How can decision trees be tuned for non-symmetrical loss?
Theoretically, decision tree algorithms specify the feature as well as the threshold that maximize the separation between classes at each node. This can be done by solving optimization problem related to the entropy at the children nodes. 
You can modify this optimization problem by including the miss- classification costs you have in the optimization problem so the algorithm will be biased toward the most expensive class