The neonatal brain undergoes rapid maturational changes that facilitate the normal development of the nervous system and also affect the pathological response to brain injury. Electroencephalography (EEG) and analysis of sleep-wake vigilance states provide important insights into the function of the normal and diseased immature brain. While developmental changes in EEG and vigilance states are well-described in people, less is known about the normal maturational properties of rodent EEG, including the emergence and evolution of sleep-awake vigilance states. In particular, a number of developmental EEG studies have been performed in rats, but there is limited comparable research in neonatal mice, especially as it pertains to longitudinal EEG studies performed within the same mouse. In this study, we have attempted to provide a relatively comprehensive assessment of developmental changes in EEG background activity and vigilance states in wild-type mice from postnatal days 9–21. A novel EEG and EMG method allowed serial recording from the same mouse pups. EEG continuity and power and vigilance states were analyzed by quantitative assessment and fast Fourier transforms. During this developmental period, we demonstrate the timing of maturational changes in EEG background continuity, frequencies, and power and the emergence of identifiable wake, NREM, and REM sleep states. These results should serve as important control data for physiological studies of mouse models of normal brain development and neurological disease. The neonatal brain experiences rapid changes that facilitate the normal development, plasticity and growth of the nervous system and also affect the pathological response to brain injury. Electroencephalography (EEG) is a powerful tool for assessing function in the normal and diseased brain , 2. In cAnimal models are critical for understanding processes underlying normal human brain development and investigating pathophysiological mechanisms of a variety of neurological disorders affecting the neonatal and infant population. While developmental changes in human EEG have been described in detail –7, less Care and use of all mice were conducted according to an animal protocol approved by the Washington University School of Medicine (WUSM) Animal Studies Committee, and consistent with National Institutes of Health (NIH) guidelines on the Care and Use of Laboratory Animals. In addition, NIH guidelines on Rigor and Reproducibility in Preclinical Research were followed, including use of randomization, blinding, both sexes, and statistical/power analyses.Control male and female mice with a mixed genetic background (SV129/CDA/C57) were obtained from an existing colony maintained at WUSM. Although genetic background might influence EEG and sleep phenotype, the mixed background may be appropriate for future studies of genetic mouse models that involve the crossing of different parental strains. Multigravida pregnant females were acclimated to the laboratory environment 2–3 days prior to giving birth to reduce maternal stress. Date of birth was considered postnatal day 0 (P0) and litters were culled to 6–8 pups at P5.Mice were euthanized by rapid decapitation under isoflurane anesthesia, consistent with the guidelines of the Panel on Euthanasia of the American Veterinary Medical Association.Mouse pups received surgery for placement of EEG electrodes on P7-P8 . Custom Mouse pups were separated from the dam and received serial three-hour video-EEG monitoring sessions between P9 and P21  during the light phase of a 12 hr light/12 hr dark cycle. Mouse pups were placed in heated Plexiglas recording chamber with the temperature adjusted daily in order to maintain body temperature near 36°C and compensate for greater thermoregulation as the pups aged. A custom flexible cable was attached to the exposed pin header on the freely moving pup and connected to single channel AC amplifiers . Bilateral cortical EEG signals were acquired using a referential montage and the signals amplified at 20,000X with highpass (1Hz) and lowpass (100Hz) filters applied. EMG signals were filtered with highpass (10Hz) and lowpass (300Hz) filters. EEG and EMG signals were digitized at 400Hz  and pups were recorded in up to 3-hour sessions. After the recording session, pups were immediately weighed and returned to the dam.Bilateral EEG and nuchal muscle EMG files (.edf) were imported into AdInstruments LabChart software and a digital bandpass  filter applied for review. Neonatal mice P9-P14 were manually scored in 5 second epochs and vigilance state scored as awake, non-REM sleep (NREM) or REM sleep (REM) using a combination of the EEG, EMG and respective spectral power representations. As the mice aged and produced longer sleep cycles, older P17 and P21 mice were scored in 10 second epochs. Vigilance state scoring parameters were assessed and defined in the older mice based on standard criteria for adult rodents –14, 18, Percentage of time spent in each vigilance state was tabulated using LabChart as a function of recording time. Vigilance state transitions were calculated using the scored comment and averaged per hour. Scored transitions were used to determine the bout durations by calculating the state specific time spent between vigilance state changes. The percentage of total power in delta or theta ranges were calculated using sorted vigilance state spectral data in the 1–4 Hz and 4–8.5Hz frequency ranges, respectively, and excluded epochs containing obvious artifact.Young neonatal mice have varying periods of low voltage discontinuous EEG. As the mice mature the frequency of the discontinuous periods decreases and the duration becomes shorter. Qualitative assessments of discontinuous EEG were manually selected using standardized one-hour raw EEG traces from P9 to P17 pups. The duration of discontinuous periods was selected from non-artifact EEG with a greater than 50% reduction in amplitude from baseline and with an average voltage below +/- 10 μV and deviation of +/- 20 μV. A return in amplitude to baseline levels indicated the end of the discontinuous period.Total power fast Fourier transforms (FFT) were calculated using LabChart software from 1-20Hz with 512 bin size and a Hann (cosine-bell) data window using spectral data extracted from each epoch. Epochs with obvious movement artifact or epochs with vigilance state and age specific artifact thresholds were excluded. FFTs were sorted by vigilance state and left and right EEG power data were averaged and displayed with a 1Hz frequency resolution.Operated and naïve mouse pups aged P22-P25 were perfusion-fixed with 4% paraformaldehyde and cut into 45 μm sections with a freezing microtome. Sections near the placement of electrodes were stained with 0.5% cresyl violet acetate to assess electrode location and gross morphological condition of the underlying cortex or damage caused by the implanted electrodes or other methodological processes. Immunohistochemistry was performed for GFAP  followed by labeling with secondary antibody Alexa-488 conjugated goat anti-rabbit IgG  and DAPI nuclear stain to assess gliosis near the resting electrodes