Pharmacogenomic biomarkers for personalized cancer treatment

PCAS: An Integrated Tool for Multi-Dimensional Cancer Research Utilizing Clinical Proteomic Tumor Analysis Consortium Data

Proteomics offers a robust method for quantifying proteins and elucidating their roles in cellular functions, surpassing the insights provided by transcriptomics. The Clinical Proteomic Tumor Analysis Consortium database, enriched with comprehensive cancer proteomics data including phosphorylation and ubiquitination profiles, alongside transcriptomics data from the Genomic Data Commons, allow for integrative molecular studies of cancer. The ProteoCancer Analysis Suite (PCAS), our newly developed R package and Shinyapp, leverages these resources to facilitate in-depth analyses of proteomics, phosphoproteomics, and transcriptomics, enhancing our understanding of the tumor microenvironment through features like immune infiltration and drug sensitivity analysis. This tool aids in identifying critical signaling pathways and therapeutic targets, particularly through its detailed phosphoproteomic analysis. To demonstrate the functionality of the PCAS, we conducted an analysis of GAPDH across multiple cancer types, revealing a significant upregulation of protein levels, which is consistent with its important biological and clinical significance in tumors, as indicated in our prior research. Further experiments were used to validate the findings performed using the tool. In conclusion, the PCAS is a powerful and valuable tool for conducting comprehensive proteomic analyses, significantly enhancing our ability to uncover oncogenic mechanisms and identify potential therapeutic targets in cancer research.

Clinical pharmacists' knowledge, attitude, perception, and beliefs about the role of pharmacogenetic testing for genes polymorphisms when prescribing mercaptopurine

Background

Single nucleotide polymorphisms in the gene encoding proteins involved in mercaptopurine metabolism can influence drug efficacy and safety. This study aims to assess clinical pharmacists' knowledge about mercaptopurine-related genes and their polymorphisms and investigate their attitudes, perceptions, and beliefs about the need for and importance of pharmacogenetic testing for mercaptopurine.

Methods

A cross-sectional descriptive study was conducted among oncology/hematology clinical pharmacists in Saudi Arabia using an online-questionnaire developed by experts in the field. The questionnaire consists of four-sections exploring clinical pharmacists' knowledge, attitudes, perceptions, and beliefs about the importance of gene testing and genes polymorphism when prescribing mercaptopurine. Descriptive statistics were used to analyze the data in the study.

Results

A total of 41 oncology/hematology clinical pharmacists responded to the survey invitation. Almost half of them had more than 10 years of work experience, but only 17 % of them received formal training in pharmacogenetics. The overall level of knowledge about pharmacogenetics among participants was low, with a mean score of 2.8 points (1.7) out of 8 items. However, around 76 % agreed that it is important to perform pharmacogenetic screening prior to prescribing mercaptopurine, and almost 93 % state that it will influence their dosage recommendation. Most of the participants had a good perception (95.1 %) of their role in genetic testing for medication selection, dosing, and monitoring; however, about 10 % of surveyed pharmacists reported not being completely responsible about recommending pharmacogenetic testing. The surveyed pharmacists had a good belief in the importance of pharmacogenetic testing and their overall attitude was positive toward the use of pharmacogenetic testing, with emphasis on the importance of training on the proper assessment and interpretation of pharmacogenetic tests.

Conclusions

Pharmacists demonstrated good perception and positive attitude toward pharmacogenetic testing, despite the low level of knowledge and limited formal training. Thus, more attention to developing national guidelines on pharmacogenetic testing is warranted to ensure successful pharmacogenetic testing implementation.

A conceptual framework for cautious escalation of anticancer treatment: How to optimize overall benefit and obviate the need for de-escalation trials

BACKGROUND

The developmental workflow of the currently performed phase 1, 2 and 3 cancer trial stages lacks essential information required for the determination of the optimal efficacy threshold of new anticancer regimens. Due to this there is a serious risk of overdosing and/or treating for an unnecessary long time, leading to excess toxicity and a higher financial burden for society. But often post-approval de-escalation trials for dose-optimization and treatment de-intensification are not performed due to failing resources and time. Therefore, the developmental workflow needs to be restructured toward cautious systemic cancer treatment escalation, in order to guarantee optimal efficacy and sustainability.

METHODS

In this manuscript we discuss opportunities to produce the information needed for cautious escalation, based on models of cancer growth and cancer kill kinetics as well as exploratory biomarkers, for the purpose of designing the optimal phase 3 superiority trial. Subsequently, we compare the sample size needed for a phase 3 superiority trial, followed by a necessary de-escalation trial with the sample size needed for a multi-arm phase 3 trial with intervention arms of differing intensity. All essential items are structured within a Framework for Cautious Escalation (FCE). The discussion uses illustrations from the breast cancer setting, but aims to be applicable for all cancers.

RESULTS

The FCE is a promising model of clinical development in oncology to prevent overtreatment and associated issues, especially with regard to the number of repetitive treatment cycles. It will hopefully increase the relevance and success rate of clinical trials, to deliver improved patient-centric outcomes.

