The aim of this study was to compare the efficacy and safety of 2 approaches for intra-coronary administration of tirofiban (aspiration catheter versus guiding catheter) in patients over 60 years of age undergoing percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI). It has been suggested that the administration of tirofiban by intra-coronary injection could promote drug absorption in the diseased region and enhance the inhibition of platelet aggregation, decreasing bleeding rates, but little is known about the comparative efficiency and safety of using guiding catheter versus aspiration catheter for delivery.Eighty-nine patients over 60 years of age with STEMI undergoing PCI were randomly divided into 2 groups according to the injection route for intracoronary administration of tirofiban [guiding catheter (n = 41) and aspiration catheter (n = 48)]. Baseline features, epicardial and myocardial perfusion, major adverse cardiac and cerebrovascular events (MACCEs), and bleeding rate were compared.P > .05). The patients in the aspiration catheter group generally had a higher incidence of cerebral vascular disease. Compared with those in the guiding catheter group, patients in the aspiration catheter group obtained more favorable myocardial perfusion (P < .05). In-hospital and at 3-month and 6-month follow-ups, the MACCE rate and frequency of bleeding events were similar between the 2 groups (P > .05).No differences in age, gender, and history of hypertension, hypercholesterolemia, diabetes, and so on were observed (Intra-coronary delivery of tirofiban through aspiration catheter led to better myocardial perfusion in STEMI patients over 60 years of age undergoing PCI compared with intra-coronary injection of tirofiban through guiding catheter. The 2 delivery routes were associated with similar rates of MACCEs and bleeding events. This procedure is thus recommended in patients with STEMI who can undergo PCI for the infarct-related artery (IRA) within 12 hours of symptom onset and if the door-to-balloon time can be within 90 minutes. Epicardial thrombolysis in myocardial infarction (TIMI) 3 flow can be achieved in the IRA in >90% of patients undergoing PPCI. However, epicardial blood flow does not necessarily equate to myocardial perfusion. After the operation, angina pectoris and even heart failure, sudden death, and other serious cardiovascular adverse events are possible. At present, it is believed that this is because sufficient and effective blood flow perfusion in the myocardium is not achieved, leading to hibernation, stunning, and necrosis of the myocardium. Thus, even with TIMI 3 flow after successful PCI may, effective myocardial perfusion may not be achieved.–7 Excessive platelet activation and aggregation play an important role in the progression of acute coronary syndrome (ACS). Glycoprotein (GP) IIb/IIIa antagonists can effectively block the binding of fibrinogen to platelet glycoprotein IIb/IIIa receptor and the adhesion of platelets and damaged endothelial cells. One such antagonist, tirofiban, was shown to improve myocardial perfusion by inhibiting platelet aggregation. According to the ESC/ACCF/AHA guidelines, the use of GP IIb/IIIa inhibitors (GPIs) is reasonable as bailout therapy in the event of angiographic evidence of a large thrombus, slow or no reflow, or other thrombotic complications.,11 However, the incidence of hemorrhage is also increased. Elderly patients (>60 ys of age) with coronary heart disease have complex clinical risk factors, leading to the occurrence of more complications. The incidence of bleeding is higher in these patients after strengthening of anti-platelet therapy. Tirofiban is administered through both intravenous and intra-coronary artery delivery. It has been suggested that the administration of tirofiban by intra-coronary injection can promote drug absorption in the diseased region and enhance the inhibition of platelet aggregation, thereby decreasing bleeding risk. Intra-coronary administration of tirofiban can be achieved using either a guiding catheter or an aspiration catheter, and little is known about the comparative efficiency and safety for these 2 delivery methods. Therefore, the present study investigated the efficacy and safety of using the 2 different methods of tirofiban administration to improve myocardial perfusion and clinical outcomes in STEMI patients >60 years of age undergoing PPCI.Primary percutaneous coronary intervention (PPCI) is currently considered the most effective treatment option for ST-segment elevation myocardial infarction (STEMI).22.1Between January 2012 and January 2017, a total of 369 STEMI patients underwent PPCI with 12 hours of symptom onset in our hospital. Among them, patients who were over 60 years of age were consecutively enrolled in this study. STEMI was defined as chest pain of >30 minutes duration and electrocardiogram (ECG) changes with ST segment elevation of >2 mm in at least 2 precordial leads and >1 mm in the limb leads. Patients were excluded if they had contraindications for the use of GPIs , as were patients with cardiogenic shock at admission or patients with noncardiac conditions that could interfere with compliance with the protocol or require interruption of thienopyridine treatment. Informed consent was obtained from individual patients, and the study protocol was approved by the Institutional Review Board on Human Research. Finally, 89 patients were randomly divided into 2 groups according to the method used to administer the loading dose of tirofiban: the guiding catheter group (n = 41) and the aspiration catheter group (n = 48). The patients received. An intra-coronary injection of 10 μg/kg tirofiban  was applied according to the thrombus burden.All the patients received 300 mg aspirin and 300 to 600 mg Clopidogrel and 100 U/kg unfractionated heparin (UFH) in the catheterization laboratory. PPCI was performed via the radial artery approach, using standard 6F or 7F guiding catheters. In the aspiration catheter group, patients received an intra-coronary target injection of tirofiban using the Export aspiration catheter . The patients continued to receive 100 mg aspirin daily, 75 mg Clopidogrel daily (for 1 year), and 20 mg Atorvastatin daily at the physician's discretion. Other drugs, such as angiotensin converting enzyme inhibitors (ACEI) and beta blockers, were prescribed according to current guidelines and the patient's condition.2.2–15 The first frame of TMPFC was defined as the frame that clearly demonstrated the first appearance of myocardial blush beyond the IRA (F1). The last frame of TMPFC was then defined as the frame in which contrast or myocardial blush disappeared (F2). The TMPFC is therefore F2–F1 frame counts at a filming rate of 15 frames/sec, or (F2–F1) × 2 frame counts at the corrected filming rate of 30 frames/sec.The TIMI Myocardial Perfusion Frame Count (TMPFC) was used to standardize and quantify myocardial perfusion by timing the filling and clearance of contrast in the myocardium using cine-angiographic frame-counting.TIMI grades were used to assess the forward blood flow and were graded as follows: Grade 0, no blood perfusion in the IRA and occlusion of distal vessels without blood flow; grade 1, distal stenosis of the coronary artery without blood flow; grade 2, distal stenosis of the coronary artery with blood flow, but with slow complete filling; and grade 3, blood flow similar to normal coronary artery blood flow. Hematoma was considered significant if there was a hematoma >5 cm at the access site. Major adverse cardiac and cerebrovascular events (MACCEs) included cardiovascular mortality, myocardial infarction, heart failure, cerebrovascular accident, and need for urgent revascularization.The patients were followed up in the hospital and 6 months after PCI for mortality, myocardial infarction, cerebrovascular events, need for urgent revascularization, bleeding, heart failure, and hematoma. Major bleeding was defined as a >5 g/dL decrease in the hemoglobin level or need for transfusion; medium bleeding was defined as a 3 to 5 g/dL decrease in the hemoglobin level; and minor bleeding was defined as a <3 g/dL decrease in the hemoglobin level.2.3t test or nonparametric Mann-Whitney U test whenever the data did not appear to have a normal distribution. Categorical variables were compared using the Pearson χ2 or the Fisher exact test, as appropriate. P ≤.05 were considered statistically significant.Statistical analyses were conducted using the statistical software SPSS version 16.0 for Windows . The data are presented as mean ± standard deviation (SD) for numerical variables and raw numbers and percentages (%) for categorical variables. Continuous variables were compared using the student 33.1The 89 STEMI patients  enrolled in this study had a mean age of 69.9 years. A total of 89 IRAs were analyzed: 47 in the left anterior descending artery (LAD) system, 27 in the right coronary artery (RCA) system, 12 in the left circumflex artery (LCX) system, and 3 in the left main coronary artery (LM) system. Table 3.2P > .05). On assessment of myocardial perfusion based on the TMPFC, patients who received intracoronary administration of tirofiban through an aspiration catheter had a lower TMPFC than those who received tirofiban through a guiding catheter .As shown in Table 3.3P > .05). As shown in Table Follow-up information was available at 6 months for all patients. The incidence of bleeding in the aspiration catheter group appeared lower than that in the guiding catheter group, but the difference was not found to be significant  is a serious type of coronary heart disease characterized by a high incidence, acute onset, and high mortality. The incidence of slow flow in patients with AMI treated by PCI has been reported to be about 10% to 30%. In our study, the preoperative and postoperative TIMI grades did not differ. Therefore, the epicardial blood flow grade cannot reflect the degree of myocardial perfusion. TMPFC is a quantitative index for assessing myocardial perfusion, and it allows quantification of TIMI myocardial perfusion grading (TMPG). TMPFC was confirmed to be independent predictor of 30-day and 6-month MACCE rates. The mean TMPFC in normal arteries was shown to be 83.47 ± 17.96 frames . We found that patients treated with intracoronary administration of tirofiban through an aspiration catheter had a lower TMPFC than those who received tirofiban through a guiding catheter (87.95 ± 12.39 vs 94.36 ± 15.87), suggesting that administration of tirofiban through aspiration catheter would improve myocardial perfusion in STEMI patients >60 years of age undergoing PPCI, compared with intracoronary injection of tirofiban through guiding catheter. Improved myocardial perfusion was associated with improved survival of stunned myocardium, which may contribute to improved outcomes.Compared with thrombolytic therapy, PPCI to open the IRA for STEMI patients has shown better outcomes. Slow flow or no-reflow after opening the IRA is one of the major complications of PPCI, and acute or sub-acute thrombosis is the main cause of the most serious complications and major adverse cardiac events (MACEs) after PCI. Consistent with the effect of tirofiban, other GP IIb/IIIa inhibitors such as eptifibatide and abciximab improve long-term outcomes in high-risk patients with AMI following PCI. Comparisons of the need for vascular access and major bleeding complications were not possible due to low rates of these events. By modifying the route of administration of eptifibitide, the clinical effect might be preserved without increasing the risks of short-term mortality and procedural