Predictive Immune Biomarkers Research Articles

432 (PB420): A multi-omics approach using transcriptomic and spatial analysis to identify predictive immune biomarkers of response and toxicity following chemotherapy in head and neck cancers

432 (PB420): A multi-omics approach using transcriptomic and spatial analysis to identify predictive immune biomarkers of response and toxicity following chemotherapy in head and neck cancers

Immunotherapy in Neuroendocrine Neoplasms: A Diamond to Cut.

A raise in the incidence of NENs is expected. Therefore, the identification of new therapeutic strategies, such as immunotherapy, remains crucial. To date, immune checkpoint inhibitors as monotherapy have shown modest activity in unselected NENs. Although immunotherapy combos (plus another immune agents or chemotherapy, among others) are potentially more active than single agents, this has not been uniformly confirmed, even in high-grade NENs. Other immunotherapeutic strategies under development include bispecific antibodies, targeting specific tumor antigens like DLL3, and cell therapy. Currently, no predictive immune biomarkers are available to guide clinical decisions. A comprehensive tumor molecular profiling approach needs to be developed for the selection of patients with NEN who could potentially benefit from immunotherapy. Ideally, clinical trials should incorporate this tumor molecular profiling to identify predictive biomarkers and improve efficacy. Achieving this goal requires an international collaborative effort.

Abstract 2803: Identification of microbiome and predictive immune biomarkers in patients with advanced non-small lung cancer treated with pembrolizumab with or without chemotherapy

Abstract Background: Recent studies have shown that gut microbiota modulates antitumor immunity, and affect clinical efficacy of immune checkpoint inhibitors, including anti-programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1) inhibitors. We aimed to explore the metagenomic profiles of previously untreated, treatment naïve, stage IV non-small cell lung cancer (NSCLC) treated with pembrolizumab, an anti-PD1 agent with or without platinum-based chemotherapy and identify distinct ecological niche between responder and non-responder groups. Methods: We performed metagenome sequencing, whole exome sequencing (WES), and RNA sequencing (RNA-seq) on 50 patients with stage IV NSCLC, including adenocarcinoma (N=31) and squamous cell carcinoma (N=22). We employed Dirichlet Multinomial Mixtures (DMM) community typing to cluster microbial community profiling data at the genus level and analyzed the changes in microbial composition and diversity between enterotype 1 (E1, N=27) and enterotype 2 (E2, N=23). Mutation landscape including genomic alterations in relation to immune response was analyzed between responders and non-responders using WES data. Differentially expressed genes (DEGs), Gene Set Enrichment Analysis (GSEA), and immune deconvolution analysis were carried out using RNA-seq data to analyze molecular profiling of two enterotypes between the responder (N=15) and non-responder (N=35) groups. Results: The two enterotypes exhibited distinct microbial abundances and alpha diversity indices. Abundances of members of Bacteroidaceae (P=0.011) and Oscillospiraceae (P<0.001) were higher in E2 members, and enrichment of Lactobacillaceae (P=0.006) and Bifidobacteriaceae (P=0.041) were higher in E1. Compared to E1, the number of observed bacterial species (P<0.001) and Shannon index (P<0.001) was significantly higher in E2, indicating a healthier gut environment. The proportion of responders was higher in E2 than in E1 cluster. Comparison between responders and non-responder groups within each enterotype revealed that Lactobacillaceae showed an association with the response in E2 cluster. Mutation landscape revealed TP53 mutation as the most common mutation, more frequently observed in the E2 (100%) than in the E1 (56%) cluster. Conclusion: We explored the association between the gut microbiome and clinical response to pembrolizumab with or without combination with chemotherapy and found that E2 cluster enriched with Lactobacillaceae is associated with treatment response. Further analysis with larger cohort is ongoing. Citation Format: Jii Bum (Joy) Lee, Su-Jin Choi, Gang-Taik Lee, Junwon Yang, Hyun-Seok Oh, Arim Min, Bo-Eun Kwon, Seong-san Kang, Sujeong Baek, Dong Kwon Kim, Jae Hwan Kim, Sun Min Lim, Min Hee Hong, Kyoung-Ho Pyo, Byoung Chul Cho. Identification of microbiome and predictive immune biomarkers in patients with advanced non-small lung cancer treated with pembrolizumab with or without chemotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2803.

An update on methods for detection of prognostic and predictive biomarkers in melanoma.