Large-Scale Machine Learning Analysis Reveals DNA Methylation and Gene Expression Response Signatures for Gemcitabine-Treated Pancreatic Cancer

Background: Gemcitabine is a first-line chemotherapy for pancreatic adenocarcinoma (PAAD), but many PAAD patients do not respond to gemcitabine-containing treatments. Being able to predict such nonresponders would hence permit the undelayed administration of more promising treatments while sparing gemcitabine life-threatening side effects for those patients. Unfortunately, the few predictors of PAAD patient response to this drug are weak, none of them exploiting yet the power of machine learning (ML). Methods: Here, we applied ML to predict the response of PAAD patients to gemcitabine from the molecular profiles of their tumors. More concretely, we collected diverse molecular profiles of PAAD patient tumors along with the corresponding clinical data (gemcitabine responses and clinical features) from the Genomic Data Commons resource. From systematically combining 8 tumor profiles with 16 classification algorithms, each of the resulting 128 ML models was evaluated by multiple 10-fold cross-validations. Results: Only 7 of these 128 models were predictive, which underlines the importance of carrying out such a large-scale analysis to avoid missing the most predictive models. These were here random forest using 4 selected mRNAs [0.44 Matthews correlation coefficient (MCC), 0.785 receiver operating characteristic-area under the curve (ROC-AUC)] and XGBoost combining 12 DNA methylation probes (0.32 MCC, 0.697 ROC-AUC). By contrast, the hENT1 marker obtained much worse random-level performance (practically 0 MCC, 0.5 ROC-AUC). Despite not being trained to predict prognosis (overall and progression-free survival), these ML models were also able to anticipate this patient outcome. Conclusions: We release these promising ML models so that they can be evaluated prospectively on other gemcitabine-treated PAAD patients.

Learning from the nexus of autoimmunity and cancer

The immune system plays critical roles in both autoimmunity and cancer, diseases at opposite ends of the immune spectrum. Autoimmunity arises from loss of T cell tolerance against self, while in cancer, poor immunity against transformed self fails to control tumor growth. Blockade of pathways that preserve self-tolerance is being leveraged to unleash immunity against many tumors; however, widespread success is hindered by the autoimmune-like toxicities that arise in treated patients. Knowledge gained from the treatment of autoimmunity can be leveraged to treat these toxicities in patients. Further, the understanding of how T cell dysfunction arises in cancer can be leveraged to induce a similar state in autoreactive T cells. Here, we review what is known about the T cell response in autoimmunity and cancer and highlight ways in which we can learn from the nexus of these two diseases to improve the application, efficacy, and management of immunotherapies.

Interpretable Machine Learning Models to Predict the Resistance of Breast Cancer Patients to Doxorubicin from Their microRNA Profiles

Doxorubicin is a common treatment for breast cancer. However, not all patients respond to this drug, which sometimes causes life-threatening side effects. Accurately anticipating doxorubicin-resistant patients would therefore permit to spare them this risk while considering alternative treatments without delay. Stratifying patients based on molecular markers in their pretreatment tumors is a promising approach to advance toward this ambitious goal, but single-gene gene markers such as HER2 expression have not shown to be sufficiently predictive. The recent availability of matched doxorubicin-response and diverse molecular profiles across breast cancer patients permits now analysis at a much larger scale. 16 machine learning algorithms and 8 molecular profiles are systematically evaluated on the same cohort of patients. Only 2 of the 128 resulting models are substantially predictive, showing that they can be easily missed by a standard-scale analysis. The best model is classification and regression tree (CART) nonlinearly combining 4 selected miRNA isoforms to predict doxorubicin response (median Matthew correlation coefficient (MCC) and area under the curve (AUC) of 0.56 and 0.80, respectively). By contrast, HER2 expression is significantly less predictive (median MCC and AUC of 0.14 and 0.57, respectively). As the predictive accuracy of this CART model increases with larger training sets, its update with future data should result in even better accuracy.

Dissecting cell fate dynamics in pediatric glioblastoma through the lens of complex systems and cellular cybernetics

Cancers are complex dynamic ecosystems. Reductionist approaches to science are inadequate in characterizing their self-organized patterns and collective emergent behaviors. Since current approaches to single-cell analysis in cancer systems rely primarily on single time-point multiomics, many of the temporal features and causal adaptive behaviors in cancer dynamics are vastly ignored. As such, tools and concepts from the interdisciplinary paradigm of complex systems theory are introduced herein to decode the cellular cybernetics of cancer differentiation dynamics and behavioral patterns. An intuition for the attractors and complex networks underlying cancer processes such as cell fate decision-making, multiscale pattern formation systems, and epigenetic state-transitions is developed. The applications of complex systems physics in paving targeted therapies and causal pattern discovery in precision oncology are discussed. Pediatric high-grade gliomas are discussed as a model-system to demonstrate that cancers are complex adaptive systems, in which the emergence and selection of heterogeneous cellular states and phenotypic plasticity are driven by complex multiscale network dynamics. In specific, pediatric glioblastoma (GBM) is used as a proof-of-concept model to illustrate the applications of the complex systems framework in understanding GBM cell fate decisions and decoding their adaptive cellular dynamics. The scope of these tools in forecasting cancer cell fate dynamics in the emerging field of computational oncology and patient-centered systems medicine is highlighted.

