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Purpose:An astonishing amount of work has been put out in recent years to pinpoint biomarkers as possible resources for enhancing psychiatric preventive care, diagnosis, therapeutic response, and therapeutic development. In contrast to those other ailments, psychological illnesses include a diverse range of symptoms that are grouped into diagnostic categories. As a result, persons that have the same psychological disorder have a large deal of therapeutic variation. The above aspect, together with our poor understanding of the neurochemical imbalances driving dissociative ailments, leads to the existing pharmaceutical choices' low effectiveness. In this regard, the discovery of biomarkers in psychology or psychiatry, or any other clinical mental health sciences area is turning out to be crucial for facilitating diagnosis and through the creation of markers that enable the stratification of individuals inside this condition, which may then result in more targeted therapy choices. This evaluation presents a practical description of therapeutic development along with an overview of the idea and several categories of biomarkers to throw light on the subject. Additionally, the developments in this area were compiled and divided into the following groups, including "genetics, transcriptomics, proteomics, metabolomics, and epigenetics". Objective:The main objective of this paper is to shed some light on the area of neuropsychological disorder and its variations as well as common biomarker assessment methods to detect it. Another core objective of this paper is to rule out "omics" techniques used in the domain of neuropsychology or general psychiatry to detect some complicated and common mental disorders. Design/Methodology/Approach:This clinical paper is prepared by using secondary data from various official and authentic websites and journal papers. In order to make the paper more accurate and scientific in nature, primary data has also been utilized to build this paper. Expert opinions from neuropsychologists, general psychiatrists, and researchers who are doing research in the domain of neuropsychology have also been taken to guarantee the reliability of the paper. Findings/Result:Despite the positive outcomes, there have been few biomarker studies, particularly those that focus on the treatment of mental illnesses. This study's outcome makes a comment on the difficulties that will need to be overcome in the possible future in order to generate credible, trustworthy, and widely applicable biomarkers for mental diseases and their intervention. An essential first step towards the construction of more customized therapy is the detection of characteristics that forecast response to therapy since doing so will decrease drug switching based on trial and error and facilitate the development of novel successful medicines. Originality and Value:A scientific and more comprehensive approach has been taken to provide better information about neuropsychological/general psychiatric disorders and their detection by using histological or any other clinically proven methods. A new stratagem is applied to shed light on the topic of neuropsychological illnesses and their detection by using the biomarker method. Paper Type: Clinical analysis/interpretive paper

The primary goal of precision medicine is to maximize the benefit‐risk relationships for individual patients by delivering the right drug to the right patients at the right dose. To achieve this goal, it has become increasingly important to assess gene‐drug interactions (GDIs) in clinical settings. The US Food and Drug Administration (FDA) periodically updates the table of pharmacogenetic/genomic (PGx) biomarkers in drug labeling on their website. As described herein, an effort was made to categorize various PGx biomarkers covered by the FDA‐PGx table into certain groups. There were 2 major groups, oncology molecular targets (OMT) and drug‐metabolizing enzymes and transporters (DMETs), which constitute ~70% of all biomarkers (~33% and ~35%, respectively). These biomarkers were further classified whether their labeling languages could be actionable in clinical practice. For OMT biomarkers, ~70% of biomarkers are considered actionable in clinical practice as they are critical for the selection of appropriate drugs to individual patients. In contrast, ~30% of DMET biomarkers are considered actionable for the dose adjustments or alternative therapies in specific populations, such as CYP2C19 and CYP2D6 poor metabolizers. In addition, the GDI results related to some of the other OMT and DMET biomarkers are considered to provide valuable information to clinicians. However, clinical GDI results on the other DMET biomarkers can possibly be used more effectively for dose recommendation. As the labels of some drugs already recommend the precise doses in specific populations, it will be desirable to have clear language for dose recommendation of other (or new) drugs if appropriate.

HCC surveillance should be performed as recommended in patients with chronic hepatitis B and patients with cirrhosis to reduce HCC‐related mortality. Semiannual abdominal ultrasound and AFP is currently the best strategy to maximize early detection; however, this strategy misses more than one‐third of HCCs at an early stage, highlighting the need for better tests. MRI‐based imaging strategies may increase sensitivity for early HCC detection, although there are potential concerns about cost‐effectiveness and radiological capacity. In parallel, there are several promising blood‐based protein and genomic biomarker panels that have shown high sensitivity for early HCC detection in early case‐control studies. These biomarkers still require validation in large cohort studies but have potential to truly transform how we perform HCC surveillance among at‐risk patients.