The approval of immunotherapy for stage II-IV melanoma has underscored the need for improved immune-based predictive and prognostic biomarkers. For resectable stage II-III patients, adjuvant immunotherapy has proven clinical benefit, yet many patients experience significant adverse events and may not require therapy. In the metastatic setting, single agent immunotherapy cures many patients but, in some cases, more intensive combination therapies against specific molecular targets are required. Therefore, the establishment of additional biomarkers to determine a patient's disease outcome (i.e., prognostic) or response to treatment (i.e., predictive) is of utmost importance. Multiple methods ranging from gene expression profiling of bulk tissue, to spatial transcriptomics of single cells and artificial intelligence-based image analysis have been utilized to better characterize the immune microenvironment in melanoma to provide novel predictive and prognostic biomarkers. In this review, we will highlight the different techniques currently under investigation for the detection of prognostic and predictive immune biomarkers in melanoma.

Vulnerability to immune therapy in BRAF- and MYB-altered pediatric gliomas.

2066 Background: The frequency and expression of predictive immune biomarkers in pediatric gliomas is relatively unknown. Here, we sought to define predictive signatures of immunotherapy responders by examining immune checkpoint expression, immune cell population signatures, and gene amplifications in a variety of pediatric gliomas. Methods: We profiled a cohort of pediatric, adolescent, and young adult glioma samples submitted to Caris Life Sciences (Phoenix, AZ) for analysis (N = 207). The cohort was further stratified by driver mutations: IDH mutant (N = 103), H3-3A mutant (N = 36), MYB-altered (N = 4), BRAF-altered (N = 39), and IDH wild type [IDH WT] (N = 25). De-identified next generation DNA sequencing (592-gene or whole exome) and RNA (whole transcriptome) sequencing were used to determine tumor-infiltrating immune cell signatures and immune checkpoint protein expression. Transcriptomic signatures predictive of response to immunotherapy (T cell inflamed score) and replication stress response defect (RSRD) score were calculated on transcripts per million (TPM) values. Immune cell fractions were estimated using RNA deconvolution (quanTIseq). Results: BRAF-altered tumors showed the highest IFN gamma signature (a clinically relevant inflammatory biomarker), a high pro-inflammatory M1 macrophage signature, and very high CD4+/CD8+ T cell signatures compared to other tumor groups. IDH mutant, but not WT gliomas, were relatively deficient in T cell infiltration but revealed an increase in dendritic cell signatures. IDH WT gliomas showed more T cell enrichment but higher immune suppressive checkpoint expression. H3-3A mutant tumors showed relatively low immune cell infiltration and immune checkpoint expression. Pro-inflammatory M1 macrophages were lower in the high-grade glioma microenvironment compared to low-grade. Immune suppressive M2 macrophages were elevated in the IDH WT microenvironment relative to other gliomas. There was no apparent difference in the frequency of PD-1+ T cells and tumor-expressed PD-L1 among the various molecularly defined pediatric glioma groups. Conclusions: In general, BRAF- and MYB-altered gliomas displayed high immune activation relative to other tumors. IDH WT tumors have an immune suppressive microenvironment with relatively high immune checkpoint expression. Based on predictive markers, BRAF- and MYB-driven gliomas show signatures suggesting the possibility of a greater response to immunotherapies than IDH WT/mutant gliomas or H3-3A gliomas.

Booster vaccination is required to elicit and maintain COVID-19 vaccine-induced immunity in SIV-infected macaques

ABSTRACT Prolonged infection and possible evolution of SARS-CoV-2 in patients living with uncontrolled HIV-1 infection highlight the importance of an effective vaccination regimen, yet the immunogenicity of COVID-19 vaccines and predictive immune biomarkers have not been well investigated. Herein, we report that the magnitude and persistence of antibody and cell-mediated immunity (CMI) elicited by an Ad5-vectored COVID-19 vaccine are impaired in SIV-infected macaques with high viral loads (> 105 genome copies per ml plasma, SIVhi) but not in macaques with low viral loads (< 105, SIVlow). After a second vaccination, the immune responses are robustly enhanced in all uninfected and SIVlow macaques. These responses also show a moderate increase in 70% SIVhi macaques but decline sharply soon after. Further analysis reveals that decreased antibody and CMI responses are associated with reduced circulating follicular helper T cell (TFH) counts and aberrant CD4/CD8 ratios, respectively, indicating that dysregulation of CD4+ T cells by SIV infection impairs the COVID-19 vaccine-induced immunity. Ad5-vectored COVID-19 vaccine shows no impact on SIV loads or SIV-specific CMI responses. Our study underscores the necessity of frequent booster vaccinations in HIV-infected patients and provides indicative biomarkers for predicting vaccination effectiveness in these patients.

