Prevalence of depression is higher in patients with cancer than in the general population. Sustained systemic inflammation has been associated with depressive behavior and it has been reported that depressed patients commonly display alterations in their immune system. We previously showed that cancer in mice induces a systemic environment that promotes neutrophil activation and leukocytosis. We thus hypothesized that the peripheral systemic response to a solid tumor leads to endothelial activation, which may promote inflammatory changes in the brain with behavioral consequences. Using the Lewis lung carcinoma (LLC) model, we show that tumor growth induces a progressive increase in peripheral inflammation as observed by elevated interleukin-6 (IL-6). In behavioral studies, tumor-bearing mice showed no sign of motor, coordination or short term working memory deficits as assessed by rotarod, balance-beam, and novel object recognition tests. However, there was an impairment in the grip strength test and interestingly, an anxious and despair-like phenotype in the elevated plus-maze, and tail suspension tests, respectively. Immunostaining of perfused brains revealed fibrin accumulation in the vasculature with some leakage into the parenchyma, a process known to activate endothelial cells. Taken together, our results suggest that the inflamed and prothrombotic systemic environment created by the growth of a peripherally-located solid tumor induces endothelial activation, accumulation of fibrin in the brain and astrocyte activation, perhaps leading to depressive-like behavior. Prevalence of depression is higher in patients with cancer than in the general population and is associated with increased morbidity. Hearing distressful news such as a cancer diagnosis can factor in to a depressive state. However, it has also been postulated that activation of pro-inflammatory cytokines as well as alterations of the hemostatic system can contribute to depression (reviewed in ). PeriphAstrocytes make up the most mass in the brain and are the main support cell to neurons as well as the endothelium, providing a liaison between the two. They are vital for structural normalcy in the brain and play a hefty role in maintenance of homeostasis in the CNS. Astrocytes are also implicated in development and maintenance of the blood-brain barrier. Forming prominent borders that line all interfaces between CNS and blood vessels, astrocytes can display anti-inflammatory barrier functions preventing neurotoxic inflammation or a pro-inflammatory phenotype resulting in a cytotoxic profile .Systemic inflammation and vascular dysfunctions are often observed in cancer patients and mouse models of cancer ,7. Our gHere we show, following the subcutaneous injection of Lewis Lung carcinoma (LLC) cells which induces a mild systemic inflammatory environment ,9, that The LLC cell line was purchased from ATCC and maintained in high glucose Dulbecco’s Modified Eagle’s Medium (DMEM) + L-GlutaMAX-1 . LLC cells originated from C57BL/6 mice and were characterized by ATCC according to the cell line authentification testing  and were used within 6 months after resuscitation. All cell culture products were purchased from Life Technologies/Invitrogen.All animal procedures were performed using adult, female (6 to 10 weeks), C57BL/6J mice (The Jackson Laboratory) and approved by the Institutional Animal Care and Use Committee of Boston Children’s Hospital . Animals were group-housed, at a maximum of five per cage, on a 12-hour light/dark cycle. Food and water were provided ad libitum. Number of animals used in this study was based on previous work done with this model system .5) cells were inoculated subcutaneously in the right flank. The tumor volume was calculated using the formula V = lw2 x 0.4 where l is the length and w the width [3. In this study, no animals were euthanized prior to study endpoints. Analgesic were not administered at any point during the study until euthanization. Blood was collected using EDTA-coated capillaries and analyzed with a Hemavet hematology analyzer (Drew Scientific). At sacrifice, blood was collected into 3.2% sodium citrate, centrifuged at 6000 rpm for plasma collection and centrifuged again at 12000 rpm to remove any contaminating cells. For brain collection, animals were perfused with 15ml of PBS under ketamine and xylazine anesthesia.LLC 5 x 105 cells whe width ,12. MicePlasma IL-6 and fibrinogen were assessed using Mouse IL-6 ELISA (Abcam) and Mouse fibrinogen ELISA (Genway Biotech), following manufacturer’s instructions. The VWF ELISA was performed as previously described  using thWe used the C57BL/6 stratin of mice because they are syngeneic to the cancer cells. The behavioral experiments were performed at the Kirby Biology Center which is part of the Dept of Neurobiology at Harvard Medical School. The Neurodevelopmental Behavior Core has a housing facility within the footprint to minimize transfer stress to the testing rooms. Mice were left for at least 30 min to acclimatize to the test room before experiments were begun. The testing was performed in rooms with low light (30 lux) to reduce stress. The person performing the test left the room during each test to minimize any influence on the behavior of the mouse during the trial.Animals were allowed to adapt to the chamber for 5 minutes without objects prior to testing. The test consisted of 2 phases: Sample Phase, where animals were allowed to explore an arena containing two identical objects until they had investigated both objects for a total period of 20 seconds; Recall Phase, where animals were allowed 5 minutes to explore the arena and two objects, one of which was the same as in the sample phase and one of which was novel. The recall phase occurred 10 minutes after the sample phase. The test started by the animal being placed into the arena at the far side against the wall and opposite to where the objects were located. Behavior was recorded remotely by a camera and the amount of time in seconds spent sniffing the objects was analysed using Ethovision XT v11.5. Only time spent with the nose in contact with an object was considered as investigation of that object. Objects in both phases of the test were alternated across trials to control for possible side bias. Objects and arena were cleaned thoroughly between trials with 10% bleach solution. Time spent with the novel object was divided by the time spent with the familiar object to obtain a novelty ratio for each mouse.The elevated plus maze consisted of a device with two “open” arms that had no walls and two “closed” arms (30x5cm) with walls arranged to create a “plus” shape. The area where the arms cross in the center formed the decision zone. The entire apparatus was raised 85 cm above the floor (Lafayette Instruments). Mice were placed on the center platform of the maze, facing a closed arm, and allowed to explore the apparatus for 5 minutes. The maze was cleaned between subjects with 10% bleach and dried. Behavior was recorded remotely by a camera and the total time spent in the open, center, and closed compartments was analysed using (Noldus Ethovision XT v11.5). Time spent on the open arm was divided by the time in the closed arms to calculate % time on the open arms for each mouse.Mice were suspended by the tail  for a test duration of 6 minutes about 30–50 cm above the surface of a table covered with soft padding material. The duration of immobility was scored during the last 4 minutes of the test for each mouse using ethovision XT v11.5.The beam was a narrow metal rod (1m long and 1cm wide) 50 cm above the bench surface with a black plastic box at one end in which cage mates of the test mouse were placed to encourage the test mouse to walk across the beam. A camera was located 90 degrees, perpendicular to the beam such that the whole length of the beam could be captured. The mice were trained the day before the first test to traverse the beam in both directions. The following day the test mouse was placed at one end of the beam and allowed to freely traverse along the beam in one direction. Having completed the first trip the mouse was then tested in the opposite direction. This allowed both sides of the animal to be assessed for foot slips in post-hoc review of the video recordings. Time taken to traverse the beam, total footsteps and number of foot slips for each mouse were scored by an observer blind to group.Animals were placed onto the device on the day prior to test and trained to remain on the rotating rod for a period 5 min. The rod rotated at a fixed speed of 4 rpm during training. If a mouse fell off the rod, it was placed back onto the device without interruption of the rotations. The following day mice were placed on the rod at 4 rpm for ten seconds to adjust to the rod speed after which the rod accelerated at 0.1rpm/sec. Each mouse completed five trials on the test day with a minimum of five minutes rest between each trial. The mean time spent on the rod was calculated from the 5 trials for each mouse.Motor function and coordination were evaluated by the grip test as modified from Moran et al., PNAS 1995. For this test, a mouse was placed in the center of a semi-flexible wooden rod  fixed between two vertical supports, elevated 40 cm from the benchtop. Mice were rated within 30 seconds with the following scoring system: 0, mouse fails to grab onto the rod and/or falls off; 1, mouse hangs onto the rod by one or both forepaws; 2, as for 1, and attempts to climb onto the rod; 3, hangs onto rod by one or both forepaws plus one or both hindpaws; 4, mouse hangs on with forepaws and hindpaws plus the tail wrapped around rod; 5, mouse escapes to the supports.Brains were harvested from perfused animals and snap frozen on dry ice. Cryosections were fixed with zinc fixative and stained with a sheep anti-fibrinogen antibody , and incubated with anti-sheep Alexa-488 or Alexa-555 (Invitrogen) as secondary antibodies