Search

Abstract Background Genomic tests are available to predict breast cancer recurrence and to guide clinical decision making. These predictors provide recurrence risk scores along with a measure of uncertainty, usually a confidence interval. The confidence interval conveys random error and not systematic bias. Standard tumor sampling methods make this problematic, as it is common to have a substantial proportion (typically 30-50%) of a tumor sample comprised of histologically benign tissue. This "normal" tissue could represent a source of non-random error or systematic bias in genomic classification. Methods To assess the performance characteristics of genomic classification to systematic error from normal contamination, we collected 55 tumor samples and paired tumor-adjacent normal tissue. Using genomic signatures from the tumor and paired normal, we evaluated how increasing normal contamination altered recurrence risk scores for various genomic predictors. Results Simulations of normal tissue contamination caused misclassification of tumors in all predictors evaluated, but different breast cancer predictors showed different types of vulnerability to normal tissue bias. While two predictors had unpredictable direction of bias (either higher or lower risk of relapse resulted from normal contamination), one signature showed predictable direction of normal tissue effects. Due to this predictable direction of effect, this signature (the PAM50) was adjusted for normal tissue contamination and these corrections improved sensitivity and negative predictive value. For all three assays quality control standards and/or appropriate bias adjustment strategies can be used to improve assay reliability. Conclusions Normal tissue sampled concurrently with tumor is an important source of bias in breast genomic predictors. All genomic predictors show some sensitivity to normal tissue contamination and ideal strategies for mitigating this bias vary depending upon the particular genes and computational methods used in the predictor.

Abstract Background Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex in vitro/in vivo datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods. Results The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated in vitro assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA. Conclusion We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.

Abstract Background Computational methods to predict transcription factor binding sites (TFBS) based on exhaustive algorithms are guaranteed to find the best patterns but are often limited to short ones or impose some constraints on the pattern type. Many patterns for binding sites in prokaryotic species are not well characterized but are known to be large, between 16–30 base pairs (bp) and contain at least 2 conserved bases. The length of prokaryotic species promoters (about 400 bp) and our interest in studying a small set of genes that could be a cluster of co-regulated genes from microarray experiments led to the development of a new exhaustive algorithm targeting these large patterns. Results We present Searchpattool, a new method to search for and select the most specific (conservative) frequent patterns. This method does not impose restrictions on the density or the structure of the pattern. The best patterns (motifs) are selected using several statistics, including a new application of a z-score based on the number of matching sequences. We compared Searchpattool against other well known algorithms on a Bacillus subtilis group of 14 input sequences and found that in our experiments Searchpattool always performed the best based on performance scores. Conclusion Searchpattool is a new method for pattern discovery relative to transcription factor binding sites for species or genes with short promoters. It outputs the most specific significant patterns and helps the biologist to choose the best candidates.

Introduction: The topoisomerase 1 (TOP1) inhibitor irinotecan is a standard-of-care agent for relapsed Ewing sarcoma (EWS), but its efficacy is limited by chemical instability, rapid clearance and reversibility, and dose-limiting toxicities, such as diarrhea. Indenoisoquinolines (IIQs) represent a new class of clinical TOP1 inhibitors designed to address these limitations., Methods: In this study, we evaluated the preclinical efficacy of three IIQs (LMP400, LMP744, and LMP776) in relevant models of EWS. We characterized the pharmacokinetics of IIQs in orthotopic xenograft models of EWS, optimized the dosing regimen through tolerability studies, and tested the efficacy of IIQs in a panel of six molecularly heterogeneous EWS patient-derived xenograft (PDX) models. For each PDX, we conducted whole genome and RNA sequencing, and methylation analysis., Results: We show that IIQs potently inhibit the proliferation of EWS cells in vitro, inducing complete cell growth inhibition at nanomolar concentrations via induction of DNA damage and apoptotic cell death. LMP400 treatment induced ≥30% tumor regression in two of six PDX models, with more durable regression compared to irinotecan treatment in one of these models. RNA sequencing of PDX models identified a candidate predictive biomarker gene signature for LMP400 response. These data, along with pharmacogenomic data on IIQs in sarcoma cell lines, are available at a new interactive public website: https://discover.nci.nih.gov/rsconnect/EwingSarcomaMinerCDB/., Discussion: Our findings suggest that IIQs may be promising new agents for a subset of EWS patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Lee, Szabova, Collins, Gordon, Johnson, Householder, Jorgensen, Lu, Bassel, Elloumi, Peer, Nelson, Varriano, Varma, Roberts, Ohler, Figg, Sharan, Pommier and Heske.)

