Cancer pharmacogenomics, challenges in implementation, and patient-focused perspectives

PGxDB: an interactive web-platform for pharmacogenomics research

Pharmacogenomics, the study of how an individual's genetic makeup influences their response to medications, is a rapidly evolving field with significant implications for personalized medicine. As researchers and healthcare professionals face challenges in exploring the intricate relationships between genetic profiles and therapeutic outcomes, the demand for effective and user-friendly tools to access and analyze genetic data related to drug responses continues to grow. To address these challenges, we have developed PGxDB, an interactive, web-based platform specifically designed for comprehensive pharmacogenomics research. PGxDB enables the analysis across a wide range of genetic and drug response data types - informing cell-based validations and translational treatment strategies. We developed a pipeline that uniquely combines the relationship between medications indexed with Anatomical Therapeutic Chemical (ATC) codes with molecular target profiles with their genetic variability and predicted variant effects. This enables scientists from diverse backgrounds - including molecular scientists and clinicians - to link genetic variability to curated drug response variability and investigate indication or treatment associations in a single resource. With PGxDB, we aim to catalyze innovations in pharmacogenomics research, empower drug discovery, support clinical decision-making, and pave the way for more effective treatment regimens. PGxDB is a freely accessible database available at https://pgx-db.org/.

Current stage of preclinical and clinical development of guggulsterone in cancers: Challenges and promises

Throughout human history, the utilization of medicinal herbs has been recognized as a crucial defense against various ailments, including cancer. Natural products with potential anticancer properties, capable of inducing apoptosis in cancer cells, have garnered substantial attention. One such agent under investigation is guggulsterone (GS), a phytosterol derived from the gum resin of the Commiphora mukul tree. This review aims to provide a comprehensive summary of recent studies elucidating the anticancer molecular mechanisms and molecular targets of GS, guiding future research and potential applications as an adjuvant drug in cancer therapy. Recent in vivo and in vitro studies have explored the biological activities of the active ingredients in Commiphora mukul. Specifically, GS emerges as a potential cancer chemopreventive and therapeutic agent. The investigations delve into the impact of GS on constitutively activated survival pathways, including Janus kinase/signal transducer and activator of transcription (JAK/STAT), nuclear factor-kappa B (NF-kB), and PI3-kinase/AKT signaling pathways. These pathways regulate antiapoptotic and proinflammatory genes, exerting control over growth and inflammatory responses. The findings highlight the potential of GS in disrupting survival pathways crucial for cancer cell viability. The inhibition of JAK/STAT, NF-kB, and PI3-kinase/AKT signaling pathways positions GS as a promising candidate for cancer therapy. The review synthesizes evidence from diverse studies, underscoring the multifaceted biological activities of GS in cancer prevention and treatment. To advance our understanding, future clinical and translational studies are imperative to determine effective doses in humans. Additionally, there is a need for the development of new pharmaceutical forms of GS to optimize therapeutic effects. This comprehensive review provides a foundation for ongoing research, offering insights into the potential of GS as a valuable addition to the armamentarium against cancer.

Evolution of predictive risk factor analysis for chemotherapy-related toxicity

The causes of variation in toxicity to the same treatment regimen among seemingly similar patients remain largely unknown. There was tremendous optimism that the patient's germline genome would be strongly predictive of treatment-related toxicity and could be used to personalize treatment and improve therapeutic outcomes. However, there has been limited success in discovering robust pharmacogenetic predictors of treatment-related toxicity and even less progress in translating the few validated predictors into clinical practice. It is apparent that identification of toxicity predictors that can be used to predict and prevent treatment-related toxicity will require thinking beyond germline genomics. To that end, we propose an integrated biomarker discovery approach that recognizes that a patient's toxicity risk is determined by the cumulative effects of a broad range of "omic" and non-omic factors. This commentary describes the limited success in discovering and translating clinical and pharmacogenetic toxicity predictors into clinical practice. We illustrate the evolution of cancer toxicity biomarker discovery and translation through studies of taxane-induced peripheral neuropathy, which is one of the most common and debilitating side effects of cancer treatment. We then discuss the opportunities for discovering non-genomic (e.g., metabolomic, lipidomic, transcriptomic, proteomic, microbiomic, medical, behavioral, environmental) and integrated biomarkers that may be more strongly predictive of toxicity risk and the potential challenges with translating integrated biomarkers into clinical practice. This integrated biomarker discovery approach may circumvent some of the major limitations in toxicity biomarker science and move precision oncology treatment forward so that patients receive maximum treatment benefit with minimal toxicity.

