TIS GEP Use Cases

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October 10, 2023

Introduction to the TIS Gene Expression Profile

Innovations in multiplex gene expression analysis are revolutionizing translational research and clinical practices by enabling identification of gene signatures involved in various biological processes. Gene expression profiling (GEP) has proven particularly valuable in cancer research, revealing biological mechanisms relevant in cancer onset and progression and, in turn, patient-specific signatures enabling differentiation of cancer subtypes. GEP signatures have demonstrated value in identifying patients that could benefit from certain cancer treatments, unraveling a drug’s mechanism of action, and in evaluating treatment effects.

This article discusses the significance of the Tumor Inflammation Signature (TIS), a crucial gene expression profile with high relevance in the field of immuno-oncology developed by Merck in collaboration with NanoString. This article also highlights examples of research studies demonstrating the utility of TIS gene expression profile in different stages of the drug development process when used alone and in combination with other biomarkers.

TIS Gene Expression Profile Quantifies a Peripherally Suppressed Adaptive Immune Response

TIS is a gene expression signature, or profile, consisting of 18 genes, covering four areas of biology related to T- and natural killer cells, antigen-presenting cells, IFN- γ biology, and T cell exhaustion (Figure 1).

Illustration of how a Tumor Inflammation Signature covers 4 areas of biology — **Figure 1.** Composed of 18 different genes, TIS is a gene expression profile that covers 4 areas of biology – T Cell/NK cells, antigen presenting cells, IFNg biology, and T Cell exhaustion.

Indicating the infiltration level of tumors by T-cells, the TIS score, along with tumor antigenicity, has shown value as a predictor of patient response to pembrolizumab therapy.¹ Gene expression profiling biomarkers like TIS derive their reliability from quantifying and weighting the relative expression levels of multiple genes to generate a score. The derived scores are compared to a predetermined cutoff value. The cutoff values for TIS can be adjusted to be specific to tumor type and treatment setting. A pan-cancer threshold, or cut-off, is thus possible for widespread use of the TIS assay, making TIS a gene expression profiling predictor of cancer treatment response across all tumor types examined.¹ ²

Developed as a tool for predicting patient response to pembrolizumab anti-PD1 therapy, TIS gene expression signature was trained and validated against a large cohort of 11 solid tumor types. TIS score was then fully validated in 9 different tumor types (Figure 2A).² At this point, the TIS signature and associated TIS score were approved for use as an investigational use-only (IUO) assay on the nCounter® Analysis System platform.

TIS Gene Expression Profile Improves Patient Stratification for Immune Checkpoint Inhibitor (ICI) Therapy

Figure 2A. Scatter plot showing distribution of TIS GEP scores across 244 patients from Keynote-012 and Keynote-028 representing 9 tumor types. Y-axis: PFS in days. X-axis: T cell-inflamed GEP score = Tumor Inflammation Signature score values from -1.2 to 0.8. Red dots represent patients exhibiting complete or partial response to anti-PD1 therapy enriched in the higher end of the TIS score. Blue triangles represent patients with stable disease. Black dots represent patients in the “Other” outcome category. (Figure reproduced with permission from Ayers M. et al., IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade., J of Clin Invest., 2017, 127:2930)

**Figure 2B.** i. Kaplan Myer curve indicating increased number of days with PFS in response to Anti-PD1 therapy for GEP^hi cohort relative to GEP^lo cohort. ii. ROC illustrates variable sensitivity and specificity of GEP in individual tumor type compared to pan-tumor GEP score. (Figure reproduced with permission from Cristescu R et al., Pan Tumor Genomic Biomarkers for PD-1 Checkpoint Blockade-based Immunotherapy. *Science*, 2018, 362.197)

Correlation of high TIS score with treatment response is also observed in studies using RNA-Seq. In patients with advanced urothelial carcinoma, TIS scores quantified by RNA-Seq successfully predicted patient response to pembrolizumab.³ Using the same score weights as the nCounter study, Figure 2B,⁴ higher TIS scores were also observed in responders compared to non-responders in a separate study.³

Similarly, a different retrospective analysis of immuno-oncology (IO)-naïve melanoma patients treated with pembrolizumab showed a trend toward higher rates of patients with progression-free-survival (PFS) and overall survival (OS) associated with a high TIS score;⁵ these results were further supported across multiple tumor types by a pan-tumor study of pembrolizumab-treated patients.⁶