Immunotherapeutic targeting of cell surface proteins is an increasingly effective cancer therapy. However, given the limited number of current targets, the identification of new surface proteins, particularly those with biological importance, is critical. Here, we uncover delta-like non-canonical Notch ligand 1 (DLK1) as a cell surface protein with limited normal tissue expression and high expression in multiple refractory adult metastatic cancers including small cell lung cancer (SCLC) and adrenocortical carcinoma (ACC), a rare cancer with few effective therapies. In ACC, ADCT-701, a DLK1 targeting antibody-drug conjugate (ADC), shows potent in vitro activity among established cell lines and a new cohort of patient-derived organoids as well as robust in vivo anti-tumor responses in cell line-derived and patient-derived xenografts. However, ADCT-701 efficacy is overall limited in ACC due to high expression and activity of the drug efflux protein ABCB1 (MDR1, P-glycoprotein). In contrast, ADCT-701 is extremely potent and induces complete responses in DLK1 + ACC and SCLC in vivo models with low or no ABCB1 expression. Genetic deletion of DLK1 in ACC dramatically downregulates ABCB1 and increases ADC payload and chemotherapy sensitivity through NOTCH1-mediated adrenocortical de-differentiation. Single cell RNA-seq of ACC metastatic tumors reveals significantly decreased adrenocortical differentiation in DLK low or negative cells compared to DLK1 positive cells. This works identifies DLK1 as a novel immunotherapeutic target that regulates tumor cell plasticity and chemoresistance in ACC. Our data support targeting DLK1 with an ADC in ACC and neuroendocrine neoplasms in an active first-in-human phase I clinical trial (NCT06041516)., Competing Interests: Nitin Roper and Jaydira Del Rivero have received research funding from ADC Therapeutics for this study. The other authors have no competing interests to report.

Adrenocortical carcinoma (ACC) is a rare and highly heterogeneous disease with a notably poor prognosis due to significant challenges in diagnosis and treatment. Emphasizing on the importance of precision medicine, there is an increasing need for comprehensive genomic resources alongside well-developed experimental models to devise personalized therapeutic strategies. We present ACC_CellMinerCDB, a substantive genomic and drug sensitivity database (available at https://discover.nci.nih.gov/acc_cellminercdb) comprising ACC cell lines, patient-derived xenografts, surgical samples, and responses to more than 2,400 drugs examined by the NCI and National Center for Advancing Translational Sciences. This database exposes shared genomic pathways among ACC cell lines and surgical samples, thus authenticating the cell lines as research models. It also allows exploration of pertinent treatment markers such as MDR-1, SOAT1, MGMT, MMR, and SLFN11 and introduces the potential to repurpose agents like temozolomide for ACC therapy. ACC_CellMinerCDB provides the foundation for exploring larger preclinical ACC models., Significance: ACC_CellMinerCDB, a comprehensive database of cell lines, patient-derived xenografts, surgical samples, and drug responses, reveals shared genomic pathways and treatment-relevant markers in ACC. This resource offers insights into potential therapeutic targets and the opportunity to repurpose existing drugs for ACC therapy., (©2024 The Authors; Published by the American Association for Cancer Research.)

Sarcomas are a diverse group of rare malignancies composed of multiple different clinical and molecular subtypes. Due to their rarity and heterogeneity, basic, translational, and clinical research in sarcoma has trailed behind that of other cancers. Outcomes for patients remain generally poor due to an incomplete understanding of disease biology and a lack of novel therapies. To address some of the limitations impeding preclinical sarcoma research, we have developed Sarcoma_CellMinerCDB, a publicly available interactive tool that merges publicly available sarcoma cell line data and newly generated omics data to create a comprehensive database of genomic, transcriptomic, methylomic, proteomic, metabolic, and pharmacologic data on 133 annotated sarcoma cell lines. The reproducibility, functionality, biological relevance, and therapeutic applications of Sarcoma_CellMinerCDB described herein are powerful tools to address and generate biological questions and test hypotheses for translational research. Sarcoma_CellMinerCDB (https://discover.nci.nih.gov/SarcomaCellMinerCDB) aims to contribute to advancing the preclinical study of sarcoma., Competing Interests: The authors declare no competing interests.

Current treatment options for metastatic adrenocortical carcinoma (ACC) have limited efficacy, despite the common use of mitotane and cytotoxic agents. This study aimed to identify novel therapeutic options for ACC. An extensive drug screen was conducted to identify compounds with potential activity against ACC cell lines. We further investigated the mechanism of action of the identified compound, TAK-243, its synergistic effects with current ACC therapeutics, and its efficacy in ACC models including patient-derived organoids and mouse xenografts. TAK-243, a clinical ubiquitin-activating enzyme (UAE) inhibitor, showed potent activity in ACC cell lines. TAK-243 inhibited protein ubiquitination in ACC cells, leading to the accumulation of free ubiquitin, activation of the unfolded protein response, and induction of apoptosis. TAK-243 was found to be effluxed out of cells by MDR1, a drug efflux pump, and did not require Schlafen 11 (SLFN11) expression for its activity. Combination of TAK-243 with current ACC therapies (e.g., mitotane, etoposide, cisplatin) produced synergistic or additive effects. In addition, TAK-243 was highly synergistic with BCL2 inhibitors (Navitoclax and Venetoclax) in preclinical ACC models including patient-derived organoids. The tumor suppressive effects of TAK-243 and its synergistic effects with Venetoclax were further confirmed in a mouse xenograft model. These findings provide preclinical evidence to support the initiation of a clinical trial of TAK-243 in patients with advanced-stage ACC. TAK-243 is a promising potential treatment option for ACC, either as monotherapy or in combination with existing therapies or BCL2 inhibitors., Significance: ACC is a rare endocrine cancer with poor prognosis and limited therapeutic options. We report that TAK-243 is active alone and in combination with currently used therapies and with BCL2 and mTOR inhibitors in ACC preclinical models. Our results suggest implementation of TAK-243 in clinical trials for patients with advanced and metastatic ACC., (© 2024 The Authors; Published by the American Association for Cancer Research.)