Drug-biomarker co-development in oncology – 20 years and counting

Circulating tumor cells with increasing aneuploidy predict inferior prognosis and therapeutic resistance in small cell lung cancer

AIMS

Treatment resistance commonly emerges in small cell lung cancer (SCLC), necessitating the development of novel and effective biomarkers to dynamically assess therapeutic efficacy. This study aims to evaluate the clinical utility of aneuploid circulating tumor cells (CTCs) for risk stratification and treatment response monitoring.

METHODS

A total of 126 SCLC patients (two cohorts) from two independent cancer centers were recruited as the study subjects. Blood samples were collected from these patients and aneuploid CTCs were detected. Aneuploid CTC count (ACC) and aneuploid CTC score (ACS), were used to predict progression-free survival (PFS) and overall survival (OS). The performance of the ACC and the ACS was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

Compared to ACC, ACS exhibited superior predictive power for PFS and OS in these 126 patients. Moreover, both univariate and multivariate analyses revealed that ACS was an independent prognostic factor. Dynamic ACS changes reflected treatment response, which is more precise than ACC changes. ACS can be used to assess chemotherapy resistance and is more sensitive than radiological examination (with a median lead time of 2.8 months; P < 0.001). When patients had high ACS levels (> 1.115) at baseline, the combination of immunotherapy and chemotherapy resulted in longer PFS (median PFS, 7.7 months; P = 0.007) and OS (median OS, 16.3 months; P = 0.033) than chemotherapy alone (median PFS, 4.9 months; median OS, 13.6 months).

CONCLUSIONS

ACS could be used as a biomarker for risk stratification, treatment response monitoring, and individualized therapeutic intervention in SCLC patients.

Uncovering PROTAC Sensitivity and Efficacy by Multidimensional Proteome Profiling: A Case for STAT3

Proteolysis-targeting chimera (PROTAC) is a powerful technology that can effectively trigger the degradation of target proteins. The intricate interplay among various factors leads to a heterogeneous drug response, bringing about significant challenges in comprehending drug mechanisms. Our study applied data-independent acquisition-based mass spectrometry to multidimensional proteome profiling of PROTAC (DIA-MPP) to uncover the efficacy and sensitivity of the PROTAC compound. We profiled the signal transducer and activator of transcription 3 (STAT3) PROTAC degrader in six leukemia and lymphoma cell lines under multiple conditions, demonstrating the pharmacodynamic properties and downstream biological responses. Through comparison between sensitive and insensitive cell lines, we revealed that STAT1 can be regarded as a biomarker for STAT3 PROTAC degrader, which was validated in cells, patient-derived organoids, and mouse models. These results set an example for a comprehensive description of the multidimensional PROTAC pharmacodynamic response and PROTAC drug sensitivity biomarker exploration.

BioKA: a curated and integrated biomarker knowledgebase for animals

Biomarkers play an important role in various area such as personalized medicine, drug development, clinical care, and molecule breeding. However, existing animals' biomarker resources predominantly focus on human diseases, leaving a significant gap in non-human animal disease understanding and breeding research. To address this limitation, we present BioKA (Biomarker Knowledgebase for Animals, https://ngdc.cncb.ac.cn/bioka), a curated and integrated knowledgebase encompassing multiple animal species, diseases/traits, and annotated resources. Currently, BioKA houses 16 296 biomarkers associated with 951 mapped diseases/traits across 31 species from 4747 references, including 11 925 gene/protein biomarkers, 1784 miRNA biomarkers, 1043 mutation biomarkers, 773 metabolic biomarkers, 357 circRNA biomarkers and 127 lncRNA biomarkers. Furthermore, BioKA integrates various annotations such as GOs, protein structures, protein-protein interaction networks, miRNA targets and so on, and constructs an interactive knowledge network of biomarkers including circRNA-miRNA-mRNA associations, lncRNA-miRNA associations and protein-protein associations, which is convenient for efficient data exploration. Moreover, BioKA provides detailed information on 308 breeds/strains of 13 species, and homologous annotations for 8784 biomarkers across 16 species, and offers three online application tools. The comprehensive knowledge provided by BioKA not only advances human disease research but also contributes to a deeper understanding of animal diseases and supports livestock breeding.

Assessment of HER2 in Gastric-Type Endocervical Adenocarcinoma and its Prognostic Significance

As the most common type of human papillomavirus-independent endocervical adenocarcinomas (ECAs), gastric-type endocervical adenocarcinomas (GEAs) account for approximately 10% of all ECAs. Although anti-HER2 therapy has been proven effective in many cancers, it has not been used in ECAs, including GEAs, which is at least partly due to the lack of a well-defined guideline. Limited available data regarding HER2 in GEAs and ECAs have considerable variations likely caused by variations in the tumor type selection, testing methods, and scoring criteria. Here, we selected 58 GEA cases to examine the HER2 status using immunohistochemistry and fluorescent in situ hybridization and investigate the prognostic value and their association with other known or potential prognostic factors. When strong complete or lateral/basolateral membranous reactivity in ≥10% tumor cells was used to define HER2 positivity, relatively high prevalence of HER2 overexpression (10/58[17.2%]) and amplification (9/58 [15.5%]), as well as high immunohistochemistry-fluorescent in situ hybridization concordance rate (9/10 [90%]) was found in GEAs. A lateral/basolateral staining pattern ("U-shaped") was observed, at least focally, in most of HER2-positive (3+) and equivocal (2+) tumors. Notably, considerable heterogeneity of HER2 expression was observed in HER2 positive and equivocal cases (80.0% and 83.3%, respectively). HER2 overexpression and amplification were associated with worse progression-free survival (P = .047 and P = .032, respectively). Programmed death-ligand 1 expression was associated with worse progression-free survival (P = .032), whereas mutant-type p53 demonstrated no prognostic significance. Our work laid a solid foundation for the eventual development of a future standard HER2 testing guideline for GEAs.