Background Because of the growing number of actionable biomarkers in non-small cell lung cancer (NSCLC), sufficient tissue availability for testing is becoming a greater challenge. Liquid biopsy offers a potential solution by complementing standard tissue-based methods. In this study, the authors analyzed the concordance of actionable genomic alterations sequenced from circulating tumor DNA (ctDNA; Guardant360) and tissue (Oncomine Focus Assay). Methods From September 2015 to May 2018, 421 paired plasma and tissue samples from patients with advanced NSCLC who had previously undergone tissue testing by standard methods were collected. Both types of samples were available for 287 patients (262 in cohort 1 [treatment-naive] and 25 in cohort 2 [treatment failure]), and only 1 sample type was available for 134 patients (50 in cohort 3 [plasma only] and 84 in cohort 4 [tissue only]). Results In cohort 1, 198 samples (77.6%) showed concordance between tissue and plasma next-generation sequencing (NGS). Among the discordant cases, plasma testing detected additional genomic alterations in 11 patients (4.2%). In 50 patients without tissue-based NGS results (cohort 3), the ctDNA-based test detected genomic alterations in 20 samples (40.0%). The median allele frequency (AF) of mutations identified with ctDNA-based NGS (0.74%) was lower than that identified with the tissue-based NGS test (13.90%). Clinical responses to matched targeted therapy occurred, regardless of the ctDNA AF. Upfront ctDNA-based testing identified 60.4% of patients with genomic alterations. In addition, ctDNA-based testing uncovered 12.0% more actionable alterations when it was performed after tissue-based NGS testing. Conclusions The results indicate that a ctDNA-based test identifies additional patients with actionable genomic alterations and could, therefore, be used to complement traditional tissue-based testing for NSCLC. Lay summary Circulating tumor DNA (ctDNA)-based next-generation sequencing (NGS) testing is becoming essential as the number of actionable genomic biomarker increases for the treatment selection of non-small cell lung cancer. This study demonstrates the additive value of ctDNA-based testing in addition to tissue-based NGS and standard of care-based biomarker testing for detecting additional patients with actionable genomic alterations. Clinical responses have also been observed in patients with a low allele frequency detected by ctDNA-based NGS testing.

Precision therapy for a subgroup of genetically defined metastatic castration-resistant prostate cancer patients may become a reality in the near future. DNA damage repair gene mutated prostate cancer might be vulnerable to treatment with PARP inhibitors (PARPi). PARPi clinical trials for prostate cancer investigate both germline and somatic genomic alterations of 43 genes for the applicability as genomic biomarker of PARPi sensitivity. Clinical trials with preliminary results show that BRCA2 and BRCA1, but also ATM, additionally BRIP1, FANCA, CDK12 and PALB2 may affect clinical end points, and may be potential candidates for genome-guided patient selection in PARPi treatment of prostate cancer.

Although the application of human mesenchymal stem cells (hMSCs) to repair damaged or diseased tissues has proven relatively effective, both the donor-to-donor variability in ex vivo expansion rates and the maintenance of stemness remain a bottleneck to widespread translation. Previous work from this laboratory stratified donors into those yielding hMSCs with high- or low-growth capacity; global transcriptomic analysis revealed that high-growth-capacity hMSCs were characterized by a loss of the gene encoding glutathione S-transferase theta 1 (GSTT1). These GSTT1-null hMSCs demonstrated increased proliferative rates, clonogenic potential, and longer telomeres compared with low-growth capacity hMSCs that were GSTT1-positive. Thus, this study identifies GSTT1 as a novel genomic DNA biomarker for hMSC scalability.

Recent discoveries elucidating the genetic underpinnings of glial neoplasms have revealed myriad recurrent alterations that have clinical value by improving accuracy of diagnosis and prognosis. Furthermore, this wealth of genomic information provides the basis for targeted therapies and the subsequent design of biomarker-based clinical trials. This review summarizes the current landscape of clinically relevant molecular alterations in gliomas and describes the role of routine molecular testing in context of treatment planning for standards of care and clinical trials.