THU178 - Predictive immune biomarkers to safely discontinue nucleos (t)ide analogue treatment in HBeAg negative chronic hepatitis B (NUC-B study)

THU178 - Predictive immune biomarkers to safely discontinue nucleos (t)ide analogue treatment in HBeAg negative chronic hepatitis B (NUC-B study)

Immune Profiling To Predict Treatment Response From Extracorporeal Photopheresis In Graft-Versus-Host Disease

Graft-versus-host disease (GVHD) is a major cause of morbidity and mortality in patients receiving stem cell transplant. Extracorporeal photopheresis has demonstrated efficacy in a proportion of GVHD patients that are steroid refractory. However, given the expense, the challenging logistics, and unpredictable GVHD response to ECP, a predictive immune biomarker to assist with the selection of patients to treat with ECP can make this treatment modality more efficacious and cost-effective. One of the challenges of using leukocyte as biomarkers is that any single leukocyte population does not adequately describe the immune system. We have recently described an approach to characterize the immune system in a comprehensive manner. We have used a combination of whole blood quantitative flow cytometry and bioinformatics to generate immune profiles among patients with GVHD who were prescribed ECP. GVHD grading and clinical responses were confirmed by serial physical exams and review of medical records over a 6 month period. Response to ECP was defined as a decrease in clinical grade of GVHD (IBMTR and NIH consensus criteria) at the end of treatment. Peripheral blood drawn prior to initiation of ECP was used to generate a patient's immune phenotype by analyzing 33 lymphoid and myeloid surface marker combinations by flow cytometry. Immune phenotypes from 27 age-matched healthy volunteers were used as controls.From July 2011 to April 2013, 20 patients were enrolled (3 aGVHD, 17 cGVHD). At the time of this abstract, 15 patients were evaluable for treatment response, with 6 responders and 9 non-responders (median time on treatment 5.28 months, range 0.25 – 6 months). Using multivariate analysis by principal component analysis and unsupervised hierarchical clustering, we identified clustering patterns (immune profiles) in the composite immune phenotypes of the GVHD patients and controls. The healthy controls clustered together into a single immune profile. Four of the six responders clustered closely together and away from the 9 non-responders and healthy controls. Anova analysis identified 21 individual markers that were different among the clusters. Using non-parametric Wilcoxon comparison, we found that patients who responded to ECP had lower numbers of lymphocytes (p=2.5x10-12), CD4 T cells (p=0.002), CD4+CD69+ T cells (p=0.02), and CD8+CTLA4+ (p=1.8x10-5) than healthy controls and non-responders.To our knowledge, this is the first report of a bioinformatics approach to comprehensively examine systemic immune profiles of GVHD patients. Our results provide the proof-of-concept for using this method to identify an immune profile as a predictive biomarker in the treatment of GVHD. Our data supports the need for a larger study to definitively define the immune profiles and validate this approach. Disclosures:No relevant conflicts of interest to declare.

Multiplex Immunofluorescence Tyramide Signal Amplification for Immune Cell Profiling of Paraffin-Embedded Tumor Tissues.

Every day, more evidence is revealed regarding the importance of the relationship between the response to cancer immunotherapy and the cancer immune microenvironment. It is well established that a profound characterization of the immune microenvironment is needed to identify prognostic and predictive immune biomarkers. To this end, we find phenotyping cells by multiplex immunofluorescence (mIF) a powerful and useful tool to identify cell types in biopsy specimens. Here, we describe the use of mIF tyramide signal amplification for labeling up to eight markers on a single slide of formalin-fixed, paraffin-embedded tumor tissue to phenotype immune cells in tumor tissues. Different panels show different markers, and the different panels can be used to characterize immune cells and relevant checkpoint proteins. The panel design depends on the research hypothesis, the cell population of interest, or the treatment under investigation. To phenotype the cells, image analysis software is used to identify individual marker expression or specific co-expression markers, which can differentiate already selected phenotypes. The individual-markers approach identifies a broad number of cell phenotypes, including rare cells, which may be helpful in a tumor microenvironment study. To accurately interpret results, it is important to recognize which receptors are expressed on different cell types and their typical location (i.e., nuclear, membrane, and/or cytoplasm). Furthermore, the amplification system of mIF may allow us to see weak marker signals, such as programmed cell death ligand 1, more easily than they are seen with single-marker immunohistochemistry (IHC) labeling. Finally, mIF technologies are promising resources for discovery of novel cancer immunotherapies and related biomarkers. In contrast with conventional IHC, which permits only the labeling of one single marker per tissue sample, mIF can detect multiple markers from a single tissue sample, and at the same time, deliver extensive information about the cell phenotypes composition and their spatial localization. In this matter, the phenotyping process is critical and must be done accurately by a highly trained personal with knowledge of immune cell protein expression and tumor pathology.

