Clinical Validation Data Research Articles

Association of GATA3 expression in triple-positive breast cancer with overall survival and immune cell infiltration

Breast cancer remains a leading cause of cancer-related mortality among women, with triple-positive breast cancer (TPBC) being a particularly aggressive subtype. GATA binding protein 3 (GATA3) plays a crucial role in the luminal differentiation of breast epithelium and T-cell differentiation. However, the relationship between GATA3 and immune infiltration in TPBC remains unclear. This study collected and analyzed TPBC data from The Cancer Genome Atlas (TCGA), METABRIC, and GSE123845 databases. Univariate and multivariate Cox regression analyses, along with Kaplan–Meier survival analyses, were employed to assess the prognostic value of GATA3 and other clinical features. Subsequently, Gene Set Enrichment Analysis (GSEA) was conducted to explore the potential biological functions and regulatory mechanisms of GATA3 in TPBC. Additionally, ssGSEA analysis revealed the connection between GATA3 and immune infiltration. And the effects of neoadjuvant chemotherapy and immunotherapy on GATA3 expression were also explored. Finally, clinical samples were used to detect the relationship between GATA3 expression and tumor infiltrating lymphocyte (TIL) levels. Our results demonstrated that GATA3 was significantly overexpressed in TPBC tissues compared to normal tissues (P < 0.05). A positive correlation between GATA3 mRNA and protein levels was observed (R = 0.55, P < 0.05). Notably, high GATA3 expression was associated with poor overall survival (HR = 1.24, 95% confidence interval (CI) 1.25–11.76, P < 0.05). GSEA indicated significant enrichment of immune-related gene sets in low GATA3 expression groups. Furthermore, pathologic complete response (pCR) patients exhibited significantly lower GATA3 expression compared to residual disease (RD) patients. Mutation analysis revealed higher PIK3CA and TP53 mutation rates in high GATA3 expression groups. Finally, clinical validation data showed that the degree of TILs was significantly higher in the low GATA3 expression group. In conclusion, this study suggests that high GATA3 expression may be associated with poor prognosis and may reduce immune infiltration in TPBC.

Deep-learning-based sampling position selection on color Doppler sonography images during renal artery ultrasound scanning

Accurate selection of sampling positions is critical in renal artery ultrasound examinations, and the potential of utilizing deep learning (DL) for assisting in this selection has not been previously evaluated. This study aimed to evaluate the effectiveness of DL object detection technology applied to color Doppler sonography (CDS) images in assisting sampling position selection. A total of 2004 patients who underwent renal artery ultrasound examinations were included in the study. CDS images from these patients were categorized into four groups based on the scanning position: abdominal aorta (AO), normal renal artery (NRA), renal artery stenosis (RAS), and intrarenal interlobular artery (IRA). Seven object detection models, including three two-stage models (Faster R-CNN, Cascade R-CNN, and Double Head R-CNN) and four one-stage models (RetinaNet, YOLOv3, FoveaBox, and Deformable DETR), were trained to predict the sampling position, and their predictive accuracies were compared. The Double Head R-CNN model exhibited significantly higher average accuracies on both parameter optimization and validation datasets (89.3 ± 0.6% and 88.5 ± 0.3%, respectively) compared to other methods. On clinical validation data, the predictive accuracies of the Double Head R-CNN model for all four types of images were significantly higher than those of the other methods. The DL object detection model shows promise in assisting inexperienced physicians in improving the accuracy of sampling position selection during renal artery ultrasound examinations.

Developing an AI-assisted digital auscultation tool for automatic assessment of the severity of mitral regurgitation: protocol for a cross-sectional, non-interventional study

IntroductionMitral regurgitation (MR) is the most common valvular heart disorder, with a morbidity rate of 2.5%. While echocardiography is commonly used in assessing MR, it has many limitations, especially for...

Cancer Radiomic and Perfusion Imaging Automated Framework: Validation on Musculoskeletal Tumors.

