Cell Dispersal Influences Tumor Heterogeneity and Introduces a Bias in NGS Data Interpretation

Anticancer properties of complexes derived from bidentate ligands

Cancer is the abnormal division and multiplication of cells in an organ or tissue. It is the second leading cause of death globally. There are various types of cancer such as prostate, breast, colon, lung, stomach, liver, skin, and many others depending on the tissue or organ where the abnormal growth originates. Despite the huge investment in the development of anticancer agents, the transition of research to medications that improve substantially the treatment of cancer is less than 10%. Cisplatin and its analogs are ubiquitous metal-based anticancer agents notable for the treatment of various cancerous cells and tumors but unfortunately accompanied by large toxicities due to low selectivity between cancerous and normal cells. The improved toxicity profile of cisplatin analogs bearing bidentate ligands has motivated the synthesis of vast metal complexes of bidentate ligands. Complexes derived from bidentate ligands such as β-diketones, diolefins, benzimidazoles and dithiocarbamates have been reported to possess 20 to 15,600-fold better anticancer activity, when tested on cell lines, than some known antitumor drugs currently on the market, e.g. cisplatin, oxaliplatin, carboplatin, doxorubicin, and 5-fluorouracil. This work discusses the anticancer properties of various metal complexes derived from bidentate ligands, for possible application in chemotherapy. The results discussed were evaluated by the IC50 values as obtained from cell line tests on various metal-bidentate complexes. The structure-activity relationship study of the complexes discussed, revealed that hydrophobicity is a key factor that influences anticancer properties of molecules.

Dynamic contrast-enhanced MRI radiomics model predicts epidermal growth factor receptor amplification in glioblastoma, IDH-wildtype

PURPOSE

To develop and validate a dynamic contrast-enhanced (DCE) MRI-based radiomics model to predict epidermal growth factor receptor (EGFR) amplification in patients with glioblastoma, isocitrate dehydrogenase (IDH) wildtype.

METHODS

Patients with pathologically confirmed glioblastoma, IDH wildtype, from January 2015 to December 2020, with an EGFR amplification status, were included. Patients who did not undergo DCE or conventional brain MRI were excluded. Patients were categorized into training and test sets by a ratio of 7:3. DCE MRI data were used to generate volume transfer constant (Ktrans) and extracellular volume fraction (Ve) maps. Ktrans, Ve, and conventional MRI were then used to extract the radiomics features, from which the prediction models for EGFR amplification status were developed and validated.

RESULTS

A total of 190 patients (mean age, 59.9; male, 55.3%), divided into training (n = 133) and test (n = 57) sets, were enrolled. In the test set, the radiomics model using the Ktrans map exhibited the highest area under the receiver operating characteristic curve (AUROC), 0.80 (95% confidence interval [CI], 0.65-0.95). The AUROC for the Ve map-based and conventional MRI-based models were 0.74 (95% CI, 0.58-0.90) and 0.76 (95% CI, 0.61-0.91).

CONCLUSION

The DCE MRI-based radiomics model that predicts EGFR amplification in glioblastoma, IDH wildtype, was developed and validated. The MRI-based radiomics model using the Ktrans map has higher AUROC than conventional MRI.

Immunotherapeutic Approaches in Ovarian Cancer

Ovarian cancer (OC) is gynecological cancer, and diagnosis and treatment are continuously advancing. Next-generation sequencing (NGS)-based diagnoses have emerged as novel methods for identifying molecules and pathways in cancer research. The NGS-based applications have expanded in OC research for early detection and identification of aberrant genes and dysregulation pathways, demonstrating comprehensive views of the entire transcriptome, such as fusion genes, genetic mutations, and gene expression profiling. Coinciding with advances in NGS-based diagnosis, treatment strategies for OC, such as molecular targeted therapy and immunotherapy, have also advanced. Immunotherapy is effective against many other cancers, and its efficacy against OC has also been demonstrated at the clinical phase. In this review, we describe several NGS-based applications for therapeutic targets of OC, and introduce current immunotherapeutic strategies, including vaccines, checkpoint inhibitors, and chimeric antigen receptor (CAR)-T cell transplantation, for effective diagnosis and treatment of OC.

Currently favored sampling practices for tumor sequencing can produce optimal results in the clinical setting

Tumor heterogeneity is a consequence of clonal evolution, resulting in a fractal-like architecture with spatially separated main clones, sub-clones and single-cells. As sequencing an entire tumor is not feasible, we ask the question whether there is an optimal clinical sampling strategy that can handle heterogeneity and hypermutations? Here, we tested the effect of sample size, pooling strategy as well as sequencing depth using whole-exome sequencing of ovarian tumor specimens paired with normal blood samples. Our study has an emphasis on clinical application—hence we compared single biopsy, combined local biopsies and combined multi-regional biopsies. Our results show that sequencing from spatially neighboring regions show similar genetic compositions, with few private mutations. Pooling samples from multiple distinct regions of the primary tumor did not increase the overall number of identified mutations but may increase the robustness of detecting clonal mutations. Hypermutating tumors are a special case, since increasing sample size can easily dilute sub-clonal private mutations below detection thresholds. In summary, we compared the effects of sampling strategies (single biopsy, multiple local samples, pooled global sample) on mutation detection by next generation sequencing. In view of the limitations of present tools and technologies, only one sequencing run per sample combined with high coverage (100–300 ×) sequencing is affordable and practical, regardless of the number of samples taken from the same patient.

