Biomarker Selection Research Articles

The impact of single gene testing (SGT) on subsequent comprehensive genomic profiling (CGP) success in community oncology practice for advanced non-small cell lung cancer (NSCLC): Results from a prospective observational reference laboratory testing program.

6506 Background: CGP concurrently tests tumor tissue for guideline-recommended predictive biomarkers for optimal therapy selection and identification of clinical trials in NSCLC patients. A common practice in the community oncology setting is to first order SGT, then consider CGP when SGT results are negative or after treatment failure. Given the limited tissue available and the scope of alterations not tested by SGT, we sought to characterize the effects of prior SGT on subsequent CGP testing success, and therapeutic opportunities identified by CGP beyond SGT for NSCLC patients in the community setting. Methods: Reference laboratory information systems were used to prospectively contact clinicians across 80 community practices who ordered at least 1 SGT for guideline-recommended genomic variants in NSCLC. CGP was offered for their patients prior to SGT or upon receipt of negative SGT results. SGT included individual assays for BRAF, EGFR, KRAS, MET exon 14 skipping mutations; ALK, RET, and ROS1 rearrangements; and PD-L1 IHC . CGP included DNA-seq for mutations, copy number variants in 523 genes including guideline-recommended genomic variants, MSI, and tumor mutational burden; RNA-seq for rearrangements/fusions/splice variants; and PD-L1 IHC. Results: Among a total of 580 NSCLC patients with CGP ordered (May 2021-December 2022), 168 (29%) had ≥1 SGT ordered prior to CGP (median=5). No patients had all SGT performed, with untested cases ranging from 10% for ALK to 90% for MET exon 14. The same FFPE tissue block was used for CGP in 150/168 (89%) of cases with prior negative SGT. Compared to CGP-only cases, CGP for cases with prior negative SGT was canceled twice as often at tissue review (16% vs 7%; p=.001), had higher DNA extraction failures (13% vs 8%; p=.09), and lower DNA sequencing success (70% vs 83%, p<.001). CGP detected guideline-recommended variants in 51% of all cases. Among prior negative SGT cases, frequencies were higher in genes where SGT was performed less often and is less sensitive than CGP RNA-seq ( RET fusions , MET exon 14 skipping). CGP also identified guideline-recommended variants in genes with no SGT offered during the study period ( ERBB2 mutations , NTRK2/3 fusions), as well as variants with emerging evidence for FDA expedited program-designated clinical trial therapies in 28% of patients, including NRG1 fusions and BRAF non-V600E , KEAP1, KRAS non-G12C, NFE2L2, STK11, and TP53 Y220C mutations. Conclusions: Prior negative SGT doubled subsequent CGP test cancellations for NSCLC due to tissue insufficiency and increased CGP DNA extraction failures. SGT practice in the community oncology setting does not meet practice guideline recommendations and negatively impacts the potential benefit of subsequent CGP for NSCLC patients.

A phase 2 study of the WEE1 inhibitor AZD1775 in SETD2-deficient advanced solid tumor malignancies.

3104 Background: AZD1775 is an inhibitor of the WEE1 kinase, leading to loss of genome integrity. Pathogenic SETD2 mutations lead to genome-wide loss of histone H3K36 trimethylation (H3K36me3) and hypersensitivity to WEE1 inhibition with a synthetic lethal effect. AZD1775 was studied in 2 cohorts of patients: A) Patients (pts) with locally advanced/metastatic solid tumor malignancy other than clear cell renal cell carcinoma (ccRCC) and B) pts with locally advanced/metastatic ccRCC. Methods: Eligible pts had a locally advanced/metastatic cancer with progression on ≥ 1 prior systemic therapy (including prior tyrosine kinase inhibitor or checkpoint inhibitor for cohort B), and a pathogenic SETD2 mutation by CLIA-certified next generation sequencing panel. Pts received AZD1775 at a starting dose of 300 mg PO daily on days 1-5, 8-12 of each 21-day cycle. The primary objective was to determine the objective response rate (ORR) by RECIST 1.1 criteria in each cohort. Planned sample size was 9 pts in each cohort in stage 1 of a Simon two-stage design. Secondary objectives were to determine the clinical benefit rate (CBR) (defined as partial response or stable disease [SD] > 4 months), duration of response (DOR), and safety. Correlative assays include the evaluation of H3K36me3 mark by immunohistochemistry (IHC) in archival tumor tissue. Results: Between 05/2019 and 10/2021, 18 pts whose tumor harbored a pathogenic mutation in SETD2 were enrolled (9/cohort). The median age was 60 years (range 45 – 74), ECOG PS 0 in 8 (44.4%). One patient remains on treatment; 17 have discontinued (disease progression, n = 9; adverse event, n = 4; patient withdrawal, n = 2; patient death, n = 1). The median duration of treatment was 1.89 months (range 0.75 – 13.5). There were no confirmed objective responses and therefore enrollment was halted following accrual to stage 1. Tumor regression of any magnitude was observed in 5/17 (29%) evaluable pts. Stable disease was the best overall response in 10/17 (59%) evaluable pts, including 3 pts with SD for > 4 months. One patient with RCC remains on treatment with SD for 14+ months duration. The most common adverse events of any grade were nausea (59%), anemia (41%), diarrhea (41%), and neutropenia (41%). Twelve pts (67%) experienced a Grade ≥ 3 adverse event. Evaluation of H3K36me3 using IHC as an exploratory biomarker is underway. Conclusions: AZD1775 given as monotherapy failed to achieve an objective response in SETD2-altered RCC and other solid tumor malignancies. However, a subset of pts derived clinical benefit as manifested by tumor regressions and/or durable stable disease. Evaluation of AZD1775 in combination with other DNA damage-inducing agents that cause replication stress may be warranted, potentially with the integration of the H3K36me3 mark as a biomarker for patient selection. Clinical trial information: NCT03284385 .

