Development of a multigenomic liquid biopsy (PROSTest) for prostate cancer in whole blood

INTRODUCTION

We describe the development of a molecular assay from publicly available tumor tissue mRNA databases using machine learning and present preliminary evidence of functionality as a diagnostic and monitoring tool for prostate cancer (PCa) in whole blood.

MATERIALS AND METHODS

We assessed 1055 PCas (public microarray data sets) to identify putative mRNA biomarkers. Specificity was confirmed against 32 different solid and hematological cancers from The Cancer Genome Atlas (n = 10,990). This defined a 27-gene panel which was validated by qPCR in 50 histologically confirmed PCa surgical specimens and matched blood. An ensemble classifier (Random Forest, Support Vector Machines, XGBoost) was trained in age-matched PCas (n = 294), and in 72 controls and 64 BPH. Classifier performance was validated in two independent sets (n = 263 PCas; n = 99 controls). We assessed the panel as a postoperative disease monitor in a radical prostatectomy cohort (RPC: n = 47).

RESULTS

A PCa-specific 27-gene panel was identified. Matched blood and tumor gene expression levels were concordant (r = 0.72, p < 0.0001). The ensemble classifier ("PROSTest") was scaled 0%-100% and the industry-standard operating point of ≥50% used to define a PCa. Using this, the PROSTest exhibited an 85% sensitivity and 95% specificity for PCa versus controls. In two independent sets, the metrics were 92%-95% sensitivity and 100% specificity. In the RPCs (n = 47), PROSTest scores decreased from 72% ± 7% to 33% ± 16% (p < 0.0001, Mann-Whitney test). PROSTest was 26% ± 8% in 37 with normal postoperative PSA levels (<0.1 ng/mL). In 10 with elevated postoperative PSA, PROSTest was 60% ± 4%.

CONCLUSION

A 27-gene whole blood signature for PCa is concordant with tissue mRNA levels. Measuring blood expression provides a minimally invasive genomic tool that may facilitate prostate cancer management.

Development and validation of a multigenomic liquid biopsy (PROSTest) for prostate cancer detection

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Background: A crucial requirement in prostate cancer (PCa) management is an accurate, easily measurable, liquid biopsy that can define the molecular pathology of an individual PCa. We report the development and clinical validation of a novel PCa-specific, multi-genomic biomarker. Methods: We identified candidate mRNA biomarkers in PCa-Adeno transcriptomes ( n=1,159) using several strategies: co-expression networks, differential expression, and functional enrichment. mRNA transcripts were screened in an independent tumor tissue ( n=50) set and validated as biomarkers in the TCGA-PRAD ( n=500) dataset. An amalgam of Random Forest, Gradient Boosted Machines and Support Vector Machines, all standard machine learning classifiers, was used to develop a classification algorithm and probability score in a peripheral blood gene expression test cohort ( n=430). This multigene biomarker was validated in two independent clinical blood sample sets (Set I: PCa n=77, controls n=54; Set II: PCa n=132, controls/BPH n=99) to determine as PCa-specificity and diagnostic efficacy Clinical utility was evaluated versus Gleason scores, T-staging and PSA ( n=209) and in a prostatectomy cohort ( n=47). Results: The pipeline identified 27 of PCa gene markers in the tumor tissue set and TCGA-PRAD dataset. Gene expression was significantly correlated ( r=0.72, p<0.0001) in matched tissue/blood samples. The PROSTest (scale: 0-100) ensemble algorithm (developed in blood) had a sensitivity for PCa of 92.2% (95% CI: 83.8-97.1%; Set I) and 95.0% (95% CI: 89.9-98%. Set II). The specificity was 100% for Set I (95% CI: 93.4-100%) and 100% for Set II (95% CI: 96.3-100%). PCa scores were significantly ( p<0.0001) lower for controls (Set I: 17±4; Set II: 18±4) and BPH (19±6) to PCa; 82±19 (Set I) and 80±19 (Set II). The AUROC was 0.98±0.01. PROSTest scores were elevated ( p<0.05) in T2-4 and were significantly correlated with Gleason ( r=0.93, p<0.02). In contrast, PSA from matched samples was not associated ( p=NS) with clinically significant disease (Gleason 7-10 or T2-4 tumors). In head-to-head comparisons, the PROSTest was considerably more accurate than PSA for detecting significant disease (z-statistic: 2.43, p=0.015). In the R0 prostatectomy cohort, all scores were elevated (72±7) and significantly decreased post-surgery (26±8, p<0.0001, n=37). Individuals with residual disease ( n=10) exhibited elevated (60±4) post-surgical scores. Conclusions: The PROSTEst is a multigenomic blood-based PCR tool that accurately (>90%) identifies prostate cancer. It is significantly more accurate than PSA for the detection and stratification of clinically significant prostate disease. A multigenomic liquid biopsy for PCA provides a real-time, non-invasive method for detection of a PCa and may facilitate the early identification of residual/recurrent disease.

