scholarly journals From Genetic Alterations to Tumor Microenvironment: The Ariadne’s String in Pancreatic Cancer

Cells ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 309 ◽  
Author(s):  
Chiara Bazzichetto ◽  
Fabiana Conciatori ◽  
Claudio Luchini ◽  
Francesca Simionato ◽  
Raffaela Santoro ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Tumor Microenvironment ◽  
Current Knowledge ◽  
Myeloid Cells ◽  
Genetic Alterations ◽  
Molecular Characteristics ◽  
Precision Oncology ◽  
Cancer Type ◽  
Tumor Type ◽  
Driver Genes

The threatening notoriety of pancreatic cancer mainly arises from its negligible early diagnosis, highly aggressive progression, failure of conventional therapeutic options and consequent very poor prognosis. The most important driver genes of pancreatic cancer are the oncogene KRAS and the tumor suppressors TP53, CDKN2A, and SMAD4. Although the presence of few drivers, several signaling pathways are involved in the oncogenesis of this cancer type, some of them with promising targets for precision oncology. Pancreatic cancer is recognized as one of immunosuppressive phenotype cancer: it is characterized by a fibrotic-desmoplastic stroma, in which there is an intensive cross-talk between several cellular (e.g., fibroblasts, myeloid cells, lymphocytes, endothelial, and myeloid cells) and acellular (collagen, fibronectin, and soluble factors) components. In this review; we aim to describe the current knowledge of the genetic/biological landscape of pancreatic cancer and the composition of its tumor microenvironment; in order to better direct in the intrinsic labyrinth of this complex tumor type. Indeed; disentangling the genetic and molecular characteristics of cancer cells and the environment in which they evolve may represent the crucial step towards more effective therapeutic strategies

scholarly journals Pan-cancer detection of driver genes at the single-patient resolution

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Joel Nulsen ◽  
Hrvoje Misetic ◽  
Christopher Yau ◽  
Francesca D. Ciccarelli
Keyword(s):  
False Positive Rate ◽  
Genetic Alterations ◽  
Therapeutic Interventions ◽  
Precision Oncology ◽  
Cancer Type ◽  
Rare Cancer ◽  
Driver Genes ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

Abstract Background Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions. Results We present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways. Conclusions sysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types (https://github.com/ciccalab/sysSVM2).

scholarly journals Pan-cancer detection of driver genes at the single-patient resolution

2020 ◽  
Author(s):  
Joel Nulsen ◽  
Hrvoje Misetic ◽  
Christopher Yau ◽  
Francesca D. Ciccarelli
Keyword(s):  
False Positive Rate ◽  
Genetic Alterations ◽  
Therapeutic Interventions ◽  
Precision Oncology ◽  
Cancer Type ◽  
Rare Cancer ◽  
Driver Genes ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

ABSTRACTBackgroundIdentifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions.ResultsWe present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways.ConclusionssysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types (https://github.com/ciccalab/sysSVM2).

scholarly journals A computational method for prioritizing targeted therapies in precision oncology: performance analysis in the SHIVA01 trial

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Istvan Petak ◽  
Maud Kamal ◽  
Anna Dirner ◽  
Ivan Bieche ◽  
Robert Doczi ◽  
...  
Keyword(s):  
Clinical Benefit ◽  
Progressive Disease ◽  
Genetic Alterations ◽  
Outcome Data ◽  
Computational Method ◽  
Molecular Profile ◽  
Precision Oncology ◽  
Driver Genes ◽  
Oncology Clinical Trial ◽  
Molecularly Targeted Agents

