed at a relatively late stage and approximately 25% of patients with CRC have metastatic disease (mCRC) when first presented to their physicians. An additional 30% of newly diagnosed patients with localized resectable CRC subsequently develop metastatic recurrence.

"EP2105740 describes that some proteins, including Apolipoprotein AI (APOA1), are regulated by c-myc overexpression in subjects suffering from or being susceptile to cancer. Consequently, EP2105740 describes the use of the biomarker APOA1 in the diagnosis, prognosis and/or treatment monitoring of cancer, in particular of lung cancer, but not a prediction of the effectiveness of a treatment, let alone an immunotherapy.

"WO2010/076322 describes a method for predicting a response to and/or benefit from chemotherapy in a patient suffering from cancer involving (i) classifying a tumor into at least two classes, (ii) determining in a tumor sample the expression of at least one marker gene indicative of a response to chemotherapy for a tumor in each respective class, (iii) and depending on said gene expression, predicting said response and/or benefit; wherein one marker gene is CXCL13. WO2010/076322 also does not describe a prediction of the effectiveness of a treatment, let alone an immunotherapy.

"Similarly, WO2010/003773 describes methods for predicting an outcome of cancer in a patient suffering from cancer, said patient having been previously diagnosed as node positive and treated with cytotoxic chemotherapy; wherein one marker gene is CXCL13. WO2010/003773 does not describe a prediction of the effectiveness of an immunotherapy.

"EP 1 777 523 A1 relates to the prognosis of the outcome of a cancer in a patient, which prognosis is based on the quantification of one or several biological markers that are indicative of the presence of, or alternatively the level of, the adaptive immune response of said patient against said cancer. Overall, an extremely large number of markers is disclosed. Furthermore, EP 1 777 523 A1 relates to a prognosis (and not prediction) for the outcome of a cancer in a patient, based on the detection and/or the quantification, of one or more biological markers indicative of the presence of, or alternatively of the level of, the adaptive immune response of said patient against said cancer at the tumor site.

"Despite the recent progress in the diagnosis and management of many cancers described above, such as, for example, RCC and CRC, still biological markers are needed that can be used to achieve an improved diagnosis, and in particular a prediction of whether a beneficial effect of an immunotherapy can be expected, in order to further improve the survival and to better adjust the treatment of people in need. Furthermore, the markers should also allow for a prediction of the outcome of said treatment of cancer. It is therefore an object of the present invention to provide respective biological markers and diagnostic, predictive and prognostic methods."

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventors' summary information for this patent application: "In an aspect of the present invention, said object is solved by providing a method for predicting an effect of an immunotherapy in a cancer patient, comprising a) determining the level of at least one marker selected from the group consisting of Apolipoprotein A1 (ApoA1), CCL17/TARC, eosinophils (in absolute numbers or %), monocytes (in absolute numbers or %), CD95/Fas, aspartate aminotransferase/serum glutamic oxaloaceticacid transaminase (ASAT/SGOT), cancer antigen 19-9 (CA19-9), lactate dehydrogenase (LDH), threonine, immunoglobulin E (IgE), and matrix metalloproteinase 3 (MMP-3) in a sample from said cancer patient, wherein a higher (or increased) level of the marker compared to the median of a given cancer patient population is indicative for a beneficial effect of an immunotherapy for said patient, or b) determining the level of at least one marker selected from the group consisting of CXCL13/BCA-1, neutrophils (in %), interleukin 6 (IL-6) and short-chain acylcarnitines in a sample from said cancer patient, wherein a lower (or decreased) level of the marker compared to the median (+/-10%) of a given cancer patient population is indicative for a beneficial effect of an immunotherapy for said patient.

BRIEF DESCRIPTION OF THE DRAWINGS

"FIG. 1 shows a Kaplan-Meier analysis for the effect of (A) ApoA1, (B) CXCL13/BCA-1, (C) monocytes and (D) CCL17/TARC on overall survival in all patients and subgroups. For each parameter, the upper figure shows the survival of all patients having high (red lines) vs. low (green lines) values of the parameter. The lower figure shows a subgroup analysis (+CY vs -CY) for patients with high values of the parameter (red vs blue lines), and for patients with low values of the parameter (green vs yellow lines).

