DETERMINING VIABILITY AND TREATMENT OF DISEASE AGENTS

Abstract

Predicting viability and treatment of disease agents is described herein. In an example, a system accesses a disease agent transcriptome data of a disease agent. The system generates a disease agent viability score by applying a classifier to the disease agent transcriptome. The classifier defines a universal transcriptome signature for a viability of the disease agent in different host-relevant contexts. The system generates a viability state of the disease agent by determining a deviation of the disease agent viability score from a viability threshold of the universal transcriptome signature for viability and determines a treatment recommendation based on the viability state of the disease agent. The system outputs the treatment recommendation.

Description

Claims

1. A computer-implemented method comprising: (a) accessing a disease agent transcriptome of a disease agent;(b) generating a disease agent viability score by applying a classifier to the disease agent transcriptome, the classifier defining a universal transcriptome signature for a viability of the disease agent in a plurality of different host-relevant contexts;(c) generating a viability state of the disease agent by determining a deviation of the disease agent viability score from a viability threshold of the universal transcriptome signature for viability;(d) determining a treatment recommendation based on the viability state of the disease agent; and(e) outputting the treatment recommendation.

2. The computer-implemented method of claim 1, wherein the classifier was trained using a training data set comprising a plurality of viable disease agent transcriptomes, and wherein the classifier was tested on a testing data set comprising a first set of untreated disease agent transcriptomes and a second set of treated disease agent transcriptomes, the training data set and the testing data set derived from the disease agent being grown under the plurality of different host-relevant contexts with drug treatment and without drug treatment to define the universal transcriptome signature for viability.

3. The computer-implemented method of claim 1, wherein the viability threshold is set as a lower limit of a viable transcriptome space defined by the classifier.

4. The computer-implemented method of claim 1, wherein the classifier is a single-class support vector machine.

5. The computer-implemented method of claim 1, wherein the disease agent viability score is a weighted sum of a plurality gene expression ranks generated by the classifier and rank normalized.

6. The computer-implemented method of claim 1, wherein the disease agent is a cell, and the disease agent transcriptome is obtainable from the cell.

7. The computer-implemented method of claim 1, wherein the disease agent is Mycobacterium tuberculosis and a host of the disease agent is a mammal.

8. The computer-implemented method of claim 1, wherein the disease agent transcriptome comprises a subset of mRNA transcripts produced by primer-directed amplification, the subset of mRNA transcripts comprising one or more weighted features selected by bootstrapping and rank ordering based on weights determined by the primer-directed amplification.

9. The computer-implemented method of claim 8, wherein the primer-directed amplification is reverse transcription loop-mediated isothermal amplification (LAMP).

10. The computer-implemented method of claim 1, wherein determining the treatment recommendation comprises: comparing the viability state of the disease agent to one or more single-drug treatment viability states of the disease agent, the one or more single-drug treatment viability states produced by: (i) generating one or more single-drug treatment viability scores by an application of the classifier to a plurality of single-drug treatment transcriptomes of the disease agent grown under a plurality of single-drug treatment conditions, and (ii) generating the one or more single-drug treatment viability states by a determination of another deviation of the one or more single-drug treatment viability scores from the viability threshold of the universal transcriptome signature for viability.

11. The computer-implemented method of claim 10, wherein determining the treatment recommendation further comprises: comparing the viability state of the disease agent and the one or more single-drug treatment viability states of the disease agent with a multi-drug viability state, the multi-drug viability state imputed by an application of the classifier to an average of a plurality of disease agent transcriptomes and one or more single drug treatment transcriptomes.

12. The computer-implemented method of claim 11, wherein the average is a geometric mean.

13. The computer-implemented method of claim 1, wherein determining the treatment recommendation comprises evaluating an efficacy of a drug treatment for the disease agent.

14. The computer-implemented method of claim 1, further comprising: facilitating the treatment recommendation for a host of the disease agent.

15. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform a set of actions including: (a) accessing a disease agent transcriptome of a disease agent;(b) generating a disease agent viability score by applying a classifier to the disease agent transcriptome, the classifier defining a universal transcriptome signature for viability of the disease agent in a plurality of different host-relevant contexts;(c) generating a viability state of the disease agent by determining a deviation of the disease agent viability score from a viability threshold of the universal transcriptome signature;(d) determining a treatment recommendation for the disease agent based on the viability state of the disease agent; and(e) outputting the treatment recommendation.

16. The computer-program product of claim 15, wherein determining the treatment recommendation comprises: comparing the viability state of the disease agent to one or more single-drug treatment viability states of the disease agent, the one or more single-drug treatment viability states produced by a process comprising an application of the classifier to a plurality of single-drug treatment transcriptomes of the disease agent grown under a plurality of single-drug treatment conditions.

17. The computer-program product of claim 16, wherein determining the treatment recommendation further comprises: comparing the viability state and the one or more single-drug treatment viability states with a multi-drug treatment viability state.

18. The computer-program product of claim 17, wherein the multi-drug treatment viability state is imputed.

19. The computer-program product of claim 18, wherein the multi-drug treatment viability state is produced by an imputation comprising an application of the classifier to an average of a plurality of disease agent transcriptomes and one or more single-drug treatment transcriptomes.

20. A system comprising: a microfluidic device for receiving a sample of a host subject and producing disease agent transcriptome data of a disease agent from the sample;one or more data processors; anda non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform a set of actions including: (a) accessing a disease agent transcriptome of the disease agent;(b) generating a disease agent viability score by applying a classifier to the disease agent transcriptome, the classifier defining a universal transcriptome signature for viability of the disease agent in a plurality of different host-relevant contexts;(c) generating a viability state of the disease agent by determining a deviation of the disease agent viability score from a viability threshold of the universal transcriptome signature;(d) determining a treatment recommendation for the disease agent based on the viability state of the disease agent; and(e) outputting the treatment recommendation.