The TIS gene expression profile appears to be enriched for a response to anti-PD1 given that higher TIS scores better predicted response to pembrolizumab treatment compared to nivomulab treatment.⁵ Nivolumab response was also associated with higher TIS scores in a pan-tumor cohort, though not to the same degree as pembrolizumab. Independent cohorts of NSCLC patients treated with nivolumab were analyzed.⁷⁸ Baseline samples showing significantly higher TIS scores better predicted response to treatment than lower scores,⁷ confirming the significant association observed between TIS score and clinical response (PR or CR versus SD or progression).⁸

Using TIS scores to prospectively select patients in clinical trials

At least two Phase 1 clinical trials have used TIS scores to prospectively select immunotherapy-naïve NSCLC patients for treatment with combination anti-PD1 + ICOS agonist, or anti-PD1 alone – Jounce SELECT Clinical Trial (Figure 3). Having already established a cut-off and base level threshold score for TIS, the assay was deployed to prospectively enroll patients into the Jounce SELECT Clinical Trial. A novel biomarker which correlated with both high TIS-scores and good treatment-response rates was discovered. This novel biomarker combined with TIS shows patients treated with ICOS agonist have a high emergence of CD4 T cells.⁹ Called TIS^VOPRA score, the new multivariate biomarker was used to demonstrate clinical benefit, as patients with high or positive TIS^VOPRA scores have significantly better progression-free survival, PFS, and overall survival, OS, than those with negative scores.⁹ NanoString’s IO 360™ panel and BC 360™ panel enable the assessment of 40 additional gene signatures besides TIS, supporting assessment of critical biological mechanisms in cancer in combination with any other established biomarker such as TIS.

TIS^vopra Predicts Clinical Benefit

**Figure 3.** Subjects who are TIS^VOPRA positive had a higher 6 moth PFs (20.1% vs. 3.7%) and significantly higher median OS (16.9 months vs. 6.2 months, p-value 0.0062) than those who were TIS^VOPRA negative. PDL-1 and ICOS IHC are not predictors of clinical benefit with vopra alone or in combination with nivo (data not shown). (Figure reproduced with permission from Yap TA et al., *J Clin Oncol* 2020 38:5_suppl, 14-14)

Using TIS Gene Expression Profile to Measure Treatment Effect

This section highlights studies that have shown that TIS scores can be used to evaluate changes in response to treatment. For instance, TIS score changes in both responders and non-responders after treatment with pembrolizumab or immune-checkpoint therapy. This suggests that TIS scores can effectively measure treatment effects in immuno-oncology trials and other relevant research studies.

One study comparing screening samples from PD-1-refractory advanced melanoma patients to samples after 15 days of pembrolizumab and HDAC-inhibitor SNDX-275/MS-275 combination treatment found that TIS scores slightly increase. Post-treatment TIS scores significantly increased in both responders and non-responders, with a greater increase observed in responders.¹⁰ Another study in IO-naïve melanoma patients showed TIS scores increasing after immune-checkpoint therapy with a combination of ipilimumab (anti-CTLA4) and high dose interferon neoadjuvant (UPCI 11-063).¹¹ On balance, these studies support the assertion that TIS scores can be a measure of response in patients’ post-treatment samples.

TIS scores appear to be immunotherapy-treatment specific. Researchers examining IO-naïve progressed breast cancer patients before and after treatment with a combination of chemotherapy and bevacizumab found that several gene expression profiling signatures, including TIS, showed little- to no- increase in TIS score in either pCR or RD patients as expected for non-IO treatments.¹² Since the TIS gene expression profile does not change in response to chemotherapy and anti-VEGF treatment, its value lies in its high specificity for assessing treatment effects in immuno-oncology relevant tumors.

TIS as a Measurement in Multi-Component, Composite, Biomarkers

Understandably, the predictive power of single biomarkers is sometimes incomplete. Composite biomarkers have the potential to yield improved sensitivity and specificity over single biomarkers. As multi-omic approaches and artificial intelligence advance and are applied more broadly in medical research, multi-component biomarkers will likely become more prevalent. Today, observations are limited to associations between individual gene signatures, and these have yet to develop as composite biomarkers. For instance, in a comparison of multiple gene expression profiling signatures across several cancer types (melanoma, bladder, HNSCC, TNBC, gastric, RCC and ovarian), TIS was shown to have a high predictive value of response to pembrolizumab treatment across all tumor types. Other signatures also correlated highly with response to treatment across all tumor types, including angiogenesis, MDSCs, and TGFβ.⁴ Another study in patients with AML correlated TIS and other immune signatures with response to bi-specific antibody (anti-CD3xCD123) therapy and found that pre-treatment bone marrow samples have higher scores for TIS, inflammatory cytokines, T_reg, CD8, IFNg, PD-1, PD-L1 and T cell exhaustion than non-AML bone marrow. Post-treatment TIS scores were high in responders for AML, as were scores for all others tested except T cell exhaustion. A combination of these biomarkers could reliably predict patient response.¹³

