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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

340
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
340
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  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Patient-specific Computational Models Predict Prognosis In B Cell Lymphoma By Quantifying Pro-proliferative And Anti-apoptotic Signatures From Genetic Sequencing Data.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Patient-specific Computational Models Predict Prognosis In B Cell Lymphoma By Quantifying Pro-proliferative And Anti-apoptotic Signatures From Genetic Sequencing Data.

Related Experiment Video

Predictive Immune Modeling of Solid Tumors
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Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

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Patient-specific computational models predict prognosis in B cell lymphoma by quantifying pro-proliferative and anti-apoptotic signatures from genetic sequencing data.

Richard Norris1, John Jones1, Erika Mancini2

  • 1Department of Clinical and Experimental Medicine, Brighton and Sussex Medical School, Brighton, UK.

Blood Cancer Journal
|July 4, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

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VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma
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VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

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From a 2DE-Gel Spot to Protein Function: Lesson Learned From HS1 in Chronic Lymphocytic Leukemia
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From a 2DE-Gel Spot to Protein Function: Lesson Learned From HS1 in Chronic Lymphocytic Leukemia

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Related Experiment Videos

Predictive Immune Modeling of Solid Tumors
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Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

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VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma
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VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

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From a 2DE-Gel Spot to Protein Function: Lesson Learned From HS1 in Chronic Lymphocytic Leukemia
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From a 2DE-Gel Spot to Protein Function: Lesson Learned From HS1 in Chronic Lymphocytic Leukemia

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Mathematical models predict that combined anti-apoptotic (AA) and pro-proliferative (PP) signaling mutations worsen blood cancer prognosis. Identifying patients with simultaneous AA and PP signaling (AAPP) reveals novel, distinct prognostic subgroups for personalized treatment strategies.

Area of Science:

  • Computational biology and bioinformatics
  • Hematologic oncology and cancer genomics

Background:

  • Genetic heterogeneity and co-occurring mutations in blood cancers significantly influence clinical outcomes.
  • Predicting the combined effects of mutations on complex signaling networks remains a challenge.

Purpose of the Study:

  • To utilize mathematical modeling to predict the impact of co-occurring mutations on cellular signaling and cell fates in diffuse large B cell lymphoma and multiple myeloma.
  • To identify patient subgroups with distinct prognostic profiles based on combined signaling pathway activation.

Main Methods:

  • Development and application of mathematical models to simulate cellular signaling pathways.
  • Integration of patient-specific mutational profiles into personalized lymphoma models.
  • Analysis of discovery and validation cohorts to correlate signaling states with clinical prognosis.

Main Results:

  • Simulations predicted adverse prognosis when mutations induced both anti-apoptotic (AA) and pro-proliferative (PP) signaling.
  • Identification of a subgroup (8-25% of patients) with simultaneous upregulation of AA and PP signaling (AAPP) across classifications.
  • Patients with neither, one (AA or PP), or both (AAPP) signaling states exhibited good, intermediate, and poor prognoses, respectively.
  • AAPP signaling combined with genetic/clinical predictors created distinct prognostic categories, with AAPP patients in poor prognosis clusters showing significantly shorter survival.

Conclusions:

  • Personalized computational models can identify novel, risk-stratified patient subgroups in blood cancers.
  • The simultaneous upregulation of anti-apoptotic and pro-proliferative signaling (AAPP) defines a poor prognostic group.
  • This approach offers a valuable tool for developing risk-adapted clinical trials and personalized treatment strategies.