Determinants of targeted cancer therapy use in community oncology practice: a qualitative study using the Theoretical Domains Framework and Rummler-Brache process mapping

BACKGROUND

Precision medicine holds enormous potential to improve outcomes for cancer patients, offering improved rates of cancer control and quality of life. Not all patients who could benefit from targeted cancer therapy receive it, and some who may not benefit do receive targeted therapy. We sought to comprehensively identify determinants of targeted therapy use among community oncology programs, where most cancer patients receive their care.

METHODS

Guided by the Theoretical Domains Framework, we conducted semi-structured interviews with 24 community cancer care providers and mapped targeted therapy delivery across 11 cancer care delivery teams using a Rummler-Brache diagram. Transcripts were coded to the framework using template analysis, and inductive coding was used to identify key behaviors. Coding was revised until a consensus was reached.

RESULTS

Intention to deliver precision medicine was high across all participants interviewed, who also reported untenable knowledge demands. We identified distinctly different teams, processes, and determinants for (1) genomic test ordering and (2) delivery of targeted therapies. A key determinant of molecular testing was role alignment. The dominant expectation for oncologists to order and interpret genomic tests is at odds with their role as treatment decision-makers' and pathologists' typical role to stage tumors. Programs in which pathologists considered genomic test ordering as part of their staging responsibilities reported high and timely testing rates. Determinants of treatment delivery were contingent on resources and ability to offset delivery costs, which low- volume programs could not do. Rural programs faced additional treatment delivery challenges.

CONCLUSIONS

We identified novel determinants of targeted therapy delivery that potentially could be addressed through role re-alignment. Standardized, pathology-initiated genomic testing may prove fruitful in ensuring patients eligible for targeted therapy are identified, even if the care they need cannot be delivered at small and rural sites which may have distinct challenges in treatment delivery. Incorporating behavior specification and Rummler-Brache process mapping with determinant analysis may extend its usefulness beyond the identification of the need for contextual adaptation.

A blockchain-based framework to support pharmacogenetic data sharing

The successful implementation of pharmacogenetics (PGx) into clinical practice requires patient genomic data to be shared between stakeholders in multiple settings. This creates a number of barriers to widespread adoption of PGx, including privacy concerns related to the storage and movement of identifiable genomic data. Informatic solutions that support secure and equitable data access for genomic data are therefore important to PGx. Here we propose a methodology that uses smart contracts implemented on a blockchain-based framework, PGxChain, to address this issue. The design requirements for PGxChain were identified through a systematic literature review, identifying technical challenges and barriers impeding the clinical implementation of pharmacogenomics. These requirements included security and privacy, accessibility, interoperability, traceability and legal compliance. A proof-of-concept implementation based on Ethereum was then developed that met the design requirements. PGxChain's performance was examined using Hyperledger Caliper for latency, throughput, and transaction success rate. The findings clearly indicate that blockchain technology offers considerable potential to advance pharmacogenetic data sharing, particularly with regard to PGx data security and privacy, large-scale accessibility of PGx data, PGx data interoperability between multiple health care providers and compliance with data-sharing laws and regulations.

