3047 Background: Expressed RNA can capture mutations, changes in expression levels due to methylation, and provide information on cell of origin, growth, and proliferation status. We developed an approach to isolate fragmented RNA from peripheral blood plasma and explored its potential to be used in liquid biopsy. Methods: Peripheral blood cfRNA was extracted from patients with neoplasms in B-cell (#105), T-cell (#16), Myeloid (#73), and from solid tumors (#44), Normal individuals (#51), and reactive post-transplant (#137). RNA was sequenced using a 1459-gene panel. Expression profile was generated using Cufflinks. Results: cfRNA levels of various solid tumor biomarkers (CA-125, CA-15-3, CEA 8, Keratin19, Keratin6A...) were significantly higher (P < 0.0001) in samples from solid tumors as compared with normal control. Similarly, cfRNA lymphoid markers (CD19, CD22, CD79A, and CD79B...) and cfRNA myeloid markers (CD33, CD14, CD117, CD56...) were all higher in B-cell lymphoid neoplasms and myeloid neoplasms, respectively (P < 0.0001), as compared with control. In evaluating the host immune system, cfRNA CD4:CD8B and CD3D:CD19 ratios in normal controls were as expected (median: 5.92 and 6.87, respectively) and were significantly lower in solid tumors (median 3.40 and 2.23, respectively, P < 0.0002). Solid tumor cfRNA showed CTLA4:CD8B ratio significantly higher in tumors than in normal (median 0.74 vs 0.19, P = 0.0001), while there was no difference in cfRNA PD-L1:CD8B ratio (median 1.45 vs 1.77, P = 0.96). Similar distinct patterns are noted for various cytokine and chemokines. cfRNA was highly predictive of diagnosis (AUC > 0.98) of solid tumors, B-cell lymphoid neoplasms, T-cell lymphoid neoplasms, and myeloid neoplasms as compared with normal control. When a specific neoplastic disease was considered against all cases including control and other neoplasms, the AUC varied between 0.77 and 0.949. Conclusions: This data shows that liquid biopsy using targeted sequencing of cfRNA in patients with various types of cancer provides comprehensive and reliable information on the neoplastic disease as well as the host. [Table: see text]
Background: Human epidermal growth factor receptor-2 (HER2) and hormone receptors are typically used as binary biomarkers for selecting breast cancer therapy. There is a need to explore the clinical relevance of these biomarkers as continuous variables. This is particularly relevant for the new class of antibody-drug conjugates (ADC), in which a relatively low HER2 expression level is adequate for targeting tumor cells. We explored the potential of RNA profiling, determined by next generation sequencing (NGS), to provide more flexible clinical biomarkers as compared with immunohistochemistry (IHC) or fluorescent in situ hybridization (FISH). Methods: Clinical and laboratory data from 57 breast cancers collected by the COTA real-world data company were used to study biomarker levels as detected by routine clinical transcriptomic tests. HER2 (ERBB2), estrogen receptor alpha (ESR1), and androgen receptor (AR) mRNA levels were compared with reported HER2 and estrogen receptor (ER) IHC and FISH results. Results: RNA levels accurately reflected and predicted HER2 amplification (see table below). Importantly, RNA data showed significant variation and overlap in the levels of ERBB2 mRNA between cases scored by IHC as zero, 1+, and 2+. This variation correlated with progression-free survival (PFS). Similarly, the ESR1 RNA levels accurately reflected ER status and demonstrated significant variation between positive cases. RNA data also showed significant variations in the AR levels. Patients wssith high AR mRNA levels had significantly better PFS (P=0.05). Patients expressing high ER and AR levels had significantly better PFS than those expressing low ESR1 and AR levels (P=0.03). Conclusions: These findings suggest that RNA analysis using NGS is an alternative to IHC and FISH. RNA provides continuous data that can determine cut-off points predicting response to therapy and should be explored in predicting ADC response. ERBB2 mRNA levels (FPKM) in various HER2 IHC groupss IHC Score Valid N Mean Median Minimum Maximum Quartile Range Std.Dev. Zero 19 231 243 63 537 170 110 Zero vs one 3 151 155 45 253 208 104 Zero to one vs one 18 397 302 172 845 365 222 One Vs two 13 495 423 159 1094 273 284 Two vs three 4 7523 7649 937 13859 7183 5310 Citation Format: Maher Albitar, Andre Goy, Andrew Pecora, Deena Graham, Donna Donna McNamara, Ahmad Ahmad Charifa, Andrew IP, Wanlong Ma, Stanley Waintraub. Real-world transcriptomic biomarkers as replacement for immunohistochemistry and FISH studies in breast cancer [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 967.