Early Identification of Residual Disease After Neuroendocrine Tumor Resection Using a Liquid Biopsy Multigenomic mRNA Signature (NETest)

INTRODUCTION

Surgery is the only cure for neuroendocrine tumors (NETs), with R0 resection being critical for successful tumor removal. Early detection of residual disease is key for optimal management, but both imaging and current biomarkers are ineffective post-surgery. NETest, a multigene blood biomarker, identifies NETs with >90% accuracy. We hypothesized that surgery would decrease NETest levels and that elevated scores post-surgery would predict recurrence.

METHODS

This was a multicenter evaluation of surgically treated primary NETs (n = 153). Blood sampling was performed at day 0 and postoperative day (POD) 30. Follow-up included computed tomography/magnetic resonance imaging (CT/MRI), and messenger RNA (mRNA) quantification was performed by polymerase chain reaction (PCR; NETest score: 0-100; normal ≤20). Statistical analyses were performed using the Mann-Whitney U-test, Chi-square test, Kaplan-Meier survival, and area under the receiver operating characteristic curve (AUROC), as appropriate. Data are presented as mean ± standard deviation.

RESULTS

The NET cohort (n = 153) included 57 patients with pancreatic cancer, 62 patients with small bowel cancer, 27 patients with lung cancer, 4 patients with duodenal cancer, and 3 patients with gastric cancer, while the surgical cohort comprised patients with R0 (n = 102) and R1 and R2 (n = 51) resection. The mean follow-up time was 14 months (range 3-68). The NETest was positive in 153/153 (100%) samples preoperatively (mean levels of 68 ± 28). In the R0 cohort, POD30 levels decreased from 62 ± 28 to 22 ± 20 (p < 0.0001), but remained elevated in 30% (31/102) of patients: 28% lung, 29% pancreas, 27% small bowel, and 33% gastric. By 18 months, 25/31 (81%) patients with a POD30 NETest >20 had image-identifiable recurrence. An NETest score of >20 predicted recurrence with 100% sensitivity and correlated with residual disease (Chi-square 17.1, p < 0.0001). AUROC analysis identified an AUC of 0.97 (p < 0.0001) for recurrence-prediction. In the R1 (n = 29) and R2 (n = 22) cohorts, the score decreased (R1: 74 ± 28 to 45 ± 24, p = 0.0012; R2: 72 ± 24 to 60 ± 28, p = non-significant). At POD30, 100% of NETest scores were elevated despite surgery (p < 0.0001).

CONCLUSION

The preoperative NETest accurately identified all NETs (100%). All resections decreased NETest levels and a POD30 NETest score >20 predicted radiologically recurrent disease with 94% accuracy and 100% sensitivity. R0 resection appears to be ineffective in approximately 30% of patients. NET mRNA blood levels provide early objective genomic identification of residual disease and may facilitate management.