AbstractPrecision oncology is currently based on pairing molecularly targeted agents (MTA) to predefined single driver genes or biomarkers. Each tumor harbors a combination of a large number of potential genetic alterations of multiple driver genes in a complex system that limits the potential of this approach. We have developed an artificial intelligence (AI)-assisted computational method, the digital drug-assignment (DDA) system, to prioritize potential MTAs for each cancer patient based on the complex individual molecular profile of their tumor. We analyzed the clinical benefit of the DDA system on the molecular and clinical outcome data of patients treated in the SHIVA01 precision oncology clinical trial with MTAs matched to individual genetic alterations or biomarkers of their tumor. We found that the DDA score assigned to MTAs was significantly higher in patients experiencing disease control than in patients with progressive disease (1523 versus 580, P = 0.037). The median PFS was also significantly longer in patients receiving MTAs with high (1000+ <) than with low (<0) DDA scores (3.95 versus 1.95 months, P = 0.044). Our results indicate that AI-based systems, like DDA, are promising new tools for oncologists to improve the clinical benefit of precision oncology.

scholarly journals CXCL12 and Its Isoforms: Different Roles in Pancreatic Cancer?

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Alessandra Righetti ◽  
Matteo Giulietti ◽  
Berina Šabanović ◽  
Giulia Occhipinti ◽  
Giovanni Principato ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Tumor Microenvironment ◽  
Stromal Cells ◽  
Tumor Invasion ◽  
Epithelial To Mesenchymal Transition ◽  
Current Knowledge ◽  
Tumor Development ◽  
Physiological Role ◽  
Mesenchymal Transition ◽  
Splicing Isoforms

CXCL12 is a chemokine that acts through CXCR4 and ACKR3 receptors and plays a physiological role in embryogenesis and haematopoiesis. It has an important role also in tumor development, since it is released by stromal cells of tumor microenvironment and alters the behavior of cancer cells. Many studies investigated the roles of CXCL12 in order to understand if it has an anti- or protumor role. In particular, it seems to promote tumor invasion, proliferation, angiogenesis, epithelial to mesenchymal transition (EMT), and metastasis in pancreatic cancer. Nevertheless, some evidence shows opposite functions; therefore research on CXCL12 is still ongoing. These discrepancies could be due to the presence of at least six CXCL12 splicing isoforms, each with different roles. Interestingly, three out of six variants have the highest levels of expression in the pancreas. Here, we report the current knowledge about the functions of this chemokine and then focus on pancreatic cancer. Moreover, we discuss the methods applied in recent studies in order to understand if they took into account the existence of the CXCL12 isoforms.

scholarly journals Genomic and Proteomic Alterations in Desmoplastic Small Round Blue-Cell Tumors

2018 ◽  
pp. 1-9 ◽  
Author(s):  
Ajaz Bulbul ◽  
John Paul Shen ◽  
Joanne Xiu ◽  
Pablo Tamayo ◽  
Hatim Husain
Keyword(s):  
In Situ Hybridization ◽  
Ewing Sarcoma ◽  
Molecular Characteristics ◽  
Cancer Type ◽  
Tumor Type ◽  
Missense Mutations ◽  
Data Set ◽  
Therapeutic Implications ◽  
Blue Cell

Purpose Desmoplastic small round blue-cell tumors (DSRCTs) are sarcomas that contain the t(11;22) (p13;q12) translocation EWS-WT1 fusion protein. Because this is a rare tumor type, prospective clinical trials in DSRCT are challenging. Patients are treated in a manner similar to those with Ewing sarcoma; however, differences in prognosis and clinical presentation suggest fundamental differences in biology and potentially different therapeutic implications. This study aimed to characterize the molecular characteristics of DSRCT tumors to explore unique therapeutic options for this extremely rare and aggressive cancer type. Methods Thirty-five DSRCT tumors were assessed using next-generation sequencing, protein expression (immunohistochemistry), and gene amplification (chromogenic in situ hybridization or fluorescence in situ hybridization). Three patients had tumor mutational load, which was calculated as somatic nonsynonymous missense mutations sequenced with a 592-gene panel. Gene expression data were obtained for an additional seven DSRCT tumors. Molecular alterations were compared with 88 Ewing sarcomas. Results The most common alterations that distinguished DSRCTs from Ewing sarcoma included higher androgen receptor (AR), TUBB3, epidermal growth factor receptor, and TOPO2A expression. Independent analysis by RNA sequencing confirmed higher AR expression from an independent data set of EWS-WT1 fusion–positive DSRCTs compared with Ewing sarcoma and a pan-cancer analysis. DSRCTs had somatic mutations that were identified in TP53 and FOXO3, averaged five mutations per megabase, and no programmed death-ligand 1 expression was detected in any DSRCT samples. Conclusion The current analysis provides the first comparative analysis, to our knowledge, of molecular aberrations that distinguish DSRCT from Ewing sarcoma. High AR expression seems to be a defining event in these malignancies, and additional investigation of the responsiveness of AR inhibitors in this disease is encouraged.