Skin Cancer Research Goes Digital: Looking for Biomarkers within the Droplets

Skin cancer, which includes the most frequent malignant non-melanoma carcinomas (basal cell carcinoma, BCC, and squamous cell carcinoma, SCC), along with the difficult to treat cutaneous melanoma (CM), pose important worldwide issues for the health care system. Despite the improved anti-cancer armamentarium and the latest scientific achievements, many skin cancer patients fail to respond to therapies, due to the remarkable heterogeneity of cutaneous tumors, calling for even more sophisticated biomarker discovery and patient monitoring approaches. Droplet digital polymerase chain reaction (ddPCR), a robust method for detecting and quantifying low-abundance nucleic acids, has recently emerged as a powerful technology for skin cancer analysis in tissue and liquid biopsies (LBs). The ddPCR method, being capable of analyzing various biological samples, has proved to be efficient in studying variations in gene sequences, including copy number variations (CNVs) and point mutations, DNA methylation, circulatory miRNome, and transcriptome dynamics. Moreover, ddPCR can be designed as a dynamic platform for individualized cancer detection and monitoring therapy efficacy. Here, we present the latest scientific studies applying ddPCR in dermato-oncology, highlighting the potential of this technology for skin cancer biomarker discovery and validation in the context of personalized medicine. The benefits and challenges associated with ddPCR implementation in the clinical setting, mainly when analyzing LBs, are also discussed.

Efficacy and safety of immuno-chemotherapy in patients with advanced non-small-cell lung cancer harboring oncogenic mutations: a multicenter retrospective study

PURPOSE

The effect of immuno-chemotherapy on patients with advanced non-small-cell lung cancer (NSCLC) harboring oncogenic mutations remains poorly understood. This study aimed to characterize the efficacy of immuno-chemotherapy and determine the optimal treatment strategy for such patients.

METHODS

We conducted this retrospective cohort study on patients with NSCLC harboring oncogenic driver alterations and treated with an immune checkpoint inhibitor combined with chemotherapy at five institutions. The clinical characteristics and outcomes of immuno-chemotherapy for NSCLC with oncogenic mutations in a real-world setting were analyzed.

RESULTS

Among 846 patients diagnosed with advanced or recurrent NSCLC between April 2017 and April 2021, 43 patients with oncogenic mutations were treated with immuno-chemotherapy. The median age of patients was 68 (range 44-78) years; 42% of patients never smoked, and adenocarcinoma was the most common histology (95%). In patients with KRAS mutations (n = 10) or PD-L1 expression of 50% or greater (n = 10), the disease control rate was 100%. The median progression-free survival (PFS) was 5.4, 6.3, and 8.9 months in patients harboring mutations in EGFR, KRAS, and other genes, respectively (P = 0.22). Patients with PD-L1 expression of 50% or greater had significantly longer median PFS than patients with PD-L1 expression of less than 50% (16.4 vs. 5.1 months; P = 0.001). Two patients experienced grade 3 immuno-related adverse events.

CONCLUSION

Immuno-chemotherapy has a clinical benefit and is safe for patients with oncogenic mutations. Notably, patients with PD-L1 expression of 50% or more experience greater benefit from immuno-chemotherapy than those with PD-L1 expression of less than 50%.

Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles

(1) Background: Inter-tumour heterogeneity is one of cancer’s most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, single-gene markers of response are rare and/or may fail to achieve a significant impact in the clinic. Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. (2) Methods: Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. (3) Results: Combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: paclitaxel (breast cancer), binimetinib (breast cancer) and cetuximab (colorectal cancer). Interestingly, each of these multi-gene ML models identifies some treatment-responsive PDXs not harbouring the best actionable mutation for that case. Thus, ML multi-gene predictors generally have much fewer false negatives than the corresponding single-gene marker. (4) Conclusions: As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if ML algorithms were also applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations.

Lack of predictive tools for conventional and targeted cancer therapy: Barriers to biomarker development and clinical translation

Predictive tools, utilising biomarkers, aim to objectively assessthe potentialresponse toa particular clinical intervention in order to direct treatment.Conventional cancer therapy remains poorly served by predictive biomarkers, despite being the mainstay of treatment for most patients. In contrast, targeted therapy benefits from a clearly defined protein target for potential biomarker assessment. We discuss potential data sources of predictive biomarkers for conventional and targeted therapy, including patient clinical data andmulti-omicbiomarkers (genomic, transcriptomic and protein expression).Key examples, either clinically adopted or demonstrating promise for clinical translation, are highlighted. Following this, we provide an outline of potential barriers to predictive biomarker development; broadly discussing themes of approaches to translational research and study/trial design, and the impact of cellular and molecular tumor heterogeneity. Future avenues of research are also highlighted.

