Abstract Breakthroughs in targeted KRAS therapeutics (KRASi) have the potential to transform the treatment landscape for several of the most common cancers including lung, colorectal, and pancreatic. Despite the recent approvals of KRASi and the anticipation of more to come, both the rate of patient response and the durability of these responses remain significant areas requiring improvement. Biomarkers that can predict response to KRASi and guide effective patient selection and drug combination strategies will be key to realizing the full potential of this emerging therapeutic field. While most biomarkers predominantly rely on a single analyte (e.g. KRAS mutation status), Genialis’ biomarkers are constructed using high-dimensional and/or multimodal data that capture the underlying biological complexity unique to each individual patient. Genialis' ResponderID™ is a machine learning-based biomarker discovery framework that models fundamental aspects of cancer biology to predict the clinical benefit based on the patient’s own biology. Here we report progress towards the development of a first-in-class, RNA-based biomarker, ResponderID™ KRAS, capable of stratifying KRAS G12C inhibitor response in lung cancer patients using RNA sequencing data. Trained on thousands of lung cancer samples, our biomarker models therapeutic response by unifying two core KRAS biologic axes, dependency and activation, to identify those patients most likely to respond. The performance characteristics of ResponderID™ KRAS thus far has been evaluated on a real world dataset of lung cancer patients treated with Sotorasib. ResponderID™ KRAS serves as an independent biomarker designed to inform clinical trial design, select for therapeutic efficacy, identify rational combination strategies, and expedite approvals across various therapeutic contexts. Citation Format: Josh Wheeler, Anže Lovše, Klemen Žiberna, Miha Štajdohar, Luka Ausec, Janez Kokošar, Daniel Pointing, Aditya Pai, Rafael Rosengarten, Mark Uhlik. ResponderID™ KRAS: Biology-driven machine learning to personalize KRAS inhibitor therapeutics [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 6446.

Abstract Introduction: Immune checkpoint inhibitors (ICI) are an important therapeutic option for patients with triple negative breast cancer (TNBC). However, identification of patients most likely to respond is challenging. PD-L1 positivity by immunohistochemistry is the standard biomarker used for ICI therapy selection in TNBC. However, other biomarkers, such as analysis of the tumor microenvironment (TME) may be more accurate in predicting response. The XernaTM TME Panel uses RNA sequencing data and machine learning to analyze the TME, utilizing the angiogenic and immunogenic biology of the TME to classify tumors into four TME subtypes. In this study, the distribution of Xerna TME subtypes and associated genomic alterations in TNBC were investigated for their potential use in therapy selection. Methods: A total of 203 TNBC patient samples underwent tumor-normal whole-exome, whole-transcriptome sequencing testing with the OncoExTraTM assay. The whole-transcriptome expression data were analyzed using the Xerna TME Panel to assign each sample to one of four subtypes: Immune Active (IA), Immune Suppressed (IS), Immune Desert (ID) and Angiogenic (A). The IA and IS subtypes both have high immune scores that may be particularly sensitive to ICI therapy. Actionable alterations, defined as those with FDA-approved matched therapies in any cancer, with matched clinical trials, or with evidence in cancer guidelines or the literature for possible matched therapies, were also identified and associations across Xerna subtypes were explored. Results: Approximately half (100 of 203; 49.3%) of the patient samples had high (IA+IS) immune subtypes (Table 1). Targetable alterations associated with an FDA-approved therapy were present in 114 (56.2%) patients. No biomarkers were significantly associated (p < 0.05) with high (IA+IS) versus low (ID+A) immune scores. Biomarkers associated with ICI response, namely mismatch repair gene alterations (MSH2/3/6, MLH1/3, PMS1/2), high tumor mutational burden (TMB-high) and microsatellite instability were detected in only 6 (3.0%), 3 (1.5%) and 1 (0.5%) patient samples respectively, and all but 1, an MSH6 alteration, were in high immune subtype samples. Conclusions: The Xerna TME Panel classified 49.3% of TNBC patient tumors to IA or IS, suggesting they may respond to ICI therapy. Many (56.2%) patient tumors harbored alterations associated with FDA-approved therapies, providing the potential for novel combination therapies. These findings warrant further study and clinical validation in TNBC patients treated with ICI therapy. Table 1. Frequency of actionable biomarkers that were present in at least 10 (5%) TNBC patient samples. Citation Format: Gargi Basu, Janine Lobello, Snehal Thakkar, Jessica Aldrich, Matthew Halbert, Patrick Eimerman, Cynthia Flannery, Nishitha Therala, David Hall, Daniel Pointing, Lea Vohar, Roman Luštrik, Luka Ausec, Mark Uhlik, Seema Iyer, Laura Benjamin, Frederick Baehner. Prevalence of genomic alterations in Xerna tumor microenvironment subtypes in triple negative breast cancer patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-06-10.

