Identification of Casiopeina II-gly secondary targets through a systems pharmacology approach

Anda-Jáuregui, Guillermo de; Espinal-Enríquez, Jesús; Hur, Junguk; Alcalá-Corona, Sergio Antonio; Ruíz-Azuara, Lena; Hernández‐Lemus, Enrique

doi:10.1101/327718

Cited by 2 publications

(7 citation statements)

References 59 publications

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“…PD tumors showed a higher functional node activity in Cell cycle 1" (17) and Cell cycle 2" (18). Regarding TNBC, in the BL1 subtype, the activity of nodes "Immune response (B cell)" (11) and "Immune response (Interferon)" (12) was higher in CR than in PR. "Immune response (MHCII)" (9), "Immune response (chemotaxis)" (10) node activities also were higher in CR tumors but without statistics differences.…”

Section: Figure 3 Breast Cancer Network By Breast Cancer Molecular Smentioning

confidence: 96%

“…In Basal-like subtype, tumors attaining a CR showed significant differences in "Immune response (B cell)" (11) and "Immune response (Interferon)" (12) nodes activities while "Immune response (MHCII)" (9) node activity was higher in tumors showing a PR. PD tumors showed a higher functional node activity in Cell cycle 1" (17) and Cell cycle 2" (18).…”

Section: Figure 3 Breast Cancer Network By Breast Cancer Molecular Smentioning

confidence: 98%

“…Basal-like tumors were also classified according to Lehmann's subtypes. "Immune response (MHCII)" (9) node ac-tivity was higher in Luminal A and Normal-like subtypes while Basal-like tumors showed higher functional node activity in "Immune response (chemotaxis)" (10), "Immune response (B cell)" (11) and "Immune response (Interferon)" (12), as well as in "Cell cycle 1 and 2" (17 and 18) nodes (Figure 3).…”

Section: Functional Characterization Of Molecular Subtypesmentioning

confidence: 99%

“…Concerning Luminal A and Luminal B subtypes, "Immune response (MHCII)" (9), "Immune response (chemotaxis)" (10), "Immune response (B cell)" (11) and "Immune response (Interferon)" (12) functional nodes activities were higher in tumors attaining a CR, although differences were not statistically significant. As in the case of Basal-like tumors, a progressive decrease of activity in these nodes was observed from CR to SD.…”

Section: Figure 3 Breast Cancer Network By Breast Cancer Molecular Smentioning

confidence: 99%

“…These statistically inferred networks provide a deeper level of biological understanding in two main directions: giving support to previously identified biological observations, and giving new insights regarding novel biological interactions. Moreover, the transcriptional network approach has proven to be useful to unveil transcriptional regulation in breast cancer (10,11).…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Graphical Models Relate Immune Status with Response to Neoadjuvant Chemo-Therapy in Breast Cancer

Zapater-Moros

Gámez‐Pozo

Prado-Vázquez

et al. 2017

Preprint

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Breast cancer is the most frequent tumor in women and its incidence is increasing. Neoadjuvant chemotherapy has become standard of care as a complement to surgery in locally advanced or poor-prognosis early stage disease. The achievement of a complete response to neoadjuvant chemotherapy correlates with prognosis but it is not possible to predict who will obtain an excellent response. The molecular analysis of the tumor offers a unique opportunity to unveil predictive factors. In this work, gene expression profiling in 279 tumor samples from patients receiving neoadjuvant chemotherapy was performed and probabilistic graphical models were used. This approach enables addressing biological and clinical questions from a Systems Biology perspective, allowing to deal with large gene expression data and their interactions. Tumors presenting complete response to neoadjuvant chemotherapy had a higher activity of immune related functions compared to resistant tumors. Similarly, samples from complete responders presented higher expression of lymphocyte cell lineage markers, immune-activating and immune-suppressive markers, which may correlate with tumor infiltration by lymphocytes (TILs). These results suggest that the patient's immune system plays a key role in tumor response to neoadjuvant treatment. However, future studies with larger cohorts are necessary to validate these hypotheses.

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Section: Figure 3 Breast Cancer Network By Breast Cancer Molecular Smentioning

confidence: 96%

Section: Figure 3 Breast Cancer Network By Breast Cancer Molecular Smentioning

confidence: 98%

Section: Functional Characterization Of Molecular Subtypesmentioning

confidence: 99%

Section: Figure 3 Breast Cancer Network By Breast Cancer Molecular Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Probabilistic Graphical Models Relate Immune Status with Response to Neoadjuvant Chemo-Therapy in Breast Cancer

Zapater-Moros

Gámez‐Pozo

Prado-Vázquez

et al. 2017

Preprint

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Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics

Hernández‐Lemus

Martínez-García

2021

Front. Oncol.

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Cancer is a set of complex pathologies that has been recognized as a major public health problem worldwide for decades. A myriad of therapeutic strategies is indeed available. However, the wide variability in tumor physiology, response to therapy, added to multi-drug resistance poses enormous challenges in clinical oncology. The last years have witnessed a fast-paced development of novel experimental and translational approaches to therapeutics, that supplemented with computational and theoretical advances are opening promising avenues to cope with cancer defiances. At the core of these advances, there is a strong conceptual shift from gene-centric emphasis on driver mutations in specific oncogenes and tumor suppressors—let us call that the silver bullet approach to cancer therapeutics—to a systemic, semi-mechanistic approach based on pathway perturbations and global molecular and physiological regulatory patterns—we will call this the shrapnel approach. The silver bullet approach is still the best one to follow when clonal mutations in driver genes are present in the patient, and when there are targeted therapies to tackle those. Unfortunately, due to the heterogeneous nature of tumors this is not the common case. The wide molecular variability in the mutational level often is reduced to a much smaller set of pathway-based dysfunctions as evidenced by the well-known hallmarks of cancer. In such cases “shrapnel gunshots” may become more effective than “silver bullets”. Here, we will briefly present both approaches and will abound on the discussion on the state of the art of pathway-based therapeutic designs from a translational bioinformatics and computational oncology perspective. Further development of these approaches depends on building collaborative, multidisciplinary teams to resort to the expertise of clinical oncologists, oncological surgeons, and molecular oncologists, but also of cancer cell biologists and pharmacologists, as well as bioinformaticians, computational biologists and data scientists. These teams will be capable of engaging on a cycle of analyzing high-throughput experiments, mining databases, researching on clinical data, validating the findings, and improving clinical outcomes for the benefits of the oncological patients.

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Identification of Casiopeina II-gly secondary targets through a systems pharmacology approach

Cited by 2 publications

References 59 publications

Probabilistic Graphical Models Relate Immune Status with Response to Neoadjuvant Chemo-Therapy in Breast Cancer

Probabilistic Graphical Models Relate Immune Status with Response to Neoadjuvant Chemo-Therapy in Breast Cancer

Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics

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