Abstract B04: Immune modeling analysis identifies ICOS and CTLA-4 as predictive biomarkers in serous epithelial ovarian cancer

Abstract Objective: The goal of this study is to comprehensively determine the most clinically relevant immune checkpoint receptor in epithelial ovarian cancer (EOC). Methods: Stage III, Grade III EOC formalin-fixed, paraffin-embedded (FFPE) tumors from ten patients were submitted to Cofactor Genomics to undergo RNA sequencing and machine learning analysis to determine immune cell content and levels of the ten most studied immune escape genes. Patient samples were stratified by high progression-free survival (PFS) of 65 months or greater (n=5) and low PFS of 7 months or less (n=5). All patient tumors submitted were from primary debulking surgery and were naïve to chemotherapy. Immunohistochemistry (IHC) was employed to validate findings. Results: The two immune escape genes ICOS and CTLA-4 were found to be the most predictive biomarkers differentiating short and long PFS. ICOS median transcripts per million (TPM) were 418 and 1,621 in patients with short and long PFS, respectively (p&amp;lt;0.03), while median TPM for CTLA-4 were 1,294 and 4,045 in patients with short and long PFS, respectively (p&amp;lt;0.03). In addition, PD-1 exhibited a median TPM of 817 in patients with short PFS compared to 2455 in patients with long PFS (p&amp;lt;0.05). Immune cell profiling revealed a statistically significant (p&amp;lt;0.05) lower percentage of M2 macrophages in patients with a long PFS (1.07%) compared to patients with a short PFS (2.13%). Higher percentages of CD4+ T cells, CD8+ T cells, CD19+ B cells, and Tregs were detected in patients with a longer PFS compared to short; however, these differences did not reach statistical significance. It was discovered that immune escape genes PD-1, PD-L1, CTLA-4, OX40, TIM3, and ICOS all significantly correlated (p&amp;lt;0.05) to the percentage of CD8+ T cells and total immune cell content. CD47 levels significantly inversely correlated to total immune cell percentages (p&amp;lt;0.02). OX40 was the only immune escape gene that significantly correlated to percentage of CD4+ T cells (p&amp;lt;0.03). PD-1, PD-L1, CTLA-4, OX40, and ICOS all correlated significantly to the percentage of CD19+ B cells (p&amp;lt;0.02). Finally, PD-L1, CTLA-4, OX40, TIM3, and ICOS all significantly correlated to Treg levels (p&amp;lt;0.03). Conclusions: Immune modeling analysis revealed ICOS and CTLA-4 as the most predictive immune biomarkers for PFS in EOC. Interestingly, it was discovered that levels of both activating and inhibitory immune checkpoint receptors correlate to a higher percentage of immune cell populations, indicating overall immune content is important in predicting improved outcomes. Future directions include repeating this assay in a larger patient cohort to validate the predictive threshold determined for ICOS and CTLA-4. Citation Format: Nicole James, Matthew Oliver, Joyce Ou, Jenna Emerson, Katherine Miller, Erin Lips, Ashley Borgstadt, Paul DiSilvestro, Jennifer Ribeiro. Immune modeling analysis identifies ICOS and CTLA-4 as predictive biomarkers in serous epithelial ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr B04.

Practice gaps and challenges integrating new immuno-oncology agents in the treatment of cancer patients in the United States: A mixed-method study.

