The identification of genes maintaining cancer growth is critical to our understanding of tumorigenesis. We report the first in vivo genetic screen of patient-derived tumors, using metastatic melanomas and targeting 236 chromatin genes by expression of specific shRNA libraries. Our screens revealed unprecedented numerosity of genes indispensable for tumor growth (∼50% of tested genes) and unexpected functional heterogeneity among patients (<15% in common). Notably, these genes were not activated by somatic mutations in the same patients and are therefore distinguished from mutated cancer driver genes. We analyzed underlying molecular mechanisms of one of the identified genes, the Histone–lysine N-methyltransferase KMT2D, and showed that it promotes tumorigenesis by dysregulating a subset of transcriptional enhancers and target genes involved in cell migration. The assembly of enhancer genomic patterns by activated KMT2D was highly patient-specific, regardless of the identity of transcriptional targets, suggesting that KMT2D might be activated by distinct upstream signaling pathways. Significance: Drug targeting of biologically relevant cancer-associated mutations is considered a critical strategy to control cancer growth. Our functional in vivo genetic screens of patient-derived tumors showed unprecedented numerosity and interpatient heterogeneity of genes that are essential for tumor growth, but not mutated, suggesting that multiple, patient-specific signaling pathways are activated in tumors. Cancer Discov; 6(6); 650–63. ©2016 AACR. This article is highlighted in the In This Issue feature, p. 561

The E3 ubiquitin ligase (E3) WWP1 is an oncogenic factor implicated in the maintenance of different types of epithelial cancers. The role of WWP1 in haematological neoplasms remains unknown. Acute myeloid leukaemia (AML) is characterized by the expansion of malignant myeloid cells blocked at different stages of differentiation. Here, we report that the expression of WWP1 is significantly augmented in a large cohort of primary AML patients and in AML cell lines, compared to hematopoietic cells from healthy donors. We show that WWP1 inactivation severely impairs the growth of primary AML blasts and cell lines in vitro. In vivo, we observed a reduced leukaemogenic potential of WWP1-depleted AML cells upon transplantation into immunocompromised mice. Mechanistically, WWP1 inactivation induces the accumulation of its protein substrate p27^Kip1, which ultimately contributes to G₀/G₁ cell cycle arrest of AML blasts. In addition, WWP1 depletion triggers the autophagy signalling and reduces survival of leukemic cells. Collectively, our findings provide molecular insights into the anti-cancer potential of WWP1 inhibition, suggesting that this E3 is a promising biomarker and druggable target in AML.

Introduction: The study’s aim was to investigate the immunohistochemical (IHC) expression of biological markers as potential prognostic/therapeutic factors in vulvar squamous cell carcinoma (VSCC). Methodology: A series of 101 patients surgically treated at our center from 2016 to 2020 were retrospectively enrolled: 53 node-negative (Group A) and 48 node-positive (Group B). A total of 146 samples, 101 from primary tumor (T) and 45 from nodal metastases (N), were investigated. The IHC panel included: p16, p53, MLH1, MSH2, MSH6, PMS2, PD-L1, CD3, HER2/neu, ER, PR, EGFR, VEGF, and CD31. The reactions were evaluated on qualitative and semi-quantitative scales. Generalized Linear Model (GLM) and cluster analysis were performed in R statistical environment. A distance plot compared the IHC panel of T with the correspondent N. Results: In Group A: p16-positive expression (surrogate of HPV-dependent pathway) was significantly higher (20.8% vs. 6.2%, p = 0.04). In Group B: PD-L1 positivity and high EGFR expression were found, respectively, in 77.1% and 97.9% patients (T and/or N). Overall, p16-negative tumors showed a higher PD-L1 expression (60.9% vs. 50.0%). In both groups: tumoral immune infiltration (CD3 expression) was mainly moderate/intense (80% vs. 95%); VEGF showed strong/moderate-diffuse expression in 13.9% of T samples; CD31, related to tumoral microvessel density (MVD), showed no difference between groups; a mutated p53 and over-expressed PD-L1 showed significant association with nodal metastasis, with Odds Ratios (OR) of 4.26 (CI 95% = 1.14–15.87, p = 0.03) and 2.68 (CI 95% = 1.0–7.19, p < 0.05), respectively; since all mismatch repair proteins (MMR) showed a retained expression and ER, PR, and HER2/neu were negative, they were excluded from further analysis. The cluster analysis identified three and four sub-groups of molecular profiles, respectively, in Group A and B, with no difference in prognosis. The molecular signature of each N and corresponding T diverged significantly in 18/41 (43.9%) cases. Conclusions: Our results support a potential role of immune checkpoint inhibitors and anti-VEGF and anti-EGFR drugs especially in patients with worse prognosis (metastatic, HPV-independent). A panel including EGFR, VEGF, PDL1, p16, and p53 might be performed routinely in primary tumor and repeated in case of lymph node metastases to identify changes in marker expression.

Background: High-grade serous ovarian cancer (HGSOC) has poor survival rates due to a combination of diagnosis at advanced stage and disease recurrence as a result of chemotherapy resistance. In BRCA1 (Breast Cancer gene 1) - or BRCA2-wild type (BRCAwt) HGSOC patients, resistance and progressive disease occur earlier and more often than in mutated BRCA. Identification of biomarkers helpful in predicting response to first-line chemotherapy is a challenge to improve BRCAwt HGSOC management.Methods: To identify a gene signature that can predict response to first-line chemotherapy, pre-treatment tumor biopsies from a restricted cohort of BRCAwt HGSOC patients were profiled by RNA sequencing (RNA-Seq) technology. Patients were sub-grouped according to platinum-free interval (PFI), into sensitive (PFI > 12 months) and resistant (PFI < 6 months). The gene panel identified by RNA-seq analysis was then tested by high-throughput quantitative real-time PCR (HT RT-qPCR) in a validation cohort, and statistical/bioinformatic methods were used to identify eligible markers and to explore the relevant pathway/gene network enrichments of the identified gene set. Finally, a panel of primary HGSOC cell lines was exploited to uncover cell-autonomous mechanisms of resistance.Results: RNA-seq identified a 42-gene panel discriminating sensitive and resistant BRCAwt HGSOC patients and pathway analysis pointed to the immune system as a possible driver of chemotherapy response. From the extended cohort analysis of the 42 DEGs (differentially expressed genes), a statistical approach combined with the random forest classifier model generated a ten-gene signature predictive of response to first-line chemotherapy. The ten-gene signature included: CKB (Creatine kinase B), CTNNBL1 (Catenin, beta like 1), GNG11 (G protein subunit gamma 11), IGFBP7 (Insulin-like growth factor-binding protein 7), PLCG2 (Phospholipase C, gamma 2), RNF24 (Ring finger protein 24), SLC15A3 (Solute carrier family 15 member 3), TSPAN31 (Tetraspanin 31), TTI1 (TELO2 interacting protein 1) and UQCC1 (Ubiquinol-cytochrome c reductase complex assembly factor). Cytotoxicity assays, combined with gene-expression analysis in primary HGSOC cell lines, allowed to define CTNNBL1, RNF24, and TTI1 as cell-autonomous contributors to tumor resistance.Conclusions: Using machine-learning techniques we have identified a gene signature that could predict response to first-line chemotherapy in BRCAwt HGSOC patients, providing a useful tool towards personalized treatment modalities.

The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient’s tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).