Pancreatic ductal adenocarcinoma (PDAC) exhibits the poorest prognosis of all solid tumors, with a 5-year survival of less than 10%. To improve the prognosis, it is necessary to advance in the development of tools that help us in the early diagnosis, treatment selection, disease monitoring, evaluation of the response and prognosis. Liquid biopsy (LB), in its different modalities, represents a particularly interesting tool for these purposes, since it is a minimally invasive and risk-free procedure that can detect both the presence of genetic material from the tumor and circulating tumor cells (CTCs) in the blood and therefore distantly reflect the global status of the disease. In this work we review the current status of the main LB modalities (ctDNA, exosomes, CTCs and cfRNAs) for detecting and monitoring PDAC.

Low-grade and early-stage endometrioid endometrial carcinomas (EECs) have an overall good prognosis but biomarkers identifying patients at risk of relapse are still lacking. Recently, CTNNB1 exon 3 mutation has been identified as a potential risk factor of recurrence in these patients. We evaluate the prognostic value of CTNNB1 mutation in a single-centre cohort of 218 low-grade, early-stage EECs, and the correlation with beta-catenin and LEF1 immunohistochemistry as candidate surrogate markers. CTNNB1 exon 3 hotspot mutations were evaluated by Sanger sequencing. Immunohistochemical staining of mismatch repair proteins (MLH1, PMS2, MSH2, and MSH6), p53, beta-catenin, and LEF1 was performed in representative tissue microarrays. Tumours were also reviewed for mucinous and squamous differentiation, and MELF pattern. Nineteen (8.7%) tumours harboured a mutation in CTNNB1 exon 3. Nuclear beta-catenin and LEF1 were significantly associated with CTNNB1 mutation, showing nuclear beta-catenin a better specificity and positive predictive value for CTNNB1 mutation. Tumours with CTNNB1 exon 3 mutation were associated with reduced disease-free survival (p = 0.010), but no impact on overall survival was found (p = 0.807). The risk of relapse in tumours with CTNNB1 exon 3 mutation was independent of FIGO stage, tumour grade, mismatch repair protein expression, or the presence of lymphovascular space invasion. CTNNB1 exon 3 mutation has a negative impact on disease-free survival in low-grade, early-stage EECs. Nuclear beta-catenin shows a higher positive predictive value than LEF1 for CTNNB1 exon 3 mutation in these tumours. Graphical abstract

Low-grade, early-stage endometrial carcinoma (EC) is the most frequent malignant tumor of the uterine corpus. However, the molecular alterations that underlie these tumors are far from being fully understood. The purpose of this study is to describe dysregulated molecular pathways from EC patients. Sixteen samples of tumor tissue and paired healthy controls were collected and both were subjected to mass spectrometry (MS)/MS proteomic analysis. Gene ontology and pathway analysis was performed to discover dysregulated pathways and/or proteins using different databases and bioinformatic tools. Dysregulated pathways were cross-validated in an independent external cohort. Cell signaling, immune response, and cell death-associated pathways were robustly identified. The SLIT/ROBO signaling pathway demonstrated dysregulation at the proteomic and transcriptomic level. Necroptosis and ferroptosis were cell death-associated processes aberrantly regulated, in addition to apoptosis. Immune response-associated pathways showed a dominance of innate immune responses. Tumor immune infiltrates measured by immunofluorescence demonstrated diverse lymphoid and myeloid populations. Our results suggest a role of SLIT/ROBO, necroptosis, and ferroptosis, as well as a prominent role of innate immune response in low-grade, early-stage EC. These results could guide future research in this group of tumors.

There are three prognostic stratification tools used for endometrial cancer: ESMO-ESGO-ESTRO 2016, ProMisE, and ESGO-ESTRO-ESP 2020. However, these methods are not sufficiently accurate to address prognosis. The aim of this study was to investigate whether the integration of molecular classification and other biomarkers could be used to improve the prognosis stratification in early-stage endometrial cancer. Relapse-free and overall survival of each classifier were analyzed, and the c-index was employed to assess accuracy. Other biomarkers were explored to improve the precision of risk classifiers. We analyzed 293 patients. A comparison between the three classifiers showed an improved accuracy in ESGO-ESTRO-ESP 2020 when RFS was evaluated (c-index = 0.78), although we did not find broad differences between intermediate prognostic groups. Prognosis of these patients was better stratified with the incorporation of CTNNB1 status to the 2020 classifier (c-index 0.81), with statistically significant and clinically relevant differences in 5-year RFS: 93.9% for low risk, 79.1% for intermediate merged group/CTNNB1 wild type, and 42.7% for high risk (including patients with CTNNB1 mutation). The incorporation of molecular classification in risk stratification resulted in better discriminatory capability, which could be improved even further with the addition of CTNNB1 mutational evaluation.

Anal squamous cell carcinoma (ASCC) is a rare neoplasm. Chemoradiotherapy is the standard of care, with no therapeutic advances achieved over the past three decades. Thus, a deeper molecular characterization of this disease is still necessary. We analyzed 46 paraffin-embedded tumor samples from patients diagnosed with primary ASCC by exome sequencing. A bioinformatics approach focused in the identification of high-impact genetic variants, which may act as drivers of oncogenesis, was performed. The relation between genetics variants and prognosis was also studied. The list of high-impact genetic variants was unique for each patient. However, the pathways in which these genes are involved are well-known hallmarks of cancer, such as angiogenesis or immune pathways. Additionally, we determined that genetic variants in BRCA2, ZNF750, FAM208B, ZNF599, and ZC3H13 genes are related with poor disease-free survival in ASCC. This may help to stratify the patient's prognosis and open new avenues for potential therapeutic intervention. In conclusion, sequencing of ASCC clinical samples appears an encouraging tool for the molecular portrait of this disease.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with an overall 5-year survival rate of just 5%. A better understanding of the carcinogenesis processes and the mechanisms of the progression of PDAC is mandatory. Fifty-two PDAC patients treated with surgery and adjuvant therapy, with available primary tumors, normal tissue, preneoplastic lesions (PanIN), and/or lymph node metastases, were selected for the study. Proteins were extracted from small punches and analyzed by LC-MS/MS using data-independent acquisition. Proteomics data were analyzed using probabilistic graphical models, allowing functional characterization. Comparisons between groups were made using linear mixed models. Three proteomic tumor subtypes were defined. T1 (32% of patients) was related to adhesion, T2 (34%) had metabolic features, and T3 (34%) presented high splicing and nucleoplasm activity. These proteomics subtypes were validated in the PDAC TCGA cohort. Relevant biological processes related to carcinogenesis and tumor progression were studied in each subtype. Carcinogenesis in the T1 subtype seems to be related to an increase of adhesion and complement activation node activity, whereas tumor progression seems to be related to nucleoplasm and translation nodes. Regarding the T2 subtype, it seems that metabolism and, especially, mitochondria act as the motor of cancer development. T3 analyses point out that nucleoplasm, mitochondria and metabolism, and extracellular matrix nodes could be involved in T3 tumor carcinogenesis. The identified processes were different among proteomics subtypes, suggesting that the molecular motor of the disease is different in each subtype. These differences can have implications for the development of future tailored therapeutic approaches for each PDAC proteomics subtype.