Biomarker surveillance after solid organ transplant is an advancing field with promise for further elucidation through high-throughput analyses and “omics” technologies. To date, Gene Expression Profiling (GEP; AlloMapâ) is the only FDA-cleared genomic assay as surveillance for moderate-severe TCMR versus allograft quiescence after heart transplantation. Clinical validity and utility, however, have been recently established after kidney and heart transplantation, for analysis of donor-derived cell-free DNA in the assessment for potential “Allograft Tissue Injury” that may occur during diverse processes such as T-Lymphocyte mediated (TCMR) or Antibody-mediated (ABMR) allograft rejection or allograft-associated infection. These genomic analytes are currently being explored and evaluated across the spectrum of solid organ transplantation and the focus of numerous robust clinical transplant study registries. “Multi-Modality Surveillance” is currently firmly established within the armamentarium and practice algorithms of the transplant center specialist. However, such diagnostics which are performed on peripheral blood samples obtained during routine laboratory surveillance are now available and widely applicable during the inevitable clinical transition in care that is increasingly relegated to the General Practitioner. Noninvasive Biomarker assessment may prove invaluable for surveillance to thereby avoid the predominance of invasive protocol biopsy procedures. Further, during times of increased risk of infectious contagion exposure, such as the SARS-CoV-2 (CoVid-19) pandemic, laboratory Biomarker surveillance may prove invaluable in deferring routine scheduled office or clinic appointments and the inherent exposures.

Proactive identification and characterization of hazards attributable to chemicals are central aspects of risk assessments. Current legislations and trends in predictive toxicology advocate a transition from in vivo methods to nonanimal alternatives. For skin sensitization assessment, several OECD validated alternatives exist for hazard identification, but nonanimal methods capable of accurately characterizing the risks associated with sensitizing potency are still lacking. The GARD (Genomic Allergen Rapid Detection) platform utilizes exposure-induced gene expression profiles of a dendritic-like cell line in combination with machine learning to provide hazard classifications for different immunotoxicity endpoints. Recently, a novel genomic biomarker signature displaying promising potency-associated discrimination between weak and strong skin sensitizers was proposed. Here, we present the adaptation of the defined biomarker signature on a gene expression analysis platform suited for routine acquisition, confirm the validity of the proposed biomarkers, and define the GARDpotency assay for prediction of skin sensitizer potency. The performance of GARDpotency was validated in a blinded ring trial, in accordance with OECD guidance documents. The cumulative accuracy was estimated to 88.0% across 3 laboratories and 9 independent experiments. The within-laboratory reproducibility measures ranged between 62.5% and 88.9%, and the between-laboratory reproducibility was estimated to 61.1%. Currently, no direct or systematic cause for the observed inconsistencies between the laboratories has been identified. Further investigations into the sources of introduced variability will potentially allow for increased reproducibility. In conclusion, the in vitro GARDpotency assay constitutes a step forward for development of nonanimal alternatives for hazard characterization of skin sensitizers.

Recently, genomic biomarkers have been widely used clinically for prediction of the efficacy and safety of pharmacotherapy and diagnosis and prognosis of pathological conditions. Therefore, genomic biomarkers are anticipated to accelerate not only precision medicine for pharmacotherapy but also development of molecularly targeted drugs. Because the design of clinical studies involving biomarkers may differ from conventional clinical study designs, a concept paper focused on clinical studies and patient selection methods based on genomic biomarkers is desired to prompt innovative drug development. Thus, this concept paper aimed to compile and present current scientific information from the related guidelines regarding application of genomic biomarkers to clinical trials and studies for drug development. We hope that this concept paper will prompt the development of guidelines for biomarker application to drug development by industry, regulatory authorities, the medical profession, and academia.

Genomic aberrations of neuroblastoma occurring in late childhood and adolescence are still understudied. Publicly available DNA copy number profiles of 556 tumors (discovery set) and of 208 tumors obtained by array-CGH assay (validation set) were used to test if 19p loss is significantly over-represented in children and adolescents with neuroblastoma. The 19p loss occurrence was separately tested within different age groups in the discovery and validation set and the resulting P values were combined by meta-analysis and corrected by Bonferroni’s method. In both sets, 19p loss was associated with older age at diagnosis. Particularly, the lowest age group significantly associated with 19p loss (discovery set: 20%; validation set: 35%) was 6 years. The 19p loss correlated with inferior overall survival in patients over 6 years of age. Relevant tumor suppressor genes (KEAP1, DNM2, SMARCA4, SLC44A2 and CDKN2D) and microRNAs (miR-181c, miR-27a, and mirR-199a-1) are located in the genomic region involved in 19p loss. Downregulation of DNM2, SLC44A2 and CDKN2D was associated with poor patient outcome and older age. Among the recurrent NB chromosomal aberrations, only 1q gain was enriched in patients older than 6, and its presence was mutually exclusive with respect to 19p loss. Our data demonstrate that 19p loss is a genomic biomarker of NB diagnosed in older children that can predict clinical outcome.

