Strategies to design clinical studies to identify predictive biomarkers in cancer research

Whole exome sequencing and machine learning germline analysis of individuals presenting with extreme phenotypes of high and low risk of developing tobacco-associated lung adenocarcinoma

BACKGROUND

Tobacco is the main risk factor for developing lung cancer. Yet, while some heavy smokers develop lung cancer at a young age, other heavy smokers never develop it, even at an advanced age, suggesting a remarkable variability in the individual susceptibility to the carcinogenic effects of tobacco. We characterized the germline profile of subjects presenting these extreme phenotypes with Whole Exome Sequencing (WES) and Machine Learning (ML).

METHODS

We sequenced germline DNA from heavy smokers who either developed lung adenocarcinoma at an early age (extreme cases) or who did not develop lung cancer at an advanced age (extreme controls), selected from databases including over 6600 subjects. We selected individual coding genetic variants and variant-rich genes showing a significantly different distribution between extreme cases and controls. We validated the results from our discovery cohort, in which we analysed by WES extreme cases and controls presenting similar phenotypes. We developed ML models using both cohorts.

FINDINGS

Mean age for extreme cases and controls was 50.7 and 79.1 years respectively, and mean tobacco consumption was 34.6 and 62.3 pack-years. We validated 16 individual variants and 33 variant-rich genes. The gene harbouring the most validated variants was HLA-A in extreme controls (4 variants in the discovery cohort, p = 3.46E-07; and 4 in the validation cohort, p = 1.67E-06). We trained ML models using as input the 16 individual variants in the discovery cohort and tested them on the validation cohort, obtaining an accuracy of 76.5% and an AUC-ROC of 83.6%. Functions of validated genes included candidate oncogenes, tumour-suppressors, DNA repair, HLA-mediated antigen presentation and regulation of proliferation, apoptosis, inflammation and immune response.

INTERPRETATION

Individuals presenting extreme phenotypes of high and low risk of developing tobacco-associated lung adenocarcinoma show different germline profiles. Our strategy may allow the identification of high-risk subjects and the development of new therapeutic approaches.

FUNDING

See a detailed list of funding bodies in the Acknowledgements section at the end of the manuscript.

High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data

Introduction: FOLFOX and FOLFIRI chemotherapy are considered standard first-line treatment options for colorectal cancer (CRC). However, the criteria for selecting the appropriate treatments have not been thoroughly analyzed. Methods: A newly developed machine learning model was applied on several gene expression data from the public repository GEO database to identify molecular signatures predictive of efficacy of 5-FU based combination chemotherapy (FOLFOX and FOLFIRI) in patients with CRC. The model was trained using 5-fold cross validation and multiple feature selection methods including LASSO and VarSelRF methods. Random Forest and support vector machine classifiers were applied to evaluate the performance of the models. Results and Discussion: For the CRC GEO dataset samples from patients who received either FOLFOX or FOLFIRI, validation and test sets were >90% correctly classified (accuracy), with specificity and sensitivity ranging between 85%-95%. In the datasets used from the GEO database, 28.6% of patients who failed the treatment therapy they received are predicted to benefit from the alternative treatment. Analysis of the gene signature suggests the mechanistic difference between colorectal cancers that respond and those that do not respond to FOLFOX and FOLFIRI. Application of this machine learning approach could lead to improvements in treatment outcomes for patients with CRC and other cancers after additional appropriate clinical validation.

Baseline CTC Count as a Predictor of Long-Term Outcomes in High-Risk Prostate Cancer

The aim of the present study was to verify whether the baseline circulating tumor cell (CTC) count might serve as a predictor of overall survival (OS) and metastasis-free survival (MFS) in patients with high-risk prostate cancer (PCa) during a follow-up period of at least 5 years. CTCs were enumerated using three different assay formats in 104 patients: the CellSearch® system, EPISPOT assay and GILUPI CellCollector. A total of 57 (55%) patients survived until the end of the follow-up period, with a 5 year OS of 66% (95% CI: 56–74%). The analysis of univariate Cox proportional hazard models identified a baseline CTC count ≥ 1, which was determined with the CellSearch® system, a Gleason sum ≥ 8, cT ≥ 2c and metastases at initial diagnosis as significant predictors of a worse OS in the entire cohort. The CTC count ≥ 1 was also the only significant predictor of a worse OS in a subset of 85 patients who presented with localized PCa at the baseline. The baseline CTC number did not affect the MFS. In conclusion, the baseline CTC count can be considered a determinant of survival in high-risk PCa and also in patients with a localized disease. However, determining the prognostic value of the CTC count in patients with localized PCa would optimally require longitudinal monitoring of this parameter.

Ethical considerations about the collection of biological samples for genetic analysis in clinical trials

Progress in precision medicine is being achieved through the design of clinical trials that use genetic biomarkers to guide stratification of patients and assignation to treatment or control groups. Genetic analysis of biomarkers is, therefore, essential to complete their objectives, and this involves the study of biological samples from donor patients that have been recruited according to criteria previously established in the design of the clinical trial. Nevertheless, it is becoming very common that, in the solicitation of biological samples, purposes that are beyond the objectives of the stated therapeutic trial research are introduced, like the development of ill-explained exploratory studies or the use in unspecified future research. In the digital era, the sequencing of patients’ DNA needs to be considered as a serious security matter, not only for the patients, but also for their relatives. Genetic information may be easily stored, even forever, in digital files. This engenders a permanent risk of being stolen or misused in many ways. Furthermore, re-identification of sample donors is technically feasible through their genetic data. For these reasons, genetic analysis of samples collected in clinical trials should be restricted to the accomplishment of their main objectives or well justified goals.

Relative Quantification of Proteins in Formalin-Fixed Paraffin-Embedded Breast Cancer Tissue Using Multiplexed Mass Spectrometry Assays

The identification of clinically relevant biomarkers represents an important challenge in oncology. This problem can be addressed with biomarker discovery and verification studies performed directly in tumor samples using formalin-fixed paraffin-embedded (FFPE) tissues. However, reliably measuring proteins in FFPE samples remains challenging. Here, we demonstrate the use of liquid chromatography coupled to multiple reaction monitoring mass spectrometry (LC-MRM/MS) as an effective technique for such applications. An LC-MRM/MS method was developed to simultaneously quantify hundreds of peptides extracted from FFPE samples and was applied to the targeted measurement of 200 proteins in 48 triple-negative, 19 HER2-overexpressing, and 20 luminal A breast tumors. Quantitative information was obtained for 185 proteins, including known markers of breast cancer such as HER2, hormone receptors, Ki-67, or inflammation-related proteins. LC-MRM/MS results for these proteins matched immunohistochemistry or chromogenic in situ hybridization data. In addition, comparison of our results with data from the literature showed that several proteins representing potential biomarkers were identified as differentially expressed in triple-negative breast cancer samples. These results indicate that LC-MRM/MS assays can reliably measure large sets of proteins using the analysis of surrogate peptides extracted from FFPE samples. This approach allows to simultaneously quantify the expression of target proteins from various pathways in tumor samples. LC-MRM/MS is thus a powerful tool for the relative quantification of proteins in FFPE tissues and for biomarker discovery.

A novel nanobody as therapeutics target for EGFR-positive colorectal cancer therapy: exploring the effects of the nanobody on SW480 cells using proteomics approach

Background

The epidermal growth factor receptor (EGFR) overexpression is found in metastatic colorectal cancer (mCRC). Targeted molecular therapies such as monoclonal antibodies (mAbs) and tyrosine kinase inhibitors (TKI) are becoming more precise, targeting specifically for cancer therapeutics. However, there are adverse effects of currently available anti-EGFR drugs, including drug-resistant and side effects. Nanobodies can overcome these limitations. Our previous study has found that cell-penetrable nanobodies targeted at EGFR-tyrosine kinase were significantly reduced EGFR-positive lung cancer cells viability and proliferation. The aim of the present study was to determine the effect of cell-penetrable nanobody (R9VH36) on cell viability and proteomic profile in EGFR-positive human colorectal cancer cell lines.