or along the electrode track. In addition, sequential sections were stained with Fluro-Jade C (FJC) and DAPI to assess cellular necrosis or cellular damage near the electrode site.Images were acquired with a Hamamatsu NanoZoomer 2.0 pathological microscope and GFAP-immunoreactive and FJC cells in neocortex and hippocampus were counted by a blinded investigator. In images from coronal sections at ~ 2 mm posterior to bregma and ~ 500μm from midline, regions of interest were marked in neocortex by a 200 μm wide box spanning from the neocortical surface to the bottom of layer VI. Hippocampal gliosis and necrosis were quantified using 1mm x 500μm area between the CA1 pyramidal cell layer and along the granular layer of the dentate gyrus. GFAP-immunoreactive cells and FJC positive cells were counted bilaterally in the regions of interest from one section per mouse from a total of 6–8 mice per group in a blinded fashion.All statistical analysis was performed using SigmaStat 3.5 software. Quantitative differences in weight between naïve and operated mice were analyzed by two-way repeated measures ANOVA with multiple comparisons using the Holm-Sidak method. Student’s t-test was used to compare histological measures between naïve and operated mice. Quantitative differences between ages were analyzed by one-way repeated measures ANOVA with Tukey post hoc tests. Quantitative differences in FFT generated frequency bins of normal distributed data were analyzed by one-way ANOVA with Bonferroni post hoc tests and non-parametric frequency data analyzed by Kruskal-Wallis One Way Analysis of Variance on Ranks with Dunn’s post hoc tests. 1 Hz bins in the frequency range of 1–20 Hz were analyzed; multiple comparison corrections were not made because multiple, consecutive bins were found to be significant, which exceeds the predicted chance of random false positives. Quantitative data are expressed as mean ± SEM. Statistical significance was defined as p < 0.05.We developed a method to allow longitudinal assessment of EEG background and wake-sleep states by serial EEG and EMG recordings from neonatal mice between P9 to P21. Following surgical electrode implantation at P7 or P8, the exposed pin header secured on the pups’ head allowed for reversible connection of the pup in daily three-hour recording sessions from P9-P14, P17, and P21, with return to the dam in between sessions . Actual Histological analysis confirmed the expected EEG electrode placement and revealed that the wire electrodes sometimes caused mild to moderate compression and superficial tearing of layer I and/or layer II neocortex in areas near the resting electrodes  top. HowVideo, EEG and EMG data, along with corresponding power color density spectral array (CDSA) patterns, were used to determine vigilance states and EEG characteristics in the developing neonatal mouse from P9 to P21. Daily recordings were performed between P10-P14, but did not generally demonstrate statistically significant differences in analysis between consecutive days, so for simplicity data for P11 and P13 are not shown in the subsequent results and figures. In P9 mice, cortical EEG exhibits primarily a discontinuous pattern, characterized by brief segments of higher amplitude, lower frequency activity separated by periods of low amplitude, relatively suppressed activity . As the By P10, initial evidence of distinctive cortical EEG and nuchal EMG vigilance state patterns develop . The awaIn P12 mice, further maturation of cortical EEG patterns continues, making the distinction between vigilance states clearer . Slow waOverall, the EEG evolves from an invariant low amplitude discontinuous pattern into distinct vigilance state patterns with increasing continuity and power. Cortical EEG activity in P9 neonatal mice is discontinuous, with 87.9 +/- 15.9 discontinuous periods per hour and a mean 3.9 second +/- 0.5 duration . BetweenAfter vigilance state patterns develop in young neonatal mice, wakefulness comprised relatively short portion of the overall recorded time of P10 and P12 mice but increased significantly through P17-P21 . By compPrior to P14, sleep cycles are short and neonates frequently transition between vigilance states. As the pups age, fewer vigilance state transitions occur, including transitions from wakefulness into NREM, from NREM into REM, and from REM into wakefulness . There iFFT are useful to breakdown EEG waves into their respective amplitude/frequency components. Vigilance state patterns present after P10 provide distinct EEG power distributions. As neonatal mice mature, an overall increase in total power develops independent of vigilance state Figs  and 4. IThe percentage of power contained within the delta (1-4Hz) and theta (4–8.5Hz) frequency ranges of EEG are important components associated with the maturing brain and sleep patterns. Overall, the percentage of delta power decreased and the percentage of theta power increased during development independent of vigilance state . During In this study, we performed a longitudinal assessment of EEG and vigilance states in the developing neonatal mouse. Previous studies have examined different aspects of EEG and sleep ontogeny in immature rodents, particularly in rats –14, but In infants and young mammals the brain undergoes rapid development and cortical EEG can provide a means to assess some of these developmental changes. In preterm human infants and young neonatal rodent models, cortical EEG features low voltage discontinuous activity that progressively develops into a continuous pattern in a state-dependent fashion , 9, 22. th week of life [Sleep is the dominant activity in human infants and young mammals with increased sleep/wake cycle duration and increased wakefulness occurring with maturity . In neon of life , 29, 30.In our study, mice follow a similar progression over the neonatal and infantile periods with increased wakefulness and awake bout durations and corresponding decreased REM sleep. After P14, REM sleep periods follow longer NREM bouts as sleep becomes more consolidated and adult-like. In contrast with most previous rat studies, however, we found a higher percentage of NREM sleep relative to REM sleep in the neonatal and younger infantile mice. Consistent with Seelke and Blumberg , using aIn addition to the initial increase in continuity, cortical EEG in the infantile rat increases in total power across all frequencies during the P9 to P21 period independent of vigilance state , 14, 31.Overall, our study in mice closely supports most of the developmental EEG and sleep ontogeny patterns previously characterized in neonatal rats. Limitations of our study include recording sessions restricted to a few hours due to neonatal pup size, mouse dam dependence, and technical limitations associated with tethered EEG recordings in pre-weanling mice. We also only recorded during the light phase, which could introduce bias secondary to possible diurnal effects or circadian rhythms. However, prior studies indicate that rats generally do not demonstrate diurnal variation or consolidated circadian rhythms until P17 , 14, 32.S1 Dataset(XLSX)Click here for additional data file.