Limitations from commercial software applications prevent the implementation of a robust and cost-efficient high-throughput cancer imaging radiomic feature extraction and perfusion analysis workflow. This study aimed to develop and validate a cancer research computational solution using open-source software for vendor- and sequence-neutral high-throughput image processing and feature extraction. The Cancer Radiomic and Perfusion Imaging (CARPI) automated framework is a Python-based software application that is vendor- and sequence-neutral. CARPI uses contour files generated using an application of the user's choice and performs automated radiomic feature extraction and perfusion analysis. This workflow solution was validated using two clinical data sets, one consisted of 40 pelvic chondrosarcomas and 42 sacral chordomas with a total of 82 patients, and a second data set consisted of 26 patients with undifferentiated pleomorphic sarcoma (UPS) imaged at multiple points during presurgical treatment. Three hundred sixteen volumetric contour files were processed using CARPI. The application automatically extracted 107 radiomic features from multiple magnetic resonance imaging sequences and seven semiquantitative perfusion parameters from time-intensity curves. Statistically significant differences (P < .00047) were found in 18 of 107 radiomic features in chordoma versus chondrosarcoma, including six first-order and 12 high-order features. In UPS postradiation, the apparent diffusion coefficient mean increased 41% in good responders (P = .0017), while firstorder_10Percentile (P = .0312) was statistically significant between good and partial/nonresponders. The CARPI processing of two clinical validation data sets confirmed the software application's ability to differentiate between different types of tumors and help predict patient response to treatment on the basis of radiomic features. Benchmark comparison with five similar open-source solutions demonstrated the advantages of CARPI in the automated perfusion feature extraction, relational database generation, and graphic report export features, although lacking a user-friendly graphical user interface and predictive model building.

Analytical and clinical validation of oncomine Dx target (ODxT) test and local testing for identifying patients with HER2 (ERBB2)-mutant (HER2m) non–small-cell lung cancer (NSCLC) for treatment with trastuzumab deruxtecan (T-DXd) in DESTINY-Lung01/02 (DL-01/02).

9033 Background: Based on the international DL-01 trial, the US Food and Drug Administration (FDA) approved the ODxT Test as a companion diagnostic (CDx) to identify patients (pts) with NSCLC harboring activating HER2 mutations who may benefit from T-DXd. The ODxT Test uses next-generation sequencing (NGS) to detect genomic alterations in formalin-fixed, paraffin-embedded tumor tissue. The accuracy of the ODxT Test by central testing and its agreement with clinical trial assays (CTAs; NGS, polymerase chain reaction, etc.) by local testing was evaluated using analytical and clinical validation data from DL-01 cohort 2 and DL-02 arm 1. Methods: ODxT Test and Illumina TruSight Tumor170 NGS assay results were compared for analytical accuracy in NSCLC samples from DL-01 and commercial vendors. Diagnostic agreement was assessed for DL-01/02 by concordance between the ODxT Test and CTAs. Clinical accuracy per objective response rate (ORR) and duration of response (DoR) to T-DXd were evaluated in all pts with HER2m NSCLC identified by ODxT Test vs CTA. Activating HER2 mutations included single nucleotide variants and exon 20 insertions. Pts received T-DXd 6.4 mg/kg in DL-01 or 5.4 mg/kg (FDA-approved dose) in DL-02 every 3 weeks. Results: NSCLC samples from DL-01 (n = 91), DL-02 (n = 102), and commercial vendors (n = 129) were individually assessed. The ODxT Test met requirements for analytical accuracy (≥90% agreement when excluding unknown results), with a positive percent agreement (PPA) of 100% and negative percent agreement (NPA) of 99.1% for detecting HER2 mutations. Clinical accuracy of the ODxT Test also met FDA requirements (excluding unknowns) for NPA (≥98%) and PPA (95% CI lower bound ≥85%). DL-01 PPA (95% CI) was 98.0% (89.2%-100%) and NPA was 100.0% (96.6%-100%); for DL-02, PPA was 96.7% (88.7%-99.6%) and NPA was 100% (96.6%-100%). In DL-01, confirmed ORR (95% CI) for pts with HER2m NSCLC identified by central ODxT Test was 58.3% (43.2%-72.4%) vs 54.9% (44.2%-65.4%) of pts identified by local CTA; median DoR (95% CI) was 12.0 mo (5.5 mo-18.2 mo) per the ODxT Test vs 9.3 mo (5.7 mo-14.7 mo) per CTA. In DL-02 (prespecified interim analysis early cohort), ORR (95% CI) was 53.6% (33.9%-72.5%) per ODxT Test vs 53.8% (39.5%-67.8%) per CTA; median DoR (95% CI) was not estimable (NE [NE-NE]) per the ODxT Test and NE (4.2 mo-NE) per CTA. Conclusions: This study demonstrates the analytical and clinical accuracy of the ODxT Test as a CDx for detecting activating HER2 mutations in NSCLC to identify pts who may benefit from T-DXd. A high level of agreement was also shown between the ODxT Test and CTAs. Clinical trials for HER2m NSCLC may successfully use a central testing assay as a CDx while allowing adequately validated local testing assays to expedite patient enrollment. Clinical trial information: NCT03505710 , NCT04644237 .

