Applying Bioinformatic Platforms, In Vitro, and In Vivo Functional Assays in the Characterization of Genetic Variants in the GH/IGF Pathway Affecting Growth and Development

Genetic Variants Associated With Response to Platinum-Based Chemotherapy in Non-Small Cell Lung Cancer Patients: A Field Synopsis and Meta-Analysis

Background: Publications on the associations of genetic variants with the response to platinum-based chemotherapy (PBC) in NSCLC patients have surged over the years, but the results have been inconsistent. Here, a comprehensive meta-analysis was conducted to combine eligible studies for a more accurate assessment of the pharmacogenetics of PBC in NSCLC patients. Methods: Relevant publications were searched in PubMed, Scopus, and Web of Science databases through 15 May 2021. Inclusion criteria for eligible publications include studies that reported genotype and allele frequencies of NSCLC patients treated with PBC, delineated by their treatment response (sensitive vs. resistant). Publications on cell lines or animal models, duplicate reports, and non-primary research were excluded. Epidemiological credibility of cumulative evidence was assessed using the Newcastle-Ottawa Scale (NOS) and Venice criteria. Begg's and Egger's tests were used to assess publication bias. Cochran's Q-test and I2 test were used to calculate the odds ratio and heterogeneity value to proceed with the random effects or fixed-effects method. Venice criteria were used to assess the strength of evidence, replication methods and protection against bias in the studies. Results: A total of 121 publications comprising 29,478 subjects were included in this study, and meta-analyses were performed on 184 genetic variants. Twelve genetic variants from 10 candidate genes showed significant associations with PBC response in NSCLC patients with strong or moderate cumulative epidemiological evidence (increased risk: ERCC1 rs3212986, ERCC2 rs1799793, ERCC2 rs1052555, and CYP1A1 rs1048943; decreased risk: GSTM1 rs36631, XRCC1 rs1799782 and rs25487, XRCC3 rs861539, XPC rs77907221, ABCC2 rs717620, ABCG2 rs2231142, and CDA rs1048977). Bioinformatics analysis predicted possible damaging or deleterious effects for XRCC1 rs1799782 and possible low or medium functional impact for CYP1A1 rs1048943. Conclusion: Our results provide an up-to-date summary of the association between genetic variants and response to PBC in NSCLC patients.

Insulin-like growth factors and aging: lessons from Laron syndrome

The growth hormone (GH)-insulin-like growth factor-1 (IGF1) signaling pathway emerged in recent years as a key determinant of aging and longevity. Disruption of this network in different animal species, including flies, nematodes and mouse, was consistently associated with an extended lifespan. Epidemiological analyses have shown that patients with Laron syndrome (LS), the best-characterized disease under the umbrella of the congenital IGF1 deficiencies, seem to be protected from cancer. While aging and cancer, as a rule, are considered diametrically opposite processes, modern lines of evidence reinforce the notion that aging and cancer might, as a matter of fact, be regarded as divergent manifestations of identical biochemical and cellular underlying processes. While the effect of individual mutations on lifespan and health span is very difficult to assess, genome-wide screenings identified a number of differentially represented aging- and longevity-associated genes in patients with LS. The present review summarizes recent data that emerged from comprehensive analyses of LS patients and portrays a number of previously unrecognized targets for GH-IGF1 action. Our article sheds light on complex aging and longevity processes, with a particular emphasis on the role of the GH-IGF1 network in these mechanisms.

Hallmarks of cancer: The insulin-like growth factors perspective

The identification of a series of attributes or hallmarks that are shared by virtually all cancer cells constitutes a true milestone in cancer research. The conceptualization of a catalogue of common genetic, molecular, biochemical and cellular events under a unifying Hallmarks of Cancer idea had a major impact in oncology. Furthermore, the fact that different types of cancer, ranging from pediatric tumors and leukemias to adult epithelial cancers, share a large number of fundamental traits reflects the universal nature of the biological events involved in oncogenesis. The dissection of a complex disease like cancer into a finite directory of hallmarks is of major basic and translational relevance. The role of insulin-like growth factor-1 (IGF1) as a progression/survival factor required for normal cell cycle transition has been firmly established. Similarly well characterized are the biochemical and cellular activities of IGF1 and IGF2 in the chain of events leading from a phenotypically normal cell to a diseased one harboring neoplastic traits, including growth factor independence, loss of cell-cell contact inhibition, chromosomal abnormalities, accumulation of mutations, activation of oncogenes, etc. The purpose of the present review is to provide an in-depth evaluation of the biology of IGF1 at the light of paradigms that emerge from analysis of cancer hallmarks. Given the fact that the IGF1 axis emerged in recent years as a promising therapeutic target, we believe that a careful exploration of this signaling system might be of critical importance on our ability to design and optimize cancer therapies.

A Comprehensive Evaluation of the Performance of Prediction Algorithms on Clinically Relevant Missense Variants

The rapid integration of genomic technologies in clinical diagnostics has resulted in the detection of a multitude of missense variants whose clinical significance is often unknown. As a result, a plethora of computational tools have been developed to facilitate variant interpretation. However, choosing an appropriate software from such a broad range of tools can be challenging; therefore, systematic benchmarking with high-quality, independent datasets is critical. Using three independent benchmarking datasets compiled from the ClinVar database, we evaluated the performance of ten widely used prediction algorithms with missense variants from 21 clinically relevant genes, including BRCA1 and BRCA2. A fourth dataset consisting of 1053 missense variants was also used to investigate the impact of type 1 circularity on their performance. The performance of the prediction algorithms varied widely across datasets. Based on Matthews Correlation Coefficient and Area Under the Curve, SNPs&GO and PMut consistently displayed an overall above-average performance across the datasets. Most of the tools demonstrated greater sensitivity and negative predictive values at the expense of lower specificity and positive predictive values. We also demonstrated that type 1 circularity significantly impacts the performance of these tools and, if not accounted for, may confound the selection of the best performing algorithms.