Methods

The human colorectal carcinoma cell line (SW480) was treated with R9VH36, compared with gefitinib. Cell viability was monitored using the MTT cell viability assay. The proteomic profiling was analyzed by LC–MS/MS .

Results

The half-maximal inhibitory concentration (IC₅₀) values determined for R9VH36 and gefitinib against SW480 were 527 ± 0.03 nM and 13.31 ± 0.02 μM, respectively. Moreover, both the gefitinib-treated group and nanobody-treated group had completely different proteome profiles. A total 6626 differentially expressed proteins were identified. PCA analysis revealed different proteome profiling in R9VH36 experiment. There were 8 proteins in R9VH36 that significantly exhibited opposite expression directions when compared to gefitinib. These proteins are involved in DNA-damage checkpoint processes.

Conclusion

The proteomics explored those 6,626 proteins had different expressions between R9VH36 and gefitinib. There were 8 proteins in R9VH36 exhibited opposite expression direction when comparing to gefitinib. Our findings suggest that R9VH36 has the potential to be an alternative remedy for treating EGFR-positive colon cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12953-022-00190-6.

Pharmacogenetics-based personalized treatment in patients with inflammatory bowel disease: A review

The development of treatment options has revolutionized the prognosis of inflammatory bowel disease (IBD). However, a particular group of patients still experience therapeutic failure or drug side effects. Although the high inter-patient variability in therapy is associated with clinical factors, including age, disease behavior, and disease duration, they attribute only a small proportion of inter-individual variability. Thus, pharmacogenetics evaluating associations between specific genetic variations and drug responses or side effects have focused on optimizing therapeutic efficacy and minimizing toxicity in IBD treatment. Thiopurine S-methyltransferase (TPMT) and nudix hydrolase 15 (NUDT15) are well-established predictive markers of thiopurine-induced myelosuppression. Low TPMT activity is related to increased 6-thioguanine nucleotide levels, subsequently leading to myelotoxicity. NUDT15 variants are strongly associated with thiopurine-induced early leukopenia in Asians, with a lower incidence of TPMT-deficient allele. The Korean Association for the Study of Intestinal Diseases guidelines recommend pretreatment determination of NUDT15 genotypes, especially in East Asians, and NUDT15 R139C measurement has been approved for clinical use since 2019. Several studies have attempted to identify powerful genetic markers for personalized medicine. In this article, we review the identified pharmacogenetics of currently available drugs, focusing on 5-aminosalicylic acid, glucocorticosteroids, thiopurines, and anti-tumor necrosis factor-alpha agents.

Liquid Biopsy: A New Translational Diagnostic and Monitoring Tool for Musculoskeletal Tumors

Soft tissue and bone sarcomas represent a group of aggressive neoplasms often accompanied by dismal patient prognosis, especially when distant metastases are present. Moreover, effective treatment can pose a challenge, as recurrences are frequent and almost half of patients present with advanced disease. Researchers have unveiled the molecular abnormalities implicated in sarcomas' carcinogenesis, paving the way for novel treatment strategies based on each individual tumor's characteristics. Therefore, the development of new techniques aiding in early disease detection and tumor molecular profiling is imperative. Liquid biopsy refers to the sampling and analysis of patients' fluids, such as blood, to identify tumor biomarkers, through a variety of methods, including qRT-PCR, qPCR, droplet digital PCR, magnetic microbeads and digital PCR. Assessment of circulating tumor cells (CTCs), circulating free DNA (ctDNA), micro RNAs (miRs), long non-coding RNAs (lncRNAs), exosomes and exosome-associated proteins can yield a plethora of information on tumor molecular signature, histologic type and disease stage. In addition, the minimal invasiveness of the procedure renders possible its wide application in the clinical setting, and, therefore, the early detection of the presence of tumors. In this review of the literature, we gathered information on biomarkers assessed through liquid biopsy in soft tissue and bone sarcoma patients and we present the information they can yield for each individual tumor type.

Clinical Relevance of Circulating Tumor Cells in Prostate Cancer Management

Given the low specificity of the routinely used biomarker prostate-specific antigen, circulating tumor cell (CTC) enumeration seems to be particularly useful in the monitoring of prostate cancer. In this review, we focused on a few aspects of CTC enumeration in prostate malignancies: prognostic value in metastatic and non-metastatic tumors, role in the monitoring of treatment outcomes, use as a surrogate marker for survival, and other applications, mostly for research purposes. CTC enumeration, without a doubt, offers an attractive perspective in the management of prostate cancer. However, the vast majority of available data about the role of CTC in this malignancy originate from randomized studies of anticancer agents and do not necessarily translate into real-world clinical practice. Further, most studies on the application of CTC in prostate cancer patients were limited to advanced stages of this malignancy. Meanwhile, the role of CTC in the early stages of prostate cancer, in which some patients may present with occult disseminated disease, is still relatively poorly understood, and should thus be studied extensively. Other obstacles in the widespread application of CTC enumeration in routine clinical practice include considerable discrepancies in the number of cells determined with various commercially available systems.

Liquid Biopsy for Cancer Cachexia: Focus on Muscle-Derived microRNAs

Cancer cachexia displays a complex nature in which systemic inflammation, impaired energy metabolism, loss of muscle and adipose tissues result in unintentional body weight loss. Cachectic patients have a poor prognosis and the presence of cachexia reduces the tolerability of chemo/radio-therapy treatments and it is frequently the primary cause of death in advanced cancer patients. Early detection of this condition could make treatments more effective. However, early diagnostic biomarkers of cachexia are currently lacking. In recent years, although solid biopsy still remains the "gold standard" for diagnosis of cancer, liquid biopsy is gaining increasing interest as a source of easily accessible potential biomarkers. Moreover, the growing interest in circulating microRNAs (miRNAs), has made these molecules attractive for the diagnosis of several diseases, including cancer. Some muscle-derived circulating miRNA might play a pivotal role in the onset/progression of cancer cachexia. This topic is of great interest since circulating miRNAs might be easily detectable by means of liquid biopsies and might allow an early diagnosis of this syndrome. We here summarize the current knowledge on circulating muscular miRNAs involved in muscle atrophy, since they might represent easily accessible and promising biomarkers of cachexia.

Evidence of psychological and biological effects of structured Mindfulness-Based Interventions for cancer patients and survivors: A meta-review

OBJECTIVE

A large number of studies have been conducted exploring the effects of mindfulness programs on health outcomes, such as psychological and biological outcomes. However, there is substantial heterogeneity among studies and, consequently, in the systematic reviews/meta-analyses. Since clinical practice is massively informed by evidence on review studies, our main objective was to summarize the reported evidence regarding the effects of structured mindfulness-based programs on psychological, biological, and quality-of-life outcomes in cancer patients.

METHODS

We conducted a meta-review, using a literature search from inception to June 2020 in several electronic databases using a combination of keywords including MBSR, MBCT, cancer, and meta-analysis OR "systematic review" (PROSPERO registration CRD42020186511).

RESULTS

Ten studies met the eligibility criteria and were included. The main findings were beneficial small to medium effect sizes of Mindfulness-Based Stress Reduction (MBSR)/Mindfulness-Based Cognitive Therapy (MBCT)/Mindfulness-Based Cancer Recovery (MBCR) on psychological health, such as anxiety, depression, stress, and quality of life. A beneficial effect was found for biological outcomes, albeit based on a reduced number of studies. Studies were moderate homogenous regarding the intervention, population, and outcomes explored. Results on long-term follow-up seem to indicate that the effects tend not to be maintained, namely in shorter follow-ups (6 months).