The utility of blood-based molecular tools-the NETest-to monitor and evaluate the efficacy of PRRT in neuroendocrine tumors

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Background: Peptide receptor radionuclide therapy (PRRT) is an effective therapy for metastatic/inoperable neuroendocrine tumors (NETs). Tools to predict and monitor the efficacy of therapy are important adjuncts in the radio-oncology armamentarium. Standard blood biomarkers are not effective by new molecular based assays such as the PRRT Predictive Quotient (PPQ) and NETest are effective as real-time predictors and monitors of therapy. We aimed to prospectively evaluate whether: 1) the NETest functioned as a surrogate biomarker for image-based per RECIST evaluation of PRRT efficacy; 2) there was a correlation between changes in NETest levels during therapy, PPQ prediction and treatment efficacy. Methods: Three independent 177Lu-PRRT-treated GEP-NET and BPNEN cohorts (Rotterdam, Netherlands: n= 41; Bad-Berka, Germany: n= 44; Meldola, Italy: n= 72). Treatment response: RECIST1.1 [Responder (stable, partial/complete response) vs Non-Responder]. Blood sampling: pre-PRRT, prior to each cycle and 6 months (median) after completion of all cycles. PPQ (positive/negative) and NETest (0-100 score) by PCR. Stable<40; progressive > 40). CgA (ELISA) as comparator. Samples deidentified, measurement and analyses blinded. Kaplan-Meier survival and Mann-Whitney analyses. Results: 122 of 157 were evaluable. RECIST stabilization or response in 67%; 33% progressed. NETest significantly ( p< 0.0001) decreased in RECIST-“responders” (-47±3%); in “non-responders” it elevated (+79±19%, p< 0.0005). NETest monitoring accuracy 98% (119/122). Follow-up levels > 40 (progressive) vs stable (<40) significantly correlated with mPFS (not reached vs. 10 months; HR 0.04, 95%CI: 0.02-0.07). PPQ response prediction was accurate in 118 (97%); 99% accurate positive and 93% accurate negative prediction. NETest significantly ( p< 0.0001) decreased in PPQ-predicted responders (-46±3%) and remained increased in PPQ-predicted non-responders (+75±19%). Follow-up NETest categories stable vs progressive significantly correlated with PPQ prediction and mPFS (not reached vs. 10 months; HR 0.06, 95%CI: 0.03-0.12). In comparison, the standard biomarker, CgA, failed to predict or correlate with response to PRRT ( p= NS). Conclusions: NETest accurately (98%) monitors PRRT response and is an effective surrogate marker for radiological response (image concordance 98%). A NETest decrease identified responders (99%) and correlated ( > 97%) with the pretreatment PPQ response predictor.

A multi-gene colorectal cancer liquid biopsy with >90% accuracy in diagnosis and assessment of disease status

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Background: There are few blood-based biomarkers for colorectal cancer (CRC). We report a 13-gene colon cancer circulating-free mRNA to diagnose CRC. Clinical utility was assessed in surgical and chemotherapy patients. Methods: Gene identification/validation: Publicly available colon cancer transcriptomes (E-MTAB-57) to identify candidate markers using gene co-expression network enrichment, differential expression and functional enrichment analyses. Cell line/tumor tissue gene expression: Candidate gene expression was evaluated in 3 CRC cell-lines (LOVO, LS-180 and COLO320DM) and surgical resection samples (CRC adenocarcinomas: n= 33) and biomarkers validated in the TCGA-COAD database ( n= 261 adenocarcinomas, 41 mucinous adenocarcinomas). 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>7 days. 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: Thirteen candidate CRC genes blood were identified. Cell lines and tumor tissue: Expression levels were significantly elevated ( p< 0.001, 20-100-fold) in cell lines and CRC tumors. All 13 markers were confirmed in TCGA-COAD samples (average TPM ranged from 692-4405). Blood gene expression: All 13 CRC marker genes were identified in CRC blood. Levels were 54.4±1.5 ( p< 0.0001) compared to controls (9.5±1.7); AUROC:0.91±0.02, accuracy 90.5% (sensitivity 93.1%, specificity 81.2%). The matched tissue/blood correlation was r = 0.80 ( p= 0.002). Clinical Utility: In the surgical cohort, accuracy was 100% vs CEA (35%) or CA-19-9 (17%) (both p< 0.0001). Resection (R0–92%) significantly decreased levels (47±2) at follow-up ( p< 0.006). In the treated cohort, levels were elevated in PD (63±4.1) vs SD (30±3.2, p< 0.0001). Conclusions: A CRC gene marker panel in blood identified colon cancer with a diagnostic accuracy of 91%. This was significantly greater than CEA or CA-19 in CRCs. Surgical resection decreased levels. CRC score was elevated in progressive vs stable disease. A colorectal cancer liquid biopsy will facilitate image-based management.