Background The lack of an accurate blood biomarker in neuroendocrine tumor (NET) disease has hindered management. The advance of genomic medicine and the development of molecular biomarkers has provided a strategy—liquid biopsy—to facilitate real-time management. We reviewed the role of a blood mRNA-based NET biomarker, the NETest, as an in vitro diagnostic (IVD). Patients and methods A systematic review of the literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was undertaken. The methodological quality was evaluated using the QUADAS-2 tool. We identified ten original scientific papers that met the inclusion criteria. These were assessed by qualitative analysis and thereafter meta-analysis. Data were pooled and a median [95% confidence interval (CI)] diagnostic odds ratio (DOR), positive likelihood ratio (+LR), and negative likelihood ratio (−LR) were calculated. For the meta-analysis, a generic inverse variance method was undertaken using the accuracy and area under the curve (AUC) data. Results The ten studies exhibited moderate to high methodological quality. They evaluated NETest usage both as a diagnostic and as a monitoring tool. The meta-analysis identified the diagnostic accuracy of the NETest to be 95%–96% with a mean DOR of 5 853, +LR of 195, and −LR of 0.06. The NETest was 84.5%–85.5% accurate in differentiating stable disease from progressive disease. As a marker of natural history, the accuracy was 91.5%–97.8%. As an interventional/response biomarker, the accuracy was 93.7%–97.4%. The pooled AUC for the NETest was 0.954 ± 0.005, with a z-statistic of 175.06 (P Conclusions The NETest is an accurate biomarker suitable for clinical use in NET disease management. The meta-analysis supports the utility of the NETest as an IVD to establish a diagnosis and monitor therapeutic efficacy. The use of this as a biomarker provides information relevant to NET management consistent with observations regarding utility of liquid biopsies in other oncological disciplines.

Simple Summary Historically, the treatment of prostate cancer was a blanket approach for all. Prostate cancer has not benefitted from targeted treatments based on specific tumour characteristics (ie. Particular genetic or molecular patterns) the way other cancers have. This is important as studies have shown that prostate cancer patients with certain errors in their genes, such as BRCA2 or BRCA1, are more likely to have worse disease and poorer outcome. These patients can be treated successfully with a group of drugs called ‘PARP inhibitors’. This paper examines the prognostic, clinical and therapeutic role of BRCA2/BRCA1 mutations across the evolution of PCa. The impact of the inclusion of BRCA genes on genetic screening will also be outlined. Abstract Studies have demonstrated that men with Prostate Cancer (PCa) harboring BRCA2/BRCA1 genetic aberrations, are more likely to have worse disease and a poorer prognosis. A mutation in BRCA2 is known to confer the highest risk of PCa for men (8.6 fold in men ≤65 years) making BRCA genes a conceivable genomic biomarker for risk in PCa. These genes have attracted a lot of research attention however their role in the clinical assessment and treatment of PCa remains complex. Multiple studies have been published examining the relationship between prostate cancer and BRCA mutations. Here BRCA mutations are explored specifically as a biomarker for risk in PCa. It is in this context, we examined the prognostic, clinical and therapeutic role of BRCA2/BRCA1 mutations across the evolution of PCa. The impact of the inclusion of BRCA genes on genetic screening will also be outlined.

Although estrogen-receptor-positive (ER+) breast cancer is generally associated with favorable prognosis, clinical outcome varies substantially among patients. Genomic assays have been developed and applied to predict patient prognosis for personalized treatment. We hypothesize that the recurrence risk of ER+ breast cancer patients is determined by both genomic mutations intrinsic to tumor cells and extrinsic immunological features in the tumor microenvironment. Based on the Cancer Genome Atlas (TCGA) breast cancer data, we identified the 72 most common genomic aberrations (including gene mutations and indels) in ER+ breast cancer and defined sample-specific scores that systematically characterized the deregulated pathways intrinsic to tumor cells. To further consider tumor cell extrinsic features, we calculated immune infiltration scores for six major immune cell types. Many individual intrinsic features are predictive of patient prognosis in ER+ breast cancer, and some of them achieved comparable accuracy with the Oncotype DX assay. In addition, statistical learning models that integrated these features predicts the recurrence risk of patients with significantly better performance than the Oncotype DX assay. As a proof-of-concept, our study indicates the great potential of genomic and immunological features in prognostic prediction for improving breast cancer precision medicine. The framework introduced in this work can be readily applied to other cancers.Author SummaryMany genomic biomarker tests such as Oncotype DX have been developed for breast cancer and have helped guide clinical decisions. We have developed gene signatures to integrate cancer genomic and transcriptomic data to characterize the downstream effect of driver genomic events. These signatures recapitulate the de-regulated pathways underlying the corresponding driver genomic events and are more correlated with clinical phenotypes such as recurrence free survival than mutation status alone. We apply this framework to ER+ breast cancer and define gene signatures for a total of 72 most commonly observed genomic events including gene mutations, amplifications and deletions. We find that many of these gene signatures are predictive of patient prognosis in ER+ breast cancer, and some of them achieved comparable accuracy with the Oncotype DX assay. We combine these tumor-intrinsic signatures with infiltration signatures for major immune cell types (tumor-extrinsic features) to construct integrative models for prognosis prediction. The models predicts the recurrence risk of patients with significantly better performance than the Oncotype DX assay.