In conclusion, assessment of the TILs grade in residual tumors after NAC with trastuzumab might be necessary to determine patients with good prognosis among those who do not achieve pCR.Predictive utility of tumor-infiltrating lymphocytes (TILs) in HER2-positive breast cancer patients receiving neoadjuvant chemotherapy (NAC) with concurrent trastuzumab remains unclear. We examined TILs grades of pretreatment cancer tissue specimens and residual tumors after NAC with trastuzumab and determined the predictive utility of the TILs grade in pathological complete response (pCR) and the prognostic power of TILs in HER2-positive breast cancer. This cohort study included 128 HER2-positive breast cancer who received NAC with trastuzumab. TILs grading of the tumor stroma in pretreatment biopsy specimens and residual tumors after NAC with trastuzumab was categorized as low, intermediate, and high based on the criteria of the International Working Group. In current study, the pCR rate was 64.8%, and the Relapse-free survival (RFS) was significantly worse in the non-pCR group than in the pCR group. The pCR rate correlated with the TILs grade in pretreatment tumors. In 45 non-pCR patients, TILs grade was higher in the residual tumors than in the pretreatment tumors. The RFS was significantly better in residual tumors with high TILs grade than those with low TILs grade ( As a result, recent clinical trials on new drugs for breast cancer also evaluated their efficacy by assessing preoperative drug therapy with pCR as an endpoint3. Approximately 20% to 30% of all breast cancers are HER2-positive, and NAC with trastuzumab, often administered in routine practice in HER2-positive breast cancer, achieves a high rate of pCR. We previously reported response rates of 65% or higher with NAC using either paclitaxel or docetaxel, which was followed by fluorouracil, epirubicin, and cyclophosphamide (FEC), concomitantly administrated with trastuzumab4.A variety of clinical trials on neoadjuvant chemotherapy (NAC) for breast cancer demonstrated its utility in both early-stage and advanced breast cancer. Evidence to date suggest that NAC in patients for whom mastectomy is unavoidable can significantly increase the number of patients eligible for breast-conserving surgery. Furthermore, various clinical trials representative of the National Surgical Adjuvant Breast and Bowel Project (NSABP) protocols B-1810. Among the clinical trials on adjuvant chemotherapy for breast cancer, the results of the Breast International Group (BIG) 2–98 trial11 suggested an association between TILs expression and the efficacy of anthracyclines in HER2-positive breast cancer. However, the drug effect of TILs in preoperative chemotherapy combined with trastuzumab and its utility as a prognostic factor in HER2-positive breast cancer remain controversial.Variations in immune responses within the cancer tissue are predicted to depend on the characteristics of immune cells that comprise tumor-infiltrating lymphocytes (TILs); these variations are also proposed to impact drug sensitivity and prognosis in breast cancer. Many retrospective studies suggested the potential utility of TILs expression in breast cancer as a prognostic factor and a predictor of the efficacy of drug therapyWe therefore evaluated TILs expression in breast cancer tissue specimens obtained from patients before and after treatment with NAC plus trastuzumab. We also assessed the association of TILs expression with pCR and prognosis following combination treatment with NAC and trastuzumab.2 = 7.23, p = 0.0072). As shown in Table p < 0.0001), PgR negativity (p = 0.00012), high (≥30%) Ki67 labeling index (p = 0.0090), and histological grade 3 (p = 0.0056).Patient and tumor characteristics are shown in Table The entire cohort of 128 patients were categorized into those with low, intermediate, and high TILs grades based on the analysis of pretreatment tumor specimens Fig. . Brieflyp = 0.026). The pCR rate in patients with high TILs grade was 83.3%, which was significantly higher than that in those with low TILs grade , PgR negativity (p = 0.037), high (≥30%) Ki67 labeling index (p = 0.031), and histological grade 3  was 53 months . In the 128 patients, TILs grade of the primary tumor was not a prognostic factor  to stimulate natural killer cells22. In the present study, TILs expression within breast cancer tissues was enhanced after NAC with trastuzumab compared with the pretreatment evaluation in 20% of the non-pCR patients, suggesting that trastuzumab might stimulate the lymphocytes within the tumor by a mechanism that is distinct from ADCC.Trastuzumab, a molecular-targeted drug, binds to the surface HER2 antigen to inhibit its dimerization, thereby inhibiting the transduction of signals to the phosphatidylinositol 3-kinase cascade downstream of HER223. Perez et al.24 used a large transcriptomic dataset to reveal that gene subsets associated with immune function were strongly correlated with the beneficial effect of trastuzumab in HER2-positive breast cancer. These studies suggest that trastuzumab is strongly associated with the activation of killer T cells as well as ADCC. In present study, TILs grade of the primary tumor was not a significant prognostic factor. However, the prognosis of the cases with high CD8-positive TILs density was significantly better than those with low CD8-positive TILs density. For effective use of molecular-targeted drugs and immune checkpoint inhibitors involved in intratumor immune responses in the future, more in-depth analyses of differences in the characteristics of immune cells that make up TILs that are present before and after NAC with trastuzumab using biomarkers, such as programmed death 1, programmed death-ligand 1, CD4, CD8, and FOXP3, are needed to evaluate the utility of TILs expression in assessing outcomes in breast cancer patients.Trastuzumab-TDB, similar to trastuzumab, has two arms, one binding to HER2 on breast cancer cells and the other binding to CD3 on T cells, thereby triggering an anti-tumor effect by inducing a strong intratumor immune response. Recent efforts have been focused on bispecific antibodies such as trastuzumab-TDB that can utilize immune responsesIn conclusions, TILs status of primary tumors might predict pCR to NAC in combination with trastuzumab in HER2-positive breast cancer. Patients with high TILs grades in residual tumors exhibited better prognosis than those with low TILs grades in residual tumors. Evaluation of TILs in the residual tumor after NAC with trastuzumab might be necessary to predict patients who might achieve good prognosis among those without pCR.Among all patients who underwent surgery at the Division of Breast Surgery in Saitama Cancer Center between 2005 and 2011, 128 consecutive patients with HER2-positive invasive breast cancer who received NAC with trastuzumab were included in this study.in situ hybridization were performed as described previously25. Briefly, the following antibodies were used in immunohistochemical staining for subtype determination: estrogen receptor , progesterone receptor , and HER2 . HER2 amplification was achieved using an automated slide processing system  with dual in situ hybridization . Expression levels of ER, PgR, and HER2 were determined in accordance with the American Society of Clinical Oncology/College of American Pathologists criteria. Specimens with a nuclear staining rate of at least 1% were considered positive for ER and PgR. Evaluation of HER2 immunohistochemical staining was based on four grades corresponding to scores of 0, 1+, 2+, and 3+, which depended on staining intensity of cell membranes. Only specimens with a score of 2+ by HER2 immunohistochemical staining were evaluated for gene amplification by DISH, and those with a HER2 immunohistochemical score of 3+ or 2+ and positive for HER2 amplification by DISH were defined