for fibrinogen, and Cy3-conjugated GFAP (Sigma #9205) or FITC-conjugated lectin from Lycopersicon esculentum Sigma (L0401). Sections were counterstained with Hoechst-33342 to visualize all nuclei, mounted with Fluoro-gel (Electron Microscopy Sciences) and observed under an epifluorescent Axiovert microscope (Zeiss). All images were taken using the same exposure settings per magnification.Brains were harvested from perfused animals and snap frozen on dry ice. Thin sections throughout the whole brains were collected and lysed by sonication in RIPA buffer (Sigma #R0278) containing protease and phosphatase inhibitors (Thermo Scientific # 88669). The lysate was assessed for fibrin(ogen) using a mouse fibrinogen ELISA (GenWay #BB0BA2). Evan’s blue dye assay was performed on a separate set of animals and was assessed as previously described .Thin section lysates used for fibrinogen ELISA were also used for western blotting experiments. Samples were normalized for protein (BCA kit #) and added to Laemmli buffer (Bio-rad) containing 2-marcaptoethanol and boiled for 5 minutes. After cooling, samples were loaded onto 4–20% SDS-PAGE gels and transferred onto Immobilon PVDF membranes. Blots were probed for GFAP and GAPDH . Western blots were quantified using Image J64 software . For quantification, values for GFAP were normalized to GAPDH (protein loading control).Data are represented as mean ± SEM and were analyzed by One-way ANOVA followed by Bonferroni’s post-hoc analyses. All p-values were considered significant below 0.05.Peripheral solid tumors often induce a systemic inflammatory state that correlate with tumor growth in mouse models and cancer patients ,16. To fAs cancer patients and patients with chronic inflammation have been reported to suffer from depression and anxiety, we performed a battery of behavioral tests on control and tumor-bearing mice on days 11 and 17 after implantation. Tumor-bearing mice demonstrated normal motor abilities as revealed by the same number of slips in the beam walk test  and the Emerging studies associate depression-like symptom with modulation of the immune system . Indeed,Since astrocytes play a critical role in maintenance and formation of the blood brain barrier (BBB) phenotype (reviewed in ) we evalAnxiety and depression are common among patients with cancer and clinical evidence now suggests a causative effect of cancer on the initiation of depressive symptoms, as the psychological behavior is exhibited before the diagnosis of cancer . InflammDepression-like symptoms in tumor-bearing mice have been observed in mouse models of breast, ovarian and colon carcinoma ,27,28,29Although astrocyte activation has been widely related to systemic inflammation, to our knowledge, this is the first report of peripheral tumor burden leading to astrocyte activation. Our results show a progressive increase in astrocyte activation as the inflammatory/procoagulant state of tumor-bearing mice progresses. Studies showing astrocyte activation in mouse models widely use systemic injection of bacterial lipopolysaccharides (LPS). In this setting, activation of astrocytes is maintained for 24h with a peak at 6h which follow the released of inflammatory cytokines . MoreoveOur study indicates that the peripheral inflammation/procoagulant state produced by the tumor burden induces fibrin accumulation at the vessel wall in the brain. Astrocyte activation is also occurring, which could be the link to anxiety and signs of behavioral despair. Thus, our data suggest that anti-inflammatory treatment of the chronic inflammation observed in cancer patients might also treat or prevent depression. Indeed, ibuprofen has been shown to reduce the depressive behavior of tumor-bearing mice . InteresS1 Checklist(PDF)Click here for additional data file.


Low back pain (LBP) is ranked as one of the most prevalent health conditions. It is likely that some inflammatory mediators could be associated with pain and disability in these patients. Photobiomodulation therapy (PBMT) is a non-pharmacological therapy often used in patients with LBP and one of the possible mechanisms of action of therapy is modulate inflammatory mediators. However, to date there are no studies that evaluated the effects of PBMT on the levels of inflammatory mediators in patients with LBP. The aim of this study is to evaluate the acute effects of PBMT on systemic levels of inflammatory mediators and pain intensity in patients with chronic non-specific low back pain.2 (PGE2). The secondary outcomes will be levels of necrosis factor alpha (TNF-α), interleukin 6 (IL-6) and pain intensity. Biochemical and clinical outcomes will be measured at baseline and 15 minutes after the single treatment session.This is a prospectively registered, two-arm randomized placebo-controlled trial with blinded patients, assessors and therapists. Eighteen patients with chronic non-specific LBP will be randomized into 2 groups: placebo or active PBMT. The treatment will be provided in a single session. The primary outcome will be levels of prostaglandin EDespite PBMT be used in musculoskeletal disorders such as LBP, to the best of our knowledge this is the first study that will investigate a possible biological mechanism behind the positive clinical effects of PBMT on non-specific chronic low back pain.The study was approved by the Regional Research Ethics Committee. The results will be disseminated through publication in peer-reviewed international journal and conferences.NCT03859505. Some aspects can be associated with low back pain, and the identification of these aspects is helpful in both prevention and management of this condition. One of these aspects is the possible association between inflammatory mediators and low back pain, since the increased of pro inflammatory cytokines levels is often observed in painful conditions.,5Non-specific low back pain is a complex and extremely frequent condition demonstrated increase of plasmatic levels of necrosis factor alpha (TNF-α) in patients with chronic low back pain when compared to healthy individuals, for example. However, no significant correlation of TNF-α levels to pain intensity or disability were identified. Moreover, it was observed that there is an increase of TNF-α ,8 and interleukin (IL) 6 levels in patients with acute low back pain. Thus, the presence of some inflammatory mediators might be associated with pain and disability in patients with low back pain, since pro inflammatory cytokines such as IL-6, TNF-α and IL-1 contribute to the activation of nociceptors that generate potential of action and pain hyper sensibility.,5Evidences have demonstrated increase of pro inflammatory cytokines levels in patients with low back pain. Wang et al However, the mechanisms of action of available therapies to reduce pain and disability in these patients still remain uncertain. According to the abovementioned studies,–8 the modulation of inflammatory mediators levels could be considered a possible strategy in the low back pain management and thus, the use of photobiomodulation therapy (PBMT) could be interesting in this case.Several nonpharmacologic and noninvasive management options are available for patients with low back pain . PBMT is often used in the treatment of musculoskeletal disorders such as low back pain;,12 however, the exact mechanism of action through PBMT decreases pain intensity and disability in these patients is still unknown. Evidences suggest that PBMT is effective in modulate inflammatory mediators such as TNF-α, IL-1β, IL-6, IL-10 and prostaglandin E2 (PGE2), both in experimental and clinical studies,–18 contributing to pain relief,19 and tissue repair.,21 The biological responses of PBMT on these inflammatory mediators may be targeted as possible mechanism of action for its beneficial clinical effects.PBMT is a non-pharmacological and non-thermal intervention that consists in applying a non-ionized form of light, which includes LASER (light amplification by stimulated emission of radiation) and LED (light-emitting diodes) from visible to infrared spectrum.2) and pain intensity in patients with chronic non-specific low back pain.However, currently there are no studies about the effects of PBMT on inflammatory mediators in patients with non-specific low back pain, investigating whether this could be a possible mechanism of action. Therefore, the aim of this study is to evaluate the acute effects of PBMT on systemic levels of inflammatory mediators , placebo-controlled trial will be performed. The protocol of this study has been prospectively registered on Clinicaltrials.gov (NCT03859505). The study will be conducted at Department of Global Public Health and Primary Care, University of Bergen, Norway.2.2The present study will follow the ethical guidelines and was approved by the Regional Research Ethics Committee (Protocol No. 2018/1361/REK nord). Patients will be informed about all study procedures and asked to sign the Informed Consent Form prior to their enrollment in the study. Research personnel will take all appropriate and customary steps to ensure that data remain secure and that patient privacy and confidentiality will be maintained.2.32 (primary outcome of the present study) of 1.05 pg/μl (0.42 standard deviation), whereas applying the placebo in patients with non-specific low back pain resulted in levels of PGE2 of 1.52 pg/μl (0.39 standard deviation). We used the Researcher's Toolkit to calculate the sample size . Based on the aforementioned parameters used to calculate the sample, we found a sample of 9 patients per group, for a total of 18 patients. Since the phototherapy device used in the study causes no harmful thermal effects, patients of different skin colors will be recruited. The patients will be recruited through primary care doctors and physiotherapists.Since no studies assessing the effects of PBMT on inflammatory mediators in patients with non-specific low back pain are available, the number