In a trial of patients with high grade serous ovarian cancer (HGSOC), addition of the ATR inhibitor berzosertib to gemcitabine improved progression free survival (PFS) compared to gemcitabine alone but biomarkers predictive of treatment are lacking. Here we report a candidate biomarker of response to gemcitabine versus combined gemcitabine and ATR inhibitor therapy in HGSOC ovarian cancer. Patients with replication stress (RS)-high tumors (n = 27), defined as harboring at least one genomic RS alteration related to loss of RB pathway regulation and/or oncogene-induced replication stress achieve significantly prolonged PFS (HR = 0.38, 90% CI, 0.17–0.86) on gemcitabine monotherapy compared to those with tumors without such alterations (defined as RS-low, n = 30). However, addition of berzosertib to gemcitabine benefits only patients with RS-low tumors (gemcitabine/berzosertib HR 0.34, 90% CI, 0.13–0.86) and not patients with RS-high tumors (HR 1.11, 90% CI, 0.47–2.62). Our findings support the notion that the exacerbation of RS by gemcitabine monotherapy is adequate for lethality in RS-high tumors. Conversely, for RS-low tumors addition of berzosertib-mediated ATR inhibition to gemcitabine is necessary for lethality to occur. Independent prospective validation of this biomarker is required., A randomized phase 2 study recently showed that the addition of ATR inhibitor berzosertib to gemcitabine improved PFS compared to gemcitabine alone in patients with ovarian cancer. In this preplanned exploratory study, the authors demonstrate that a genomic biomarker of replication-stress is associated with outcome to gemcitabine alone and may predict which patients benefit from addition of the ATR inhibitor berzosertib.

[Abastract] The purpose of the “Micronuclei and Disease” special issue (SI) is to: (i) Determine the level of evidence for association of micronuclei (MN), a biomarker of numerical and structural chromosomal aberrations, with risk of specific diseases in humans; (ii) Define plausible mechanisms that explain association of MN with each disease; (iii) Identify knowledge gaps and research needed to translate MN assays into clinical practice. The “MN and Disease” SI includes 14 papers. The first is a review of mechanisms of MN formation and their consequences in humans. 11 papers are systematic reviews and/or meta-analyses of the association of MN with reproduction, child health, inflammation, auto-immune disease, glycation, metabolic diseases, chronic kidney disease, cardiovascular disease, eleven common cancers, ageing and frailty. The penultimate paper focuses on effect of interventions on MN frequency in the elderly. A road map for translation of MN data into clinical practice is the topic of the final paper. The majority of reviewed studies were case-control studies in which the ratio of mean MN frequency in disease cases relative to controls, i.e. the mean ratio (MR), was calculated. The mean of these MR values, estimated by meta-analyses, for lymphocyte and buccal cell MN in non-cancer diseases were 2.3 and 3.6 respectively, and for cancers they were 1.7 and 2.6 respectively. The highest MR values were observed in studies of cancer cases in which MN were measured in the same tissue as the tumour (MR = 4.9–10.8). This special issue is an important milestone in the evidence supporting MN as a reliable genomic biomarker of developmental and degenerative disease risk. These advances, together with results from prospective cohort studies, are helping to identify diseases in which MN assays can be practically employed in the clinical setting to better identify high risk patients and to prioritise them for preventive therapy.