Opportunities for Systems Biology and Quantitative Systems Pharmacology to Address Knowledge Gaps for Drug Development in Pregnancy

AbstractPregnant women are still viewed as therapeutic orphans to the extent that they are avoided as participants in mainstream clinical trials and not considered a priority for targeted drug research despite the fact that many clinical conditions exist during pregnancy for which pharmacotherapy is warranted. Part of the challenge is the uncertain risk potential that pregnant women represent in the absence of timely and costly toxicology and developmental pharmacology studies, which only partly mitigate such risks. Even when clinical trials are conducted in pregnant women, they are often underpowered and absent biomarkers and exclude evaluation across multiple stages of pregnancy where relevant development risk could have been assessed. Quantitative systems pharmacology model development has been proposed as one solution to fill knowledge gaps, make earlier and perhaps more informed risk assessment, and design more informative trials with better recommendations for biomarker and end point selection including design and sample size optimality. Funding for translational research in pregnancy is limited but will fill some of these gaps, especially when joined with ongoing clinical trials in pregnancy that also fill certain knowledge gaps, especially biomarker and end point evaluation across pregnancy states linked to clinical outcomes. Opportunities exist for further advances in quantitative systems pharmacology model development with the inclusion of real‐world data sources and complimentary artificial intelligence/machine learning approaches. The successful coordination of the approach reliant on these new data sources will require commitments to share data and a diverse multidisciplinary group that seeks to develop open science models that benefit the entire research community, ensuring that such models can be used with high fidelity. New data opportunities and computational resources are highlighted in an effort to project how these efforts can move forward.

Randomized phase 2 study of nab-paclitaxel and gemcitabine with or without tocilizumab as first-line treatment in patients with advanced pancreatic cancer (PACTO).

4147 Background: Pancreatic cancer (PC) presents one of the most aggressive malignancies for which currently available treatments are modestly effective. We report the findings of a randomized phase 2 trial (NCT02767557) to test the efficacy of gemcitabine/nab-paclitaxel (gem/nab) with or without tocilizumab (toc), as first-line treatment in patients with locally advanced or metastatic PC. Methods: Prior to randomization, a safety cohort of 6 participants received gem 1000 mg/m2, nab 125 mg/m2 (days 1, 8, and 15 of each 28-day cycle) and toc 8 mg/kg (day 1 of each cycle). Participants were then randomized 1:1 to receive gem/nab or gem/nab/toc. Patients had modified Glasgow Prognostic Score of 1 or 2 and were stratified by performance status (PS) and stage. The primary endpoint was the overall survival (OS) rate at 6 months (OS6). Secondary end points included median progression-free survival (PFS), median OS, overall response rate (ORR), and safety. Results: A total of 147 patients were treated; 141 were randomized to receive gem/nab/toc (n = 70), or gem/nab (n = 71). As of January 9, 2023, the median follow-up was 8.1 months (IQR 4.2–13.9). OS6 was 68.6 % (95% CI, 56.3-78.1) in gem/nab/toc group and 62.0% (95% CI, 49.6-72.1) in gem/nab group (p = 0.41). No significant differences in the median OS or PFS were observed between the gem/nab/toc group and the gem/nab group (OS, 8.4 versus 8.0 months; HR, 0.75; 95% CI, 0.54-1.05; p = 0.10), and (PFS, 5.6 versus 5.5 months; HR, 0.85; 95% CI, 0.61-1.20; p = 0.36). The 12, 18 and 24-month OS rates were 37.1% (95% CI, 26.0%-48.3%), 27.1% (17.4%-37.8%), 10% (4.4%-18.3%) for gem/nab/toc and 28.2% (18.3%-38.9%), 7.0% (2.6%-14.5%), 2.8% (0.5%-8.8%) for gem/nab, respectively. The ORR was 37.1% (95% CI, 25.9%-49.5%) in gem/nab/toc group and 35.2% (24.2%-47.5%) in gem/nab group. The incidence of grade 3 or higher treatment related adverse events was 88.2% in gem/nab/toc and 63.4% in gem/nab group (p < 0.001). Of those, neutropenia (55.3% versus 16.9%), and thrombocytopenia (40.8% versus 11.3%) were most common. Two and one treatment-related deaths, respectively, in gem/nab/toc and gem/nab group, occurred during the study. Conclusions: In patients with advanced PC, the addition of tocilizumab to gem/nab did not result in improved OS rate at 6 months. Although more patients were alive at 18 months in the gem/nab/toc, long-term survival rates exceeding 24 months were not different between groups. The biomarkers for selection of patients who might benefit from gem/nab combined with tocilizumab are to be identified. Clinical trial information: NCT02767557 .