CONCLUSIONS

This meta-review brings a broad perspective on the actual evidence regarding MBSR/MBCT/MBCR. We expect to contribute to future project design, focused on developing high-quality studies and exploring the moderating effects that might contribute to biased results, as well as exploring who might benefit more from MBSR/MBCT/MBCT interventions.

Heterogenous presence of neutrophil extracellular traps in human solid tumours is partially dependent on IL-8

Neutrophil extracellular traps (NETs) are webs of extracellular nuclear DNA extruded by dying neutrophils infiltrating tissue. NETs constitute a defence mechanism to entrap and kill fungi and bacteria. Tumours induce the formation of NETs to the advantage of the malignancy via a variety of mechanisms shown in mouse models. Here, we investigated the presence of NETs in a variety of human solid tumours and their association with IL-8 (CXCL8) protein expression and CD8⁺ T-cell density in the tumour microenvironment. Multiplex immunofluorescence panels were developed to identify NETs in human cancer tissues by co-staining with the granulocyte marker CD15, the neutrophil marker myeloperoxidase and citrullinated histone H3 (H3Cit), as well as IL-8 protein and CD8⁺ T cells. Three ELISA methods to detect and quantify circulating NETs in serum were optimised and utilised. Whole tumour sections and tissue microarrays from patients with non-small cell lung cancer (NSCLC; n = 14), bladder cancer (n = 14), melanoma (n = 11), breast cancer (n = 31), colorectal cancer (n = 20) and mesothelioma (n = 61) were studied. Also, serum samples collected retrospectively from patients with metastatic melanoma (n = 12) and NSCLC (n = 34) were ELISA assayed to quantify circulating NETs and IL-8. NETs were detected in six different human cancer types with wide individual variation in terms of tissue density and distribution. At least in NSCLC, bladder cancer and metastatic melanoma, NET density positively correlated with IL-8 protein expression and inversely correlated with CD8⁺ T-cell densities. In a series of serum samples from melanoma and NSCLC patients, a positive correlation between circulating NETs and IL-8 was found. In conclusion, NETs are detectable in formalin-fixed human biopsy samples from solid tumours and in the circulation of cancer patients with a considerable degree of individual variation. NETs show a positive association with IL-8 and a trend towards a negative association with CD8⁺ tumour-infiltrating lymphocytes. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Circulating and Tumor-Infiltrating NK Cells From Clear Cell Renal Cell Carcinoma Patients Exhibit a Predominantly Inhibitory Phenotype Characterized by Overexpression of CD85j, CD45, CD48 and PD-1

Although natural killer (NK) cells infiltrate clear cell renal cell carcinomas (ccRCC), the most frequent malignancy of the kidney, tumor progression suggests that they become dysfunctional. As ccRCC-driven subversion of NK cell effector functions is usually accompanied by phenotypic changes, analysis of such alterations might lead to the identification of novel biomarkers and/or targets in immuno-oncology. Consequently, we performed a phenotypic analysis of peripheral blood NK cells (PBNK) and tumor-infiltrating NK cells (TINK) from ccRCC patients. Compared to HD, PBNK from ccRCC patients exhibited features of activated cells as shown by CD25, CD69 and CD62L expression. They also displayed increased expression of DNAM-1, CD48, CD45, MHC-I, reduced expression of NKG2D, and higher frequencies of CD85j⁺ and PD-1⁺ cells. In addition, compared to PBNK from ccRCC patients, TINK exhibited higher expression of activation markers, tissue residency features and decreased expression of the activating receptors DNAM-1, NKp30, NKp46, NKp80 and CD16, suggesting a more inhibitory phenotype. Analysis of The Cancer Genome Atlas (TCGA) revealed that CD48, CD45, CD85j and PD-1 are significantly overexpressed in ccRCC and that their expression is associated with an NK cell infiltration signature. Calculation of z-scores revealed that their expression on PBNK, alone or combined, distinguished ccRCC patients from HD. Therefore, these molecules emerge as novel potential biomarkers and our results suggest that they might constitute possible targets for immunotherapy in ccRCC patients.

Soluble Neuropilin-1 is an independent marker of poor prognosis in early breast cancer

Background

Neuropilin-1 (NRP-1) is a transmembrane protein that acts as a multifunctional non-tyrosine kinase receptor with an established role in development and immunity. NRP-1 also regulates tumor biology, and high expression levels of tissue NRP-1 have been associated with a poor prognosis. Recently, ELISA-based quantification of soluble NRP-1 (sNRP-1) has become available, but little is known about the prognostic value of sNRP-1 in malignancies.

Materials and methods

We measured sNRP-1 in the serum of 509 patients with primary early breast cancer (BC) at the time of diagnosis using ELISA.

Results

Mean serum values of sNRP-1 were 1.88 ± 0.52 nmol/l (= 130.83 ± 36.24 ng/ml). SNRP-1 levels weakly correlated with age, and were higher in peri- and postmenopausal patients compared to premenopausal patients, respectively (p < 0.0001). Low levels of sNRP-1 were associated with a significant survival benefit compared to high sNRP-1 levels at baseline (p = 0.005; HR 1.94; 95%CI 1.23–3.06). These findings remained significant after adjustment for tumor stage including lymph node involvement, grading, hormone receptor, HER2 status, and age (p = 0.022; HR 1.78; 95%CI 1.09–2.91).

Conclusion

Our findings warrant further investigations into the prognostic and therapeutic potential of sNRP-1 in BC.

Targeted drug delivery strategies for precision medicines

Progress in the field of precision medicine has changed the landscape of cancer therapy. Precision medicine is propelled by technologies that enable molecular profiling, genomic analysis, and optimized drug design to tailor treatments for individual patients. Although precision medicines have resulted in some clinical successes, the use of many potential therapeutics has been hindered by pharmacological issues, including toxicities and drug resistance. Drug delivery materials and approaches have now advanced to a point where they can enable the modulation of a drug's pharmacological parameters without compromising the desired effect on molecular targets. Specifically, they can modulate a drug's pharmacokinetics, stability, absorption, and exposure to tumours and healthy tissues, and facilitate the administration of synergistic drug combinations. This Review highlights recent progress in precision therapeutics and drug delivery, and identifies opportunities for strategies to improve the therapeutic index of cancer drugs, and consequently, clinical outcomes.

Cell detachment ratio on pH-responsive chitosan: A useful biometric for prognostic judgment and drug efficacy assessment in oncology

The inherently unpredictable complexity of tumors impedes the widespread practice of the molecular biomarkers in outcome prediction. Alternatively, from the biophysical perspective, this study sought to investigate the applicability of the cell detachment ratio (CDR) derived from pH-responsive chitosan as a biometrical identifier for the disease state in cancer prognostic judgment and drug efficacy assessment. In the targeted therapy model, the repression of tumor dissemination in cells harboring aberrant ErbB signals (human non-small cell lung cancer cell line PC9 and breast cancer cell line BT474) were first demonstrated both in vitro and in vivo. Consequently, the corresponding CDR profile goes synchronously with the extent of cancer regression in response to the medication. Definitive integrins that drive the cell detachment were also verified through CDR examination following the integrin functional blockade. Conclusively, CDR is a promising clinical index for evaluation of the metastatic cell behaviors in terms of the cell detachment.

In Vitro Anticancer Effects of Stilbene Derivatives: Mechanistic Studies on HeLa and MCF-7 Cells

BACKGROUND AND OBJECTIVE

The growing prevalence of cancer and the resulting chemoresistance exert a huge burden on healthcare systems and impose a great challenge to public health around the world. In efforts to develop new chemotherapeutic agents for cancer treatment, a class of heterocyclic compounds i.e. triazine-based molecules were investigated as anticancer agents.