of patients per group in the present study was calculated based on a pilot study. This pilot study was recently conducted by our research group with 3 patients per group in order to estimate the sample size. A β value of 20% and an α of 5% were used to calculate the sample size. The pilot study showed that applying PBMT in patients with non-specific low back pain resulted in levels of PGE2.4Inclusion criteria:Patients with chronic non-specific low back pain ;Persistent low back pain for at least 3 months;Pain intensity of at least 3 points, measured by Pain Numerical Rating Scale;Aged between 18 and 65 years;Both genders.Exclusion criteria:Patients with severe skin diseases ;,26Patients with low back pain associated with nerve root compromise ;Serious spinal pathologies such as fractures, tumors, inflammatory and infectious diseases;Serious cardiovascular or metabolic disorders;Previous spinal surgery;Pregnancy.2.5The patients will be welcomed by the study's blinded assessor who will determine whether they will be eligible to participate in the study. Subsequently, a file will be completed with the patient's sociodemographic data and clinical history. Next, the primary and secondary outcomes of the study will be collected and all eligible patients will be randomized and allocated into 2 interventions groups: active PBMT or placebo PBMT. Fifteen minutes after the end of the single treatment session, the primary and secondary outcomes of the study will be collected again. The Consolidated Standards of Reporting Trials flowchart summarizing experimental procedures and patients is shown in Figure 2.6Prior to the single treatment session, patients will be randomized into their respective intervention groups: placebo or active PBMT. The randomization will be generated by a computer program (Excel Office 2010) and performed by a participating researcher not involved with the recruitment or evaluation of patients. This same researcher will be responsible for programming the PBMT device according to the result of the randomization (active or placebo mode) and will be instructed not to disclose the programmed intervention to the therapist, any of the patients or other researchers involved in the study until its completion. Concealed allocation will be achieved through the use of sequentially numbered, sealed and opaque envelopes. To ensure blinding for therapists and patients, the device will emit the same sounds and the same information on the display regardless of the programmed mode. Thus, patient and therapist will be blinded throughout the treatment.2.7Patients will undergo the treatment (active PBMT or placebo), according to prior randomization, in a single session:1.2) and LaserShower (emitter with an area of 20 cm2) cluster probes as emitters. Nine sites will be irradiated on the patient's lumbar region: 3 central sites on top of the spinous processes , using the SE25; in the same direction, but laterally, 3 sites on the left and 3 on the right , using the LaserShower (LS). PBMT irradiation sites and parameters were chosen based on a previous study. The treatment will be performed in a single session and the patients will receive a total dose of 220.05 J. Table Active PBMT: The PBMT will be performed using the Multi Radiance Medical Super Pulsed Laser MR4 console , with the SE25  and secondary outcomes (levels of TNF-α and IL-6 and pain intensity) of the study will be obtained at baseline and 15 minutes after the single treatment session by an assessor who will not be aware of patients’ allocation to their treatment groups:The primary outcome (levels of PGE2 (inflammatory mediators): measured by enzyme-linked immunosorbent assay (ELISA) method. Blood samples (10 ml) will be collected by a qualified nurse  and will be obtained from the antecubital vein. One hour after collection, each sample will be centrifuged at 3000 rpm for 20 minutes. Pipettes will be used to transfer the serum to Eppendorf tubes, which will be stored at −80°C until analysis. The levels of TNF-α, IL-6 and PGE2 in the blood samples will be determined by ELISA, using a commercial kit and following the manufacturer's instructions . Spectrophotometric readings will be performed in a SpectraMax Plus 384 Absorbance Plate Reader  with 450-nm wavelength and correction to 570 nm. The results of TNF-α and IL-6 will be expressed in pg/ml and the results of PGE2 will be expressed in pg/μl.Levels of TNF-α, IL-6 and PGE Pain Numerical Rating Scale evaluates pain intensity levels perceived by the patient on an 11-point scale ranging from 0 to 10, with 0 being ‘no pain’ and 10 ‘the worst possible pain. Patients will be instructed to score the level of pain intensity based at the time of evaluation.Pain intensity: measured by the Pain Numerical Rating Scale.2.9 The findings will be tested for their normality using the Shapiro-Wilk test. Parametric data will be expressed as mean and standard deviation and non-parametric data as median and respective upper and lower limits. Parametric data will be analyzed by two-way repeated measures analysis of variance  with post-hoc Bonferroni correction. Non-parametric data will be analyzed using the Friedman test and, secondarily, the Wilcoxon signed-rank test.The statistical analysis will be conducted following the principles of intention-to-treat analysis.2.10The datasets generated and analyzed during the current study will be available from the corresponding author on reasonable request.3Despite PBMT be used in musculoskeletal disorders such as low back pain, to the best of our knowledge this is the first study that will investigate a possible biological mechanism behind the positive clinical effects of PBMT on non-specific chronic low back pain. We strongly believe that this investigation can be helpful in the management of this condition. Over the years, it has been believed that some subgroups of this population can benefit from specific treatments, responding favorably to specific therapies.,31 In this way, knowing the mechanism of action of available therapies becomes an important aspect in this process.Non-specific low back pain is a complex health condition and several treatment options may be used for symptoms relief. cytochrome c oxidase modulation and ATP synthesis, besides modulating some inflammatory biomarkers.,18 Thus, if PBMT is able to modulate inflammatory mediators in non-specific low back pain, patients with increased levels of these mediators might be more responsive and consequently benefit more with the use of therapy.PBMT triggers positive effects acting in different ways, including increase of microcirculation,It is important to highlight that the present study is innovative and present high methodological quality . The sample size was calculated to provide the appropriate statistical power to detect precise differences for the primary outcomes of the study; however, the sample size has not powered enough to be correlated with the secondary outcome (pain intensity). One of the limitations of the study is that although the PBMT parameters were based on previous studies, there was no optimization and only one dose will be tested.3.1The study will be disseminated through publication in peer-reviewed international journal and conferences.SST, LOPC, ECPLJ and JMB contributed to the concept and design of the study, established the hypothesis and wrote the original proposal. SST, LOPC, JJ, MBS, IFN, ECPLJ and JMB contributed significantly in creating the manuscript. ECPLJ and JMB performed critical revisions of the manuscript. SST wrote the final version of the manuscript. All authors read and approved the final version of the manuscript.Conceptualization: Shaiane Silva Tomazoni, Leonardo Oliveira Pena Costa, Jon Joensen, Martin Bjørn Stausholm, Ingvill Fjell Naterstad, Ernesto Cesar Pinto Leal-Junior, Jan Magnus Bjordal.Funding acquisition: Shaiane Silva Tomazoni.Investigation: Ernesto Cesar Pinto Leal-Junior.Methodology: Shaiane Silva Tomazoni, Jon Joensen, Martin Bjørn Stausholm, Ingvill Fjell Naterstad, Ernesto Cesar Pinto Leal-Junior.Project administration: Shaiane Silva Tomazoni, Jon Joensen.Supervision: Leonardo Oliveira Pena Costa, Jan Magnus Bjordal.Writing – original draft: Shaiane Silva Tomazoni, Leonardo Oliveira Pena Costa, Ernesto Cesar Pinto Leal-Junior, Jan Magnus Bjordal.Writing – review & editing: Shaiane Silva Tomazoni, Leonardo Oliveira Pena Costa, Ernesto Cesar Pinto Leal-Junior, Jan Magnus Bjordal.


Retraction Note: Acta Neuropathol Communhttps://doi.org/10.1186/s40478-019-0709-3The authors have retracted this article  because The conclusion in the original paper was that NAWM is more similar to WM control than to MS lesions . This was based on the low number of significantly differentially expressed genes (DEGs) between NAWM and control WM. Now, the number of DEGs in the NAWM increased significantly compared to control WM: several new genes have been discovered, and the authors also found 16 unique genes in NAWM that are differentially expressed and are not present in any lesion type .The conclusion in the original paper that chronic active lesion was the most distinct lesion type is still valid, as a new Venn diagram  However, although CHI3L1 is present in chronic active lesions by astrocytes in the rim, verified with immunohistochemistry (IHC) and RNAscope, its gene expression is not significant. (iii) The authors also found TGFbeta-R2 in a repair specific network  and verified its presence in astrocytes in remyelinating lesions by RNAscope and IHC. However, TGFBR2 is significantly upregulated in other lesion types as well.The authors have repaired the labels and re-analyzed the data. While many of the conclusions remained unchanged and validated by other methods, some conclusions cannot hold anymore. A detailed description of the major changes is as followed:All authors agree to this retraction. The authors have been offered to submit a revised manuscript for further peer review.