Milestones of Precision Medicine: An Innovative, Multidisciplinary Overview

Although the concept of precision medicine, in which healthcare is tailored to the molecular and clinical characteristics of each individual, is not new, its implementation in clinical practice has been heterogenous. In some medical specialties, precision medicine has gone from being just a promise to a reality that achieves better patient outcomes. This is a fact if we consider, for example, the great advances made in the genetic diagnosis and subsequent treatment of countless hereditary diseases, such as cystic fibrosis, which have improved the life expectancy of many of the affected children. In the field of oncology, the development of targeted therapies has prolonged the survival of patients with breast, lung, colorectal, melanoma, and hematological malignancies. In other disciplines, clinical milestones are perhaps less well known, but no less important. The current challenge is to expand and generalize the use of technologies that are central to precision medicine, such as massively parallel sequencing, to improve the management (prevention and treatment) of complex conditions such as cardiovascular, kidney, or autoimmune diseases. This process requires investment in specialized expertise, multidisciplinary collaboration, and the nationwide organization of genetic laboratories for diagnosis of specific diseases.

Meta-Analysis of ABCG2 and ABCB1 Polymorphisms With Sunitinib-Induced Toxicity and Efficacy in Renal Cell Carcinoma

Background: ABCG2 and ABCB1 are genes related to the pharmacokinetics of sunitinib and have been associated with its toxicity and efficacy. However, the results have been controversial. This study aimed to evaluate the associations of ABCG2 and ABCB1 polymorphisms with sunitinib-induced toxicity and efficacy in renal cell carcinoma (RCC) by meta-analysis. Methods: PubMed, EMBASE, Cochrane Library, and Web of Science were systematically searched for studies investigating the associations of the ABCG2 rs2231142 polymorphism with sunitinib-induced toxicity and the associations of the ABCB1 rs1128503 and ABCB1 rs2032582 polymorphisms with sunitinib-induced toxicity and clinical outcomes. The associations were evaluated by effect size (ES) with 95% confidence intervals (CIs). Results: Eight and five studies were included in the toxicity and efficacy analysis, respectively, including a total of 1081 RCC patients. The ABCG2 rs2231142 A allele was associated with an increased risk of sunitinib-induced thrombocytopenia and hand-foot syndrome (HFS) in Asians (ES = 1.65, 95% CI = 1.15-2.36, p = 0.006; ES = 1.52, 95% CI = 1.02-2.27, p = 0.041). However, the ABCG2 rs2231142 polymorphism was not associated with sunitinib-induced hypertension or neutropenia (ES = 1.09, 95% CI = 0.69-1.73, p = 0.701; ES = 0.87, 95% CI = 0.57-1.31, p = 0.501). Compared with the C allele, the ABCB1 rs1128503 T allele was associated with a decreased risk of sunitinib-induced hypertension but worse progression-free survival (PFS) (ES = 0.44, 95% CI = 0.26-0.77, p = 0.004; ES = 1.36, 95% CI = 1.07-1.73, p = 0.011). There was no significant association between the T allele or C allele of ABCB1 rs1128503 and overall survival (OS) (ES = 0.82, 95% CI = 0.61-1.10, p = 0.184). The ABCB1 rs2032582 T allele was associated with worse PFS than the other alleles (ES = 1.46, 95% CI = 1.14-1.87, p = 0.003), while there was no significant association between the T allele or other alleles and sunitinib-induced hypertension, HFS, or OS (ES = 0.77, 95% CI = 0.46-1.29, p = 0.326; ES = 1.02, 95% CI = 0.65-1.62, p = 0.919; ES = 1.32, 95% CI = 0.85-2.05, p = 0.215). Conclusion: The results indicate that the ABCG2 rs2231142 polymorphism may serve as a predictor of sunitinib-induced thrombocytopenia and HFS in Asians, while the ABCB1 rs1128503 polymorphism may serve as a predictor of sunitinib-induced hypertension, and both the ABCB1 rs1128503 and rs2032582 polymorphisms may serve as predictors of PFS in RCC. These results suggest a possible application of individualized use of sunitinib according to the genetic background of patients.

Osteosarcoma, personalized medicine, and tissue engineering; an overview of overlapping fields of research

INTRODUCTION

Osteosarcoma is a common bone malignancy in patients of all ages. Surgical and chemotherapy interventions fail to shrink tumor growth and metastasis. The development of efficient patient-specific therapeutic strategies for osteosarcoma is of great interest in tissue engineering and personalized medicine. The present manuscript aimed to review the advancements in tissue engineering and personalized medicine strategies to overcome osteosarcoma and the relevant biological aspects as well as the current tumor models in vitro and in vivo.

RESULTS

Tissue engineering and personalized medicine contribute to gene/cell engineering and cell-based therapies specific to genomic and proteomic profiles of individual patients to improve the current treatment options. Also, tissue engineering scaffolds provide physical support to missing bones, could trap cancer cells and deliver immune cells. Taken together, these strategies suppress tumor growth, angiogenic potential, and the subsequent metastasis as well as elicit desirable immune responses against tumor mass.