Network-based assessment of HDAC6 activity predicts preclinical and clinical responses to the HDAC6 inhibitor ricolinostat in breast cancer

Inhibiting individual histone deacetylase (HDAC) is emerging as well-tolerated anticancer strategy compared with pan-HDAC inhibitors. Through preclinical studies, we demonstrated that the sensitivity to the leading HDAC6 inhibitor (HDAC6i) ricolinstat can be predicted by a computational network-based algorithm (HDAC6 score). Analysis of ~3,000 human breast cancers (BCs) showed that ~30% of them could benefice from HDAC6i therapy. Thus, we designed a phase 1b dose-escalation clinical trial to evaluate the activity of ricolinostat plus nab-paclitaxel in patients with metastatic BC (MBC) (NCT02632071). Study results showed that the two agents can be safely combined, that clinical activity is identified in patients with HR⁺/HER2^- disease and that the HDAC6 score has potential as predictive biomarker. Analysis of other tumor types also identified multiple cohorts with predicted sensitivity to HDAC6i's. Mechanistically, we have linked the anticancer activity of HDAC6i's to their ability to induce c-Myc hyperacetylation (ac-K148) promoting its proteasome-mediated degradation in sensitive cancer cells.

Transcriptomics, molecular docking, and cross-resistance profiling of nobiletin in cancer cells and synergistic interaction with doxorubicin upon SOX5 transfection

BACKGROUND

Nobiletin is a polymethoxylated flavone from citrus fruit peels. Among other bioactivities, it acts antioxidative, anti-inflammatory, neuroprotective, and cardiovascular-protective. Nobiletin exerts profound anticancer activity in vitro and in vivo but the underlying mechanisms are not well understood.

PURPOSE

The aim was to unravel the multiple modes of action against cancer cells by bioinformatic and transcriptomic techniques and their verification by molecular pharmacological methods.

METHODS

The in silico methods used were COMPARE analysis of transcriptomic data, signaling pathway analysis, transcription factor binding motif analysis in promoter sequences of target genes, and molecular docking. The in vitro methods used were resazurin assay, isobologram analysis, generation of stably SOX5-tranfected cells, and Western blotting.

RESULTS

Nobiletin was cytotoxic against a wide range of cell lines from different tumor types, including diverse phenotypes to established anticancer drugs (e.g., P-glycoprotein, ABCB5, p53, EGFR). Cross-resistance profiling with 83 standard anticancer drugs revealed a correlation to antihormonal anticancer drugs, which can be explained by the phytoestrogenic features of nobiletin. Transcriptomic analysis showed that the responsiveness of tumor cells was predictable by their specific mRNA expression profile. Nobiletin bound to the transcription factor SOX5 in silico. SOX5 conferred resistance to the control drug doxorubicin but collateral sensitivity to nobiletin in HEK293 cells transfected with a lentiviral GFP-tagged pLOCORF-SOX5 vector. The combination of nobiletin and doxorubicin synergistically killed HEK293-SOX5 cells in isobologram analyses, implying attractive new treatment options.

CONCLUSION

Nobiletin represents an interesting candidate for cancer therapy with broad-spectrum activity and multiple modes of action. The identification of novel targets (i.e., SOX5) may allow its use for targeted tumor therapy in individualized treatment protocols.

Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer

Background

Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development.

Methods

Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients.

Results

Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence.

Conclusion

Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-022-09349-x.

Current Prognostic and Predictive Biomarkers for Endometrial Cancer in Clinical Practice: Recommendations/Proposal from the Italian Study Group

Endometrial carcinoma (EC) is the most common gynecological malignant disease in high-income countries, such as European countries and the USA. The 2020 edition of the World Health Organization (WHO) Classification of Tumors of the Female Genital Tract underlines the important clinical implications of the proposed new histomolecular classification system for ECs. In view of the substantial genetic and morphological heterogeneity in ECs, both classical pthological parameters and molecular classifiers have to be integrated in the pathology report. This review will focus on the most commonly adopted immunohistochemical and molecular biomarkers in daily clinical characterization of EC, referring to the most recent published recommendations, guidelines, and expert opinions.