Background In advanced colorectal cancer (CRC), analysis of the tumor microenvironment (TME) may be useful as a predictive biomarker, particularly supporting the use of immunotherapies and anti-angiogenic therapies.1 The XernaTM TME Panel utilizes RNA sequencing data and machine learning to analyze the angiogenic and immunogenic biology of the TME to classify tumors into four TME subtypes.2 In this study, we investigated the distribution of Xerna TME subtypes and associated genomic alterations in CRC for their potential use in therapy selection. Methods A total of 336 CRC patient samples underwent testing with the OncoExTraTM assay. This assay utilizes whole-exome, whole-transcriptome sequencing to identify actionable alterations, defined as those with FDA-approved matched therapies in any cancer, with matched clinical trials, or with evidence in cancer guidelines or the literature for possible matched therapies. The whole-transcriptome expression data were analyzed using the Xerna TME Panel to assign each sample to one of four subtypes: Immune Active (IA), Immune Suppressed (IS), Immune Desert (ID) and Angiogenic (A). Biomarker associations were explored. Results Approximately half (49.4%) of the patient samples had high (IA+IS) versus low (ID+A) immune subtypes, and 247 (73.5%) harbored targetable alterations associated with an FDA-approved therapy. Several biomarkers were significantly associated (p<0.05) with Xerna subtypes, most of which were over-represented in high immune subtypes (19 of 21), with 13 indicative of defective DNA repair (table 1). Microsatellite instability (MSI-high) and high tumor mutational burden (TMB-high) were detected in 30 (8.9%) and 37 (11.0%) patient samples, with 28 (16.9%) and 33 (19.9%) occurring within high immune subtypes (IA+IS), respectively. Some MSI-high and TMB-high samples occurred in low immune subtypes (ID+A), perhaps indicating a lower propensity for response to ICI therapy. Of note, 138 of 306 (45.1%) MSI-low and 133 of 299 (44.5%) TMB-low samples were in the high immune subtypes, suggestive of possible sensitivity to ICI therapy. Actionable KRAS/NRAS, and BRAF alterations were detected in 162 (48.2%) and 23 (6.8%) patients respectively, though none were significantly associated with TME subtypes. Conclusions The Xerna TME Panel classified 49.4% of CRC patients to IA or IS subtypes who may benefit from ICI therapy, including many lacking biomarkers currently used for this therapy decision. Most (73.5%) patients harbored alterations associated with FDA-approved therapies, providing the potential for novel combination therapies.3 These findings warrant further study and clinical validation in CRC patients treated with ICI therapy. References Huyghe N, Benidovskaya E, Stevens P, Van den Eynde M. Biomarkers of Response and Resistance to Immunotherapy in Microsatellite Stable Colorectal Cancer: Toward a New Personalized Medicine. Cancers (Basel). 2022 Apr 29;14(9):2241. doi: 10.3390/cancers14092241. PMID: 35565369; PMCID: PMC9105843. Uhlik M, Pointing D, Iyer S, Ausec L, Štajdohar M, Cvitkovič R, Žganec M, Culm K, Santos VC, Pytowski B, Malafa M, Liu H, Krieg AM, Lee J, Rosengarten R, Benjamin L. Xerna™ TME Panel is a machine learning-based transcriptomic biomarker designed to predict therapeutic response in multiple cancers. Front Oncol. 2023 May 12;13:1158345. doi: 10.3389/fonc.2023.1158345. PMID: 37251949; PMCID: PMC10213262. Yang Z, Wu G, Zhang X, Gao J, Meng C, Liu Y, Wei Q, Sun L, Wei P, Bai Z, Yao H, Zhang Z. Current progress and future perspectives of neoadjuvant anti-PD-1/PD-L1 therapy for colorectal cancer. Front Immunol. 2022 Sep 9;13:1001444. doi: 10.3389/fimmu.2022.1001444. PMID: 36159842; PMCID: PMC9501688. Ethics Approval The study was approved by WCG IRB Ethics Board, approval number 20181863.

