extfieldsofstudy: Psychology Medicine

s2fieldsofstudy: Biology Psychology

text:
Preparatory (cid:2) -band oscillations reﬂect spatial gating independently of predictions regarding target identity

Preparatory (cid:2) -band oscillations reﬂect spatial gating independently of predictions regarding target identity. J Neurophysiol modulations of cortical (cid:2) -band oscillations are a reliable index of the voluntary allocation of covert spatial attention. It is currently unclear whether attentional cues containing information about a target’s identity (such as its visual orientation), in addition to its location, might additionally shape preparatory (cid:2) modulations. Here, we explore this question by directly comparing spatial and feature-based attention in the same visual detection task while recording brain activity using magnetoencephalography (MEG). At the behavioral level, preparatory feature-based and spatial attention cues both improved performance and did so independently of each other. Using MEG, we replicated robust (cid:2) lateralization following spatial cues: in preparation for a visual target, (cid:2) power decreased contralaterally and increased ipsilaterally to the attended location. Critically, however, preparatory (cid:2) lateralization was not signiﬁcantly modulated by predictions regarding target identity, as carried via the behaviorally effective feature-based attention cues. Furthermore, nonlateralized (cid:2) power during the cue-target interval did not differentiate between uninformative cues and cues carrying feature-based predic- tions either. Based on these results we propose that preparatory (cid:2) modulations play a role in the gating of information between spatially segregated cortical regions and are therefore particularly well suited for spatial gating of information. NEW & NOTEWORTHY The present work clariﬁes if and how human brain oscillations in the (cid:2) -band support multiple types of anticipatory attention. Using magnetoencephalography, we show that posterior (cid:2) -band oscillations are modulated by predictions regarding the spatial location of an upcoming visual target, but not by feature- based predictions regarding its identity, despite robust behavioral beneﬁts. This provides novel insights into the functional role of preparatory (cid:2) mechanisms and suggests a limited speciﬁcity with which they may operate.

WHEN EXPECTING A task-relevant stimulus, we often have multiple types of predictive information, for example, about its spatial location and identity. In such cases, we can optimize behavior using both space-and feature-based attention (Egner et al. 2008;White et al. 2015). In this study, we investigate whether and how oscillatory rhythms in the ␣-band reflect multiple types of anticipatory attention signals.
The role of ␣-band (8-to 14-Hz) oscillations in spatial attention has been an active area of research for over a decade. In preparation for an upcoming visual target, attention to a particular location decreases posterior ␣ power contralaterally to the attended location and increases ␣ power ipsilaterally to the attended location (Kelly et al. 2006;Sauseng et al. 2005;Siegel et al. 2008;Thut et al. 2006;Worden et al. 2000). Similar ␣ lateralization with attentional allocation is also observed in the somatosensory system when anticipating tactile stimuli (Haegens et al. 2011a;van Ede et al. 2011). Such preparatory ␣ modulations have been proposed to reflect functional inhibition of sensory regions processing irrelevant information and/or a release of inhibition of those regions processing relevant information (Foxe and Snyder 2011;Jensen et al. 2012;Jensen and Mazaheri 2010;Klimesch 2012). Critically, these ␣ modulations have been shown to be functionally relevant, predicting task performance on a trial-by-trial basis Mathewson et al. 2009;Romei et al. 2010;van Dijk et al. 2008).
Despite the wealth of literature on ␣ oscillation in preparatory spatial attention, it is currently not clear whether ␣ lateralization is modulated by content predictions in addition to spatial predictions. Content predictions can be hypothesized to influence preparatory ␣ modulations in at least two ways. First, content predictions may strengthen ␣ modulations. The opportunity to preload the anticipated target identity into working memory might lead to stronger engagement in relevant (contralateral) visual areas. Conversely, identity predictions could also be hypothesized to lead to weaker ␣ modulations, since only those populations coding for the expected target features need to upregulate their excitability (as putatively indexed by the ␣ modulation), as opposed to the larger cortical population coding for all possible target identities.
Here, we independently manipulated spatial and featurebased preparatory attention while recording brain activity using magnetoencephalography (MEG). In contrast to our hypotheses, target-identity predictions did not significantly modulate ␣ oscillations despite having a clear benefit on performance. We propose that ␣ power that can be detected at the spatial scales accessible to MEG does not show significant modulations when task-relevant feature levels compete for representations within brain regions.

Participants
All experimental protocols were reviewed and approved by the Central University Research Ethics Committee of the University of Oxford. Twenty volunteers took part in the experiment (9 male, 18 -35 yr, all right handed). All participants had normal or correctedto-normal vision and were naïve to the purpose of the experiment. Participants gave written informed consent before taking part and received financial compensation (£15/h for sessions involving MEG and £10/h for sessions involving only behavioral testing).

Procedures
To examine the role of brain rhythms in preparatory feature-based and spatial attention, we asked participants to perform a targetdetection task on two peripheral orientation stimuli, which were preceded by a cue. Figure 1 provides a task schematic of the experimental task. Participants completed two experimental sessions on separate days. An initial behavioral session was conducted to provide training on the behavioral task. Task performance was accompanied by MEG recordings in a second session, always completed within one week from the training session.

Task and Stimuli
The task was programmed in MATLAB version 7.10 (MathWorks) and presented using the Psychophysics Toolbox in Matlab (Kleiner et al. 2007). Stimuli were back projected (Panasonic PT D7700E) on a screen at a viewing distance of 120 cm with a spatial resolution of 1,280 by 1,024 pixels and a refresh rate of 60 Hz. Stimuli appeared against a uniform midgray background.
The task involved detecting the presence of a target orientation stimulus (horizontal or vertical grating) within an array of two peripherally presented gratings (left and right visual field). Arrays were preceded by a centrally presented attentional cue that could contain information regarding the target's location (left/right), its identity (vertical/horizontal), or both (see Fig. 1B). Spatial and feature cueing was thus independently manipulated, resulting in the following four different conditions: neutral cueing (N), spatial-only cueing (S), feature-only cueing (F), and combined spatial-feature cueing (SF). A target stimulus was present in 50% of trials. When the target was present, a distracting tilted-orientation grating stimulus (45°) occurred on the other visual field. Target-absent arrays always contained two tilted gratings. These could have the same or different orientations. Participants were instructed to maintain central fixation during task performance.
Trials began with a 1,000-to 1,500-ms (randomly determined) presentation of a central fixation dot (0.1°visual angle in width and height). This was followed by the presentation of a central cue stimulus for 200 ms. The cue stimulus consisted of a diamond shape (1.6°visual angle in width and height) around a plus sign in its center. To cue spatial location (left vs. right) the left or right half of the diamond shape was highlighted in blue or magenta. To cue the feature identity (horizontal vs. vertical) the vertical or horizontal line of the plus sign was highlighted in blue or magenta (see Fig. 1B). The highlighting color was counterbalanced between participants, with the other color serving as the default "background" color of the cue stimulus (i.e., the neutral cue was either all blue or all magenta). Participants were explicitly told that cues did not predict whether the target would be present or absent but that, instead, if the target would be present, it would occur at the cued location and be of the cued Here, the parts of the cue highlighted in blue indicate the relevant feature dimension and/or spatial location while the background color is magenta. Highlight and background color were counterbalanced between participants. Feature and spatial cueing were independently manipulated, resulting in the following 4 conditions: neutral cues (N), feature-only cues (F), spatial-only cues (S), and combined spatial-feature cues (SF). C: behavioral results in the magnetoencephalography (MEG) session. Valid spatial cueing led to shorter reaction times (RTs, in ms) and higher accuracy. Valid feature cueing shortened RTs and improved accuracy. Feature and spatial cueing did not interact. Error bars reflect Ϯ 1 SE. identity (if this information was contained in the cue). Following a cue-target interstimulus interval of 1,200 ms, a stimulus array was presented for 50 ms and then backward masked (after an interval of 67 ms) for 283 ms. Stimulus arrays consisted of either a horizontally or vertically oriented target Gabor patch (Gaussian-vignetted sinusoidal gratings) and a Ϯ45°tilted distracter Gabor patch (target present) or two distracter tilted Gabor patches (target absent). Gabor patch stimuli (diameter: 2°of visual angle) were presented at a contrast for which the target detection task was performed at 75% accuracy (see below for details of the staircasing procedure) with a spatial frequency of three cycles per degree. The Gaussian envelope had a space constant of 0.44°. The orientations of the target and distracter stimuli were pseudorandomly selected on each trial. A target was present on one-half of the trials (randomly selected at the beginning of the experiment). Gabor patches were presented Ϯ5.2°from the vertical meridian and 3°below the horizontal meridian. Backward-mask stimuli were constructed by applying a Gaussian-vignette to the convolution of 100% contrast square-wave gratings at four orientations of 90, 180, 45, and Ϫ45°angle. Stimuli and masks were presented atop 10% contrast luminance pedestals that were present throughout the entire experiment. Participants responded (target present or absent) by making a right-handed index or middle finger button press during a response period of up to 2,000 ms. Response mappings were counterbalanced between participants. The subsequent intertrial interval was 1,000 ms (Ϯ500 ms). All conditions were equiprobable and randomized.
Participants performed a calibration session before the behavioral training and MEG experimental session using an adaptive psychophysical staircase procedure to estimate the threshold contrast for perceiving the Gabors. Task difficulty was adjusted for each participant by titrating the contrast of the Gabor patches for which the target detection task was performed at 75% accuracy when only neutral cues were used.
Behavioral training session. Before the MEG session, on a separate day, participants were trained on the behavioral task to ensure they were familiar with the task and could efficiently use the different cue types. Participants completed a total of 288 trials of the experimental task (split into three runs) in a MEG mock scanner. In each run, short rest periods were introduced every 32 trials. The first run served as a practice run to familiarize participants with the task, and stimulus contrast was set to 50%. All cues were included in this run. The second run comprised the staircase procedure starting at 50% contrast and was repeated if necessary. Only neutral cues were included in the staircase run. In the third run, the contrast was set to the threshold estimated from the staircase, and spatial, feature, and spatial-feature (but no neutral) cues were included.
MEG session. Whole head MEG recordings were acquired with a 306-channel Vectorview system (Elekta-Neuromag, Helsinki, Finland) at the Oxford Center for Human Brain Activity (OHBA). The system contains 306 sensors: 102 magnetometers and 204 orthogonally oriented planar gradiometers (102 latitudinal gradiometers, 102 longitudinal gradiometers). The MEG scanner was housed in a threelayer magnetically shielded room (Imedco). The data were sampled at 1,000 Hz.
Additional electrodes were used to record eye movements (EOG) and heart rate (ECG) during MEG acquisition. A vertical pair of EOG electrodes was placed above and below the left eye to detect blinks. To detect lateral eye movements, a horizontal pair was placed with one electrode to the left of the left orbit and the other electrode to the right of the right orbit. Electrodes were placed on the left and right wrist to record ECG. In addition, eye movements were monitored online and recorded at 1,000 Hz with a remote infrared eye tracker (EyeLink 1000; SR research) controlled via the Psychophysical Toolbox in Matlab version 7.10 (MathWorks). Four magnetic coils served as head-position indicator (HPI) coils. These were positioned on each mastoid bone and on each side of the forehead near the hairline. The positions of the HPI coils relative to three fiducial points and the subject's head shape were digitized using a Polhemus 3D tracking system (EastTrach 3D; Polhemus). HPI coils were activated at the beginning of each block to localize the participant's head with respect to the sensor array and to monitor the subject's head position to correct for any head movements later during analysis.
During the recording session, participants sat in a reclining chair and supported their head against the back and top of the MEG helmet.
Participants were asked to remain as still as possible during the recording session and were continuously monitored by a video camera. Participants were given a fiberoptic button box and made responses using their right and left index finger. Stimuli were projected on a screen placed around 120 cm in front of the subject. MEG data were recorded in five blocks. Participants completed a total of 640 trials of the experimental task split into five blocks of 128 trials each. The four experimental conditions (SF, S, F, N) were presented in randomized order, resulting in a total of 160 trials/condition.