Identification of the receptor of oncolytic virus M1 as a therapeutic predictor for multiple solid tumors

Over the last decade, oncolytic virus (OV) therapy has shown its promising potential in tumor treatment. The fact that not every patient can benefit from it highlights the importance for defining biomarkers that help predict patients' responses. As particular self-amplifying biotherapeutics, the anti-tumor effects of OVs are highly dependent on the host factors for viral infection and replication. By using weighted gene co-expression network analysis (WGCNA), we found matrix remodeling associated 8 (MXRA8) is positively correlated with the oncolysis induced by oncolytic virus M1 (OVM). Consistently, MXRA8 promotes the oncolytic efficacy of OVM in vitro and in vivo. Moreover, the interaction of MXRA8 and OVM studied by single-particle cryo-electron microscopy (cryo-EM) showed that MXRA8 directly binds to this virus. Therefore, MXRA8 acts as the entry receptor of OVM. Pan-cancer analysis showed that MXRA8 is abundant in most solid tumors and is highly expressed in tumor tissues compared with adjacent normal ones. Further study in cancer cell lines and patient-derived tumor tissues revealed that the tumor selectivity of OVM is predominantly determined by a combinational effect of the cell membrane receptor MXRA8 and the intracellular factor, zinc-finger antiviral protein (ZAP). Taken together, our study may provide a novel dual-biomarker for precision medicine in OVM therapy.

Systematic optimization of targeted and multiplexed MS-based screening workflows for protein biomarkers

Background: The capability of targeted MS-based methods to simultaneously measure multiple analytes with high selectivity and sensitivity greatly facilitates the discovery and quantitation of novel biomarkers. However, the complexity of biological samples is a major bottleneck that requires extensive sample preparation. Results: This paper reports a generic workflow to optimize surrogate peptide-based protein biomarker screening for seven human proteins in a multiplexed manner without the need for any specific affinity reagents. Each step of the sample processing and LC-MS methods is systematically assessed and optimized for better analytical performance. Conclusion: The established method is used for the screening of multiple myeloma patient samples to determine which proteins could be robustly measured and serve as potential biomarkers of the disease.

Novel FLT3/AURK multikinase inhibitor is efficacious against sorafenib-refractory and sorafenib-resistant hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the sixth most common type of cancer and has a high mortality rate worldwide. Sorafenib is the only systemic treatment demonstrating a statistically significant but modest overall survival benefit. We previously have identified the aurora kinases (AURKs) and FMS-like tyrosine kinase 3 (FLT3) multikinase inhibitor DBPR114 exhibiting broad spectrum anti-tumor effects in both leukemia and solid tumors. The purpose of this study was to evaluate the therapeutic potential of DBPR114 in the treatment of advanced HCC.

Methods

Human HCC cell lines with histopathology/genetic background similar to human HCC tumors were used for in vitro and in vivo studies. Human umbilical vein endothelial cells (HUVEC) were used to evaluate the drug effect on endothelial tube formation. Western blotting, immunohistochemical staining, and mRNA sequencing were employed to investigate the mechanisms of drug action. Xenograft models of sorafenib-refractory and sorafenib-acquired resistant HCC were used to evaluate the tumor response to DBPR114.

Results

DBPR114 was active against HCC tumor cell proliferation independent of p53 alteration status and tumor grade in vitro. DBPR114-mediated growth inhibition in HCC cells was associated with apoptosis induction, cell cycle arrest, and polyploidy formation. Further analysis indicated that DBPR114 reduced the phosphorylation levels of AURKs and its substrate histone H3. Moreover, the levels of several active-state receptor tyrosine kinases were downregulated by DBPR114, verifying the mechanisms of DBPR114 action as a multikinase inhibitor in HCC cells. DBPR114 also exhibited anti-angiogenic effect, as demonstrated by inhibiting tumor formation in HUVEC cells. In vivo, DBPR114 induced statistically significant tumor growth inhibition compared with the vehicle control in multiple HCC tumor xenograft models. Histologic analysis revealed that the DBPR114 treatment reduced cell proliferation, and induced apoptotic cell death and multinucleated cell formation. Consistent with the histological findings, gene expression analysis revealed that DBPR114-modulated genes were mostly related to the G2/M checkpoint and mitotic spindle assembly. DBPR114 was efficacious against sorafenib-intrinsic and -acquired resistant HCC tumors. Notably, DBPR114 significantly delayed posttreatment tumor regrowth and prolonged survival compared with the regorafenib group.

Conclusion

Our results indicated that targeting AURK signaling could be a new effective molecular-targeted agent in the treatment of patients with HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12929-022-00788-0.

Probabilistic Boolean Modeling of Pre-clinical Tumor Models for Biomarker Identification in Cancer Drug Development

As high-throughput sequencing experiments become more widely used in pre-clinical and clinical settings, pharmacogenetic and pharmacogenomic biomarker development plays an increasingly important role in oncology drug development pipelines and programs. Consequently, computer-based learning approaches have entered into use at multiple stages in pre-clinical and clinical pipelines. However, few approaches are available to identify interpretable and implementable biomarkers of response early in the drug development process when only small pre-clinical data packages are available. To address the need for early-stage biomarker development using pre-clinical tumor models, we have adapted the previously published Probabilistic Target Inhibitor Map (PTIM) platform to the challenge of biomarker hypothesis development, and denoted this approach the Probabilistic Target Map-Biomarker (PTM-Biomarker). In this article, we detail the history and design philosophy of PTM-Biomarker, and present two case studies using the biomarker discovery tool to illustrate its utility in guiding cancer drug development. © 2021 Wiley Periodicals LLC.