Clinical outcomes in phase 1 clinical trials (Ph1-CT) when treating patients (pts) with novel immunotherapy (IT) agents at early lines for advanced disease.

2586 Background: Cancer pts can be offered IT drugs within Ph1-CT before having exhausted standard of care (SoC) therapies. However, whether this strategy may improve pts’ outcomes remains unclear. We assess the benefits of receiving novel IT compounds within Ph1-CT at early lines for advanced disease. Methods: We retrospectively reviewed a correlative series of pts with advanced solid tumors treated with IT within Ph1-CT at the Phase 1 Unit of Catalan Institute of Oncology (ICO), Barcelona, Spain, from January 2018 to July 2022. Primary aim was to assess clinical outcomes measured by median progression-free survival (mPFS) and median overall survival (mOS) according to number of prior lines (PL) for recurrent/metastatic disease, prior IT, availability of alternative SoC in our country, IT biomarker-selected population and grade 3-4 toxicity (G3-4 Tox) by multivariate analysis (MVA). Overall response rate (ORR) was evaluated by RECIST 1.1. Clinical benefit rate (CBR) was defined as complete/partial response + stable disease for ≥6 months (m). PFS/OS were calculated by Kaplan-Meier method. Log-rank test was used for comparisons. MVA was performed by Cox regression. Results: A total of 228 pts were assessed: IT monotherapy = 49 (21.5%), IT combinations = 179 (78.5%) (IT+IT = 111 [48.7%], IT+targeted therapy = 24 [10.5%], IT+others = 44 [19.3%]). Forty-nine (21.5%) pts were included according to IT biomarkers (PDL1, TIM3 expression, MSI-H, others). Median age was 62 y (24-83), 62.7% were men. All pts had ECOG ≤1. The 4 most frequent cancer types were HNSCC (19.7%), NSCLC (15.4%), glioblastoma (10.5%) and urothelial (10.1%). Number of PL: 0 = 33 (14.5%) pts, 1 = 92 (40.3%) pts, ≥2 = 103 (45.2%) pts. Sixty-three (27.6%) pts had received prior IT and 159 (69.7%) had alternative available SoC. Overall G3-4 Tox rate was 21.5% (20.6% in pts IT-treated). ORR and CBR were higher in pts IT-naïve than in pts IT-treated (21.2% vs 12.7% [p .18] and 35.7% vs 25.4% [p .26], respectively). With a median follow-up of 19m, overall mPFS and mOS were 3.5m (2.7-3.9) and 9.8m (8.5-11.5), respectively. In the MVA, lesser number of PL, SoC availability and biomarker selection positively impacted on mOS. After Ph1-CT treatment, 111 (48.7%) pts received further treatment: 19.3% within clinical trial, 29.4% with SoC. Conclusions: In our cohort of pts treated within IT Ph1-CT, those with less PL of treatment, with available alternative SoC and/or selected by biomarker achieved higher PFS and OS. These results suggest that treating pts with novel IT agents at early lines might positively impact on survival. [Table: see text]

Surface Potential Modulation in Boronate-Functionalized Magnetic Nanoparticles Reveals Binding Interactions: Toward Magnetophoretic Capture/Quantitation of Sugars from Extracellular Matrix.