failure.,28 A meta-analysis provided evidence of a net clinical benefit for intracoronary versus intravenous abciximab administration, with the highest benefit observed in high-risk ACS patients, such as those with reduced baseline left ventricular ejection fraction (LVEF).Tirofiban is a platelet GP IIb/IIIa inhibitor and one of the most powerful anti-platelet aggregation drugs. After administration for 5 minutes, platelet aggregation can be inhibited up to 96%, which can reduce the incidence of MACCEs. The Serbia STEMI Register study showed that tirofiban administration with PPCI, following pretreatment with 600 mg clopidogrel, improved the primary outcome after 30 days and after 1 year without an increase in major bleeding. found that intracoronary injection of tirofiban prevents microcirculation dysfunction during delayed PCI in AMI patients. Moreover, a meta-analysis showed that compared with intravenous administration of tirofiban, intracoronary administration of tirofiban significantly increased TIMI grade 3 flow  and TMP grade 3 flow  while reducing the incidence of MACEs  in ACS patients. Intraregional administration yielded favorable outcomes in terms of myocardial tissue reperfusion as evidenced by the improved TIMI flow grade, reduced incidence of cardiac thin filament complex (CTFC), complete ST-segment resolution, and reduced incidence of MACEs without an increase in the incidence of in-hospital major bleeding events. The intralesional administration of GPIs can be recommended as the preferred regimen to guard against no-reflow.In addition, Sun et al The intra-coronary administration of tirofiban can reduce the systemic effects of drugs, increasing the local drug concentration, and thus, possibly leading to better anti-platelet aggregation and anti-inflammatory effects.,35 Intracoronary abciximab reduces the occurrence of death and MACEs in ACS. Moreover, the drug can be administered to the distal of end of a vascular lesion via aspiration catheter. Administration of tirofiban via an aspiration catheter can increase the drug concentration within microcirculation blood vessels to better inhibit the expression of the platelet surface receptors and cannot be cross-linked with fibrinogen.Our study showed that the administration of tirofiban via aspiration catheter could further improve the myocardial perfusion level in STEMI patients >60 years of age, without increasing the incidence of bleeding events. Elderly patients with coronary heart disease have complex clinical risk factors and experience more complications than younger patients. The incidence of bleeding was shown to be higher after strengthening of anti-platelet therapy, especially with intravenous administration.5Our study demonstrated that intra-coronary delivery of tirofiban through aspiration catheter may improve myocardial perfusion and long-term prognosis for STEMI patients over 60 years of age undergoing PCI compared with intracoronary injection of tirofiban through guiding catheter.5.1The study is limited by the small sample size; a larger study with adequate statistical power is required to validate this approach. In addition, this was a single-center clinical study, and larger prospective and multicenter studies assessing the value of this conclusion are warranted. Moreover, more precise techniques such as cardiac magnetic resonance imaging should be used to evaluate myocardial perfusion and cardiac function in future studies.Writing – original draft: Sigan Hu.Writing – review & editing: Hongju Wang.Data curation: Jian Zhu, Miaonan Li, Dasheng Gao.Funding acquisition: Miaonan Li.Conceptualization: Hui Li.Formal analysis: Hui Li, Heng Zhang.


Direct electronic communication with sensory areas of the neocortex is a challenging ambition for brain-computer interfaces. Here, we report the first successful neural decoding of English words with high intelligibility from intracortical spike-based neural population activity recorded from the secondary auditory cortex of macaques. We acquired 96-channel full-broadband population recordings using intracortical microelectrode arrays in the rostral and caudal parabelt regions of the superior temporal gyrus (STG). We leveraged a new neural processing toolkit to investigate the choice of decoding algorithm, neural preprocessing, audio representation, channel count, and array location on neural decoding performance. The presented spike-based machine learning neural decoding approach may further be useful in informing future encoding strategies to deliver direct auditory percepts to the brain as specific patterns of microstimulation. Heelan, Lee et al. collect recordings from microelectrode arrays in the auditory cortex of macaques to decode English words. By systematically characterising a number of parameters for decoding algorithms, the authors show that the long short-term memory recurrent neural network (LSTM-RNN) outperforms six other decoding algorithms. This area is accessible to chronic implantation of MEAs for large channel-count broadband recording (see “Methods” section). Prior seminal research into the STG has provided understanding of the hierarchical processing of auditory objects by producing detailed maps of the cellular level characteristics through microwire recordings6. The connection of non-human primate (NHP) research to human speech processing has been reviewed7. Additional techniques to acquire global maps of the NHP auditory system (via tracking metabolic pathways) include fMRI8 and 2-deoxyglucose (2-DG) autoradiography9.Electrophysiological mapping by single intracortical electrodes has provided much insight in revealing the functional neuroanatomical areas of the primate auditory cortex. Directly relevant to this work is the investigation of the role of the macaque secondary auditory cortex in processing complex sounds through intracortical microelectrode array (MEA) population recordings. Whereas the core of the secondary auditory cortex lies in the lateral sulcus, portions of the adjacent belt and parabelt lie on the superior temporal gyrus11. This research has demonstrated encoding properties based on spectrotemporal receptive fields (STRFs) of the primary sensory neurons in A1. Further, studies in marmosets have yielded key insights into neural representation and auditory processing by mapping neuronal spectral response beyond A1 into the belt and parabelt regions of the secondary auditory cortex through intrinsic optical imaging12. Results suggest that the secondary auditory cortex contains distributed spectrotemporal representations in accordance with findings from microelectrode mapping of the macaque STG4. Another recent study examined feedback-dependent vocal control in marmosets and showed how the feedback-sensitive activity of auditory cortical neurons predicts compensatory vocal changes13. Importantly, this work also demonstrated how electrical microstimulation of the auditory cortex rapidly evokes similar changes in speech motor control for vocal production .Other relevant animal studies have examined the primary auditory cortex (A1) in ferrets (single neuron electrophysiology) to unmask representations underlying tonotopic maps22. While surface electrodes are thought to report neural activity over large populations of cells as field potentials, relatively accurate reconstructions of human and artificial sounds have been achieved in short-term recordings of patients during clinical epilepsy assessment. The recordings generally focus on multichannel low-frequency (0–300 Hz) local field potential (LFP) activity, such as the high-gamma band (70–150 Hz), using linear and nonlinear regression models. LFP decoding has been used to reconstruct intelligible audio directly from brain activity during single-trial sound presentations19. When combined with deep learning techniques, electrophysiology data recorded by ECoG grids has been decoded to reconstruct English language words and sentences23. These studies provided insight into how the STG encodes more complex auditory aspects such as envelope, pitch, articulatory kinematics, spectrotemporal modulation, and phonetic content in addition to basic spectral and temporal content. One study augmented findings from ECoG studies by recording from an MEA implanted on the anterior STG of the patient where the single or multiunit activity showed selectivity to phonemes and words. This work provided evidence that the STG is involved in high-order auditory processing24.We also note relevant human research which mainly deployed intracranial electrocorticographic (ECoG) surface electrode arrays in the STG25 (NPT), we demonstrated the effects of decoding algorithm, neural preprocessing, audio representation, channel count, and array location on decoding performance by evaluating thousands of unique neural decoding models. Through this methodology, we achieved high fidelity audio reconstructions of English words and macaque calls through the successful decoding of multiunit spiking activity.In this paper, we investigated whether accurate low-order spectrotemporal features can be reconstructed from high spatial and temporal resolution MEA-based signals recorded in the higher-order auditory cortex. Specifically, we explored how different decoding algorithms affect reconstruction quality when availed to large channel count neural population recordings of spikes. We implanted two 96-channel intracortical MEAs in the parabelt areas of the secondary auditory cortex in the rhesus macaque model and demonstrated the successful decoding of multiunit spiking activity to reconstruct intelligible English words and macaque call audio see Fig. . Further26 was prepared to give the reader a concise view of this paper. We used 96-channel intracortical MEAs to wirelessly record28 broadband (30 kS/s) neural activity targeting Layer 4 of the STG in two NHPs (see “Methods” section). We played audio recordings of 5 English words and a single macaque call over a speaker in a random order. Using a microphone, we recorded the audio playback synchronously with neural data.A Supplementary Summary MovieWe performed a large-scale neural decoding grid-search to explore the effects of various factors on reconstructing audio from the subject’s neural activity. This grid-search included all steps of the neural decoding pipeline including the audio representation, neural feature extraction, feature/target preprocessing, and neural decoding algorithm. In total, we evaluated 12,779 unique decoding models. Table 15. We present examples in a Supplementary Correlation Movie29 that demonstrates various reconstructions and their corresponding correlation scores. This movie aims to provide subjective context to the reader regarding the intelligibility of our experimental results. Additionally, we evaluated neural decoding models with other metrics that quantified reconstruction performance on specific audio aspects including sound envelope, pitch, loudness, and speech intelligibility . We have also provided a Summary Movie that describes the presented findings26.During our analysis, we primarily used the mean Pearson correlation between the target and predicted audio mel-spectrogram bands as a performance metric for neural decoding models30 representation (128 bands) of the audio as target variables (one target per band) and multiunit spike counts as neural features (one feature per MEA channel) (see “Methods” section). Both the mel-spectrogram bands and multiunit spike counts were binned into 40 ms time bins, and all audio was reconstructed on a bin-by-bin basis .Neural decoding models regressed audio targets on neural features. We used the