A genomic blood test (NETest) identifies neuroendocrine transformation of prostate cancer

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Background: Neuroendocrine-like differentiation (NELD) and an aggressive phenotype are key features of castration-resistant prostate cancer (CRPC). Current blood-based biomarkers cannot detect these treatment-refractory variants. Our aim was to evaluate the NETest, a blood-based 51-marker gene neuroendocrine detection tool, as a CRPC-diagnostic versus prostate cancer (PCA). Methods: In silico evaluation: NETest gene identification in the TCGA-PRAD ( n= 500 PCA) and CRPC RNAseq datasets (cBIOPortal: dbGap-phs000909.v.p1, tissue samples: n= 47, including 15 CRPC). Blood gene expression: PCA: n= 50, CRPC: n= 40, hormone-sensitive PCA: n= 75 and benign prostatic hyperplasia (BPH: n= 41). NETest assay: Normalized gene expression, algorithmically assessed and scored: 0-100. Cut-off 20. PSA: ECLIA diagnostic assay: cut-off 4ng/L, > 10ng/ml = actionable value. Statistics: ANOVA, AUROC analyses and sensitivity/specificity metrics. Data is mean±SEM. Results: RNAseq: Two (4%) of the 51 NETest genes were identified in TCGA-PCA. In contrast, all 51 NETest genes (100%) were identified in CRPC tumors. Thirty-three (65%) were detected as upregulated (1.09-1425-fold vs. normal tissue). Blood-PCR: 49/51 (96%) NETest genes detected in CRPC blood. NELD-gene expression was significantly upregulated ( > 2-fold, p< 0.01) in CRPC vs. PCA ( TPH1, PNMA2, SSTR etc). NETest scores were elevated in CRPC (79±2.8) (ANOVA, p< 0.0001) vs. PCA (22±2) and BPH (23±3). The AUC differentiating CRPC from PCA was 0.93 ( p< 0.0001). NETest was elevated in 94% of CRPC vs. 13% PCA and 15% BPH (both p< 0.001). The diagnostic sensitivities and specificities were 94% and 87%, respectively. PSA: PSA was elevated in CRPC (220±372ng/ml). This was different to PCA (14±20ng/ml, p< 0.0001) and BPH (10.3±5.7ng/ml, p< 0.003). The AUC for CRPC vs. PCA/BPH was 0.70 ( p= 0.10). PSA > 10ng/ml occurred in 70% of CRPC, 60% of PCA ( p= NS) and 39% of BPH ( p< 0.05). The AUC for NETest (0.93) was significantly better than PSA (z-statistic: 4.63, p< 0.0001). Conclusions: The NETest is a liquid biopsy that detects neuroendocrine neoplasia genes in the blood and accurately identifies NELD in castration-resistant prostate cancer. We anticipate that the NETest could be used to provide real-time information relevant to the evolving neuroendocrine status of a PCA during therapy.