Phenylboronic acids (BAs) are important synthetic receptors that bind reversibly to cis-diols enabling their use in molecular sensing. When conjugated to magnetic iron oxide nanoparticles, BAs have potential for application in separations and enrichment. Realizing this will require a new understanding of their inherent binding modes and measurement of their binding capacity and their stability in/extractability from complex environments. In this work, 3-aminophenylboronic acid was functionalized to superparamagnetic iron oxide nanoparticles (MNPs, core diameter 8.9 nm) to provide stable aqueous suspensions of functionalized particles (BA-MNPs). The progress of sugar binding and its impact on BA-MNP colloidal stability were monitored through the pH-dependence of hydrodynamic size and zeta potential during incubation with a range of saccharides. This provided the first direct observation of boronate ionization pKa in grafted BA, which in the absence of sugar shifted to a slightly more basic pH than free BA. On exposure to sugar solutions under MNP-limiting conditions, pKa moved progressively to lower pH as maximum capacity was gradually attained. The pKa shift is shown to be greater for sugars with greater BA binding affinity, and on-particle sugar exchange effects were inferred. Colloidal dispersion of BA-MNPs after binding was shown for all sugars at all pHs studied, which enabled facile magnetic extraction of glucose from agarose and cultured extracellular matrix expanded in serum-free media. Bound glucose, quantified following magnetophoretic capture, was found to be proportional to the solution glucose content under glucose-limiting conditions expected for the application. The implications for the development of MNP-immobilized ligands for selective magnetic biomarker capture and quantitation from the extracellular environment are discussed.

The mechanism and clinical application of DNA damage repair inhibitors combined with immune checkpoint inhibitors in the treatment of urologic cancer.

Urologic cancers such as kidney, bladder, prostate, and uroepithelial cancers have recently become a considerable global health burden, and the response to immunotherapy is limited due to immune escape and immune resistance. Therefore, it is crucial to find appropriate and effective combination therapies to improve the sensitivity of patients to immunotherapy. DNA damage repair inhibitors can enhance the immunogenicity of tumor cells by increasing tumor mutational burden and neoantigen expression, activating immune-related signaling pathways, regulating PD-L1 expression, and reversing the immunosuppressive tumor microenvironment to activate the immune system and enhance the efficacy of immunotherapy. Based on promising experimental results from preclinical studies, many clinical trials combining DNA damage repair inhibitors (e.g., PARP inhibitors and ATR inhibitors) with immune checkpoint inhibitors (e.g., PD-1/PD-L1 inhibitors) are underway in patients with urologic cancers. Results from several clinical trials have shown that the combination of DNA damage repair inhibitors with immune checkpoint inhibitors can improve objective rates, progression-free survival, and overall survival (OS) in patients with urologic tumors, especially in patients with defective DNA damage repair genes or a high mutational load. In this review, we present the results of preclinical and clinical trials of different DNA damage repair inhibitors in combination with immune checkpoint inhibitors in urologic cancers and summarize the potential mechanism of action of the combination therapy. Finally, we also discuss the challenges of dose toxicity, biomarker selection, drug tolerance, drug interactions in the treatment of urologic tumors with this combination therapy and look into the future direction of this combination therapy.

Correlation between biomarkers and treatment outcomes in diverse cancers: a systematic review and meta-analysis of phase I and II immunotherapy clinical trials

BackgroundMany immuno-oncology (IO) trials are conducted without biomarker selection. We performed a meta-analysis of phase I/II clinical trials evaluating immune checkpoint inhibitors (ICIs) to determine the association between biomarkers and clinical outcomes, if any. MethodsA PubMed search for phase I/II clinical trials with drugs approved by the Food and Drug Administration (labelled, off-label, combined with investigational ICIs or other treatment modalities) from 2018 to 2020 was performed. The objective response rate (ORR), progression-free survival (PFS) and overall survival (OS) were compared between biomarker-positive and biomarker-negative groups, using studies that explored the correlation of biomarkers with outcomes. ResultsOverall, 174 clinical studies that included 19,178 patients were identified, and 132 studies investigated>30 correlative biomarkers that included PD-L1 expression (≥1%, 111 studies), tumour mutational burden (20 studies) and microsatellite instability/mismatch repair deficiency (10 studies). Overall, 123, 46 and 30 cohorts (drugs, tumour types or biomarkers) with 11,692, 3065, and 2256 patient outcomes for ORR, PFS and OS, respectively, were analysed in correlation with biomarkers. Meta-analyses demonstrated that ICIs in patients with biomarker-positive tumours were associated with higher ORR (odds ratio 2.15 [95% CI, 1.79–2.58], p < 0.0001); and longer PFS (hazard ratio [HR] 0.55 [95% CI, 0.45–0.67], p < 0.0001), and OS (HR 0.65 [95% CI, 0.53–0.80], p < 0.0001) compared with those with biomarker-negative tumours. Significance for ORR and PFS was retained in multivariate analysis (p < 0.001) (OS, not included owing to the small number of trials reporting OS). ConclusionOur data suggest that IO biomarkers should be used in patient selection for ICIs. Prospective studies are warranted.