DISCUSSION

Advanced and high-throughput gene and protein identification technologies have facilitated the recognition of genomic and proteomic profiles of patients to design and develop patient-specific treatments. The pre-clinical studies showed promising outcomes to inhibit tumor growth and invasion but controversial results compared to clinical investigations make the importance of more clinical reports inevitable. The experimental tumor models assist the evolution of effective treatments by understanding the mechanisms of tumor progression.

CONCLUSION

Tissue engineering and personalized medicine strategies seem encouraging alternatives to conventional therapies against osteosarcoma. Modeling the tumor microenvironment coupled with pre-clinical results give new intelligence into the translation of strategies into the clinic.

Nanomedicine and Early Cancer Diagnosis: Molecular Imaging using Fluorescence Nanoparticles

Incorporating nanotechnology into fluorescent imaging and magnetic resonance imaging (MRI) has shown promising potential for accurate diagnosis of cancer at an earlier stage than the conventional imaging modalities. Molecular imaging (MI) aims to quantitatively characterize, visualize, and measure the biological processes or living cells at molecular and genetic levels. MI modalities have been exploited in different applications including noninvasive determination and visualization of diseased tissues, cell trafficking visualization, early detection, treatment response monitoring, and in vivo visualization of living cells. High-affinity molecular probe and imaging modality to detect the probe are the two main requirements of MI. Recent advances in nanotechnology and allied modalities have facilitated the use of nanoparticles (NPs) as MI probes. Within the extensive group of NPs, fluorescent NPs play a prominent role in optical molecular imaging. The fluorescent NPs used in molecular and cellular imaging can be categorized into three main groups including quantum dots (QDs), upconversion, and dyedoped NPs. Fluorescent NPs have great potential in targeted theranostics including cancer imaging, immunoassay- based cells, proteins and bacteria detections, imaging-guided surgery, and therapy. Fluorescent NPs have shown promising potentials for drug and gene delivery, detection of the chromosomal abnormalities, labeling of DNA, and visualizing DNA replication dynamics. Multifunctional NPs have been successfully used in a single theranostic modality integrating diagnosis and therapy. The unique characteristics of multifunctional NPs make them potential theranostic agents that can be utilized concurrently for diagnosis and therapy. This review provides the state of the art of the applications of nanotechnologies in early cancer diagnosis focusing on fluorescent NPs, their synthesis methods, and perspectives in clinical theranostics.

QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction

Affinity fingerprints report the activity of small molecules across a set of assays, and thus permit to gather information about the bioactivities of structurally dissimilar compounds, where models based on chemical structure alone are often limited, and model complex biological endpoints, such as human toxicity and in vitro cancer cell line sensitivity. Here, we propose to model in vitro compound activity using computationally predicted bioactivity profiles as compound descriptors. To this aim, we apply and validate a framework for the calculation of QSAR-derived affinity fingerprints (QAFFP) using a set of 1360 QSAR models generated using K_i, K_d, IC₅₀ and EC₅₀ data from ChEMBL database. QAFFP thus represent a method to encode and relate compounds on the basis of their similarity in bioactivity space. To benchmark the predictive power of QAFFP we assembled IC₅₀ data from ChEMBL database for 18 diverse cancer cell lines widely used in preclinical drug discovery, and 25 diverse protein target data sets. This study complements part 1 where the performance of QAFFP in similarity searching, scaffold hopping, and bioactivity classification is evaluated. Despite being inherently noisy, we show that using QAFFP as descriptors leads to errors in prediction on the test set in the ~ 0.65-0.95 pIC₅₀ units range, which are comparable to the estimated uncertainty of bioactivity data in ChEMBL (0.76-1.00 pIC₅₀ units). We find that the predictive power of QAFFP is slightly worse than that of Morgan2 fingerprints and 1D and 2D physicochemical descriptors, with an effect size in the 0.02-0.08 pIC₅₀ units range. Including QSAR models with low predictive power in the generation of QAFFP does not lead to improved predictive power. Given that the QSAR models we used to compute the QAFFP were selected on the basis of data availability alone, we anticipate better modeling results for QAFFP generated using more diverse and biologically meaningful targets. Data sets and Python code are publicly available at https://github.com/isidroc/QAFFP_regression .