Accupuncture-like transcutaneous electrical nerve stimulation (ALTENS) therapy has been shown in prospective studies to be effective in the treatment of radiation-induced xerostomia. Those studies treated patients within 27 months from end of radiation with ALTENS delivered in the clinic using a Codetron unit. However, that unit is no longer produced and there is limited data on success of ALTENS when delivered at home.A 50-year old man with xerostomia, 8 years post-radiation for T4N1 squamous cell carcinoma of the tonsillar fossa, was given ALTENS with a currently commercially available unit from Girish Surgical. He used the unit at home, 20 minutes daily for 8 weeks.After 8 weeks of ALTENS therapy the patient saw a reduction in the Self-Reported University of Michigan Xerostomia-Related Quality of Life Scale from 20 to 1.This case report demonstrates (1) the Girish Surgical unit is effective, (2) self-administration of ALTENS in patients who cannot come to clinic regularly may be practical and (3) ALTENS can still offer durable benefit to patients even 8 years after chemoradiation therapy. Currently approved treatment options are limited and not always effective. The most commonly used therapy is pilocarpine, which increases salivary flow in the major salivary glands by stimulating M3 muscarinic receptors, but is associated with significant adverse side effects.3Xerostomia is a common toxicity associated with chemoradiation in the treatment of head and neck cancer. This occurs in the vast majority of patients treated with radiation to the head and neck as a result of damage to the salivary glands, causing hyposalivation and chronic oral dryness. Patients with xerostomia are at higher risk of oral infections and frequently experience problems with eating, speaking and swallowing.7 Reports generated from single and multi-institutional studies have demonstrated benefit in patients initiating ALTENS therapy within 3–27 months of completing chemoradiation.7 In addition, a previous case report found ALTENS therapy to be effective in a patient 5 years removed from completion of chemoradiation.8 Unfortunately, the TENS unit produced by Codetron  used to conduct these studies is no longer manufactured. The treatment protocol for these patients also required them to receive therapy at a clinical centre twice weekly for 12 weeks.7 A different TENS unit, produced by Girish Surgical  is available and in current production. This unit was purchased, instruction on its use was given to a patient in the clinic  treatment has emerged as a promising new modality to improve saliva production and related symptoms without the associated cholinergic side effects caused by sympathomimetic agents.e clinic , and theA 50-year old male with a history of T4N1M0 squamous cell carcinoma of the tonsillar fossa underwent definitive treatment with chemoradiation. Radiation was delivered from 20 August 2007 through 8 October 2007 to the oropharynx, entire neck and supraclavicular region using intensity-modulated radiation therapy. A total dose of 7,500 cGy was delivered in 125 cGy fractions given twice daily, 5 days per week. Overall treatment time for these 60 fractions was 51 days. The patient tolerated treatment relatively well, however he developed trouble swallowing and xerostomia that persisted post-radiation. These toxicities did not improve over the next 8 years of follow up.8 were given at baseline and upon completion at 8 weeks to assess response and saw a reduction in score from 20 to 1. In addition, the patient reported improvements in salivary function and related symptoms that included ‘being moist more frequently, having less food stuck to teeth, and less dryness upon waking up from sleep’. These benefits have been maintained since completion of therapy 6 months ago.ALTENS therapy is available to all patients free of charge at Roswell Park Cancer Institute (RPCI). This involves using a Codetron eight-channel TENS unit and requires the patient to travel to RPCI twice a week to receive therapy. As this patient lives over an hour from Buffalo and Codetron TENS units are no longer produced in the United States, an eight-channel TENS unit was purchased from Girish Surgical in India. The patient was taught how to self-administer ALTENS and then given the unit to carry out therapy at home for the duration of treatment. A single round of daily treatment was self-administered for 8 weeks totalling 56 applications. Self-reported University of Michigan Xerostomia-Related Quality of Life Scale (XeQOLS) surveys4 conducted a 46 patient, single institution, phase I–II trial demonstrating an ALTENS approach without invasive needles that improved whole saliva production and related symptoms in patients when initiated within 27 months of chemoradiation completion. The multi-institutional, 48 patient, phase II trial that followed showed similar efficacy with a positive treatment response in 86% of patients using the self-reported XeQOLS surveys to evaluate response at baseline and 6 months post-treatment.5 Eligibility for ALTENS was limited to those within 24 months of completing chemoradiation.5A series of studies have suggested that ALTENS may improve radiation-induced xerostomia. Wong et al.6 found ALTENS was just as efficacious as pilocarpine but produced significantly less grade 3 or less adverse events (20·8–61·6%). A secondary analysis of this study using the RTOG-modified University of Washington Head and Neck Symptom Score to assess overall health-related quality of life demonstrated consistently lower scores, indicating better function, for patients randomised to the ALTENS arm compared with those randomised to pilocarpine.7In a 148 patient, phase III multi-institutional randomised study comparing ALTENS with pilocarpine therapy, Wong et al.9 With the Codetron TENS unit no longer in production, it became necessary to find an alternative 8-channel TENS unit to be used with ALTENS therapy. There have been no current or previous studies evaluating the effectiveness of a Girish Surgical TENS unit with ALTENS therapy or examining the efficacy of having a patient self-administer treatment with a travelling unit.Since the publication of these studies, 1 case report has reported the effectiveness of initiating ALTENS in a patient 5 years removed from completing chemoradiation.As a result of these clinical studies of ALTENS and the current report, it may be of value to continue evaluating the effectiveness of the Girish Surgical unit as a suitable replacement for the Codetron unit and the efficacy of ALTENS therapy in patients suffering from radiation-induced xerostomia who have been post-chemoradiation for an extended period of time. In addition, the possibility of allowing patients to self-administer treatment can be a practical solution to expanding ALTENS therapy to patients unable to come to clinic regularly. In this patient, we observed a compelling drop in XeQOLS score from 20 to 1 and the patient self-reported a noticeable improvement in symptoms. The Girish Surgical unit offers a promising replacement to the Codetron unit and has the potential to alleviate xerostomia symptoms in those well beyond post-chemoradiation completion.ALTENS has proven to be effective in alleviating symptoms of radiation-induced xerostomia, however the Codetron TENS unit used in these studies is no longer available. Our findings suggest the Girish Surgical TENS unit is effective, self-administration of ALTENS in patients who cannot come to clinic regularly may be practical, and ALTENS can still offer durable benefit to patients even 8 years after chemoradiation completion. Self-administration of ALTENS has the further advantage that patients may continue it until they are satisfied with the response as opposed to some arbitrary limit on the number of treatments.


In this work, we assess the reproducibility and prognostic value of CT-derived radiomic features for resectable pancreatic ductal adenocarcinoma (PDAC). Two radiologists contoured tumour regions on pre-operative CT of two cohorts from two institutions undergoing curative-intent surgical resection for PDAC. The first (n = 30) and second cohorts (n = 68) were used for training and validation of proposed prognostic model for overall survival (OS), respectively. Radiomic features were extracted using PyRadiomics library and those with weak inter-reader reproducibility were excluded. Through Cox regression models, significant features were identified in the training cohort and retested in the validation cohort. Significant features were then fused via Cox regression to build a single radiomic signature in the training cohort, which was validated across readers in the validation cohort. Two radiomic features derived from Sum Entropy and Cluster Tendency features were both robust to inter-reader reproducibility and prognostic of OS across cohorts and readers. The radiomic signature showed prognostic value for OS in the validation cohort with hazard ratios of 1.56 (P = 0.005) and 1.35 (P = 0.022), for the first and second reader, respectively. CT-based radiomic features were shown to be prognostic in patients with resectable PDAC. These features may help stratify patients for neoadjuvant or alternative therapies. As the only definitive treatment, about 20% of PDAC cases are eligible for surgical resection2 with these patients having a 5-year survival of 19%3.Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer-related death in the US with an extremely poor prognosis. Over the past four decades, the 5-year survival rate has only marginally increased from 3% to 8.5%6.The most common clinicopathologic factors significantly associated with 5-year survival are lymph node status, tumour size, margin status at surgery, histological tumour grade, and receipt of adjuvant chemotherapyradiomics has shown early promise in exploiting the latent information in medical images and establishing links between quantitative imaging biomarkers, and patient outcome and response to systemic chemotherapy and radiation9. Radiomics refers to the extraction and analysis of a large amount of quantitative features from medical images11. These quantitative imaging features can be used to build prognostic models to risk-stratify patients based on different clinical outcomes such as survival. The ability to capture the entirety of a tumour gives radiomics the capability of assessing one of the key features of cancer, heterogeneity. Radiomic parameters related to heterogeneity have been shown to be a prognostic factor for patient outcome in other cancer sites such as lung12.As an evolving paradigm in cancer biomarker discovery and validation, 13. Other radiomic feature classes include morphological features to capture ROI shape characteristics14 and edge detection features that highlight the boundaries of objects in the ROI15.Radiomic features include different classes of quantitative imaging features that each captures a different property of a region of interest (ROI). Fist-order  and second-order statistical features  are among the most frequently used radiomic features where the former are calculated using the histogram of grey-level pixels, regardless of the spatial relationship among the pixels, and the latter are calculated using grey level co-occurrence matrices (GLCM)16. As a standard-of-care imaging modality, CT images can be used to extract radiomic features with no extra image acquisition cost to the healthcare system, thus providing comprehensive information on the phenotypic and textural structure of