A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer

Non-small cell lung cancer (NSCLC), the primary histological form of lung cancer, accounts for about 25%—the highest—of all cancer deaths. As NSCLC is often undetected until symptoms appear in the late stages, it is imperative to discover more effective tumor-associated biomarkers for early diagnosis. Topological data analysis is one of the most powerful methodologies applicable to biological networks. However, current studies fail to consider the biological significance of their quantitative methods and utilize popular scoring metrics without verification, leading to low performance. To extract meaningful insights from genomic data, it is essential to understand the relationship between geometric correlations and biological function mechanisms. Through bioinformatics and network analyses, we propose a novel composite selection index, the C-Index, that best captures significant pathways and interactions in gene networks to identify biomarkers with the highest efficiency and accuracy. Furthermore, we establish a 4-gene biomarker signature that serves as a promising therapeutic target for NSCLC and personalized medicine. The C-Index and biomarkers discovered were validated with robust machine learning models. The methodology proposed for finding top metrics can be applied to effectively select biomarkers and early diagnose many diseases, revolutionizing the approach to topological network research for all cancers.

What Can Current Biomarker Data Tell Us About the Risks of Lung Cancer Posed by Heated Tobacco Products?

Heated tobacco products (HTPs) are marketed as less harmful alternatives to cigarettes, but the lung cancer risk of HTPs is unknown. In the absence of epidemiological data, assessing the risks of HTPs relies on biomarker data from clinical trials. This study examined existing biomarker data to determine what it tells us about the lung cancer risk posed by HTPs. We identified all biomarkers of exposure and potential harm measured in HTP trials and evaluated their appropriateness based on ideal characteristics for measuring lung cancer risk and tobacco use. The effects of HTPs on the most appropriate biomarkers within cigarette smokers switched to HTPs and compared to continued cigarette smoking or cessation were synthesized. Sixteen out of eighty-two biomarkers (7 exposure and 9 potential harm) measured in HTP trials have been associated with tobacco use and lung cancer, dose-dependently correlated with smoking, modifiable upon cessation, measured within an appropriate timeframe, and had results published. Three of the exposure biomarkers significantly improved in smokers who switched to HTPs and were not significantly different from cessation. The remaining 13 biomarkers did not improve-in some instances worsening upon switching to HTPs-or were inconsistently affected across studies. There were no appropriate data to estimate the lung cancer risk of HTPs in non-smokers. The appropriateness of existing biomarker data in assessing lung cancer risk of HTPs, both relative to cigarettes and their absolute risk, is limited. Furthermore, findings on the most appropriate biomarkers were conflicting across studies and largely showed no improvement following a switch to HTPs. Biomarker data are fundamental to assessing the reduced risk potential of HTPs. Our evaluation suggests much of the existing biomarker data on HTPs is inappropriate for determining the risk of lung cancer posed by HTPs. In particular, there is a paucity of data on the absolute lung cancer risk of HTPs, which could be obtained from comparisons to smokers who quit and never smokers exposed to or using HTPs. There is an urgent need for further exploration of the lung cancer risks posed by HTPs, via clinical trials and, in the long-term, confirmation of these risks via epidemiological studies. However, careful consideration should be given to biomarker selection and study design to ensure both are appropriate and will provide valuable data.

Short and long term prediction of seizure freedom in drug-resistant focal epilepsy surgery

BackgroundThe selection of candidates for drug-resistant focal epilepsy surgery is essential to achieve the best post-surgical outcomes. Objective: To develop two prediction models for seizure freedom in the short and long-term follow-up and from them to create a risk calculator in order to individualize the selection of candidates for surgery and future therapies in each patients. MethodsA sample of 64 consecutive patients who underwent epilepsy surgery at two Cuban tertiary health institutions between 2012 and 2020 constituted the basis for the prediction models. Two models were obtained through the novel methodology, based on biomarker selection reached by resampling methods, cross-validation and high-accuracy index measured through the area under the receiving operating curve (ROC) procedure. ResultsThe first, to pre-operative model included five predictors: epilepsy type, seizures per month, ictal pattern, interictal EEG topography and normal or abnormal magnetic resonance imaging,. it's precision was 0.77 at one year, and with four years and more 0.63. The second model including variables from the trans-surgical and post-surgical stages: the interictal discharges in the post-surgical EEG, incomplete or complete resection of the epileptogenic zone, the surgical techniques employed and disappearance of the discharge in post-resection electrocorticography; the precision of this model was 0.82 at one year, and with four years and more 0.97. ConclusionsThe introduction of trans-surgical and post-surgical variables increase the prediction of the pre-surgical model. A risk calculator was developed using these prediction models, which could be useful as an accurate tool to improve the prediction in epilepsy surgery.