as HER2-positive breast cancer4.Immunohistochemistry and 2) every week or 4 cycles of docetaxel (75 mg/m2) every 3 weeks, followed by 4 cycles of FEC 75  every 3 weeks. All patients also received 4 mg/kg trastuzumab on day 1 of the treatment and 2 mg/kg trastuzumab every week thereafter, for a total of 24 cycles. Trastuzumab was used for 6 months as adjuvant therapy. In addition, ER-positive breast cancer patients underwent postoperative endocrine therapy with tamoxifen or an aromatase inhibitor. We reported the utility as well as the eligibility and exclusion criteria for this protocol previously4.The details of NAC were as follows: 12 cycles of paclitaxel  and conducted in full compliance with the Declaration of Helsinki.26, was categorized into three groups by modifying the International Working Group criteria18: low (TILs: 0% to 10%), moderate (TILs: 10% to 40%), and high (TILs: 40% to 90%).Hematoxylin/eosin-stained samples were prepared from formalin-fixed, paraffin-embedded sections of 4 μm slices from core needle biopsy specimens in all patients as well as surgical specimens in non-pCR patients. A pathologist specialized in breast pathology used an optical microscope at 200–400 × magnification to determine whether mononuclear immune cells interposing between tumor nests were stromal TILs. Other immune cells present in tumor specimens were not evaluated. Considering the heterogeneity of TILs within tissue, the distribution of TILs was evaluated using all core needle biopsy samples. In surgical specimens from non-pCR patients, residual TILs in sites with the highest residual tumor concentration were evaluated. If the pathological effect of treatment was strong but the amount of residual tumor was low, lymphocytes aggregation surrounding degenerating cancer cells were evaluated as TILs. The TILs grade, as previously reportedThe immunohistochemical expression of CD8 in TILs was evaluated in primary tumors using core needle biopsy specimens. The source of the primary antibody of CD8 was FLEX Monoclonal Mouse Anti-Human CD8, Dako, Copenhagen, Denmark. Staining was performed automatically using an automated immunohistochemistry instrument . High CD8 expression was defined as number of CD8-positive TILs > 25 in one high power field.4, which categorizes pathological response into six histological grades  based on the degree of morphological changes in the primary tumor as a result of NAC treatment. Grade 1a was defined as “mild change in cancer cells regardless of the area or marked changes in cancer cells in less than one-third of total cancer cells,” whereas grade 1b was defined as “marked changes in one-third or more but less than two-thirds of cancer cells.” Grade 2a was defined as “marked changes in two-thirds or more of cancer cells, but with clear cancer nests,” and grade 2b was defined as “an effect extremely close to a complete response (grade 3), but with a very small amount of residual cancer cells.” Finally, grade 3 was defined as “disappearance of all cancer cells” and was equivalent to pCR in the NSABP B-18 trial1. Residual noninvasive cancers or axillary lymph node metastases are not considered in the determination of the Japanese Breast Cancer Society grades.Grading of the pathological response to NAC was performed in accordance with the Japanese Breast Cancer Society criteriap value of 0.05 or less was considered to indicate a significant difference.Associations of TILs grades with pCR rate and clinicopathological factors were evaluated using the chi squared and Fisher’s exact tests. In addition, the association between the TILs grade and prognosis based on RFS was determined using the Kaplan Meier method and the log rank test. RFS was defined as the period from the day of surgery to the day of relapse in any organ including ipsilateral breast recurrence. Statistical analyses were conducted using the SPSS statistical software version 22.0 , and a This study was approved by the Institutional Review Board of Saitama Cancer Center (Reference number: 534). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.Supplementary Information


Next-generation sequencing techniques revolutionized the study of RNA expression by permitting whole transcriptome analysis. However, sequencing reads generated from nested and multi-copy genes are often either misassigned or discarded, which greatly reduces both quantification accuracy and gene coverage.Here we present count corrector (CoCo), a read assignment pipeline that takes into account the multitude of overlapping and repetitive genes in the transcriptome of higher eukaryotes. CoCo uses a modified annotation file that highlights nested genes and proportionally distributes multimapped reads between repeated sequences. CoCo salvages over 15% of discarded aligned RNA-seq reads and significantly changes the abundance estimates for both coding and non-coding RNA as validated by PCR and bedgraph comparisons.http://gitlabscottgroup.med.usherbrooke.ca/scott-group/coco.The CoCo software is an open source package written in Python and available from Bioinformatics online. Detection and quantification of RNA transcripts are critical steps to understand the mechanism of gene expression and its impact on cell function. Traditionally, transcript abundance has been evaluated using techniques that target one known RNA sequence at a time, as in the case of quantitative RT-PCR. More recently, the development of RNA-sequencing (RNA-seq) techniques revolutionized transcriptome analysis by providing the tools necessary to study, at least in theory, all RNA transcripts simultaneously. Diverse library preparation protocols exist, the most commonly used ones focussing on particular classes of RNA through enrichment steps. Such strategies include polyA enrichment, non-ribosomal RNA (rRNA) enrichment (e.g. rRNA depletion), small RNA enrichment and enrichment for RNAs bound to specific factors  which are often produced from multiple genes and/or nested in other genes . AccordiAbundance estimation from RNA-seq data generally requires a pipeline that aligns reads to a reference genome and then assigns them to annotated genes  and HISATo increase the overall accuracy of RNA quantification and monitor the expression pattern of overlapping and repetitive genes, we developed a count corrector (CoCo) pipeline that rescues and correctly assigns otherwise ambiguous sequencing reads. CoCo employs the read assignment function featureCounts from Subread . TGIRT displays high processivity and fidelity, providing an accurate view of the ribodepleted transcriptome  was obtained from Ensembl  and withThe correct_annotation module of CoCo produces a modified annotation file in which any exon position overlapping a snoRNA, small Caja-body RNA (scaRNA), snRNA, tRNA or miRNA is removed, resulting in a gapped annotation file . To do sThe counts obtained from CoCo’s correct_count module were normalized by the length of the main transcript of the gene and by the read count to give transcripts per million (TPM) as described further in (https://github.com/Population-Transcriptomics/pairedBamToBed12) and bedtools genomecov (v2.25.0) to produce a bedgraph of a paired-end dataset. For single-end datasets , BAM files were converted to bedgraphs using only genomecov (v2.25.0). The bedgraphs were visualized using IGV (BAM files were converted to Bedgraph files with CoCo’s correct_bedgraph module  which ushttp://ensembl.org/help/faq? id=468). The group ‘other’ corresponds to all other biotypes not listedEnsembl biotypes protein_coding, pseudogene and long_noncoding were pooled as recommended (CoCo was compared with the following read assignment tools: featureCounts . The parhttp://rnomics.med.usherbrooke.ca/). Droplet Digital PCR (ddPCR) reactions were prepared using 10 μl of 2× QX200 ddPCR EvaGreen Supermix (Bio-Rad), 10 ng (3 µl) cDNA, 100 nM final (2 µl) primer pair