the tumour. Neoadjuvant therapy has been shown to improve the survival of patients with resectable PDAC17. If radiomic features can identify patients with more aggressive disease, it might help select patients most in need of neoadjuvant treatment.In PDAC, computed tomography (CT) is the main diagnostic tool for assessment of local extent of disease and surgical planning18, renal clear cell carcinoma20, and metastatic colorectal cancer21, there is scarcity of multicentre radiomics research on PDAC. Our preliminary results were published on a single small cohort (n = 30) exploring a limited number of radiomic features for prognostication  showing two features were predictors of overall survival (OS) for PDAC patients undergoing curative intent surgical resection22. Another study investigated radiation induced changes in CT radiomic features (8 first-order features) in a cohort of 20 patients with pancreatic head cancer over a period of 5 weeks and association between changes in radiomic features and pathologic response was reported9. Recently, a larger cohort of 161 patients with resected PDAC was analyzed to study the prognostic accuracy of radiomic features combined with preoperative serum carbohydrate antigen 19-9 (CA19-9 levels) and pathology score (The Brennan score) where it was reported that adding the pathology score to radiomic features and serum cancer antigen improves the prognostic power of the model23.Although previous studies have shown the prognostic value of CT radiomic features for different cancer sites including non-small cell lung cancer24. The purpose of this study was to assess the reproducibility and prognostic value of CT-derived radiomic features for resectable PDAC.In this retrospective study, we aimed to address the shortcomings of the previous radiomic studies of PDAC by including assessment of reproducibility across different readers, using data from different institutions and CT scanners, and using separate training and validation sets. To achieve this goal and further validate CT radiomic parameters as prognostic biomarkers in PDAC patients, we investigated these parameters in two separate pre-operative cohorts from two institutions and contoured by two radiologists with different levels of expertise through the analysis of a comprehensive set of radiomic features with a standard analytic library (PyRadiomics version 2.0.1)This retrospective study was approved by the Research Ethics Board of Sunnybrook Health Sciences Centre and University Health Network and all methods were carried out in accordance with relevant guidelines and regulations. Two cohorts from two separate institutions consisting of 30 and 68 patients undergoing curative intent surgical resection for PDAC from 2007–2012 and 2008–2013, respectively, who had pre-operative contrast-enhanced CT available for analysis and were part of ongoing studies where survival data was being collected were included. Patients were resectable and had not received neo-adjuvant treatment. To minimize the effect of post-operative complications on outcomes analyses, patients who died within 90 days after surgery were excluded. Institutional review board approval was obtained for this study from both institutions and the need for written informed patient consent was waived. The demographic information for both cohorts is shown in Table 22.We previously used the first cohort (n = 30) in a pilot study where only few in-house developed radiomic features extracted from single reader contours were investigated for prognostic value of OS in PDAC patientsPatients underwent contrast-enhanced CT with a biphasic pancreas protocol consisting of arterial or pancreatic phase and portal venous phase acquisitions. As CT scans were not all from the same institution, the exact contrast bolus volume, timing, and injection rate varied over the time period. In addition, there was inconsistent timing related to variation in CT protocols during the arterial/pancreatic phase imaging. This resulted in variable enhancement of the tumour and background pancreas. As a result, in many cases, the tumour was inconsistently visualized on the arterial/pancreatic phase. The portal phase was consistent in timing and enhancement of background tissue across the entire cohorts. For these reasons, all pancreatic cancer boundaries were drawn on the portal venous phase of acquisition as this phase was most consistent across all exams. CT images were reconstructed with 5 mm and 2 mm intervals for the first cohort and second cohort, respectively. Detector width was 40 mm and kV was 120 kVp for the portal phase for both cohorts. Examination was performed on a 64 row multidetector helical CT .25 was used by an experienced radiologist with 18 years of experience as an oncologic imager (Reader 1) and a radiology research fellow (Reader 2) blinded to patient outcome, to review the images and contour the ROIs on the slice with the largest visible cross section of the tumour on the portal venous phase. To differentiate the tumour and the pancreas background, relative contrast difference was considered and in cases where tumour boundary was not clear, tumour boundary was defined by the presences of pancreatic or common bile duct cut-off and review of pancreatic phase images22.An in-house developed volume of region contouring tool (ProCanVAS)24. To remove the fat and stents, the images were thresholded where voxels with HU (Hounsfield unit) <−10 and >500 were excluded from the analysis. We used a subset of well-known PyRadiomics features, which include first order features and second order features extracted from GLCM matrix using different filters . In total, 410 radiomic features were extracted which included different classes of features listed in Table Feature extraction was performed on the ROI using the PyRadiomics library version 2.0.1) in Python.0.1 in Pradiomic signature, which is both robust to inter-reader reproducibility and prognostic of OS across the cohorts and readers. Constructing a single radiomic signature instead of using a set of features reduces feature space dimension mitigating multiple testing problem. In addition, a multi-feature signature accounts for the inter-feature interactions which usually leads to improved predictive modeling compared to individual features26.We used the first cohort (n = 30) and second cohort (n = 68) as the training and validation datasets, respectively. The goal was to build a single 29. We excluded features with ICC < 0.3 to eliminate unstable features among the contours of both readers. The more reproducible features were then tested for their ability to predict OS in the training cohort using a Cox proportional-hazards regression model30 where the features were treated as continuous variables. This was done using Reader 1 contours as Reader 1 was the more experienced radiologist. Any features that were not prognostic of OS in the training cohort were eliminated. A feature selection method (LASSO31) was applied to significant features (P < 0.05) in the training cohort to select the ones with best prognostic power.First, individual radiomic features of both Reader 1 and 2 in the training set were evaluated for their inter-reader reproducibility by calculating Intraclass Correlation Coefficients (ICC) for each pair of features. ICC, which represents how strongly the features in the same class resemble each other, is generally regarded as poor if less than 0.332. The feature was considered validated if its adjusted P-value was <0.05.Each radiomic feature derived above in the training cohort was then tested in the validation cohort. This was done by retesting these selected features on both Reader 1 and 2 contours in the validation cohort using a univariate Cox regression model and Wald test. Given that this was the validation phase, to control the multiple testing problem, false discovery rate (FDR) control was appliedAs a final step, the remaining significant features in the training cohort were run through a Cox regression model to generate a single radiomic signature. To validate the reproducibility of the constructed radiomic signature, the ICC was calculated in both training and validation cohorts. To validate the prognostic value of the constructed radiomic signature, Cox regression and Wald test were run in the validation cohort for both Reader 1 and 2 contours.Clinical factors that may be prognostic of OS were also used in univariate Cox regression models. These factors include age, sex, tumour size, grade, N stage, margin, and CA 19-9 levels.Out of 410 initial radiomic features generated from PyRadiomics library, 133 features were removed due to having zero or constant values for all patients. Out of 277 remaining features, 251 features had ICC > 0.3 among the contours of two readers in the training cohort. When the Cox regression model followed by feature selection was applied to these 251 reproducible features in the training cohort with Reader 1 contours, 3 features were significant (P < 0.05). These 3 features were then assessed using Cox regression in the validation cohort with both readers’ contours, and 2 remained significant after applying FDR multiple testing control (P < 0.05). Table Figures As it can be seen from Table μx and μy are the mean gray level intensities of the marginal row and column probabilities of px and py, respectively where Cluster Tendency is a measure of groupings of pixels with similar gray-level values:1 is original_glcm_SumEntropy and F2 is squareroot_glcm_ClusterTendency.The radiomics signature derived from these 2 radiomic signatures combined is shown in Equation The hazard ratios in the validation cohort for the radiomic signature were 1.56 : 1.15–2.13) and 1.35 (CI: 1.04–1.75) for Reader 1 and 2, respectively. The P-values in the validation cohort for the radiomic signature were 0.005 and 0.022 for Reader 1 and Reader 2, respectively with ICC value of 0.63 . To investigate whether the radiomic signature adds prognostic value to the model built with N stage, we tested a bivariate Cox regression model using N stage and radiomic signature and it was found that the bivariate model (N stage plus radiomic signature) is significantly different (with improved performance) than the univariate model  . This indicates that adding radiomic signature to the clinical factor model (N stage) further improves the prognosis performance. CA19-9 levels were available for a subset of patients; 25 and 39 patients for training and validation cohorts, respectively. In the subset, CA19-9 factor was only significant in the validation cohort with P-value of 0.047 and hazard ratio of 1.37 (CI: 1.00-1.88). Adding radiomic signature to CA19-9 model significantly improved the prognostic performance of OS  confirming that the radiomic signature adds to the prognostic power of the model with of CA19-9, which is an established clinical biomarker.Table 33. Better treatment options, fundamental understanding of the disease and earlier detection methods are needed. In this exploratory study, we evaluated the potential of radiomic features in PDAC on CT as part of early validation. We have demonstrated the potential of a radiomic signature as a prognostic biomarker in PDAC that can be used across different CT scanners and readers. Although radiomic features have been found to be prognostic of patient outcome in different cancer sites such as lung18, kidney20, and colorectal cancer21, there is limited work on PDAC23. These studies are all single institution exploring a limited number of radiomic features. In