Background: Few therapeutic options exist for anti-PD-1 refractory, metastatic melanoma patients, and today’s biomarkers are insufficient to aid in defining who should receive potential combinatorial immunotherapies. Results from a phase Ib, multicenter study (NCT02680184) showed that a combination of vidutolimod and pembrolizumab provided a best overall response of 23.5% in patients with metastatic or unresectable cutaneous melanoma who had received prior anti-PD-1 therapy. The Xerna™ TME Panel consists of an artificial neural net that utilizes the expression of ~100 genes involved in angiogenesis and tumor immune biologies to classify patient samples into one of four tumor microenvironment (TME) biomarker subtypes: Angiogenesis (A), Immune Active (IA), Immune Desert (ID), or Immune Suppressed (IS). We hypothesized that the IS subtype is predictive of vidutolimod + pembrolizumab benefit in this cohort compared to the other TME subtypes (A, IA, and ID). Methods: Total RNASeq was performed on FFPE biopsies collected from a subset of patients prior to therapy (N=38) and 2 weeks post-initiation of therapy (N=10). Gene expression data was analyzed using the Xerna TME Panel algorithm to assign a TME subtype. Correlational analyses between TME subtypes, response to therapy, and other hallmark gene signatures were performed. Results: Overall response rate in the pretreatment cohort available for biomarker analysis was 26%, comparable to the entire vidutolimod/pembrolizumab arm. The cohort had a skewed distribution of TME subtypes with high prevalence of IS (34%) and ID (45%), indicative of immune therapy-refractory biologies. An overall response of 54% was observed in the IS subtype, compared with 12% in the other subtypes combined. Comparison with other “hallmark” gene signatures confirmed enrichment of immune and angiogenesis biologies in the IS subtype, but none of these individual hallmark signatures were found to differentiate between responders and non-responders. The Xerna TME Panel demonstrated superior classification performance across all criteria compared to a baseline classifier, including accuracy (0.76 vs. 0.62), sensitivity (0.70 vs. 0.27) and specificity (0.79 vs. 0.74). Among matched post-treatment samples, 70% revealed a change in TME subtype compared to their pre-treatment status. Three of the post-treatment samples represented changes from an immune-low subtype to an immune-high subtype, including one complete responder with a pre-treatment ID that changed to IA while on therapy, illustrating how the TME Panel may be used to interpret the mechanism of drug response. Conclusions: The Xerna TME Panel shows potential activity as a predictive and pharmacodynamic biomarker for the combination of vidutolimod and pembrolizumab in anti-PD-1 refractory melanoma patients. Citation Format: Mark T. Uhlik, Daniel Pointing, Luka Ausec, Miha Stajdohar, Robert Cvitkovic, Matjaz Zganec, Seema Iyer, Hong Liu, Art Krieg, Laura Benjamin. The XernaTM TME Panel potentially predicts response to a combination of the TLR9 agonist vidutolimod and PD-1 inhibitor pembrolizumab in metastatic melanomas with prior anti-PD-1 treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3270.

