Clinical Targeted Next-Generation Sequencing Shows Increased Mutational Load in Endometrioid-type Endometrial Adenocarcinoma With Deficient DNA Mismatch Repair:

Detection of driver mutations and genomic signatures in endometrial cancers using artificial intelligence algorithms

Analyzed endometrial cancer (EC) genomes have allowed for the identification of molecular signatures, which enable the classification, and sometimes prognostication, of these cancers. Artificial intelligence algorithms have facilitated the partitioning of mutations into driver and passenger based on a variety of parameters, including gene function and frequency of mutation. Here, we undertook an evaluation of EC cancer genomes deposited on the Catalogue of Somatic Mutations in Cancers (COSMIC), with the goal to classify all mutations as either driver or passenger. Our analysis showed that approximately 2.5% of all mutations are driver and cause cellular transformation and immortalization. We also characterized nucleotide level mutation signatures, gross chromosomal re-arrangements, and gene expression profiles. We observed that endometrial cancers show distinct nucleotide substitution and chromosomal re-arrangement signatures compared to other cancers. We also identified high expression levels of the CLDN18 claudin gene, which is involved in growth, survival, metastasis and proliferation. We then used in silico protein structure analysis to examine the effect of certain previously uncharacterized driver mutations on protein structure. We found that certain mutations in CTNNB1 and TP53 increase protein stability, which may contribute to cellular transformation. While our analysis retrieved previously classified mutations and genomic alterations, which is to be expected, this study also identified new signatures. Additionally, we show that artificial intelligence algorithms can be effectively leveraged to accurately predict key drivers of cancer. This analysis will expand our understanding of ECs and improve the molecular toolbox for classification, diagnosis, or potential treatment of these cancers.

FDA-Approved and Emerging Next Generation Predictive Biomarkers for Immune Checkpoint Inhibitors in Cancer Patients

A patient's response to immune checkpoint inhibitors (ICIs) is a complex quantitative trait, and determined by multiple intrinsic and extrinsic factors. Three currently FDA-approved predictive biomarkers (progra1mmed cell death ligand-1 (PD-L1); microsatellite instability (MSI); tumor mutational burden (TMB)) are routinely used for patient selection for ICI response in clinical practice. Although clinical utility of these biomarkers has been demonstrated in ample clinical trials, many variables involved in using these biomarkers have poised serious challenges in daily practice. Furthermore, the predicted responders by these three biomarkers only have a small percentage of overlap, suggesting that each biomarker captures different contributing factors to ICI response. Optimized use of currently FDA-approved biomarkers and development of a new generation of predictive biomarkers are urgently needed. In this review, we will first discuss three widely used FDA-approved predictive biomarkers and their optimal use. Secondly, we will review four novel gene signature biomarkers: T-cell inflamed gene expression profile (GEP), T-cell dysfunction and exclusion gene signature (TIDE), melanocytic plasticity signature (MPS) and B-cell focused gene signature. The GEP and TIDE have shown better predictive performance than PD-L1, and PD-L1 or TMB, respectively. The MPS is superior to PD-L1, TMB, and TIDE. The B-cell focused gene signature represents a previously unexplored predictive biomarker to ICI response. Thirdly, we will highlight two combined predictive biomarkers: TMB+GEP and MPS+TIDE. These integrated biomarkers showed improved predictive outcomes compared to a single predictor. Finally, we will present a potential nucleic acid biomarker signature, allowing DNA and RNA biomarkers to be analyzed in one assay. This comprehensive signature could represent a future direction of developing robust predictive biomarkers, particularly for the cold tumors, for ICI response.

Targeted sequencing with a customized panel to assess histological typing in endometrial carcinoma

The two most frequent types of endometrial cancer (EC) are endometrioid (EEC) and serous carcinomas (SC). Differential diagnosis between them is not always easy. A subset of endometrial cancers shows misleading microscopical features, which cause problems in differential diagnosis, and may be a good scenario for next-generation sequencing. Previous studies have assessed the usefulness of targeted sequencing with panels of generic cancer-associated genes in EC histological typing. Based on the analysis of TCGA (The Cancer Genome Atlas), EEC and SC have different mutational profiles. In this proof of principle study, we have performed targeted sequencing analysis with a customized panel, based on the TCGA mutational profile of EEC and SC, in a series of 24 tumors (16 EEC and 8 SC). Our panel comprised coding and non-coding sequences of the following genes: ABCC9, ARID1A, ARID5B, ATR, BCOR, CCND1, CDH19, CHD4, COL11A1, CSDE1, CSMD3, CTCF, CTNNB1, EP300, ERBB2, FBXW7, FGFR2, FOXA2, KLLN, KMT2B, KRAS, MAP3K4, MKI67, NRAS, PGAP3, PIK3CA, PIK3R1, PPP2R1A, PRPF18, PTEN, RPL22, SCARNA11, SIN3A, SMARCA4, SPOP, TAF1, TP53, TSPYL2, USP36, and WRAP53. Targeted sequencing validation by Sanger sequencing and immunohistochemistry was performed in a group of genes. POLE mutation status was assessed by Sanger sequencing. The most mutated genes were PTEN (93.7%), ARID1A (68.7%), PIK3CA (50%), and KMT2B (43.7%) for EEC, and TP53 (87.5%), PIK3CA (50%), and PPP2R1A (25%) for SC. Our panel allowed correct classification of all tumors in the two categories (EEC, SC). Coexistence of mutations in PTEN, ARID1A, and KMT2B was diagnostic of EEC. On the other hand, absence of PTEN, ARID1A, and KMT2B mutations in the presence of TP53 mutation was diagnostic of SC. This proof of concept study demonstrates the suitability of targeted sequencing with a customized endometrial cancer gene panel as an additional tool for confirming histological typing.

DNA mismatch repair in cancer

Microsatellite instability (MSI) refers to the hypermutator phenotype secondary to frequent polymorphism in short repetitive DNA sequences and single nucleotide substitution, as consequence of DNA mismatch repair (MMR) deficiency. MSI secondary to germline mutation in DNA MMR proteins is the molecular fingerprint of Lynch syndrome (LS), while epigenetic inactivation of these genes is more commonly found in sporadic MSI tumors. MSI occurs at different frequencies across malignancies, although original methods to assess MSI or MMR deficiency have been developed mostly in LS related cancers. Here we will discuss the current methods to detect MSI/MMR deficiency with a focus of new tools which are emerging as highly sensitive detector for MSI across multiple tumor types. Due to high frequencies of non-synonymous mutations, the presence of frameshift-mutated neoantigens, which can trigger a more robust and long-lasting immune response and strong TIL infiltration with tumor eradication, MSI has emerged as an important predictor of sensitivity for immunotherapy-based strategies, as showed by the recent FDA's first histology agnostic-accelerated approval to immune checkpoint inhibitors for refractory, adult and pediatric, MMR deficient (dMMR) or MSI high (MSI-H) tumors. Moreover, it is known that MSI status may predict cancer response/resistance to certain chemotherapies. Here we will describe the complex interplay between the genetic and clinical-pathological features of MSI/dMMR tumors and the cancer immunotherapy, with a focus on the predictive and prognostic role of MMR status for immune checkpoint inhibitors (ICIs) and providing some suggestions on how to conceive better predictive markers for immunotherapy in the next future.