MATERIALS AND METHODS

New triazine hybrids of stilbene were synthesized and evaluated as anticancer agents for cervical (HeLa) and breast (MCF-7) carcinoma cells. The compound (7e), sodium (E)-6,6'-(ethene-1,2- diyl)bis(3-((4-chloro-6-((3-luorophenyl)amino)-1,3,5-triazin-2-yl)amino)benzenesulfonate) was found to be most potent among synthesized derivatives and was explored further for detailed mechanistic studies.

RESULTS

In a set comprised of twelve derivatives, compound 7e, sodium (E)-6,6'-(ethene-1,2-diyl)bis(3-((4- chloro-6-((3-luorophenyl)amino)-1,3,5-triazin-2-yl)amino)benzenesulfonate) was found most potent inhibitor for HeLa and MCF-7 cells.

DISCUSSION

The present study has revealed that compound 7e may activate mitochondrial pathway of apoptosis in HeLa and MCF-7 cells which was assessed by DNA binding studies, estimation of the release of Lactate Dehydrogenase (LDH), fluorescence imaging, production of Reactive Oxygen Species (ROS) in cancer cells, analysis of cell cycle by flow cytometry, change in Mitochondrial Membrane Potential (MMP) and activation of caspase-9 and caspase-3.

CONCLUSION

Compound 7e may serve as a lead in designing new anticancer compounds based on stilbene scaffold.

Whole exome sequencing characterization of individuals presenting extreme phenotypes of high and low risk of developing tobacco-induced lung adenocarcinoma

Background

Tobacco is the main risk factor for developing lung cancer. Yet, some heavy smokers do not develop lung cancer at advanced ages while others develop it at young ages. Here, we assess for the first time the genetic background of these clinically relevant extreme phenotypes using whole exome sequencing (WES).

Methods

We performed WES of germline DNA from heavy smokers who either developed lung adenocarcinoma at an early age (extreme cases, n=50) or did not present lung adenocarcinoma or other tumors at an advanced age (extreme controls, n=50). We selected non-synonymous variants located in exonic regions and consensus splice sites of the genes that showed significantly different allelic frequencies between both cohorts. We validated our results in all the additional extreme cases (i.e., heavy smokers who developed lung adenocarcinoma at an early age) available from The Cancer Genome Atlas (TCGA).

Results

The mean age for the extreme cases and controls was respectively 49.7 and 77.5 years. Mean tobacco consumption was 43.6 and 56.8 pack-years. We identified 619 significantly different variants between both cohorts, and we validated 108 of these in extreme cases selected from TCGA. Nine validated variants, located in relevant cancer related genes, such as PARP4, HLA-A or NQO1, among others, achieved statistical significance in the False Discovery Rate test. The most significant validated variant (P=4.48×10⁻⁵) was located in the tumor-suppressor gene ALPK2.

Conclusions

We describe genetic variants associated with extreme phenotypes of high and low risk for the development of tobacco-induced lung adenocarcinoma. Our results and our strategy may help to identify high-risk subjects and to develop new therapeutic approaches.

Characterization of the perioperative changes of exosomal immune-related cytokines induced by prostatectomy in early-stage prostate cancer patients

BACKGROUND

Myeloid-derived suppressor cells (MDSCs) are relevant in prostate cancer microenvironment collaborating in tumor development. The main tumor marker used in this disease, prostate-specific antigen (PSA), does not provide information related to this tumor microenvironment. Cancer cells secrete exosomes carrying bioactive molecules contributing to MDSCs recruitment and induction. The aim of this study was to characterize the perioperative changes of exosomal cytokines relevant in MDSCs recruitment induced by prostatectomy in prostate cancer patients.

METHODS

Blood was drawn from 26 early-stage prostate cancer patients before and after radical prostatectomy and from 16 healthy volunteers. Serum exosomes were separated by precipitation. Cytokines related with MDSC cell recruitment and activation CCL2, CXCL2, CXCL5, CXCL8, CXCL12, MIF, S100A9 and TGF-ß were measured in serum and serum-derived exosomes using immunometric assays.

RESULTS

All cytokines were detected both in serum and exosomes, except for CXCL12, which was detected only in serum. Exosomes were enriched specially in MIF, TGF-ß and CXCL2. Presurgical MIF levels in exosomes correlated negatively with serum PSA. Also, presurgical TGF-ß decreased both in serum and exosomes as Gleason score rises. Patientś presurgical exosomes had increased CCL2, CXCL5 and TGF-ß levels than exosomes from healthy controls. These differences were not observed when cytokines were analyzed in serum, except for TGF-ß. Cytokine levels of CCL2, CXCL5 decreased in patients' postsurgical exosomes, while TGF-ß further increased. On the contrary, S100A9 levels were lower in patientś presurgical exosomes but increased after radical prostatectomy.

CONCLUSIONS

Blood exosomal content in cytokines constitute an attractive source to evaluate MDSCs immunomodulators providing additional information related to tumor microenvironment in prostate cancer.

The use of emerging safety biomarkers in nonclinical and clinical safety assessment - The current and future state: An IQ DruSafe industry survey

Pharmaceutical and biotechnology companies rarely disclose their use of translational emerging safety biomarkers (ESBs) during drug development, and the impact of ESB use on the speed of drug development remains unclear. A cross-industry survey of 20 companies of varying size was conducted to understand current trends in ESB use and future use prospects. The objectives were to: (1) determine current ESB use in nonclinical and clinical drug development and impact on asset advancement; (2) identify opportunities, gaps, and challenges to greater ESB implementation; and (3) benchmark perspectives on regulatory acceptance. Although ESBs were employed in only 5-50% of studies/programs, most companies used ESBs to some extent, with larger companies demonstrating greater nonclinical use. Inclusion of ESBs in investigational new drug applications (INDs) was similar across all companies; however, differences in clinical trial usage could vary among the prevailing health authority (HA). Broader implementation of ESBs requires resource support, cross-industry partnerships, and collaboration with HAs. This includes generating sufficient foundational data, demonstrating nonclinical to clinical translatability and practical utility, and clearly written criteria by HAs to enable qualification. If achieved, ESBs will play a critical role in the development of next-generation, translationally-tailored standard laboratory tests for drug development.

BioPETsurv: Methodology and open source software to evaluate biomarkers for prognostic enrichment of time-to-event clinical trials

Biomarkers can be used to enrich a clinical trial for patients at higher risk for an outcome, a strategy termed "prognostic enrichment." Methodology is needed to evaluate biomarkers for prognostic enrichment of trials with time-to-event endpoints such as survival. Key considerations when considering prognostic enrichment include: clinical trial sample size; the number of patients one must screen to enroll the trial; and total patient screening costs and total per-patient trial costs. The Biomarker Prognostic Enrichment Tool for Survival Outcomes (BioPETsurv) is a suite of methods for estimating these elements to evaluate a prognostic enrichment biomarker and/or plan a prognostically enriched clinical trial with a time-to-event primary endpoint. BioPETsurv allows investigators to analyze data on a candidate biomarker and potentially censored survival times. Alternatively, BioPETsurv can simulate data to match a particular clinical setting. BioPETsurv's data simulator enables investigators to explore the potential utility of a prognostic enrichment biomarker for their clinical setting. Results demonstrate that both modestly prognostic and strongly prognostic biomarkers can improve trial metrics such as reducing sample size or trial costs. In addition to the quantitative analysis provided by BioPETsurv, investigators should consider the generalizability of trial results and evaluate the ethics of trial eligibility criteria. BioPETsurv is freely available as a package for the R statistical computing platform, and as a webtool at www.prognosticenrichment.com/surv.