mel-spectrogramData were collected from a total of 3 arrays implanted in 2 NHPs (RPB and CPB arrays in NHP-0 and an RPB array in NHP-1). Data for each array were analyzed independently (i.e. no pooling across arrays or NHPs). Unless explicitly stated, all models were trained on 5 English words using all 96 neural channels from the NHP-0 RPB array. Each array data set contained To mitigate decoding model overfitting, data collected from a given array was sequentially concatenated across recording sessions, and the resulting array data sets were sequentially split into training (80%), validation (10%), and testing (10%) sets. The primary motivation for this work was to enable future active listening tasks that will consist of a limited number of English words . Results are shown in Fig. We evaluated seven different neural decoding algorithms including the Kalman filter, Wiener filter, Wiener cascade, dense neural network (NN), simple recurrent NN (RNN), gated recurrent unit (GRU) RNN, and long short-term memory (LSTM) RNN (see “Methods” section). Each neural network consisted of a single hidden layer and an output layer. All models were trained on Google Cloud Platform n1-highmem-96 machines with Intel Skylake processors (see “Methods” section). We calculated a mean Pearson correlation between the target and predicted mel-spectrogram by calculating the correlation coefficient for each spectrogram band and averaging across bandsWe observed the Kalman filter (trained in 0.42 s) provided the lowest overall performance with the top model achieving a 0.57 mean validation correlation (MVC) and 0.68 mean training correlation (MTC). The top Wiener filter (trained in 2 s) performed similarly to the Kalman filter on the validation set (0.60 MVC) but showed an increased ability to fit the training set (0.78 MTC). A Wiener cascade (trained in 3 min and 33 s) of degree 4 beat the top Wiener filter with a MVC and MTC of 0.67 and 0.82, respectively.A basic densely connected neural network (trained in 55 s) performed similarly to the top Wiener cascade decoder with a slightly improved MVC (0.69) and MTC (0.94). While the top simple RNN (trained in 2 min and 22 s) (0.94 MTC) decoder fit the training data as well as the dense neural network, it did generalize better to unseen data (0.78 MVC). Lastly, top GRU RNN (trained in 3 h and 46 min)  and LSTM RNN (trained in 3 h and 37 min)  decoders achieved similar performance with the LSTM RNN providing the best overall performance of all evaluated decoders on the validation set. The LSTM RNN also showed the highest robustness to overfitting with the top model performing only 8% worse on the validation set compared to the training set. For a comparison of how different neural network sizes affected performance, please see Supplementary Fig. 31 using MVC values for the top-performing models (see “Methods” section). We found the LSTM RNN significantly outperformed all other decoding algorithms on the validation set  and spectro-temporal modulation index33 (STMI) metrics. For a description of these metrics, please see “Methods” section.In addition to examining the mean correlation performance of neural decoding algorithms on the mel-spectrogram bands, we investigated how decoding algorithms recovered specific audio aspects including envelope, pitch, and loudness. We also quantified audio reconstruction intelligibility using the extended short-time objective intelligibilityp-value p-value p-value p-value p-value p-value p-value p-value p-value p-value p-value p-value p-value p-value p-value We found LSTM RNN decoders provided the best performance across all evaluated metrics see Fig. . The LSTTo prepare neural features for decoding models, we bandpass filtered the raw neural data and calculated unsorted multiunit spike counts across all channels.We first bandpass filtered the raw 30 kS/s neural data using a 2nd-order elliptic filter in preparation for threshold-based multiunit spike extraction. To explore the effect of the filter’s low and high cutoff frequencies, we performed a grid-search that evaluated 99 different bandpass filters. For each filter, four different LSTM RNN neural decoding models were evaluated. We found that using a low cutoff of 500–600 Hz and a high cutoff of 2000–3000 Hz (shown with yellow dotted lines) provided a marginal improvement in decoding performance see Fig. .Fig. 4A 34. These noise levels were multiplied by a scalar “threshold factor” to set an independent spike threshold for every channel . We extracted negative threshold crossings from the filtered neural data  and binned them into spike counts over non-overlapping windows corresponding to the audio target sampling (see “Audio representation” section).We used multiunit  spike counts as neural features for decoding models. After filtering, we calculated a noise level for each neural channel over the training set using median absolute deviationWhile we observed optimal values for the threshold factor, we found no clear pattern across decoding algorithms. The Wiener cascade was the most sensitive to threshold factor with a 9.1% difference between the most and least optimal values. GRU RNN decoders were the least sensitive to threshold factor with a 1.1% difference between the best and worst-performing values.Except for the Kalman filter which processed a single input at a time, we used a window of sequential values for each feature as inputs to the decoding models. Each window was centered on the current prediction with its size controlled by a “feature window span” hyperparameter . We evaluated three different feature window spans .Both the GRU RNN and LSTM RNN showed higher performance with a larger feature window span (16); however, the other decoding algorithms achieved top performance with a smaller value (8). The LSTM RNN showed the most sensitivity to this hyperparameter with a 13.4% difference between a feature window span of 16 and 4. The Wiener filter was the least sensitive with a 3.1% difference between the best and worst-performing values. These results demonstrated the importance of determining the optimal feature window span when leveraging LSTM RNN neural decoding models.35. Therefore, to investigate the effect of channel count on decoding performance, we ordered neural channels according to the highest neural activity  over the training data set. For seven different channel counts , we selected the top most active channels and built LSTM RNN decoding models using only the subselected channels.Previous work has shown that increased neuronal firing rates correlate strongly with feature importance when using LSTM RNN models for neural decodingAs shown in Fig. 30. We examined two hyperparameters for this process including the number of mel-bands in the representation and the hop size for the short-time fourier transform (STFT). We evaluated four different values for the number of mel-bands  and five different hop size values . To reconstruct audio, the mel-spectrogram was first inverted, and the Griffin-Lim algorithm36 was used to reconstruct phase information.We converted raw recorded audio to a mel-frequency spectrogramDuring preliminary studies, we observed increased decoder performance  when using fewer mel-bands and longer hop sizes. However, the process of performing mel-compression with these hyperparameter settings caused reconstructions to become unintelligible even when generated directly from the target mel-spectrograms . Therefore, by subjectively listening to the audio reconstructions generated from the target mel-spectrograms, we determined that a hop size of 40 ms and 128 mel-bands provided the best trade-off of decoder performance and result intelligibility.To investigate the effect of audio complexity on decoding performance, we subselected different sets of sounds from the recorded audio data prior to building decoding models. Audio data sets containing 1 through 5 sounds were evaluated using the following English words: “tree”, “good”, “north”, “cricket”, and “program” . For each audio data set, we then varied the channel count  across seven different values .37.As shown in Fig. p-value We implanted two NHPs (NHP-0 and NHP-1) with 96-channel MEAs in the rostral parabelt (RPB) and caudal parabelt (CPB) of the STG . By successfully learning to predict these higher frequency spectrogram bands, neural decoding models achieved a higher average correlation across the full spectrogram than when those same bands contained more noise. While adding the macaque call improved the average correlation scores, it also decreased the top ESTOI score from a 0.59 to a 0.56 on the validation set.Given an audio data set of 5 English words and decoding all 96 channels of neural data, the top performing neural decoder was a LSTM RNN (2048 recurrent units) model that achieved a 0.98 mean training correlation, 0.88 mean validation correlation, and 0.79 mean testing correlation. This model successfully reconstructed intelligible audio from RPB STG neural spiking activity with a validation ESTOI of 0.59 and a testing ESTOI of 0.54. Models were ranked by validation set mean mel-spectrogram correlation.While the primary objective of this work was to enable future active listening NHP tasks that will consist of a limited number of English words .We combined two sets of methods as part of our larger motivation to take steps towards human cortical and speech prostheses while leveraging the accessibility of the macaque model to chronic electrophysiology. First, the use of 96-channel intracortical MEAs was hypothesized to yield a new view of STG neural activity via direct recording of neural population (spiking) dynamics. To our knowledge, such multichannel implants have not been implemented in a macaque STG except for work that focused on the primary A1 and, to a lesser extent, the STG in this animal model24.We performed an end-to-end neural decoding grid-search to explore the effects of signal properties, algorithms, and hyperparameters on reconstructing audio from full-broadband neural data recorded in STG. This computational experiment resulted in neural decoding models that successfully decoded neural activity into intelligible audio on the training, validation, and test data sets. Among the seven evaluated decoding algorithms, the Kalman filter and Wiener filter showed the worst performance as compared to the Wiener cascade and neural network decoders. This suggests that spiking activity in the parabelt region has a nonlinear relationship to the spectral content of sounds. This finding agrees with previous studies in which linear prediction models performed poorly in decoding STG activity39, and recent work from our group has demonstrated the feasibility of using LSTM RNN decoders for real-time neural decoding40.In this work, we found that recurrent neural networks outperformed other common neural decoding algorithms across six different metrics that measured audio reconstruction quality relative to target audio. These metrics evaluated reconstruction accuracy on mel-spectrogram bands, sound envelope, pitch, and loudness as well as two different intelligibility metrics. LSTM RNN decoders achieved the top scores across all six metrics. These findings are consistent with those of other studies in decoding neural activity recorded from the human motor cortex41.While LSTM RNN achieved the top performance on all evaluated metrics, we found no significant difference in performance between the LSTM RNN and GRU RNN across all performance metrics. Conversely, we observed a significant difference in performance from simple RNN decoders on three of the six metrics. These results indicated