PD-L1 immunohistochemistry (IHC) is routinely used to predict the clinical response to immune checkpoint inhibitors (ICIs); however, multiple assays and antibodies have been used. This study aimed to evaluate the potential of targeted transcriptome and artificial intelligence (AI) to determine PD-L1 RNA expression levels and predict the ICI response compared to traditional IHC. RNA from 396 solid tumors samples was sequenced using next-generation sequencing (NGS) with a targeted 1408-gene panel. RNA expression and PD-L1 IHC were assessed across a broad range of PD-L1 expression levels. The geometric mean naïve Bayesian (GMNB) classifier was used to predict the PD-L1 status. PD-L1 RNA levels assessed by NGS demonstrated robust linearity across high and low expression ranges, and those assessed using NGS and IHC (Tumor cells (TC), Tumor Proportion Score (TPS) and tumor-infiltrating immune cells (ICs) were highly correlated (Tables 1). RNA sequencing provided in-depth information on the tumor microenvironment and immune response, including CD19, CD22, CD8A, CTLA4, and PD-L2 expression status. Sub-analyses showed a sustained correlation of mRNA expression with IHC (TPS and ICs) across different solid tumor types. Machine learning showed high accuracy in predicting PD-L1 status, with the area under the curve varying between 0.988 and 0.920. Targeted transcriptome sequencing combined with AI is highly useful for predicting PD-L1 status. Measuring PD-L1 mRNA expression by NGS is comparable to measuring PD-L1 expression by IHC for predicting ICI response. RNA expression has the added advantages of being amenable to standardization and avoidance of interpretation bias, along with an in-depth evaluation of the tumor microenvironment. Correlation between PD-L1 expression levels and PD-L1 IHC results IHC test results Variable Cases (N) Mean Median Range Lower Quartile Upper Quartile 10th Percentile 90th Percentile Std. Dev. TC<1% CD274 223.00 4.49 2.97 0.00 - 25.99 1.79 5.73 1.07 9.16 4.32 TC>1% CD274 90.00 14.87 10.11 0.62 - 77.53 4.60 18.62 2.95 32.35 15.75 TC<10% CD274 267.00 5.17 3.36 0.00 - 72.72 1.94 6.43 1.14 10.71 6.14 TC>10% CD274 46.00 20.81 14.03 2.90 - 77.53 8.67 26.32 4.21 51.02 17.33 IC<1% CD274 133.00 3.95 2.66 0.00 - 25.99 1.71 4.03 0.99 7.95 4.56 IC>1% CD274 129.00 9.38 5.43 0.29 - 77.53 3.21 10.31 1.74 19.05 12.13 IC<10% CD274 226.00 5.69 3.03 0.00 - 72.72 1.92 6.09 1.15 12.25 8.24 IC>10% CD274 36.00 12.50 8.52 0.29 - 77.53 4.72 13.80 4.18 31.83 13.95 TPS<1% CD274 143.00 3.36 2.35 0.00 - 25.99 1.41 3.58 0.53 7.40 3.87 TPS>1% CD274 207.00 10.19 5.63 0.29 - 133.81 3.03 11.65 1.74 22.25 14.74 TPS<10% CD274 252.00 4.25 2.87 0.00 - 72.72 1.74 4.96 0.92 8.70 5.81 TPS>10% CD274 98.00 15.50 9.67 0.29 - 133.81 4.91 18.74 2.90 31.87 18.57 TPS<30% CD274 319.00 5.35 3.32 0.00 - 72.72 1.94 6.43 1.06 12.00 6.48 TPS>30% CD274 31.00 28.50 18.62 2.90 - 133.81 11.47 35.94 6.67 52.69 27.31 Citation Format: Ahmad Charifa, Alfonso Lam, Hong Zhang, Andrew Ip, Andrew Pecora, Stanley Waintraub, Deena Graham, Donna McNamara, Martin Gutierrez, Andrew Jennis, Ipsa Sharma, Jeffrey Estella, Wanlong Ma, Andre Goy, Maher Albitar. Predicting PD-L1 status in solid tumors using transcriptomic data and artificial intelligence algorithms. [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 4337.
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