Divining responder populations from survival data

BACKGROUND

Biomarkers that predict treatment response are the foundation of precision medicine in clinical decision-making and have the potential to significantly improve the efficiency of clinical trials. Such biomarkers may be identified before clinical testing but many trials enroll unselected populations. We hypothesized that time-varying treatment effects in unselected trials may result from identifiable responder subpopulations that may have associated biomarkers.

MATERIALS AND METHODS

We first simulated scenarios of clinical trials with biomarker populations of varying prevalence and prognostic and predictive associations to illustrate the impact of subgroup-specific effects on overall population estimates. To show a real-world example of time-dependent treatment effects resulting from a prognostic and predictive biomarker, we re-analyzed data from a published clinical trial (RTOG, Radiation Therapy Oncology Group, 9402). We then demonstrated a quantitative framework to fit survival data from clinical trials using statistical models incorporating known estimates of biomarker prevalence and prognostic value to prioritize predictive biomarker hypotheses.

RESULTS

Our simulation studies demonstrate how biomarker subgroups that are both predictive and prognostic can manifest as time-dependent treatment effects in overall populations. RTOG 9402 provides a representative example where 1p/19q co-deletion and IDH mutation biomarker-specific effects led to time-varying treatment effects and a considerable deviation from proportional hazards in the overall trial population. Finally, using biomarker data from The Cancer Genome Atlas, we were able to generate statistical models that correctly identified and prioritized a commonly used biomarker through retrospective analysis of published clinical trial data.

CONCLUSIONS

Biomarkers that are both predictive and prognostic can result in characteristic changes in survival results. Retrospectively analyzing survival data from clinical trials may highlight potential indications for which an underlying predictive biomarker may be found.

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.

Exploiting Circulating MicroRNAs as Biomarkers in Psychiatric Disorders

Non-invasive peripheral biomarkers play a significant role in both disease diagnosis and progression. In the past few years, microRNA (miRNA) expression changes in circulating peripheral tissues have been found to be correlative with changes in neuronal tissues from patients with neuropsychiatric disorders. This is a notable quality of a biomolecule to be considered as a biomarker for both prognosis and diagnosis of disease. miRNAs, members of the small non-coding RNA family, have recently gained significant attention due to their ability to epigenetically influence almost every aspect of brain functioning. Empirical evidence suggests that miRNA-associated changes in the brain are often translated into behavioral changes. Current clinical understanding further implicates their role in the management of major psychiatric conditions, including major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ). This review aims to critically evaluate the potential advantages and disadvantages of miRNAs as diagnostic/prognostic biomarkers in psychiatric disorders as well as in treatment response.

Study Design Considerations for Cancer Biomarker Discoveries

Background

Biomarker discovery studies have generated an array of omic data, however few novel biomarkers have reached clinical use. Guidelines for rigorous study designs are needed.

Content

Biases frequently occur in sample selection, outcome ascertainment, or unblinded sample handling and assaying process. The principles of a prospective-specimen collection and retrospective-blinded-evaluation (PRoBE) design can be adapted to mitigate various sources of biases in discovery. We recommend establishing quality biospecimen repositories using matched two-phase designs to minimize biases and maximize efficiency. We also highlight the importance of taking the clinical context into consideration in both sample selection and power calculation for discovery studies.

Summary

Biomarker discovery research should follow rigorous design principles in sample se- lection to avoid biases. Consideration of clinical application and the corresponding biomarker performance characteristics in study designs will lead to a more fruitful discovery study.

Impact

Appropriate study designs will improve the quality and clinical rigor of biomarker discovery studies.

Preanalytical Variables for the Genomic Assessment of the Cellular and Acellular Fractions of the Liquid Biopsy in a Cohort of Breast Cancer Patients

Liquid biopsy allows assessment of multiple analytes, providing temporal information with potential for improving understanding of cancer evolution and clinical management of patients. Although liquid biopsies are intensely investigated for prediction and response monitoring, preanalytic variables are of primary concern for clinical implementation, including categories of collection method and sample storage. Herein, an integrated high-density single-cell assay workflow for morphometric and genomic analysis of the liquid biopsy is used to characterize the effects of preanalytical variation and reproducibility of data from a breast cancer cohort. Following prior work quantifying performance of commonly used blood collection tubes, this study completes the analysis of four time points to assay (24, 48, 72, and 96 hours), demonstrating precision up to 48 hours after collection for assay sensitivity, highly reproducible rare cell enumeration, morphometric characterization, and high efficiency and capacity for single-cell genomic analysis. For the cell-free analysis, both freezing and use of fresh plasma produced similar quality and quantity of cell-free DNA for sequencing. The genomic analysis (copy number variation and single-nucleotide variation) described herein is broadly applicable to liquid biopsy platforms capable of isolating cell-free and cell-based DNA. Morphometric parameters and genomic signatures of individual circulating tumor cells were evaluated in relation to patient clinical response, providing preliminary evidence of clinical validity as a potential biomarker aiding clinical diagnostics or monitoring progression.

Confidence interval estimation for the changepoint of treatment stratification in the presence of a qualitative covariate-treatment interaction

The goal in stratified medicine is to administer the "best" treatment to a patient. Not all patients might benefit from the same treatment; the choice of best treatment can depend on certain patient characteristics. In this article, it is assumed that a time-to-event outcome is considered as a patient-relevant outcome and a qualitative interaction between a continuous covariate and treatment exists, ie, that patients with different values of one specific covariate should be treated differently. We suggest and investigate different methods for confidence interval estimation for the covariate value, where the treatment recommendation should be changed based on data collected in a randomized clinical trial. An adaptation of Fieller's theorem, the delta method, and different bootstrap approaches (normal, percentile-based, wild bootstrap) are investigated and compared in a simulation study. Extensions to multivariable problems are presented and evaluated. We observed appropriate confidence interval coverage following Fieller's theorem irrespective of sample size but at the cost of very wide or even infinite confidence intervals. The delta method and the wild bootstrap approach provided the smallest intervals but inadequate coverage for small to moderate event numbers, also depending on the location of the true changepoint. For the percentile-based bootstrap, wide intervals were observed, and it was slightly conservative regarding coverage, whereas the normal bootstrap did not provide acceptable results for many scenarios. The described methods were also applied to data from a randomized clinical trial comparing two treatments for patients with symptomatic, severe carotid artery stenosis, considering patient's age as predictive marker.

Association of TRAP1 with infliximab-induced mucosal healing in Crohn's disease

BACKGROUND AND AIM

Anti-tumor necrosis factor (TNF) agents, such as infliximab (IFX), have been increasingly used to induce and maintain disease remission in patients with Crohn's disease (CD). Despite a considerable non-response rate, little is known about the genetic predictors of response to anti-TNF therapy in CD. Our aim in this study was to investigate the genetic factors associated with response to anti-TNF therapy in patients with CD.

METHODS

We performed a two-stage genome-wide association study (GWAS) to identify loci influencing the response to IFX among Korean patients with CD, comprising 42 good responders with mucosal healing and 70 non-responders. The achievement of mucosal healing was assessed by endoscopy and imaging. The functional significance of TRAP1 (TNF receptor associated protein 1) was examined using dextran sodium sulfate-induced colitis model in TRAP1 transgenic mice.

RESULTS

The GWAS identified rs2158962, an intronic single nucleotide polymorphism (SNP) of TRAP1, significantly associated with mucosal healing (odds ratio = 4.94; P_combined  = 1.35 × 10^-7 ). In the dextran sodium sulfate-induced acute colitis, TRAP1 transgenic mice showed a better response to IFX than the wild-type mice.

CONCLUSIONS

The TRAP1 gene is associated with mucosal healing in CD patients following IFX therapy. Identifying the genetic predictors of mucosal healing to anti-TNF therapy can prevent patients from exposure to ineffective therapies.