scholarly journals The Intricate Metabolism of Pancreatic Cancers

2021 ◽  
pp. 77-88
Author(s):  
Felipe Camelo ◽  
Anne Le
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Tumor Microenvironment ◽  
Poor Prognosis ◽  
Cancer Metabolism ◽  
Genetic Alterations ◽  
Cancer Growth ◽  
Cancer Death ◽  
Pancreatic Cancers ◽  
Close Relationship

AbstractCurrently, approximately 95% of pancreatic cancers are pancreatic ductal adenocarcinomas (PDAC), which are the most aggressive form and the fourth leading cause of cancer death with extremely poor prognosis [1]. Poor prognosis is primarily attributed to the late diagnosis of the disease when patients are no longer candidates for surgical resection [2]. Cancer cells are dependent on the oncogenes that allow them to proliferate limitlessly. Thus, targeting the expression of known oncogenes in pancreatic cancer has been shown to lead to more effective treatment [3]. This chapter discusses the complexity of metabolic features in pancreatic cancers. In order to comprehend the heterogeneous nature of cancer metabolism fully, we need to take into account the close relationship between cancer metabolism and genetics. Gene expression varies tremendously, not only among different types of cancers but also within the same type of cancer among different patients. Cancer metabolism heterogeneity is often prompted and perpetuated not only by mutations in oncogenes and tumor-suppressor genes but also by the innate diversity of the tumor microenvironment. Much effort has been focused on elucidating the genetic alterations that correlate with disease progression and treatment response [4, 5]. However, the precise mechanisms by which tumor metabolism contributes to cancer growth, survival, mobility, and aggressiveness represent a functional readout of tumor progression (Fig. 1).

scholarly journals Genomic characterization of meningiomas.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 2020-2020
Author(s):  
Priscilla Kaliopi Brastianos ◽  
Peleg Horowitz ◽  
Sandro Santagata ◽  
Robert T. Jones ◽  
Aaron McKenna ◽  
...  
Keyword(s):  
Statistical Significance ◽  
Point Mutations ◽  
Suppressor Gene ◽  
Genetic Alterations ◽  
Chromosome 1 ◽  
Tumor Type ◽  
Driver Genes ◽  
Genetic Changes ◽  
Fresh Frozen ◽  
Copy Number Changes

2020 Background: Understanding the genetic alterations in cancer has lead to groundbreaking discoveries in targeted therapies. Meningiomas are among the most common primary brain tumors, with approximately 18,000 new cases diagnosed annually. Though certain genes have been associated with the development of meningiomas, most notably the tumor suppressor gene neurofibromatosis 2 (NF2), the genetic changes that drive meningiomas remain poorly understood. Our objective was to comprehensively characterize the somatic genetic alterations of meningiomas to gain insight into the molecular pathways that drive this disease. Methods: Fresh frozen specimens and paired blood were collected from 16 consented patients. DNA was extracted from regions of high tumor purity determined by evaluation of H&E slides. Whole-genome sequencing from 10 tumor-normal pairs and whole-exome sequencing from 6 tumor-normal pairs was carried out. We performed an unbiased screen for point mutations, insertions-deletions, rearrangements and copy-number changes across the exomes and genomes. Recurrent (potential driver) events were then analyzed with additional algorithms for statistical significance. Results: Alterations in the NF2 gene were present in 9 of 16 patients. Multiple novel rearrangements and recurrent non-NF2 mutations were also identified in the cohort. Massive genomic rearrangement termed chromothripsis was observed in chromosome 1 in one sample, which has never previously been described in meningiomas, and represents a potentially new mechanism of malignant transformation in this tumor type. Conclusions: While NF2 mutations appear to drive a majority of these tumors, our analysis has uncovered additional potential driver genes in meningiomas, particularly in those tumors negative for NF2 alterations. To our knowledge, this is the first study to comprehensively characterize the totality of somatic genetic alterations in meningiomas, and brings us closer to the development of new therapeutic targets for this disease.

