Proton therapy- the modality of choice for future radiation therapy management of Prostate Cancer?

BACKGROUND

Proton Therapy (PR) is an emerging treatment for prostate cancer (Pca) patients. However, limited and conflicting data exists regarding its ability to result in fewer bladder and rectal toxicities compared to Photon Therapy (PT), as well as its cost efficiency and plan robustness.

MATERIALS AND METHODS

An electronic literature search was performed to acquire eligible studies published between 2007 and 2018. Studies comparing bladder and rectal dosimetry or Gastrointestinal (GI) and Genitourinary (GU) toxicities between PR and PT, the plan robustness of PR relative to motion and its cost efficiency for Pca patients were assessed.

RESULTS

28 studies were eligible for inclusion in this review. PR resulted in improved bladder and rectal dosimetry but did not manifest as improved GI/GU toxicities clinically compared to PT. PR plans were considered robust when specific corrections, techniques, positioning or immobilisation devices were applied. PR is not cost effective for intermediate risk Pca patients; however PR may be cost effective for younger or high risk Pca patients.

CONCLUSION

PR offers improved bladder and rectal dosimetry compared to PT but this does not specifically translate to improved GI/GU toxicities clinically. The robustness of PR plans is acceptable under specific conditions. PR is not cost effective for all Pca patients.

The incoherent feed-forward loop accelerates the response-time of the gal system of Escherichia coli

Complex gene regulation networks are made of simple recurring gene circuits called network motifs. One of the most common network motifs is the incoherent type-1 feed-forward loop (I1-FFL), in which a transcription activator activates a gene directly, and also activates a repressor of the gene. Mathematical modeling suggested that the I1-FFL can show two dynamical features: a transient pulse of gene expression, and acceleration of the dynamics of the target gene. It is important to experimentally study the dynamics of this motif in living cells, to test whether it carries out these functions even when embedded within additional interactions in the cell. Here, we address this using a system with incoherent feed-forward loop connectivity, the galactose (gal) system of Escherichia coli. We measured the dynamics of this system in response to inducing signals at high temporal resolution and accuracy by means of green fluorescent protein reporters. We show that the galactose system displays accelerated turn-on dynamics. The acceleration is abolished in strains and conditions that disrupt the I1-FFL. The I1-FFL motif in the gal system works as theoretically predicted despite being embedded in several additional feedback loops. Response acceleration may be performed by the incoherent feed-forward loop modules that are found in diverse systems from bacteria to humans.

The coherent feedforward loop serves as a sign-sensitive delay element in transcription networks

Recent analysis of the structure of transcription regulation networks revealed several "network motifs": regulatory circuit patterns that occur much more frequently than in randomized networks. It is important to understand whether these network motifs have specific functions. One of the most significant network motifs is the coherent feedforward loop, in which transcription factor X regulates transcription factor Y, and both jointly regulate gene Z. On the basis of mathematical modeling and simulations, it was suggested that the coherent feedforward loop could serve as a sign-sensitive delay element: a circuit that responds rapidly to step-like stimuli in one direction (e.g. ON to OFF), and at a delay to steps in the opposite direction (OFF to ON). Is this function actually carried out by feedforward loops in living cells? Here, we address this experimentally, using a system with feedforward loop connectivity, the L-arabinose utilization system of Escherichia coli. We measured responses to step-like cAMP stimuli at high temporal resolution and accuracy by means of green fluorescent protein reporters. We show that the arabinose system displays sign-sensitive delay kinetics. This type of kinetics is important for making decisions based on noisy inputs by filtering out fluctuations in input stimuli, yet allowing rapid response. This information-processing function may be performed by the feedforward loop regulation modules that are found in diverse systems from bacteria to humans.

Structure and function of the feed-forward loop network motif

Engineered systems are often built of recurring circuit modules that carry out key functions. Transcription networks that regulate the responses of living cells were recently found to obey similar principles: they contain several biochemical wiring patterns, termed network motifs, which recur throughout the network. One of these motifs is the feed-forward loop (FFL). The FFL, a three-gene pattern, is composed of two input transcription factors, one of which regulates the other, both jointly regulating a target gene. The FFL has eight possible structural types, because each of the three interactions in the FFL can be activating or repressing. Here, we theoretically analyze the functions of these eight structural types. We find that four of the FFL types, termed incoherent FFLs, act as sign-sensitive accelerators: they speed up the response time of the target gene expression following stimulus steps in one direction (e.g., off to on) but not in the other direction (on to off). The other four types, coherent FFLs, act as sign-sensitive delays. We find that some FFL types appear in transcription network databases much more frequently than others. In some cases, the rare FFL types have reduced functionality (responding to only one of their two input stimuli), which may partially explain why they are selected against. Additional features, such as pulse generation and cooperativity, are discussed. This study defines the function of one of the most significant recurring circuit elements in transcription networks.