A blood-based multi-mRNA liquid biopsy with >90% accuracy for diagnosis and assessment of prostate cancers

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Background: There are a paucity of blood-based biomarkers with clinical utility for prostate cancer (PCa). We developed a circulating mRNA (27-gene) prostate cancer signature to diagnose and manage PCa. Methods: Gene identification: Publicly available PCa transcriptome sets ( n= 1,159 samples) were evaluated and compared with normal blood-based transcriptomes using gene co-expression network enrichment, differential expression and functional enrichment analyses to identify candidate markers. Gene expression evaluation: Seven PCA cell lines and two normal prostate epithelial lines were used to assess candidate genes. Marker genes were determined in PCa tumor tissue ( n= 50) and validated in the TCGA-PRAD ( n= 500) dataset. Blood gene expression: Set #I: PCA: n= 132, BPH: n= 44, controls n= 55. Set #II: n= 50 (biochemical recurrence [BCR]). We constructed an artificial intelligence PCa model using classification algorithm analyses. Scoring: normalized algorithmically analyzed gene expression (0 to 100), positive score >20. PSA: BPH ( n= 44) and PCa ( n= 132). Clinical score assessment: Surgical cohort: ( n= 47), samples: pre-surgical and post: 1 week - 14 months. Statistics: Kruskal-Wallis, Pearson-correlation, Fisher’s and AUROC analyses (Mean±SEM). Results: Transcriptomic analysis identified 27 candidates. Cell lines/tissue: Expression levels were significantly elevated ( p< 0.001, 2.1-35.8-fold) in cell lines and PCa surgical samples. All 27 markers were confirmed in TCGA-PRAD samples (average TPM: 58 to 10,366). Blood: In Set#I, levels in PCa were 47±2 ( p< 0.0001) compared to BPH (19±1) and controls (18±0.5); AUROC: 0.92 (BPH) and 0.94 (controls), with an accuracy of 85-88%, a sensitivity of 86% and specificities 82 and 93%. For PSA, the AUROC (PCa vs. BPH) was 0.51 ( p= 0.88). PSA was positive in 86% of BPH and was > 10ng/ml in 30%. PSA was positive in 83% of PCa and > 10ng/ml in 40% (Fisher’s p= 0.28). PSA accuracy ( > 10ng/ml) was 48%. Levels in Set#II (BCR) were 44±3. ProstaTest-was positive in 48 (96%). Surgical cohort ( n= 47): Prostatest accuracy 100% pre-surgery. Resection decreased levels (KW-statistic: 57.4, p< 0.0001) from 52±1 to 23.5±2. Conclusions: A 27-gene blood signature was developed for PCa that exhibited a diagnostic accuracy of 92%; significantly better than PSA (48%, p< 0.0001). Surgical resection significantly ( p< 0.0001) decreased levels. Biochemical recurrence was accurately detected (96%). A multi-gene prostate cancer liquid biopsy is likely to have clinical utility in both diagnosis and monitoring of PCa.

A 13-gene colorectal cancer liquid biopsy with greater than 90 percent accuracy in diagnosis and assessment of disease status

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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>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<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<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<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<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.

PRRT neuroendocrine tumor response assessment using blood transcript analysis: The NETest