Paclitaxel Response Can Be Predicted With Interpretable Multi-Variate Classifiers Exploiting DNA-Methylation and miRNA Data

To address the problem of resistance to paclitaxel treatment, we have investigated to which extent is possible to predict Breast Cancer (BC) patient response to this drug. We carried out a large-scale tumor-based prediction analysis using data from the US National Cancer Institute’s Genomic Data Commons. These data sets comprise the responses of BC patients to paclitaxel along with six molecular profiles of their tumors. We assessed 10 Machine Learning (ML) algorithms on each of these profiles and evaluated the resulting 60 classifiers on the same BC patients. DNA methylation and miRNA profiles were the most informative overall. In combination with these two profiles, ML algorithms selecting the smallest subset of molecular features generated the most predictive classifiers: a complexity-optimized XGBoost classifier based on CpG island methylation extracted a subset of molecular factors relevant to predict paclitaxel response (AUC = 0.74). A CpG site methylation-based Decision Tree (DT) combining only 2 of the 22,941 considered CpG sites (AUC = 0.89) and a miRNA expression-based DT employing just 4 of the 337 analyzed mature miRNAs (AUC = 0.72) reveal the molecular types associated to paclitaxel-sensitive and resistant BC tumors. A literature review shows that features selected by these three classifiers have been individually linked to the cytotoxic-drug sensitivities and prognosis of BC patients. Our work leads to several molecular signatures, unearthed from methylome and miRNome, able to anticipate to some extent which BC tumors respond or not to paclitaxel. These results may provide insights to optimize paclitaxel-therapies in clinical practice.

Pharmacogenomic Biomarkers in Docetaxel Treatment of Prostate Cancer: From Discovery to Implementation

Prostate cancer is the fifth leading cause of male cancer death worldwide. Although docetaxel chemotherapy has been used for more than fifteen years to treat metastatic castration resistant prostate cancer, the high inter-individual variability of treatment efficacy and toxicity is still not well understood. Since prostate cancer has a high heritability, inherited biomarkers of the genomic signature may be appropriate tools to guide treatment. In this review, we provide an extensive overview and discuss the current state of the art of pharmacogenomic biomarkers modulating docetaxel treatment of prostate cancer. This includes (1) research studies with a focus on germline genomic biomarkers, (2) clinical trials including a range of genetic signatures, and (3) their implementation in treatment guidelines. Based on this work, we suggest that one of the most promising approaches to improve clinical predictive capacity of pharmacogenomic biomarkers in docetaxel treatment of prostate cancer is the use of compound, multigene pharmacogenomic panels defined by specific clinical outcome measures. In conclusion, we discuss the challenges of integrating prostate cancer pharmacogenomic biomarkers into the clinic and the strategies that can be employed to allow a more comprehensive, evidence-based approach to facilitate their clinical integration. Expanding the integration of pharmacogenetic markers in prostate cancer treatment procedures will enhance precision medicine and ultimately improve patient outcomes.

Pharmacogenomic analyses of sunitinib in patients with pancreatic neuroendocrine tumors

Aim: Evaluate associations between clinical outcomes and SNPs in patients with well-differentiated pancreatic neuroendocrine tumors receiving sunitinib. Patients & methods: Kaplan–Meier and Cox proportional hazards models were used to analyze the association between SNPs and survival outcomes using data from a sunitinib Phase IV (genotyped, n = 56) study. Fisher’s exact test was used to analyze objective response rate and genotype associations. Results: After multiplicity adjustment, progression-free and overall survivals were not significantly correlated with SNPs; however, a higher objective response rate was significantly associated with IL1B rs16944 G/A versus G/G (46.4 vs 4.5%; p = 0.001). Conclusion: IL1B SNPs may predict treatment response in patients with pancreatic neuroendocrine tumors. VEGF pathway SNPs are potentially associated with survival outcomes.

The arrival of predictive biomarkers for monitoring therapy response to natural compounds in cancer drug discovery

Intrinsic or acquired drug resistance, adverse drug reactions and tumor heterogeneity between and within cancer patients limit the efficacy of clinical management of advanced cancers. To overcome these barriers, predictive biomarkers have recently emerged to guide medical oncologists in the selection of cancer patients who will respond to various anticancer treatments and to improve the toxicity to benefit ratio. Notably, targeted therapy has significantly benefited from these advances, but the application of predictive biomarkers have been a bit slower with some drugs derived from natural sources such as trabectedin, cabazitaxel and alvocidib. In this paper, we discuss some recent advances regarding the use of cancer biomarkers to predict efficacy of some selected natural compounds with a focus on human clinical studies.

Trends in interorganizational transactions in personalized medicine development

Personalized medicine is an innovative concept that allows patients with a validated companion diagnosis (CoDx) to receive treatment using the most suitable drug. Currently, a major movement in the pharmaceutical industry involves the integrated use of multiple resources from external sources. To ascertain preferable interorganizational collaborations and their suitable exits, we compared the related transactions in personalized and nonpersonalized cancer drugs. We found that there were significantly more of some alliance deals in personalized medicine, and that market licenses, one of the exits, were well correlated with other alliances only in personalized medicine. Furthermore, four types of collaboration mode were identified, and more active collaborations with external partners were found to lead to more successful outcomes in personalized medicine development.