Trained and validated as a predictor of response to anti-PD1 monotherapy treatment, recent studies have compared the predictive power of TIS with that of other biomarkers such as Tumor Mutational Burden (TMB). In a study measuring 4 of the genes from TIS in combination with TMB in IO-treated melanoma patients, the best survival rates in all three treatment groups – nivolumab alone, ipilimumab alone, or the combination – were predicted by the highest TMB and TIS-related gene expression profiling scores; the next best predictor was a high gene expression profile and low TMB score, Figure 4.¹⁴

**Figure 4.** Composite biomarkers, TMB-high and Tumor Inflammation GEP-high scores are best predictors of overall survival for melanoma patients treated with nivolumab, ipilimumab or a combination of both therapies (Figure reproduced with permission from Hodi et al., TMB and Inflammatory Gene Expression Associated with Clinical Outcomes following Immunotherapy in Advanced Melanoma, Cancer Immunol Research, 2021;9(10):1202-1213)

Conclusion

Taken together, tumor inflammation biology represented in the TIS GEP score has been shown to have multiple applications in drug development research – for prospective and retrospective patient stratification and in evaluating IO-therapeutic response. TIS has been shown to be useful in evaluating the effect of chemotherapy and bispecific drug modalities (such as anti-VEGF) in tumors. Other studies highlighted the value of combining the established gene expression profiling biomarker TIS with other gene signatures and multi-omic biomarkers, such as tumor mutational burden (TMB). These studies showed how a multi-variate composite biomarker approach can help improve the accuracy of biomarkers looking to improve the success of not only immunotherapy but other drug modalities. Continued assessment of established biomarkers such as TIS are likely to derive value in the quest for biomarkers of response, uncovering drug mechanism of action, and in evaluating treatment effects both in vitro and ex vivo.

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Accessing TIS for Your Research

As an important predictor of immunotherapy treatment response, TIS can be accessed in several ways for your research. TIS is included in several gene signature panels available for the nCounter® Analysis System. The PanCancer IO 360™ panel includes TIS among its 48 signatures, as do the Breast Cancer 360™, Tumor Signaling 360™, CAR-T Characterization , Immune Exhaustion, and Autoimmune panels. With Panel Plus you can also spike in the TIS signature into another pre-built panel. Panel Plus allows spike in of up to 55 genes. The TIS signature is comprised of 18 genes. You can access TIS signature analysis using NanoString’s Data Analysis Services. Finally several Contract Research Organizations can help you with your TIS and signature studies.

Visit our Gene Expression Profiling Signatures page for more information.