Clinical trial designs targeting patient subgroups with certain genetic characteristics may enhance the efficiency of developing drugs for cardiovascular disease (CVD). To evaluate the extent to which genetic knowledge translates to the CVD pipeline, we analyzed how genomic biomarkers are utilized in trials. Phase II and III trial protocols for investigational new drugs for CVD and risk factors were evaluated for prospective and exploratory genomic biomarker use; drug targets were evaluated for the presence of evidence that genetic variations can impact CVD risk or drug response. We identified 134 programs (73 unique drug targets) and 147 clinical trials. Less than 1% (n = 1/147) trials used a genomic biomarker prospectively for in-trial enrichment despite 32% (n = 23/73) of the drug targets having evidence of genetic variations. Additionally, 46% (n = 68/147) of the trials specified exploratory biomarker use. The results highlight an opportunity for more targeted CVD drug development by leveraging genomic biomarker knowledge.

Alcaligenes javaensis is a food-borne, Gram-negative bacterium, which has the closest relationship with Alcaligenes faecalis commonly causing contamination of clinical equipment and infection on humans. Hence, the development of a biomarker for members of genus Alcaligenes including A.javaensis is needed for early detection of this bacterium. This research aimed to screen the performance of 5 restriction enzymes consisting of EcoRV, Hind III, NotI, SalI, and BgIII as singles and in combinations to produce specific pattern of restricted, genomic DNA fragments of A.javaensis. Total DNA was first extracted from subcultured A.javaensis cells using DNA isolation kit and the obtained DNA isolate was then subjected to enzyme restrictions in vitro. Later, the restricted DNA fragments were evaluated using agarose gel electrophoresis method. Results showed that only the EcoRV-BgIII-HindIII combination within a restriction process of 18 h could produce smaller-sized DNA bands, while those from other combinations (5 enzymes as singles and their 2-combinations) could not do so. As conclucion, the combination of EcoRV-BgIII-HindIII is the most potential among the evaluated endonucleases to be used as a genomic biomarker for A.javaensis. Article DOI: https://dx.doi.org/10.20319/lijhls.2019.51.6272 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

For clinical trials, molecular heterogeneity has played a more important role recently. Many novel clinical trial designs prospectively incorporate molecular information to evaluation of treatment ...

Recent advances in surgical techniques, bladder cancer molecular biology discoveries, access to point-of-care genomic biomarker platforms, and therapy options in metastatic bladder cancer patients suggest that significant improvements in the options and outcomes for patients with muscle-invasive bladder cancer (MIBC) are achievable. Indeed, multiple MIBC clinical trials with practice-changing impact are ongoing. This chapter provides a synopsis of the key populations, designs, and endpoints under investigation in these important MIBC trials.

INTRODUCTION: LUME-Colon 1 (NCT02149108) was a global, placebo-controlled phase III study of nintedanib in advanced colorectal cancer (CRC). Pre-specified biomarker analyses investigated the association of CRC consensus molecular subtypes (CMS) and tumor genomic and circulating biomarkers with clinical outcomes. MATERIALS AND METHODS: Archival tumor tissue, cell-free DNA (cfDNA), and plasma samples were collected for genomic, transcriptomic, and proteomic analyses to investigate potential associations between CRC CMS and other biomarkers with nintedanib response and clinical outcomes. RESULTS: Of the 765 treated patients, 735, 245, and 192 patient samples were analyzed in the circulating protein, tumor tissue, and cfDNA datasets, respectively. Patients were classified as CMS1 (1.7%), CMS2 (27.7%), CMS3 (0.9%), CMS4 (51.5%), or unclassified (18.2%). Unclassified/mixed CMS was associated with longer overall survival (OS) with nintedanib vs. CMS2 or CMS4 (interaction P-value = .0086); no association was observed for CMS4. Gene expression-based pathway analysis revealed an association between vascular endothelial growth factor-related signaling and OS for nintedanib (P = .0498). The most frequently detected somatic mutations were APC (72.0% [tumor tissue] vs. 56.8% [cfDNA]), TP53 (47.1% vs. 34.9%), KRAS (40.8% vs. 28.6%), and PIK3CA (16.6% vs. 11.5%); concordance rates were > 80%. Median OS differences were observed for APC and TP53 mutations vs. wild-type in cfDNA, indicating a potential prognostic value. Circulating ANG-2, CA-9, CEACAM1, collagen-IV, IGFBP-1, ICAM-1, IL-8, and uPAR were potentially prognostic for both OS and progression-free survival. CONCLUSION: We demonstrated the feasibility of large-scale biomarker analyses and CMS classification within a global clinical trial, and identified signals suggesting a potential for greater nintedanib treatment response in the unclassified/mixed CMS subgroup, despite these tumors showing heterogeneous patterns of CMS mixtures. Our results revealed a high degree of concordance in somatic mutations between tumor tissue and cfDNA. Associations with prognosis for cfDNA somatic mutations, as well as several protein-based biomarkers, may warrant further investigation in future trials. ispartof: CLINICAL COLORECTAL CANCER vol:20 issue:1 pages:84-+ ispartof: location:United States status: published