solutions and 5ul molecular grade sterile water (Wisent) for a 20 μl total reaction. Each reaction mix was converted to droplets with the QX200 droplet generator (Bio-Rad). Droplet-partitioned samples were then transferred to a 96-well plate, sealed and cycled in a C1000 deep well Thermocycler (Bio-Rad) under the following cycling protocol: 95°C for 5 min (DNA polymerase activation), followed by 50 cycles of 95°C for 30 s (denaturation), 59°C for 1 min  and 72°C for 30 s (extension) followed by post-cycling steps of 4°C for 5 min and 90°C for 5 min  and an infinite 12°C hold for reading using the QX200 reader (Bio-Rad). The concentration reported is in copies/μl of the final 1× ddPCR reaction (using QuantaSoft software from Bio-Rad). All primer sequences are available in Digital PCR was performed by the Université de Sherbrooke RNomics Platform  thTo test the quantification accuracy of the CoCo pipeline, we examined its capacity to correctly assign and quantify sequencing reads using diverse RNA-seq datasets, and compared its quantification to those of the main read assignment pipelines available. Most comparisons we report involve samples generated using a library preparation protocol that accurately detects both coding and non-coding RNA classes, thanks to the use of the TGIRT reverse transcriptase and ribodepletion . AlignedTo evaluate the impact of CoCo on RNA detection and quantification, we compared the aligned read assignments obtained using the CoCo pipeline to values obtained using five currently available and commonly used read assignment pipelines, described in Section 2 and in In order to accurately quantify read counts from nested genes and distinguish their reads from the host gene reads, CoCo carries out a background correction. This is achieved by subtracting the average read count of the feature in which the nested gene is encoded from the read count assigned to the nested gene. This correction does not significantly change the final read counts of genes nested in host gene features (most often introns) that are expressed at low levels . HoweverA large number of genes, in particular those producing non-coding RNA, exist in more than one copy. The RNA produced from most of these different forms of repeated features cannot be quantified by standard read assignment modules due to their identical or near identical sequences that result in multimapping of sequencing reads e.g. . As a coTo experimentally validate the accuracy of the CoCo-based quantification, we chose nine overlapping or repeated genes and examined their abundance using ddPCR, comparing these values to sequencing abundance estimates. These nine genes include seven snoRNAs overlapping with protein-coding genes and two repeated gene families (RN7SK and RN7SL2). In the case of the multimapped genes RN7SK and RN7SL2, their abundance was estimated to be respectively two and four times higher,  using thThe capacity to detect transcripts from repeated and overlapping genes not only provides new information about the expression dynamics of these genes but may also alter the overall distribution of read counts. Accordingly, we examined the impact of using CoCo on the overall distribution of transcript estimates in the human genome. Comparison of the read assignments obtained using the CoCo pipeline or the standard featureCounts pipeline indicates that while 65.8% of the read pairs in fragmented datasets were assigned identically by both strategies and 18.6% were assigned by neither, >15% were only assigned by CoCo . Most ofMost of the read pairs only assigned by CoCo (15.7%) originate from multimapped genes, representing 12.9% of all aligned read pairs . As expeAs a consequence of the CoCo correction, the overall distribution of the human transcriptome was modified considerably. The proportion of all read counts attributed to protein-coding genes in fragmented datasets was reduced by 12%, representing 74% of all assigned reads using the standard pipeline but only 62% using CoCo  were chosen for comparison to the CoCo pipeline because they are widely used and easily implemented standalone pipelines. These tools were compared considering typical examples of nested and multimapped genes in datasets covering five library preparation methodologies  to investigate their wide applicability. HTSeq-count, STAR, Cufflinks and featureCounts using standard parameter values generally performed poorly to quantify nested and multimapped genes, both according to comparisons to bedgraphs and ddPCR quantification. These four tools failed to detect many nested genes and did not accurately quantify many multimapped genes. In contrast, RSEM, featureCounts with optimized parameter values and most often CoCo performed much better at quantifying nested and multimapped genes, both according to bedgraph and ddPCR comparisons, displaying good capacity for both small and long RNA-seq, fragmented or not, single and paired-end sequencing, regardless of the strandedness and type of libraries . RSEM’s Accurately quantifying multimapped reads has been investigated in several studies. featureCounts annotates such reads, making it easy for CoCo’s correct_count module to distribute them according to uniquely mapped reads. featureCounts itself provides an option to deal with multimapped reads, by splitting them equally between all members of a multimapped group . However, a significant proportion of these members are likely not expressed, particularly in the large families of non-coding RNAs with tens or even hundreds of copies, making CoCo’s strategy  more realistic. Evaluating the accuracy of multimapped read assignment is difficult since unlike mRNA, most non-coding RNA lack external unique sequences that could be used to differentiate between genes with shared sequences. In building our pipeline for the quantification of nested and multimapped genes, we opted for a computationally quick and simple solution that enables the evaluation of the overall abundance of RNA generated from genes with shared sequence. While we cannot guarantee the accuracy of the abundance for each individual gene from a repeated family, the overall abundance of the RNA generated from these repeated families is accurately determined by CoCo as validated experimentally by ddPCR analysis and gene-specific analyses (One of the most surprising observations in the application of CoCo is how many genomic regions do not have proper annotation and how this mis- or lacking annotation may affect the overall interpretation of transcript distribution. Indeed, while CoCo was able to recuperate the ambiguous reads due to gene overlap with small non-coding RNAs and multimapping, about 18.6% of all read pairs remained unassigned. Examination of these read pairs shows that the great majority align to unannotated regions in the genome , indicatThe read assignment corrections shown here are essential steps to study the human transcriptome. Most of the multimapped and overlapping reads originate from highly expressed genes and thus any changes in their read assignment will significantly affect the overall distribution of the human transcriptome. In addition, accurate quantification of nested and multimapped genes not only enhances the detection of these types of RNA but also corrects the quantification of their protein-coding host genes. Thus, any sequence analysis pipeline that ignores or fails to accurately detect nested and multimapped genes would likely result in significant changes in read count distribution and ultimately in incorrect expression estimates, for large proportions of the transcriptome. Indeed, as sequencing depth increases and the capacity to simultaneously detect both coding and non-coding RNA improves, read assignment tools like CoCo will become essential for any sequencing analysis pipeline.btz433_Supplementary_DataClick here for additional data file.