Analysis
Behavioral analysis. Hypotheses regarding the effects of experimental parameters on recall accuracy and reaction times (RTs, in ms) in correct trials were tested using analysis of variance (ANOVA) and t-tests (Bonferroni corrected where appropriate). To guide our interpretation of the MEG results, we only used participants' performance during the MEG session.
MEG analysis. Analysis of the MEG data was performed using a combination of custom-written MATLAB scripts, in-house OHBA software library, SPM8 (Litvak et al. 2011; http://www.fil.ion.ucl. ac.uk/spm), and FieldTrip ; http://www. ru.nl/fcdonders/fieldtrip/). All time samples were corrected with respect to the refresh delay of the projector (measured online with a photodiode). MEG analysis was performed in two main steps as follows: 1) preprocessing of the data to remove artifacts present in the raw data and 2) sensor-level analysis consisting of the time-frequency decomposition of the MEG data to examine the time-frequency dynamics of preparatory ␣-band modulations. The epoch of interest for analyses was the period between cue and target onset.
PREPROCESSING. The raw MEG data were visually inspected to remove any channels with excessive noise. The data were then denoised using Maxfilter signal-space separation (Taulu et al. 2005), and compensated for changes in head position within session using the MaxMove software, both implemented in MaxFilter version 2.2 (Elekta Neuromag). The Maxfilter software works by mathematically transforming the data to a set of virtual sensors, which is made possible by location information provided by the MEG sensor array and each person's continuous head position signal recorded from the HPI coils. Transforming the data into virtual sensors gives a standard reference frame and allows for data to be combined across recordings. The data were subsequently downsampled to 250 Hz. After applying a high-pass filter at 1 Hz, the data were then epoched with respect to each cue onset (from Ϫ2 to ϩ3 s). This time window encompassed both cue-target interval activity (from 0 s onward) and target-related activity (from 1.4 s onward). The resulting epochs were manually inspected for artifacts. Systematic artifacts (including eye blinks and heart beats) were identified and regressed out of the data using the following procedure. Independent component analysis (ICA) was used to decompose the sensor data for each session into 150 temporally independent components (tICs) and associated sensor topographies using FastICA (http://research.ics.aalto.fi/ica/fastica). Artifact components were manually identified by the combined inspection of the spatial topography, time course, kurtosis of the time course, and frequency spectrum for all components. Artifactual components were then rejected by subtracting them out of the data (in the majority of cases 3-5 components were removed from each subject). Finally, data were imported into Fieldtrip and inspected using the semiautomatic rejection tool to discard any remaining trials with excessive variance at 8 -14 Hz. TIME-FREQUENCY ANALYSIS. Time-frequency analysis of the timedomain sensor-space signal was performed using a Fast-Fourier transformation algorithm for the range of frequencies between 3 and 30 Hz. The interval Ϫ1 to ϩ2.5 s centered on cue onset was used for analysis. Data from within a 300-ms sliding time window, which was advanced in steps of 60 ms, was Fourier transformed and multiplied with a Hanning taper in 1-Hz steps. As a result of the 300-ms sliding time window, all depicted time points incorporate signal from Ϯ150 ms around that time point. We then averaged the resulting power spectra over trials within each condition of interest. Magnetometers were discarded, and the power time series in the gradiometer pairs were combined (Cartesian sum, i.e., summing the squares of the latitudinal and longitudinal gradiometer activity and taking the square root), resulting in a 102 sensor combined planar gradiometer map of sensor space time-frequency power.
For each participant, we first contrasted the power spectra between left and right spatial (SF and S) cues in all MEG channels and expressed this as a percentage change {i.e., (left -right)/(left ϩ right)] ϫ 100}. Second, to examine effects of feature information on cueinduced ␣ lateralization, we calculated lateralization indexes for spatial-only (S) cues and for spatial-feature (SF) cues separately and compared them. Concretely, for clusters consisting of left and right MEG channels, we calculated the normalized difference in power between trials in which the target was expected contralateral or ipsilateral to those channels and also expressed this as a percentage change: [(contralateral -ipsilateral)/(contralateral ϩ ipsilateral)] ϫ 100. We subsequently collapsed this contralateral vs. ipsilateral metric across left and right channel clusters. The lateralization indexes were calculated two times using different channel selections: first, including all left and all right MEG channels and, second, focusing on ␣ power in visual areas by focusing on a subset of the most informative MEG channels, which we will refer to as visual channels. To identify visual channels, we selected channels above left and right visual areas by plotting the group-level grand average ␣ lateralization (from the analysis described above, i.e., averaged over all types of spatial cues) and manually selected the five left and right channels that showed the maximal response (power suppression contralateral and increase ipsilateral).
Next, to examine the effects of feature information on ␣ lateralization in more detail, we compared the time courses of contralateral and ipsilateral responses following spatial-feature (SF) vs. spatial-only (S) cues. In contrast to the analysis described above, this time, we completed the analysis on visual channels only.
Finally, to examine effects of feature cueing on nonlateralized ␣ power, we also contrasted power spectra in feature-only (F) cueing to neutral (N) cueing conditions. This analysis was done on all channels and on visual channels.
At the group level, we tested for significance using cluster-based permutation statistics (5,000 permutations, ␣-level 0.05), which circumvent the multiple-comparisons problem by evaluating the full dataspace under a single permutation distribution of the largest cluster (Maris and Oostenveld 2007). Unless noted otherwise, clustering was performance across space (channels) and time (0 -1.4 s from cue onset), with regard to data that were averaged in the apriori-defined frequency band of interest (i.e., the ␣-band; 8 -14 Hz).

Behavioral Results
Repeated-measures ANOVAs were carried out on accuracy and RTs (RTs from correct trials only) with the factors spatial cue (present, absent) and feature cue (present, absent). Behavioral data are depicted in Fig. 1 To assess whether feature information alone improved performance, we compared feature-only with neutral cues and spatial-feature with spatial-only cues using repeated-measures t-tests.