addition, only one reader contour has been used for the analysis, and standard radiomic libraries are not used in most of these studies. By using an open source code library (PyRadiomics24), there is an opportunity for other centres to validate the findings presented in this study. If further validated, this signature could be used to help select patients that may benefit from neoadjuvant treatment.PDAC has a very low survival rate10 where the number of features is far greater than the number of patients in the dataset. This challenge combined with the reproducibility issues inherent in different readers annotating the same image differently, and inconsistency in images acquired by different scanners, which might lead to unreliable features, cast doubt on the reproducibility of radiomic features as prognostic biomarkers for cancer. In this study, the main goal was to address these challenges by generating a single radiomic signature using the contours of two readers on two cohorts from two institutions where the first cohort was used for radiomic signature discovery and the second cohort was used for validation. This allowed us to separate the training and testing data and thus, to perform a proper validation of the generated radiomic signature.Radiomics studies for cancer prognosis are usually limited by the “Large P, small N” dataset problem34 but also different readers.Excluding features with low agreement between the readers ensured the reproducibility of the final radiomic signature. The radiomic signature was generated by combining the features that were significant in the training cohort and remained significant in the validation cohort after multiple testing correction. It is encouraging to observe that the radiomic signature that was generated in the training cohort remained significant in the validation cohort for both readers. This confirms the reproducibility of radiomic features as cancer biomarkers across not only different scanners/institutions which has also been shown in other studies for different cancer sites such as lungIt was interesting to observe that a significant number of radiomic features (251 out of 277) were robust with respect to inter-reader variability in ROI contouring. The fact that out of 277 robust features (with moderate and high ICC), only 3 were found to be prognostic of OS in the training cohort may be due to small sample size (n = 30). A larger sample size will increase the probability of finding more prognostic features in the training phase.9, joint entropy22, and sum entropy35) have been shown to be prognostic of OS for different cancer sites. Entropy measures the degree of randomness or non-uniformity in the image and it has been hypothesized that it can act as a surrogate for tumour heterogeneity. The comparison of pairs of synchronous metastases from the same primary tumour has shown that entropy in each pair is highly correlated suggesting that it is capable of representing tumour biological characteristics36. It is promising to note that one of the radiomic features validated in this work is also based on entropy-related features (Sum Entropy), which strengthens the hypothesis that this specific radiomic feature may capture the underlying tumour phenotype.As an indicator of tumour heterogeneity, entropy-related radiomic features  was not significant. This may be in part related to the poor definition of cancer margins and high interobserver variability in size measure on CT of PDAC. This indicates that features such as entropy that capture tumour characteristics beyond size may be needed for prognostication of PDAC.Although tumour size measured as the maximal diameter of the mass on gross pathologic examination has been shown to be a histopathologic feature for prognosis of OS37 were available for a subset of patients in both cohorts and it was significant only in the validation cohort. Similar to N stage, the radiomic signature improved the prognostic power when combined with CA19-9.Out of other clinical factors available for both cohorts , only N stage, which is a postoperative factor was prognostic of OS in the validation cohort. It is important to note that the radiomic signature improved the prognostic power when added to N stage model. This indicates that the radiomic signature harbours prognostic information not necessarily captured by N stage factor. This, combined with the fact that radiomic signature is a preoperative biomarker, reconfirms the prognostic value of radiomic signature as a potentially reliable biomarker for PDAC. CA19-9 levels which had been shown to be associated with the OS of PDAC37 were not available for all patients. In future studies, the added prognostic value of radiomic signature to these preoperative biomarkers will be investigated using the full cohorts. Nevertheless, when these biomarkers are obtained, which is after diagnosis, radiomic features are readily available with no extra cost. Thus, a reliable radiomic signature with prognostic power is of significant value independent of other preoperative biomarkers.Limitations of this work was the relatively small sample, and outcome was limited to overall survival. We hope to extend this work to larger cohorts and multicentre studies with more clinical outcome and genomics data soon. Moreover, CA19-9 and carcinoembryonic antigen (CEA) which have been shown to be associated with the OS of PDACOnce validated, these biomarkers may have a role in selection of patients who should undergo neoadjuvant treatment with chemotherapy and/or radiation therapy prior to surgery. This also provides a potential signature to be tested in different PDAC populations such as unresectable patients. Further work on histologic correlates such as tumour stroma which is a potential druggable target in this disease would also be of interest.In this study, we have demonstrated a set of imaging biomarkers and a signature that are both reproducible across different readers and CT scanners and prognostic in preoperative patients. These parameters provide a reasonable starting set of quantitative measures for prospective validation in future trials in surgical candidates with PDAC.Conventional staging CT-based radiomic features related to Sum Entropy and Cluster Tendency show promise for prognostication of OS for PDAC patients undergoing surgical resection across different institutions.The Sunnybrook Health Sciences Centre and University Health Network Research Ethics Boards approved these retrospective single institution studies and waived the requirement for informed consent.


E. coli. The transcriptome data provides clear expression signals and thus represents an extremely favorable test setting. The methods estimate activity changes of all TFs, which we expect to be highest in the KD TF. However, only in 15 out of 54 cases, the KD TFs ranked in the top 5%. We show that this poor overall performance cannot be attributed to a low effectiveness of the knockdown or the specific regulatory network provided as background knowledge. Further, the ranks of regulators related to the KD TF by the network or pathway are not significantly different from a random selection. In general, the result overlaps of different methods are small, indicating that they draw very different conclusions when presented with the same, presumably simple, inference problem. These results show that the investigated methods cannot yield robust TF activity estimates in knockdown schemes.Numerous methods have been developed trying to infer actual regulatory events in a sample. A prominent class of methods model genome-wide gene expression as linear equations derived from a transcription factor (TF) – gene network and optimizes parameters to fit the measured expression intensities. We apply four such methods on experiments with a TF-knockdown (KD) in human and  Gene regulation also plays an important role in the development and progression of various diseases3. Thus, the elucidation of human regulatory relationships is an important research field and many methods attempting to infer the actual regulatory events in a given sample have been proposed.The regulation of gene expression is a fundamental biological mechanism in all living species. It determines the cells’ unique properties and enables them to adapt to the organism’s development, cellular function, the environment and external stimuli1. Several algorithms, such as biRte4 (Bayesian inference of context-specific regulator activities), ISMARA5 , RABIT6  and RACER7 , have been presented to model genome-wide gene expression and regulation via the activity and relationships of transcription factors. These models allow for the application of mathematical optimization to find parameters that minimize the divergence of predicted and measured expression intensities8. They all consider the topology of the regulatory TF – gene network to be given and try to infer the actual TF activity developed in a certain disease or under a specific experimental condition. Their primary output is a ranked list of TFs, sorted by their activity in a given group of samples. A detailed description of the specific methods evaluated in this paper is given in the materials and methods section at the end of the paper. Several studies reported that such methods can be used to identify biomarkers for specific phenotypes in human cell lines and in vivo samples, for example in innate immunity5, ageing related changes5 or acute myeloid leukemia7.In eukaryotes, gene expression is mainly regulated by a complex network of transcription factors (TFs) which activate or repress gene transcription8. We used a publicly available human TF – gene network9 together with experimental data from TCGA10 (The Cancer Genome Atlas) for three cancer types to identify key biomarkers for these specific diseases. The results showed that all methods seemed to detect strong signals and find biologically relevant information, but sensitivity was low and the mutual result overlaps from different methods were small. We suspected that the complexity of gene regulation in cancer was one reason for the questionable performance and low consistency of different methods’ results. Therefore, we here focus on much less complex data and use knockdown experiments to evaluate different methods on estimating TF activity changes. We suppose that the highest change in activity will occur in the knocked down TF when comparing case and control samples. Many datasets of such high-throughput experiments for certain experimental conditions and different species have been published and are available in public repositories like GEO11 (Gene Expression Omnibus). In this straightforward and for the methods favorable setting, we expected that the methods would consistently be able to identify the knocked down TF.We previously compared different methods for estimating regulatory activity qualitatively and quantitatively in detail4, ISMARA5, RABIT6 and RACER7, to infer transcription factor activity from gene expression data in knockdown (KD) experiments. We downloaded transcriptome data of four publically available KD experiments from the GEO11 repository including different TF knockdowns in human and E. coli cell lines. To better distinguish KD TFs from cell lines or other abbreviations, we will set TFs in italics. The three chosen experiments in human cell lines  contain data from 8 knocked down genes  and the double knockdown C/EBPβ & STAT3. The selected experiment in E. coli (GEO identifier GSE112116) comprises 5 knocked down genes  and the double knockdown