Nanotherapeutic approaches to overcome distinct drug resistance barriers in models of breast cancer

Targeted delivery of drugs to tumor cells, which circumvent resistance mechanisms and induce cell killing, is a lingering challenge that requires innovative solutions. Here, we provide two bioengineered strategies in which nanotechnology is blended with cancer medicine to preferentially target distinct mechanisms of drug resistance. In the first 'case study', we demonstrate the use of lipid-drug conjugates that target molecular signaling pathways, which result from taxane-induced drug tolerance via cell surface lipid raft accumulations. Through a small molecule drug screen, we identify a kinase inhibitor that optimally destroys drug tolerant cancer cells and conjugate it to a rationally-chosen lipid scaffold, which enhances anticancer efficacy in vitro and in vivo. In the second 'case study', we address resistance mechanisms that can occur through exocytosis of nanomedicines. Using adenocarcinoma HeLa and MCF-7 cells, we describe the use of gold nanorod and nanoporous vehicles integrated with an optical antenna for on-demand, photoactivation at ~650 nm enabling release of payloads into cells including cytotoxic anthracyclines. Together, these provide two approaches, which exploit engineering strategies capable of circumventing distinct resistance barriers and induce killing by multimodal, including nanophotonic mechanisms.

Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models

The goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a crucial task of precision oncology. Machine learning methods have been employed to predict drug sensitivity based on the multiple omics data available for large panels of cancer cell lines. However, there are no comprehensive guidelines on how to properly train and validate such machine learning models for drug sensitivity prediction. In this paper, we introduce a set of guidelines for different aspects of training gene expression-based predictors using cell line datasets. These guidelines provide extensive analysis of the generalization of drug sensitivity predictors and challenge many current practices in the community including the choice of training dataset and measure of drug sensitivity. The application of these guidelines in future studies will enable the development of more robust preclinical biomarkers.

Reproducibility of scoring criteria for HER2 immunohistochemistry in endometrial serous carcinoma: a multi-institutional interobserver agreement study

Targeted anti-human epidermal growth factor receptor 2 (HER2) therapy has recently been proven to improve progression-free and overall survival of patients with advanced stage or recurrent endometrial serous carcinoma. To date, no specific pathology HER2 testing or scoring guidelines exist for endometrial cancer. However, based on evidence from the recent successful clinical trial and comprehensive pre-trial pathologic studies, a new set of HER2 scoring criteria have been proposed for endometrial serous carcinoma-distinct from the existing breast and gastric cancer-specific criteria. We present the first study assessing interobserver agreement of HER2 scores using the proposed serous endometrial cancer-specific scoring system. A digitally scanned set of 40 HER2-immunostained slides of endometrial serous carcinoma were sent to seven gynecologic pathologists, who independently assigned HER2 scores for each slide following a brief tutorial. Follow-up fluorescent in situ hybridization (FISH) for HER2 gene amplification was performed on cases with interobserver disagreement when a 2+ HER2 score was assigned by at least one observer. Complete agreement of HER2 scores among all 7 observers was achieved on 15 cases, and all but one case had an agreement by at least 4 observers. The overall agreement was 72.3% (kappa 0.60), 77.5% (kappa 0.65), and 83.3% (kappa 0.65), using four (0 to 3+ ), three (0/1+ , 2+ , 3+ ), or two (0/1+ , 2/3+ ) HER2 scoring categories, respectively. Based on the combination of HER2 immunostaining scores and FISH, the interobserver disagreement may have potentially resulted in a clinically significant difference in HER2 status only in three tumors. We conclude, that the proposed serous endometrial cancer-specific HER2 scoring criteria are reproducible among gynecologic pathologists with moderate to substantial interobserver agreement rates comparable to those of previously reported in breast and gastric carcinomas. Our findings significantly strengthen the foundation for establishing endometrial cancer-specific HER2 scoring guidelines in the future.

HER2 Testing and Reporting in Endometrial Serous Carcinoma: Practical Recommendations for HER2 Immunohistochemistry and Fluorescent In Situ Hybridization: Proceedings of the ISGyP Companion Society Session at the 2020 USCAP Annual Meeting

Anti-HER2 therapy has recently emerged as an effective targeted treatment approach for patients with advanced stage and recurrent endometrial serous carcinoma, resulting in significantly prolonged progression-free and overall survival when combined with the standard chemotherapy regimen. Consequently, there is an increasing clinical demand in pathology laboratories for HER2 testing of these tumors. This article provides an overview of the unique characteristics of HER2 protein expression and gene amplification in endometrial serous carcinoma and summarizes the HER2 scoring criteria used for patient enrollment in the recent clinical trial. Following the experience of guideline-development in other tumor types, the trial criteria should serve as the basis for future endometrial carcinoma-specific HER2 testing and scoring recommendations, to ensure therapeutic response in new patient cohorts. Thus, based on the clinical trial, the author proposes a specific HER2 testing algorithm for endometrial serous carcinoma to guide the current clinical practice. Future studies are necessary to refine and adjust these criteria to allow for appropriate triaging of patients and maximize the clinical benefit from HER2-targeted therapy.

TM4SF4 and LRRK2 Are Potential Therapeutic Targets in Lung and Breast Cancers through Outlier Analysis

Purpose

To find biomarkers for disease, there have been constant attempts to investigate the genes that differ from those in the disease groups. However, the values that lie outside the overall pattern of a distribution, the outliers, are frequently excluded in traditional analytical methods as they are considered to be ‘some sort of problem.’ Such outliers may have a biologic role in the disease group. Thus, this study explored new biomarker using outlier analysis, and verified the suitability of therapeutic potential of two genes (TM4SF4 and LRRK2).