IL16 PRODUCTION IS A MECHANISM OF RESISTANCE TO BTK INHIBITORS AND TO R‐CHOP

Introduction: Covalent and non-covalent BTK inhibitors (BTKi) are used or being explored for the treatment of patients with B-cell malignancies, such as marginal zone lymphoma (MZL), mantle cell lymphoma (MCL), chronic lymphocytic leukemia (CLL) and diffuse large B-cell lymphoma (DLBCL) of the activated B-cell type (ABC). The identification of mechanisms of resistance can lead to improvements in the current therapeutic approaches. We had reported IL16 production in a MZL model of secondary resistance to ibrutinib (Arribas ENA2019). Here, we expand the initial observation to additional lymphoma types and treatments modalities, performing therapeutic interventions to block it and providing clinical validation data on its expression and role in patients. Methods: VL51 (marginal zone lymphoma, MZL) parental (PAR) and Ibrutinib-resistant (RES) cells underwent RNA (mRNA & miRNA by RNA-Seq), DNA (promoter methylation, WES) and secretome (Cytokine array) profiling. RES cells exhibited resistance to covalent and non-covalent BTK inhibitors. No DNA copy number variations or mutations were detected by WES in RES compared to PAR cells, including those affecting BTK or PLCG2 genes. Results: Multi-omics (DNA, mRNA, miRNA, secretome) characterization of RES and PAR cells identified hypomethylation, up-regulation and secretion of IL16 in RES but not in PAR cells. Elevated levels of p-AKT and p-ERK was observed in RES compared to PAR, suggesting a signaling activation in RES cells. Stimulation with recombinant IL16 reduced sensitivity to BTKi not only in VL51 PAR cells, but also in models derived from MCL (REC1) and CLL (MEC1). Conversely, the use of IL16 blocking antibody recovered sensitivity to BTKi in RES cells, along with other lines with high expression of IL16 and low sensitivity to ibrutinib derived from MCL (MAVER1) and MZL (SSK41). IL16 is part of the ABC-DLBCL signature. We observed that high IL16 expression was associated with shorter overall survival in ABC but not in germinal center like DLBCL patients (n = 233, log-rank test p = 0.013, multivariate Cox: HR (95% CI) 0.43 (0.22–0.85), p = 0.016). IL16 stimulation also decreased sensitivity to RCHOP in ABC DBCL cells (OCILy10, HBL1). Finally, secreted levels of IL16 in the serum of CLL patients (n = 17) with resistance to ibrutinib (in absence of BTK or PLCG2 mutations) were significantly higher compared to patients responding to the drug and paired for clinical features. Conclusions: Starting from a MZL model of secondary resistance to ibrutinib, we showed that IL16 can sustain resistance to BTK inhibitors also in CLL and MCL. Furthermore, increased expression of IL16 associated with shorter outcome in ABC DLBCL patients and the interleukin induced in vitro resistance to RCHOP. The demonstration that IL16 blocking therapies recovered sensitivity to therapies can lead to novel therapeutic approaches to overcome resistance in lymphoma patients. The research was funded by: Swiss National Science Foundation (SNSF 31003A_163232/1) Keyword: molecular targeted therapies Conflicts of interests pertinent to the abstract A. Arribas Educational grants: Astra Zeneca

Systematic gene-disease relationship (GDR) curation unveils 61 gene-disease associations and highlights the impact on genetic testing

PurposeWith this study, we aimed to explore the gene-disease relationship (GDR) evidence for 109 gene-disease pairs and the significance of a large Biodatabank for this classification. MethodsThe Clinical Genome Resource (ClinGen) Clinical Validity Framework for evaluation of GDR was applied. Most of the assessed genes were without a phenotype entry in the Online Mendelian Inheritance in Man (OMIM) database. Our Biodatabank with genetic data from over 670,000 previously tested individuals, in addition to data available in literature and public databases, were used for gene curation. ResultsWe confirmed 61 GDR (Definitive: 4 genes, Strong: 22 genes, Moderate: 35 genes). For 84 of 109 gene-disease pairs, a higher score was reached when using data from our Biodatabank in addition to externally obtainable data. This increased the final level of classification in 21 of the genes. Over 400 patients received a genetic report with clinically relevant variants in these 61 genes. ConclusionOur results demonstrate the importance of careful assessment of gene clinical validity data, along with the use of genetic data repositories. Implementation of the ClinGen Clinical Validity Framework for assessment of GDR is relatively straightforward. We encourage diagnostic laboratories to implement such a system and contribute to closing the knowledge gap in genetic research and diagnostics.

110P Clinical validation of a 50-gene hotspot NGS panel in Asian patients with solid tumors

The advent of next-generation sequencing (NGS) technologies had seen rapid adoption by clinical laboratories. Herein, we report the clinical validation data of a laboratory developed test-APEX Tissue (MiRXES), which enables simultaneous detection of single-nucleotide variants (SNVs), insertions/deletions (InDels), copy number amplifications and fusions through both DNA and RNA sequencing.