The Relationship between Insomnia and the Pathophysiology of Major Depressive Disorder: An Evaluation of a Broad Selection of Serum and Urine Biomarkers.

Insomnia exhibits a clinically relevant relationship with major depressive disorder (MDD). Increasing evidence suggests that insomnia is associated with neurobiological alterations that resemble the pathophysiology of MDD. However, research in a clinical population is limited. The present study, therefore, aimed to investigate the relationship between insomnia and the main pathophysiological mechanisms of MDD in a clinical sample of individuals with MDD. Data were extracted from three cohorts (N = 227) and included an evaluation of depression severity (Quick Inventory of Depressive Symptomatology, QIDS-SR16) and insomnia severity (QIDS-SR16 insomnia items) as well as serum and urine assessments of 24 immunologic (e.g., tumour necrosis factor α receptor 2 and calprotectin), neurotrophic (e.g., brain-derived neurotrophic factor and epidermal growth factor), neuroendocrine (e.g., cortisol and aldosterone), neuropeptide (i.e., substance P), and metabolic (e.g., leptin and acetyl-L-carnitine) biomarkers. Linear regression analyses evaluating the association between insomnia severity and biomarker levels were conducted with and without controlling for depression severity (M = 17.32), antidepressant use (18.9%), gender (59.0% female; 40.5% male), age (M = 42.04), and the cohort of origin. The results demonstrated no significant associations between insomnia severity and biomarker levels. In conclusion, for the included biomarkers, current findings reveal no contribution of insomnia to the clinical pathophysiology of MDD.

Evaluation of the selectivity of several OATP1B biomarkers using relative activity factor method.

In recent years, some endogenous substrates of organic anion transporting polypeptide 1B (OATP1B) have been identified and characterized as potential biomarkers to assess OATP1B-mediated clinical drug-drug interactions (DDIs). However, quantitative determination of their selectivity to OATP1B are still limited. In this study, we developed a relative activity factor (RAF) method to determine the relative contribution of hepatic uptake transporters OATP1B1, OATP1B3, OATP2B1, and sodium-taurocholate co-transporting polypeptide (NTCP) on hepatic uptake of several OATP1B biomarkers, including coproporphyrins I (CPI), CPIII, sulfate conjugates of bile acids: glycochenodeoxycholic acid sulfate (GCDCA-S), glycodeoxycholic acid sulfate (GDCA-S), and taurochenodeoxycholic acid sulfate (TCDCA-S). RAF values for OATP1B1, OATP1B3, OATP2B1, and NTCP were determined in cryopreserved human hepatocytes and transporter transfected cells using pitavastatin, cholecystokinin, resveratrol-3-O-β-D-glucuronide, and taurocholic acid (TCA) as reference compounds, respectively. OATP1B1-specific pitavastatin uptake in hepatocytes was measured in the absence and presence of 1 µM estropipate, while NTCP-specific TCA uptake was measured in the presence of 10 µM rifampin. Our studies suggested that CPI was a more selective biomarker for OATP1B1 than CPIII, while GCDCA-S and TCDCA-S were more selective to OATP1B3. OATP1B1 and OATP1B3 equally contributed to hepatic uptake of GDCA-S. The mechanistic static model, incorporating the fraction transported (ft) of CPI/III estimated by RAF and in vivo elimination data, predicted several perpetrator interactions with CPI/III. Overall, RAF method combined with pharmacogenomic and DDI studies is a useful tool to determine the selectivity of transporter biomarkers and facilitate the selection of appropriate biomarkers for DDI evaluation. Significance Statement We developed a new RAF method to quantitatively determine the contribution of hepatic uptake transporters OATP1B1, OATP1B3, OATP2B1, and NTCP on several OATP1B biomarkers (CPI, CPIII, GCDCA-S, GDCA-S, and TCDCA-S) and evaluated their predictivity on perpetrator-biomarker interactions. Our studies suggest that RAF method is a useful tool to determine the selectivity of transporter biomarkers. This method combined with pharmacogenomic and DDI studies will facilitate mechanistic interpretation and modeling of biomarker data and the selection of appropriate biomarkers for DDI evaluation.

Classification of tuberculosis-related programmed cell death-related patient subgroups and associated immune cell profiling.