Materials and Methods

Modified Tukey’s fences outlier analysis was carried out to identify new biomarkers using the public gene expression datasets. And we verified the presence of the selected biomarkers in other clinical samples via customized gene expression panels and tissue microarrays. Moreover, a siRNA-based knockdown test was performed to evaluate the impact of the biomarkers on oncogenic phenotypes.

Results

TM4SF4 in lung cancer and LRRK2 in breast cancer were chosen as candidates among the genes derived from the analysis. TM4SF4 and LRRK2 were overexpressed in the small number of samples with lung cancer (4.20%) and breast cancer (2.42%), respectively. Knockdown of TM4SF4 and LRRK2 suppressed the growth of lung and breast cancer cell lines. The LRRK2 overexpressing cell lines were more sensitive to LRRK2-IN-1 than the LRRK2 under-expressing cell lines

Conclusion

Our modified outlier-based analysis method has proved to rescue biomarkers previously missed or unnoticed by traditional analysis showing TM4SF4 and LRRK2 are novel target candidates for lung and breast cancer, respectively.

Analytical Challenges in Development of Chemoresistance Predictors for Precision Oncology

Chemoresistance, i.e., tumor insensitivity to chemotherapy, shortens life expectancy of cancer patients. Despite the availability of new treatment options, initial systemic regimens for solid tumors are dominated by a set of standard chemotherapy drugs, and alternative therapies are used only when a patient has demonstrated chemoresistance clinically. Chemoresistance predictors use laboratory parameters measured on tissue samples to predict the patient's response to chemotherapy and help to avoid application of chemotherapy to chemoresistant patients. Despite thousands of publications on putative chemoresistance predictors, there are only about a dozen predictors that are sufficiently accurate for precision oncology. One of the major reasons for inaccuracy of predictors is inaccuracy of analytical methods utilized to measure their laboratory parameters: an inaccurate method leads to an inaccurate predictor. The goal of this study was to identify analytical challenges in chemoresistance-predictor development and suggest ways to overcome them. Here we describe principles of chemoresistance predictor development via correlating a clinical parameter, which manifests disease state, with a laboratory parameter. We further classify predictors based on the nature of laboratory parameters and analyze advantages and limitations of different predictors using the reliability of analytical methods utilized for measuring laboratory parameters as a criterion. Our eventual focus is on predictors with known mechanisms of reactions involved in drug resistance (drug extrusion, drug degradation, and DNA damage repair) and using rate constants of these reactions to establish accurate and robust laboratory parameters. Many aspects and conclusions of our analysis are applicable to all types of disease biomarkers built upon the correlation of clinical and laboratory parameters.

A guide for bioinformaticians: 'omics-based drug discovery for precision oncology

Bioinformatics-centric drug development is inevitable in the era of precision medicine. Clinical 'omics information, including genomics, epigenomics, transcriptomics, and proteomics, provides the most comprehensive molecular landscape in which each patient's pathological history is delineated. Hence, the capability of bioinformaticians to manage integrative 'omics data is crucial to current drug development. Bioinformatics can accelerate drug development from initial time-consuming discoveries to the clinical stage by providing information-guided solutions. However, many bioinformaticians do not have opportunities to participate in drug discovery programs. As a starting point for bioinformaticians with no prior drug development experience, here we discuss bioinformatics applications during drug development with a focus on working-level omics-based methodologies.

CircRNAs in anticancer drug resistance: recent advances and future potential

CircRNAs are a novel class of RNA molecules with a unique closed continuous loop structure. CircRNAs are abundant in eukaryotic cells, have unique stability and tissue specificity, and can play a biological regulatory role at various levels, such as transcriptional and posttranscriptional levels. Numerous studies have indicated that circRNAs serve a crucial purpose in cancer biology. CircRNAs regulate tumor behavioral phenotypes such as proliferation and migration through various molecular mechanisms, such as miRNA sponging, transcriptional regulation, and protein interaction. Recently, several reports have demonstrated that they are also deeply involved in resistance to anticancer drugs, from traditional chemotherapeutic drugs to targeted and immunotherapeutic drugs. This review is the first to summarize the latest research on circRNAs in anticancer drug resistance based on drug classification and to discuss their potential clinical applications.

Machine learning approaches to drug response prediction: challenges and recent progress

Cancer is a leading cause of death worldwide. Identifying the best treatment using computational models to personalize drug response prediction holds great promise to improve patient's chances of successful recovery. Unfortunately, the computational task of predicting drug response is very challenging, partially due to the limitations of the available data and partially due to algorithmic shortcomings. The recent advances in deep learning may open a new chapter in the search for computational drug response prediction models and ultimately result in more accurate tools for therapy response. This review provides an overview of the computational challenges and advances in drug response prediction, and focuses on comparing the machine learning techniques to be of utmost practical use for clinicians and machine learning non-experts. The incorporation of new data modalities such as single-cell profiling, along with techniques that rapidly find effective drug combinations will likely be instrumental in improving cancer care.