MEG Results
Modulations of posterior ␣-band lateralization by spatial and feature cueing. To assess the spatial cueing effect, we compared ␣-band power following left vs. right spatial cues (S, SF). To this end, we compared ␣ power (averaged over 8 -14 Hz) from 0 to 1.4 s post cue onset, between left and right spatial cues. A cluster permutation test on this contrast in the ␣-band (8 -14 Hz), considering all time points from 0 to 1.4 s and all MEG channels, revealed a significant positive cluster over left posterior sensors from 0.32 to 1.3 s (P ϭ 0.0044) and a just-significant negative cluster over right posterior sensors from 0.44 to 0.92 s after cue onset (P ϭ 0.0452). Figure 2A depicts this contrast, averaged over all significant time points of the positive cluster. Next, we calculated ␣ lateralization indexes, combining left and right cues to calculate contralateral and ipsilateral preparatory activity, and then contrasting the two (see Fig. 2B). To assess how ␣ lateralization following spatial cues was modulated by the presence of feature information, we calculated the ␣ lateralization indexes separately for spatial-feature (SF) and spatial-only (S) cues, first using all left and right MEG channels and then using a subset of the left and right visual channels (see METHODS for details) only. Figure  2C depicts the time courses of the ␣ lateralization indexes for the two conditions using all channels, and Fig. 2D shows the same using visual channels only. A comparison of the ␣ lateralization indexes over all channels (considering all time points and frequencies between 3 and 30 Hz) revealed one significant positive cluster (4 Hz, 0.44 -0.62 s, P ϭ 0.025), showing a stronger (possibly faster) lateralization when the cue contained no feature information, in the spatial-only cueing condition (S). Similarly, this was the case when comparing ␣ lateralization indexes over visual channels only (1 positive cluster, 6 -16 Hz, 0.32-0.62 s, P ϭ 0.004).
It is conceivable that this difference reflects differences in the demands to interpret the cue stimulus. It may be more straightforward to interpret S cues compared with SF cues, leading to a faster deployment of spatial attention with S compared with SF cues. Indeed, and critically, in the most relevant preparatory period just before the anticipated target onset (at 1.4 s), the modulations by spatial attention were virtually indistinguishable between spatial-feature and spatial-only cues (i.e., spatial cues with and without target identity predictions). This was the case no matter what channel selection we used.
Contralateral and ipsilateral ␣-band responses relative to neutral cues. To evaluate ␣ lateralization more carefully, we then looked separately at contralateral and ipsilateral ␣-band power changes in visual channels. To this end, we overlaid the time courses of spatial-only, combined spatial-feature, and neutral cues, normalized by a precue baseline. As suggested by Fig. 3, the attentional modulation (vs. neutral) appeared slightly delayed for combined spatial-feature compared with spatial-only cues in both contra-and ipsilateral visual channels. Indeed, cluster-permutation tests, averaging over ␣-band power and visual channels, revealed a significant difference between ipsilateral responses following spatial-feature vs. spatial-only cues (1 negative cluster from 0.38 to 0.68 s, P ϭ 0.006, see Fig. 3B). However, there was no difference between contralateral responses following spatial-feature vs. spatialonly cues (see Fig. 3A). Again, in both contra-and ipsilateral channels, these conditions became virtually indistinguishable toward the end of the cue-target interval. We further noted that both spatial-feature and spatial-only cues elicited significant contralateral and ipsilateral responses compared with neutral cues (contralateral spatial-feature vs. neutral: 1 negative cluster from 0.44 to 0.62 s, P ϭ 0.024; contralateral spatial-only vs. neutral: 1 negative cluster from 0.44 to 0.56 s, P ϭ 0.047; ipsilateral spatial-feature vs. neutral: 1 positive cluster from 0.86 to 1.4 s, P ϭ 0.002; ipsilateral spatial-only vs. neutral: 1 positive cluster from 0.44 to 1.4 s, P Ͻ 0.001; see Fig. 3, A and B).
Modulations of nonlateralized ␣-band power by feature cueing. As stated above, it is feasible that the observed difference in ␣ lateralization between spatial-feature and spatial-only cues from 0.44 to 0.62 s after cue onset reflects low-level differences in cue processing (as spatial-feature cues are more complex than spatial-only cues). Alternatively, however, this initially reduced ␣ lateralization with feature information could also be due to a global modulation linked to preparatory feature-based attention (Bichot et al. 2005;Jehee et al. 2011;Martinez-Trujillo and Treue 2004;McAdams and Maunsell 2000;Saenz et al. 2002;Serences and Boynton 2007;Treue and Martínez Trujillo 1999) whereby ␣ power might be attenuated in a nonlateralized fashion (thereby potentially countering and/or delaying the lateralization effect). If so, then one would expect such global modulation also to show in the contrast between pure feature (F) and neutral (N) cues. However, cluster permutation tests on the averaged ␣-band power (8 -14 Hz), considering all time points from 0 s to 1.4 s and all channels (Fig. 4A), or all visual channels (Fig. 4B), revealed no significant clusters.
We also explored other frequency bands across all channels and time points but did not find any robust effects. Concretely, we compared power in the predefined (4 -8 Hz)-, ␣ (8 -14 Hz)-, ␤ (14 -30 Hz)-, and ␥ (40 -100 Hz)-bands between feature-only and neutral cues. Cluster-based permutation tests, averaging over respective frequency bands and considering all time points from 0 to 1.4 s and all MEG channels, only revealed one significant positive cluster in the -band from 0.152 to 0.508 s (P ϭ 0.003) that centered on medial frontal channels (see Fig. 5). Although this is a potentially interesting effect, its relative early timing suggests that it, too, may reflect differences in cue processing rather than a sustained effect of anticipatory attention. Moreover, this analysis was only exploratory and did not correct for multiple comparison across frequencies.

DISCUSSION
In this study, we set out to investigate whether predictions about target identity, in addition to predictions about locations, modulate preparatory attentional ␣ modulations. We considered two hypotheses: target identity predictions may strengthen ␣ modulation because of relevant cortical areas showing stronger engagement (e.g., by loading the target template in working memory), or target identity predictions may lead to weaker ␣ modulations, since only selected populations coding for the expected target feature modulate their excitability (as opposed Fig. 3. Time courses of contralateral (A) and ipsilateral (B) ␣ power responses, averaged over visual channels, for SF (blue line) and S (green line) cues, together with ␣ power responses following N cues (black line), time locked to cue onset. The dotted and solid blue bars denote significant differences between N cueing and S cueing conditions and between N and SF cueing conditions, respectively (P Ͻ 0.05). to the larger cortical population coding for all possible target identities). Our results do not provide support for either hypothesis. Preparing for the spatial location of an upcoming visual target stimulus elicited robust ␣ lateralization. However, when observers additionally held predictions about the identity of the upcoming target (and prepared both for the spatial location and the orientation of the upcoming target), ␣ lateralization was not further modulated apart from an early difference that was likely related to delayed deployment of attention with more complex cues (i.e., there was no ␣ modulation in the most relevant preparatory time window). Of course we cannot rule out that both hypothesized mechanisms were at play, counteracting one another (i.e., stronger engagement of a smaller population), but we consider it highly unlikely that this would result in a perfectly balanced activation pattern. It is key to note that the lack of an observed modulation of ␣ lateralization (and nonlateralized power) by feature-based attention was not due to the fact that the feature cues were not effective because they did elicit a clear behavioral performance benefit. Similarly, it is important to highlight that neutral cues were effective at shifting attention, since they provided useful information for preparation (Nobre and Rohenkohl 2014;Posner 1980) and only lack information about the most likely feature/ location of the target.

Relation to Other Studies of Feature-Based Attention and ␣ Oscillations
The role of ␣ power in spatial attention has been studied extensively, and several studies have demonstrated spatially specific modulations of ␣ power in preparation for an upcoming target stimulus (e.g., Sauseng et al. 2005;Thut et al. 2006;Worden et al. 2000), in line with the findings reported here. Fewer studies have assessed the effects of feature-based attention and target identity predictions on ␣ power. Snyder and Foxe (2010) examined whether ␣ mechanisms are also involved in nonspatial preparatory attention using a featuredimension cueing task where either color or motion information was task relevant in different trials. ␣ power modulations were observed bilaterally along the dorsal stream in motion area MT when motion was task relevant or along the ventral stream in color area V4 when color was task relevant. This study seems similar to ours, but there is a key difference between the experimental designs. In our task, participants were cued to attend to local feature identities (horizontal vs. vertical), processed by overlapping neural populations, whereas Snyder and Foxe (2010) cued participants to attend to feature dimensions, processed by large nonoverlapping specialized visual modules, and no specific target identity was known in advance.
In another related study, Mayer and colleagues showed modulation of ␣ power over task-relevant brain regions by stimulus identity predictions (Mayer et al. 2016). Participants viewed a letter sequence consisting of two parts: initially, letter identity was unknown and revealed over successive trials as letter visibility increased. Following that, letter visibility was lowered back down to subthreshold levels. Participants made subjective ratings to each stimulus in a sequence regarding its detectability and identity. This design allowed comparing ␣ power preceding letters whose identity was unknown (in the first half of a sequence) with letters whose identity was known (in the second half of a sequence), under identical sensory input. It was found that prestimulus ␣ power increased over left occipital areas when stimulus identity was known compared with when it was not. Source-space analysis of the effect identified a set of areas in left temporal cortex previously linked to top-down letter processing and supramodal representations of letters. This study seems remarkably similar to ours, since here predictions were made regarding actual feature values as opposed to feature dimensions. However, we used simple orientation stimuli while Mayer et al. (2016) used more complex letter stimuli processed in more high-level areas, a potentially important difference as discussed below.
In contrast to our findings, another recent MEG study (de Lange et al. 2013) reported an increase in preparatory ␣ power over occipital areas when observers held expectations about the feature composition of an upcoming stimulus, in that case its motion direction, compared with when observers did not have expectations. The authors proposed that this increase in ␣ power with expectation was driven by selective modulation of subpopulations of direction-selective cells, challenging our findings. It is possible that modulations in oscillatory activity differ as a function of predictions about motion direction vs. stimulus orientation. However, there are at least two additional relevant points to consider that might account for this apparent discrepancy with our findings. First, neutral and informative cues were not perceptually matched in the previous study, which might have driven differences in their sensory processing that are unrelated to preparatory neural dynamics. The authors acknowledged this possibility. Second, in our task, the cue-related prediction was orthogonal to the decision required for task performance (as participants performed a detection task), whereas in de Lange et al. (2013) it was. When predictions are also informative about the decisions and responses required, they may influence additional neural populations.
Finally, another recent study by van Diepen et al. (2016) examined the role of ␣ activity in spatial and feature-based attention. Participants performed a forced-choice identification task to a letter that appeared inside a target shape presented alongside a distracter shape. Spatial attention was manipulated using predictive cues, whereas feature-based attention was manipulated by varying target-distracter similarity (target shapes were always red, whereas distracter shapes could be presented in one of four possible colors ranging from red to yellow). The authors showed that, even when spatial cues were uninformative, ␣ lateralization to target stimuli emerged in low-similarity distracter trials. Given that van Diepen et al.
(2016) focused on target processing and manipulated featurebased attention by varying target-distracter similarity, their results do not directly inform our question of how target content predictions affect ␣ modulations in preparation for an upcoming target, and it is difficult to relate their findings to our results and the literature discussed above.
Albeit in different ways, the findings by Mayer et al. (2016) and Snyder and Foxe (2010) have thus suggested parallels between the role of ␣ mechanisms in selective spatial attention and feature-based attention. One proposal that could reconcile the findings of these two studies with ours is that ␣ oscillations play a role in gating information between distant nonoverlapping neural populations. We elaborate on this below.