NGS testing patterns in advanced non-small cell lung cancer (aNSCLC) and metastatic breast cancer (mBC): OneOncology (OO) sites compared to Flatiron Health Nationwide (NAT).

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 288-288
Author(s):  
Ari M. Vanderwalde ◽  
Esprit Ma ◽  
Elaine Yu ◽  
Tania Szado ◽  
Richard Price ◽  
...  
Keyword(s):  
Advanced Disease ◽  
Metastatic Breast ◽  
Precision Oncology ◽  
Cancer Type ◽  
Tumor Type ◽  
Future Studies ◽  
Testing Rate ◽  
Testing Patterns ◽  
Test Result ◽  
Over Time

288 Background: Personalized treatment (tx) decisions can be improved through diagnostic tests with NGS by detecting different actionable mutations. OO, a research-focused network of community practices, has a network-wide precision oncology initiative and has advocated for NGS testing in advanced cancers since 2019. This study evaluated NGS testing patterns in aNSCLC and mBC populations descriptively in OO community sites and Flatiron Health NAT. Methods: This study used the Flatiron Health EHR derived de-identified database from [1] four OO sites, and [2] NAT. Patients (pts) diagnosed (Dx) with aNSCLC (stage ≥ IIIb) or mBC from 1/1/2015 to 5/31/2020, aged ≥ 18 years, had ≥ 1 visit ≤ 90 days (d) of advanced or metastatic Dx, and had ≥ 1 biomarker test were included. NAT NGS was confirmed via abstraction from patient records. Descriptive analyses were conducted to assess NGS testing patterns and pts characteristics by tumor type. Results: Of biomarker tested pts at OO vs. NAT (community:academic: 90%:10% aNSCLC; 93%:7% mBC), 2,029 of 3,152 (64%) OO vs. 13,681 of 29,572 (46%) NAT in aNSCLC and 514 of 1,282 (40%) OO vs. 2,458 of 12,175 (20%) NAT in mBC received NGS ± other tests. Testing rate of all 5 aNSCLC biomarkers (ALK, BRAF, EGFR, ROS-1, and KRAS) was higher with NGS vs. other tests for OO (87% vs. 6%) and NAT (87% vs. 11%). In mBC, a higher testing rate of BRCA with NGS vs. other tests (OO: 68% vs. 26%, NAT: 71% vs. 28%) and similar testing rate on HER2 (OO: 98% vs. 98%, NAT: 100% vs. 99%). Median time from Dx to NGS test result at OO vs. NAT was 33 d vs. 32 d in aNSCLC and 70 d vs. 188 d in mBC. NGS testing rates increased over time, with higher rates at OO vs. NAT [Table]. Pts with NGS vs. other tests were slightly younger in aNSCLC (OO: 68 y vs. 70 y, p = 0.001; NAT: 69 y vs. 70 yr, p < 0.001) and mBC (OO: 61 y vs. 67 y, p < 0.001; NAT: 61 y vs. 66 y, p < 0.001), and slightly more commercially insured in aNSCLC (OO: 48% vs. 45%, p = 0.3; NAT: 37% vs. 33%, p < 0.001) and mBC (OO: 54% vs. 48% OO, p = 0.053; NAT: 42 % vs. 36 %, p < 0.001). Conclusions: The adoption of NGS differed by cancer type and NGS testing rates have increased over time in aNSCLC and mBC. While some pts may have received testing outside of the Flatiron network, OO had a higher NGS uptake than NAT, and had a shorter time to testing in mBC that was possibly related to a network wide strategy recommending testing at Dx of advanced disease. Future studies on tx pattern after NGS testing are warranted to improve the actionability of NGS to foster personalized tx. [Table: see text]