11028 Background: Previous research has indicated challenges integrating new immuno-oncology agents (IOAs) and predictive immune biomarkers into practice. Barriers, clinical gaps and underlying causalities explaining these challenges, however, are poorly understood. Methods: A mixed-methods educational needs assessment was conducted with physicians from 6 specialties (oncology, interventional radiology, pathology, pulmonology, emergency medicine and rheumatology), clinical pharmacists, physician assistants and advanced nurse practitioners involved in the care of cancer patients in the United States. Semi-structured interviews and discussion groups were thematically analyzed to identify challenges, barriers and underlying causalities. Qualitative findings subsequently informed the development of online surveys, which served to quantify findings. The following findings pertain to oncologists. Results: A total of 660 health care providers participated in the study, in which 17 interviews and 88 surveys were completed with oncologists. Seventy-two percent reported sub-optimal knowledge of the interactions between IOAs and the tumor’s micro-environment, while 62% reported sub-optimal skills determining which IOA to select based on this information. Oncologists reported sub-optimal knowledge of best practices for using IOAs to treat cancer in presence of an autoimmune disease (74%-80% depending on condition), and sub-optimal skills weighing the risks and benefits of prescribing IOAs for these profiles (66%-77%). In addition, 50% of oncologists reported feeling overwhelmed by the volume of new IOAs being made available. Many oncologists expressed doubts regarding the clinical benefit (59%) and innovative nature (43%) of emerging IOAs. Finally, 46% reported limited skills identifying viable treatment options based on pharmacodiagnostic test reports. Barriers to having predictive biomarkers inform treatment decisions included sub-optimal communication between specialists regarding specimen requirements and desired biomarker information. Conclusions: This study demonstrates the need to further support healthcare professionals as they face challenges integrating new IOAs and predictive immune biomarkers into practice. Given the wide array of IOAs becoming available each year, addressing the knowledge, skills, confidence and attitude gaps identified in this study could help improve health care delivery and potentially optimize outcomes for cancer patients.

A randomized phase II study of cabozantinib and nivolumab versus nivolumab in recurrent endometrial cancer.

6010 Background: The efficacy of treatment for recurrent endometrial cancer (EC) remains limited. Vascular endothelial growth factor and inflammatory chemokines are proangiogenic factors and immune modulators involved in immune suppression. Reprogramming the tumor microenvironment by combining antiangiogenic and immunotherapy (IO) could enhance antitumor responses. Methods: A 2:1 randomized phase 2 trial compared the combination of cabozantinib and nivolumab (Arm A) versus nivolumab (Arm B) in recurrent EC. Primary endpoint was progression free survival (PFS) assessed by RECIST 1.1 (NCT03367741). Women with recurrent measurable EC were eligible. There were no limits on prior therapy, but at least one prior platinum-based chemotherapy was required. Patients (pts) were stratified according to MSI status and assessed by CT every 8 weeks. Cabozantinib was given at 40 mg daily (Arm A) and nivolumab at 240 mg, on D1 and D15 of a 28-day cycle for 4 cycles, followed by 480 mg every 4 weeks (Arms A &amp; B). Pts with carcinosarcoma or prior IO were enrolled in an exploratory cohort and received combination treatment (Arm C). A baseline biopsy was required for all pts. CyTOF analysis was performed on fresh biopsies. Results: 76 evaluable pts were enrolled (Arm A: 36, Arm B: 18, Arm C: 9 carcinosarcoma, and 20 post IO including 7 pts crossed over from Arm B). 55% of pts had received ≥3 prior lines of therapy. Two pts were MSI high in Arm A and none in Arm B. The Kaplan-Meier estimated median PFS was 5.3 (95% CI: 3.5-9.5) months in Arm A and 1.9 (95% CI: 1.6-3.8) months in Arm B, with a log-rank p = 0.07, which met the significance level of 0.1 used for sample size calculation. Objective response rate (ORR) was 25% for Arm A and 16.7% for Arm B; stable disease (SD) was seen in 44.4% vs 11.1%, respectively. Clinical benefit (ORR+SD) was significantly higher in arm A vs B (p &lt; 0.001). In Arm C-carcinosarcoma, one patient had a partial response (11.9 months duration) and four SD. In Arm C-prior IO, six pts responded and eight had SD. The most common related AEs in Arm A were diarrhea (47.2%), elevated liver enzymes (44.4%), fatigue (38.9%), anorexia, hypertension, and nausea (30.6%), mainly grade 1/2. Preliminary CyTOF analysis across treatment arms identified multiple immune subsets for further interrogation including activated CD8+ and CD4+ T cells. Conclusions: Cabozantinib plus nivolumab demonstrates improved PFS compared to nivolumab in heavily pre-treated women with recurrent EC. In-depth CyTOF analysis of the tumor microenvironment to identify predictive immune biomarkers of response is ongoing. Clinical trial information: NCT03367741.

Multiplexed immunohistochemistry for immune cell phenotyping, quantification and spatial distribution in situ.