that the addition of gating units to the recurrent cells provided significant decoding improvements when reconstructing audio from STG neural activity. However, the addition of memory cells (present in LSTM RNN but not GRU RNN) did not significantly improve reconstruction performance. These results demonstrated that similar decoding performance was achieved whether the decoder used all available neural history  or learned which history was most important . The similarity of the LSTM RNN and GRU RNN has also been observed when modeling polyphonic music and speech signalsIn addition to the effect of decoding algorithm selection, we also showed that optimizing the neural frequency content via a bandpass filter prior to multiunit spike extraction provided a marginal decoding performance improvement. These results raise the question whether future auditory neural prostheses may achieve practically useful decoding performance while sampling at only a few kilohertz. We observed no consistent effect when varying the threshold factor used in extracting multiunit spike counts. When using an LSTM RNN neural decoder, longer windows of data and more LSTM nodes in the network improved decoding performance. In general, we found the complexity of the decoding task to affect the optimal channel count with increased channel counts improving performance on more complex audio data sets. However, decoders overfit the training data when the number of neural channels was too high relative to the audio complexity . Due to these results, we will continue to grid-search threshold factor, LSTM network size, and the number of utilized neural channels during future experiments.While we observed some improvement in decoding performance from the MEAs implanted in the rostral parabelt STG compared to the array implanted in the caudal parabelt STG, this work does not fully answer the question whether location specificity of the MEA is critical within the overall cortical implant area, or whether reading out from the spatially extended cortical network in the STG is relatively agnostic to the precise location of the multichannel probe. Nonetheless, we plan to utilize information simultaneously recorded from two or more arrays in future work to test if this enables the decoding of increasingly complex audio . We are developing an active listening task for future experiments that will allow the NHP subject to directly report different auditory percepts.23. On a numerical basis, our analysis achieved higher mean Pearson correlation and ESTOI values possibly due to the multichannel spike-based neural recordings and some advantage in such intracortical recordings compared to surface ECoG approaches. In our work with macaques, we also chose to restrict the auditory stimulus to relatively low complexity and short temporal duration (single words). Parenthetically, we note that the broader question of necessary required and optimal neural information versus task complexity has been approached theoretically42.When comparing our results with previous efforts in reconstructing auditory representations from cortical activity, we note especially the work with human ECoG data23, and future work will explore these methods in place of mel-compression/Griffin-Lim algorithm. Given the goal of comparing different decoding models with our offline neural computational toolkit, we have not approached in this work the regime of real-time processing of neural signals such as required for a practical brain–computer interface. However, based on our previous work, the trained LSTM model could be implemented on a Field-Programmable Gate Array (FPGA) to achieve real-time latencies40. While we will continue to improve the performance of our neural decoding models, the presented results provide one starting point for future neural encoding work to “write in” neural information by patterned microstimulation44 to elicit naturalistic audio sensations. Such future work can leverage the results presented here in guiding steps towards the potential development of an auditory cortical prosthesis.We also found that the audio representation and processing pipeline are critical to generating intelligible reconstructed audio from neural activity. Other studies have shown benefits from using deep learning to enable the audio representation/reconstructionThis work included two male adult rhesus macaques. The animals had two penetrating MEAs  implanted in STG each providing 96 channels of broadband neural recordings. All research protocols were approved and monitored by Brown University Institutional Animal Care and Use Committee, and all research was performed in accordance with relevant NIH guidelines and regulations.46. Those two parabelt areas are closely connected to the anterior lateral belt and medial lateral belt which show selectivity for the meaning of sound (“what”) and the location of the sound source (“where”), respectively4.The rostral and caudal parabelt regions of STG have been shown to play a role in auditory perception47. To enhance the longevity of the recording environment, we staged our surgical procedure in two steps. First, we created a custom planar titanium mesh designed to fit the curvature of the skull. This mesh was designed using a 3D-printed skull model of the target area (acquired by MRI and CT imaging) and was coated with hydroxyapatite to accelerate osseointegration. This mesh was initially implanted and affixed with multiple screws providing a greater surface area for osseointegration on the NHP’s skull. Post-surgical CT and MRI scans were combined to generate a 3D model showing the location of the mesh in relation to the skull and brain.Our institutional experience with implanting MEAs in NHPs has suggested that there is a non-trivial failure rate for MEA titanium pedestalsSeveral weeks after the first mesh implantation procedure, we devised a surgical technique to access the parabelt region. A bicoronal incision of the skin was performed. The incision was carried down to the level of the zygomatic arch on the left and the superior temporal line on the right. The amount of temporal muscle in the rhesus macaques prevented an inferior temporal craniotomy. To provide us with lower access on the skull base, we split the temporal muscle in the plane perpendicular to our incision  which were then retracted anteriorly and posteriorly. This allowed us to have sufficient inferior bony exposure to plan a craniotomy over the middle temporal gyrus and the Sylvian fissure.47.The mesh, lateral sulcus, superior temporal sulcus, and central sulcus served as reference locations to guide the MEA insertion see Fig. . MEA arrWe collected broadband neural data to characterize mesoscale auditory processing of STG during a passive listening task. While NHP subjects were restrained in a custom chair located in an echo suppression chamber using AlphaEnviro acoustic panels, complex sounds  were presented through a focal loudspeaker. The subjects were trained to remain stable during the training session to minimize audio artifacts.The passive listening task was controlled with a PC running MATLAB . Animals were rewarded after every session using a treat reward. Within one session, 30 stimuli representations were played at ~1 s intervals. Data from 5 to 6 sessions in total were collected in one day. Computer synthesized English words were chosen to have different lengths (varying number of syllables) and distinct spectral contents  . Audio was recorded at 30 kS/s synchronously with the neural data using a microphone connected to an NSP auxiliary analog input. The NSP broadcasted the data to a desktop computer over a local area network for long-term data storage using the Blackrock Central Software Suite. Importantly, the synchronous recording of neural and audio data by a single machine aligned the neural and audio data for offline neural decoding analysis.We used MEAs from Blackrock Microsystems with an iridium oxide electrode tip coating that provided a mean electrode impedance of around 50 kOhm. Iridium oxide was chosen with future intracortical microstimulation in mind. The MEA electrode length was 1.5 and 0.4 mm pitch for high-density grid recording. Intracortical signals were streamed wirelessly at 30 kS/s per channel  using a CerePlex W wireless recording device25 (NPT) for performing large-scale distributed neural processing experiments in the cloud. The NPT is fully compatible with the dockex computational experiment infrastructure48 . This enabled us to integrate software modules coded in different programming languages to implement processing pipelines. In total, the presented neural decoding analysis was composed of 26 NPT software modules coded in the Python, MATLAB, and C++ programming languages.We developed a neural processing toolkitDockex was used to launch, manage, and monitor experiments run on Google Cloud Platform (GCP) Compute Engine virtual machines (VMs). For this work, experiments were dispatched to a cluster of 10 VMs providing a total of 960 virtual CPU cores and over 6.5 terabytes of memory. In this work, GPUs were not used during the training or reconstruction since multiprocessing on GPUs was not yet supported by dockex; however, future experiments will leverage GPUs for accelerating deep learning. Machine learning experiment configuration files implemented the described grid-search by instantiating NPT modules with different combinations of parameters and defining dependencies between modules for synchronized execution and data passing. Dockex automatically extracted concurrency from these experiment configurations thereby scaling NPT execution across all available cluster resources. The presented neural processing experiments evaluated 12,779 unique neural decoding models.49 library to perform spike extraction via a threshold crossing technique. Raw neural data was first scaled to units of microvolts and filtered using a 2nd-order bandpass elliptic filter with grid-searched cutoff frequencies. A noise level  of each channel was estimated over the training set using the median absolute deviation to minimize the interference of spikes34. We set thresholds for each individual channel by multiplying the noise levels by a threshold factor. We detected negative threshold crossings on the full-broadband 30 kS/s data and then binned them into counts.We used multiunit spike counts see Fig.  as neura50 was used to calculate the short-time Fourier transform spectrogram with an FFT window of 2048. This spectrogram was then compressed to its mel-scaled spectrogram to reduce its dimensionality to the number of mel-bands. These mel-bands served as the target data for the evaluated neural decoders. Audio files were recovered from the mel-bands by inverting the mel-spectrogram and using the Griffin-Lim algorithm36 to recover phase information. All targets were standardized to zero-mean using scikit-learn51 transformers fit on the training data.We manually labeled raw audio to designate the begin and end time indices for single sound trials. The audio data contained five different English words as well as a single macaque call. We subselected sounds to create target audio data sets for the neural decoders with varying complexity. The librosa audio analysis library52 including a Kalman filter, Wiener filter, Wiener cascade, dense neural network, simple recurrent RNN, GRU RNN, and LSTM RNN. Each neural network consisted of a single hidden layer and an output layer. Hidden units in the dense neural network and simple RNN used rectified linear unit activations53. The GRU RNN and LSTM RNN used tanh activations for hidden units. All output layers