KRAS Mutant Allele Fraction in Circulating Cell-Free DNA Correlates With Clinical Stage in Pancreatic Cancer Patients

Background: The research on circulating tumor DNA (ctDNA) in pancreatic cancer (PC) has emerged recently. Although the detection rate of the KRAS mutation in ctDNA was relatively consistent with that in tumor tissue, whether the KRAS mutant allele fraction (MAF) differed was still not reported. So far, the clinical application of ctDNA detection in PC remains inconclusive. Methods: Plasma samples were collected from 110 PC and 52 pancreatic benign (PB) disease patients. The detection of KRAS mutation in ctDNA was performed using droplet digital PCR and compared with that in matched tumor tissue. We assessed the utility of KRAS MAFs in ctDNA and tissue for pancreatic malignancy assessment. Results: We found that KRAS MAF in ctDNA of PC patients was higher than that of PB patients, and was obviously associated with tumor staging and distant metastasis. However, KRAS MAF in ctDNA was significantly different from that in matched tissue. KRAS MAF in tumor tissue had no significant correlation with the clinical status. In addition, a ROC curve analysis revealed that mutant KRAS ctDNA combined with CA19-9 could increase the sensitivity rate of early-stage PC prediction, compared with CA19-9 test alone. Conclusion: The MAF of KRAS in ctDNA was related to the clinical stage of PC (p = 0.001). Mutant KRAS ctDNA could improve the sensitivity in early diagnosis of PC as a complement to CA19-9. Our study suggested that KRAS mutation in ctDNA could be a valuable circulating biomarker for the malignancy assessment in PC.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

A new class of regression model for a bounded response with application in the study of the incidence rate of colorectal cancer

Response variables in medical sciences are often bounded, e.g. proportions, rates or fractions of incidence of some disease. In this work, we are interested to study if some characteristics of the population, e.g. sex and race which can explain the incidence rate of colorectal cancer cases. To accommodate such responses, we propose a new class of regression models for bounded response by considering a new distribution in the open unit interval which includes a new parameter to make a more flexible distribution. The proposal is to obtain compound power normal distribution as a base distribution with a quantile transformation of another family of distributions with the same support and then is to study some properties of the new family. In addition, the new family is extended to regression models as an alternative to the regression model with a unit interval response. We also present inferential procedures based on the Bayesian methodology, specifically a Metropolis–Hastings algorithm is used to obtain the Bayesian estimates of parameters. An application to real data to illustrate the use of the new family is considered.

Existing and novel biomarkers for precision medicine in systemic sclerosis

The discovery and validation of biomarkers resulting from technological advances in the analysis of genomic, transcriptomic, lipidomic and metabolomic pathways involved in the pathogenesis of complex human diseases have led to the development of personalized and rationally designed approaches for the clinical management of such disorders. Although some of these approaches have been applied to systemic sclerosis (SSc), an unmet need remains for validated, non-invasive biomarkers to aid in the diagnosis of SSc, as well as in the assessment of disease progression and response to therapeutic interventions. Advances in global transcriptomic technology over the past 15 years have enabled the assessment of microRNAs that circulate in the blood of patients and the analysis of the macromolecular content of a diverse group of lipid bilayer membrane-enclosed extracellular vesicles, such as exosomes and other microvesicles, which are released by all cells into the extracellular space and circulation. Such advances have provided new opportunities for the discovery of biomarkers in SSc that could potentially be used to improve the design and evaluation of clinical trials and that will undoubtedly enable the development of personalized and individualized medicine for patients with SSc.

MiTPeptideDB: a proteogenomic resource for the discovery of novel peptides

Motivation

The principal lines of research in MS/MS based Proteomics have been directed toward the molecular characterization of the proteins including their biological functions and their implications in human diseases. Recent advances in this field have also allowed the first attempts to apply these techniques to the clinical practice. Nowadays, the main progress in Computational Proteomics is based on the integration of genomic, transcriptomic and proteomic experimental data, what is known as Proteogenomics. This methodology is being especially useful for the discovery of new clinical biomarkers, small open reading frames and microproteins, although their validation is still challenging.

Results

We detected novel peptides following a proteogenomic workflow based on the MiTranscriptome human assembly and shotgun experiments. The annotation approach generated three custom databases with the corresponding peptides of known and novel transcripts of both protein coding genes and non-coding genes. In addition, we used a peptide detectability filter to improve the computational performance of the proteomic searches, the statistical analysis and the robustness of the results. These innovative additional filters are specially relevant when noisy next generation sequencing experiments are used to generate the databases. This resource, MiTPeptideDB, was validated using 43 cell lines for which RNA-Seq experiments and shotgun experiments were available.

Availability and implementation

MiTPeptideDB is available at http://bit.ly/MiTPeptideDB.

Supplementary information

Supplementary data are available at Bioinformatics online.

Translating RB1 predictive value in clinical cancer therapy: Are we there yet?

The retinoblastoma RB1 gene has been identified in the 80s as the first tumor suppressor. RB1 loss of function, as well alterations in its pathway, occur in most human cancers and often have prognostic value. RB1 has a key role in restraining cell cycle entry and, along with its family members, regulates a myriad of cellular processes and affects cell response to a variety of stimuli, ultimately determining cell fate. Consistently, RB1 status is a crucial determinant of the cell response to antitumoral therapies, impacting on the outcome of both traditional and modern anti-cancer strategies, including precision medicine approaches, such as kinase inhibitors, and immunotherapy. Despite many efforts however, the predictive value of RB1 status in the clinical practice is still underused, mainly owing to the complexity of RB1 function, to differences depending on the cellular context and on the therapeutic strategies, and, not-lastly, to technical issues. Here, we provide an overview of studies analyzing the role of RB1 in response to conventional cytotoxic and cytostatic therapeutic agents in different cancer types, including hormone dependent ones. We also review RB1 predictive value in the response to the last generation CDK4/6 inhibitors, other kinase inhibitors, and immunotherapy and discuss new emerging non-canonical roles of RB1 that could impact on the response to antitumoral treatments.

Identification of Tumor Antigens Among the HLA Peptidomes of Glioblastoma Tumors and Plasma

Glioblastoma multiforme (GBM) is the most aggressive brain tumor with poor prognosis to most patients. Immunotherapy of GBM is a potentially beneficial treatment option, whose optimal implementation may depend on familiarity with tumor specific antigens, presented as HLA peptides by the GBM cells. Further, early detection of GBM, such as by a routine blood test, may improve survival, even with the current treatment modalities. This study includes large-scale analyses of the HLA peptidome (immunopeptidome) of the plasma-soluble HLA molecules (sHLA) of 142 plasma samples, and the membranal HLA of GBM tumors of 10 of these patients' tumor samples. Tumor samples were fresh-frozen immediately after surgery and the plasma samples were collected before, and at multiple visits after surgery. In total, this HLA peptidome analysis involved 52 different HLA allotypes and resulted in the identification of more than 35,000 different HLA peptides. Strong correlations were observed in the signal intensities and in the repertoires of identified peptides between the tumors and plasma-soluble HLA peptidomes of the individual patients, whereas low correlations were observed between these HLA peptidomes and the tumors' proteomes. HLA peptides derived from Cancer/Testis Antigens (CTAs) were selected based on their presence among the HLA peptidomes of the patients and absence of expression of their source genes from any healthy and essential human tissues, except from immune-privileged sites. Additionally, peptides were selected as potential biomarkers if their levels in the plasma-sHLA peptidome were significantly reduced after the removal of tumor mass. The CTAs identified among the analyzed HLA peptidomes provide new opportunities for personalized immunotherapy and for early diagnosis of GBM.