Tuberculosis (TB) is the deadliest communicable disease in the world with the exception of the ongoing COVID-19 pandemic. Programmed cell death (PCD) patterns play key roles in the development and progression of many disease states such that they may offer value as effective biomarkers or therapeutic targets that can aid in identifying and treating TB patients. The Gene Expression Omnibus (GEO) was used to gather TB-related datasets after which immune cell profiles in these data were analyzed to examine the potential TB-related loss of immune homeostasis. Profiling of differentially expressed PCD-related genes was performed, after which candidate hub PCD-associated genes were selected via a machine learning approach. TB patients were then stratified into two subsets based on the expression of PCD-related genes via consensus clustering. The potential roles of these PCD-associated genes in other TB-related diseases were furtherexamined. In total, 14 PCD-related differentially expressed genes (DEGs) were identified and highly expressed in TB patient samples and significantly correlated with the abundance of many immune cell types. Machine learning algorithms enabled the selection of seven hub PCD-related genes that were used to establish PCD-associated patient subgroups, followed by the validation of these subgroups in independent datasets. These findings, together with GSVA results, indicated that immune-related pathways were significantly enriched in TB patients exhibiting high levels of PCD-related gene expression, whereas metabolic pathways were significantly enriched in the other patient group. Single cell RNA-seq (scRNA-seq) further highlighted significant differences in the immune status of these different TB patient samples. Furthermore, we used CMap to predict five potential drugs for TB-related diseases. These results highlight clear enrichment of PCD-related gene expression in TB patients and suggest that this PCD activity is closely associated with immune cell abundance. This thus indicates that PCD may play a role in TB progression through the induction or dysregulation of an immune response. These findings provide a foundation for further research aimed at clarifying the molecular drivers of TB, the selection of appropriate diagnostic biomarkers, and the design of novel therapeutic interventions aimed at treating this deadly infectious disease.

Identification of immune-related genes and patient selection for hepatocellular carcinoma immunotherapy.

Hepatocellular carcinoma (HCC) is a malignant disease with a poor prognosis. Among the treatment strategies for HCC, tumor immunotherapy (TIT) is a promising research hotspot, in which identifying novel immune-related biomarkers and selecting suitable patient population are urgent issues to be solved. In this study, an abnormal expression map of HCC cell genes was constructed using public high-throughput data from 7,384 samples (3,941 HCC vs. 3,443 non-HCC tissues). Through single-cell RNA sequencing (scRNA-seq) cell trajectory analysis, the genes defined as potential drivers of HCC cell differentiation and development were selected. By screening for both immune-related genes and those associated with high differentiation potential in HCC cell development, a series of target genes were identified. Coexpression analysis was performed using Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) to find the specific candidate genes involved in similar biological processes. Subsequently, nonnegative matrix factorization (NMF) was conducted to select patients suitable for HCC immunotherapy based on the coexpression network of candidate genes. HSP90AA1, CDK4, HSPA8, HSPH1, and HSPA5 were identified as promising biomarkers for prognosis prediction and immunotherapy of HCC. Through the use of our molecular classification system, which was based on a function module containing 5 candidate genes, patients with specific characteristics were found to be suitable candidates for TIT. These findings provide new insights into the selection of candidate biomarkers and patient populations for future HCC immunotherapy.

Class I HDAC inhibitor entinostat synergizes with PLK1 inhibitors in MYC-amplified medulloblastoma cells

PurposeWe and others have demonstrated that MYC-amplified medulloblastoma (MB) cells are susceptible to class I histone deacetylase inhibitor (HDACi) treatment. However, single drug treatment with HDACi has shown limited clinical efficacy. We hypothesized that addition of a second compound acting synergistically with HDACi may enhance efficacy.MethodsWe used a gene expression dataset to identify PLK1 as a second target in MB cells and validated the relevance of PLK1 in MB. We measured cell metabolic activity, viability, and cycle progression in MB cells after treatment with PLK1-specific inhibitors (PLK1i). Chou–Talalay synergy calculations were used to determine the nature of class I HDACi entinostat and PLK1i interaction which was validated. Finally, the clinical potential of the combination was assessed in the in vivo experiment.ResultsMYC-amplified tumor cells are highly sensitive towards treatment with ATP-competitive PLK1i as a monotherapy. Entinostat and PLK1i in combination act synergistically in MYC-driven MB cells, exerting cytotoxic effects at clinically relevant concentrations. The downstream effect is exerted via MYC-related pathways, pointing out the potential of MYC amplification as a clinically feasible predictive biomarker for patient selection. While entinostat significantly extended survival of mice implanted with orthotopic MYC-amplified MB PDX, there was no evidence of the improvement of survival when treating the animals with the combination.ConclusionThe combination of entinostat and PLK1i showed synergistic interaction in vitro, but not in vivo. Therefore, further screening of blood–brain barrier penetrating PLK1i is warranted to determine the true potential of the combination as no on-target activity was observed after PLK1i volasertib treatment in vivo.