IntroductionMost predictive biomarkers approved for clinical use measure single analytes such as genetic alteration or protein overexpression. We developed and validated a novel biomarker with the aim of achieving broad clinical utility. The Xerna™ TME Panel is a pan-tumor, RNA expression-based classifier, designed to predict response to multiple tumor microenvironment (TME)-targeted therapies, including immunotherapies and anti-angiogenic agents.MethodsThe Panel algorithm is an artificial neural network (ANN) trained with an input signature of 124 genes that was optimized across various solid tumors. From the 298-patient training data, the model learned to discriminate four TME subtypes: Angiogenic (A), Immune Active (IA), Immune Desert (ID), and Immune Suppressed (IS). The final classifier was evaluated in four independent clinical cohorts to test whether TME subtype could predict response to anti-angiogenic agents and immunotherapies across gastric, ovarian, and melanoma datasets.ResultsThe TME subtypes represent stromal phenotypes defined by angiogenesis and immune biological axes. The model yields clear boundaries between biomarker-positive and -negative and showed 1.6-to-7-fold enrichment of clinical benefit for multiple therapeutic hypotheses. The Panel performed better across all criteria compared to a null model for gastric and ovarian anti-angiogenic datasets. It also outperformed PD-L1 combined positive score (>1) in accuracy, specificity, and positive predictive value (PPV), and microsatellite-instability high (MSI-H) in sensitivity and negative predictive value (NPV) for the gastric immunotherapy cohort.DiscussionThe TME Panel’s strong performance on diverse datasets suggests it may be amenable for use as a clinical diagnostic for varied cancer types and therapeutic modalities.

Background Despite immune checkpoint inhibitor (ICI) monotherapy approvals in NSCLC, SOC predominately utilizes combinations of ICI with non-targeted chemotherapy or precision therapies targeting oncogenic drivers. Biomarkers guiding these clinical decisions rely on tumor genotyping to identify actionable mutations, tumor mutational burden (TMB) and on immunohistochemistry for PD-L1 expression. Currently, neither PD-L1 nor TMB perform adequately for ICI patient selection.1 Emerging evidence indicates a more complete profile of the tumor microenvironment (TME) may improve selection of patients likely to respond to ICI.2 The Xerna machine learning-based RNA sequencing biomarker assay classifies tumors into four TME subtypes; Immune Active (IA), Immune Suppressed (IS), Immune Desert (ID) and Angiogenic (A) . This classification identifies tumors likely to benefit from ICI (IA and IS) or anti-angiogenic agents (ID and A).3 We examined the distribution of actionable oncogenic driver mutations across Xerna TME subtypes to investigate the potential use for therapy selection. Methods Biomarker prevalence, and Xerna TME subtype classification, were determined for 104 metastatic lung cancer cases previously analyzed using the OncomapTM ExTra test, tumor-normal whole-exome and whole-transcriptome sequencing. DNA variants and high TMB (≥10 mut/Mb) were identified from DNA sequencing, and RNA expression levels were used to assign tumors to Xerna subtypes. Biomarker and associations were compared using Fisher's Exact Test. The study was approved by WCG IRB Ethics Board, approval number 20181863. Results In total, 53% of cases had high (IA+IS) vs. low (ID+A) Xerna immune subtypes and 60% harbored targetable oncogenic driver mutations (table 1). Actionable EGFR and KRAS mutations were detected in 31% and 20% of cases respectively, while high TMB was detected in 27% of cases. High TMB was significantly higher in IA (62%) vs. IS (14%) or A (13%) categories (p<0.05). Although no significant associations between Xerna subtype and oncogenic drivers were observed, EGFR mutations were least frequent in IA tumors (15%) while 33% of the IS subtype contained KRAS mutations (10% G12C). Conclusions The Xerna TME panel identified a high prevalence of patients who may benefit from ICI (IA+IS) and harbored actionable oncogenic drivers. Within this group, the prevalence of targetable oncogenic drivers within the IS phenotype, such as KRAS G12C, may represent the potential for novel ICI combination therapies.4 These findings further highlight the importance of adding TME analysis to comprehensive biomarker testing in NSCLC References Steuer CE, Ramalingam SS. Advances in Immunotherapy and Implications for Current Practice in Non-Small-Cell Lung Cancer. JCO Oncol Pract. 2021 Nov;17(11):662–668. doi: 10.1200/OP.21.00305. Epub 2021 Jun 25. Erratum in: JCO Oncol Pract. 2022 Mar;18(3):244. Horvath L, Thienpont B, Zhao L, Wolf D, Pircher A. Overcoming immunotherapy resistance in non-small cell lung cancer (NSCLC) – novel approaches and future outlook. Mol Cancer. 2020 Sep 11;19(1):141. Iyer, S., Ausec, L., Pointing, D., Zganec, M., Cvitkovic, R., Stajdohar, M., ... & Uhlik, M. T. (2022). Xerna? TME Panel: A pan-cancer RNA-based investigational assay designed to predict patient responses to angiogenic and immune targeted therapies.? Cancer Research,? 82(12_Supplement), 1232–1232. Mugarza E, van Maldegem F, Boumelha J, Moore C, Rana S, Llorian Sopena M, East P, Ambler R, Anastasiou P, Romero-Clavijo P, Valand K, Cole M, Molina-Arcas M, Downward J. Therapeutic KRASG12C?inhibition drives effective interferon-mediated antitumor immunity in immunogenic lung cancers. Sci Adv. 2022 Jul 22;8(29) Ethics Approval The study was approved by WCG IRB Ethics Board, approval number 20181863.