Discovery of Lipid Metabolism-Related Genes for Predicting Tumor Immune Microenvironment Status and Prognosis in Prostate Cancer.

Background Reprogramming of lipid metabolism is closely associated with tumor development, serving as a common and critical metabolic feature that emerges during tumor evolution. Meanwhile, immune cells in the tumor microenvironment also undergo aberrant lipid metabolism, and altered lipid metabolism also has an impact on the function and status of immune cells, further promoting malignant biological behavior. Consequently, we focused on lipid metabolism-related genes for constructing a novel prognostic marker and evaluating immune status in prostate cancer. Methods Information about prostate cancer patients was obtained from TCGA and GEO databases. The NMF algorithm was conducted to identify the molecular subtypes. The least absolute shrinkage and selection operator (Lasso) regression analysis was applied to establish a prognostic risk signature. CIBERSORT algorithm was used to calculate immune cell infiltration levels in prostate cancer. External clinical validation data were used to validate the results. Results Prostate cancer samples were divided into two subtypes according to the NMF algorithm. A six-gene risk signature (PTGS2, SGPP2, ALB, PLA2G2A, SRD5A2, and SLC2A4) was independent of prognosis and showed good stability. There were significant differences between risk groups of patients with respect to the infiltration of immune cells and clinical variables. Response to immunotherapy also differed between different risk groups. Furthermore, the mRNA expression levels of the signature genes were verified in tissue samples by qRT-PCR. Conclusion We constructed a six-gene signature with lipid metabolism in prostate cancer to effectively predict prognosis and reflect immune microenvironment status.

Abstract 752: Unveiling Korean cancer-associated gene using large-size cancer germline mutation data

Abstract We are focusing on the germline mutation data to understand the possible cancer-causing gene mutation that has a chance of inheritability to the next generation. Cancer genomics has been already widely studied around the world in a pan-cancer manner. But we found that the majority of the ethnicity of the data sequenced is mainly the US and European origin. For the Korean cancer cohort, it is clear that the lack of sample size was the bottleneck for ethnicity-specific studies. It is necessary that there is a limited number of the study contribute to the knowledge that Korean specifically applicated to explore the possible association between germline mutation and cancer occurrences. The objective of this study is to discover the Korean-specific candidate genes that associate with the tumor.Our study collected around 3000 Korean-specific samples consisting of various cancer types and normal control. We also gathered normal control samples for 354 Super-Normal (tumor-free, age &amp;lt; 65, med-checked) samples, and 1094 Korean normal (U1K) samples. Whole Exome Sequencing has been applied for every sample except U1K cohort that has already been whole genome sequenced. Genome-Wide Association Test (GWAS) has been executed between Korean-specific cancer patients and Korean normal samples. The study has been dichotomously analyzed, common variants are studied by GWAS fashion, and the rare variants are studied by gene-level aggregated fashion. Not only statistically, but also functionally we make use of the knowledge that Nonsense Mediated Decay (NMD) information to break the limitation of the unexplained gene mutation that does not follow the Central Dogma. Clinical validation data is also utilized. In a clinical aspect, we validated the dataset with a Clinvar and decided whether the mutation has a piece of clinical evidence or not.By conducting a series of studies, we have successfully found that some candidate genes which are known for DNA binding genes including PABPC1, PABPC3, and ZNF717 were significantly associated. Indeed, a cell division cycle associated gene like CDC27 also represents a significant association with cancer phenotype. Furthermore, ATXN3, SEPT2, PCMTD1 mutation is observed more in the cancer cohort. We are performing comparison studies to further elucidate which ethnic group represents more gene mutation on different cancer types.In summary, we propose the method that presents the possible germline mutation that is associated with phenotype by various factors, and also proposed Korean-specific candidate genes that associate with cancer. Citation Format: Gangpyo Yuh, Seulki Song, Eunju Cho, Miso Kim, Hyery Kim, Youngil Koh, Jookyung Park, Sungsoo Yoon, Sehoon Lee. Unveiling Korean cancer-associated gene using large-size cancer germline mutation data [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 752.

Clinical validation of a multicancer detection blood test by circulating cell-free DNA (cfDNA) methylation sequencing: The THUNDER study.