SP-0530 Biomarkers for patient selection and monitoring

SP-0530 Biomarkers for patient selection and monitoring

Abstract #1368667: Effects of Sodium-Glucose Co-transporter 2 Inhibitors on Adipokines and Inflammatory Markers in People Living with Type 2 Diabetic Mellitus

Type 2 diabetes mellitus is a chronic metabolic disease associated with high cardiovascular disease (CVD) risks. SGLT-2 inhibitors reduce HbA1c and show favorable effects on body weight, blood pressure, lipid profile, arterial stiffness, and endothelial function. More importantly, SGLT-2 inhibitors have demonstrated impressive cardioprotective and renoprotective effects. The main mechanisms of cardioprotective effects have been attributed to improvement in cardiac cell metabolism, improvement in ventricular loading conditions, inhibition of the Na+/H+ exchanger in myocardial cells, alteration in adipokines and cytokines production. This research aimed to observe the effects of SGLT-2 inhibitors on select inflammatory biomarkers, adiponectin, chosen metabolic and anthropometric variables. Compare changes of serum adiponectin between baseline with 16-week after SGLT-2 inhibitors therapy and other variables such as BMI, fasting plasma glucose, HbA1c, for instance, among people with type 2 diabetic mellitus. At 16-week after SGLT-2 inhibitors treatment, a striking increment in mean serum adiponectin by 1.25 mcg/mL (95% CI: 0.02, 2.48) or 81% was revealed. Furthermore, they showed a reduction of mean BMI 1.12 % (95% CI: -1.74%, -0.50%), systolic blood pressure 1 mmHg (95% CI: -1.85, -0.72), diastolic blood pressure 1 mmHg (95% CI: -1.65, -0.52), fasting plasma glucose 34 mg/dL (95% CI: -51.66, -17.10), and HbA1c 0.73% (95% CI: -1.25%, -0.21%). No between-group differences were expressed with the other biomarkers. Serious side effects such as ketoacidosis, hypoglycemic status, genitourinary tract infection, or acute kidney injury were not appeared. SGLT-2 inhibitors can improve changes in serum adiponectin which appreciatively benefit in cardiovascular outcomes among type 2 diabetes.

Detection of PD-1 for flow cytometry immunophenotyping using commercial antibodies in clinical studies with check point inhibitors

Abstract Precision for Medicine (Precision) has optimized the use of commercial anti-PD-1 and anti-IgG4 reagents to detect both free PD-1 and/or Pembro-bound PD-1 in pharmacodynamic studies when Pembrolizumab, a concomitant check point inhibitor, is used in combination with a new therapy. Precision has developed and qualified flow immunophenotyping assays to quantify PD-1 on CD4+ and CD8+ T cells using a commercial PD-1 clone (PD1.3.1.3) and an anti-IgG4. This enabled detection of both free and drug bound PD-1 receptor. We compared detection of PD-1 in PHA stimulated healthy donors treated with saturating Pembrolizumab or no drug treatment. There was not a significant difference observed in PD-1 quantification in the presence or absence of Pembrolizumab, which demonstrated that our approach was able to accurately measure total PD-1 whether free or bound. As Pembrolizumab was titrated from the saturating concentration there was a dose-dependent decrease in detection of PD-1. Here we will demonstrate the detection of PD-1 using the above methodology compared to other commercial PD-1 antibody clones. In clinical trials which incorporate checkpoint inhibitors like either PD-1 or PD-L1, the selection of commercial reagents are key considerations to detect these receptors when designing a sensitive, selective, and accurate biomarker assay.

The association between commuting and cardiovascular disease: A biomarker-based analysis of cross-sectional cohort data from the UK Biobank

This study used cross-sectional UK Biobank data to estimate the influence of active and passive commuting modes and commuting distance on cardiovascular disease (CVD) -related biomarkers as measures of health outcomes. The analysis applied logistic regression to assess the risk of exhibiting individual biomarker values outside a predefined reference interval and standard linear regression to estimate the relation between commuting practices and a composite CVD index. The study sample comprised 208,893 UK Biobank baseline survey participants aged 40 to 69 who use various modes of transport to commute to work at least once a week. Participants were recruited and interviewed between 2006 and 2010 at 22 centers geographically dispersed across England, Scotland, and Wales. The data set included these participants' sociodemographic and health-related information, including lifestyle indicators and biological measures. The primary outcome was a shift from low to high-risk blood serum levels in eight cardiovascular biomarkers: total cholesterol, low density lipoprotein, high density lipoprotein, triglycerides, apolipoprotein A and B, C-reactive protein, and lipoprotein (a). Our results indicated a small negative association between the composite risk index for CVD biomarkers and weekly commuting distance. Although estimates for active commuting modes (cycling, walking) may admittedly be sensitive to different covariate adjustments, our specifications show them to be positively associated with select CVD biomarkers. Commuting long distances by car is negatively associated with CVD-related biomarkers, while cycling and walking might be positively associated. This biomarker-based evidence, although limited, is less susceptible to residual confounding than that from distant outcomes like CVD mortality.