"FIG. 2A shows the distribution of values of the univariate biomarkers ApoA1, CXCL13/BCA-1, monocytes and CCL17/TARC, as indicated in the figure, of the -CY (gray) and +CY (black) group, showing either no T-cell response (no), single peptide response (1) or multipeptide response (>1). Error bars represent the standard error of the mean. FIG. 2B shows a different way of depicting the distribution of values of the multivariate biomarker in patients of the -CY (gray) and +CY (black) group, showing either no T-cell response (no), single peptide response (1) or multipeptide response (>1). Dots represent single values, lines represent the mean.

"FIG. 3 shows the Kaplan Meier analysis of the effect of a combination of ApoA1 and CCL17/TARC on overall survival in all patients and subgroups. In A), the biomarker-positive population was defined to consist of patients with at least one of the two parameters within the positive range (score=1 or 2), while biomarker-negative patients showed no parameter within the positive range (score=0). In B), the biomarker-positive population was defined to consist only of those patients showing both parameters within the positive range (score=2), while patients with at least one parameter in the negative range (score=0 or 1) were considered biomarker-negative. The upper figures show the survival of all biomarker-positive patients (green lines) vs all biomarker-negative patients (red lines). The lower figures show subgroup analyses (+CY vs -CY) for biomarker-positive patients (green lines vs yellow lines) and biomarker-negative patients (red lines vs blue lines).

"FIG. 4A shows the mean of the marker consisting of the combination of ApoA1 and CCL17/TARC in patients of the -CY (gray) and +CY (black) group, showing either no T-cell response (no), single peptide response (1) or multipeptide response (>1). Error bars represent the standard error of the mean. FIG. 4B shows a different way of depicting the distribution of values of the binary biomarker in patients of the -CY (gray) and +CY (black) group, showing either no T-cell response (no), single peptide response (1) or multipeptide response (>1). Dots represent single values, lines represent the mean.

"FIG. 5 shows a Kaplan-Meier analysis for the effect of the multivariate biomarker on overall survival. The upper figure shows the survival of all patients having high (red lines) vs. low (green lines) values of the parameter. The lower figure shows a subgroup analysis (+CY vs -CY) for patients with high values of the parameter (red vs blue lines), and for patients with low values of the parameter (green vs yellow lines). The value of 0.019076043 points was used as cut-off separating biomarker high and low patients.

"FIG. 6A shows the mean values of the multivariate biomarker in patients of the -CY (gray) and +CY (black) group, showing either no T-cell response (no), single peptide response (1) or multipeptide response (>1). Error bars represent the standard error of the mean. FIG. 6B shows a different way of depicting the distribution of values of the multivariate biomarker in patients of the -CY (gray) and +CY (black) group, showing either no T-cell response (no), single peptide response (1) or multipeptide response (>1). Dots represent single values, lines represent the mean."

For more information, see this patent application: WEINSCHENK, Toni; Singh, Harpreet; Mahr, Andrea; Fritsche, Jens. Methods of Using Biomarkers for Predicting the Outcome of an Immunotherapy against Cancer. Filed January 17, 2014 and posted August 28, 2014. Patent URL: http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&u=%2Fnetahtml%2FPTO%2Fsearch-adv.html&r=3348&p=67&f=G&l=50&d=PG01&S1=20140821.PD.&OS=PD/20140821&RS=PD/20140821

Keywords for this news article include: Amino Acids, Apolipoproteins, Biological Factors, Biological Products, Blood Cells, Bone Marrow Cells, CD Antigens, CD4 Antigens, CD8 Antigens, CXC Chemokines, Cancer Vaccines, Chemokine CXCL13, Chemotherapy, Cytokines, Differentiation, Drugs, Epitopes, Gastroenterology, Genetics, HIV Receptors, Hemic and Immune Systems, Immatics Biotechnologies GmbH, Immunology, Interleukin-16 Receptors, Membrane Proteins, Monocytes, Mononuclear Leukocytes, Mononuclear Phagocyte System, Myeloid Cells, Oncology, Peptides, Phagocytes, Proteomics, Renal Cell Carcinoma, T-Lymphocyte Antigens, Therapy, Tumors.

Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2014, NewsRx LLC


For more stories covering the world of technology, please see HispanicBusiness' Tech Channel



Source: Cancer Vaccine Week


Story Tools






HispanicBusiness.com Facebook Linkedin Twitter RSS Feed Email Alerts & Newsletters