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Toward developing a metastatic breast cancer treatment strategy that incorporates history of response to previous treatments
- Source :
- BMC Cancer, Vol 21, Iss 1, Pp 1-12 (2021), BMC cancer, vol 21, iss 1, BMC Cancer
- Publication Year :
- 2021
- Publisher :
- BMC, 2021.
-
Abstract
- Background Information regarding response to past treatments may provide clues concerning the classes of drugs most or least likely to work for a particular metastatic or neoadjuvant early stage breast cancer patient. However, currently there is no systematized knowledge base that would support clinical treatment decision-making that takes response history into account. Methods To model history-dependent response data we leveraged a published in vitro breast cancer viability dataset (84 cell lines, 90 therapeutic compounds) to calculate the odds ratios (log (OR)) of responding to each drug given knowledge of (intrinsic/prior) response to all other agents. This OR matrix assumes (1) response is based on intrinsic rather than acquired characteristics, and (2) intrinsic sensitivity remains unchanged at the time of the next decision point. Fisher’s exact test is used to identify predictive pairs and groups of agents (BH p Results Of the 90 compounds, 57 have sensitivity profiles significantly associated with those of at least one other agent, mostly targeted drugs. Nearly all associations are positive, with (intrinsic/prior) sensitivity to one agent predicting sensitivity to others in the same or a related class (OR > 1). In vitro conditional response patterns clustered compounds into five predictive classes: (1) DNA damaging agents, (2) Aurora A kinase and cell cycle checkpoint inhibitors; (3) microtubule poisons; (4) HER2/EGFR inhibitors; and (5) PIK3C catalytic subunit inhibitors. The apriori algorithm implementation made further predictions including a directional association between resistance to HER2 inhibition and sensitivity to proteasome inhibitors. Conclusions Investigating drug sensitivity conditioned on observed sensitivity or resistance to prior drugs may be pivotal in informing clinicians deciding on the next line of breast cancer treatments for patients who have progressed on their current treatment. This study supports a strategy of treating patients with different agents in the same class where an associated sensitivity was observed, likely after one or more intervening treatments.
- Subjects :
- 0301 basic medicine
Oncology
Cancer Research
Resistance
Drug Resistance
Datasets as Topic
Drug Screening Assays
0302 clinical medicine
Software Design
Surgical oncology
Ductal
Cluster Analysis
Data Mining
Medicine
Breast
Cancer
media_common
EGFR inhibitors
screening and diagnosis
Tumor
Drug Substitution
Carcinoma, Ductal, Breast
Disease Management
Metastatic breast cancer
lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Combined Modality Therapy
Neoadjuvant Therapy
Progression-Free Survival
Detection
Exact test
Treatment Outcome
5.1 Pharmaceuticals
6.1 Pharmaceuticals
030220 oncology & carcinogenesis
Public Health and Health Services
Female
Development of treatments and therapeutic interventions
Algorithms
Research Article
Drug
medicine.medical_specialty
media_common.quotation_subject
Clinical Decision-Making
Oncology and Carcinogenesis
Aurora A kinase
Breast Neoplasms
Antineoplastic Agents
lcsh:RC254-282
Cell Line
03 medical and health sciences
Breast cancer
Recommendation algorithm
Clinical Research
Cell Line, Tumor
Internal medicine
Breast Cancer
Genetics
Humans
Oncology & Carcinogenesis
Salvage Therapy
Internet
business.industry
Carcinoma
Evaluation of treatments and therapeutic interventions
Antitumor
Odds ratio
medicine.disease
4.1 Discovery and preclinical testing of markers and technologies
030104 developmental biology
Drug Resistance, Neoplasm
Quality of Life
Neoplasm
Drug Screening Assays, Antitumor
business
Subjects
Details
- Language :
- English
- ISSN :
- 14712407
- Volume :
- 21
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- BMC Cancer
- Accession number :
- edsair.doi.dedup.....fa5fec2928fa85e102749dfc44f9b3b7