Label-free toxicology screening of primary human mesenchymal cells and iPS-derived neurons

The high-throughput, label-free Corning Epic assay has applications in drug discovery, pharmacogenomics, cell receptor signaling, cell migration, and viral titration. The utility of Epic technology for biocompatibility testing has not been well established. In manufacturing of medical devices, in vitro and in vivo biocompatibility assessments are mandatory, according to ISO 10993. The new medical device regulation MDR 745/2017 specifies that ex vivo assays that can closely recapitulate in vivo scenarios are needed to better evaluate biomedical devices. We propose herein that Epic technology—which enables detection of variations in cell mass distribution—is suitable for biocompatibility screening of compounds. In this study, we challenged primary human osteoblasts, endothelial cells, and neurons derived from induced pluripotent stem cells with specific concentrations of methyl methacrylate (MMA). Polymeric MMA has long been applied in cranioplasty, where it makes contact with multiple cell types. Application of Epic technology yielded real-time cytotoxicity profiles for all considered cell types. The results were compared with those from microscopic observation of the same culture plate used in the Epic analyses. The Epic assay should be further examined for its utility for cell biology, genomics, and proteomics companion assays. Our results suggest that Epic technology can be applied to biocompatibility evaluation of human cells in medical device development.

Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine

Nowadays, the personalized approach to health care and cancer care in particular is becoming more and more popular and is taking an important place in the translational medicine paradigm. In some cases, detection of the patient-specific individual mutations that point to a targeted therapy has already become a routine practice for clinical oncologists. Wider panels of genetic markers are also on the market which cover a greater number of possible oncogenes including those with lower reliability of resulting medical conclusions. In light of the large availability of high-throughput technologies, it is very tempting to use complete patient-specific New Generation Sequencing (NGS) or other "omics" data for cancer treatment guidance. However, there are still no gold standard methods and protocols to evaluate them. Here we will discuss the clinical utility of each of the data types and describe a systems biology approach adapted for single patient measurements. We will try to summarize the current state of the field focusing on the clinically relevant case-studies and practical aspects of data processing.

Influence of the ABCB1 polymorphisms on the response to Taxane-containing chemotherapy: a systematic review and meta-analysis

PURPOSE

Multidrug resistance mediated by ABCB1 has been perceived to be one of the obstacles for cancer chemotherapy. This meta-analysis was performed to verify the effect of the ABCB1 rs1045642 and rs1128503 polymorphisms on the response to Taxane-containing chemotherapy.

METHODS

Pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were employed to evaluate the impact of these two ABCB1 polymorphisms. R scripts were developed to perform the meta-analysis.

RESULTS

A total of nine articles (including nine studies for rs1045642 and five for rs1128503) were collected in our systematic review. However, our meta-analysis showed no significant effect of these two ABCB1 polymorphisms on the response to Taxane-containing regimens.

CONCLUSIONS

This study highlights the unsuitability of relying on the ABCB1 rs1045642 and rs1128503 polymorphisms as therapeutic response biomarkers of Taxane-containing chemotherapy. Further polycentric studies in larger and multiracial populations are needed to validate the conclusions.

Real-Time Reverse-Transcription Quantitative Polymerase Chain Reaction Assay Is a Feasible Method for the Relative Quantification of Heregulin Expression in Non-Small Cell Lung Cancer Tissue

In preclinical studies, heregulin (HRG) expression was shown to be the most relevant predictive biomarker for response to patritumab, a fully human anti–epidermal growth factor receptor 3 monoclonal antibody. In support of a phase 2 study of erlotinib ± patritumab in non–small cell lung cancer (NSCLC), a reverse-transcription quantitative polymerase chain reaction (RT-qPCR) assay for relative quantification of HRG expression from formalin-fixed paraffin-embedded (FFPE) NSCLC tissue samples was developed and validated and described herein. Test specimens included matched FFPE normal lung and NSCLC and frozen NSCLC tissue, and HRG-positive and HRG-negative cell lines. Formalin-fixed paraffin-embedded tissue was examined for functional performance. Heregulin distribution was also analyzed across 200 NSCLC commercial samples. Applied Biosystems TaqMan Gene Expression Assays were run on the Bio-Rad CFX96 real-time PCR platform. Heregulin RT-qPCR assay specificity, PCR efficiency, PCR linearity, and reproducibility were demonstrated. The final assay parameters included the Qiagen FFPE RNA Extraction Kit for RNA extraction from FFPE NSCLC tissue, 50 ng of RNA input, and 3 reference (housekeeping) genes (HMBS, IPO8, and EIF2B1), which had expression levels similar to HRG expression levels and were stable among FFPE NSCLC samples. Using the validated assay, unimodal HRG distribution was confirmed across 185 evaluable FFPE NSCLC commercial samples. Feasibility of an RT-qPCR assay for the quantification of HRG expression in FFPE NSCLC specimens was demonstrated.