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Background: Peptide receptor radionuclide therapy (PRRT) is effective for metastatic/inoperable neuroendocrine tumors (NETs). Imaging response assessment is most effective after treatment completion. Blood biomarkers such as PRRT Predictive Quotient (PPQ) and NETest are effective in real-time. PPQ predicts PRRT efficacy, NETest monitors disease. We prospectively evaluated: 1) NETest as a surrogate biomarker for RECIST; 2) Correlation of NETest levels, PPQ prediction and treatment efficacy. Methods: Three independent 177Lu-PRRT-treated GEP-NET and BPNEN cohorts (Rotterdam, Netherlands: n=41; Bad-Berka, Germany: n=44; Meldola, Italy: n=72). Treatment response: RECIST1.1 [Responder (stable, partial/complete response) vs Non-Responder]. Blood sampling: pre-PRRT, prior to each cycle and 6 months (median) after completion of all cycles. PPQ (positive/negative) and NETest (0-100 score) by PCR. Stable<40; progressive >40). CgA (ELISA) as comparator. Samples deidentified, measurement and analyses blinded. Kaplan-Meier survival and Mann-Whitney analyses. Results: 122 of 157 were evaluable. RECIST stabilization or response in 67%; 33% progressed. NETest significantly ( p<0.0001) decreased in RECIST-“responders” (-47±3%); in “non-responders” it elevated (+79±19%, p<0.0005). NETest monitoring accuracy 98% (119/122). Follow-up levels >40 (progressive) vs stable (<40) significantly correlated with mPFS (not reached vs. 10 months; HR 0.04, 95%CI: 0.02-0.07). PPQ response prediction was accurate in 118 (97%); 99% accurate positive and 93% accurate negative prediction. NETest significantly ( p<0.0001) decreased in PPQ-predicted responders (-46±3%) and remained increased in PPQ-predicted non-responders (+75±19%). Follow-up NETest categories stable vs progressive significantly correlated with PPQ prediction and mPFS (not reached vs. 10 months; HR 0.06, 95%CI: 0.03-0.12). CgA failed to identify response to PRRT ( p=NS). Conclusions: NETest accurately (98%) monitors PRRT response and is an effective surrogate marker for radiological response (image concordance 98%). A NETest decrease identified responders and correlated (>97%) with the pretreatment PPQ response predictor.

Blood Chromogranin A Is Not Effective as a Biomarker for Diagnosis or Management of Bronchopulmonary Neuroendocrine Tumors/Neoplasms

BACKGROUND

Identification of circulating tumor markers for clinical management in bronchopulmonary (BP) neuroendocrine tumors/neoplasms (NET/NEN) is of considerable clinical interest. Chromogranin A (CgA), a "universal" NET biomarker, is considered controversial as a circulating biomarker of BPNEN.

AIM

Assess utility of CgA in the diagnosis and management of BPNEN in a multicentric study.

MATERIAL AND METHODS

CgA diagnostic metrics were assessed in lung NET/NENs (n = 200) and controls (n = 140), randomly assigned to a Training and Test set (100 BPC and 70 controls in each). Assay specificity was evaluated in neoplastic lung disease (n = 137) and nonneoplastic lung disease (n = 77). CgA efficacy in predicting clinical status was evaluated in the combined set of 200 NET/NENs. CgA levels in bronchopulmonary neuroendocrine tumor (BPNET) subtypes (atypical [AC] vs. typical [TC]) and grade was examined. The clinical utility of an alteration of CgA levels (±25%) was evaluated in a subset of 49 BPNET over 12 months. CgA measurement was by NEOLISATM kit (EuroDiagnostica).

RESULTS

Sensitivity and specificity in the training set were 41/98%, respectively. Test set data were 42/87%. Training set area under receiver operator characteristic analysis differentiated BPC from control area under the curve (AUC) 0.61 ± 0.05 p = 0.015. Test set the data were AUC 0.58 ± 0.05, p = 0.076. In the combined set (n = 200), 67% BPNET/NEN (n = 134) had normal CgA levels. CgA levels did not distinguish histological subtypes (TC vs. AC, AUC 0.56 ± 0.04, p = 0.21), grade (p = 0.45-0.72), or progressive from stable disease (AUC 0.53 ± 0.05 p = 0.47). There was no correlation of CgA with Ki-67 index (Pearson r = 0.143, p = 0.14). For nonneoplastic diseases (chronic obstructive pulmonary disorder and idiopathic pulmonary fibrosis), CgA was elevated in 26-37%. For neoplastic disease (NSCLC, squamous cell carcinoma), CgA was elevated in 11-16%. The neuroendocrine SCLC also exhibited elevated CgA (50%). Elevated CgA was not useful for differentiating BPNET/NEN from these other pathologies. Monitoring BPNET/NEN over a 12-month period identified neither CgA levels per se nor changes in CgA were reflective of somatostatin analog treatment outcome/efficacy or the natural history of the disease (progression).

