Pathway Instability Is an Effective New Mutation-Based Type of Cancer Biomarkers

Kantelhardt

et al. 2020

Cancers

Background: Inevitable recurrence after radiochemotherapy is the major problem in the treatment of glioblastoma, the most prevalent type of adult brain malignancy. Glioblastomas are notorious for a high degree of intratumor heterogeneity manifest through a diversity of cell types and molecular patterns. The current paradigm of understanding glioblastoma recurrence is that cytotoxic therapy fails to target effectively glioma stem cells. Recent advances indicate that therapy-driven molecular evolution is a fundamental trait associated with glioblastoma recurrence. There is a growing body of evidence indicating that intratumor heterogeneity, longitudinal changes in molecular biomarkers and specific impacts of glioma stem cells need to be taken into consideration in order to increase the accuracy of molecular diagnostics still relying on readouts obtained from a single tumor specimen. Methods: This study integrates a multisampling strategy, longitudinal approach and complementary transcriptomic investigations in order to identify transcriptomic traits of recurrent glioblastoma in whole-tissue specimens of glioblastoma or glioblastoma stem cells. In this study, 128 tissue samples of 44 tumors including 23 first diagnosed, 19 recurrent and 2 secondary recurrent glioblastomas were analyzed along with 27 primary cultures of glioblastoma stem cells by RNA sequencing. A novel algorithm was used to quantify longitudinal changes in pathway activities and model efficacy of anti-cancer drugs based on gene expression data. Results: Our study reveals that intratumor heterogeneity of gene expression patterns is a fundamental characteristic of not only newly diagnosed but also recurrent glioblastomas. Evidence is provided that glioblastoma stem cells recapitulate intratumor heterogeneity, longitudinal transcriptomic changes and drug sensitivity patterns associated with the state of recurrence. Conclusions: Our results provide a transcriptional rationale for the lack of significant therapeutic benefit from temozolomide in patients with recurrent glioblastoma. Our findings imply that the spectrum of potentially effective drugs is likely to differ between newly diagnosed and recurrent glioblastomas and underscore the merits of glioblastoma stem cells as prognostic models for identifying alternative drugs and predicting drug response in recurrent glioblastoma. With the majority of recurrent glioblastomas being inoperable, glioblastoma stem cell models provide the means of compensating for the limited availability of recurrent glioblastoma specimens.

Section: Analysis Of Molecular Pathway Activationmentioning

confidence: 99%

Intratumoral Heterogeneity and Longitudinal Changes in Gene Expression Predict Differential Drug Sensitivity in Newly Diagnosed and Recurrent Glioblastoma

Kim

Kantelhardt

et al. 2020

Cancers

“…Pearson correlation was then calculated for the obtained SDs and the respective numbers of biosamples per cancer type (Figure 1a). Similarly, mutation data were investigated using normalized gene mutation rates and their SDs (Figure 1b) [46]. In both cases, we found no significant correlation between SDs and sample sizes for the thirteen tumor types investigated.…”

Section: Biosample Setsmentioning

confidence: 88%

“…where NMR n,g is NMR of gene n in sample g; N mut(n,g) is the number of mutations for gene n in sample g; Length CDS(n) is the length of coding DNA sequence (CDS) of gene n in nucleotides [27,46]. The gene CDS normalization was done to eliminate a bias caused by different gene lengths [46], calculated NMR values are given in Supplementary Table S3. Averaged NMR profiles for thirteen cancer types under investigation are shown on Supplementary Table S4.…”

Section: Mutation Datamentioning

confidence: 99%

Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology

Zolotovskaia

Petrov³

et al. 2020

IJMS

Self Cite

Inter-patient molecular heterogeneity is the major declared driver of an expanding variety of anticancer drugs and personalizing their prescriptions. Here, we compared interpatient molecular heterogeneities of tumors and repertoires of drugs or their molecular targets currently in use in clinical oncology. We estimated molecular heterogeneity using genomic (whole exome sequencing) and transcriptomic (RNA sequencing) data for 4890 tumors taken from The Cancer Genome Atlas database. For thirteen major cancer types, we compared heterogeneities at the levels of mutations and gene expression with the repertoires of targeted therapeutics and their molecular targets accepted by the current guidelines in oncology. Totally, 85 drugs were investigated, collectively covering 82 individual molecular targets. For the first time, we showed that the repertoires of molecular targets of accepted drugs did not correlate with molecular heterogeneities of different cancer types. On the other hand, we found that the clinical recommendations for the available cancer drugs were strongly congruent with the gene expression but not gene mutation patterns. We detected the best match among the drugs usage recommendations and molecular patterns for the kidney, stomach, bladder, ovarian and endometrial cancers. In contrast, brain tumors, prostate and colorectal cancers showed the lowest match. These findings provide a theoretical basis for reconsidering usage of targeted therapeutics and intensifying drug repurposing efforts.

“…The AUC value is the universal characteristic of biomarker robustness depending on its sensitivity and specificity (Green DM 1966). This statistical approach is broadly applicable to different types of biomarkers in oncology (Chen et al 2019;Tanioka et al 2018;Liu et al 2018;Zolotovskaia et al 2018;Lezhnina et al 2014;Borisov et al 2014). It positively correlates with the quality of a biomarker and varies from 0.5 till 1.…”

Section: Differentially Regulated Genes and Molecular Pathwaysmentioning

confidence: 99%

RNA sequencing profiles and diagnostic signatures linked with response to ramucirumab in gastric cancer

Cold Spring Harb Mol Case Stud

Poddubskaya

Baranova

et al. 2020

Gastric cancer (GC) is the fifth cancer type by associated mortality. Proportion of early diagnosis is low, and most patients are diagnosed at the advanced stages. First line therapy standardly includes fluoropyrimidines and platinum compounds with trastuzumab for HER2positive cases. For the recurrent disease there are several alternative options including ramucirumab, a monoclonal therapeutic antibody that inhibits VEGF-mediated tumor angiogenesis by binding with VEGFR2, alone or in combination with other cancer drugs. However, overall response rate rate following ramucirumab or its combinations is 30-80% of the patients, suggesting that personalization of drug prescription is needed to increase efficacy of treatment. We report here original tumor RNA sequencing profiles for 15 advanced GC patients linked with data on clinical response to ramucirumab or its combinations. Three genes showed differential expression in the tumors-responders vs non-responders: CHRM3, LRFN1 and TEX15. Of them, CHRM3 was upregulated in the responders. Using bioinformatic platform Oncobox we simulated ramucirumab efficiency and compared output model results with actual tumor response data. An agreement was observed between predicted and real clinical outcomes (AUC ≥ 0.7). These results suggest that RNA sequencing may be used to personalize prescription of ramucirumab for GC and indicate on potential molecular mechanisms underlying ramucirumab resistance. The RNA sequencing profiles obtained here are fully compatible with the previously published Oncobox Atlas of Normal Tissue Expression (ANTE) data.