used linear activations, and no dropout54 was used. We used the adam optimization algorithm55 to train the dense neural network and RMSprop56 for all recurrent neural neural networks. We sequentially split the data set  represents the proportion of frames where the relative pitch error is higher than 20%63. This resulted in loudness values with units of loudness units relative to full scale (LUFS) where 1 LUFS = 1 dB. For the purpose of comparing overall loudness reconstruction accuracy across decoding algorithms, we calculated the absolute value of the momentary decibel difference between the target loudness  and reconstructed  audio signals through a one third octave filter bank to model signal transduction in the cochlear inner hair cells. The subband temporal envelopes for both signals are then calculated before performing normalization across the rows and columns of the spectrogram matrices. The intermediate intelligibility is defined as the signed length of the orthogonal projection of the noisy normalized vector onto the clean normalized vector.The ESTOI algorithm estimates the average intelligibility of noisy audio samples across a group of normal-hearing listeners33. STMI utilizes a model of the mammalian auditory system by first generating a neural representation of the audio (auditory spectrogram) and then processing that neural representation with a bank of modulation selective filters to estimate the spectral and temporal modulation content. Here, we utilized the STMI specific to speech samples 65 Python Library to perform the Friedman tests and the scikit-posthocs66 Python library to perform the Conover tests.For all other performance metrics, we first split the reconstructed validation audio into non-overlapping blocks of 2 s in length. A given performance metric was then calculated for each block, and a Friedman test was performed to test the multisample null hypothesis. For all evaluated metrics, this process rejected the null hypothesis, and we then applied Conover post-hoc multiple comparisons tests to calculate the significance of differences between decoding algorithms. We utilized the STACData was collected from 2 NHPs using a total of three different arrays. For each array, we utilized Further information on research design is available in the Supplementary MaterialDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Reporting Summary


Patient-derived xenograft (PDX) models are in vivo models of human cancer that have been used for translational cancer research and therapy selection for individual patients. The Jackson Laboratory (JAX) PDX resource comprises 455 models originating from 34 different primary sites (as of 05/08/2019). The models undergo rigorous quality control and are genomically characterized to identify somatic mutations, copy number alterations, and transcriptional profiles. Bioinformatics workflows for analyzing genomic data obtained from human tumors engrafted in a mouse host  must address challenges such as discriminating between mouse and human sequence reads and accurately identifying somatic mutations and copy number alterations when paired non-tumor DNA from the patient is not available for comparison.We report here data analysis workflows and guidelines that address these challenges and achieve reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from PDX models that lack genomic data from paired non-tumor tissue for comparison. Our workflows incorporate commonly used software and public databases but are tailored to address the specific challenges of PDX genomics data analysis through parameter tuning and customized data filters and result in improved accuracy for the detection of somatic alterations in PDX models. We also report a gene expression-based classifier that can identify EBV-transformed tumors. We validated our analytical approaches using data simulations and demonstrated the overall concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA).https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows.The analysis workflows that we have developed to accurately predict somatic profiles of tumors from PDX models that lack normal tissue for comparison enable the identification of the key oncogenic genomic and expression signatures to support model selection and/or biomarker development in therapeutic studies. A reference implementation of our analysis recommendations is available at The online version of this article (10.1186/s12920-019-0551-2) contains supplementary material, which is available to authorized users. Patient-derived xenograft (PDX) models are in vivo models of human cancer that have been used for translational cancer research and therapy selection for individual patients –8. PreviThe Jackson Laboratory (JAX) PDX resource comprises 455 PDX cancer models originating from 34 different primary sites . The models undergo rigorous quality control and are genomically characterized to identify somatic mutations, copy number alterations, and transcriptional profiles  that we developed specifically for the analysis of genomic data generated from PDX tumors. Our workflows incorporate commonly used software and public databases but are tailored to address the specific challenges of PDX genomics data analysis through parameter tuning and customized data filters and result in improved accuracy for the detection of somatic alterations in PDX models relative to analyses that lacked custom filters. We also report a gene expression-based classifier that can identify EBV-transformed tumors. Finally, to demonstrate the effectiveness of our workflows, we show the overall concordance of the genomic and transcriptomic profiles of the PDX models in the JAX PDX resource with relevant tumor types from The Cancer Genome Atlas (TCGA).Here we describe bioinformatics analysis workflows and guidelines  and homogenized in Nuclei Lysis Buffer (Promega) using a gentleMACS dissociator (Miltenyi Biotec Inc). DNA was isolated using the Wizard Genomic DNA Purification Kit (Promega) according to manufacturer’s protocols. DNA quality and concentration were assessed using a Nanodrop 2000 spectrophotometer (Thermo Scientific), a Qubit dsDNA BR Assay Kit on a Qubit Fluorometer (Thermo Scientific), and the Genomic DNA ScreenTape on a 4200 TapeStation (Agilent Technologies). Libraries were prepared using the Hyper Prep Kit (KAPA Biosystems) and SureSelectXT Target Enrichment System with the JAX Cancer Treatment Profile (CTP) targeted panel of 358 related genes (Agilent Technologies) , 32, accTissues preserved in RNAlater were homogenized in TRIzol (ThermoFisher Scientific) using a gentleMACS dissociator (Miltenyi Biotec Inc). Total RNA was isolated using the miRNeasy Mini kit (Qiagen) according to manufacturer’s protocols, including the optional DNase digest step. RNA quality and concentration were assessed using the RNA 6000 Nano LabChip assay on the 2100 Bioanalyzer instrument and Nanodrop 2000 spectrophotometer (Thermo Scientific). Prior to 2016, non-stranded libraries were constructed using TruSeq RNA Library Prep Kit v2 (Illumina). Stranded libraries were prepared using the KAPA mRNA HyperPrep Kit (KAPA Biosystems), according to the manufacturer’s instructions. Briefly, the protocol entails isolation of polyA containing mRNA using oligo-dT magnetic beads, RNA fragmentation, first and second strand cDNA synthesis, ligation of Illumina-specific adapters containing a unique barcode sequence for each library, and PCR amplification. Libraries were checked for quality and concentration using the DNA 1000 assay (Agilent Technologies) and quantitative PCR (KAPA Biosystems), according to the manufacturers’ instructions. Libraries were pooled and sequenced 75 bp paired-end on the NextSeq 500 (Illumina) using NextSeq High Output Kit v2 reagents (Illumina), or 100 bp paired-end on the HiSeq2500 (Illumina) using TruSeq SBS v3 reagents (Illumina).DNA samples were sent to the Genotyping Core at the Hussman Institute for Human Genomics (University of Miami) for genotyping on the Genome-Wide Human SNP Array 6.0 (Affymetrix). Quality control on the CEL files was carried out using the standard Contrast QC metric from the Affymetrix Genome Wide SNP 6.0 array manual.DNA sequence data generated from PDX tumors underwent initial data processing as follows: (i) sequence reads with 70% of the bases having a quality score < 30 (Q30) were discarded, (ii) bases with quality scores less than Q30 were trimmed from the 3′ end of the read, (iii) sequence reads with < 70% of bases remain after trimming were discarded, (iv) both reads from pair-end sequencing were discarded if either read was discarded. If < 50% of the total reads remained following the preprocessing steps, the sample was removed from the analysis. Following the initial data processing step described above, mouse reads were identified and filtered out using Xenome v1.0.0 . Only reSequence reads that passed all pre-processing steps were mapped to the reference human genome  using the BWA-MEM alignment tool with ALT-Aware mapping  using the UnifiedGenotyper, was used for variant discovery analysis [https://broadinstitute.github.io/picard/), and (iii) recalibrating the base quality scores as the variant calling algorithms rely heavily on the quality scores assigned to the individual base calls in each sequence read. Pindel [The GATK best practices workflow :High quality variants from both variant callers in the PDX samples were obtained based on GATK hard filtering (see below), and have a read depth (DP) of ≥140 and allele frequency (ALT_AF) of ≥5%. These DP and ALT_AF thresholds were optimized using a set of known and validated mutations and samples reported earlier for the JAX CTP targeted panel sequencing at high coverage (average 941 X) , 32. The(i) for point mutations, QD < 2.0, FS > 60.0, MQ < 40.0, MQRankSum < − 12.5, ReadPosRankSum < − 8.0.(ii) for indels, QD < 2.0, FS > 200.0, ReadPosRankSum < − 20.0.In addition, we verified that these default thresholds were able to detect all the known mutations in the CTP samples . The avehttps://ckbhome.jax.org/) [Variants were annotated for their effect  using SnpEff v4.3  based onax.org/) , 47 via Since normal samples from patients whose tumors were used to generate the PDX models were unavailable in most cases, we generated a dataset of putative human germline variants using data from several public resources: (i) dbSNP, (ii) 1000 Genomes Project, (iii) ExAC database with MAF ≥1%, and (iv) a compendium of variants from 20 normal human blood samples available in JAX  were considered to be putative false positive (FP) mutations. The rationale for this data filtering step was based on our observation that the maximum recurrent frequency of somatic mutated base positions was 6% across a compendium of TCGA tumor samples . Thus, we would expect that any mutated loci recurring across PDX samples at significantly higher rates to likely be FP. Systematic technical errors in sequencing and/or mapping are possible explanations for the common recurrent non-somatic mutations identified PDX models.Variants not in our aggregated dataset of putative germline variants described above but occurred at a frequency of 25% or greater across all PDX models (https://ckbhome.jax.org/) with 1) known or predicted gain or loss of protein function, 2) potential treatment approach for any cancer type and 3) drug sensitivity and resistance effects in clinical or preclinical studies. In addition, as Pindel results contained a large number of FPs, we only included those that were present in the JAX-CKB by the same criteria.An exception to the germline and false positives exclusion process was made