Genetic Polymorphisms in CYP2C19 Cause Changes in Plasma Levels and Adverse Reactions to Anlotinib in Chinese Patients With Lung Cancer

Background: Anlotinib is a small molecular multi-targeting tyrosine kinase inhibitor. Growing evidence indicates that treatment efficacy, and toxicity varies considerably between individuals. Therefore, this study aimed to investigate the relationship between cytochrome P450 (CYP450) gene polymorphisms, drug concentrations, and their adverse reactions in anlotinib-treated patients with lung cancer.

Methods: We enrolled 139 patients with lung cancer, treated with anlotinib. Twenty loci in the following five genes of the CYP450 family were genotyped: CYP450 family 3 subfamily A member 5 (CYP3A5), 3 subfamily A member 4 (CYP3A4), 2 subfamily C member 9 (CYP2C9), 2 subfamily C member 19 (CYP2C19), and 1 subfamily A member 2 (CYP1A2). Data on adverse reactions were collected from patients, and plasma anlotinib concentrations were measured.

Results: There were significant variances in plasma trough concentration (3.95–52.88 ng/ml) and peak plasma concentration (11.53–42.8 ng/ml) following administration of 8 mg anlotinib. Additionally, there were significant differences in the plasma trough concentration (5.65–81.89 ng/ml) and peak plasma concentration (18.01–107.18 ng/ml) following administration of 12 mg anlotinib. Furthermore, for CYP2C19-rs3814637, the peak plasma concentrations of mutant allele T carriers (TT+CT) were significantly higher than those of wildtypes (CC). For CYP2C19-rs11568732, the peak plasma concentrations of the mutant allele G carriers (GT+GG) were significantly higher than those of the wild-type (TT). More importantly, the incidence rates of hypertension and hemoptysis (peripheral lung cancer) with TT+CT in rs3814637 and GT+GG in rs11568732 were significantly higher than those with CC and TT.

Conclusions: The plasma trough and peak concentrations varied significantly for both 8 and 12 mg of anlotinib. Single-nucleotide polymorphisms in CYP2C19 are significantly associated with hypertension, hemoptysis, and anlotinib peak concentrations. Polymorphisms in CYP450 may explain inter-individual differences in anlotinib-related adverse reactions.

Influence of NSAIDs and methotrexate on CD73 expression and glioma cell growth

Glioblastoma (GBM) is the most malignant and deadly brain tumor. GBM cells overexpress the CD73 enzyme, which controls the level of extracellular adenosine, an immunosuppressive molecule. Studies have shown that some nonsteroidal anti-inflammatory drugs (NSAIDs) and methotrexate (MTX) have antiproliferative and modulatory effects on CD73 in vitro and in vivo. However, it remains unclear whether the antiproliferative effects of MTX and NSAIDS in GBM cells are mediated by increases in CD73 expression and adenosine formation. The aim of this study was to evaluate the effect of the NSAIDs, naproxen, piroxicam, meloxicam, ibuprofen, sodium diclofenac, acetylsalicylic acid, nimesulide, and ketoprofen on CD73 expression in GBM and mononuclear cells. In addition, we sought to understand whether the effects of MTX may be mediated by CD73 expression and activity. Cell viability and CD73 expression were evaluated in C6 and mononuclear cells after exposure to NSAIDs. For analysis of the mechanism of action of MTX, GBM cells were treated with APCP (CD73 inhibitor), dipyridamole (inhibitor of adenosine uptake), ABT-702 (adenosine kinase enzyme inhibitor), or caffeine (P1 adenosine receptor antagonist), before treatment with MTX and AMP, in the presence or not of mononuclear cells. In summary, only MTX increased the expression of CD73 in GBM cells decreasing cells viability by mechanisms independent of the adenosinergic system. Further studies are needed to understand the role of MTX in the GBM microenvironment.