10544 Background: The development of ultra-sensitive genomic and epigenomic assays enables the early detection of multiple cancers in parallel. However, large-scale prospective clinical validation data are rare. Here, we report clinical validation data from the THUNDER (The Unintrusive Detection of EaRly-stage cancers, NCT04820868) study, which evaluates the performance of ELSA-seq among 6 cancer types in lung, colorectum, liver, esophagus, pancreas and ovary, which account for 50% of cancer morbidity and 62% of cancer mortality. Methods: This prospective case-control study consists of four stages: marker discovery, model training, validation, and independent validation. A customized panel covering 161,984 CpG sites was established using public data and in-house data. Cancer patients were pre-specified into the training and validation sets, and healthy controls (HC) were age-matched. Two multi-cancer detection blood test (MCDBT-1 and MCDBT-2) models with different cut-offs were established from the retrospectively collected training set and tested in the validation set. An independent validation set was enrolled prospectively, matched by age, and tested with the locked MCDBT-1/2 models. An interception model was then applied based on the model performance and China cancer incidence data to infer potential positive predictive value (PPV) and clinical utility in real-world practice. Results: In total, the training set consisted of 399 cases and 626 HC; the validation set consisted of 301 cases and 123 HC; and the independent validation set consisted of 505 cases and 505 HC. In the training set, the specificities were 98.9% (95% confidence interval [CI], 97.7%‒99.5%) and 99.7% (98.8%‒100.0%) for MCDBT-1/2 models, respectively. In the independent validation set ( n = 856, the rest to be sequenced and reported), MCDBT-1 and MCDBT-2 yielded sensitivity of 76.2% (72.0%‒80.0%) and 70.2% (65.8%‒74.4%) in 6 cancers with specificity of 96.3% (93.9%‒97.9%) and 99.3% (97.8%‒99.8%), respectively. Stage I‒III sensitivity was 68.5% (63.1%‒73.5%) and 60.8% (55.3%‒66.2%) for MCDBT-1/2 models, respectively. The prediction accuracy of top predicted origin was 79.1% (74.5%‒83.2%) and 83.0% (78.4%‒87.0%) for MCDBT-1/2, respectively. The interception model projected an estimated PPV of 3.1% and 12.4% for MCDBT-1/2 models, respectively. MCDBT-1 could reduce 5-year cancer mortality by 20.3%‒24.6% and reduce late-stage incidence by 51.7%‒61.7%, and MCDBT-2 could reduce 5-year cancer mortality by 15.9%‒19.9% and reduce late-stage incidence by 40.9%‒49.2%. Conclusions: cfDNA methylation-based MCDBT-1/2 models can effectively identify multiple cancers simultaneously at early stages with promising sensitivity, specificity, and accuracy of predicted origin. Their performances are to be further validated in a prospective interventional study (NCT05227534).

Recommendations for Defining and Reporting Adherence Measured by Biometric Monitoring Technologies: Systematic Review.

BackgroundSuboptimal adherence to data collection procedures or a study intervention is often the cause of a failed clinical trial. Data from connected sensors, including wearables, referred to here as biometric monitoring technologies (BioMeTs), are capable of capturing adherence to both digital therapeutics and digital data collection procedures, thereby providing the opportunity to identify the determinants of adherence and thereafter, methods to maximize adherence.ObjectiveWe aim to describe the methods and definitions by which adherence has been captured and reported using BioMeTs in recent years. Identifying key gaps allowed us to make recommendations regarding minimum reporting requirements and consistency of definitions for BioMeT-based adherence data.MethodsWe conducted a systematic review of studies published between 2014 and 2019, which deployed a BioMeT outside the clinical or laboratory setting for which a quantitative, nonsurrogate, sensor-based measurement of adherence was reported. After systematically screening the manuscripts for eligibility, we extracted details regarding study design, participants, the BioMeT or BioMeTs used, and the definition and units of adherence. The primary definitions of adherence were categorized as a continuous variable based on duration (highest resolution), a continuous variable based on the number of measurements completed, or a categorical variable (lowest resolution).ResultsOur PubMed search terms identified 940 manuscripts; 100 (10.6%) met our eligibility criteria and contained descriptions of 110 BioMeTs. During literature screening, we found that 30% (53/177) of the studies that used a BioMeT outside of the clinical or laboratory setting failed to report a sensor-based, nonsurrogate, quantitative measurement of adherence. We identified 37 unique definitions of adherence reported for the 110 BioMeTs and observed that uniformity of adherence definitions was associated with the resolution of the data reported. When adherence was reported as a continuous time-based variable, the same definition of adherence was adopted for 92% (46/50) of the tools. However, when adherence data were simplified to a categorical variable, we observed 25 unique definitions of adherence reported for 37 tools.ConclusionsWe recommend that quantitative, nonsurrogate, sensor-based adherence data be reported for all BioMeTs when feasible; a clear description of the sensor or sensors used to capture adherence data, the algorithm or algorithms that convert sample-level measurements to a metric of adherence, and the analytic validation data demonstrating that BioMeT-generated adherence is an accurate and reliable measurement of actual use be provided when available; and primary adherence data be reported as a continuous variable followed by categorical definitions if needed, and that the categories adopted are supported by clinical validation data and/or consistent with previous reports.

FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies

To assess key trends, strengths, and gaps in validation studies of the Food and Drug Administration (FDA)-regulated imaging-based artificial intelligence/machine learning (AI/ML) algorithms. We audited publicly available details of regulated AI/ML algorithms in imaging from 2008 until April 2021. We reviewed 127 regulated software (118 AI/ML) to classify information related to their parent company, subspecialty, body area and specific anatomy type, imaging modality, date of FDA clearance, indications for use, target pathology (such as trauma) and findings (such as fracture), technique (CAD triage, CAD detection and/or characterization, CAD acquisition or improvement, and image processing/quantification), product performance, presence, type, strength and availability of clinical validation data. Pertaining to validation data, where available, we recorded the number of patients or studies included, sensitivity, specificity, accuracy, and/or receiver operating characteristic area under the curve, along with information on ground-truthing of use-cases. Data were analyzed with pivot tables and charts for descriptive statistics and trends. We noted an increasing number of FDA-regulated AI/ML from 2008 to 2021. Seventeen (17/118) regulated AI/ML algorithms posted no validation claims or data. Just 9/118 reviewed AI/ML algorithms had a validation dataset sizes of over 1000 patients. The most common type of AI/ML included image processing/quantification (IPQ; n=59/118), and triage (CADt; n=27/118). Brain, breast, and lungs dominated the targeted body regions of interest. Insufficient public information on validation datasets in several FDA-regulated AI/ML algorithms makes it difficult to justify clinical applications since their generalizability and presence of bias cannot be inferred.

Biomarkers for detection of clinically significant prostate cancer: contemporary clinical data and future directions.

Use of serum prostate-specific antigen (PSA) testing for early detection of prostate cancer appears to reduce cancer-specific mortality. Due to the limited specificity of PSA for clinically significant [Grade Group (GG) ≥2] cancer, however, screening carries substantial risks, including frequent unnecessary prostate biopsies and overdetection of non-aggressive cancers. To that end, serum and urine biomarkers with improved specificity for GG ≥2 cancer have been proposed for clinical use following PSA. In the current article, we present clinical validation data for five such biomarkers: PHI, 4Kscore, SelectMDx, ExoDx, and MPS. For all studies, we specify the study population (overall biopsy referral vs. pre-specified PSA ranges), previous biopsy status (biopsy-naïve vs. previous negative biopsy), and the proportion of subjects diagnosed with GG ≥2 cancer. Outcomes include test performance characteristics: sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Published data were used to compute the number of unnecessary biopsies avoided and number of GG ≥2 cancers missed if the biomarker had been used clinically to select for prostate biopsy. The evidence review is preceded by a primer on these and other clinically-relevant summary statistics.

Abstract PS2-40: Analytical and clinical validation of the trucheck™ platform for diagnostic triaging of symptomatic cases suspected of breast cancer

Abstract Background: trucheckTM is a non-invasive micro-biopsy-like approach for diagnostic triaging of symptomatic individuals suspected of Breast Cancer. trucheckTM is based on the detection of Circulating Ensembles of Tumor Associated Cells (C-ETACs: EpCAM+, Pan-CK+, CD45±) of Breast Cancer origin (GCDFP15+, GATA3+); such C-ETACs are ubiquitous in blood samples of patients with Breast Cancer and unexpected in asymptomatic individuals as well as in individuals with Benign Breast conditions. Methods: Analytical validation was performed using control cell lines for EpCAM (SKBR-3), Pan-CK (SKBR-3), CD45 (PBMCs), GCDFP15 (SKBR-3) and GATA3 (MCF-7) respectively. Known amounts of control cells were spiked into healthy donor blood and their recovery rates determined by immunocytochemistry (ICC) to establish Sensitivity, Specificity, Accuracy, Limit of Detection, Linearity and Precision. Clinical Validation was performed using 15 mL peripheral blood collected from 1,527 participants. An initial Retrospective Clinical Pre-validation was performed using blood samples collected from 547 known cases of Breast Cancer and 19 known cases of other (non-Breast) solid organ Cancers. In a subset of 20 Breast Cancer cases with metastases to the Lungs or Liver, C-ETACs were evaluated for markers associated Lung (Napsin-A, TTF-1, p40) and Liver (Hep-Par 1, Glypican-3) primaries. Prospective Clinical Validation was performed on blood samples collected prior to any invasive procedure from 961 symptomatic cases suspected of Breast Cancer. Results: Analytical Validation based on recovery of spiked control cells indicated 94.0% Sensitivity, 100% Specificity, 97.0% Accuracy, 93.2% - 96.7% Precision and significant linearity (R2≥0.99) for all ICC markers. Clinical Pre-validation indicated 89.4% Sensitivity and 100% Specificity. C-ETACs from the known Breast Cancer cases with Lung and Liver metastases were negative for Lung- and Liver-cancer specific ICC markers, respectively, while C-ETACs from non-Breast Cancer samples were negative for Breast Cancer-specific ICC markers. In the Prospective Clinical Validation, histopathological evaluation (HPE) of biopsied tumor tissue indicated Breast Cancer in 848 cases and benign conditions in 113 of the 961 suspected symptomatic cases. C-ETAC-based trucheckTM approach had 91.9% Sensitivity, 98.3% Specificity and 96.9% Accuracy. Conclusion: Analytical and Clinical Validation data establish the viability of C-ETAC-based trucheckTM for diagnostic triaging of symptomatic individuals suspected of Breast Cancer. Individuals positive for Breast Cancer-specific C-ETACs can be prioritized for further clinical procedures whereas C-ETAC negative individuals can be considered for alternate diagnoses. Citation Format: Dadasaheb Akolkar, Darshana Patil, Pradip Fulmali, Pooja Fulmali, Revati Patil, Kiran Bendale, Archana Adhav, Shoeb Patel, Shabista Khan, Raja Dasarathan, Vishal Ranjan, Rohit Chougule, Pradyumna Shejwalkar, Akshay Ainwale, Mohini Garte, Rahul Sonawane, Madhav Purane, Yogeshwar Chaudhari, Prajakta Sagar, Shubhangi Nerkar, Vineet Datta, Cynthe Sims, Prashant Kumar, Ajay Srinivasan, Rajan Datar. Analytical and clinical validation of the trucheck™ platform for diagnostic triaging of symptomatic cases suspected of breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS2-40.