How Specific Can Preparatory ␣ Modulations Be Employed?
The functional role underlying ␣ power modulations has been proposed to be the gating of information flow via active inhibition of non-task-relevant cortical areas (e.g., Jensen and Mazaheri 2010;Klimesch et al. 2007), for example, by modulating neuronal spike rates (Haegens et al. 2011b). However, this account leaves open the specificity with which this mechanism can operate. We speculate that ␣ modulations are predominantly involved in the gating of information processed by large nonoverlapping sensory area, be it between different retinotopical locations or different cortical areas that are specialized for processing different visual features [e.g., dorsal vs. ventral feature dimensions as in Snyder and Foxe (2010) but not in the specific gating of information at the level of particular features that are coded in neighboring cortical columns]. Importantly, this would mean that ␣ mechanisms may be able to support feature-based attention (and identity predictions) only in tasks where information gating occurs between distant cortical areas that are specialized for processing different feature dimensions, or relatively high-level feature representations. Fine-grained overlapping areas coding for early level feature values may not be targetable by ␣ mechanisms.
A likely explanation for this is that (EEG measured) ␣ modulations reflect an aggregate measure that occurs at a larger scale than, for example, orientation columns. This explanation can account for the differences between the findings reported here and the findings reported by Snyder and Foxe (2010) despite the apparent similarity of these two studies. Concretely, we used specific orientations (vertical vs. horizontal) that are represented in interdigitating populations. In contrast, Snyder and Foxe (2010) used non-identity-specific feature dimensions (color vs. motion), which are coded in distant nonoverlapping dorsal vs. ventral regions. Similarly, Mayer et al. (2016) reported ␣ modulations by identity predictions in relatively high-level cortical processing areas. In other words, the crucial distinction is not between feature-based vs. space-based attention but about what relevant populations are engaged in a task, and ␣ modulations cannot operate at a level of specificity such as microcolumns.
The findings of a recent study (Harvey et al. 2013) suggest that it may be possible to measure finer-grained differences in target-induced ␣ with higher spatial resolution using electrocorticography (ECoG). Harvey et al. (2013) recorded separate fMRI and ECoG data from occipital and parietal areas of an epileptic patient while the patient viewed drifting checkerboard bards of varying orientations. BOLD responses were used to estimate population receptive fields (pRF) for each stimulus. V1 electrodes in which pRF surround was stimulated showed increased ␣ power compared with electrodes in which pRF center was stimulated, and this pattern was observed between electrodes only 1 cm apart with partially overlapping visual field representations. These findings show that stimulus-induced ␣ modulations can be highly localized, with differences evident between nearby V1 locations.

Neural Mechanisms of Preparatory Feature-Based Attention
Given that we observed a robust behavioral benefit following feature-only cues, this must be the result of some neural processes. We focused on both global and lateralized modulations of ␣ power and did not observe any robust signature of preparatory feature-based attention. Going beyond our initial hypotheses, we also conducted exploratory analysis in other frequency bands, but these analyses also did not yield conclusive results. We may not have been able to look properly at some putative anticipatory modulatory mechanisms. Additionally, there may have been some exogenous feature-based priming (Theeuwes 2013) given that our cues highlighted the same visual features as the target stimuli. What the neural substrates are of the reported performance benefit by preparatory feature-based cues, alike the ones used here, thus remains an important avenue for future investigations.


s2fieldsofstudy: Biology Medicine

text:
Inhibition of the PI3K/AKT/mTOR pathway activates autophagy and compensatory Ras/Raf/MEK/ERK signalling in prostate cancer

The PI3K/AKT/mTOR pathway is frequently activated in advanced prostate cancer, due to loss of the tumour suppressor PTEN, and is an important axis for drug development. We have assessed the molecular and functional consequences of pathway blockade by inhibiting AKT and mTOR kinases either in combination or as individual drug treatments. In established prostate cancer cell lines, a decrease in cell viability and in phospho-biomarker expression was observed. Although apoptosis was not induced, a G1 growth arrest was observed in PTEN null LNCaP cells, but not in BPH1 or PC3 cells. In contrast, when the AKT inhibitor AZD7328 was applied to patient-derived prostate cultures that retained expression of PTEN, activation of a compensatory Ras/ MEK/ERK pathway was observed. Moreover, whilst autophagy was induced following treatment with AZD7328, cell viability was less affected in the patient-derived cultures than in cell lines. Surprisingly, treatment with a combination of both AZD7328 and two separate MEK1/2 inhibitors further enhanced phosphorylation of ERK1/2 in primary prostate cultures. However, it also induced irreversible growth arrest and senescence. Ex vivo treatment of a patient-derived xenograft (PDX) of prostate cancer with a combination of AZD7328 and the mTOR inhibitor KU-0063794, signiicantly reduced tumour frequency upon re-engraftment of tumour cells. The results demonstrate that single agent targeting of the PI3K/AKT/mTOR pathway triggers activation of the Ras/MEK/ERK compensatory pathway in nearpatient samples. Therefore, blockade of one pathway is insuficient to treat prostate cancer in man.

INTRODUCTION
Prostate cancer is the most common cancer in men, and the second most common cause of cancer death in men in the UK (Cancer Research UK, 2014). Advanced prostate tumours initially respond well to androgen deprivation therapy (ADT). However, in most cases, ADT ultimately fails and castration resistant prostate cancer (CRPC) emerges, which is almost inevitably fatal [1]. Although novel therapeutic agents, such as abiraterone and enzalutamide have demonstrated improved overall survival in metastatic CRPC, new www.impactjournals.com/oncotarget/ Oncotarget, 2017, Vol. 8, (No. 34), pp: 56698-56713

Research Paper
therapies are urgently needed [2][3][4]. There is also a growing realisation that early adoption of chemotherapy either before or with hormone therapy, results in greatly increased survival [5].
The PI3K/AKT/mTOR pathway is over-activated in many human cancers, including prostate cancer, and has emerged as a target for small molecule therapies [6][7][8]. However, inhibition of the PI3K pathway alone has proven to be insufficient for treatment of cancer, due to the existence of feedback loops and cross-talk with other pathways, including the Ras/Raf/MEK/ERK [9][10][11], and androgen receptor signalling pathways [8,12,13]. Therefore, combined targeting of the PI3K/ AKT/mTOR and the Ras/MEK/ERK pathways has been proposed as an alternative approach in a range of different cancers, including multiple myeloma (MM), melanoma and prostate cancer [6,14,15]. In multiple myeloma, combined blockade of the PI3K and MAPK pathways resulted in significantly enhanced cell death in the majority of tested cell lines [14]. A combination of the mTORC1 inhibitor, rapamycin and the MEK inhibitor, PD325901 inhibited growth of both androgen-responsive (CWR22Rv1 and CASP 2.1) and androgen-independent (CASP 1.1) prostate cancer cell lines. Blockade of both pathways, using a novel sorafenib derivative, NSK-01105, was shown to reduce cell proliferation and induce apoptosis in LNCaP and PC3 cells, and xenografted tumours of the same cell lines [16].
Preclinical assessment of potential new prostate cancer therapies is routinely performed in a limited number of cell lines and xenografts derived from them. The acknowledged heterogeneity between responses in different patients is therefore not addressed and may be the cause of the high attrition rate in oncology drug development [17]. In general, such approaches allow measurements of drug efficacy, but can prove misleading in determining the clinical applicability of drugs and drug combinations. Additionally, the presence of cancer stem cells (CSCs), in a range of cancers including leukaemia [18], breast [19], brain [20], colon [21], pancreas [22], lung [23] and prostate [24][25][26], has been suggested to be responsible for therapy resistance [27]. In prostate cancer, basal phenotype CSCs, CD44 + /α 2 β 1 hi / CD133 + are the most likely source of treatment-resistant cells, serving as a reservoir for tumour recurrence after castration therapy [24,28,29].
To address intra-tumour heterogeneity and differences between patients, we investigated the role of PI3K/ AKT/mTOR signalling in a range of prostate cancer primary cultures and PDXs. By measuring the signalling and cellular responses after treatment with small molecule inhibitors of the pathway, we demonstrate a surprising, but consistent disparity between the 'standard' cell line models and patient-derived cells.

Novel AKT and mTOR inhibitors reduce prostate cell viability
As AKT and mTOR kinases are crucial regulators of prostate cancer cell growth, proliferation and survival, treatment of high grade tumours with an AKT kinase inhibitor (AZD7328) and mTOR kinase inhibitor (KU-0063794), both ATP-competitive small molecules, has been proposed as a promising treatment strategy in oncology [6][7][8]. To establish the efficacy of the small molecule inhibitors, two PTEN-positive cell lines, BPH1 (benign) and P4E6 (localised cancer) and two PTEN-negative cell lines LNCaP (lymph node metastasis) and PC3 (bone metastasis) were treated with a range of concentrations (0.01 -30 μM) of AZD7328 or KU-0063794, for up to 48 hours. An MTS assay was performed to investigate the effect of AZD7328 and KU-0063794 on cell viability. LNCaP cells were the most sensitive to both inhibitors with an EC 50 of 0.4 μM (0.3-0.5) for AZD7328 and 1.03 μM (0.8-1.3) for KU-0063794 (Table 1). PC3 cells were similarly susceptible to KU-0063794, but were 27 fold less sensitive to AZD7328 (11.0 μM). Although treatment with AZD7328 reduced the viability of both PTEN-negative cell lines, the PTEN-positive cells lines (BPH1 and P4E6) were less sensitive to the AKT inhibitor and their viability was only slightly reduced. Specifically, the EC 50 for the KU-0063794 were approximately 5 -15 fold higher for BPH1 (5.8 μM) and P4E6 (10.2 μM) in comparison to LNCaP and PC3 (Table 1). Taken together, these results suggest that AZD7328 and KU-0063794's ability to reduce cell viability is dependent on PTEN status, with PTEN-negative cell lines being more susceptible to AKT and mTOR blockade.
To validate these findings in a 'near-patient' setting, primary cultures were generated from prostate cancer biopsies. Biopsies from patients with benign disease were used as normal controls. The cells were treated with increasing concentrations (1 -50 µM) of AZD7328 or KU-0063794 for up to 72 hours and cell viability was assessed. The results show that AZD7328 and KU-0063794 are less effective in patient-derived cultures (Supplementary Figure  1) indicating that combined treatment should be considered.
As mTOR kinase is responsible for maintaining metabolic homeostasis, the effect of treatment was measured using trypan blue exclusion to assess viability, and in a larger cohort of patients (n=6). Primary cultures were treated with AZD7328 and KU-0063794, either alone or in combination. We observed a significant decrease in the viability (~ 30%) with 3 μM KU-0063794 treatment at 24 and at 48 hours (P< 0.015) ( Figure 1A). Interestingly, a significant decrease in cell viability was observed in the majority of samples following combined treatment with 3 μM of either inhibitor at 24 hours and 48 hours (P < 0.006) ( Figure 1A). Additionally, a significant advantage of combination treatment over AZD7328 alone was observed at 48 hours. Importantly, these data show that there is variability in response to treatment between patients.
Since prostate epithelial cells are organised in a hierarchy [24,[29][30][31], the effect of treatment was assessed on the viability of Stem-like Cells (SC), Transit Amplifying (TA) and Committed Basal (CB) cells. Primary epithelial cancer cultures (derived from patients with Gleason 9, n=3 and Gleason 7 cancer, n=1) were treated with either 3 μM AZD7328, 3 μM KU-0063794 or a combination of 3 μM AZD7328 + 3 μM KU-0063794, for 72 hours. The cell subpopulations were isolated following treatment, and viability was assessed using trypan blue exclusion. Treatment with 3 μM AZD7328 reduced the viability of stem-like cells in 3 of 4 samples with the highest reduction (78%), in a Gleason 9 sample (H149/12) ( Figure 1B). Moreover, treatment with 3 μM KU-0063794 resulted in a decrease in the viability of SCs derived from all Gleason 9 samples, whilst stem cell numbers were not affected by treatment from the Gleason 7 culture. Interestingly, a combination of 3 μM AZD7328 + 3 μM KU-0063794 resulted in a significant decrease (P = 0.0044) in SC numbers in all cancer cultures ( Figure 1B).
In contrast to the effect on stem cell numbers, TA and CB cell numbers were not affected with either inhibitor alone or a combination of both ( Figure 1B). These results reflect prostate cancer intra tumoural heterogeneity as well as differences between patient samples further emphasizing the need for patient stratification.