Potential of whole-genome sequencing-based pharmacogenetic profiling

Pharmacogenetics represents a major driver of precision medicine, promising individualized drug selection and dosing. Traditionally, pharmacogenetic profiling has been performed using targeted genotyping that focuses on common/known variants. Recently, whole-genome sequencing (WGS) is emerging as a more comprehensive short-read next-generation sequencing approach, enabling both gene diagnostics and pharmacogenetic profiling, including rare/novel variants, in a single assay. Using the example of the pharmacogene CYP2D6, we demonstrate the potential of WGS-based pharmacogenetic profiling as well as emphasize the limitations of short-read next-generation sequencing. In the near future, we envision a shift toward long-read sequencing as the predominant method for gene diagnostics and pharmacogenetic profiling, providing unprecedented data quality and improving patient care.

Liquid biopsy for cancer management: a revolutionary but still limited new tool for precision medicine

The term liquid biopsy is used in contraposition to the traditional "solid" tissue biopsy. In the oncology field it has opened a new plethora of clinical opportunities as tumor-derived material is shedded into the different biofluids from where it can be isolated and analyzed. Common biofluids include blood, urine, saliva, cerebrospinal fluid (CSF), pleural effusion or bile. Starting from these biological specimens several analytes can be isolated, among which we will review the most widely used: circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating tumor RNA (ctRNA), proteins, metabolites, and exosomes. Regarding the nature of the biomarkers it will depend on the analyte, the type of tumor and the clinical application of the liquid biopsy and it includes, somatic point mutations, deletions, amplifications, gene-fusions, DNA-methylated marks, tumor-specific miRNAs, proteins or metabolites. Here we review the characteristics of the analytes and the methodologies used for their isolation. We also describe the applications of the liquid biopsy in the management of patients with cancer, from the early detection of cancers to treatment guidance in patients with advanced tumors. Finally, we also discuss some current limitations and still open questions.

Polymorphisms of Drug-Metabolizing Enzymes and Transporters Contribute to the Individual Variations of Erlotinib Steady State Trough Concentration, Treatment Outcomes, and Adverse Reactions in Epidermal Growth Factor Receptor-Mutated Non-Small Cell Lung Cancer Patients

Background

Erlotinib is presently the first line treatment for non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) active mutation. An increasing number of evidences show that the treatment efficacy and toxicities are considerably heterogeneous among individuals. Hence, it is necessary to find biological predictors for further individualized treatment of erlotinib in NSCLC patients.

Methods

Our present study enrolled 87 cases of NSCLC patients who had been administrated erlotinib with a fixed dose (150 mg/d). Eleven polymorphisms in seven genes of drug-metabolizing enzymes and transporters were genotyped and the steady state trough concentrations were also determined.

Results

There were significant variances in the steady-state erlotinib trough plasma concentrations, ranging from 315.6 ng/ml to 4479.83 ng/ml. Erlotinib steady state trough concentration was remarkably lower in current smoking patients. The steady state trough concentration of GG in rs1048943 of CYP1A1 was significantly higher than that of AA allele carriers. The polymorphism of CYP1A2 was significantly associated with the severity of skin rash, and the development of diarrhea was associated with SNPs in ABCB1 and CYP3A5. We also observed that GG allele in CYP1A1 was accompanied with a longer PFS in our study.

Conclusion

A large variability of erlotinib steady state trough concentration was found among Chinese Han population. SNPs in CYP1A1 appeared to influence the steady state trough concentration of erlotinib. Correlation between CYP1A2 polymorphisms and severity of skin rash was observed, together with the correlation between the development of diarrhea and SNPs in ABCB1 and CYP3A5.

Pharmaco-Geno-Proteo-Metabolomics and Translational Research in Cancer

The diagnosis, prognosis and treatment of cancer has had a great improvement due to the "omics" technologies such as genomics, proteomics, epigenomics, pharmacogenomics, and metabolomics. The technological progress of these technologies has allowed precision medicine to become a clinical reality. The study of different biomolecules such as DNA, RNA and proteins has helped to detect alterations in genes, changes in gene expression profiles and loss or gain of protein function, which allows us to make associations and better understand the cancer biology. Data obtained from different "omics" technologies gives a complementary spectrum of information that helps us to understand and unveil new information for a better diagnosis, prognosis, prediction of new molecular targets of anticancer therapies, etc. This chapter presents a general landscape of the interaction between the Pharmaco-Geno-Proteo-Metabolomic and translational medicine research in cancer.

