Wren Laboratories (United States)

Gastroenteropancreatic neuroendocrine neoplasms (GEP‐NENs) are challenging to diagnose and manage. Because there is a critical need for a reliable biomarker, we previously developed the NETest, a liquid biopsy test that quantifies the expression of 51 neuroendocrine tumor (NET)‐specific genes in blood using real‐time PCR (NETest 1.0). In this study, we have leveraged our well‐established laboratory approach (blood collection, RNA isolation, qPCR) with contemporary supervised machine learning methods and expanded training and testing sets to improve the discrimination and calibration of the NETest algorithm (NETest 2.0). qPCR measurements of RNA‐stabilized blood‐derived gene expression of 51 NET markers were used to train two supervised classifiers. The first classifier trained on 78 Controls and 162 NETs, distinguishing NETs from controls; the second, trained on 134 stable disease samples, 61 progressive disease samples, differentiated stable from progressive NET disease. In all cases, 80% of data was retained for model training, while remaining 20% were used for performance evaluation. The predictive performance of the AI system was assessed using sensitivity, specificity, and Area under Received Operating Characteristic curves (AUROC). The algorithm with the highest performance was retained for validation in two independent validation sets. Validation Cohort #I consisted of 277 patients and 186 healthy controls from the United States, Latin America, Europe, Africa and Asia, while Validation Cohort #II comprised 291 European patients from the Swiss NET Registry. A specificity cohort of 147 gastrointestinal, pancreatic and lung malignancies (non‐NETs) was also evaluated. NETest 2.0 Algorithm #1 (Random Forest/gene expression normalized to ATG4B) achieved an AUROC of 0.91 for distinguishing NETs from controls (Validation Cohort #I), with a sensitivity of 95% and specificity of 81%. In Validation Cohort #II, 92% of NETs with image‐positive disease were detected. The AUROC for differentiating NETs from other malignancies was 0.95; the sensitivity was 92% and specificity 90%. NETest 2.0 Algorithm #2 (Random Forest/gene expression normalized to ALG9) demonstrated an AUROC of 0.81 in Validation Cohort #I and 0.82 in Validation Cohort #II for differentiating stable from progressive disease, with specificities of 81% and 82%, respectively. Model performance was not affected by gender, ethnicity or age. Substantial improvements in performance for both algorithms were identified in head‐to‐head comparisons with NETest 1.0 (diagnostic: p = 1.73 × 10−9; prognostic: p = 1.02 × 10−10). NETest 2.0 exhibited improved diagnostic and prognostic capabilities over NETest 1.0. The assay also demonstrated improved sensitivity for differentiating NETs from other gastrointestinal, pancreatic and lung malignancies. The validation of this tool in geographically diverse cohorts highlights their potential for widespread clinical use.

Abstract Introduction: Prostate cancer is the most frequently diagnosed cancer across 112 countries. Among American men, an estimated 299, 010 new cases and 35,250 deaths are projected for 2024. In this study, the transcriptomic profile of prostate cancer patients from geographically and ethnically diverse cohorts (USA-Caucasian, RSA-Black) were compared to identify gene expression patterns associated with prostate cancer. Methods: Whole blood samples were collected from PCa and controls. Controls had the same age-range as PCa with no clinical indication of prostatic disease and serum PSA levels below 1.5 ng/ml. RNA was isolated, sequencing was carried out on Novaseq 6000, and data analyzed using PartekFlow to identify differential gene expression patterns (RSA-PCa: 6, RSA-Control: 6, USA-PCa: 7, USA-Control: 11). qRT-PCR was undertaken on a cohort of 27 biomarkers (PROSTest) related to PCa in whole blood. Results: Differential expression analysis identified distinct patterns in USA and RSA groups. In the USA PCa cohort, 2,193 genes were differentially expressed compared to the controls with fold change (FC) >1, p-value (p) <0.05, while in the RSA PCa cohort, 1,627 genes were differentially expressed compared to the controls. Pathway analyses identified enrichment for immune responsive genes in the RSA cohorts. We identified that 26 of 27 Wren’s PROSTest genes detectable in both groups. RNAseq and normalized PCR gene expression were well- correlated (r=0.44, p=0.0012, n=30 pairs). Conclusion: Based on this RNAseq study, there may be geographic differences related to ethnicity in overall blood RNA gene expression. We specifically identified alterations in immune function gene expression between the USA and RSA. Importantly, PROSTest genes were detected in both populations and exhibited a significant correlation. Development of gene signatures for biomarker discovery should, however, consider specific ethnicities and geographical regions. Citation Format: Srinivas V Koduru, Mark Kidd, Ané Pieters, S E Nagel, Robert P Millar, Abdel B Halim. Transcriptomic analysis of whole blood samples from prostate cancer patients: A comparative study of patients from geographically diverse populations [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B062.