Comprehensive Outline of Whole Exome Sequencing Data Analysis Tools Available in Clinical Oncology

Whole exome sequencing (WES) enables the analysis of all protein coding sequences in the human genome. This technology enables the investigation of cancer-related genetic aberrations that are predominantly located in the exonic regions. WES delivers high-throughput results at a reasonable price. Here, we review analysis tools enabling utilization of WES data in clinical and research settings. Technically, WES initially allows the detection of single nucleotide variants (SNVs) and copy number variations (CNVs), and data obtained through these methods can be combined and further utilized. Variant calling algorithms for SNVs range from standalone tools to machine learning-based combined pipelines. Tools for CNV detection compare the number of reads aligned to a dedicated segment. Both SNVs and CNVs help to identify mutations resulting in pharmacologically druggable alterations. The identification of homologous recombination deficiency enables the use of PARP inhibitors. Determining microsatellite instability and tumor mutation burden helps to select patients eligible for immunotherapy. To pave the way for clinical applications, we have to recognize some limitations of WES, including its restricted ability to detect CNVs, low coverage compared to targeted sequencing, and the missing consensus regarding references and minimal application requirements. Recently, Galaxy became the leading platform in non-command line-based WES data processing. The maturation of next-generation sequencing is reinforced by Food and Drug Administration (FDA)-approved methods for cancer screening, detection, and follow-up. WES is on the verge of becoming an affordable and sufficiently evolved technology for everyday clinical use.

Use of the Ion PGM and the GeneReader NGS Systems in Daily Routine Practice for Advanced Lung Adenocarcinoma Patients: A Practical Point of View Reporting a Comparative Study and Assessment of 90 Patients

Background: With the integration of various targeted therapies into the clinical management of patients with advanced lung adenocarcinoma, next-generation sequencing (NGS) has become the technology of choice and has led to an increase in simultaneously interrogated genes. However, the broader adoption of NGS for routine clinical practice is still hampered by sophisticated workflows, complex bioinformatics analysis and medical interpretation. Therefore, the performance of the novel QIAGEN GeneReader NGS system was compared to an in-house ISO-15189 certified Ion PGM NGS platform. Methods: Clinical samples from 90 patients (60 Retrospectively and 30 Prospectively) with lung adenocarcinoma were sequenced with both systems. Mutations were analyzed and EGFR, KRAS, BRAF, NRAS, ALK, PIK3CA and ERBB2 genes were compared and sampling time and suitability for clinical testing were assessed. Results: Both sequencing systems showed perfect concordance for the overlapping genes. Correlation of allele frequency was r² = 0.93 for the retrospective patients and r² = 0.81 for the prospective patients. Hands-on time and total run time were shorter using the PGM system, while the GeneReader platform provided good traceability and up-to-date interpretation of the results. Conclusion: We demonstrated the suitability of the GeneReader NGS system in routine practice in a clinical pathology laboratory setting.

Pathological Bases and Clinical Impact of Intratumor Heterogeneity in Clear Cell Renal Cell Carcinoma

PURPOSE OF REVIEW

Intratumor heterogeneity is an inherent event in tumor development that is receiving much attention in the last years since it is responsible for most failures of current targeted therapies. The purpose of this review is to offer clinicians an updated insight of the multiple manifestations of a complex event that impacts significantly patient's life.

RECENT FINDINGS

Clear cell renal cell carcinoma is the most common renal tumor and a paradigmatic example of a heterogeneous neoplasm. Next-generation sequencing has demonstrated that intratumor heterogeneity encompasses genetic, epigenetic, and microenvironmental variability. Currently accepted protocols of tumor sampling seem insufficient in unveiling intratumor heterogeneity with reliability and need to be updated. This variability challenges the precise morphological diagnosis, its molecular characterization, and the selection of optimal personalized therapies in clear cell renal cell carcinoma, a neoplasm traditionally considered chemo- and radio-resistant. We review the state of the art of the different approaches to intratumor heterogeneity in clear cell renal cell carcinomas, from the simple morphology to the most sophisticated massive sequencing tools.

Intratumor and Intertumor Heterogeneity in Melanoma

Melanoma is a cancer that exhibits one of the most aggressive and heterogeneous features. The incidence rate escalates. A high number of clones harboring various mutations contribute to an exceptional level of intratumor heterogeneity of melanoma. It also refers to metastases which may originate from different subclones of primary lesion. Such component of the neoplasm biology is termed intertumor and intratumor heterogeneity. These levels of tumor heterogeneity hinder accurate diagnosis and effective treatment. The increasing number of research on the topic reflects the need for understanding limitation or failure of contemporary therapies. Majority of analyses concentrate on mutations in cancer-related genes. Novel high-throughput techniques reveal even higher degree of variations within a lesion. Consolidation of theories and researches indicates new routes for treatment options such as targets for immunotherapy. The demand for personalized approach in melanoma treatment requires extensive knowledge on intratumor and intertumor heterogeneity on the level of genome, transcriptome/proteome, and epigenome. Thus, achievements in exploration of melanoma variety are described in details. Particularly, the issue of tumor heterogeneity or homogeneity given BRAF mutations is discussed.