ArticleTheranostics2019
Decoding cancer heterogeneity: studying patient-specific signaling signatures towards personalized cancer therapy.
Article in Theranostics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 42 citations in OpenAlex.
- ProteinNetworkSight: a user-friendly platform for transforming co-expression patterns into actionable therapeutic insights through interactive network visualization.Nucleic acids research · 2026Article
- The role of KDM5B in creating synthetic vulnerabilities in combination with radiotherapy in melanoma cells.Cell communication and signaling : CCS · 2026Article
- Peptide Arrays as Tools for Unraveling Tumor Microenvironments and Drug Discovery in Oncology.Cells · 2026Review
- The responses of HNSCC patients to immunotherapy are shown by two novel co-expression patterns.NPJ precision oncology · 2025Article
- Regulation of the brain tumor microenvironment by focused ultrasound.Molecular therapy. Oncology · 2025Review
- Combination of bulk RNA and single-cell sequencing unveils PANoptosis-related immunological ecology hallmarks and classification for clinical decision-making in hepatocellular carcinoma.Scientific reports · 2024Article
- Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning.Science advances · 2024Article
- Transcriptome free energy can serve as a dynamic patient-specific biomarker in acute myeloid leukemia.NPJ systems biology and applications · 2024Article
- Patient-specific signaling signatures predict optimal therapeutic combinations for triple negative breast cancer.Molecular cancer · 2024Article
- Advances in Glioblastoma Therapy: An Update on Current Approaches.Brain sciences · 2023Review
- Impact of elastic substrate on the dynamic heterogeneity of WC256 Walker carcinosarcoma cells.Scientific reports · 2023Article
- Understanding Glioblastoma Signaling, Heterogeneity, Invasiveness, and Drug Delivery Barriers.International journal of molecular sciences · 2023Review
- Overexpressed kinetochore genes are used by cancer cells as genome destabilizers and transformation catalysts.Translational oncology · 2023Article
- Edge and modular significance assessment in individual-specific networks.Scientific reports · 2023Article
- The immunogenic radiation and new players in immunotherapy and targeted therapy for head and neck cancer.Frontiers in oral health · 2023Review
- Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance.Genome medicine · 2022Article
- Overcoming resistance to EGFR monotherapy in HNSCC by identification and inhibition of individualized cancer processes.Theranostics · 2022Article
- Article
- Overcoming resistance to BRAFNPJ precision oncology · 2021Article
- Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data.Entropy (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The past years have witnessed a rapid increase in the amount of large-scale tumor datasets. The challenge has now become to find a way to obtain useful information from these masses of data that will allow to determine which combination of FDA-approved drugs is best suited to treat the specific tumor. Various statistical analyses are being developed to extract significant signals from cancer datasets. However, tumors are still being assigned to pre-defined categories (breast luminal A, triple negative, etc.), conceptually contradicting the vast heterogeneity that is known to exist among tumors, and likely overlooking unique tumors that must be addressed and treated individually. We present herein an approach based on information theory that, rather than searches for what makes a tumor similar to other tumors, addresses tumors individually and unbiasedly, and impartially decodes the critical patient-specific molecular network reorganization in every tumor.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.