Clinical and analytical validation of FoundationOne Liquid CDx, a novel 324-Gene cfDNA-based comprehensive genomic profiling assay for cancers of solid tumor origin.

As availability of precision therapies expands, a well-validated circulating cell-free DNA (cfDNA)-based comprehensive genomic profiling assay has the potential to provide considerable value as a complement to tissue-based testing to ensure potentially life-extending therapies are administered to patients most likely to benefit. Additional data supporting the clinical validity of cfDNA-based testing is necessary to inform optimal use of these assays in the clinic. The FoundationOne®Liquid CDx assay is a pan-cancer cfDNA-based comprehensive genomic profiling assay that was recently approved by FDA. Validation studies included >7,500 tests and >30,000 unique variants across >300 genes and >30 cancer types. Clinical validity results across multiple tumor types are presented. Additionally, results demonstrated a 95% limit of detection of 0.40% variant allele fraction for select substitutions and insertions/deletions, 0.37% variant allele fraction for select rearrangements, 21.7% tumor fraction for copy number amplifications, and 30.4% TF for copy number losses. The limit of detection for microsatellite instability and blood tumor mutational burden were also determined. The false positive variant rate was 0.013% (approximately 1 in 8,000). Reproducibility of variant calling was 99.59%. In comparison with an orthogonal method, an overall positive percent agreement of 96.3% and negative percent agreement of >99.9% was observed. These study results demonstrate that FoundationOne Liquid CDx accurately and reproducibly detects the major types of genomic alterations in addition to complex biomarkers such as microsatellite instability, blood tumor mutational burden, and tumor fraction. Critically, clinical validity data is presented across multiple cancer types.

Biomarkers of Crohn's Disease to Support the Development of New Therapeutic Interventions.

Currently, 2 coprimary end points are used by health authorities to determine the effectiveness of therapeutic interventions in patients with Crohn's disease (CD): symptomatic remission (patient-reported outcome assessment) and endoscopic remission (ileocolonoscopy). However, there is lack of accepted biomarkers to facilitate regulatory decision-making in the development of novel therapeutics for the treatment of CD. With support from the Helmsley Charitable Trust, Critical Path Institute formed the Crohn's Disease Biomarkers preconsortium (CDBpC) with members from the pharmaceutical industry, academia, and nonprofit organizations to evaluate the CD biomarker landscape. Biomarkers were evaluated based on biological relevance, availability of biomarker assays, and clinical validation data. The CDBpC identified the most critical need as pharmacodynamic/response biomarkers to monitor disease activity in response to therapeutic intervention. Fecal calprotectin (FC) and serum C-reactive protein (CRP) were identified as biomarkers ready for the regulatory qualification process. A number of exploratory biomarkers and potential panels of these biomarkers was also identified for additional development. Given the different factors involved in CD and disease progression, a combination of biomarkers, including inflammatory, tissue injury, genetic, and microbiome-associated biomarkers, will likely have the most utility. The primary focus of the Inflammatory Bowel Disease Regulatory Science Consortium will be development of exploratory biomarkers and the qualification of FC and CRP for IBD. The Inflammatory Bowel Disease Regulatory Science Consortium, focused on tools to support IBD drug development, will operate in the precompetitive space to share data, biological samples for biomarker testing, and assay information for novel biomarkers.

