1. Toward developing a metastatic breast cancer treatment strategy that incorporates history of response to previous treatments
- Author
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Olow, Aleksandra K, Veer, Laura van ’t, and Wolf, Denise M
- Subjects
Clinical Research ,Breast Cancer ,Cancer ,4.1 Discovery and preclinical testing of markers and technologies ,5.1 Pharmaceuticals ,Evaluation of treatments and therapeutic interventions ,Development of treatments and therapeutic interventions ,Detection ,screening and diagnosis ,6.1 Pharmaceuticals ,Algorithms ,Antineoplastic Agents ,Breast Neoplasms ,Carcinoma ,Ductal ,Breast ,Cell Line ,Tumor ,Clinical Decision-Making ,Cluster Analysis ,Combined Modality Therapy ,Data Mining ,Datasets as Topic ,Disease Management ,Drug Resistance ,Neoplasm ,Drug Screening Assays ,Antitumor ,Drug Substitution ,Female ,Humans ,Internet ,Neoadjuvant Therapy ,Progression-Free Survival ,Quality of Life ,Salvage Therapy ,Software Design ,Treatment Outcome ,Metastatic breast cancer ,Resistance ,Recommendation algorithm ,Oncology and Carcinogenesis ,Public Health and Health Services ,Oncology & Carcinogenesis - Abstract
BackgroundInformation 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.MethodsTo 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 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.ConclusionsInvestigating 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.
- Published
- 2021