scholarly journals Pancreatic Cancer Signaling Pathways, Genetic Alterations, and Tumor Microenvironment: The Barriers Affecting the Method of Treatment

Biomedicines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 373
Author(s):  
Darya Javadrashid ◽  
Amir Baghbanzadeh ◽  
Afshin Derakhshani ◽  
Patrizia Leone ◽  
Nicola Silvestris ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Tumor Microenvironment ◽  
Signaling Pathways ◽  
Immune Checkpoint ◽  
Therapeutic Strategy ◽  
Genetic Alterations ◽  
Ductal Adenocarcinoma ◽  
Therapeutic Approaches ◽  
Ras Mutation ◽  
Direct Targeting

Genetic alterations, especially the K-Ras mutation, carry the heaviest burden in the progression of pancreatic precursor lesions into pancreatic ductal adenocarcinoma (PDAC). The tumor microenvironment is one of the challenges that hinder the therapeutic approaches from functioning sufficiently and leads to the immune evasion of pancreatic malignant cells. Mastering the mechanisms of these two hallmarks of PDAC can help us in dealing with the obstacles in the way of treatment. In this review, we have analyzed the signaling pathways involved in PDAC development and the immune system’s role in pancreatic cancer and immune checkpoint inhibition as next-generation therapeutic strategy. The direct targeting of the involved signaling molecules and the immune checkpoint molecules, along with a combination with conventional therapies, have reached the most promising results in pancreatic cancer treatment.

scholarly journals Personalized First-Line Treatment of Metastatic Pancreatic Neuroendocrine Carcinoma Facilitated by Liquid Biopsy and Computational Decision Support

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1850
Author(s):  
Judita Szkukalek ◽  
Róbert Dóczi ◽  
Anna Dirner ◽  
Ákos Boldizsár ◽  
Ágnes Varga ◽  
...  
Keyword(s):  
Decision Support ◽  
Liquid Biopsy ◽  
Genetic Alterations ◽  
Tumor Burden ◽  
Precision Oncology ◽  
Tumor Type ◽  
First Line ◽  
First Line Therapy ◽  
Line Therapy ◽  
Liver Functions

Background: We present the case of a 50-year-old female whose metastatic pancreatic neuroendocrine tumor (pNET) diagnosis was delayed by the COVID-19 pandemic. The patient was in critical condition at the time of diagnosis due to the extensive tumor burden and failing liver functions. The clinical dilemma was to choose between two registered first-line molecularly-targeted agents (MTAs), sunitinib or everolimus, or to use chemotherapy to quickly reduce tumor burden. Methods: Cell-free DNA (cfDNA) from liquid biopsy was analyzed by next generation sequencing (NGS) using a comprehensive 591-gene panel. Next, a computational method, digital drug-assignment (DDA) was deployed for rapid clinical decision support. Results: NGS analysis identified 38 genetic alterations. DDA identified 6 potential drivers, 24 targets, and 79 MTAs. Everolimus was chosen for first-line therapy based on supporting molecular evidence and the highest DDA ranking among therapies registered in this tumor type. The patient's general condition and liver functions rapidly improved, and CT control revealed partial response in the lymph nodes and stable disease elsewhere. Conclusion: Deployment of precision oncology using liquid biopsy, comprehensive molecular profiling, and DDA make personalized first-line therapy of advanced pNET feasible in clinical settings.

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