Comprehensive genomic profiling enables genomic biomarker detection in advanced solid tumors. Here, to evaluate the utility of circulating tumor DNA (ctDNA) genotyping, we compare trial enrollment using ctDNA sequencing in 1,687 patients with advanced gastrointestinal (GI) cancer in SCRUM-Japan GOZILA (no. UMIN000016343), an observational ctDNA-based screening study, to enrollment using tumor tissue sequencing in the same centers and network (GI-SCREEN, 5,621 patients). ctDNA genotyping significantly shortened the screening duration (11 versus 33 days, P

Background: HER2-positive (HER2+) breast cancer (BC) comprises all the four PAM50 molecular subtypes. Among these, the HER2-Enriched (HER2-E) appear to be associated with higher pathological complete response (pCR) rates following anti-HER2-based regimens. Here, we present a meta-analysis to validate the association of the HER2-E subtype with pCR following anti-HER2-based neoadjuvant treatments with or without chemotherapy (CT). Methods: A systematic literature search was performed in February 2019. The primary objective was to compare the association between HER2-E subtype (versus others) and pCR. Selected secondary objectives were to compare the association between 1) HER2-E subtype and pCR in CT-free studies, 2) HER2-E subtype within hormone receptor (HR)-negative and HR+ disease and 3) HR-negative disease (versus HR+) and pCR in all patients and within HER2-E subtype. A random-effect model was applied. The Higgins��� I2 was used to quantify heterogeneity. Results: Sixteen studies were included, 5 of which tested CT-free regimens. HER2-E subtype was significantly associated with pCR in all patients (odds ratio [OR] = 3.50, p < 0.001, I2 = 33%), in HR+ (OR = 3.61, p < 0.001, I2 = 1%) and HR-negative tumors (OR = 2.28, p = 0.01, I2 = 47%). In CT-free studies, HER2-E subtype was associated with pCR in all patients (OR = 5.52, p < 0.001, I2 = 0%) and in HR + disease (OR = 4.08, p = 0.001, I2 = 0%). HR-negative status was significantly associated with pCR compared to HR + status in all patients (OR = 2.41, p < 0.001, I2 = 30%) and within the HER2-E subtype (OR = 1.76, p < 0.001, I2 = 0%). Conclusions: The HER2-E biomarker identifies patients with a higher likelihood of achieving a pCR following neoadjuvant anti-HER2-based therapy beyond HR status and CT use. Future trial designs to escalate or de-escalate systemic therapy in HER2+ disease should consider this genomic biomarker.

Enrichment design is a common strategy in personalized medicine in which treatments are given only to patients who are tested positive on a genomic biomarker. Such a targeted trial, as compared to the untargeted trials in which all patients receive the treatments, can substantially reduce the sample size needed for a study as demonstrated in the literature for two-arm trials. To fill in the gaps in the existing literature, we consider trials to evaluate a targeted treatment by comparing pre–post trial outcomes to investigate the intervention effect after the treatment. We investigate the relative efficiency in terms of sample size reduction of an enrichment design against the conventional design, focusing on binary endpoints. The effects of misclassification of the genomic classifier on the relative efficiency are also investigated.