Validation of the RayStation Monte Carlo dose calculation algorithm using a realistic lung phantom.

PurposeOur purposes are to compare the accuracy of RaySearch's analytical pencil beam (APB) and Monte Carlo (MC) algorithms for clinical proton therapy and to present clinical validation data using a novel animal tissue lung phantom.MethodsWe constructed a realistic lung phantom composed of a rack of lamb resting on a stack of rectangular natural cork slabs simulating lung tissue. The tumor was simulated using 70% lean ground lamb meat inserted in a spherical hole with diameter 40 ± 5 mm carved into the cork slabs. A single‐field plan using an anterior beam and a two‐field plan using two anterior‐oblique beams were delivered to the phantom. Ion chamber array measurements were taken medial and distal to the tumor. Measured doses were compared with calculated RayStation APB and MC calculated doses.ResultsOur lung phantom enabled measurements with the MatriXX PT at multiple depths in the phantom. Using the MC calculations, the 3%/3 mm gamma index pass rates, comparing measured with calculated doses, for the distal planes were 74.5% and 85.3% for the APB and 99.1% and 92% for the MC algorithms. The measured data revealed up to 46% and 30% underdosing within the distal regions of the target volume for the single and the two field plans when APB calculations are used. These discrepancies reduced to less than 18% and 7% respectively using the MC calculations.ConclusionsRaySearch Laboratories' Monte Carlo dose calculation algorithm is superior to the pencil‐beam algorithm for lung targets. Clinicians relying on the analytical pencil‐beam algorithm should be aware of its pitfalls for this site and verify dose prior to delivery. We conclude that the RayStation MC algorithm is reliable and more accurate than the APB algorithm for lung targets and therefore should be used to plan proton therapy for patients with lung cancer.

Clinical analysis of lectin-like oxidized low-density lipoprotein receptor-1 in patients with in-stent restenosis after percutaneous coronary intervention.

In-stent restenosis (ISR) is the most common complication associated with percutaneous coronary intervention (PCI). Although some studies have reported an association between lectin-like oxidized low-density lipoprotein receptor-1 (LOX-1) and ISR, not enough clinical validation data are available to support this link. Here, we report our cross-sectional study aimed at exploring the feasibility of LOX-1 as a biomarker for the prognostic diagnosis of patients undergoing PCI.Three groups were included: ISR group, including 99 patients with ISR diagnosed with coronary arteriography (CAG) after PCI; lesion group, comprising 87 patients with coronary artery stenosis (<50%) diagnosed with CAG after PCI; and control group, consisting of 96 volunteers with no coronary artery disease. The levels of LOX-1 were measured in each patient by using an enzyme-linked immunosorbent assay, and their general information as well as laboratory parameters were recorded and followed up during a period of 2 years.LOX-1 levels gradually increased after PCI along with the progression of the lesion in the 3 groups. The levels of LOX-1 were significantly higher in the ISR group than in the other 2 groups (P < .001). LOX-1 levels were correlated with the levels of uric acid (UA) (r = 0.289, P = .007), creatinine (CREA) (r = .316, P = .003), and high-density lipoprotein cholesterol (HDL-C) (r = -0.271, P = .012), whereas no statistically significant correlation was detected with the Gensini score (r = 0.157, P = .141). The sensitivity and specificity of LOX-1 were 81.5% and 55.7%, respectively, with the most optimal threshold (5.04 μg/L). The area under curve (AUC) of the receiver operator characteristic (ROC) curve of LOX-1 was 0.720, and LOX-1 had the highest AUC compared with CREA, UA, and HDL-C, both individually and in combination.A high level of LOX-1 in the early period after PCI has a certain predictive power and diagnostic value for ISR. However, the level of LOX-1 is not related to the Gensini score of coronary artery after PCI, and CREA and UA, which are weakly related to LOX-1, have no obvious synergy in the diagnosis of ISR with LOX-1.

Validación de una batería para evaluar el reconocimiento de emociones a través del rostro y del cuerpo utilizando estímulos dinámicos

This paper presents validation studies of a new neuropsychological assessment battery, locally developed, designed to assess facial and body expression recognition of basic and complex emotions, with video stimuli. We analyzed item difficulty and item discrimination power of the items (Study 1), convergent validity and reliability (Study 2), and the effect of demographic variables, gender, age and educational level, on the battery tests (Study 3). Results showed that the four emotional tests of the battery had adequate psychometric properties, with appropriate difficulty and discrimination indexes, and satisfactory indicators of validity and reliability. Age and educational level affected performance in all battery tests, but there weren’t significative differences between men and women. In future works it is expected test the performance of the battery in pathological populations in order to obtain clinical validation data.