Effect of immediate cold formalin fixation on phosphoprotein IHC tumor biomarker signal in liver tumors using a cold transport device

Phosphoproteins are the key indicators of signaling network pathway activation. Many disease treatment therapies are designed to inhibit these pathways and effective diagnostics are required to evaluate the efficacy of these treatments. Phosphoprotein IHC have been impractical for diagnostics due to inconsistent results occurring from technical limitations. We have designed and tested a novel cold transport device and rapid cold plus warm formalin fixation protocol using phosphoproteins IHC. We collected 50 liver tumors that were split into two experimental conditions: 2 + 2 rapid fixation (2 hours cold then 2 hour warm formalin) or 4 hour room-temperature formalin. We analyzed primary hepatocellular carcinoma (n = 10) and metastatic gastrointestinal tumors (n = 28) for phosphoprotein IHC markers pAKT, pERK, pSRC, pSTAT3, and pSMAD2 and compared them to slides obtained from the clinical blocks. Expression of pERK and pSRC, present in the metastatic colorectal carcinoma, were better preserved with the rapid processing protocol while pSTAT3 expression was detected in hepatocellular carcinoma. Differences in pSMAD2 expression were difficult to detect due to the ubiquitous nature of protein expression. There were only 3 cases expressing pAKT and all exhibited a dramatic loss of signal for the standard clinical workflow. The rapid cold preservation shows improvement in phosphoprotein preservation.

Delivery of polymeric nanostars for molecular imaging and endoradiotherapy through the enhanced permeability and retention (EPR) effect

Expression levels of biomarkers are generally unknown at initial diagnosis. The development of theranostic probes that do not rely on biomarker availability would expand therapy options for cancer patients, improve patient selection for nanomedicine and facilitate treatment of inoperable patients or patients with acquired therapy resistance. Herein, we report the development of star polymers, also known as nanostars, that allow for molecular imaging and/or endoradiotherapy based on passive targeting via the enhanced permeability and retention (EPR) effect. Methods: We synthesised a star copolymer, consisting of 7-8 centre-cross-linked arms that were modified with Gd³⁺ for magnetic resonance imaging (MRI), and functionalised either with ⁸⁹Zr for in vivo quantification and positron emission tomography (PET) imaging, or with ¹⁷⁷Lu for endoradiotherapy. ¹H longitudinal relaxivities were determined over a continuum of magnetic field strengths ranging from 0.24 mT - 0.94 T at 37 °C (nuclear magnetic relaxation dispersion (NMRD) profile) and T ₁-weighted MRI contrast enhancement was visualized at 3 T and 7 T. PET imaging and ex vivo biodistribution studies were performed in mice bearing tumours with high EPR (CT26) or low EPR (BxPC3) characteristics. Therapy studies were performed in mice with high EPR tumours and mean absorbed organ doses were estimated for a standard human model. Results: The star copolymer with Gd³⁺ displayed a significantly superior contrast enhancement ability (T ₁ = 0.60 s) compared to the standard clinical contrast agent Gadovist (T ₁ = 1.0 s). Quantification of tumour accumulation using the radiolabelled nanostars in tumour-bearing mice demonstrated an exceptionally high uptake in tumours with high EPR characteristics (14.8 - 21.7 %ID/g). Uptake of the star polymers in tumours with low EPR characteristics was significantly lower (P<0.001), suggesting passive tumour accumulation of the nanostars via the EPR effect. Survival of mice treated with high dose ¹⁷⁷Lu-labelled star polymers was significantly higher than survival of mice treated with lower therapy doses or control mice (P=0.001), demonstrating the utility of the ¹⁷⁷Lu-labelled star polymers as platforms for endoradiotherapy. Conclusion: Our work highlights the potential of star polymers as probes for the molecular imaging of cancer tissue or for the passive delivery of radionuclides for endoradiotherapy. Their high functionalisability and high tumour accumulation emphasises their versatility as powerful tools for nanomedicine.

Design of a New Peptide Substrate Probe of the Putative Biomarker Legumain with Potential Application in Prostate Cancer Diagnosis Ex Vivo

The lysosomal endoprotease legumain (asparaginyl endoprotease) has been proposed as a putative biomarker in prostate tumours, in which the enzyme is markedly overexpressed. Overexpression, coupled with highly selective specificity for cleavage of substrates at the C-terminus of asparagine (Asn) residues, make legumain an attractive biochemical target for potential diagnosis, prognosis and treatment. We report the design, synthesis, characterisation and preliminary evaluation of a new rhodamine-B (Rho-B)-labelled legumain peptide substrate probe5[Rho-Pro-Ala-Asn-PEG-AQ(4-OH)] and its selective targeting to lysosomes in PC3 prostate cancer cells. Probe5was efficiently activated by recombinant human legumain to afford the high quantum yield reporter fluorophore tripeptide4b(Rho-Pro-Ala-Asn-OH) with concomitant release of intense fluorescence. Furthermore, probe5was activated upon incubation with homogenates derived from fresh-frozen tissue material of prostatectomy specimens. Probe5represents a new viable biochemical tool for probing the activity of legumain with the potential to be used in ex vivo diagnostics in the cancer pathology laboratory.