Circulating tumor DNA in advanced prostate cancer: transitioning from discovery to a clinically implemented test

The genomic landscape of metastatic castration-resistant prostate cancer (mCRPC) differs from that of the primary tumor and is dynamic during tumor progression. The real-time and repeated characterization of this process via conventional solid tumor biopsies is challenging. Alternatively, circulating cell-free DNA (cfDNA) containing circulating tumor DNA (ctDNA) can be obtained from patient plasma using minimally disruptive blood draws and is amenable to sequential analysis. ctDNA has high overlap with the genomic sequences of biopsies from metastases and has the advantage of being representative of multiple metastases. The availability of techniques with high sensitivity and specificity, such as next-generation sequencing (NGS) and digital PCR, has greatly contributed to the development of the cfDNA field and enabled the detection of genomic alterations at low ctDNA fractions. In mCRPC, a number of clinically relevant genomic alterations have been tracked in ctDNA, including androgen receptor (AR) aberrations, which have been shown to be associated with an adverse outcome to novel antiandrogen therapies, and alterations in homologous recombination repair (HRR) genes, which have been associated with a response to PARP inhibitors. Several clinical applications have been proposed for cfDNA analysis, including its use as a prognostic tool, as a predictive biomarker, to monitor tumor response and to identify novel mechanisms of resistance. To date, the cfDNA analysis has provided interesting results, but there is an urgent need for these findings to be confirmed in prospective clinical trials.

Identification of mutations associated with acquired resistance to sunitinib in renal cell cancer

Sunitinib is one of the most widely used targeted therapeutics for renal cell carcinoma (RCC), but acquired resistance against targeted therapies remains a major clinical challenge. To dissect mechanisms of acquired resistance and unravel reliable predictive biomarkers for sunitinib in RCC, we sequenced the exons of 409 tumor-suppressor genes and oncogenes in paired tumor samples from an RCC patient, obtained at baseline and after development of acquired resistance to sunitinib. From newly arising mutations, we selected, using in silico prediction models, six predicted to be deleterious, located in G6PD, LRP1B, SETD2, TET2, SYNE1, and DCC. Consistently, immunoblotting analysis of lysates derived from sunitinib-desensitized RCC cells and their parental counterparts showed marked differences in the levels and expression pattern of the proteins encoded by these genes. Our further analysis demonstrates essential roles for these proteins in mediating sunitinib cytotoxicity and shows that their loss of function renders tumor cells resistant to sunitinib in vitro and in vivo. Finally, sunitinib resistance induced by continuous exposure or by inhibition of the six proteins was overcome by treatment with cabozantinib or a low-dose combination of lenvatinib and everolimus. Collectively, our results unravel novel markers of acquired resistance to sunitinib and clinically relevant approaches for overcoming this resistance in RCC.

The Dynamic Use of EGFR Mutation Analysis in Cell-Free DNA as a Follow-Up Biomarker during Different Treatment Lines in Non-Small-Cell Lung Cancer Patients

Epidermal growth factor receptor (EGFR) mutational testing in advanced non-small-cell lung cancer (NSCLC) is usually performed in tumor tissue, although cfDNA (cell-free DNA) could be an alternative. We evaluated EGFR mutations in cfDNA as a complementary tool in patients, who had already known EGFR mutations in tumor tissue and were treated with either EGFR-tyrosine kinase inhibitors (TKIs) or chemotherapy. We obtained plasma samples from 21 advanced NSCLC patients with known EGFR tumor mutations, before and during therapy with EGFR-TKIs and/or chemotherapy. cfDNA was isolated and EGFR mutations were analyzed with the multiple targeted cobas EGFR Mutation Test v2. EGFR mutations were detected at baseline in cfDNA from 57% of patients. The semiquantitative index (SQI) significantly decreased from the baseline (median = 11, IQR = 9.5-13) to the best response (median = 0, IQR = 0-0, p < 0.01), followed by a significant increase at progression (median = 11, IQR = 11-15, p < 0.01) in patients treated with either EGFR-TKIs or chemotherapy. The SQI obtained with the cobas EGFR Mutation Test v2 did not correlate with the concentration in copies/mL determined by droplet digital PCR. Resistance mutation p.T790M was observed at progression in patients with either type of treatment. In conclusion, cfDNA multiple targeted EGFR mutation analysis is useful for treatment monitoring in tissue of EGFR-positive NSCLC patients independently of the drug received.

Statistical Methods for Identifying Biomarkers from miRNA Profiles of Cancers

Biomarkers play important roles in early diagnosis and treatment plan for cancer patients and the importance is growing. With advances in high-throughput molecular profiling technology for various types of molecules such as DNA, RNA, proteins, or metabolites, it is now possible to perform massive profiling analysis that allows accelerating discovery of novel biomolecules. Because no single marker is sufficiently accurate for clinical use, the cancer biomarker is developed in the form of multiple biomarker panels. No single marker is sufficiently accurate for clinical use, and thus cancer biomarkers are developed in the form of multiple biomarker panels. Of various types of molecular biomarkers, microRNA (miRNA) has emerged as a class of promising cancer biomarker recently. MiRNAs are small noncoding RNAs that regulate gene expression. The chapter overviews the process of identification of biomarker panels from miRNA profiles focusing on statistical methods. Introduction to molecular cancer biomarkers is touched first. From sample design to miRNA profiling process is reviewed in the method section.Statistical methods for biomarker development are introduced according to three typical purposes of molecular biomarkers: tumor subtype classification, early detection, and prediction of treatment response or prognosis of patients. Example codes for R program are provided as well for selected methods.

Development of a Highly Multiplexed SRM Assay for Biomarker Discovery in Formalin-Fixed Paraffin-Embedded Tissues

The search for novel and clinically relevant biomarkers still represents a major clinical challenge and mass-spectrometry-based technologies are essential tools to help in this process. In this application, we demonstrate how selected reaction monitoring (SRM) can be applied in a highly multiplexed way to analyze formalin-fixed paraffin-embedded (FFPE) tissues. Such an assay can be used to analyze numerous samples for narrowing down a list of potential biomarkers to the most relevant candidates. The use of FFPE tissues is of high relevance in this context as large sample collections linked with valuable clinical information are available in hospitals around the world. Here we describe in detail how we proceeded to develop such an assay for 200 proteins in breast tumor FFPE tissues. We cover the selection of suitable peptides, which are different in FFPE compared to fresh frozen tissues and show how we deliberately biased our assay toward proteins with a high probability of being measurable in human clinical samples.

Maytansine-bearing antibody-drug conjugates induce in vitro hallmarks of immunogenic cell death selectively in antigen-positive target cells

Oncology treatment has been revolutionized by the introduction of immune checkpoint inhibitor drugs, which enable 20-40% of patients to generate anti-tumor immune responses. Combination treatment approaches with chemotherapeutic drugs may enable responses in the remaining patient cohorts. In this regard, a handful of drugs are promising due to their ability to induce immunogenic cell death in target cells. However, these agents are systemically delivered and indiscriminately cytotoxic to proliferating cells. By contrast, antibody-drug conjugates can selectively deliver a cytotoxic payload to a tumor, sparing most healthy cells. The ability of antibody-drug conjugates to induce immunogenic cell death in target cells has not yet been determined, although preclinical in vivo studies suggest this possibility. Here, we describe for the first time production of the in vitro hallmarks of immunogenic cell death - ecto-calreticulin and secreted ATP and HMGB1 protein - by cells in response to treatment with antibody-drug conjugates bearing a maytansine payload.

