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Genomic analysis of paired tumor–normal samples and clinical data can be used to match patients to cancer therapies or clinical trials. We analyzed 500 patient samples across diverse tumor types using the Tempus xT platform by DNA-seq, RNA-seq and immunological biomarkers. The use of a tumor and germline dataset led to substantial improvements in mutation identification and a reduction in false-positive rates. RNA-seq enhanced gene fusion detection and cancer type classifications. With DNA-seq alone, 29.6% of patients matched to precision therapies supported by high levels of evidence or by well-powered studies. This proportion increased to 43.4% with the addition of RNA-seq and immunotherapy biomarker results. Combining these data with clinical criteria, 76.8% of patients were matched to at least one relevant clinical trial on the basis of biomarkers measured by the xT assay. These results indicate that extensive molecular profiling combined with clinical data identifies personalized therapies and clinical trials for a large proportion of patients with cancer and that paired tumor–normal plus transcriptome sequencing outperforms tumor-only DNA panel testing. Genomic profiling of cancer samples yields more therapeutic options by including germline, RNA-seq and immunotherapy biomarkers.

BackgroundSolid tumors comprise >90% of cancers. Metastatic colorectal cancer, non-small cell lung cancer, and pancreatic cancer are among the leading causes of cancer-related mortality (5-year overall survival: 14%, 6%, and 3%, respectively).1Chimeric antigen receptor (CAR) T-cell therapy demonstrated clinical outcomes in hematologic malignancies.2 3 However, translating engineered T-cell therapies to solid tumors proves difficult due to a lack of tumor-specific targets that discriminate cancer cells from normal cells. In previous studies, the use of a carcinoembryonic antigen T-cell receptors and mesothelin CARs both resulted in dose-limiting on-target, off-tumor toxicities.4 5 TmodTM CAR T-cell therapy addresses these challenges by leveraging dual receptors to create a robust AND NOT signal integrator capable of killing tumor cells, while leaving healthy cells intact (figure 1).6 Tmod platform technology is a versatile system that may be applied to T cells and natural killer cells in autologous and allogeneic settings.HLA LOH offers a definitive tumor versus normal discriminator target for CAR T-cell therapy.6 7 The 2 receptors comprise an activator that recognizes an antigen present on the surface of normal and tumor cells and a blocker that recognizes a second surface antigen from an allele lost only in tumor cells. HLA LOH has been observed in ~13% across all solid tumors and up to 33% of pancreatic cancers.8 New technologies have shown higher HLA LOH rates; however, it is unclear whether patients with HLA LOH in their primary tumor tissues are at higher risk for recurrence. BASECAMP-1 is an observational study with key objectives: 1) To determine and identify patients with somatic HLA LOH eligible for Tmod CAR T-cell therapy, and 2) Subsequent leukapheresis and manufacturing feasibility for future Tmod CAR T-cell trials.MethodsBASECAMP-1 (NCT04981119) patient eligibility has 2 parts (figure 2): 1) Patients will be initially screened to identify germline HLA-A*02 heterozygosity by central next-generation sequencing (NGS). If HLA-A*02 heterozygosity is confirmed, primary archival tumor tissue will be analyzed by xT-Onco NGS testing9 to determine if somatic tumor HLA-A*02 LOH is present; 2) If the tumor demonstrates HLA-A*02 LOH and the patient screens eligible, the patient will undergo leukapheresis. Patients enrolled in the study who undergo leukapheresis will be evaluated for safety 7 days post-leukapheresis and followed for relapsed status. Banked T cells will be available for subsequent autologous Tmod CAR T-cell therapy at the time of relapse.Abstract 491 Figure 1Illustration of the Tmod T cell engaging with tumor cells with somatic loss of HLA-A*02 and with normal cellsAbstract 491 Figure 2Study schema. HLA, human leukocyte antigen; LOH, loss of heterozygosity; NGS, next generation sequencingTrial RegistrationNCT04981119ReferencesAmerican Cancer