Toward developing a metastatic or neoadjuvant breast cancer treatment strategy that incorporates history of response to previous treatments
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 is assuming (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<0.05). Recommendation systems are used to make further drug recommendations based on past ‘history’ of response.
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 sensitivity was previously observed.
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Posted 27 May, 2020
On 29 Aug, 2020
Received 23 Aug, 2020
Received 23 Aug, 2020
On 10 Aug, 2020
On 09 Aug, 2020
Received 26 Jul, 2020
Received 26 Jul, 2020
Received 26 Jul, 2020
On 25 Jul, 2020
Received 29 Jun, 2020
Received 27 Jun, 2020
On 24 Jun, 2020
Received 24 Jun, 2020
On 15 Jun, 2020
On 15 Jun, 2020
On 09 Jun, 2020
On 08 Jun, 2020
Invitations sent on 01 Jun, 2020
On 13 May, 2020
On 13 May, 2020
On 12 May, 2020
On 12 May, 2020
Toward developing a metastatic or neoadjuvant breast cancer treatment strategy that incorporates history of response to previous treatments
Posted 27 May, 2020
On 29 Aug, 2020
Received 23 Aug, 2020
Received 23 Aug, 2020
On 10 Aug, 2020
On 09 Aug, 2020
Received 26 Jul, 2020
Received 26 Jul, 2020
Received 26 Jul, 2020
On 25 Jul, 2020
Received 29 Jun, 2020
Received 27 Jun, 2020
On 24 Jun, 2020
Received 24 Jun, 2020
On 15 Jun, 2020
On 15 Jun, 2020
On 09 Jun, 2020
On 08 Jun, 2020
Invitations sent on 01 Jun, 2020
On 13 May, 2020
On 13 May, 2020
On 12 May, 2020
On 12 May, 2020
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 is assuming (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<0.05). Recommendation systems are used to make further drug recommendations based on past ‘history’ of response.
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 sensitivity was previously observed.
Figure 1
Figure 2
Figure 3