Rapid tissue processing using a temperature-controlled collection device to preserve tumor biomarkers

Precision tissue diagnostics rely on high quality input specimens so that assay results are not affected by artifact, but advances in collection and processing of tissue specimens have lagged behind innovations in diagnostic assay development. Therefore, we have designed and evaluated a novel surgical tissue collection device that maintains and monitors sample temperature and motion throughout transport so that the major preanalytical variable of tissue temperature can be controlled and measured. This device, in combination with an improved cold–hot tissue fixation protocol affords optimal biomarker preservation in less overall time, thereby simultaneously improving diagnostic quality and turnaround time. We collected 50 primary and metastatic liver tumors using a novel transport device. Tissue was fixed using a rapid cold–hot fixation protocol and immunohistochemical assays were used to assess the performance of the device, in comparison to control tissue preserved using standard clinical fixation protocol. Two pathologists evaluated the IHC studies in a blinded fashion to determine the immunophenotype of each tumor. The observed IHC staining intensities and the clinical impressions of the immunophenotypes did not differ between tissue collected with the novel device and control tissue, while improvements in processing time were achieved. The novel cold transport device and rapid fixation protocol can be successfully and safely combined and used to monitor specimen conditions, thus preserving the diagnostic utility of specimens and improving the overall turn-around time of the diagnostic process.

Precise Immunodetection of PTEN Protein in Human Neoplasia

PTEN is a major tumor-suppressor protein whose expression and biological activity are frequently diminished in sporadic or inherited cancers. PTEN gene deletion or loss-of-function mutations favor tumor cell growth and are commonly found in clinical practice. In addition, diminished PTEN protein expression is also frequently observed in tumor samples from cancer patients in the absence of PTEN gene alterations. This makes PTEN protein levels a potential biomarker parameter in clinical oncology, which can guide therapeutic decisions. The specific detection of PTEN protein can be achieved by using highly defined anti-PTEN monoclonal antibodies (mAbs), characterized with precision in terms of sensitivity for the detection technique, specificity for PTEN binding, and constraints of epitope recognition. This is especially relevant taking into consideration that PTEN is highly targeted by mutations and posttranslational modifications, and different PTEN protein isoforms exist. The precise characterization of anti-PTEN mAb reactivity is an important step in the validation of these reagents as diagnostic and prognostic tools in clinical oncology, including their routine use in analytical immunohistochemistry (IHC). Here, we review the current status on the use of well-defined anti-PTEN mAbs for PTEN immunodetection in the clinical context and discuss their potential usefulness and limitations for a more precise cancer diagnosis and patient benefit.

Precise definition of PTEN C-terminal epitopes and its implications in clinical oncology

Anti-PTEN monoclonal antibodies (mAb) are arising as important tools for immunohistochemistry (IHC) and protein quantification routine analysis in clinical oncology. Although an effort has been made to document the reliability of tumor tissue section immunostaining by anti-PTEN mAb, and to standardize their IHC use in research and in the clinical practice, the precise topological and biochemical definition of the epitope recognized by each mAb has been conventionally overlooked. In this study, six commercial anti-PTEN mAb have been validated and characterized for sensitivity and specificity by IHC and FISH, using a set of prostate and urothelial bladder tumor specimens, and by immunoblot, using PTEN positive and PTEN negative human cell lines. Immunoblot precise epitope mapping, performed using recombinant PTEN variants and mutations, revealed that all mAb recognized linear epitopes of 6–11 amino acid length at the PTEN C-terminus. Tumor-associated or disease-associated mutations at the PTEN C-terminus did not affect subcellular localization or PIP3 phosphatase activity of PTEN in cells, although resulted in specific loss of reactivity for some mAb. Furthermore, specific mimicking-phosphorylation mutations at the PTEN C-terminal region also abolished binding of specific mAb. Our study adds new evidence on the relevance of a precise epitope mapping in the validation of anti-PTEN mAb for their use in the clinics. This will be substantial to provide a more accurate diagnosis in clinical oncology based on PTEN protein expression in tumors and biological fluids.

Biomarker-Drug and Liquid Biopsy Co-development for Disease Staging and Targeted Therapy: Cornerstones for Alzheimer's Precision Medicine and Pharmacology

Systems biology studies have demonstrated that different (epi)genetic and pathophysiological alterations may be mapped onto a single tumor’s clinical phenotype thereby revealing commonalities shared by cancers with divergent phenotypes. The success of this approach in cancer based on analyses of traditional and emerging body fluid-based biomarkers has given rise to the concept of liquid biopsy enabling a non-invasive and widely accessible precision medicine approach and a significant paradigm shift in the management of cancer. Serial liquid biopsies offer clues about the evolution of cancer in individual patients across disease stages enabling the application of individualized genetically and biologically guided therapies. Moreover, liquid biopsy is contributing to the transformation of drug research and development strategies as well as supporting clinical practice allowing identification of subsets of patients who may enter pathway-based targeted therapies not dictated by clinical phenotypes alone. A similar liquid biopsy concept is emerging for Alzheimer’s disease, in which blood-based biomarkers adaptable to each patient and stage of disease, may be used for positive and negative patient selection to facilitate establishment of high-value drug targets and counter-measures for drug resistance. Going beyond the “one marker, one drug” model, integrated applications of genomics, transcriptomics, proteomics, receptor expression and receptor cell biology and conformational status assessments during biomarker-drug co-development may lead to a new successful era for Alzheimer’s disease therapeutics. We argue that the time is now for implementing a liquid biopsy-guided strategy for the development of drugs that precisely target Alzheimer’s disease pathophysiology in individual patients.