Society. Cancer Facts & Figures 2021. Atlanta: American Cancer Society; 2021.Neelapu S, Locke F, Bartlett N, et al. Axicabtagene ciloleucel CAR T-cell therapy in refractory large B-cell lymphoma. N Engl J Med 2017;377(26):2531–2544.Maude S, Laetsch T, Buechner J, et al. Tisagenlecleucel in children and young adults with B-cell lymphoblastic leukemia. N Engl J Med 2018;378(5):439–448.Parkhurst M, Yang J, Langan R, et al. T cells targeting carcinoembryonic antigen can mediate regression of metastatic colorectal cancer but induce severe transient colitis. Mol Ther 2011;19(3):620–626.Haas AR, Tanyi JL, O’Hara MH, et al. Phase I study of lentiviral-transduced chimeric antigen receptor-modified T cells recognizing mesothelin in advanced solid cancers. Mol Ther. 2019;27(11):1919–1929.Hamburger A, DiAndreth B, Cui J, et al. Engineered T cells directed at tumors with defined allelic loss. Mol Immunol 2020;128:298–310.Hwang M, Mog B, Douglass J, et al. Targeting loss of heterozygosity for cancer-specific immunotherapy. Proc Natl Acad Sci U S A 2021;118(12):e2022410118.The Cancer Genome Atlas (TCGA) Research Network. https://www.cancer.gov/tcga. Accessed June 2021.Perera J, Mapes B, Lau D, et al. Detection of human leukocyte antigen class I loss of heterozygosity in solid tumor types by next-generation DNA sequencing. J Immunother Cancer 2019;7(suppl 1):103.

Background: Oncogenic cancers, such as human papillomavirus (HPV) and Epstein Barr virus (EBV), account for 10 to 12% of all cancers. Knowing the viral status of a tumor is essential as it may change patients’ treatment options. Numerous clinical trials investigating the benefit of radiation or chemotherapy dose reduction for HPV positive head and neck cancers have shown promising results. Additionally, virus-associated tumors are more likely to present higher levels of inflammation and immune infiltration, which make them good candidates for immunotherapy. We developed a novel viral detection algorithm using the patient’s tumor whole transcriptome profiles to predict viral status. As RNA-sequencing of clinical samples becomes increasingly common, the use of gene expression data for viral status and insights about the tumor immune microenvironment may impact clinical treatment decisions for a larger number of patients. Methods: To develop RNA based classifiers of viral status from tumor transcriptomes, we used the TCGA cervical cancer, head and neck cancer, and gastric cancer cohorts with known viral statuses. To identify predictive genes, we performed a 10-fold cross validation on the TCGA cohorts and trained a Logistic Regression with a L1 regularisation. Since L1 regularization leads to sparse coefficients, only a small subset of genes had non-zero coefficients at each split. Only the genes with non-zero coefficients in more than 80% of the splits were included in the final model. Using this subset of genes, we again trained Logistic Regression classifiers with a L1 regularisation on the TCGA dataset and validated our models on Tempus cohorts. Results: Our model can predict the HPV subtype with a 99% specificity and 99% sensitivity, and the EBV subtype with a 99% specificity and 100% sensitivity in the TCGA cohorts. In our Tempus validation cohorts, we report 96% specificity and a 88% sensitivity for HPV, and a 97% specificity and a 100% sensitivity for EBV. Subsequent analysis of whole transcriptome data showed a significant increase of the interferon gamma signature, the cytolytic index and the immunotherapy target IDO1 in EBV positive patients in both cohorts. We observed the same pattern in the HPV cohorts, though the increase was not significant in the Tempus cohorts due to the small sample size. Conclusions: Our models accurately predicts viral infection in tumors using RNA expression data. We confirm viral infections are generally associated with an upregulation of immune responses. Viral detection based on whole transcriptome data could become a useful clinical tool in combination with existing methods, providing insights about the viral status and tumor microenvironment with one test. Citation Format: Ariane Lozac’hmeur, Denise Lau, Aly Khan. Predicting oncogenic viral status using RNA signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 135.