Use of Pharmacogenetic Drugs by the Dutch Population

Introduction

The Dutch Pharmacogenetics Working Group (DPWG) indicated a list of actionable genotypes that affect patients’ response to more 50 drugs; these drugs which show variable effects based on patients’ genetic traits were named as pharmacogenetics (PGX) drugs. Preemptive genetic testing before using these drugs may protect certain patients from serious adverse reactions and could help in avoiding treatment failures. The objectives of this study include identifying the rate of PGX drug usage among Dutch population, estimating the level of users who carry the actionable genotypes and determining the main genes involved in drug’s effect variability.

Methods

Usage of PGX drugs over 2011–2017 by the insured population (an average of 11.4 million) in outpatient clinics in Netherlands was obtained from the publically available GIP databank. The data of 45 drugs were analyzed and their interactions with selected pharmacogenes were estimated. Frequency of actionable genotypes of 249 Dutch parents was obtained from the public database: Genome of Netherlands (GoNL), to identify the pattern of genetic characteristics of Dutch population.

Results

Over a 7 year period, 51.3 million exposures of patients to PGX drugs were reported with an average of 5.3 exposures per each drug user. One quarterof the exposures (12.4 million) are predicted to be experienced by individuals with actionable genotypes (risky exposures). Up to 60% of the risky exposures (around 7.5 million) were related to drugs metabolized by CYP2D6. SLCO1B1, and CYP2C19 were also identified among the top genes affecting response of drugs users (involved in about 22 and 12.4% of the risky exposures, respectively). Cardiovascular medications were the top prescribed PGX drug class (43%), followed by gastroenterology (29%) and psychiatry/neurology medications (15%). Women use more PGX drugs than men (55.8 vs. 44.2%, respectively) with the majority (84%) of users in both sexes are above 45 years.

Conclusion

PGX drugs are commonly used in Netherlands. Preemptive panel testing for CYP2D6, SLCO1B1, and CYP2C19 only could be useful to predict 95% of vulnerable patients’ exposures to PGX drugs. Future studies to assess the economic impact of preemptive panel testing on patients of older age are suggested.

The Patient in Precision Medicine: A Systematic Review Examining Evaluations of Patient-Facing Materials

Precision medicine (PM) has the potential to tailor healthcare to the individual patient by using their genetic information to guide treatment choices. However, this process is complex and difficult to understand for patients and providers alike. With a recent push in the healthcare community to understand the patient experience and engage patients in their care, it is important to give patients the opportunity to learn about PM. We performed a systematic review to identify previous work assessing the quality of patient-facing PM materials from 2008 to July 2018. Ten studies were identified, which used varying methods and measures. A qualitative assessment was conducted to compare key elements of the studies, including study design, characteristics of the participant population, what measurements were used to assess the PM materials, understandability, preference, psychological reactions, and the type of PM materials being assessed. The studies identified provide important groundwork by highlighting consistent aspects of design that aid in comprehension. Eight of the ten studies focused on the content and organization of genomic test results, while the remaining two assessed educational tools. Two main design elements that appeared across the studies were appropriately designed visual aids and simplified language. The studies identified were limited by the participant populations that were used, which were primarily white and well educated. Only one study attempted to oversample patient populations typically underrepresented in this type of research. Through our systematic review, it is evident that the breadth of knowledge in this field is limited in scope and that more work must be done to ensure that patients can engage in their care when faced with PM.