Academic Discovery of Anticancer Drugs: Historic and Future Perspectives

The identification and prosecution of meritorious anticancer drug targets and the discovery of clinical candidates represent an extraordinarily time- and resource-intensive process, and the current failure rate of late-stage drugs is a critical issue that must be addressed. Relationships between academia and industry in drug discovery and development have continued to change over time as a result of technical and financial challenges and, most importantly, to the objective of translating impactful scientific discoveries into clinical opportunities. This Golden Age of anticancer drug discovery features an increased appreciation for the high-risk, high-innovation research conducted in the nonprofit sector, with the goals of infusing commercial drug development with intellectual capital and curating portfolios that are financially tenable and clinically meaningful. In this review, we discuss the history of academic-industry interactions in the context of antidrug discovery and offer a view of where these interactions are likely headed as we continue to reach new horizons in our understanding of the immense complexities of cancer biology.

PATRI, a Genomics Data Integration Tool for Biomarker Discovery

The availability of genomic datasets in association with clinical, phenotypic, and drug sensitivity information represents an invaluable source for potential therapeutic applications, supporting the identification of new drug sensitivity biomarkers and pharmacological targets. Drug discovery and precision oncology can largely benefit from the integration of treatment molecular discriminants obtained from cell line models and clinical tumor samples; however this task demands comprehensive analysis approaches for the discovery of underlying data connections. Here we introduce PATRI (Platform for the Analysis of TRanslational Integrated data), a standalone tool accessible through a user-friendly graphical interface, conceived for the identification of treatment sensitivity biomarkers from user-provided genomics data, associated with information on sample characteristics. PATRI streamlines a translational analysis workflow: first, baseline genomics signatures are statistically identified, differentiating treatment sensitive from resistant preclinical models; then, these signatures are used for the prediction of treatment sensitivity in clinical samples, via random forest categorization of clinical genomics datasets and statistical evaluation of the relative phenotypic features. The same workflow can also be applied across distinct clinical datasets. The ease of use of the PATRI tool is illustrated with validation analysis examples, performed with sensitivity data for drug treatments with known molecular discriminants.

New tools for old drugs: Functional genetic screens to optimize current chemotherapy

Despite substantial advances in the treatment of various cancers, many patients still receive anti-cancer therapies that hardly eradicate tumor cells but inflict considerable side effects. To provide the best treatment regimen for an individual patient, a major goal in molecular oncology is to identify predictive markers for a personalized therapeutic strategy. Regarding novel targeted anti-cancer therapies, there are usually good markers available. Unfortunately, however, targeted therapies alone often result in rather short remissions and little cytotoxic effect on the cancer cells. Therefore, classical chemotherapy with frequent long remissions, cures, and a clear effect on cancer cell eradication remains a corner stone in current anti-cancer therapy. Reliable biomarkers which predict the response of tumors to classical chemotherapy are rare, in contrast to the situation for targeted therapy. For the bulk of cytotoxic therapeutic agents, including DNA-damaging drugs, drugs targeting microtubules or antimetabolites, there are still no reliable biomarkers used in the clinic to predict tumor response. To make progress in this direction, meticulous studies of classical chemotherapeutic drug action and resistance mechanisms are required. For this purpose, novel functional screening technologies have emerged as successful technologies to study chemotherapeutic drug response in a variety of models. They allow a systematic analysis of genetic contributions to a drug-responsive or −sensitive phenotype and facilitate a better understanding of the mode of action of these drugs. These functional genomic approaches are not only useful for the development of novel targeted anti-cancer drugs but may also guide the use of classical chemotherapeutic drugs by deciphering novel mechanisms influencing a tumor’s drug response. Moreover, due to the advances of 3D organoid cultures from patient tumors and in vivo screens in mice, these genetic screens can be applied using conditions that are more representative of the clinical setting. Patient-derived 3D organoid lines furthermore allow the characterization of the “essentialome”, the specific set of genes required for survival of these cells, of an individual tumor, which could be monitored over the course of treatment and help understanding how drug resistance evolves in clinical tumors. Thus, we expect that these functional screens will enable the discovery of novel cancer-specific vulnerabilities, and through clinical validation, move the field of predictive biomarkers forward. This review focuses on novel advanced techniques to decipher the interplay between genetic alterations and drug response.

Physical biology of the cancer cell glycocalyx

The glycocalyx coating the outside of most cells is a polymer meshwork comprising proteins and complex sugar chains called glycans. From a physical perspective, the glycocalyx has long been considered a simple 'slime' that protects cells from mechanical disruption or against pathogen interactions, but the great complexity of the structure argues for the evolution of more advanced functionality: the glycocalyx serves as the complex physical environment within which cell-surface receptors reside and operate. Recent studies have demonstrated that the glycocalyx can exert thermodynamic and kinetic control over cell signalling by serving as the local medium within which receptors diffuse, assemble and function. The composition and structure of the glycocalyx change markedly with changes in cell state, including transformation. Notably, cancer-specific changes fuel the synthesis of monomeric building blocks and machinery for production of long-chain polymers that alter the physical and chemical structure of the glycocalyx. In this Review, we discuss these changes and their physical consequences on receptor function and emergent cell behaviours.