Inhibition of AKT and mTOR does not induce cell cycle arrest in primary cultures
To determine whether the inhibitors directly influenced the cell cycle, two BPH and three cancer cultures were treated for 72 hours, with 5 μM AZD7328, 5 μM KU-0063794, and a combination of 5 μM AZD7328 + 5 μM KU-0063794 and were subsequently analysed by flow cytometry. The results revealed no significant difference in cell cycle distribution (p value >0.3) ( Figure 1C).

Expression of the tumour suppressor PTEN in prostate cancer patient cultures
Loss of the tumour suppressor gene, PTEN is observed in 40% of advanced prostate cancers [32,33]. Given the importance of PTEN in regulating the activity of the PI3K pathway, we determined the status of the PTEN gene as well as expression, in our patient-derived cultures. DNA analysis was performed on 7 samples for which matched lymphocytes and tumour biopsies were available. As a mutational hot spot has been reported in exon 5 of the PTEN gene (http://atlasgeneticsoncology.org/Genes/ PTENID158.html), we focused our analysis on exon 5. The results showed that missense mutations were found in tumour DNA of three samples (H135/11, H224/12 and H278/13), but not in their corresponding lymphocytes (Supplementary Figure 2A). However, it is not clear whether those mutations have resulted in translation of a functional PTEN and be affecting functionality of the PTEN protein and this will need to be investigated further.
Next, we determined whether tumour cells showed loss of heterozygosity at the PTEN gene locus 10p23 using two LOH probes ( Table 2) described in Dahia et al [34]. LOH analysis using Probe 1 (dinucleotide repeat in PTEN intron 2 (AFMa086wg9) showed that 2/7 samples (H135/11 and H377/13) were heterozygous and both cultures showed a relative loss of heterozygosity (expressed as reduction in peak intensity) in tumour DNA. Interestingly, the H135/11 sample which Had a missense mutation in exon 5 and LOH, showed a significant reduction in cell viability of stem-like cells following treatment with a combination of AZD7328 and KU-0063794, indicating higher susceptibility to the drugs. LOH analysis using probe 2 (G/T sequence polymorphism in PTEN intron 8) identified heterozygosity in a further 2 samples (H224/12 and H237/12). However, only H224/12 showed a relative loss of heterozygosity in the tumour DNA in comparison to the lymphocyte DNA.
Western blotting results showed that PTEN protein was detected in all primary cultures tested (Supplementary Figure  2B). The presence of PTEN in our cultures could possibly explain the limited efficacy of the AZD7328 and KU-0063794 in comparison to PTEN-null cell lines, such as LNCaP.

Treatment with AZD7328 and KU-0063794 reduces levels of phospho-biomarkers and induces cell cycle arrest in LNCaP cells
In order to validate the specificity of the inhibitors on the PI3K/AKT/mTOR pathway, we then determined the activity of selected phospho-biomarkers, by western blotting. PTEN-positive (BPH1) and PTEN-negative (LNCaP and PC3) cell lines were treated with 1 μM AZD7328 and KU-0063794 for 2 hours. The results showed a reduction in phospho-PRAS40 (T246), which is a substrate of AKT kinase, in all the cell lines, following treatment with AZD7328 ( Figure 2A). However, phospho-FOXO (T24/T32) levels decreased only in LNCaP cells.

Figure 1: Cell viability decreases following inhibition of AKT and mTOR in patient-derived prostate cultures. (A)
Primary cultures derived from cancers (n=5) and normal prostate (n=1) were treated with either 3 μM AZD7328, 3 μM KU-0063794 or a combination of 3 μM AZD7328 + 3 μM KU-0063794 in triplicate for 24 hours and 48 hours. Following treatment, the cells were harvested, stained with trypan blue and counted. The percentage of viable cells was calculated and normalized to the vehicle control (0.04% DMSO). Significant differences (p value < 0.05) in cell viability are indicated on the graphs. (B) Primary prostate cancer samples H252/12, H163/12, H149/12, and H135/11 were treated with 3 μM AZD7328, 3 μM KU-0063297 or a combination of 3 μM AZD7328 + 3 μM KU-0063794 for 72 hours. Following treatment, the cells were harvested, sorted into committed basal (CB), transit amplifying (TA) and stem-like cells (SC) and counted using trypan blue exclusion. Percentage of viable cells was calculated and normalized relative to the vehicle control (0.06% DMSO). Error bars represent the standard deviation. Significant difference (p <0.05) in cell viability was only observed in stem cell fraction treated with the combination of 3 μM AZD7328 + 3 μM KU-0063794 in comparison to vehicle control (indicated on the graph). (C) Cell cycle distribution remains unchanged in primary prostate cultures after treatment with AZD7328 and KU-0063794. Five prostate cultures (2 BPH and 3 cancers) were treated with 5 μM AZD7328, 5 μM KU-0063794 or 5 μM AZD7328 + 5 μM KU-0063794 for 72 hours. Following treatment, non-adherent cells were removed and remaining cells were harvested, fixed with 70% ice-cold ethanol, and stained with propidium iodide and analysed by flow cytometry. Control cells were treated with 0.1% DMSO. The signal was recorded in the PE channel and debris (subG1 phase) were excluded from the analysis.
Treatment with KU-0063794 resulted in a significant reduction of phospho-S6 (S235/236) (mTORC1 substrate) and phospho-AKT (S473) (mTORC2 substrate) in all three cell lines. This result confirmed the specificity of the inhibitors and their efficacy at a concentration of 1 μM, especially in LNCaP cells, where reduction of all respective biomarkers was observed.
Next, the effect of AKT or mTOR inhibition on cell cycle distribution was analysed. The results showed that the malignant LNCaP cells were the most affected. The majority of cells (86%) arrested in G0/G1 following 72-hour treatment with 1 μM KU-0063794, and 81% after treatment with 1 μM AZD7328, compared to 65% for the vehicle control ( Figure 2B). Cell cycle distribution in BPH1 and PC3 cells did not change significantly after treatment with either of the inhibitors.

Treatment of patient-derived cultures with AZD7328 induces autophagy and an increase in Phospho-ERK1/2 levels
As there was more variability in primary cultures' response to low concentrations of the inhibitors, we next investigated whether treatment with higher concentrations   The results showed evidence of autophagosomes in the cytoplasm in the cells treated with AZD7328, but not with KU-0063794 ( Figure 3B). Autophagy was also measured by Western blot; conversion of LC3B-I to LC3B-II is suggestive of autophagy, which was observed after treatment with AZD7328 ( Figure 3C and 3D). In addition, apoptosis was measured using cleaved PARP ( Figure 3D). The results also demonstrated an increase in cleaved PARP levels in cells treated with AZD7328, but not KU-0063794 ( Figure 3D), suggesting that despite activation of autophagy, cells died via apoptosis. Additionally, treatment with AZD7328 caused an increase in phospho-ERK1/2 levels in all analysed cultures ( Figure 3C and 3D), indicating an activation of the MAPK pathway in those cells. Treatment with KU-0063794 did not increase levels of phospho-ERK1/2 in any of the cancer cultures. However, an increase in MAPK pathway activity was observed in KU-0063794-treated BPH cultures ( Figure 3C).
As EGF is an activating ligand of the MAPK pathway and is a component of primary cell culture media used in our laboratory, we sought to determine whether the EGF in the culture medium affected the outcome of treatment with the inhibitors. Selected phospho-biomarker levels were determined in H268/12 (BPH culture) following treatment with 5 μM AZD7328, 5 μM KU-0063794 or a combination of 5 + 5. The results showed that treatment with AZD7328 resulted in a 9-fold increase in phospho-AKT in the presence of EGF, whereas in the absence of EGF, a 12.1-fold increase in expression was observed (Supplementary Figure 3). Phosphorylation of ERK1/2 increased by 2.8-fold following treatment with AZD7328 (in the presence of EGF) and by 1.2 in the absence of EGF, which suggests that the ERK activation may be mediated via signalling from growth factors, such as EGF. However, phosphorylation of S6 remained at a comparable level following treatment, either in the presence or absence of EGF. We concluded that addition of EGF to the stem cell media affected, but did not mask the outcome of treatment. Therefore, in subsequent experiments EGF was included in the culture medium for primary cells.

inhibitors further enhances phosphorylation of ERK1/2 in patient-derived cultures but induces senescence
To establish whether inhibition of MEK1/2 would be sufficient to overcome the compensatory effect of MAPK pathway activation, following inhibition of AZD7328, two small molecule inhibitors of MEK1/2, AZD6244 and RO-5126766, were tested. Following confirmation of inhibitory activity of both MEK1/2 inhibitors in prostate epithelial cultures (Supplementary Figure 4), combination treatment with AZD7328 + AZD6244 or AZD7328 + RO-512 were performed in patient-derived cancer cultures, for 72 hours. The results from Western blotting showed a further increase in phospho-ERK1/2 levels after treatment with either of the combinations ( Figure 4A and 4B), even after treatment with a high concentration (up to 25 µM) of the MEK1/2 inhibitors. This suggests that despite confirmed inhibitory activity against MEK1/2 kinase, neither AZD6244 nor RO-512 inhibitor is capable of preventing ERK1/2 phosphorylation following compensatory activation of MAPK pathway. However, despite this increase in phospho-ERK1/2 levels, the effect of combined treatment results in cellular senescence as detected by the acidic β-galactosidase staining ( Figure 4C).

