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Research article
Toward developing a metastatic or neoadjuvant breast cancer treatment strategy that incorporates history of response to previous treatments
Aleksandra K. Olow
Merck Sharp and Dohme
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0113-8306
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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.
Keywords
Metastatic breast cancer, resistance, recommendation algorithm
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CURRENT STATUS: Under Revision
Version 1
Posted 27 May, 2020
No community comments so far
Editorial decision: Major revision
On 29 Aug, 2020
Review #7 received
Received 23 Aug, 2020
Review #8 received
Received 23 Aug, 2020
Reviewer #8 agreed
On 10 Aug, 2020
Reviewer #7 agreed
On 09 Aug, 2020
Review #6 received
Received 26 Jul, 2020
Review #4 received
Received 26 Jul, 2020
Review #5 received
Received 26 Jul, 2020
Reviewer #6 agreed
On 25 Jul, 2020
Review #3 received
Received 29 Jun, 2020
Review #2 received
Received 27 Jun, 2020
Reviewer #5 agreed
On 24 Jun, 2020
Review #1 received
Received 24 Jun, 2020
Reviewer #3 agreed
On 15 Jun, 2020
Reviewer #4 agreed
On 15 Jun, 2020
Reviewer #2 agreed
On 09 Jun, 2020
Reviewer #1 agreed
On 08 Jun, 2020
Reviewers invited
Invitations sent on 01 Jun, 2020
First submitted
On 13 May, 2020
Editor assigned
On 13 May, 2020
Submission checks complete
On 12 May, 2020
Editor invited
On 12 May, 2020
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This preprint is under consideration at BMC Cancer. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Toward developing a metastatic or neoadjuvant breast cancer treatment strategy that incorporates history of response to previous treatments
Aleksandra K. Olow
Merck Sharp and Dohme
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0113-8306
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 27 May, 2020
No community comments so far
Editorial decision: Major revision
On 29 Aug, 2020
Review #7 received
Received 23 Aug, 2020
Review #8 received
Received 23 Aug, 2020
Reviewer #8 agreed
On 10 Aug, 2020
Reviewer #7 agreed
On 09 Aug, 2020
Review #6 received
Received 26 Jul, 2020
Review #4 received
Received 26 Jul, 2020
Review #5 received
Received 26 Jul, 2020
Reviewer #6 agreed
On 25 Jul, 2020
Review #3 received
Received 29 Jun, 2020
Review #2 received
Received 27 Jun, 2020
Reviewer #5 agreed
On 24 Jun, 2020
Review #1 received
Received 24 Jun, 2020
Reviewer #3 agreed
On 15 Jun, 2020
Reviewer #4 agreed
On 15 Jun, 2020
Reviewer #2 agreed
On 09 Jun, 2020
Reviewer #1 agreed
On 08 Jun, 2020
Reviewers invited
Invitations sent on 01 Jun, 2020
First submitted
On 13 May, 2020
Editor assigned
On 13 May, 2020
Submission checks complete
On 12 May, 2020
Editor invited
On 12 May, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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