The impact of DNA damage response gene polymorphisms on therapeutic outcomes in late stage ovarian cancer

Late stage epithelial ovarian cancer has a dismal prognosis. Identification of pharmacogenomic markers (i.e. polymorphisms) to stratify patients to optimize individual therapy is of paramount importance. We here report the retrospective analysis of polymorphisms in 5 genes (ATM, ATR, Chk1, Chk2 and CDK12) involved in the cellular response to platinum in a cohort of 240 cancer patients with late stage ovarian cancer. The aim of the present study was to evaluate associations between the above mentioned SNPs and patients’ clinical outcomes: overall survival (OS) and progression free survival (PFS). None of the ATM, ATR, Chk1 and Chk2 polymorphisms was found to significantly affect OS nor PFS in this cohort of patients. Genotype G/G of CDK12 polymorphism (rs1054488) predicted worse OS and PFS than the genotype A/A-A/G in univariate analysis. The predictive value was lost in the multivariate analysis. The positive correlation observed between this polymorphism and age, grade and residual tumor may explain why the CDK12 variant was not confirmed as an independent prognostic factor in multivariate analysis.The importance of CDK12 polymorphism as possible prognostic biomarker need to be confirmed in larger ovarian cancer cohorts, and possibly in other cancer population responsive to platinum agents.

Change in platelet count as a prognostic indicator for response to primary tyrosine kinase inhibitor therapy in metastatic renal cell carcinoma

OBJECTIVE

To evaluate change in platelet count as an indicator of response to primary tyrosine kinase inhibitor (TKI) therapy for metastatic renal cell carcinoma (mRCC).

PATIENTS AND METHODS

We conducted a multicentre retrospective analysis of patients with mRCC undergoing primary TKI therapy from May 2005 to August 2014. Change in platelet count was defined as post-treatment platelet count after the first cycle of treatment minus the pretreatment platelet count. Response Evaluation Criteria in Solid Tumours were used to define partial response (PR), stable disease (SD) and progressive disease (PD). Analysis was conducted between subgroups with stable/increased (+ΔPlt) and decreased (-ΔPlt) counts. The primary outcome was overall survival (OS), determined using Kaplan-Meier analysis. Multivariable analysis was conducted for risk factors associated with PD.

RESULTS

A total of 115 patients with mRCC were analysed, of whom 19 (16.6%) had a +ΔPlt and 96 (83%) a -ΔPlt. More patients with a +ΔPlt had a Karnofsky score <80 (42.1 vs 14.6%; P = 0.005) and >2 metastatic sites (78.9 vs 51%; P = 0.041). More patients with +ΔPlt than with -ΔPlt had PD (89.4 vs 19.1%; P < 0.001) and more of those with -ΔPlt than with +ΔPlt had SD/PR (80.9 vs 10.6%; P < 0.001). Multivariable analysis showed that +ΔPlt (odds ratio [OR] 5.36, P < 0.001), Karnofsky score < 80 (OR 2.96, P = 0.002) and >2 metastatic sites at presentation (OR 1.87, P = 0.013) were risk factors for PD. Kaplan-Meier analysis showed a lower 5-year OS in patients with +ΔPlt than in those with -ΔPlt (23 vs 53%; P < 0.0001). +ΔPlt had a sensitivity of 48.6%, a specificity of 97.4%, a positive predictive value of 89.5% and a negative predictive value of 80.9% for PD.

CONCLUSIONS

Patients with a -ΔPlt were more likely to respond to TKI therapy and had longer OS. +ΔPlt above baseline had a high specificity for PD after primary TKI. Further investigation is required to determine the utility of ΔPlt.

A decade of pharmacogenomics research on tyrosine kinase inhibitors in metastatic renal cell cancer: a systematic review

OBJECTIVE

The individual response to targeted tyrosine kinase inhibitors (TKIs) in the treatment of metastatic renal cell cancer (mRCC) is highly variable. Outlined in this article are findings on potential biomarkers for TKI treatment outcome in mRCC and an evaluation of the status of clinical implementation.

METHODS

Articles were selected by two independent reviewers using a systematic search in five medical databases on renal cell carcinoma, TKIs, and pharmacogenetics.

RESULTS

Many researchers have focused on predictive biomarkers for treatment outcome of targeted therapies in mRCC patients. Attempts to explain differences in efficacy and toxicity of TKIs by use of genetic variants in genes related to the pharmacokinetics and pharmacodynamics of the drug have been successful.

CONCLUSION

Most findings on potential biomarkers have not been validated and therefore biomarker testing to guide choice of therapy and dose in mRCC is not yet feasible.

Ancestral experience as a game changer in stress vulnerability and disease outcomes

Stress is one of the most powerful experiences to influence health and disease. Through epigenetic mechanisms, stress may generate a footprint that propagates to subsequent generations. Programming by prenatal stress or adverse experience in parents, grandparents, or earlier generations may thus be a critical determinant of lifetime health trajectories. Changes in regulation of microRNAs (miRNAs) by stress may enhance the vulnerability to certain pathogenic factors. This review explores the hypothesis that miRNAs represent stress-responsive elements in epigenetic regulation that are potentially heritable. Recent findings suggest that miRNAs are key players linking adverse early environments or ancestral stress with disease risk, thus they represent useful predictive disease biomarkers. Since miRNA signatures of disease are potentially heritable, big data management platforms will be vital to harness multi-generational information and capture succinct yet potent biomarkers capable of directing preventative treatments. This feature would offer a unique window of opportunity to advance personalized medicine.