Background: Cyclin-dependent kinase (CDK) 4/6 inhibitors are being evaluated in the adjuvant setting for patients with resected early stage hormone receptor positive (HR+) and HER2 negative (HER2-) breast cancer (BC). However, biomarkers that predict benefit from this class of agents are unknown. We have recently reported results from the phase II neoadjuvant NeoPalAna trial which demonstrated that palbociclib (Pal) enhanced the anti-proliferative activity when added upon anastrozole (Ana) monotherapy in estrogen receptor (ER) positive and HER2 negative breast cancers. Interestingly, a small group of patients was resistant to Pal, exhibiting persistent tumor cell proliferation (Ki67 >2.7%) on the combination of Ana and Pal. In this study, we evaluated the utility of a research algorithm for the 70-gene signature (70-GS) in identifying Pal resistant versus sensitive patients. Methods: Serial biopsies were collected from patients at four treatment timepoints: baseline (BL), cycle 1 day 1 (C1D1) following 28 days of Ana monotherapy, cycle 1 day 15 (C1D15) at 2 weeks post the addition of Pal, and at surgery (Surg). RNA was extracted from frozen tumor biopsies at each timepoint and run on Agilent full genome microarrays (GSE93204) at Washington University. As an exploratory analysis, genes from the GPL8253 array that match the 70-GS were used to calculate a research approximation of the 70-GS index (r-GS). The distribution of the r-GS across Ki67 response groups was evaluated. Results: Ki67 had previously been measured at each timepoint, and used to classify patients as being either Ana-sensitive (C1D1 Ki67 ≤2.7%), Pal-sensitive (C1D1 Ki67 >2.7%, C1D15 Ki67 ≤2.7%), or Pal-resistant (C1D15 Ki67 >2.7%). The r-GS was differentially regulated between sensitive (AI or Pal) and Pal-resistant groups at BL (p=0.012), C1D1 (p=0.039), and C1D15 (p=0.022). The r-GS values varied widely across patients at BL, and generally became more positive (more low risk) with treatment. There was no correlation between Ki67 levels and r-GS. Furthermore, gene expression analysis was performed to elucidate the difference between Pal-sensitive vs. Pal-resistant patients, and Ana-sensitive vs. Pal-sensitive patients. Conclusions: While on-treatment Ki67 indicated drug responsiveness, baseline r-GS significantly stratified patients into sensitive (Ana or Pal) versus Pal-resistant groups in the neoadjuvant setting. This preliminary finding suggests that the 70-GS may have clinical utility in identifying patients resistant to Pal for future studies. Additionally, results of the gene expression analysis may help to further develop genomic biomarkers for Pal and Ana sensitivity and resistance. Citation Format: Hoog JW, Treece T, Blumencranz L, Audeh W, Sanati S, Ellis MJ, Ma CX. Genomic biomarker for resistance to palbociclib in the NeoPalAna trial [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-09-19.

Introduction Although a growing list of essential genomic/immune-based biomarkers are linked to approved non-small-cell lung cancer (NSCLC) therapies worldwide, few reports have detailed the evolution of NSCLC predictive biomarker assessment in routine clinical practice. Methods We retrospectively reviewed the first one thousand plus NSCLC patient specimens from our institution analyzed for predictive biomarkers from 2004 to 2017 and evaluated patterns of testing as well as correlation with clinical-pathologic characteristics. Results The majority of 1009 NSCLC patients had advanced stages of adenocarcinoma with most tissues obtained from the lung, mediastinal/hilar nodes, or pleura. The majority of testing was performed on cytology or small biopsy specimens. All were tested for EGFR mutations, 895 for ALK rearrangement, 841 for KRAS mutation, 537 for ROS1 rearrangement, and 179 using comprehensive genomic profiling. Implementation of near-universal genomic biomarker testing at our institution for EGFR, ALK, ROS1 and PD-L1 all occurred within the first year following evidence of clinical activity or regulatory body approval of an associated inhibitor. The overall testing failure rate after use of the best specimen for the most common tests was ≤5.5%. A quarter of tumors had a driver oncogene identified (EGFR/ALK/ROS1/BRAF V600E) with an approved oral targeted therapy, with the highest prevalence in those patients with no or light (≤15 pack-years) history of tobacco use. Conclusions Tumor biomarker testing using clinical NSCLC specimens in routine oncologic care evolves rapidly following approval of targeted therapies linked to diagnostic assays. Our practice’s decade plus experience highlights the rapid evolution of biomarker testing and confirms the therapeutic relevance of such testing in all patients—particularly those patients with light/no history of tobacco use.

In vitro gene expression signatures to predict toxicological responses can provide mechanistic context for regulatory testing. We previously developed the TGx-28.65 genomic biomarker from a database of gene expression profiles derived from human TK6 cells exposed to 28 well-known compounds. The biomarker comprises 65 genes that can classify chemicals as DNA damaging or non-DNA damaging. In this study, we applied the TGx-28.65 genomic biomarker in parallel with the in vitro micronucleus (MN) assay to determine if two chemicals of regulatory interest at Health Canada, disperse orange (DO: the orange azo dye 3-[[4-[(4-Nitrophenyl)azo]phenyl] benzylamino]propanenitrile) and 1,2,4-benzenetriol (BT: a metabolite of benzene) are genotoxic or non-genotoxic. Both chemicals caused dose-dependent declines in relative survival and increases in apoptosis. A strong significant increase in MN induction was observed for all concentrations of BT; the top two concentrations of DO also caused a statistically significant increase in MN, but these increases were