While numerous anti-angiogenic and immune targeting therapies have become standard-of-care treatments for oncology, predictive biomarkers for these agents have been either entirely lacking or challenged by inconsistencies across indications. We have developed and validated the Xerna TME Panel as a novel machine learning-based RNA-sequencing biomarker assay that guides patient selection for tumor microenvironment (TME)-targeted therapies across multiple tumor types. Gene expression data sets from both public sources and clinical practice representing over 5000 samples across 7 different tumor types were analyzed using the Xerna TME Panel. The Xerna TME Panel consists of an artificial neural net that learns complex gene expression interactions between angiogenesis and tumor immune biologies and robustly classifies patient samples into one of four TME biomarker subtypes: Angiogenesis (A), Immune Suppressed (IS), Immune Active (IA), or Immune Desert (ID). The vast majority (>75%) of all samples were assigned a TME class designation with confidence scores in the upper quartile and had nearly bimodal distributions for biomarker-positive versus -negative classifications. When compared to other independent gene signatures, such as those describing angiogenesis/mesenchymal biology, inflammation, and immune suppression, the expression profiles from the Xerna TME subtypes showed enrichment of those biological processes. Each TME subtype represented between ~15-40% of subjects of each tumor type, indicating balanced representation of subgroups within the patient populations. The Xerna TME designations were prognostic across tumor types, with “A” tumors generally associated with the worst survival and “IA” tumors associated with the best survival. The predictive ability of the Xerna TME Panel to enrich for tumor responses to targeted therapies in gastric cancer was also evaluated. In a ramucirumab+paclitaxel clinical cohort, the Xerna TME Panel high Angiogenesis score tumors (A and IS) demonstrated a 48% response rate compared to a 31% for low Angiogenesis score tumors (IA and ID). In an immune checkpoint inhibitor (ICI) cohort, high Immune score tumors (IA and IS) showed a response rate of 34% vs. 5% for low Immune score tumors (A and ID). Within the microsatellite stable patients (MSS), which historically have low response rates to ICIs, the Xerna TME Panel was able to enrich for responses between Immune high vs. Immune low score patients (25% vs. 3%). Currently in use to prospectively enroll patients into a Phase 3 ovarian cancer clinical trial and in development as a companion diagnostic (CDx) assay, the Xerna TME Panel is a robust, pan-cancer biomarker assay capable of characterizing TME dominant biologies to further advance the matching of patients with targeted therapeutics. Citation Format: Seema Iyer, Luka Ausec, Daniel Pointing, Matjaz Zganec, Robert Cvitkovic, Miha Stajdohar, Valerie Chamberlain Santos, Kerry Culm, Mokenge Malafa, Jeeyun Lee, Rafael Rosengarten, Laura Benjamin, Mark T. Uhlik. Xerna࣪ TME Panel: A pan-cancer RNA-based investigational assay designed to predict patient responses to angiogenic and immune targeted therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1232.