There was no significant enrichment of the mice and human smoking-related genes in mild COPD signature. These data suggest that murine smoke models are strongly representative of molecular processes of human smoking but less of COPD.Genome-wide mRNA profiling in lung tissue from human and animal models can provide novel insights into the pathogenesis of chronic obstructive pulmonary disease (COPD). While 6 months of smoke exposure are widely used, shorter durations were also reported. The overlap of short term and long-term smoke exposure in mice is currently not well understood, and their representation of the human condition is uncertain. Lung tissue gene expression profiles of six murine smoking experiments (n = 48) were obtained from the Gene Expression Omnibus (GEO) and analyzed to identify the murine smoking signature. The “human smoking” gene signature containing 386 genes was previously published in the lung eQTL study . A signature of mild COPD containing 7 genes was also identified in the same study. The lung tissue gene signature of “severe COPD” (n = 70) contained 4,071 genes and was previously published. We detected 3,723 differentially expressed genes in the 6 month-exposure mice datasets (FDR <0.1). Of those, 184 genes (representing 48% of human smoking) and 1,003 (representing 27% of human COPD) were shared with the human smoking-related genes and the COPD severity-related genes, respectively. There was 4-fold over-representation of human and murine smoking-related genes (P = 6.7 × 10 Although the exact mechanisms of pathophysiology are unknown, it is widely accepted that COPD is under genetic and environmental control with cigarette smoking being the most important modifiable risk factor in the Western world2.Chronic obstructive pulmonary disease (COPD) affects 300 million people and is currently the third leading cause of death worldwide8. While these studies report genes associated with disease phenotypes or smoking, follow up in vitro and in vivo studies are required to disentangle mechanism and establish causality. Of the in vivo models, mice are commonly used to determine the effects of cigarette smoking in the pathogenesis of COPD (reviewed in references15). Generally, 6 months of smoke exposure is used to induce histological and functional abnormalities in murine lungs that mimic those of human disease including emphysema, airway remodeling and pulmonary hypertension, though the changes are relatively mild compared with those observed in long-term human smokers16. However, more recent methods can replicate these features as well as the impairment of lung function in 8 weeks with nose-only exposure18. Shorter exposure times are generally used to model inflammatory mechanisms19. How gene expression profiles compare between short term and long term smoke exposure is currently not well understood. Moreover, although mice are commonly employed to model COPD, the extent to which murine experiments mimic the human condition is uncertain.Recent advances in genomics have enabled genome-wide mRNA profiling to gain novel insights into COPD pathogenesisThe availability of genome-wide transcriptomic signatures in lung tissue enables comparisons between human and murine models following short- and long-term cigarette smoke exposures. The aims of this study were to compare and contrast the molecular changes in murine models following short and long term exposures with the molecular changes in human lungs induced by cigarette smoke. Most importantly, we sought to determine, if the human “COPD” lung tissue gene expression signature is captured in murine lungs exposed to cigarette smoke.The six murine studies involving short term smoke exposure are summarized in Table Principal component analysis (PCA) performed on the 10,634 common genes led to the exclusion of one sample of air exposed mice from study GSE55127, which was a clear outlier. The resulting PCA plot shows that the 6 studies were homogenous in terms of expression changes and demonstrated clustering based on smoke-exposure status , gpnmb (glycoprotein nmb) and cd84  with the 3,687 genes related to short term exposure in mice is shown in Fig. The 139 overlapping genes were enriched in numerous gene ontology processes related to defense and immune response, glycosphingolipid and ceramide catabolic processes Table .Table 4Get al. dataset included 70 former smokers with COPD from GOLD grades 1 to 44. Using this dataset we identified 4,071 genes that were differentially expressed between patients in GOLD 3/4 vs. GOLD 1/2. A total of 1,003 “smoking” genes (27%) from the short-term murine smoking experiments overlapped with the “severe COPD signature” from the Faner et al. Comparison of the human smoking and COPD signature showed that 116 “smoking” genes (30%) from the lung eQTL dataset overlapped with the “severe COPD signature” of Faner et al.  as well as the human smoking and severe COPD signatures . The results also showed almost 4-fold enrichment of human smoking genes in the mice smoking signature (P = 6.7 × 10−26). Of the lung tissue disease signatures, the severe COPD signature from the study of Faner et al. was over-represented in the short term murine smoking signature  and was also over-represented in the 6 month smoking models . Interestingly though, the mild COPD signature was not over-represented in the mice or human smoking signatures.To quantify the extent of overlap among the different studies, we used a Fisher’s exact test to determine whether there was significant enrichment of the human smoking or disease signatures in murine smoking signatures Table . DiffereTo extend the gene expression findings to large scale human genetic studies of lung function we investigated whether any of the genes whose expression was related to smoking in both human and murine lungs were under genetic control in human lung tissue (i.e. were lung expression quantitative trait loci [eQTLs]). We found that 60 of the 139 genes have significant eQTLs (10% FDR) with a total of 7834 eQTLs.20 and the UKBilEVE studies21. The results for SNPs with p < 0.05 are shown in Supplementary Table 1 in the SpiroMeta GWAS .Next, we restricted the analysis to the most significant eQTL per probeset  which led to a final SNP list of 73 (some SNPs were top eQTLs for more than one probeset). The 73 eQTLs were tested for associations with lung function in publically available large-scale genome-wide association studies (GWASs) datasets: SpiroMetaet al. reported the overlap of mice and human smoking signatures and identified many overlapping genes, but very few that were shared with COPD signature22. Earlier, Morissette et al. also investigated the overlap of genes differentially affected by smoking in both mice and human lung tissues8. They found an enrichment of genes that were significantly