Value of Supportive Care Pharmacogenomics in Oncology Practice

Genomic medicine provides opportunities to personalize cancer therapy for an individual patient. Although novel targeted therapies prolong survival, most patients with cancer continue to suffer from burdensome symptoms including pain, depression, neuropathy, nausea and vomiting, and infections, which significantly impair quality of life. Suboptimal management of these symptoms can negatively affect response to cancer treatment and overall prognosis. The effect of genetic variation on drug response-otherwise known as pharmacogenomics-is well documented and directly influences an individual patient's response to antiemetics, opioids, neuromodulators, antidepressants, antifungals, and more. The growing body of pharmacogenomic data can now guide clinicians to select the safest and most effective supportive medications for an individual patient with cancer from the very first prescription. This review outlines a theoretical patient case and the implications of using pharmacogenetic test results to personalize supportive care throughout the cancer care continuum.

IMPLICATIONS FOR PRACTICE

Integration of palliative medicine into the cancer care continuum has resulted in increased quality of life and survival for patients with many cancer types. However, suboptimal management of symptoms such as pain, neuropathy, depression, and nausea and vomiting continues to place a heavy burden on patients with cancer. As demonstrated in this theoretical case, pharmacogenomics can have a major effect on clinical response to medications used to treat these conditions. Recognizing the value of supportive care pharmacogenomics in oncology and application into routine practice offers an objective choice for the safest and most effective treatment compared with the traditional trial and error method.

Integration of Germline Pharmacogenetics Into a Tumor Sequencing Program

PURPOSE

Evidence-based guidelines inform treatment decisions for patients for whom germline genetic information is available. Our real-time tumor sequencing program, which makes precision treatment decisions for patients with cancer, produces matched germline information, providing a unique opportunity to efficiently implement pharmacogenetics and benefit patients.

METHODS

The germline genetic database from the Michigan Oncology Sequencing (MI-Oncoseq) program was searched for 21 clinically actionable polymorphisms in five cancer-relevant genes: TPMT, DPYD, CYP2C19, CYP3A5, and UGT1A1. Residual germ line DNA was sent to an external Clinical Laboratory Improvement Amendments-approved laboratory for confirmatory genotyping. The medical records of MI-Oncoseq patients with actionable phenotypes were searched for receipt of relevant drugs and to determine whether having genetic information at the time of treatment would have led to a treatment recommendation.

RESULTS

All nine variants in TPMT, DPYD, and CYP2C19 that were detected in MI-Oncoseq were confirmed by external genotyping. Genotype determinations could not be made for CYP3A5*3, UGT1A1*28, or UGT1A1*80. On the basis of retrospective assessment of 115 adult and pediatric patient records, 4.3% (n = 5) had a potentially clinically actionable phenotype for TPMT, DPYD, or CYP2C19 and received a relevant medication. After accounting for differences in adult and pediatric recommendations, three of these patients could have received a treatment recommendation at the time of prescribing.

CONCLUSION

Germline genotype determinations for TPMT, DPYD, and CYP2C19 can be used to make evidence-based treatment recommendations in MI-Oncoseq patients. Although the proportion of patients for whom recommendations can be made is small, this added value to MI-Oncoseq and patient care comes at no additional genotyping cost. Pharmacogenetic assessment should be integrated into tumor sequencing programs that genotype matched germline DNA; however, the complexity and additional cost of implementing pharmacogenetics remain challenging.

Deep learning in pharmacogenomics: from gene regulation to patient stratification

This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future, deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical and demographic datasets.

Pharmacogenetics and the treatment of chronic myeloid leukemia: how relevant clinically? An update

Despite the excellent efficacy and improved clinical responses obtained with imatinib mesylate (IM), development of resistance in a significant proportion of chronic myeloid leukemia (CML) patients on IM therapy have emerged as a challenging problem in clinical practice. Resistance to imatinib can be due to heterogeneous array of factors involving BCR/ABL-dependent and BCR/ABL-independent pathways. Although BCR/ABL mutation is the major contributory factor for IM resistance, reduced bio-availability of IM in leukemic cells is also an important pharmacokinetic factor that contributes to development of resistance to IM in CML patients. The contribution of polymorphisms of the pharmacogenes in relation to IM disposition and treatment outcomes have been studied by various research groups in numerous population cohorts. However, the conclusions arising from these studies have been highly inconsistent. This review encompasses an updated insight into the impact of pharmacogenetic variability on treatment response of IM in CML patients.

