Influence of pharmacogenetic variability on the pharmacokinetics and toxicity of the aurora kinase inhibitor danusertib

Objectives Danusertib is a serine/threonine kinase inhibitor of multiple kinases, including aurora-A, B, and C. This explorative study aims to identify possible relationships between single nucleotide polymorphisms in genes coding for drug metabolizing enzymes and transporter proteins and clearance of danusertib, to clarify the interpatient variability in exposure. In addition, this study explores the relationship between target receptor polymorphisms and toxicity of danusertib. Methods For associations with clearance, 48 cancer patients treated in a phase I study were analyzed for ABCB1, ABCG2 and FMO3 polymorphisms. Association analyses between neutropenia and drug target receptors, including KDR, RET, FLT3, FLT4, AURKB and AURKA, were performed in 30 patients treated at recommended phase II dose-levels in three danusertib phase I or phase II trials. Results No relationships between danusertib clearance and drug metabolizing enzymes and transporter protein polymorphisms were found. Only, for the one patient with FMO3 18281AA polymorphism, a significantly higher clearance was noticed, compared to patients carrying at least 1 wild type allele. No effect of target receptor genotypes or haplotypes on neutropenia was observed. Conclusions As we did not find any major correlations between pharmacogenetic variability in the studied enzymes and transporters and pharmacokinetics nor toxicity, it is unlikely that danusertib is highly susceptible for pharmacogenetic variation. Therefore, no dosing alterations of danusertib are expected in the future, based on the polymorphisms studied. However, the relationship between FMO3 polymorphisms and clearance of danusertib warrants further research, as we could study only a small group of patients

Pharmacogenetics of telatinib, a VEGFR-2 and VEGFR-3 tyrosine kinase inhibitor, used in patients with solid tumors

Purpose Telatinib is an orally active small-molecule tyrosine kinase inhibitor of kinase insert domain receptor (KDR; VEGFR-2) and fms-related tyrosine kinase 4 (FLT4; VEGFR-3). This study aims at the identification of relationships between single nucleotide polymorphisms (SNPs) in genes encoding for transporter proteins and pharmacokinetic parameters in order to clarify the significant interpatient variability in drug exposure. In addition, the potential relationship between target receptor polymorphisms and toxicity of telatinib is explored. Methods Blood samples from 33 patients enrolled in a phase I dose-escalation study of telatinib were analyzed. For correlation with dose normalized AUC(0–12), ATP-binding cassette (ABC) B1 (ABCB1), ABCC1, and ABCG2 were the genes selected. For correlation with telatinib toxicity, selected genes were the drug target genes KDR and FLT4. Results No association between dose normalized AUC(0–12) and drug transporter protein polymorphisms was observed. In addition, no association between toxicity and KDR or FLT4 genotype or haplotype was seen. Conclusions Our pharmacogenetic analysis could not reveal a correlation between relevant gene polymorphisms and clinical and pharmacokinetic observations of telatinib

A single digital droplet PCR assay to detect multiple KIT exon 11 mutations in tumor and plasma from patients with gastrointestinal stromal tumors

__Background:__ Gastrointestinal stromal tumors (GISTs) are characterized by oncogenic KIT mutations that cluster in two exon 11 hotspots. The aim of this study was to develop a single, sensitive, quantitative digital droplet PCR (ddPCR) assay for the detection of common exon 11 mutations in both GIST tumor tissue and in circulating tumor DNA (ctDNA) isolated from GIST patients' plasma. __Methods:__ A ddPCR assay was designed using two probes that cover both hotspots. Available archival FFPE tumor tissue from 27 consecutive patients with known KIT exon 11 mutations and 9 randomly selected patients without exon 11 mutations were tested. Plasma samples were prospectively collected in a multicenter bio-databank from December 2014. ctDNA was analyzed of 22 patients with an exon 11 mutation and a baseline plasma sample. __Results:__ The ddPCR assay detected the exon 11 mutation in 21 of 22 tumors with exon 11 mutations covered by the assay. Mutations in ctDNA were detected at baseline in 13 of 14 metastasized patien

Predicting clinical benefit from everolimus in patients with advanced solid tumors, the CPCT-03 study

Background: In this study, our aim was to identify molecular aberrations predictive for response to everolimus, an mTOR inhibitor, regardless of tumor type. Methods: To generate hypotheses about potential markers for sensitivity to mTOR inhibition, drug sensitivity and genomic profiles of 835 cell lines were analyzed. Subsequently, a multicenter study was conducted. Patients with advanced solid tumors lacking standard of care treatment options were included and underwent a pre-treatment tumor biopsy to enable DNA sequencing of 1,977 genes, derive copy number profiles and determine activation status of pS6 and pERK. Treatment benefit was determined according to TTP ratio and RECIST. We tested for associations between treatment benefit and single molecular aberrations, clusters of aberrations and pathway perturbation. Results: Cell line screens indicated several genes, such as PTEN (P = 0.016; Wald test), to be associated with sensitivity to mTOR inhibition. Subsequently 73 patients were included, of which 59 started treatment with everolimus. Response and molecular data were available from 43 patients. PTEN aberrations, i.e. copy number loss or mutation, were associated with treatment benefit (P = 0.046; Fisher's exact test). Conclusion: Loss-of-function aberrations in PTEN potentially represent a tumor type agnostic biomarker for benefit from everolimus and warrants further confirmation in subsequent studies