for variants from GATK that were annotated as clinically relevant in JAX CKB. We rescued any filtered variants that were curated into the proprietary JAX-Clinical Knowledgebase  varying sequencing coverage, 2) spiked-in mutations representative of the different tumor types, and 3) different proportions of spiked-in mouse sequence contamination .To evaluate the effect of each filter used in our workflow, we modified the somatic mutation workflow by: (i) omitting Xenome to filter mouse reads, and (ii) mapping to the reference sequence using BWA-MEM. Each modified workflow was used to process each PDX simulated library and each set of results, with and without quality filters, was used to compute the lists of true positive, false positive, true negative and false negative variants. As such, we can calculate the range of sensitivities and specificities of the predicted variants for all the simulated PDX models. We compared the distributions of precision, recall and F1-score /) for different variations of the variant calling workflow on the simulated datasets. Furthermore, we compared the predicted allele frequencies of the true positives of each sample with the input by correlation.rsem-calculate-expression) with default parameters. We further normalized the expression estimate (expected_count from RSEM) using upper quantile normalization of non-zero expected counts and scaling to 1000.Prior to alignment to the human transcriptome, sequences from PDX tumors were processed for sequence quality. Only sequences with base qualities ≥30 over 70% of read length were used in downstream analyses. Quality trimmed reads were then analyzed using the default parameters of Xenome v1.0.0 (k = 25)  to separA gene signature for identifying putative EBV-associated lymphomas was derived by calculating the fold change of the average expression of the expressed genes between 20 EBV-associated lymphomas and 100 non-EBV tumors based on the Z-score transformation of upper-quantile normalized RNA-Seq counts (RSEM). 24 up-regulated and 24 downregulated genes, selected based on the highest and lowest fold change values, were used to define the list of classifier genes . The mouse content for each PDX sample was estimated by the mouse reads proportion computed by Xenome of the mutation calling pipeline for the CTP sequencing of the same PDX sample.PennCNV-Affy and Affymetrix Power Tools –54 were The same normalization steps as the single-tumor analysis were applied in which both tumor and normal CEL files were normalized with 300 HapMap samples, followed by the CNA analysis using the tumor-normal version of ASCAT 2.4.3.2 ratio of total copy number relative to diploid state (copy number 2) and predicted ploidy from ASCAT. A segment was defined as LOH when the major-allele copy number was ≥0.5 and the minor-allele copy number was ≤0.1. Gene-level copy number and LOH were estimated by intersecting the genome coordinates of copy number segments with genome coordinates of genes . In cases where a segment boundary was contained within a gene’s coordinates, the most conservative (lowest) estimate of copy number was used to represent the copy number of the entire intact gene, and the gene was annotated with the number of overlapping segmentsThe resultant copy number segments were annotated with loss of heterozygosity (LOH) and log2 ratio of the copy number relative to the average ploidy of the sample or diploid state with a threshold of ±0.4 respectively. We compiled a list of genes with focal copy number alterations that were significantly amplified (n = 273) or deleted (n = 820) in the 8 tumor types . Using this set of genes, we compared the proportion of genes that would be classified as gain and loss when using different baselines (diploid state 2 or ASCAT predicted ploidy) for PDX models listed in Additional file The low-level copy number gain or loss of a gene was defined by the log2(CN/ploidy) ≤ + 1) and high-level gain (log2(CN/ploidy) > + 1). The expression fold change of each gene was calculated as the log2(TPM + 1) relative to the mean expression across PDX samples with a stringent normal copy number state (− 0.4 ≤ log2(CN/ploidy) ≤ 0.4). The significance of expression changes of each gene for the entire PDX resource with copy number gain or loss relative to the normal state was calculated using the Student’s t-Test.Using annotations from the Cancer Gene Census resource  we analyWe calculated the distribution of mutational load  of the CTP genes for 6 tumor types with at least 10 models in the PDX resource  from the RNA-Seq data of 6 tumor types in TCGA [http://firebrowse.org/). Non-expressed genes across all tumor types were removed (log2(TPM + 1) < 2), and the top 1000 most varying genes based on the variance of their z-scores of log2(TPM + 1) across all tumor types were selected to cluster the TCGA samples by hierarchical clustering. These 1000 most varying genes were intersected, by common gene symbols, with the PDX expression data. The symbols of genes that mapped to multiple genomic locations were removed, leaving 993 overlapping genes. These 993 genes were then used to cluster the PDX samples by hierarchical clustering. The frequencies of over-expression and under-expression of each gene is defined by the z-scores of log2(TPM + 1) of > + 1 and < − 1 respectively. Correlation of the gene expression frequencies in each tumor type was computed using Pearson correlation. The differential gene expression of each tumor type compared to all other tumor types was computed using limma and edgeR based on expected counts from RSEM with TMM-normalization and voom transformation [p-value < 0.05, log (fold change) > 1) or down-regulated  < − 1) genes were obtained for the PDX resource and TCGA patient cohorts separately. The significance of overlap of each set of genes between PDX and TCGA RNA-Seq data was determined using hypergeometric p-value.6 tumor types with at least 10 models in the PDX resource were selected for comparison with TCGA  with a threshold of ±0.4 respectively. The amplification or deletion calls of each gene for the TCGA samples were provided  by FireBrowse (http://firebrowse.org/). Using the list of genes with focal copy number alterations that were significantly amplified (n = 273) or deleted (n = 820) in the 8 tumor types from the GISTIC 2.0 analysis from the TCGA FireBrowse website, we calculated the copy number gain and loss frequencies of these genes for each tumor type in the PDX resource and TCGA cohorts using the respective gain and loss calls.Eight tumor types with at least 10 models in the PDX resource  was highly similar to the overall distribution of tumor types in the PDX resource with Pearson correlation coefficient > 0.9   [BRAF V600E and KRAS G12C) are present in the dbSNP database and occurred at frequencies that fell within our exclusion criteria. Two EGFR indels that were not called by GATK initially, but that were detected by Pindel were rescued as they were annotated clinically relevant.To address the balance of false positive and false negative mutation calls, we “rescued” single nucleotide variants and indels that were initially filtered out as germline using curated annotations available in the JAX Clinical Knowledgebase  , 47 . However, we also observed that EBV-associated lymphoma tumors display a distinct and highly reproducible expression pattern  for human CD45 is the primary screen for EBV-associated lymphomas in the JAX PDX Resource and is performed as one component of routine Quality Control procedures  mouse strain from The Jackson Laboratory (strain 005557), on the human SNP array. The signal intensity from mouse DNA was negligible , we observed the overall high similarity between the segmented copy number profiles analyzed with and without the paired-normal sample  and tumor suppressor genes (n = 40) [n = 194), the over and under-expression were computed relative to the “normal” expression of each gene estimated by the average expression in samples with normal copy number state. While this approach did not account for all mechanisms of gene regulation, we were able to better estimate the normal expression for genes compared to using the mean expression (z-score) across all tumor types, which could be biased for frequently aberrated genes such as MYC, which tends to be frequently amplified and over-expressed across many tumor types [p < 0.05)  , relativ(n = 40) , 72, 73.or types , 75. MosBecause we lacked paired-normal samples for most models in the JAX PDX resource, we were unable to experimentally validate the somatic mutations predicted by our workflows. As an alternative approach to validation, we compared the genomic and transcriptomic profiles for the JAX models to data for the same tumor type available from TCGA and assessed the overall concordance of patterns in mutation frequency, gene expression, and copy number alterations.p-values (p < 1 × 10− 4) by Fisher’s exact test , which demonstrated the replicability of TCGA expression signatures in the PDX resource. The frequencies of over- and under-expression for the top-varying genes for each tumor type displayed better correlation for the same tumor type for PDX versus TCGA compared to other tumor types , despite different sample sizes and the different proportion of tumor types , copy number alterations and gene expression from PDX sequencing or array data require balancing sensitivity and specificity , 70, espRemove mouse reads with Xenome  , 76, 77 Optimize quality thresholds  to filter for high quality variant calls for any given capture panel and sequencing coverage.Adjust quality filters to detect low frequency variants (> 5%) with high confidence in order to detect clinically-relevant variants that are present in minor clones.Filter variant calls to remove those that are likely germline variants –45 to imFilter highly recurrent (> maximum somatic mutation frequency) mutations to remove false positives arising from sequencing or analysis-related errors.Reinstate (rescue) variants with putative clinical relevance , 78–81 tWe recommend using multiple data sources for germline variants and cancer relevant variants to fine-tune workflows for calling somatic mutations to achieve a reasonable balance of false-negative and false positives. Variant calling pipelines should be re-run periodically as new data about germline and cancer-relevant variants become available. Our major recommendations for data analysis workflows for calling somatic mutations for PDX tumors in the absence of paired-normal samples include the following:Normalize SNP array signal intensity with a large number of normal samples , 71 thatEstimate copy number gains and losses using copy number ratio relative to overall cancer genome ploidyWe recommend using publicly available data sets to generate a proxy for sex-matched normal samples in order to estimate copy number alterations in an engrafted PDX tumors. Our major recommendations for data analysis workflows for estimating copy number alterations by SNP arrays for PDX tumors in the absence of paired-normal samples include the following:Although not evaluated for the work reported here, data analysis methods have been developed by other research groups to estimate copy number alterations in absence of paired-normal samples from whole-exome or whole-genome sequence data –86. CompScreen for EBV-transformed lymphomas using lymphoma expression classifier score in addition to a primary screen of paraffin embedded tumor samples using immunohistochemistry for human CD45+ cells.Use mean expression across samples of all tumor types (z-score) or average expression across samples with “normal” copy number state for comparison of gene expression levels.We recommend using gene expression data from engrafted tumors for both quality assurance of PDX models in addition to comparing gene expression levels. Our major recommendations for data analysis workflows for comparing gene expression levels for PDX tumors in the absence of paired-tissue RNA include the following:The availability of RNA-Seq data for engrafted tumors provides an opportunity for predicting fusion genes and various software tools are available for this purpose , 88. BenTo assess the quality of our genomic characterization workflows, we compared the results of our workflows for PDX tumors with data from TCGA. Overall, our analysis results demonstrated that patterns of frequently mutated genes, copy number alterations, and gene expression were comparable in PDX and TCGA samples of the same tumor type. Using colorectal cancer as an example, we demonstrated that pathways known to be perturbed in this cancer were altered consistently in PDX and TCGA tumors   that we have developed for the analysis of genomic data generated from PDX tumors lacking paired non-tumor tissue result in accurate detection of somatic alterations in PDX models. We demonstrate the effectiveness of our workflows by validating with simulated data. In addition, we show that there is high concordance of the genomic and transcriptomic profiles of the PDX models in the JAX PDX resource with relevant tumor types from The Cancer Genome Atlas (TCGA).In conclusion, the bioinformatics analysis workflows and guidelines Additional file 2:Supplementary Tables S1, S13 and S14. (XLSX 94 kb)