modulated by cigarette smoke in humans and in mice, and that the majority of biological functions modulated by cigarette smoke in humans were also affected in mice8. Both studies, however, did not compare short vs. long term smoke exposures of mice and did not identify genetic variants relating to the expression of genes of interest.Pre-clinical animal models represent a valuable tool for understanding the pathogenesis of COPD and identifying novel therapeutics and biomarkers. However, to date, there has been a scarcity of data that have directly compared molecular profiles in the lungs of smoke-exposed mice that have been used to model COPD against those of human lungs in order to determine how (and if) ‘disease’ in these animals is representative of the human condition. A recent study by Yun −309), suggesting that acute transcriptomic changes in the lungs related to cigarette smoking are largely retained over longer term, when morphologic appearances of emphysema, airway remodeling and mild pulmonary hypertension become measurable in mice; (2) a significant overlap of genes in smoke-exposed murine lungs (48% from short-term exposure and 44% from long-term exposures) with those of human lungs explanted from current smokers. There was a 3.8 fold enrichment of the human “smoking” lung signature in the murine lungs (p = 1.6–6.7 × 10−26); and (3) a 1.4 fold enrichment of severe COPD gene expression signature in the human “smoking” lung signature (p = 3.5 × 10−3), with a 1.2 fold enrichment in short-term smoke- exposed murine (p = 4.2 × 10−5) and a 1.4 fold enrichment in long-term smoke-exposed murine lungs (p = 2.3 × 10−12). There were 48 genes that were common to the lungs of both smoked-exposed mice and current smokers and severe COPD, suggesting that the long term smoking exposure of mice results in transcriptomic changes that are also found in severe COPD patients even following smoking cessation. Of these the association of the gene encoding for cathepsin S was also supported in large scale human genetics studies of lung function. Finally, the murine and human smoking signatures were not over-represented in mild COPD signature, suggesting that overall mice models are better representative of smoking but less so of COPD in humans.By directly comparing and contrasting the gene expression profiles of smoke-exposed (both long and short-term) murine lungs against a large number of human lungs of current and ex smokers across the full spectrum of COPD severity , we have made several important observations. They include: (1) the identification of overlapping 3,723 and 3,106 genes that were differentially expressed in short and long-term smoke exposure in murine lungs, respectively , which is an endogenous thrombin inhibitor that protects against vascular remodeling and atherosclerosis via its inhibition of thrombin in the vascular wall30. It may also play a role in enhancing cell motility and promoting metastasis in non-small cell lung cancer31.The smoking genes that overlapped between murine and human lung tissue included aryl hydrocarbon receptor repressor (AHRR), cytochrome P450 family 1 subfamily B member 1 (CYP1B1), C-X-C motif chemokine ligand 16 (CXCL16), NAD(P)H quinone dehydrogenase 1 (NQO1) and serpin family D member 1 (SERPIND1), all of which were up-regulated in the lung tissues of smokers. The AHRR gene has been well studied and a large number of publications have reported changes in its methylation and expression related to smoking34. The significant enrichment of human smoking signatures in the murine lung following short and long-term smoke exposure suggests that mice models of smoking do, in fact, reflect molecular changes that occur with smoking in humans. There are some caveats, however. For instance, we found that for ~6% of the human “smoking” lung genes the change in gene expression in the murine lungs was in the opposite direction. This may be due to different molecular responses to smoking between human and mice lungs. Alternatively, it may reflect the duration of cigarette smoke exposure between humans and mice. The duration of smoke exposure for mice ranged from 6–24 weeks compared to years of smoking in humans. However, this can be considered representative in a mouse that have an average life span of 1.5–2 years.Almost half (48%) of genes making up the human smoking signature overlapped with those differentially expressed in the murine smoked lung. The overlapping genes were enriched in processes related to host defense and immune responses including those that involve glycosphingolipid and ceramide catabolic pathways. These processes are well known to be affected by smoking35 and plays an active role in adaptive immune responses36. The major inhibitor of cathepsin S is cystatin C which was recently identified as a COPD causal gene using an integrative genomics approach37.Using integrative genomics we showed that 43% of the overlapping smoking signature genes were under genetic control in lung tissue. The SNP that showed the strongest association with lung function in large scale genetic studies was an eQTL for the cathepsin S gene (CTSS). The CTSS gene encodes an elastin-degrading proteinase which is highly expressed by macrophages and dendritic cells38. Finally, the cellular heterogeneity of murine and human lung tissue samples may have limited our ability to detect overlapping signatures.Our current study has some limitations. First, the sample sizes for the studies included may have led to false negative results. Second, the unit of analysis in this study was gene expression, yet translation and post translational modifications of proteins in lung tissue may also be similar or different between mice and human and between smokers with and without COPD. Third, mice have different pathophysiology compared to humans. For example, studies have shown that in humans, the loss of small airways proceeds the development of emphysema before COPD is detectable with spirometryIn conclusion, the current study uncovered a strong similarity between short and long term smoking effects on lung transcriptome in mice and a strong overlap with the human smoking signature. The study additionally uncovered genes common to smoking and COPD signatures in mice and humans which warrants further study.42. Briefly, non-tumour lung specimens were collected from patients undergoing lung surgery at three different sites: Institut Universitaire de Cardiologie et de Pneumologie de Québec (IUCPQ), Laval University , University of British Columbia  and University of Groningen , which contained 51,627 non-control probesets and data were normalized using RMA43. Genotyping was performed using the Illumina Human1M-Duo BeadChip array. Genotype imputation was undertaken using the 1000 G reference panel. Following standard microarray and