Combined inhibition of AKT and mTOR decreases tumour frequency in a patient-derived xenograft model
To determine whether AKT and mTOR blockade could inhibit tumour growth, tumour cells were harvested from a human prostate cancer xenograft, (derived from a patient with hormone-naïve Gleason 7 disease), and were subsequently treated with either 10 μM AZD7328, 10 μM KU-0063794 or a combination of 10 + 10, for 72 hours. Following treatment, the cells were engrafted (at limiting dilutions) into Rag2 -/-γC -/mice to determine the effect of treatment on tumour frequency. The results showed that xenograft tumour cells were still able to initiate tumours following treatment with either inhibitor or a combination of both (Table 4). However, tumour frequency was significantly reduced with the combination treatment (P =0.010) ( Table 5). In contrast, neither AZD7328 nor KU-0063794 blockade affected tumour outgrowth.
An additional effect of treatment with a combination of the inhibitors was an 8-day increase in the tumour latency compared to vehicle control (38 days and 30 days, respectively). There was no difference in tumour latency following treatment with either inhibitor alone.

DISCUSSION
The PI3K/AKT/mTOR and the Androgen Receptor signalling pathways are important drivers of prostate cancer growth and progression. Despite progress in prostate cancer treatment with the development of second generation anti-androgen agents, patient outcome remains unsatisfactory. Following the development of endocrine resistance, patients are typically treated with docetaxel, which is effective in 40 -55 % of patients [4,[35][36][37]. Targeting of the PI3K/AKT/mTOR pathway has been investigated in a number of studies as an alternative treatment for patients with over-activation of the pathway [38,39]. Since a reciprocal feedback between the AR and the PI3K pathways has been demonstrated in prostate cancer, a combination of inhibitors targeting the PI3K pathway and anti-androgens, such as abiraterone and enzalutamide is currently in clinical trials [8,40]. However, targeting androgen-dependent prostate cells does not eradicate the stem cell component of prostate tumours, which we and others have shown to have an ARnegative phenotype [24,28,41]. Therefore alternative combinations of drugs are urgently required if we are to progress in the treatment of prostate cancer and the potential of PI3K pathway inhibitors has to be fully elucidated.
The aim of this study was to determine the importance of the PI3K/AKT/mTOR pathway in human prostate cancer. This was performed by targeting the pathway with AKT and mTOR inhibitors in prostate cancer cell lines, primary cell cultures and a 'near-patient' xenograft. Activity of the small molecule inhibitors against AKT and mTOR kinases used in this study have also been tested in a number of other tumour types [42,43].
The results of treatment with AZD7328 and KU-0063794 showed that PTEN-negative LNCaP cells were the most susceptible to treatment. Treatment of primary cultures (derived from patients with BPH and cancer) confirmed variability in response between patient samples, even from patients with the same Gleason grade. In contrast to established prostate cancer cell lines, PTEN   [32,33,44]. This discrepancy can be explained because the majority of cultures used in this study were derived from treatment naïve patients with early stage disease, whereas PTEN is more frequently lost in advanced prostate cancers. Only 12% of radical prostatectomy samples show complete loss of PTEN [45]. Nevertheless these cultures are relevant because it is imperative that combinations of inhibitors that delay progression to advanced disease, after initial androgen deprivation therapy or radiotherapy, are identified.
Targeting the PI3K/AKT/mTOR pathway has been proposed as a clinical strategy to eliminate cancer stem cells. For example, treatment of anaplastic thyroid cancer with a PI3K inhibitor, LY294002, attenuated the self-renewal of cancer stem cells and induced cell differentiation [46]. Similar findings were also reported in acute myeloid leukaemia and in breast cancer stem cells, where it was shown that the PI3K/AKT/mTOR pathway was critical for cancer stem cell proliferation and survival [47,48]. Moreover, treatment with a chemotherapeutic drug cisplatin, results in a significant increase in breast cancer stem cells, which could be partially attributed to the activation of the PI3K/AKT/mTOR pathway [49]. However, combined treatment with cisplatin and the mTOR inhibitor rapamycin synergistically inhibited cancer stem cell-mediated primary and metastatic cancer growth [49]. These recent findings emphasise the need to test combination treatments, as a more successful approach to targeting cancer stem cells. Our findings suggest that despite differences between patients' responses, targeting stem cells with a combination of AZD7328 and KU-0063794 might potentially be more successful than a monotherapy with either drug alone. Further investigation is needed to identify differences between treatment responders and non-responders, in a more efficient stratification of patients in the clinic.
Treatment with high concentrations (≥ 25 μM) of AKT and mTOR inhibitors induced dramatic morphological changes, such as vacuolation of cells and membrane blebbing. Although these concentrations are much higher than exposures achieved in man by AKT and mTOR inhibitors of similar potency, the biology invoked as a consequence of greater target inhibition is of interest. Membrane blebs are spherical outgrowths of the plasma membrane that occur upon membrane detachment from the underlying cytoskeleton and are indicative of apoptosis [50]. However, we observed no increase in cleaved PARP expression after mTOR inhibitor treatment. This could be due to a minority of cells showing apoptotic features, and therefore being undetectable by western blotting. Increased vacuolation of cells following treatment with the AKT inhibitor was morphologically indicative of autophagy and the formation of autophagosomes. Autophagy is a cellular catabolic degradation process in response to starvation or stress, in which cellular organelles, proteins and cytoplasm are digested and recycled to maintain metabolism [51,52]. A clear increase in the conversion of LC3-B I to LC3-B II (autophagy marker) demonstrated by both western blotting and immunofluorescence confirmed the morphological evidence. Treatment with the ATPcompetitive AKT inhibitor (AZD7328) does induce autophagy in human bladder cancer cell lines, which can be overcome by treating the cells with chloroquine [53]. However, autophagy can also prevent tumourigenesis, acting as a tumour suppressor [52,54]. Accumulation of the autophagy substrate p62/SQSTM1 is sufficient to both activate the MAPK pathway and increase cell proliferation in PI3K-expressing mammary 3D structures [54]. Increased cell proliferation and MAPK activation, due to inhibition of autophagy, was only evident under 3D culture conditions, and not in monolayer cells [54]. In 3D, the cells treated with AZD7328 showed the expected increase in autophagy, as well as activation of the MAPK pathway.
We observed activation of the MAPK pathway following treatment with AZD7328, and so the primary cultures were also treated with a combination of AZD7328 and MEK1/2 inhibitors to determine the outcome of inhibiting this compensatory pathway. Surprisingly, a combined treatment with AZD7328 and either AZD6244 or RO-512 resulted in an increased phosphorylation of ERK1/2 to even greater levels than treatment with AZD7328 alone. Davies and colleagues have previously demonstrated activity and efficiency of the AZD6244 in a large panel of human cancer cell lines as well as in xenografts derived from them [55]. It has been reported that a majority of cell lines with a mutation in KRAS, NRAS or BRAF genes showed a significantly increased sensitivity to MEK inhibition, resulting in cell growth inhibition and induction of apoptosis [55]. In the human colon carcinoma xenograft HCT-116, a combination treatment of the AZD6244 and docetaxel resulted in enhanced tumour growth inhibition, in comparison to monotherapy [56]. However, the rate of mutations in Ras/ MEK/ERK pathway in clinical prostate cancer is very low; only up to 7% of clinical samples have activating point mutations of Ras and around 4 % are positive for BRAF, the most common activating mutation of RAF [57]. Similarly, efficacy of RO-512 has been demonstrated in a number of human cancer cell lines and also in the HCT-116 xenograft model [58]. However, in our patient-derived cultures, neither of the MEK1/2 inhibitors was able to overcome the compensatory ERK1/2 activation caused by inhibition of AKT. www.impactjournals.com/oncotarget It is unclear why combination treatment of prostate cancer primary cells with AZD7328 and MEK1/2 inhibitors did not reduce ERK1/2 phosphorylation. A possible explanation is that we could be observing a profound stress response where other MAPKs are activated and although phospho-ERK1/2 antibody we used does not cross-react with the corresponding phosphorylated residues of either JNK/ SAPK or p38 MAP kinases, this phenomenon will have to be investigated further. However, acidic β-galactosidase staining of treated cultures revealed that the cells became senescent after the combination treatment. Senescence is frequently triggered in tumours in response to chemotherapy and radiotherapy treatment in cancer patients [59,60] and it has been demonstrated that PTEN-loss-induced cellular senescence inhibits tumorigenesis in vivo in a human xenograft model of prostate cancer [61]. Induction of senescence in cancer cells could indeed be a good therapeutic strategy for treatment of prostate cancer if it established stable disease [61].
The PI3K/AKT/mTOR and the Ras/MEK/ERK pathways are the two major hyper-activated pathways, which promote cell proliferation, survival and metastasis in human cancer [11,62]. Significant activation of MAPK signalling has been observed in both primary and in metastatic cancer [63]. Activation of the MAPK pathway has been implicated in resistance to the PI3K/AKT/mTOR signalling inhibition in other studies [64]. Here, we present for the first time, evidence that the MAPK pathway acts as a compensatory pathway in primary prostate epithelial cell cultures following AKT inhibition. However, the possibility of a third compensatory pathway cannot be discounted by our data.
Despite our observation that the Ras/MEK/ERK compensatory pathway is activated in primary cells following the PI3K/AKT/mTOR inhibition, the ex vivo treatment of the PDX with a combination of KU-0063794 (mTOR inhibitor) and AZD7328 (AKT inhibitor) showed an effective reduction in tumour outgrowth. Therefore this combination treatment alongside anti-androgen therapy has the potential to be more effective than single treatments. However, due to the compensatory pathway and the further possibility of as yet unknown effects, the outcome of drug combinations may not be predictable and may have to be determined on a patient specific basis. The utilisation of primary cell cultures from multiple individual patients will allow elucidation of mechanisms of action of drugs, mechanisms of resistance and variation in patient response, at a level of which cannot be predicted using the standard panel of available prostate cancer cell lines.