It is increasingly recognized that a deep characterization of the immune microenvironment is required for the identification of prognostic and predictive immune biomarkers. Recent advances in the field of tissue imaging resulted in the development of fluorescence multiplex IHC technologies enabling quantitative assessment of immune phenotypes and functional orientation of immune cells in a way similar to flow cytometry, while simultaneously providing tissue context and spatial distribution. Multiplex immunofluorescent technology to FFPE tumor tissue is applied to characterize immune infiltration and PD-L1 expression. A panel consists of five protein markers: CD8, CD68, PD-L1, CK, and SOX10. The assay workflow is fast, optimized and compatible with existing instrumentation. The resulting images can be analyzed with routinely used software for digital pathology enabling the quantification of dynamic range of expression, co-localization and co-expression of markers in the whole tissue. In this chapter, we provide the protocol for the use of the UltiMapper™ I/O PD-L1 multiplex assay, from the bench to the image analysis, as well as an overview of the current multiplex image analysis solutions. Such deep profiling could guide the development of strategies to better select immune checkpoint molecules and a better stratification of patients who will potentially benefit from immunotherapies.

Abstract P4-08-30: Prognostication of immune related gene expression in patients with triple negative breast cancer

Abstract Introduction: To date, the role of immunotherapy with check point inhibitors and/or vaccines in the treatment of breast cancer (BC) is still debating, and the main focus of immunotherapy in BC is on triple negative subtype as a target population in many ongoing clinical trials. Translational research into identifying predictive and prognostic immune biomarkers is of particular clinical relevance, but, there are currently no definite prognostic and predictive immune biomarkers in BC, especially in triple negative breast cancer(TNBC). We investigated the expression profiles of immune genes in patients with TNBC to identify the prognostic value of immune genes in search of clinical implications. Methods : We investigated expression profiles of 770 pan-cancer immune related genes using the nCounter mRNA expression assay (NanoString®) from paraffin-embedded tumor tissues in 200 patients diagnosed as TNBC who received curative surgery at Samsung Medical Center from 2000 to 2004. We analyzed the relationship between stage adjusted level of gene expressions and patients' survival outcomes using Cox regression model. Results: Of 770 genes, 186 genes were selected from univariate analysis with clinical stage adjustment. In multivariate analysis using Cox regression, expressions of CD1B, CD45, CD53, CT45A1, GTF3C1, IL11RA, IL1RN, LRRN3, MAPK1, NEFL, PRKCE, SPACA3 and RANKL were associated with distant recurrence free survival (p&amp;lt;0.05, respectively). Among these 13 genes, expression of MAPK1, NEFL, CD45, SPACA3 and RANKL were correlated with favorable outcome in terms of distant recurrence free survival (p&amp;lt;0.05, respectively). In terms of overall survival, C3, IL1RL1, IL1RN, IL7 and PRKCE were associated with poor prognosis (p&amp;lt;0.05, respectively) and expression of SAA1 CXCL9 and RANKL resulted in favorable outcome (p&amp;lt;0.05, respectively). Table 1ParameterParameter EstimateStandard Errorp-valueHazard Ratio95% Confidence Interval(a) distant recurrence free survival Stage2.487350.680570.000312.0293.169, 45.661CD1B1.141910.2753&amp;lt;.00013.1331.826, 5.374CD531.531650.34851&amp;lt;.00014.6262.336, 9.159CT45A10.426110.134210.00151.5311.177, 1.992GTF3C11.193110.579720.03963.2971.059, 10.271IL11RA1.671120.461750.00035.3182.151, 13.146IL1RN0.980280.24657&amp;lt;.00012.6651.644, 4.321LRRN31.424170.28742&amp;lt;.00014.1542.365, 7.297MAPK1-0.542740.258240.03560.5810.35, 0.964NEFL-1.12170.335610.00080.3260.169, 0.629PRKCE2.378340.49659&amp;lt;.000110.7874.076, 28.549CD45-2.736780.43154&amp;lt;.00010.0650.028, 0.151SPACA3-0.745930.272270.00610.4740.278, 0.809RANKL-1.288920.2976&amp;lt;.00010.2760.154, 0.494(b) overall survival Stage1.359280.497810.00633.8931.468, 10.329C30.329830.150350.02831.3911.036, 1.867CXCL9-0.379190.100680.00020.6840.562, 0.834IL1RL10.679360.262940.00981.9731.178, 3.303IL1RN0.437130.172370.01121.5481.104, 2.171IL70.507280.206250.01391.6611.109, 2.488PRKCE0.835340.272910.00222.3061.35, 3.936SAA1-0.564250.13449&amp;lt;.00010.5690.437, 0.74RANKL-0.604990.234510.00990.5460.345, 0.865 Conclusion: High expression of IL1RN, PRKCE were associated with short distant recurrence free survival and overall survival in patients with TNBCs who received curative surgery. In contrast, RANKL expression resulted in prolonged distant recurrence free survival and overall survival. Citation Format: Kim J-Y, Jung HH, Lim JE, Cho EY, Lee SK, Yu JH, Lee JE, Kim SW, Nam SJ, Park YH, Ahn JS, Im Y-H. Prognostication of immune related gene expression in patients with triple negative breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-08-30.