Identification of residual disease after neuroendocrine tumor (NET) resection is critical for management. Post-surgery imaging is insensitive, expensive, and current biomarkers ineffective. We evaluated whether the NETest, a multigene liquid biopsy blood biomarker, correlated with surgical resection and could predict recurrence. Multicenter evaluation of NET resections over 24 months (n = 103): 47 pancreas, 26 small bowel, 26 lung, 2 appendix, 1 duodenum, 1 stomach. Surgery: R0 (83), R1/R2 (20). One millilitre of blood was collected at D0 and posroperative day (POD) 30. Transcript quantification by polymerase chain reaction (normal: ≤20), CgA by NEOLISA (normal ≤108 ng/mL). Standard-of-care (SoC) follow-up costs were calculated and compared to POD30 NETest-stratification approach. Analyses: Wilcoxon-paired test, Chi-square test. NETest: 103 of 103 (100%)-positive, whereas 23 of 103 (22%) were CgA-positive (Chi-square = 78, P < 0.0001).In the R0 group, the NETest decreased 59 ± 28 to 26 ± 23 (P < 0.0001); 36% (30/83) remained elevated. No significant decrease was evident for CgA. In the R1/R2 group the NETest decreased but 100% remained elevated. CgA levels did not decrease.An elevated POD30 NETest was present in R0 and 25 (83%) developed radiological recurrences. Normal score R0 s (n = 53) did not develop recurrence (Chi-square = 56, P < 0.0001). Recurrence prediction was 94% accurate with the NETest. Using the NETest to stratify postoperative imaging resulted in a cost-savings of 42%. NETest diagnosis is more accurate than CgA (100% vs 22%). Surgery significantly decreased NETest. An elevated POD30 NETest predicted recurrence with 94% accuracy and post-surgical POD30 NETest follow-up stratification decreased costs by 42%. CgA had no surgical utility. Further studies would define the accuracy and cost-effectiveness of the NETest in the detection of postoperative recurrent disease.

Gastroenteropancreatic neuroendocrine neoplasia (GEPNEN) comprises clinically as well as prognostically diverse tumour entities often diagnosed at late stage. Current classification provides a uniform terminology and a Ki67-based grading system, thereby facilitating management. Advances in the study of genomic and epigenetic landscapes have amplified knowledge of tumour biology and enhanced identification of prognostic and potentially predictive treatment subgroups. Translation of this genomic and mechanistic biology into advanced GEPNEN management is limited. ‘Targeted’ treatments such as somatostatin analogues, peptide receptor radiotherapy, tyrosine kinase inhibitors and mammalian target of rapamycin inhibitors are treatment options but predictive tools are lacking. The inability to identify clonal heterogeneity and define critical oncoregulatory pathways prior to therapy, restrict therapeutic efficacy as does the inability to monitor disease status in real time. Chemotherapy in the poor prognosis NEN G3 group, though associated with acceptable response rates, only leads to short-term tumour control and their molecular biology requires delineation to provide new and more specific treatment options.The future requires an exploration of the NEN tumour genome, its microenvironment and an identification of critical oncologic checkpoints for precise drug targeting. In the advance to personalised medical treatment of patients with GEPNEN, clinical trials need to be based on mechanistic and multidimensional characterisation of each tumour in order to identify the therapeutic agent effective for the individual tumour.This review surveys advances in NEN research and delineates the current status of translation with a view to laying the basis for a genome-based personalised medicine management of advanced GEPNEN.

Abstract Background: Neuroendocrine differentiation and the development of an aggressive phenotype are key features of castration-resistant prostate cancer (CRPC) disease. Current blood-based biomarkers cannot detect these treatment-refractory cancer variants. Aims: Evaluate the utility of the NETest, a blood-based 51-marker gene neuroendocrine detection tool, as a CRPC diagnostic and examine which functional gene clusters (hallmarks) differentiate CRPC from PCa. Methods: NETest gene identification in CRPC: Publicly available CRPC RNAseq dataset (cBIO Portal: dbGap phs000909.v.p1, tissue samples: n=47, including 15 neuroendocrine CRPC). Blood gene expression: Prostate cancers (PCa): (n=50) CRPC, (n=40) hormone-sensitive PCa (n=75), and benign prostatic hyperplasia (BPH) (n=41). NETest assay: PCR (51 genes): Expression normalized and categorized into functional classifiers. 51 markers algorithmically assessed and scored: 0-100. Cut-off 40 for progressive disease. PSA: ECLIA diagnostic assay: cut-off 4ng/L, &amp;gt;10ng/ml=critical value. Statistics: ANOVA, AUROC analyses and sensitivity/specificity metrics. Data is mean±SEM. Results: RNA tissue seq: Captured all 51 NETest genes (100%). Thirty-three (65%) genes were detected as upregulated in CRPC tumors (1.09-1425 fold elevated over normal tissue). Blood-PCR: In CRPC, detected in 49/51 (96%) NETest genes. Gene expression was significantly upregulated (p&amp;lt;0.01) in CRPC vs. PCa for somatostatin receptors (2-fold), inflammasome and fibrosome (1.9-fold), proliferome (1.6-fold) and RAF-RAS signaling (1.4-fold). NETest scores were elevated in CRPC (80±2.4) (ANOVA, p&amp;lt;0.0001) vs. PCa (36±2) and BPH (36±3). The AUC differentiating CRPC from PCa was 0.92 (p&amp;lt;0.0001). The cut-off of 40 imparted diagnostic sensitivities and specificities of 94% and 87%, respectively. PSA: PSA was elevated in CRPC (27±59ng/ml). This was not different to PCa (8.1±8ng/ml, p=0.43) but was increased vs. BPH (6.8±1.2ng/ml, p&amp;lt;0.005). The AUC for CRPC vs. PCA was 0.60 (p=0.10). PSA&amp;gt;10ng/ml occurred in 63% of CRPC, 55% of PCA (p=NS) and 24% of BPH (p&amp;lt;0.05). The AUC for NETest (0.92) was significantly better (p&amp;lt;0.0001) than PSA (0.6). Conclusion: The NETest detects neuroendocrine neoplasia genes in the blood and accurately identifies genomic hallmarks of castration-resistant prostate cancer. It performs significantly better than PSA. This multi-gene NETest liquid biopsy is likely to have clinical utility in the diagnosis of CRPC and provide a basis to stratify patients for CRPC-based therapies. Citation Format: Kambiz Rahbar, Mark Kidd, Ignat Drozdov, Alexandra Kitz, Anna Malczewska, Lisa Bodei, Pawel Rajwa, Christof Bernemann, Irvin Modlin. Diagnostic utility of the NETest in neuroendocrine transformed prostate cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1999.