CONCLUSIONS

Blood CgA levels are not clinically useful as a biomarker for lung BPNET/NEN. The low specificity and elevations in both nonneoplastic as well as other common neoplastic lung diseases identified limited clinical utility for this biomarker.

PRRT neuroendocrine tumor response monitored using circulating transcript analysis: the NETest

PURPOSE

Peptide receptor radionuclide therapy (PRRT) is effective for metastatic/inoperable neuroendocrine tumors (NETs). Imaging response assessment is usually efficient subsequent to treatment completion. Blood biomarkers such as PRRT Predictive Quotient (PPQ) and NETest are effective in real-time. PPQ predicts PRRT efficacy; NETest monitors disease. We prospectively evaluated: (1) NETest as a surrogate biomarker for RECIST; (2) the correlation of NETest levels with PPQ prediction.

METHODS

Three independent ¹⁷⁷Lu-PRRT-treated GEP-NET and lung cohorts (Meldola, Italy: n = 72; Bad-Berka, Germany: n = 44; Rotterdam, Netherlands: n = 41). Treatment response: RECIST1.1 (responder (stable, partial, and complete response) vs non-responder). Blood sampling: pre-PRRT, before each cycle and follow-up (2-12 months). PPQ (positive/negative) and NETest (0-100 score) by PCR. Stable < 40; progressive > 40). CgA (ELISA) as comparator. Samples de-identified, measurement and analyses blinded. Kaplan-Meier survival and standard statistics.

RESULTS

One hundred twenty-two of the 157 were evaluable. RECIST stabilization or response in 67%; 33% progressed. NETest significantly (p < 0.0001) decreased in RECIST "responders" (- 47 ± 3%); in "non-responders," it remained increased (+ 79 ± 19%) (p < 0.0005). NETest monitoring accuracy was 98% (119/122). Follow-up levels > 40 (progressive) vs stable (< 40) significantly correlated with mPFS (not reached vs. 10 months; HR 0.04 (95%CI, 0.02-0.07). PPQ response prediction was accurate in 118 (97%) with a 99% accurate positive and 93% accurate negative prediction. NETest significantly (p < 0.0001) decreased in PPQ-predicted responders (- 46 ± 3%) and remained elevated or increased in PPQ-predicted non-responders (+ 75 ± 19%). Follow-up NETest categories stable vs progressive significantly correlated with PPQ prediction and mPFS (not reached vs. 10 months; HR 0.06 (95%CI, 0.03-0.12). CgA did not reflect PRRT treatment: in RECIST responders decrease in 38% and in non-responders 56% (p = NS).

CONCLUSIONS

PPQ predicts PRRT response in 97%. NETest accurately monitors PRRT response and is an effective surrogate marker of PRRT radiological response. NETest decrease identified responders and correlated (> 97%) with the pretreatment PPQ response predictor. CgA was non-informative.

Utility of a ready-to-use PCR system for neuroendocrine tumor diagnosis

BACKGROUND

Multigene-based PCR tests are time-consuming and limiting aspects of the protocol include increased risk of operator-based variation. In addition, such protocols are complex to transfer and reproduce between laboratories.

AIMS

Evaluate the clinical utility of a pre-spotted PCR plate (PSP) for a novel multigene (n = 51) blood-based gene expression diagnostic assay for neuroendocrine tumors (NETs).

METHODS

A pilot study (n = 44; 8 controls and 36 NETs) was undertaken to compare CQ, normalized gene expression and algorithm-based output (NETest score). Gene expression was then evaluated between matched blood:tumor tissue samples (n = 7). Thereafter, two prospective sets (diagnostic: n = 167; clinical validation: n = 48, respectively) were evaluated for diagnostic and clinical utility value. Two independent molecular diagnostics facilities were used to assess assay reproducibility and inter-laboratory metrics. Samples were collected (per CLIA protocol) processed to mRNA and cDNA and then either run per standard assay (liquid primers) or on PSPs. Separately, matching plasma samples were analyzed for chromogranin A (CgA). Statistics included non-parametric testing, Pearson-concordance, Predictive Modeling and AUROC analyses.