Cardiovascular and Cancer Risk: The Role of Cardio-oncology

Cardio-oncology is a subspecialty of cardiology. It was created to address oncology data indicating that newly developed drugs for cancer treatment were having unanticipated cardiac side effects. Cardio-oncology designs primary and secondary risk strategies through surveillance as well as interventions to reduce cardiovascular risk (CVR), prevent cardiotoxicities, and manage the side effects that may occur. Rather than discuss in detail the cardiotoxicities of specific therapies or radiation, this review article will explore the interplay of cancer, cancer treatment, and CVR. It will examine the link between CVR and cancer risk, define mechanisms associated with cardiotoxicity, and describe screening and surveillance for patients undergoing cancer treatment. Finally, effective preventative and management strategies used to reduce the incidence of cardiotoxicities in those receiving chemotherapeutics or radiation will be presented.

The changing face of cancer diagnosis: From computational image analysis to systems biology

ᅟ: KEY POINTS: • Radiomics and radiogenomics will merge radiology, nuclear medicine, pathology and laboratory medicine. • Automation of image data analysis will change the daily routine work. • Image-guided therapy and handling complex radiogenomic data will play a major role.

CYP1A1 genetic polymorphism is a promising predictor to improve chemotherapy effects in patients with metastatic breast cancer treated with docetaxel plus thiotepa vs. docetaxel plus capecitabine

PURPOSE

A prospective study was performed to compare the outcome for metastatic breast cancer (MBC) patients treated with docetaxel plus thiotepa (DT) or docetaxel plus capecitabine (DC), and to explore the value of CYP1A1*2C polymorphisms in predicting clinical efficacy of these chemotherapies.

METHODS

MBC patients (n = 130) were randomized to treatment with DT (n = 65) or DC (n = 65). Response rate, disease control rate, progression-free and overall survival were monitored. Genotyping of CYP1A1*2C was performed in all patients.

RESULTS

DT and DC produced similar overall disease control rates (76.9 vs 69.2%), median PFS (6.7 vs. 7.5 months) and OS (20.1 vs. 21.0 months) (P > 0.05 for all comparisons); however, DT exhibited a higher rate of control of localized liver metastases (78.6 vs 41.2%, P = 0.023). Among patients homozygous for wild-type CYP1A1*1 genotype (AA), DT treatment was associated with a significantly longer PFS (8.4 vs. 6.4 months, P = 0.019) and OS (33.4 vs. 15.8 months, P = 0.018). Conversely, among patients carrying the variant CYP1A1*2C genotype (AG/GG), DC treatment was associated with a significantly longer PFS (8.4 vs. 5.5 month, P = 0.005), and OS (28.5 vs. 19.6 months, P = 0.010). After adjusting for competing risk factors, CYP1A1*2C genotype was confirmed to be an independent predictor of PFS and OS for each chemotherapy combination.

CONCLUSIONS

Overall, DT and DC result in similar clinical efficacy for MBC patients; however, efficacy for each therapy differs depending on CYP1A1*2C genotype.

Next-Generation Sequencing in Oncology: Genetic Diagnosis, Risk Prediction and Cancer Classification

Next-generation sequencing (NGS) technology has expanded in the last decades with significant improvements in the reliability, sequencing chemistry, pipeline analyses, data interpretation and costs. Such advances make the use of NGS feasible in clinical practice today. This review describes the recent technological developments in NGS applied to the field of oncology. A number of clinical applications are reviewed, i.e., mutation detection in inherited cancer syndromes based on DNA-sequencing, detection of spliceogenic variants based on RNA-sequencing, DNA-sequencing to identify risk modifiers and application for pre-implantation genetic diagnosis, cancer somatic mutation analysis, pharmacogenetics and liquid biopsy. Conclusive remarks, clinical limitations, implications and ethical considerations that relate to the different applications are provided.