ArcA & Fnr. Some of these experiments were conducted in different cell lines or conditions . Overall, we study 25 data sets , 13 from human and 12 from E. coli. For an overview of the composition of the experiments, see Figs E. coli) as input to the methods biRte, RABIT and RACER, whereas ISMARA employs an own, inaccessible underlying network. The network including information on human regulatory relationships is based on a text-mining approach9 complemented with TF – gene interactions from the public TRANSFAC database17. The network was built by text mining the entire Medline and an additional manual curation step of the top-ranking sentences. It thereby combines the content of regulatory databases with more than 300 validated regulatory relationships. The network for E. coli was retrieved from RegulonDB18. The considered datasets are described in detail in the materials and methods section at the end of the paper.Here, we compare four different methods, namely biRte19 (Algorithm for the Reconstruction of Accurate Cellular Networks) to assess the influence of the network on the results. To test whether the mere differential expression is a better predictor for TF activity changes, we calculated ranks of differential expression for the knocked down TFs and compared those to the activity ranks.We predominantly assessed the rank of the TF that was knocked down and the total number of ranked TFs. We additionally checked for aliases and we determined the ranks of neighbor TFs in the network, of co-members in a pathway and of interacting TFs. To examine whether the methods were able to detect a common signal in the data, we compared the overlap of the top 100 ranked regulators of all methods within one data set. We additionally performed the activity estimation on smaller networks and on networks inferred de-novo by ARACNE8, we are not aware of any other independent study on the performance of optimization-based algorithms for the estimation of whole genome transcription factor activity. Our previous results compared the performance on multi-omics data sets where no clear expectations could be formulated and also found very low coherence in the results of different methods. This lack of quality estimation of results led us to the new experimental design of using knock-down. However, also on this presumable much simpler problem the result overlaps are very low and the knocked-down transcription factor was only very rarely identified. We conclude that the investigated methods do not yield robust estimates of TF activity in a knockdown scheme.Apart from our own previous workE. coli, the identification of the KD TF by activity estimation yielded slightly better results compared to human cell lines. When looking not only at the KD TF but at the regulators related to the KD TF in the network or a pathway, we identified only a single case where the mean of the ranks of all related TFs was significantly smaller than expected by chance. The overlap of the top 100 ranked regulators of all methods within one data set was small and statistically insignificant. The reduction of the network size or the use of ARACNE’s inferred networks did not improve the results.Our results show that, although almost all KD TFs showed differential expression, their activity ranks were only in 15 out of 54 cases within the top 5% of all ranked TFs , both in the single KD and double KD together with STAT3, see Fig. RUNX1 was significantly upregulated in SNB19 case samples compared to the control samples. Nonetheless, we included RUNX1 in our analyses since we were only interested in finding absolute TF activity changes. In E. coli  TFs themselves were differentially expressed, which was the case for all human KD TFs except see Fig. , all KD E. coli, the number of KD TFs in the top 5% TFs was 3 in the aerobic and 5 in the anaerobic condition out of 6 data sets in either condition. As expected, the KD TFs are in about two third of the considered data sets amongst the TFs with the highest changes in differential expression.Additionally, we checked whether the differential expression per se would be a good predictor for the determination of knocked down TFs in a data set. Therefore, we computed differential expression separately in each data set, contrasting the expression of the corresponding case and control samples and evaluated the ranks of differential expression for the KD TFs. The results are shown in Supplementary Fig. E. coli). Of the 54 cases, where ranks were provided, 27 resulted from biRte, one from ISMARA, 13 from RABIT and 13 from RACER. Due to stringent filtering thresholds within the methods, no activity score was assigned to the KD TF in 37 cases, hence the ranking could not be computed in those cases.We next applied biRte, ISMARA, RABIT and RACER to determine the respective KD TFs’ ranks. The KD TFs were only in 15 out of 54 cases within the top 5% of all ranked TFs . In E. coli, the results from biRte were better with 8 out of 14 TFs in the top 5% and another two TFs in the top 10%. In all other data sets, the ranks provided by biRte for the TFs in question were quite low. ISMARA could only be applied to GSE45838 and GSE17172 since the chips from the other experiments were not supported by the online interface. In one data set (MYB) the KD TF was highly ranked (10th out of 602), but ISMARA did not provide any ranks for the two other KD TFs (BCL6 and FOXM1). Since the underlying network from ISMARA is not accessible we cannot discern whether the TF is not present in the network or was not considered important by the ranking procedure. RABIT removes TFs with insignificant cross-sample correlation from the results and therefore only provides the ranks of, on average, 56 TFs in our analyses. It did not provide any activity score for the KD TF in over half of the data sets . In human, not a single KD TF was ranked in the top 20%. However, in E. coli RABIT was able to identify AppY (rank 1) and ArcA as knocked down TFs in the anaerobic condition (rank 2 in the single KD and rank 1 in the combined KD ArcA & Fnr). In contrast, RACER ranked only one KD TF for the human data sets at all (BCL6) and did not rank any KD TF highly in E. coli. In some human data sets, RACER even reported the total number of important regulators to be zero.We observed that biRte provided ranks for nearly all KD TFs. In 3 out of the 13 human data sets, where ranks were specified, biRte ranked the knocked down TF in the top 5% (20 for human respectively EcoCyc21 for E. coli), direct neighbors in the TF – gene network, directly interacting TFs  and presumed aliases from the GeneCards23 (human) and EcoCyc21 database (E. coli)  in Fig. JUN), which is directly connected to MYB in the human regulatory network, was ranked among the top 20% TFs by two of the four methods . Previously, it was shown that JUN contributed to the transcriptional activation of MYB25. For each method and data set individually, we evaluated whether the mean of the resulting ranks of all related TFs was significantly smaller than the average rank expected at random . Only one out of 54 of the mean ranks of the estimated activity changes was significantly below the average rank: in E. coli, biRte ranked OxyR and a related TF highly in the anaerobic condition (p = 0.002). However, since this result was obtained with quite a small sample set (only two ranked TFs), we consider it not representative.We show the resulting ranks and according p-values of the KD TF and related TFs for one exemplary result . These results make sense, as the expression of FOXM1 and MYBL2 as its downstream factor were significantly associated with clinical stages and overall survival of glioma patients26 and is very high in Burkitt lymphoma27. Further, MYBL2 deregulations occurred in a broad spectrum of cancer entities29. FOXM1 is a direct target of repression by FOXO proteins. An inactivation of FOXO or overexpression of FOXM1 was associated with tumorigenesis and cancer progression30. Nevertheless, the overlap is extremely small and not significantly larger than expected at random .We found very little overlap in human cell line data. The highest overlap among three methods  occurred in E. coli, the number of common TFs from biRte, RABIT and RACER was higher, but also not significant , with a maximum overlap of 18 TFs (ArcA & Fnr knockdown in the anaerobic condition). The overlap contained, for example, ArcA, which is activated in anaerobic conditions31, NtrC, which was shown to be upregulated during the transition from anaerobic to aerobic conditions32, and AdiY, which was maximally induced under anaerobic conditions33. Although the methods do not find the knocked down TF itself, at least in our E. coli datasets they commonly find TFs biologically relevant for the condition under consideration. The results are exemplarily shown for FOXM1 and the combined ArcA & Fnr KD (anaerobic condition) in Fig. In 7, is that the regulatory networks used in the original work were much smaller compared to our networks. To assess whether the usage of a smaller network improves the results, we restricted the underlying TF – gene network to the neighborhood of each knocked down TF with a distance of two. Note that this design gives a very favorable prior to the analysis. An exemplary restricted network for FOSL2 is presented in Fig. RUNX1 and STAT3 were ranked within the top 5% and FOXM1 in the top 10% using biRte. This result was already obtained using the full network  and AppY KD (E. coli). We used these networks as input to the investigated TF activity estimation methods and ranked the resulting TF activity scores. Although the networks inferred by ARACNE have a higher density compared to our original networks, the resulting TF activity rankings are comparable  did not improve or change substantially the results.For the evaluation of the experiments based on human data, we used a gene regulatory network constructed by a text mining approachIn general, the selection of experiments might affect the outcome of the methods. We used experiments from the GEO platform, an established and extensive repository for genomic data sets, to ensure an easy and public access to the data and to allow other researchers to replicate our results. We chose data from different species, different cells of origin and cell lines, from various contributors, data measured on different arrays, and of 13 different KD TFs to ensure that our results are generalizable and depend less on the specific datasets. However, the chosen experiments had to fulfill certain criteria: obviously, they had to contain a KD scenario and we chose to include only experiments with at least three samples per condition. Further, as we wanted to include ISMARA as a method for estimating TF activity, we had to choose experiments whose Affymetrix chips were supported by its web service. All these constraints limited the number of possible data sets. Of course, the use of other experimental data, different underlying networks or additional methods might affect the results. However, since we draw our conclusions from a total of 25 evaluated data sets, amongst which we did not detect a pattern justifying an especially good or bad performance, we argue that our results show not only individual artefacts but are generalizable to the estimation of TF activity in KD studies.E. coli data. We believe that the main reason for this deficiency is the simplistic model of cellular processes used in the more complex methods like ISMARA. We can only speculate which aspects are