RESULTS

In the pilot study (n = 44), CQ values were highly concordant (r: 0.82, p<0.0001) and normalized gene expression data significantly related (p<0.0001) (Pearson-pairwise correlation). NETest values were not different (49.7±33 standard vs. 48.5±31.5 PSP) and the overall concordance in output 96%. Predictive modelling confirmed this concordance (F1 score = 0.95). Gene expression levels were highly correlated between blood and tumor tissue (R: 0.71-0.83). In the diagnostic cohort (n = 30 controls, n = 87 non-NET controls, n = 50 NET), NETest was significantly lower (p<0.0001) in controls (11±6.5) and non-NET controls (13±18) than NETs (61±31). The AUROCs were 0.93-0.97 and the diagnostic accuracy was 90-97.5%. As a diagnostic, the PSP-NETest was significantly better than CgA (accuracy: 56%, p<0.0001). For clinical samples, the PSP generated robust and accurate (>96%) scores and was significantly better (p<0.0001) than CgA. The assay protocol was consistent (r: 0.97) and reproducible (co-efficient of variation: 1.3-4.2%) across the two facilities.

CONCLUSION

The PSP protocol for the NETest has been established and prospectively tested in clinical samples. It is highly reproducible, has similar metrics (CV, categorization by control or NET) to the standard PCR assay and generates clinically concordant (>96%) NETest results. Moreover, it functions significantly more accurately than CgA.

Molecular strategies in the management of bronchopulmonary and thymic neuroendocrine neoplasms

Thoracic NETs [bronchopulmonary NETs (BPNETs) and thymic NETs (TNET)] share a common anatomic primary location, likely a common cell of origin, the "Kulchitsky cell" and presumably, a common etiopathogenesis. Although they are similarly grouped into well-differentiated [typical carcinoids (TC) and atypical carcinoids (AC)] and poorly differentiated neoplasms and both express somatostatin receptors, they exhibit a wide variation in clinical behavior. TNETs are more aggressive, are frequently metastatic, and have a lower 5-year survival rate (~50% vs. ~80%) than BPNETs. They are typically symptomatic, most often secreting ACTH (40% of tumors) but both tumor groups share secretion of common biomarkers including chromogranin A and 5-HIAA. Consistently effective and accurate circulating biomarkers are, however, currently unavailable. Surgery is the primary therapeutic tool for both BPNET and TNETs but there remains little consensus about later interventions e.g., targeted therapy, or how these can be monitored. Genetic analyses have identified different topographies (e.g., significant alterations in chromatin and epigenetic remodeling in BPNETs versus frequent chromosomal abnormalities in TNETs) but there is an absence of clinically actionable mutations in both tumor groups. Liquid biopsies, tools that can measure neoplastic signatures in peripheral blood, can potentially be leveraged to detect disease early i.e., recurrence, predict tumors that may respond to specific therapies and serve as real-time monitors for treatment responses. Recent studies have identified that mRNA transcript analysis in blood effectively identifies both BPNET and TNETs. The clinical utility of this gene expression assay includes use as a diagnostic, confirmation of completeness of surgical resection and use as a molecular management tool to monitor efficacy of PRRT and other therapeutic strategies.

Experimental substantiation for the use of mexidol and 3-hydroxypyridine fumarate in chronic myocardial injury

We studied the cardioprotective properties of mexidol and 3-hydroxypyridine fumarate in rat model of chronic myocardial injury. We found that 3-hydroxypyridine fumarate (25 mg/kg) produced more pronounced antioxidant and cardioprotective effects than mexidol (25 mg/kg).