18 Background: There are a paucity of blood-based biomarkers with clinical utility for colorectal cancer (CRC) We report the use of a 13-gene colon cancer circulating-free mRNA to diagnose CRC and identify disease burden and status Clinical utility of the assay was assessed in surgical and chemotherapy patients. Methods: Gene identification and validation: Publicly available colon cancer transcriptomes (TTs) (E-MTAB-57); gene expression in 3 CRC cell-lines (LOVO, LS-180 and COLO320DM) and validated in CRC tumors ( n=33). Tumor TT analysis: co-expression network generation and differential expression analysis compared with normal blood-based TT to identify candidate markers. Blood gene expression: CRC set (cancers: n=312, controls n=117) and a CRC artificial intelligence model constructed. Normalized gene expression algorithmically scored (0-100). Matched tumor/ blood samples were available in 33 patients. RECIST criteria Clinical score assessment: Score utility was assessed in surgical and treated cohorts: Surgical: n=37, follow&gt;7 days. Treated chemotherapy: n=75; stable disease (SD): n=20, progressive disease (PD): n=55) The relationship to CEA and CA-19-9 were assessed. Statistics: Non-parametric (Mann-Whitney), Pearson-correlation, Fisher’s and AUROC analyses (Mean±SEM). Results: Transcriptomic analysis identified a13 genes blood signature for CRC. Expression levels were significantly elevated ( p&lt;0.001, 20-100-fold) in cell lines and CRC tumors. The matched tissue/blood correlation r: 0.795 ( p=0.002). In CRC, levels were 54.4±1.5 ( p&lt;0.0001) compared to controls (9.5±1.7); AUROC: 0.91±0.02, accuracy 90.5% (sensitivity 93.1%, specificity 81.2%). Surgical cohort: CRC assay accuracy 100% vs CEA (35%) or CA-19-9 (17%) (both p&lt;0.0001). Resection (R0 – 92%) significantly decreased levels (47±2) at follow-up ( p=0.0055). Treated cohort: Levels were elevated in PD (63±4.1) vs SD (30±3.2, p&lt;0.0001). Conclusions: The diagnostic accuracy of CRC blood-signature was 91%; significantly greater than CEA or CA-19 Surgical resection decreased levels. CRC score was elevated in progressive vs stable disease. A colorectal cancer liquid biopsy has probable clinical utility in management.

The review summarizes the utility and limitations of chromogranin A (CgA) as a circulating biomarker for neuroendocrine tumors (NETs). Blood CgA measurement has numerous clinical limitations including poor assay reproducibility, low sensitivity (meta-analysis: 73%, 95% confidence interval: 0.71-0.76), and a paucity of prospective validation studies. A recent study noted elevation in 27% of NETs with a predictive value of 50% for metastases. These findings are consistent with its efficacy primarily as a monoanalyte secretory rather than multidimensional neoplastic marker. An automated CgA assay (KRYPTOR) exhibits similar metrics to the DAKO assay but is only useful in serum and routine storage diminishes its accuracy. Current studies indicate that CgA is more effective as a biomarker for cardiac disease. Given the diverse limitations of CgA, NET biomarker focus has evolved toward measurement of multiple analytes, for example, transcripts. Multianalyte algorithmic analyses perform significantly better as diagnostic (>95%) and prognostic markers (>90%) than CgA (30-74 and ∼50%, respectively) since they delineate different aspects of the biological behavior of NETs, (e.g., proliferome and metabolome). CgA is neither a reliable nor robust NET biomarker. As a monoanalyte, it is restricted by poor metrics and has limited predictive value. Its current clinical utility appears optimal in cardiovascular disease. The significance of CgA in NET disease is diminishing as other analytical approaches, particularly transcript multianalyte assays or other strategies, evolve to supersede it.