primarily responsible for the limited performance. All considered methods only used gene expression data whereas other important regulatory processes such as epigenetic mechanisms like DNA methylation, chromatin remodeling, complex promoter structures, and posttranscriptional regulatory processes via microRNAs are disregarded. The inclusion of further data types would probably change the outcome of the methods and might improve results8. Also, all models assume linear relationships between TFs and lack a notion of kinetic or temporal effects36. Although time series expression data from TF knockdown or TF induction experiments exist38, the selected methods cannot make use of this type of data. Another possible reason for the failure of the methods might be their inability to model TF self-regulation and feedback loops despite their known importance for gene regulation40.Altogether, the investigated methods for estimating TF activity were not able to robustly detect knocked down TFs neither in human nor in E. coli cell lines from the GEO repository11. We chose three different experiments for TF silencing in human cell lines  and one experiment in E. coli (GSE112116). They contain data from 8 human knocked down genes  and 5 knocked down genes  from E. coli. PCA plots for all data sets are provided in Supplementary Fig. E. coli). When multiple probes mapped to one gene we computed a t-test comparing case and control group and kept the probe with smallest p-value.We downloaded publicly available transcriptome data for different TF knockdowns in human and GSE4583812 contains data from the knock-down of BCL6 expression in human diffuse Large B-Cell Lymphoma cell lines. This experiment was performed in OCI-Ly7 and Pfeiffer GCB-DLBCL cell lines as triplicates, providing three case and three control samples per cell line. Gene expression was profiled on H-GU133plus2 Affymetrix gene chips. We analyzed the samples in dependence of their cell line origin and treated them as two independent data sets since they were clearly separated in a PCA plot . cRNA was hybridized in Affymetrix Human Genome U95 Version 2 Arrays. We used the MAS541 normalized data as provided on GEO.GSE1911415 includes 74 samples from knockdown experiments in human glioma cell line SNB19 and glioblastoma multiforme-derived brain tumour initiating cells (BTICs). shRNA-mediated silencing targeted bHLH-B2, FOSL2, RUNX1, C/EBPβ and STAT3. For SNB19, 10 control samples were available together with 4 samples with bHLH-B2 knockdown, 4 with FOSL2 knockdown and 3 samples each for C/EBPβ, STAT3 and the combined C/EBPβ & STAT3 knockdown. Data was available for C/EBPβ, STAT3, combined C/EBPβ & STAT3 knockdown and a control condition for 11 samples in each group in BTICs. RNA was hybridized on Illumina HumanHT-12v3 expression BeadChip. Since the samples were clearly separated in a PCA plot by their cell type  in both aerobic and anaerobic conditions. Additionally, three (aerobic condition) and four (anaerobic condition) wild type samples were available. Gene expression was profiled on Affymetrix E. coli Antisense Genome Arrays. We analyzed the data from the two oxygen conditions independently.9 and one from E. coli18 as input for biRte, RABIT and RACER, which are available as supplementary files. ISMARA can only be used with its own underlying regulatory network model, which is not accessible explicitly.We provided a human TF – gene network9 based on a text-mining approach complemented with TF – gene interactions from the public TRANSFAC database17 . This network includes 2894 interactions between 429 TFs and 1218 genes. The network is provided as supplementary material.We used a publicly available TF – gene network18 version 9.0, Release 9.4 and kept those interactions for which at least one entry in the column “Evidence that supports the existence of the regulatory interaction” was mentioned. The network contains 4273 interactions between 206 TFs and 1798 genes and is provided as supplementary material.We downloaded TF – gene interactions from RegulonDB8 or the according original paper. The investigated methods are publically available as web service, downloadable program or R package.The considered methods for estimating TF activity model genome-wide gene regulation as sets of equations over the activity of transcription factors. All methods assume both the set of TFs and the topology of the regulatory network to be given. By combining this background knowledge with transcriptome data, they try to infer the activity of regulators in a certain experimental condition or disease using mathematical optimization to find parameters minimizing the divergence of predicted and measured gene expression intensities. The methods predominantly produce a ranked list of TFs, sorted by their activity in a given group of samples. Here, we briefly explain the functioning of each method and state our parameter settings if applicable. For a detailed description of each method refer our previously published review4 (Bayesian inference of context-specific regulator activities) uses a probabilistic framework to estimate regulatory activities from differential gene expression data and a TF – gene network. The set of active regulators can be seen as hidden state variables which are estimated with help of the Markov Chain Monte Carlo method. Thereby, the posterior probability for each regulator and condition to influence the expression of its target genes is estimated. Simultaneously, a variable selection procedure is applied to achieve sparsity of the model.BiRteBiRte is available as a bioconductor R package. We used R version 3.2.0 with biRte version 1.10.0 and applied the method “birteLimma” to estimate regulatory activities with the options “niter” and “nburnin” set to 10000. As biRte has a randomized component, the resulting TF activities are not exactly the same for different runs. We averaged the final activity scores over 100 iterations of birteLimma.5  infers the activity of regulatory motifs (short nucleotide sequences) and thereby indirectly deduces the effects of TFs. The input signal levels, which are computed from gene expression data, are modelled linearly in terms of binding site predictions and unknown motif activities. ISMARA employs a Bayesian procedure with a Gaussian likelihood model and a Gaussian prior distribution for inferred motif activity profiles to avoid overfitting.ISMARAIn contrast to all other investigated methods, TF – gene relationships are not provided by the user. ISMARA can only be run with its proprietary underlying regulatory network model. Further, only raw data provided as CEL, FASTQ, BED, BAM or SAM files can be uploaded. Therefore, the results from ISMARA are only partly comparable to the results from other methods here.https://ismara.unibas.ch/fcgi/mara). We uploaded raw CEL files and grouped the samples according to their origin or treatment to compare the average regulatory activity between different conditions. Since Illumina chips in general and the Affymetrix E. coli Antisense Genome Array in particular are not supported, we could only run ISMARA on the data sets with BCL6 (GSE45838) and FOXM1/MYB (GSE17172) knockdown.ISMARA is available via a web service  applies a linear regression model to estimate TF activities. First, RABIT tests in each sample whether the target genes of each TF are differentially expressed. A score indicating regulatory activity is defined by the t-value (regression coefficient divided by standard error). To find a subset of TFs among those screened before, a stepwise forward selection is applied optimizing the model error. Lastly, TFs with insignificant cross-sample correlation are removed from the results.RABIThttp://rabit.dfci.harvard.edu which we adopted with the FDR option set to 1. We used the difference of expression values between case and control group as input and ordered the activity of TFs by t-value as proposed in the RABIT paper.The authors of RABIT published a C++ implementation accessible under 7  consists of a two-stage linear regression. Optimization is applied twice to reduce model complexity, where the method first infers sample-specific TF activities and uses these, in a second step, to compute general TF – gene interactions. Sparsity of the solution is obtained through elastic-net regularized generalized linear models. A supplementary feature selection procedure comparing the full model to a restricted model leaving one TF out provides the most predominant regulators.RACERWe used the available R scripts from http://www.cs.utoronto.ca/~yueli/racer.html to run RACER and set miRNA expression data, copy number variation and methylation scores, which have to be provided, to zero. The obligatory miRNA – gene network was artificially created where all dummy miRNAs and genes were connected. We computed separate models for case and control group and extracted the resulting sample-specific regulatory activities. TFs were ranked by their activity difference between the two groups.For all ranking assignments, we appointed TFs that compared equal the same rank. Subsequently, a gap was left in the ranking numbers which size was equal to the number of items that compared equal minus 1.We calculated the differential expression between case and control group for all genes in all data sets via a two-sided t-test. We ranked the genes according to the p-value of the t-test . We did not apply any multiple test correction, since we were not interested in the precise p-value but only the order of p-values to assign ranks.directly connected TFs in the network23 database version 4.8.0 Build 5 (available under www.genecards.org) for human TFs respectively synonyms from the EcoCyc21 database (ecocyc.org) for E. coli.aliases provided in the GeneCards20 2.0  for human TFs respectively from the EcoCyc21 database (ecocyc.org) for E. coli.TFs directly connected in a pathway from SignaLink22 version 2.2.2 available under http://tools.sschmeier.com/tcof/home for human.TFs directly interacting with the KD TF given in TcoF-DBIn each data set, we compared the results of each method by ranking the absolute values of the computed TF activity scores . Activities equal to zero were not considered. Therefore, the total number of ranked TFs is different in each method and data set. We predominantly assessed the rank of the TF that was knocked down in particular. Additionally to the KD TF, we evaluated the ranks (if existing) ofWe call the union of these TFs related TFs. An overview is available in Supplementary Table We computed the number of overlapping TFs in the top 10 and top 100 lists comparing all applied methods within one data set. The resulting Venn diagrams are shown in Supplementary Fig. To evaluate the influence of the human gene regulatory network, we reduced the network to the close neighborhood of the knocked down TF with a distance of 2, thus giving smaller gene regulatory networks for each knocked down TF.19 (Algorithm for the Reconstruction of Accurate Cellular Networks) to reconstruct a gene regulatory network using the FOXM1 (human) and AppY (E. coli) KD transcriptome data. We used the implementation of the “minet” bioconductor package42 in R (version 3.38) and built the mutual information matrix with Spearman’s correlation. The threshold for removing an edge in the aracne function was set to 0.1. We used the resulting gene regulatory networks as input to biRte4, RABIT6 and RACER7 and ranked the estimated TF activity scores as described above.We applied ARACNESupplementary InformationDataset 1Dataset 2