Circulating Tumor Cells in Prostate Cancer: From Discovery to Clinical Utility

BACKGROUND

Prostate cancer represents the most common non–skin cancer type in men. Unmet needs include understanding prognosis to determine when intervention is needed and what type, prediction to guide the choice of a systemic therapy, and response indicators to determine whether a treatment is working. Over the past decade, the “liquid biopsy,” characterized by the analysis of tumor cells and tumor cell products such as cell-free nucleic acids (DNA, microRNA) or extracellular vesicles circulating in the blood of cancer patients, has received considerable attention.

CONTENT

Among those biomarkers, circulating tumor cells (CTCs) have been most intensively analyzed in prostate cancer. Here we discuss recent studies on the enumeration and characterization of CTCs in peripheral blood and how this information can be used to develop biomarkers for each of these clinical contexts. We focus on clinical applications in men with metastatic castration-resistant prostate cancer, in whom CTCs are more often detected and at higher numbers, and clinical validation for different contexts of use is most mature.

SUMMARY

The overall goal of CTC-based liquid biopsy testing is to better inform medical decision-making so that patient outcomes are improved.

Artificial intelligence in drug development: present status and future prospects

Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems. Remarkable improvements in computational power coupled with advancements in AI technology could be utilised to revolutionise the drug development process. At present, the pharmaceutical industry is facing challenges in sustaining their drug development programmes because of increased R&D costs and reduced efficiency. In this review, we discuss the major causes of attrition rates in new drug approvals, the possible ways that AI can improve the efficiency of the drug development process and collaboration of pharmaceutical industry giants with AI-powered drug discovery firms.

Identification of Tumor Antigens Among the HLA Peptidomes of Glioblastoma Tumors and Plasma

Glioblastoma multiforme (GBM) is the most aggressive brain tumor with poor prognosis to most patients. Immunotherapy of GBM is a potentially beneficial treatment option, whose optimal implementation may depend on familiarity with tumor specific antigens, presented as HLA peptides by the GBM cells. Furthermore, early detection of GBM, such as by a routine blood test, may improve survival, even with the current treatment modalities. This study includes large-scale analyses of the HLA peptidome (immunopeptidome) of the plasma-soluble HLA molecules (sHLA) of 142 plasma samples, and the membranal HLA of GBM tumors of 10 of these patients' tumor samples. Tumor samples were fresh-frozen immediately after surgery and the plasma samples were collected before, and at multiple visits after surgery. In total, this HLA peptidome analysis involved 52 different HLA allotypes and resulted in the identification of more than 35,000 different HLA peptides. Strong correlations were observed in the signal intensities and in the repertoires of identified peptides between the tumors and plasma-soluble HLA peptidomes of the individual patients, whereas low correlations were observed between these HLA peptidomes and the tumors' proteomes. HLA peptides derived from Cancer/Testis Antigens (CTAs) were selected based on their presence among the HLA peptidomes of the patients and absence of expression of their source genes from any healthy and essential human tissues, except from immune-privileged sites. Additionally, peptides were selected as potential biomarkers if their levels in the plasma-sHLA peptidome were significantly reduced after the removal of tumor mass. The CTAs identified among the analyzed HLA peptidomes provide new opportunities for personalized immunotherapy and for early diagnosis of GBM.

The Significance of Liquid Biopsy in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal types of cancer. The 5-year survival rate for PDAC remains low because it is always diagnosed at an advanced stage and it is resistant to therapy. A biomarker, which could detect asymptomatic premalignant or early malignant tumors and predict the response to treatment, will benefit patients with PDAC. However, traditional biopsy has its limitations. There is an urgent need for a tumor biomarker that could easily and repeatedly sample and monitor, in real time, the progress of tumor development. Liquid biopsy could be a tool to assess potential biomarkers. In this review, we focused on the latest discoveries and advancements of liquid biopsy technology in pancreatic cancer research and demonstrated how this technology is being used in clinical applications.

Neuropilin-1 expression is associated with lymph node metastasis in breast cancer tissues

Purpose

Neuropilin-1 (NRP1) as an isoform-specific receptor for vascular endothelial growth factor and placenta growth factor in endothelial cells has been demonstrated to be expressed in breast cancer cells where it plays functional roles in cell survival, invasion, and migration. We hypothesized that an expression of NRP1 in breast cancer tissues is associated with clinicopathological data of patients and expression of the tumor suppressor miR-206.

Patients and methods

We evaluated the expression of NRP1 in 48 invasive ductal carcinomas of the breast and their corresponding adjacent noncancerous tissues (ANCTs) by means of real-time polymerase chain reaction. We also extracted data on miR-206 gene expression from the same cohort of patients to evaluate the correlation between expression levels of miR-206 and NRP1. In addition, we quantified NRP1 protein levels using the enzyme-linked immunosorbent assay technique.

Results

No significant difference was found in NRP1 expression between tumoral tissues and ANCTs. We also assessed the associations between expression levels of NRP1 and clinicopathological data of patients and found no significant associations between NRP1 transcript levels and any characteristic. However, NRP1 protein concentrations were significantly higher in patients with lymph node involvement compared with those without lymph node involvement. No correlation was found between NRP1 and miR-206 expression levels.

Conclusion

NRP1 protein levels might be an indicator of metastasis potential in breast cancer. Future studies are needed to confirm these results in larger cohorts of patients.

Genomic characterization of individuals presenting extreme phenotypes of high and low risk to develop tobacco-induced lung cancer

Single nucleotide polymorphisms (SNPs) may modulate individual susceptibility to carcinogens. We designed a genome-wide association study to characterize individuals presenting extreme phenotypes of high and low risk to develop tobacco-induced non-small cell lung cancer (NSCLC), and we validated our results. We hypothesized that this strategy would enrich the frequencies of the alleles that contribute to the observed traits. We genotyped 2.37 million SNPs in 95 extreme phenotype individuals, that is: heavy smokers that either developed NSCLC at an early age (extreme cases); or did not present NSCLC at an advanced age (extreme controls), selected from a discovery set (n = 3631). We validated significant SNPs in 133 additional subjects with extreme phenotypes selected from databases including >39,000 individuals. Two SNPs were validated: rs12660420 (pcombined  = 5.66 × 10-5 ; ORcombined  = 2.80), mapping to a noncoding transcript exon of PDE10A; and rs6835978 (pcombined  = 1.02 × 10-4 ; ORcombined  = 2.57), an intronic variant in ATP10D. We assessed the relevance of both proteins in early-stage NSCLC. PDE10A and ATP10DmRNA expressions correlated with survival in 821 stage I-II NSCLC patients (p = 0.01 and p < 0.0001). PDE10A protein expression correlated with survival in 149 patients with stage I-II NSCLC (p = 0.002). In conclusion, we validated two variants associated with extreme phenotypes of high and low risk of developing tobacco-induced NSCLC. Our findings may allow to identify individuals presenting high and low risk to develop tobacco-induced NSCLC and to characterize molecular mechanisms of carcinogenesis and resistance to develop NSCLC.

An Update on Predictive Biomarkers for Treatment Selection in Non-Small Cell Lung Cancer

It is now widely established that management of lung cancer is much more complex and cannot be centered on the binary classification of small-cell versus non-small cell lung cancer (NSCLC). Lung cancer is now recognized as a highly heterogeneous disease that develops from genetic mutations and gene expression patterns, which initiate uncontrolled cellular growth, proliferation and progression, as well as immune evasion. Accurate biomarker assessment to determine the mutational status of driver mutations such as EGFR, ALK and ROS1, which can be targeted by specific tyrosine kinase inhibitors, is now essential for treatment decision making in advanced stage NSCLC and has shifted the treatment paradigm of NSCLC to more individualized therapy. Rapid advancements in immunotherapeutic approaches to NSCLC treatment have been paralleled by development of a range of potential predictive biomarkers that can enrich for patient response, including PD-L1 expression and tumor mutational burden. Here, we review the key biomarkers that help predict response to treatment options in NSCLC patients.