Inhibitors
The AKT inhibitor AZ7328, mTOR inhibitor KU-0063794 and MEK inhibitor Selumetinib (AZD6244; ARRY-142886) were obtained from AstraZeneca, and the MEK1/2 inhibitor RO-5126766 (RO-512) was purchased from MedKoo Biosciences, Inc., NC, USA. AZ7328 is an ATP-competitive, selective inhibitor of AKT [53] that inhibits all three AKT isoforms with an IC50 of < 50 nM in isolated enzyme assays, and inhibits the phosphorylation of GSK3β in PTEN null MDA-MB-468 breast cancer cells with an IC50 of 91 nM. KU-0063794 is a highly selective inhibitor of mTOR kinase [43] and Selumetinib is a selective, non-ATP competitive inhibitor of MEK1/2 kinases [55]. RO-5126766 is an allosteric MEK inhibitor which has been shown to additionally supress feedback induction of RAF-dependent MEK phosphorylation [58].

Tissue collection and culture of tumour cells
Prostate tissue was obtained from patients undergoing radical prostatectomy or transurethral resection of the prostate (TURP) ( Table 3), with informed patient consent and approval from the local Research Ethics Committee (07/H1304/121). All patient samples were anonymized. Epithelial cultures were prepared as described previously [31]. Cell cultures were maintained in stem cell media (SCM), which consisted of keratinocyte growth medium supplemented with 5 ng/ml Epidermal Growth Factor (EGF), 50 μg/ml bovine pituitary extract (Invitrogen Ltd), 2 ng/ml stem cell factor (First Link), 100 ng/ml cholera toxin (Sigma-Aldrich Company Ltd) and 1 ng/ml GM-SCF (First Link). Epithelial cells were cultured on collagen I -coated plates in the presence of irradiated (60 Gy) STO (mouse embryonic fibroblasts) cells.

Generation of xenografts and isolation of tumour cells for ex-vivo treatment
All animal work was approved by the University of York Animal Procedures and Ethics Committee www.impactjournals.com/oncotarget and performed under a United Kingdom Home Office License. Human prostate cancer tissue was obtained from a patient with hormone naïve, Gleason 7 disease. Tumour Xenografts were derived according to the method outlined in Kroon et al., 2013 [66]. Xenografts were maintained subcutaneously in BALB/c/Rag2 -/-γC -/mice, which are highly receptive for engrafting of human tissue due to deficiencies in T, B and NK cell development [67].
15 mm 3 tumours were harvested from humanely euthanized mice and human Lin-/CD31-, were isolated according to Kroon et al., 2013 [66]. Tumour cells were resuspended in D10 medium, plated onto collagen I -dishes and treated for up to 72 hours with AKT and mTOR inhibitors. Following treatment, the cells were resuspended in Matrigel and injected subcutaneously into the flanks of a Rag2 -/-γC -/-, 2x10 5 irradiated STO were used as carrier cells. Mice were monitored weekly for tumour growth. Once the tumours were detectable they were measured every 2-3 days using a digital calliper (Duratool DC150). Tumour volume (mm 3 ) was calculated using the ellipsoidal formula: (length x width 2 )/2. Tumour initiation frequency was calculated using Extreme Limiting Dilution Analysis software available online at (http://bioinf.wehi. edu.au/software/elda/), which provided an estimate of frequency with 95% confidence intervals and includes the Pearson's chi-square test for pairwise analysis to determine differences between treatment groups.

PTEN mutational analysis and loss of heterozygosity (LOH) analysis
The Polymerase chain reaction (PCR) was used to amplify matched lymphocyte and tumour DNA from the patient primary cultures to determine mutational status and loss of heterozygosity of the PTEN gene. Standard PCR conditions were adapted for the analysis of PTEN exon 5 [34]. PCR products were column purified using Qiagen Quick PCR clean up kit (Qiagen, UK) and then directly sequenced using PTEN exon 5 sequencing primers ( Table 2). The presence of mutations was determined by using EditSeq software (DNASTAR).
Loss of heterozygosity in PTEN was assessed using two LOH probes described in Dahia et al [34]; they included a dinucleotide repeat in PTEN intron 2 (AFMa086wg9) (probe 1) and a G/T sequence polymorphism in PTEN intron 8 (probe 2). The reverse primer for intron 2 was labelled with a JOE fluorescent dye enabling PCR fragment analysis, which was performed using Applied Biosystems ABI3130XL and data analysed using Gene Mapper software (Life Technologies). LOH was identified in the heterozygous tumour samples as a reduction in one of the peaks, which corresponded to one of the alleles of the PTEN gene. The G/T polymorphism was analysed using digestion with restriction enzyme HincII (New England Biosciences), where the polymorphism is differentially cleaved in heterozygous and homozygous individuals.

Agarose gel electrophoresis
PCR products were visualised using 1.5% agarose gel electrophoresis prepared with Tris-Acetate-EDTA buffer and run at 80V. The products of the HincII digestion reaction were separated in a 3% agarose gel in Tris-Borate-EDTA buffer and run at 100V.

SDS-PAGE and Western blotting
Adherent and non-adherent cells were harvested and resuspended in Cytobuster protein extraction reagent (Novagen) supplemented with the addition of protease inhibitor cocktail (Roche) and phosphatase inhibitor cocktail (Roche). Whole cell protein lysates were quantified using the Bicinchoninic acid assay (BCA) protein assay kit (Thermo Scientific), according to manufacturer's instructions.

Flow cytometry and cell cycle analysis
Cell lines and primary prostate cells were used for cell cycle analysis following treatment. Cells were fixed in ice-cold 70% Ethanol, then incubated on ice for 30 minutes and resuspended in PBS. RNase A (at 1 mg/ml final concentration) and propidium iodide (PI; 400 µg/ml final concentration) were added, and cells incubated at 37°C for 30 min. Cells were then placed on ice, analysed on a CyAn ADP flow cytometer (Dako Cytomation) and PI fluorescence was recorded in the PE channel. To prevent bleaching of the fluorescence, samples were protected from light where possible during the procedure. The results were analysed using the Summit v4.3 software.

Immunofluorescence
Primary prostate cells were seeded at a density of 7.5 x 10 4 per well in BD-Biocoat collagen I-coated 8-well chamber slides and incubated at 37°C at 5% CO 2 for 24 hours before treatment. Following treatment, cells were fixed, permeabilised and stained as described previously [69]. Antibodies used were LC3-B (abcam, ab51520 at 1:2000) and secondary anti-rabbit Alexa Fluor 488 (Invitrogen, A-11034 at 1:300). Slides were mounted in Vectashield containing DAPI (Vector laboratories). Images were captured using a Nikon Eclipse TE300 fluorescent microscope (Nikon). Secondary only antibodies, where the primary antibody was replaced with PBS, were used as controls.

β-Galactosidase staining assay
Cells were seeded onto 10 cm dishes and, following treatment, an acidic β-Galactosidase assay (Cell Signaling Technology) was performed as an indicator of senescence. Briefly, cells were washed once with PBS and fixed for 15 minutes at room temperature, washed twice with PBS, and then incubated with β-Galactosidase staining solution, at pH 6.1, overnight at 37°C in a dry incubator. The staining results were analysed using an Evos XL transmitted light microscope (AMG) at 10x and 20x magnification.


extfieldsofstudy: Biology Medicine

text:
POLE somatic mutations in advanced colorectal cancer

Abstract Despite all the knowledge already gathered, the picture of somatic genetic changes in colorectal tumorigenesis is far from complete. Recently, germline and somatic mutations in the exonuclease domain of polymerase epsilon, catalytic subunit (POLE) gene have been reported in a small subset of microsatellite‐stable and hypermutated colorectal carcinomas (CRCs), affecting the proofreading activity of the enzyme and leading to misincorporation of bases during DNA replication. To evaluate the role of POLE mutations in colorectal carcinogenesis, namely in advanced CRC, we searched for somatic mutations by Sanger sequencing in tumor DNA samples from 307 cases. Microsatellite instability and mutation analyses of a panel of oncogenes were performed in the tumors harboring POLE mutations. Three heterozygous mutations were found in two tumors, the c.857C>G, p.Pro286Arg, the c.901G>A, p.Asp301Asn, and the c.1376C>T, p.Ser459Phe. Of the POLE‐mutated CRCs, one tumor was microsatellite‐stable and the other had low microsatellite instability, whereas KRAS and PIK3CA mutations were found in one tumor each. We conclude that POLE somatic mutations exist but are rare in advanced CRC, with further larger studies being necessary to evaluate its biological and clinical implications.

Introduction
Colorectal cancer (CRC) is one of the major causes of morbidity and mortality throughout the world and accounts for over 9% of all cancer cases diagnosed in 2012 [1]. The etiological factors and pathogenic mechanisms underlying CRC development appear to be complex and heterogeneous. The majority of CRC cases occur sporadically, arising through the sequential accumulation of multiple genetic and/or epigenetic alterations involving genes that regulate cell growth and differentiation [2,3]. Several crucial gene defects in sporadic CRC have been identified, and several molecular pathways have been described, such as the chromosomal instability (CIN), the microsatellite instability (MSI), and the CpG island methylator phenotype (CIMP) pathways [4]. These pathways are not mutually exclusive as a tumor can occasionally show features of multiple pathways, although the extent and nature of this overlap remains to be determined [2,4].
Despite the enormous progress in defining some of the common genetic and epigenetic alterations, the picture of somatic genetic changes in colorectal tumorigenesis is far from complete. To fill in the gaps, The Cancer Genome Atlas (TCGA) exome sequencing project published the result of full genomic profiling of 224 CRC samples [5]. This work confirmed many previous mutational findings in CRC and additionally identified new rare findings, such as mutations in the exonuclease domain of polymerase epsilon, catalytic subunit (POLE) gene in approximately 3% of sporadic microsatellite-stable (MSS) but hypermutated CRCs [5][6][7]. The POLE gene is located in 12q24.33 and encodes the proofreading (exonuclease) subunit of polymerase epsilon (POLE) with 2286 amino acids. Palles and collaborators, in 2013, found a subset of patients ORIGINAL RESEARCH POLE somatic mutations in advanced colorectal cancer with MSS CRC that harbor germline POLE exonuclease domain mutations (EDMs) [6]. Subsequently, Stenzinger and coworkers detected somatic POLE EDMs in 12.3% of MSS sporadic CRC [8]. Additionally, POLE EDMs were found in about 7% of sporadic endometrial cancer (EC) and, similarly to what was observed in CRC, were associated with an ultramutator and MSS phenotype [7,9]. POLE has an essential role in chromosomal DNA replication, namely in the leading-strand synthesis and recognition and removal of mispaired nucleotides by its proofreading capacity through the POLE exonuclease domain, which is crucial for the maintenance of replication fidelity [10][11][12]. The aim of this study was to evaluate the frequency and role of POLE somatic mutations in colorectal carcinogenesis, namely in advanced CRC.