Targeted next generation sequencing as a reliable diagnostic assay for the detection of somatic mutations in tumours using minimal DNA amounts from formalin fixed paraffin embedded material

Background Targeted Next Generation Sequencing (NGS) offers a way to implement testing of multiple genetic aberrations in diagnostic pathology practice, which is necessary for personalized cancer treatment. However, no standards regarding input material have been defined. This study therefore aimed to determine the effect of the type of input material (e.g. formalin fixed paraffin embedded (FFPE) versus fresh frozen (FF) tissue) on NGS derived results. Moreover, this study aimed to explore a standardized analysis pipeline to support consistent clinical decision-making. Method We used the Ion Torrent PGM sequencing platform in combination with the Ion AmpliSeq Cancer Hotspot Panel v2 to sequence frequently mutated regions in 50 cancer related genes, and validated the NGS detected variants in 250 FFPE samples using standard diagnostic assays. Next, 386 tumour samples were sequenced to explore the effect of input material on variant detection variables. For variant calling, Ion Torrent analysis software was supplemented with additional variant annotation and filtering. Results Both FFPE and FF tissue could be sequenced reliably with a sensitivity of 99.1%. Validation showed a 98.5%concordance between NGS and conventional sequencing techniques, where NGS provided both the advantage of low input DNA concentration and the detectio

Impact of CYP3A4*22 on Pazopanib Pharmacokinetics in Cancer Patients

Background and Objective: As pazopanib plasma trough concentrations are correlated with treatment outcome, we explored whether single nucleotide polymorphisms in the elimination pathway of pazopanib affect systemic pazopanib concentrations. Methods: The decreased function alleles CYP3A4 15389 C > T (*22), ABCB1 3435 C >T, ABCG2 421 C >A, and ABCG2 34G >A were analyzed within a recently developed population-pharmacokinetic model. Results: Incorporation of CYP3A4*22 in the model resulted in a 35% lower clearance for variant carriers (0.18 vs. 0.27 L/h; difference in objective function value: − 9.7; p < 0.005). Simulated median trough concentrations of cancer patients with CYP3A4*22 with 600 mg once daily or 800 mg once daily were 31 and 35 mg/L, respectively. The simulated trough concentrations for the population excluding the CYP3A4*22 carriers after 600 mg once daily or 800 mg once daily were 18 and 20 mg/L, respectively. Conclusion: This analysis shows that CYP3A4*22 heterozygotes have a substantial lower pazopanib clearance and that dose adjustments based on CYP3A4*22 status could be considered

Whole genome sequencing of metastatic colorectal cancer reveals prior treatment effects and specific metastasis features

In contrast to primary colorectal cancer (CRC) little is known about the genomic landscape of metastasized CRC. Here we present whole genome sequencing data of metastases of 429 CRC patients participating in the pan-cancer CPCT-02 study (NCT01855477). Unsupervised clustering using mutational signature patterns highlights three major patient groups characterized by signatures known from primary CRC, signatures associated with received prior treatments, and metastasis-specific signatures. Compared to primary CRC, we identify additional putative (non-coding) driver genes and increased frequencies in driver gene mutations. In addition, we identify specific genes preferentially affected by microsatellite instability. CRC-specific 1kb-10Mb deletions, enriched for common fragile sites, and LINC00672 mutations are associated with response to treatment in general, whereas FBXW7 mutations predict poor response specifically to EGFR-targeted treatment. In conclusion, the genomic landscape of mCRC shows defined changes compared to primary CRC, is affected by prior treatments and contains features with potential clinical relevance

Pan-cancer whole-genome analyses of metastatic solid tumours

Contains fulltext : 215492.pdf (publisher's version ) (Open Access)Metastatic cancer is a major cause of death and is associated with poor treatment efficacy. A better understanding of the characteristics of late-stage cancer is required to help adapt personalized treatments, reduce overtreatment and improve outcomes. Here we describe the largest, to our knowledge, pan-cancer study of metastatic solid tumour genomes, including whole-genome sequencing data for 2,520 pairs of tumour and normal tissue, analysed at median depths of 106x and 38x, respectively, and surveying more than 70 million somatic variants. The characteristic mutations of metastatic lesions varied widely, with mutations that reflect those of the primary tumour types, and with high rates of whole-genome duplication events (56%). Individual metastatic lesions were relatively homogeneous, with the vast majority (96%) of driver mutations being clonal and up to 80% of tumour-suppressor genes being inactivated bi-allelically by different mutational mechanisms. Although metastatic tumour genomes showed similar mutational landscape and driver genes to primary tumours, we find characteristics that could contribute to responsiveness to therapy or resistance in individual patients. We implement an approach for the review of clinically relevant associations and their potential for actionability. For 62% of patients, we identify genetic variants that may be used to stratify patients towards therapies that either have been approved or are in clinical trials. This demonstrates the importance of comprehensive genomic tumour profiling for precision medicine in cancer

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

Abstract: In cancer, the primary tumour’s organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here,as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA