Relevance of Fc Gamma Receptor Polymorphisms in Cancer Therapy With Monoclonal Antibodies.

Therapeutic monoclonal antibodies (mAbs), including immune checkpoint inhibitors (ICIs), are an important breakthrough for the treatment of cancer and have dramatically changed clinical outcomes in a wide variety of tumours. However, clinical response varies among patients receiving mAb-based treatment, so it is necessary to search for predictive biomarkers of response to identify the patients who will derive the greatest therapeutic benefit. The interaction of mAbs with Fc gamma receptors (FcγR) expressed by innate immune cells is essential for antibody-dependent cellular cytotoxicity (ADCC) and this binding is often critical for their in vivo efficacy. FcγRIIa (H131R) and FcγRIIIa (V158F) polymorphisms have been reported to correlate with response to therapeutic mAbs. These polymorphisms play a major role in the affinity of mAb receptors and, therefore, can exert a profound impact on antitumor response in these therapies. Furthermore, recent reports have revealed potential mechanisms of ICIs to modulate myeloid subset composition within the tumour microenvironment through FcγR-binding, optimizing their anti-tumour activity. The purpose of this review is to highlight the clinical contribution of FcγR polymorphisms to predict response to mAbs in cancer patients.

RB1 and TP53 co-mutations correlate strongly with genomic biomarkers of response to immunity checkpoint inhibitors in urothelial bladder cancer.

BACKGROUND: Muscle invasive urothelial bladder carcinoma (MIBC) present RB1 and TP53 somatic alterations in a variable percentage of tumors throughout all molecular subtypes. MIBCs with neuroendocrine features have a high response rate to immunity checkpoint inhibitors (ICIs). Whether the presence of somatic co-alterations in these 2 genes in MIBCs is relevant to their responsiveness to ICIs is not known. METHODS: The potential correlation of different genomic biomarkers of response to ICIs like tumor mutational burden (TMB), single nucleotide variants (SNV) predicted neoantigens, DNA damage response (DDR) genes, DNA somatic signatures and TILs infiltrate was explored in patients with somatic co-alterations in RB1 and TP53 (RB1&TP53) as compared with patients with no alterations in any (double wild type, DWT) or with alterations in just one of the 2 genes. The Cancer Genome Atlas (TCGA) pancancer BLCA dataset of cystectomy specimens (n = 407) with mutation, copy number alterations and transcriptomic (RNA sequencing) data as well as the IMVigor 210 study (n = 348) of metastatic urothelial bladder cancers treated with atezolizumab (PD-L1 inhibitor) with clinical response data containing transcriptomic (RNA sequencing), along with a subset (n = 274) with mutation and copy number data were used for this purpose. A novel tumor microenvironment metascore (TMM) was developed based in a LASSO regularized Cox model with predictive and prognostic ability. RESULTS: Samples with co-altered RB1&TP53: a) were enriched in immunity effectors (CD8 cytotoxic lymphocytes, NK cells) and display higher scores of a T cell inflamed signature; b) have a higher TMB, higher number of SNV predicted neoantigens and higher TILs fractions; c) have a higher number of DDR mutated and deep deleted DDR genes; d) have DNA somatic signatures 2 and 13 related to APOBEC mutagenesis. Using the IMVigor 210 dataset, RB1&TP53 samples had the highest response rate to atezolizumab and a strong correlation with TMB and TMM. The consensus molecular subtype classification in the IMVigor 210 dataset showed a significant correlation with both the response to treatment (p = 0.001, Chisquare) and the presence of RB1 and TP53 genomic alterations (p < 0.001, Chisquare). CONCLUSIONS: RB1&TP53 co-alterations are strongly associated with genomic biomarkers of response to ICIs in MIBCs.

A novel genomic signature predicting FDG uptake in diverse metastatic tumors.

BACKGROUND: Building a universal genomic signature predicting the intensity of FDG uptake in diverse metastatic tumors may allow us to understand better the biological processes underlying this phenomenon and their requirements of glucose uptake. METHODS: A balanced training set (n = 71) of metastatic tumors including some of the most frequent histologies, with matched PET/CT quantification measurements and whole human genome gene expression microarrays, was used to build the signature. Selection of microarray features was carried out exclusively on the basis of their strong association with FDG uptake (as measured by SUVmean35) by means of univariate linear regression. A thorough bioinformatics study of these genes was performed, and multivariable models were built by fitting several state of the art regression techniques to the training set for comparison. RESULTS: The 909 probes with the strongest association with the SUVmean35 (comprising 742 identifiable genes and 62 probes not matched to a symbol) were used to build the signature. Partial least squares using three components (PLS-3) was the best performing model in the training dataset cross-validation (root mean square error, RMSE = 0.443) and was validated further in an independent validation dataset (n = 13) obtaining a performance within the 95% CI of that obtained in the training dataset (RMSE = 0.645). Significantly overrepresented biological processes correlating with the SUVmean35 were identified beyond glycolysis, such as ribosome biogenesis and DNA replication (correlating with a higher SUVmean35) and cytoskeleton reorganization and autophagy (correlating with a lower SUVmean35). CONCLUSIONS: PLS-3 is a signature predicting accurately the intensity of FDG uptake in diverse metastatic tumors. FDG-PET might help in the design of specific targeted therapies directed to counteract the identified malignant biological processes more likely activated in a tumor as inferred from the SUVmean35 and also from its variations in response to antineoplastic treatments.