Immune biomarkers of response to immune-checkpoint inhibitors in head and neck squamous cell carcinoma.

Anti-programmed cell death protein 1 (PD-1) agents have become the standard of care for platinum-refractory recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) and are currently being evaluated in various disease settings. However, despite the gain in overall survival seen in some of the clinical trials, the majority of patients display primary resistance and do not benefit from these agents. Taking into consideration the potentially severe immune-related toxicities and their high cost, the search for predictive biomarkers of response is crucial. Besides Programmed death ligand-1 (PD-L1) expression, other biomarkers such as immune infiltration, tumor mutational burden or immune-gene expression profiling have been explored, but none of them has been validated in this disease. Among these, the microbiota has recently garnered tremendous interest since it has proven to influence the efficacy of PD-1 blockade in some tumor types. With the accumulating evidence on the effect of the microbiota in HNSCC tumorigenesis and progression, the study of its potential role as a predictive immune biomarker is warranted. This review examines the available evidence on emerging immune predictive biomarkers of response to anti-PD-1/PD-L1 therapy in HNSCC, introducing the microbiota and its potential use as a predictive immune biomarker in this disease.

Systemic immune signature of inflammation in metastatic melanoma (MM) patients treated with ipilimumab (IPI) and carboplatin/paclitaxel (CP).

185 Background: Only a subset of MM patients benefit from treatment with IPI. In an effort to increase response and identify predictive immune biomarkers, we combined IPI with CP. We here report a significant correlation between overall survival (OS) and biomarkers of a pre-existing inflammatory state. Methods: 30 patients were treated with C (AUC = 6) and P (175mg/m2) every 3 weeks x 5 and IPI (3mg/kg) every 3 weeks x 4. Blood was collected throughout. OS Kaplan–Meier curves were compared by the log-rank test. Cutoff thresholds defining high or low levels of chemokines, B cell populations and PD-1+CD8+ T cell populations were established from the mean value of a given variable from all patients. Neutrophil to Lymphocyte Ratio (NLR) and Systemic Immune Inflammation Index defined as Platelet x Neutrophil to Lymphocyte Ratio (SII) were calculated at baseline and tested for association with OS. SII ≥ 1375 and NLR ≥ 5 were considered as high risk groups. Results: Median OS was 16.2 months, with a 3-year OS of 36.7% for all patients. High levels of CCL3 (HR = 2.79, p = 0.0159), CCL4 (HR = 8.36, p &lt; 0.0001) and CXCL8 (HR = 3.52, p = 0.0037) were associated with worse OS. Advanced B cell differentiation before treatment was associated with worse patient outcomes. High levels of early differentiated Bm2 ( &gt; 57%) were strongly associated with better OS: HR = 0.26 p = 0.004 whereas high eBm5+Bm5 ( &gt; 14%) levels were strongly associated with worse OS: HR = 2.65, p = 0.029. Patients with higher proportions of PD-1+CD8+ T cells in circulation during treatment had poorer OS (HR = 3.84, p = 0.004) at week 10; (HR = 3.53, p = 0.005) at week 13 and (HR = 2.84, p = 0.040) at week 24. High risk SII (HR = 3.30, p = 0.0192) and NLR (HR = 2.367, p = 0.0486) were associated with worse OS. Conclusions: We have identified a significant correlation between pre-existing systemic inflammatory state and a poor response to IPI and CP. Specifically elevated CCL3, CCL4 and CXCL8, baseline B lymphocyte subset skewing, increased CD8+PD-1+ T lymphocytes, increased NLR and SII were all strongly associated to worse OS. Comprehensive immune monitoring provides evidence for new circulating biomarkers predicting outcome in MM patients treated with IPI and CP. Clinical trial information: NCT01676649.