Other Signatures of Interest

References

1
Haddad RI, Seiwert TY, Chow LQM, et al. “Influence of tumor mutational burden, inflammatory gene expression profile, and PD-L1 expression on response to pembrolizumab in head and neck squamous cell carcinoma,” Journal for ImmunoTherapy of Cancer 2022;10 :e003026. doi: 10.1136/jitc-2021-003026
2
Ayers M, Lunceford J, Nebozhyn M, et al. “IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade.” J Clin Invest. 2017;127(8):2930-2940. doi:10.1172/JCI91190
3
Grivas, “ATLAS: A phase II open-label study of rucaparib in patients with locally advanced or metastatic urothelial carcinoma (mUC)”, J Clin Oncol 37, 2019 (suppl 7S; abstr TPS496) DOI: 10.1200/JCO.2019.37.7_suppl.TPS496
4
Cristescu, R et al. “Pan Tumor Genomic Biomarkers for PD-1 Checkpoint Blockade-based Immunotherapy.” Science 362.197 (2018).
5
Warren S, Danaher P, Schatton T. “Pretreatment gene expression correlation with clinical response to pembrolizumab or nivolumab in metastatic melanoma.” Presented at: 2017 Society for Immunotherapy of Cancer Annual Meeting; November, 2017; National Harbor, MD.
6
Ott PA, Bang YJ, Piha-Paul SA, et al. “T-Cell-Inflamed Gene-Expression Profile, Programmed Death Ligand 1 Expression, and Tumor Mutational Burden Predict Efficacy in Patients Treated With Pembrolizumab Across 20 Cancers:” KEYNOTE-028. J Clin Oncol. 2019;37(4):318-327. doi:10.1200/JCO.2018.78.2276Li et al ASCO 2018 #578
7
Damotte D, Warren S, Arrondeau J, et al. “The tumor inflammation signature (TIS) is associated with anti-PD-1 treatment benefit in the CERTIM pan-cancer cohort.” J Transl Med. 2019;17(1):357. doi:10.1186/s12967-019-2100-3
8
Radosevic-Robin N. “Gene expression (GE)-based biomarkers associated with nivolumab response in a real-life cohort of patients with metastatic non-small cell lung cancer (mNSCLC).” Ann Oncol (2018) 29 (suppl_8): viii14-viii57. 10.1093/annonc/mdy269
9
Yap TA, Gainor JF, Burris HA, et al. “Association of an RNA signature (RS) with emergence of ICOS hi CD4 T cells and efficacy outcomes for the ICOS agonist vopratelimab (vopra) and nivolumab (nivo) in patients (pts) on the ICONIC trial.” J Clin Oncol 2020 38:5_suppl, 14-14
10
Johnson ML, Gabrilovich D, Wang F, Gonzalez R, Opyrchal M, Tamang D, Schmidt E, Meyers ML, Ordentlich P, Agarwala SS. “Analysis of biomarkers from a cohort of advanced melanoma patients previously exposed to immune checkpoint inhibition treated with entinostat (ENT) and pembrolizumab (PEMBRO).” Presented at: 2017 Society for Immunotherapy of Cancer Annual Meeting; November, 2017; National Harbor, MD.
11
Warren S, Vallabhaneni P, ElSawada J, White A, Ren X, Cesano A, Beechem J, Tarhini A. “Immunological profiling of baseline and resected biopsies from locally/regionally advanced/recurrent melanoma treated with neoadjuvant combination ipilimumab (3mg/kg or 10mg/kg) and high dose IFN‐α2B.” Presented at: 2017 Society for Immunotherapy of Cancer Annual Meeting; November, 2017; National Harbor, MD.
12
Li X, Warren S, Pelekanou V, et al. “Immune profiling of pre- and post-treatment breast cancer tissues from the S0800 randomized neoadjuvant trial of weekly nab-paclitaxel with or without bevacizumab and dose dense doxorubicin and cyclophosphamide.” J Clin Oncol 36, 2018 (suppl; abstr 578) DOI:10.1200/JCO.2018.36.15_suppl.578
13
Jayakumar Vadakekolathu, Catherine Lai, Stephen Reeder, Sarah E. Church, Tressa Hood, Anbarasu Lourdusamy, Michael P. Rettig, Ibrahim Aldoss, Anjali S. Advani, John Godwin, Matthew J. Wieduwilt, Martha Arellano, John Muth, Tung On Yau, Farhad Ravandi, Kendra Sweet, Heidi Altmann, Gemma A. Foulds, Friedrich Stölzel, Jan Moritz Middeke, Marilena Ciciarello, Antonio Curti, Peter J. M. Valk, Bob Löwenberg, Ivana Gojo, Martin Bornhäuser, John F. DiPersio, Jan K. Davidson-Moncada, Sergio Rutella, “TP53 abnormalities correlate with immune infiltration and associate with response to flotetuzumab immunotherapy in AML.” Blood Adv 2020; 4 (20): 5011–5024. doi: https://doi.org/10.1182/bloodadvances.2020002512
14
Hodi FS, Wolchok JD, Schadendorf D, et al. “TMB and Inflammatory Gene Expression Associated with Clinical Outcomes following Immunotherapy in Advanced Melanoma.” Cancer Immunol Res. 2021;9(10):1202-1213. doi:10.1158/2326-6066.CIR-20-0983
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Danaher, P., Warren, S., Lu, R. et al. “Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA).” j. immunotherapy cancer 6, 63 (2018). https://doi.org/10.1186/s40425-018-0367-1
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O’Donnell PH, Grivas P, Balar AV, et al. “Biomarker findings and mature clinical results from KEYNOTE-052: First-line pembrolizumab (pembro) in cisplatin-ineligible advanced urothelial cancer (UC).” J Clin Oncol 35, 2017 (suppl; abstr 4502) doi:10.1200/JCO.2017.35.15_suppl.4502

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By Espy Anguiano

For research use only. Not for use in diagnostic procedures.

TIS GEP: Use Cases in Sample Stratification, Assessment of Treatment Effect, and as a Component of a Multivariate Biomarker

Introduction to the TIS Gene Expression Profile

TIS Gene Expression Profile Quantifies a Peripherally Suppressed Adaptive Immune Response

TIS Gene Expression Profile Improves Patient Stratification for Immune Checkpoint Inhibitor (ICI) Therapy