Tumor samples
This study includes 307 patients with CRC for whom KRAS exon 2 mutations had been requested to the Genetics Department of the Portuguese Oncology Institute of Porto (IPO-Porto) between August 2008 and December 2012 and from whom there was sufficient tumor DNA/sample left for further analyses.

DNA extraction
DNA was isolated from tumor areas containing at least 50% of tumor cells, delimited by a pathologist, using the QIAamp® DNA FFPE Tissue Kit (Qiagen, Hilden, Germany) and following the manufacturer's instructions. In samples with mutations, the normal mucosa was also extracted from the same slide, to confirm the somatic status of mutations, using the same approach.

POLE mutation analysis
Somatic mutation screening of the exonuclease domain of POLE (exons 9-14) was performed by Sanger sequencing. Primer sequences were kindly provided by Professor Ian Tomlinson from the Wellcome Trust Centre for Human Genetics, University of Oxford, United Kingdom. Sanger sequencing was performed using the BigDye® Terminator v1.1 or v3.1 Cycle Sequencing Kit (Applied Biosystems, Foster City, CA, USA), following the manufacturer's instructions, and the products were analyzed on an ABI PRISM™ 310 Genetic Analyzer (Applied Biosystems) or a 3500 Genetic Analyzer (Applied Biosystems). A second DNA extraction and PCR amplification was performed in all positive samples, followed by DNA Sanger sequencing of both strands. All POLE variants were described according to the LRG_789 (NM_006231.3) and to the Human Genome Variation Society (HGVS) guidelines.

Microsatellite instability and MMR immunohistochemistry analysis
Microsatellite instability was performed in the tumors with POLE mutations, using a set of five mononucleotide microsatellite markers (BAT26, BAT25, NR21, NR22, and NR24) recommended by the National Cancer Institute as an alternative to the Bethesda panel [13]. PCR products of the microsatellite sequences were analyzed for length variations on an ABI PRISM™ 310 Genetic Analyzer (Applied Biosystems). Allele sizes were determined using the GeneMapper® software version 3.7 (Applied Biosystems). Tumors were characterized as microsatellite instability-high (MSI-H) if they had instability at >30% of loci, microsatellite instability-low (MSI-L) if unstable at <30% of loci, and MSS if they showed no instability at any loci. The tumors with POLE mutations were also assessed for MLH1, MSH2, MSH6, and PMS2 immunoexpression performed as previously described [14].
The BRAF c.1799T>A, p.Val600Glu (LRG_299, NM_004333.4) mutation was screened in the tumors with POLE mutations by PCR amplification and high-resolution melting (HRM) analysis on a LightCycler-480 II Real-Time System (Roche Applied Science) according to Mancini et al. [16]. As a confirmation of this technique, singlenucleotide primer extension (SNuPE) was performed following the SNaPshot Kit (Applied Biosystems) manufacturer's protocol.

Results
We searched for mutations in the exonuclease domain of the POLE gene (exons 9-14) in tumor DNA samples from 307 CRCs. Three heterozygous mutations were identified in two cases (T286 and T368), which corresponds to a frequency of somatic POLE mutations of 0.65%. One tumor (T286) presented one POLE missense mutation (c.857C>G, p.Pro286Arg) (Fig. 1A), and another POLE Somatic Mutations tumor (T368) presented two POLE missense mutations (c.901G>A, p.Asp301Asn (Fig. 1B); and c.1376C>T, p.Ser459Phe (Fig. 1C)). In order to determine the somatic or germline nature of the mutations found in the tumor tissues, we analyzed DNA samples extracted from normal mucosa adjacent to the tumor, and none of the mutations found in the tumors were present in normal cells of the patients (Fig. 1), indicating that all the mutations found are somatic.
Case T286, with the POLE mutation c.857C>G, p.Pro286Arg, was a man who was diagnosed at age 28 years with a moderately differentiated ascending colon adenocarcinoma. Histopathologically, the tumor displayed a cribriform pattern and it was staged as pT4NxMx, according to the TNM classification. The tumor was also tested for mutations in exons 2, 3, and 4 of the KRAS and NRAS genes, codon 600 of the BRAF gene, and exons 10 and 21 of the PIK3CA gene, and showed a mutation in PIK3CA exon 10, namely the c.1624G>A, p.Glu542Lys (previously reported by our group [15]).
Case T368, with the POLE mutations c.901G>A, p.Asp301Asn, and c.1376C>T, p.Ser459Phe, was a man who was diagnosed at age 49 years with a mucinous adenocarcinoma located in the cecum and invading the muscle layer. There were also tubular adenomatous polyps with low-grade dysplasia. The adenocarcinoma was well differentiated and, according to the TNM classification, the tumor stage was pT2N0M0 at the time of diagnosis, but 2 years later, a pelvic recurrence developed. This tumor was also investigated for KRAS, NRAS, BRAF, and PIK3CA mutations, and a mutation in KRAS exon 4 was found, namely the c.351A>C, p.Lys117Asn.
The two cases with POLE mutations were studied by immunohistochemistry, and both showed normal expression of the mismatch repair (MMR) proteins. They were also evaluated for MSI, and the tumor with the POLE mutation c.857C>G, p.Pro286Arg, displayed instability only at the BAT25 locus, being therefore classified as MSI-L. The tumor with the two POLE mutations, the c.901G>A, p.Asp301Asn, and the c.1376C>T, p.Ser459Phe, did not exhibit any alteration in the five markers, and it was consequently classified as MSS.

Discussion
In this study, we identified three POLE mutations in two of 307 CRCs, a frequency (0.65%) that is lower than some previous reports (3-12.3%) [5,8] and comparable with that of recent study of Domingo and collaborators (1%) [17]. The selection criteria could explain the differences between frequencies of somatic POLE EDMs observed. While our cases represent a consecutive series of advanced colorectal tumors not selected for MSI status, the series described by Stenzinger and coworkers, the study with the highest frequency so far described, consisted of CRCs with MSS phenotype, which likely increases the frequency of somatic POLE mutations as they have previously been seen mostly in MSS tumors [8]. On the other hand, the TCGA cases represent a series of colon or rectum adenocarcinomas of patients undergoing surgical resection, regardless of surgical stage, histologic grade, or MSI status [5], and the series described by Domingo and collaborators consisted in a study population of predominantly stage II and III CRC [17]. The three POLE EDMs here identified were the c.857C>G, p.Pro286Arg, the c.901G>A p.Asp301Asn, and the c.1376C>T p.Ser459Phe. The p.Pro286Arg and the p.Ser459Phe were previously reported as somatic mutation hotspots [18], whereas the p.Asp301Asn mutation was previously described by Stenzinger and colleagues in only one tumor [8]. All these alterations are predicted to have a direct effect on the proofreading function [6,10]. Interestingly, the POLE p.Asp301Asn and p.Ser459Phe mutations were found in the same tumor. It was not possible to clarify whether these mutations were present in the same clone (in cis or in trans) or in different clones, being therefore unknown whether the two mutations occurred simultaneously, whether the presence of one mutation resulted in another mutation in the same clone, or whether these mutations in the same tumor are an example of intratumor heterogeneity. Stenzinger and coworkers also identified six cases with two different POLE exonuclease domain mutations [8].
The two tumors harboring the POLE EDMs we here report were from patients diagnosed at a younger age (28 and 49 years) compared with the median age at diagnosis in our series (59 years), which is consistent with recent studies [17,19]. Furthermore, the p.Pro286Arg mutation was found in a tumor of a 28-year-old man, which is in agreement with two previous studies that demonstrated an association of this particular mutation with early-onset CRC [20,21]. Additionally, we observed that the tumor T286 was MSI-L, with just one positive marker, and the tumor T368 was MSS. These data concur with those reported by the TCGA project and by Stenzinger and coworkers, showing that most CRCs with POLE EDMs are not MSI-H [5,8,17]. According to the literature, these tumors with POLE EDMs have a hypermutated phenotype associated with loss of the POLE proofreading function and present a high mutational burden in the known CRC driver genes [7,22]. However, these mutations in CRC driver genes are often different from the common hotspots in those genes [7]. In our study, we observed that none of the two POLE-positive tumors presented NRAS or BRAF mutations, but tumor T286 exhibited the p.Glu542Lys PIK3CA mutation and tumor T368 displayed the p.Lys117Asn KRAS mutation. Both mutations are outside the most common hotspots in those genes, thus confirming earlier observations [7]. It is therefore likely that CRCs with POLE EDMs define a hypermutated, non-MSI-H, group of tumors [5], but it is not yet known whether this disease subset follows one of the previously established CRC pathways or whether they represent a different pathway.
We could not assess whether POLE mutations have prognostic or predictive implications due to the small number of mutations found. Nevertheless, Stenzinger and collaborators reported that patients with stage III/IV tumors harboring POLE mutations had a statistically significant increased mortality [8]. On the other hand, available data on EC suggest that patients with POLE EDMs have significantly better outcomes [9,23,24]. It is unclear how the same pathogenetic mechanism would drive opposite prognosis in the different tumor types, so further studies are necessary to comprehend the biological and clinical impact of these mutations in CRC and in other malignancies. Alterations of the proofreading function may be selectively deleterious to the cell, so it would also be important to determine whether proofreading deficiency has any effect on polymerase processivity. Furthermore, similarly to other examples of targeted therapy based on deficient DNA repair mechanisms [25,26], it would be important to explore how the hypermutated phenotype associated with POLE mutations may be used therapeutically, especially in the context of advanced CRC.