P2.04-019 A Peripheral Immune Signature Associated with Clinical Activity of Sunitinib in Thymic Carcinoma: Topic: Thymic Malignancies Clinical and Translational

We have previously reported an objective response rate of 26% and disease stabilization in 65% of patients with advanced thymic carcinoma (TC) treated with the multi kinase inhibitor sunitinib after failure of platinum-based chemotherapy. The current study investigates the impact of sunitinib on systemic immunity in patients with thymic epithelial tumors with an aim to discover blood-based, predictive immune biomarkers. Patients with thymoma and TC received sunitinib at a dose of 50 mg once daily in 6-week cycles consisting of 4 weeks of treatment followed by 2 weeks without treatment. Results from 15 patients with TC are reported here. Blood samples were collected before initiation of sunitinib therapy (Cycle 1 day 1; C1D1), and prior to treatment on day 1 of cycles 2 and 3 (C2D1; C3D1). Multiparameter flow cytometry was used to study T-cell subsets with immune checkpoint expression, four phenotypes of myeloid-derived suppressor cells (MDSCs) with CD40, and CD14+ monocytes with HLA-DR expression. Expression of 730 immune-related genes in peripheral blood was analyzed by NanoString technology. Differences in paired markers or changes in markers between two time points was evaluated by the Wilcoxon signed rank test. The Kaplan-Meier method was used to obtain estimates of progression-free survival (PFS) and overall survival (OS). Immunosuppressive Tim-3-positive Tregs declined after 2 cycles of sunitinib (p=0.024). A decrease in granulocytic MDSCs (p=0.012), lineage negative (CD3-CD19-CD56-) MDSCs (p=0.013), and immature MDSCs (p=0.01) but not monocytic MDSCs was observed after 1 cycle of sunitinib. TC patients with no objective response to sunitinib had a higher baseline immature MDSC level than responders (p=0.0044). Greater than median declines in granulocytic MDSC CD40 (C2D1 p=0.027, C3D1 p=0.0046) and lineage-MDSC CD40 (C3D1, p=0.0046) after sunitinib therapy was associated with improved PFS. Similarly, a greater than median decline in CD14+HLA-DRlo/neg monocyte levels on C2D1 was associated with longer PFS (p=0.020). Among the immune genes examined, higher baseline FEZ1 expression was associated with improved PFS and OS. Our findings suggest significant interplay between sunitinib and systemic immunity impacts therapeutic outcome in TC. Monitoring CD40 expression on specific MDSC phenotypes and FEZ1 gene expression may predict a survival benefit after treatment with sunitinib. These results, if validated in larger studies, can serve as potential blood-based predictive biomarkers in TC patients treated with sunitinib.

Predictive immune biomarker signatures in the tumor microenvironment of melanoma metastases associated with tumor-infiltrating lymphocyte (TIL) therapy

Meeting abstracts Adoptive cell therapy using autologous TIL is a promising immunotherapy for metastatic melanoma. This study was aimed at finding predictive biomarkers in TIL patients, using IHC and gene expression analysis on original tumors used to expand TIL. The purpose was to find factors

Effect of non-tuberculous Mycobacteria on host biomarkers potentially relevant for tuberculosis management.

BackgroundNon-tuberculous mycobacteria (NTM) are different from Mycobacterium tuberculosis (MTB) both in their ubiquitous environmental distribution and in their reduced capacity to cause disease. While often neglected in favour of other infectious diseases, NTM may interfere with important aspects of TB control and management, namely the efficacy of new anti-tuberculosis (TB) vaccines; the immuno-diagnostic Tuberculin skin test (TST) and QuantiFERON TB Gold In Tube assay (QFTGIT); and immune biomarkers explored for their diagnostic and/or predictive potential. Our objective was therefore to explore host immune biomarkers in children who had NTM isolated from respiratory and/or gastric specimens.Methodology and Principle FindingsThe present study was nested within a prospective cohort study of BCG-vaccinated neonates in Southern India. In this setting, immune biomarkers from peripheral blood were analyzed in 210 children aged <3 years evaluated for TB using dual-colour-Reverse-Transcriptase-Multiple-Ligation-dependent-Probe-Amplification (dcRT-MLPA) and Bio-Plex assays. The children were classified based on clinical examination, chest X-rays and mycobacterial culture reports as either: 1) TB disease, 2) NTM present and 3) controls. The study shows a down-regulation of RAB33A (p<0.001) and up-regulation of TGFβ1, IL-2 and IL-6 (all p<0.05) in children with TB disease, and that RAB33A, TGFBR2 and IL-10 (all p<0.05) were differentially expressed in children with NTM present when compared to children that were culture negative for MTB and NTM (controls).Conclusions and SignificanceCarriage of NTM may reduce the specificity of future diagnostic and predictive immune biomarkers relevant to TB management.

The quest for predictive immune biomarkers

The quest for predictive immune biomarkers