In mammary tumours, high-energy radiation is associated with induction of focal structural variants, leading to genomic instability and Met amplification. Gamma-radiation is linked to large-scale structural variants and a point mutation signature associated with oxidative stress. The genomic architecture of carcinomas, sarcomas and lymphomas arising in the same animals are significantly different. Our study illustrates the complex interactions between radiation quality, germline Trp53 deficiency and tissue/cell of origin in shaping the genomic landscape of IR-induced tumours.Ionising radiation (IR) is a recognised carcinogen responsible for cancer development in patients previously treated using radiotherapy, and in individuals exposed as a result of accidents at nuclear energy plants. However, the mutational signatures induced by distinct types and doses of radiation are unknown. Here, we analyse the genetic architecture of mammary tumours, lymphomas and sarcomas induced by high ( Mutational signatures induced by ionising radiation remain largely unexplored. Here in TP53 mutant mice, the authors characterise the genomic landscape of tumours induced by high- and low-energy radiation. In contrast, while ionizing radiation through medical exposures has been associated with increased risk of secondary malignancies4, we presently have no detailed knowledge of the types of signatures that are induced by this common source of DNA damage.Carcinogens in the environment contribute to cancer development by inducing DNA damage, resulting in specific patterns of mutations that can be diagnostic for the causative carcinogenic agent. Such “mutational signatures” help us to understand and quantify the impact of potential carcinogens on human tumour DNA, and can provide information on the mechanisms by which these agents act. Mutational signatures have been detected in thousands of human cancers6. Individuals at highest risk from radiation exposures are patients being treated for cancer by localised radiotherapy, most often using fractionated high doses of gamma rays to focally targeted sites. However lower doses of radiation also have biological effects, the consequences of which have not been adequately investigated in terms of possible genomic changes.Environmental and therapeutic IR can exist in a number of different forms, depending on radiation quality and energy, which impact how radiation interacts with biological target molecules, such as DNA. High energy gamma rays or photons are the most commonly used form of therapeutic IR and occur naturally in the earth’s atmosphere due to radioactive decay or are secondary to the action of cosmic rays. In contrast, heavy-ion or high linear energy transfer (LET) radiation is characterised by charged, high energy particles  and accounts for approximately 1% of cosmic rays7. Radiation has been proposed to cause transformation due to a “bystander effect” even in cells that have not been directly traversed by radiation particles9 but the role of this mechanism in vivo has not been established. Resolution of these questions is important not only for our understanding of mechanisms of carcinogenesis after therapeutic or accidental exposure to ionizing radiation, but also for assessment of cancer risk as a result of occupational exposure, or, increasingly, as a consequence of longer term exposure to cosmic radiation during space flights11.In fact, the exact mechanisms through which radiation induces DNA damage and its impact on the genomic landscape of radiation-induced malignancies are not well-established and have been highly controversial. Although there is general support for a direct model in which IR causes single or double strand breaks by direct collision with DNA, indirect models have also been proposed by which IR interacts with other molecules  to increase DNA damage secondary to free radical formation, or impacts the normal tissue microenvironment to promote cytokine production and transformation through mechanisms that are presently unclear12. Specifically, high LET radiation is associated with intense focal damage caused by the traversal of heavy ions through the nucleus15. In contrast, gamma radiation causes more indirect DNA damage, diffuse patterns of H2AX foci, and increased oxidative stress16.Characterising these mechanisms in human tumours is impeded by limited sample size, germline heterogeneity, complex environmental exposures and uncertainty in the dose, duration, frequency and quality of radiation exposure. Thus, the use of appropriate model systems that properly recapitulate the effects of RT in cells and animal models is pivotal. Indeed, high and low LET studies have been carried out on cells in vitro and in lower organisms, and the types of changes seen depend on radiation quality (reviewed in Durante & Cucinotta)Trp53, a gene also known as the guardian of the genome17, is critical in affecting the cellular outcome following DNA damage19. Human patients with germline TP53 mutations are highly tumour-prone21, and genomic studies have linked germline TP53 deficiency to development of genomically unstable tumours with a high incidence of chromothripsis25. To develop a deeper understanding of the effects of radiation on tumour DNA integrity, we use mouse strains with partial germline deficiency in Trp53 function, i.e., mice hemizygous for Trp53 (Trp53+/−), or homozygous for the Delta P Trp53 allele (Trp53DP/DP); hereafter Trp53ΔP). The Trp53+/−)( strain is highly susceptible to both spontaneous and radiation-induced (4 Gy gamma) lymphoma development, while the Trp53ΔP strain develops a wider range of tumours after exposure to the same radiation dose28. The Trp53ΔP mouse carries a deletion in the N-terminal proline-rich domain (PRD) of TP53, a region that is important in the regulation of human TP53 activity and stability. A common germline polymorphism at codon 72 in this region has been associated with altered cancer risk in humans30.33. We demonstrate that this low radiation dose results in the development of a wide range of malignancies in the context of germline Trp53 deficiency, but particularly in a high incidence of mammary carcinomas and angiosarcomas, which are also common radiation-associated malignancies in humans. Whole-genome sequencing of mammary tumours and whole-exome sequencing of a wider spectrum of IR-induced tumours reveals complex point mutational signatures and patterns of structural rearrangements in tumour genomes that are associated with radiation quality, as well as germline Trp53 status and tumour histology.In this study, we breed both strains of mice on to the same genetic background (FVB/N), and expose the animals to a relatively low dose (50 cGy) of gamma (low LET) or Fe-ion (high LET) radiation to explore the effects on susceptibility to tumour development in different tissues. In particular, the 50 cGy dose of Fe-ions is studied as it has been proposed by NASA and other previous studies as a realistic dose to which astronauts may be exposed during exploratory space missionsTrp53ΔP mice in a mixed 129/C57BL6 background were highly sensitive to development of several tumour types after a single exposure to whole body gamma radiation, but had a low spontaneous rate of tumour development26. We exposed 238 FVB/N mice that were Trp53 WT, hemizygous (Trp53+/−) or homozygous for the Delta P Trp53 allele (Trp53ΔP), to a single dose of either 56Fe-ion (600 MeV/amu) or gamma radiation. Most animals were exposed to a dose of 50 cGy of either radiation type, but a few received lower or higher doses  where the predominant tumour types were lymphomas and sarcomas35, the present model  more closely mimics the spectrum of radiation-induced tumours observed in humans.The most common site of tumour development was the mammary gland, regardless of radiation quality or tus Fig. . There wtus Fig. . NotablyP < 1.94e−06; Fig. Trp53+/−), as animals exposed to Fe-ion radiation survived on average 287 days, as compared to gamma radiation (358 days)  of a focused cohort of 21 mammary tumours from animals of 37. We hypothesised that a signature of reactive oxygen species (ROS)-related DNA damage may be observed as an indirect consequence of IR exposure. IR is known to generate free radicals38, which can in turn damage DNA by causing the formation of 8-Oxo-guanine adducts. We addressed this question by two different approaches: searching for de novo mutational signatures using Nonnegative Matrix Factorisation (NMF), and analysis of cosine similarity to existing trinucleotide mutational signatures in a wide range of human cancers37. When we performed de novo mutational signature extraction using WGS-derived SNVs, we observed 3 independent signatures . While these data are based on a relatively small sample size (12 gamma-induced and 9 heavy ion-induced tumours), the results are consistent with the hypothesis that 50 cGy gamma radiation results in higher levels of ROS than 50 cGy high LET radiation.Signature deconvolution using the established human COSMIC mutational signaturesion Fig. . This prINDEL signatures: A, B and C   Fig. . Interesn = 11.3/sample) as opposed to insertions (1.52) or inversions (6.1). A total of 74 intrachromosomal (ITX) and 66 interchromosomal complex translocations (CTX) were observed. The distributions of mutations across samples and chromosomes were non-homogeneous. SVs as well as smaller INDELs were clustered in some tumours  as opposed to non-focal rearrangements (including whole chromosome aneuploidy (see Methods section), as a function of germline Trp53 genotype, histological subtype and radiation quality  in the WGS data revealed striking patterns of gains or losses across many different chromosomes . This is also consistent with the data above showing that 56Fe-ion exposure is associated with increased focal deletions and insertions, supporting a model in which 56Fe-ion radiation causes direct focal damage in DNA, leading to double strand breaks (DSB). There was also an association with tumour type, as mammary tumours were strongly deletion biased (52%) as compared to lymphoma (21%) or skin tumours (15%) .To validate these observations, we carried out whole exome sequencing (WES) of 56 additional radiation-induced tumours comprising a wider range of histological types  in Met expression  showed strong evidence for whole chromosome chromothripsis, with clusters of SVs across chromosome 1 .We have explored the impact of both high and low energy radiation on tumour development in mice carrying two distinct types of germline mutation in the 55.A surprising feature of our experiments, which evaluated the effects of 50 cGy high or low LET radiation, was the exceptionally high incidence of radiation-induced mammary cancers, comprising carcinomas and a range of sarcoma types including angiosarcomas. Other models of radiation-induced tumour development have not generally resulted in a high incidence of mammary cancers. This could be due to the effects of higher radiation dose (in the 2–5 Gy range) which is associated with earlier onset haematologic malignancies and lymphomas. Alternatively, it could be due to a combination of dose and genetic background, which could result in early death due to aggressive lymphoma or sarcoma development before the time when secondary carcinomas would arise, as the median tumour free survival was 267 and 327 days for mammary and lung tumours while it was only 130 days for lymphomas. This effect is further accentuated in gamma-IR (40 day latency in lymphoma versus 331 days in mammary), which has been more frequently used in historical studies. The present model therefore more closely mimics susceptibility to human secondary breast cancer development, which is known to be increased following radiation exposure and to be influenced by both germline TP53 and somatically acquired deficiency56. Heavy-ion exposure was also associated with an increased proportion of carcinomas, and despite equal numbers of animals receiving the two radiation qualities, the incidence of the two most commonly observed tumours (mammary and lung) was significantly different for 56Fe-ions compared to gamma radiation. Furthermore, Trp53 genotype impacts tumour histology, as mice hemizygous for p53 were more likely to develop sarcomas and spindle tumours, independent of radiation quality. In contrast, the incidences of squamous cell carcinomas and lymphomas were 10-fold and 5-fold higher, respectively, in Trp53ΔP versus Trp53+/− mice  and pure (FVB/N) background mice to radiation. Mice were between 6 and 10 weeks of age. Mice exposed to HI radiation were subjected to 29 cGy, 50 cGy, 81 cGy or 1 Gy of 600 MeV/amu Mice were monitored for the development of tumours at UCSF by both visual evaluation and palpation for a minimum of 500 days, until the mouse body-condition score deteriorated, or a tumour exceeded 1 cm in diameter.At the time of sacrifice radiosensitive tissues including the skin, mammary, thymus and spleen were harvested along with the tumour and the tail as a non-radiation sensitive control. A small section of each tissue was preserved for histology and the remainder was frozen for DNA, RNA or protein extraction. H&E slides were prepared from tumour samples and examined by a board-certified pathologist to determine histological subtypes. Tumours were categorised based on histology and disease site at time of necropsy. Mammary tumours include carcinomas, spindle cell tumours, sarcomas and angiosarcomas, the latter which was verified by immunohistochemistry (CD31+). To accurately represent the range of mammary tumours identified, whole exome sequencing analysis was performed on all mammary tumour types. Only carcinomas and sarcomas (angiosarcomas were excluded) were used for whole genome sequencing.DNA was extracted from snap frozen tumour tissues using the Qiagen DNAeasy kit as per manufacturer’s instructions. Concentration and quality were determined by Nanodrop spectrophotometry and by PicoGreen (Invitrogen). RNA was extracted from snap frozen tissues using the Zymo Quick-RNA column purification kit. RNA concentration and quality were determined by Nanodrop and Agilent RNA Nano Bioanalyzer kit.58 to the GRCm38 mouse reference genome and were subsequently realigned around known InDels using GATK InDel re-aligner (v1.5-9) producing a single Binary Alignment Mapping (BAM) file for each sample.Exonic DNA was captured using the Agilent whole exome capture kit (SureSelect Mouse All Exon). Captured material was indexed and sequenced on the Illumina platform at the Wellcome Trust Sanger Institute at an minimum depth of 100×. Raw 75 bp pair end sequencing reads were aligned with BWA-aln (v0.5.10)bDNA was sheared to 450 bp fragment size using a Covaris S2. A single Illumina TruSeq v3 sequencing library was created for each strain according to manufacturer’s protocols. Each library was sequenced on a HiSeq 2000 over four lanes. Sequencing reads from each lane were aligned to the C57BL/6 J GRCm38 (mm10) mouse reference genome using BWA-MEM (0.7.13). For each library, aligned reads from each lane were merged using Picard Tools (v1.64) resulting in a single BAM file for each sample with a median coverage of 39×.59. Tumour samples were called against two genotype and strain matched controls and filtered for quality. The following filters were used: minimum of 10 q20 or greater covering bases in both tumour and normal, minimum tumour alternate allele fraction of 0.2, minimum of 3 alternate alleles in tumour, no more than 3 alternate alleles in either normal. Variants were also filtered for known variation using the Sanger variant dataset , and a pooled set of variants and variable regions observed in all control samples 59.SNV calling for exomes was performed using MuTect (version 1.1.6)Evaluating the result revealed the existence of a large number of likely germline variants in non-coding regions. To remove these variants, we selected all variants that were exactly replicated  in at least three samples without a consequential coding effect. In all cases these variants appeared to be approximately heterozygous, and no variant was consequential. As a result, we omitted all variants exactly replicated in three or greater samples without an effect on protein sequence, and all variants exactly replicated seven or more times without regard to context.Initial analysis revealed the existence of a large number of variants with the motif GGTGN. These sites were overwhelmingly associated with T > G transversions resulting in a GGGG homopolymer. Sanger sequencing showed that the majority of these variants were false positives. We imposed an additional, substantially more stringent, set of filters for these sites: minimum of 50 q20 or greater covering bases in both tumour and normal, minimum tumour alternate allele fraction of 0.2, minimum of 15 alternate alleles in tumour, normal alternate allele fraction of no greater than 0.05. This dramatically reduced the influence of this artifact. After this filtration, we took the intersection of the pass-filter variants between both controls as the keeper set for each tumour.60. Detected somatic variants were then filtered using an array of quality filters and common mouse genome variants were excluded. Tumours were called against paired tail DNA and filtered for the following; greater than or equal to 10 q20 or more bases, alternate allele fraction greater than 0.1 in tumour, and alternate allele fraction less than 0.1 in normal. In addition, we removed all variants occurring in simple repeat regions 61.For WGS. somatic variants were detected using CaVEMan, an expectation maximisation–based somatic substitution detection algorithm62). Samples were called against two normal with the following options: -l false –x 1 –M 4. Results were then filtered for germline events, recurrence, and simple repeats. Germline filtration was accomplished by identifying all sites found in controls in this cohort and flagging any tumour indel that overlapped (within 2 bases) these sites. We also repeated this strategy for germline events found in the Sanger list of indel events .Indel calling in exome samples was performed using pindel  in 3 or more samples. We also flagged regions (50 bp windows) containing three or more indels of any type. Manual evaluation of these regions revealed them to be primarily artefactual, with a small number of potentially biologically meaningful sites. As the number of artefactual variants substantially outnumbered the number of biologically meaningful sites, we omitted these regions for the calculation of indel rates but included them for the analysis of functional impact on cancer associated genes. We also removed all exome indels in simple repeat regions.pindel programme62. Resulting lesions were then filtered for exact and regional recurrence (more than three exact replicates or indels within 50 bp across the tumour cohort), and presence within simple repeat regions. Indel rates were defined similar to SNVs.  Indels were called in whole-genome samples by comparing tumour to paired control tail DNA using a modified version of the 37)). Analyses were carried out separately for single base substitutions (SBS signatures) and indels (ID signatures). We first performed de novo mutation signature extraction using NMF-based method as described in SigProfiler.The mutational catalogues from the 21 WGS samples were analyzed for mutational signatures. Signature extraction was performed using SigProfiler (conducted using methods based on nonnegative matrix factorisation (NMF) as described in Alexandrov et al. (refs. 37). The algorithm identifies the optimal combination of known human signatures that explains the observed mutation patterns (highest cosine similarity). All signatures extracted across all samples were able to be explained by a combination of signatures from the known human signatures (cosine similarity > 0.75) and thus we did not identify any signatures that would be considered novel. We performed hierarchical clustering of the samples based on the relative contribution of identified mutation signatures using both results from the de novo extraction and after signature deconvolution.For SBS signatures, extracted signatures were then compared to the set of mutational signatures deciphered from the COSMIC database as previously described  with the following options: --paired --I = 400,50 --bin_size = 250. Three samples showed consistently patterned aneuploidies that appeared to be artefactual and were omitted .CNV segmentation in whole-genome samples was performed using BICseq  remapped to mm10 with liftOverFocal rearrangement hotspots were identified by establishing the intermutation distance (the distance between any given rearrangement and its nearest neighbour) in 1 Mb windows sliding by 100 kb intervals. Enriched sites were defined as those windows with intermutation distance greater than five standard deviations greater than the genome-wide mean, with at least six unique lesions, in at least three different samples.69. Two-group analyses were performed using the non-parameteric Mann-Whitney U test. Fisher’s test was used to assess categorical data. The glm function was used to model the relationship between whether or not a chromosome was aneuploid and genotype/radiation exposure. Survival analysis was performed using Cox proportional hazards modelling and significance evaluated by the log-rank test. Plots were generated using the ggplot2 package70.Statistical analysis was performed in R (version 3.3.3)Further information on research design is available in the Supplementary InformationReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4