genotyping quality controls, data from 1,111 patients were available including 409 from Laval, 339 from UBC and 363 from Groningen. Association testing for each variant with mRNA expression in either cis (within 1 Mb of transcript start site) or in trans  was undertaken separately for each study sample, after which the results were meta-analyzed using inverse variance weighting. A genome-wide 10% false discovery rate (FDR) was applied to this analysis. The smoking gene signature in the eQTL study has been previously published7 and consisted of 386 genes that were differentially expressed between current vs. never smokers (henceforth referred to as “human smoking signature”).To compare murine lung smoke exposure induced gene expression against human smoking gene expression signatures, we used a large human dataset that has been previously described. The lung expression quantitative trait loci study (eQTLs) profiled 1,111 human lung tissue from current and ex-smokers and non-smokersThe lung eQTL study was also used to identify mild COPD signature. We performed differential expression analysis between mild COPD (FEV1 ≥80% predicted and FEV1/FVC <0.7) and controls (FEV1 ≥80% predicted and FEV1/FVC >0.7). The analysis was adjusted for age, sex and smoking status and the sample sizes were 58 mild COPD patients  and 107 control subjects . Results were combined using meta analysis using inverse variance weighting fixed effect model.All methods were carried out in accordance with relevant guidelines and regulations. Study participants informed consent was obtained from all subjects, and data access and analyses protocols were approved by the University of British Columbia Office of Research Ethics.44. GSE33512, GSE55127, GSE52509 and GSE33561 datasets were pre-processed as described in the corresponding source publications. GSE17737 and GSE18344 datasets contained samples profiled on Affymetrix Mouse Genome 430 2.0 arrays. These arrays were normalised with frozen Robust Multi-array Analysis (fRMA), a procedure that allows microarrays to be pre-processed individually or in small batches and allows data to be combined into a single dataset for further analyses45. Since different profiling platforms contain different numbers of genes, we included 10, 634 genes in the analysis that were common to all platforms. A more detailed description is provided by Dvorkin-Gheva et al.44Lung gene expression profiles of six publically available datasets (n = 48 samples) were obtained from the Gene Expression Omnibus (GEO) 44. Samples from both datasets were combined and the technical variation was removed by using Distance-Weighted Discrimination (DWD) method46.To enable comparisons with smoking signatures from longer duration of smoking, we included samples from two additional GEO datasets (GSE52509 and GSE17737) that evaluated murine lung tissue expression changes following 24 weeks of smoking exposure. GSE52509 dataset was preprocessed as described in the corresponding publication, while the samples from GSE17737 were normalized with fRMA47 to compare gene expression profiles of smoke-exposed mice from each dataset with those of control mice pooled across all experiments. T-statistics were followed by Benjamini–Hochberg adjustment for multiple testing48.We used the “limma” packageet al. to determine which genes were differentially expressed across COPD disease severity4. Briefly, lung tissue samples were obtained from 70 former smokers with COPD who required thoracic surgery because of cancer or lung transplant. RNA samples were loaded onto an Affymetrix GeneChip Human Genome U219 Array Plate . The microarray data have been deposited in GEO (GSE69818)4. We identified 4,071 genes whose expression in lung tissue was different in patients with moderate or severe COPD  grades 3, 4) and those with mild COPD . These differentially expressed genes will henceforth be referred to as “severe COPD signature”.We used data from Faner http://www.ensembl.org/info/about/publications.html). We retained only those human genes on chromosome 1 to 22, or on chromosome X or Y, based on the position information from the BioMart-Ensembl database.In order to compare results across murine and human studies, we restricted the analyses to murine genes that had a human ortholog using the BioMart-Ensembl database  test was used to test for significant over or under-representation of common genes from two different studies.Supplementary Materials


We reported that horizontal transfer of malignant traits to target cells is a potential pathway to explain cancer dissemination. Although these results were encouraging, they were never corroborated by data showing the molecular mechanisms responsible for the observed phenomenon.BRCA1-KO fibroblasts to extracellular vesicles (EVs) isolated from a colon cancer cell line (HT29) and from sera of patients with colorectal cancer. Three weeks after exposure, fibroblasts were injected subcutaneously into NOD-SCID mice. Whole genome sequencing, transcriptome analysis and RNA sequencing of cancer EVs and fibroblasts prior and after exposure to cancer EVs were performed.In the present study, we exposed Phenotypical transformation of the fibroblasts into colon cancer cells was confirmed by histopathological study of the xenotransplants. We observed that EV-mediated transfer of cancer microRNAs was responsible for the transition from a mesenchymal to an epithelial phenotype (MET) in the treated fibroblasts as well as activation of cell cycle progression and cell survival pathways. DNA and RNA sequencing suggested that cancer DNA was transferred and possibly transcribed in target cells. Furthermore, injection of colon cancer EVs in the tail vein of NOD-SCID mice determined neoplastic transformation and metastases in the lungs of the mice confirming for the first time the hypothesis that transfer of malignant epithelial cancer traits to distant target cells is a concept applicable to in vivo models.These discoveries shed new light into the molecular mechanisms behind the horizontal transfer of malignant traits and confirm the notion that metastatic disease might be reproduced through transfer of circulating genetic material.The online version of this article (10.1186/s13046-019-1248-2) contains supplementary material, which is available to authorized users. Metastatic disease is the leading cause of cancer related mortality . It is cLately, the initial enthusiasm has been curbed by the lack of consistency of data supporting the notion that the same cell is able to revert the process (MET) and therefore downregulate the expression of vimentin, upregulate the expression of CDH1 with subsequent loss of migratory freedom, adoption of apico-basal polarization and expression of the junctional complexes that are hallmarks of epithelial tissues , 9. FurtGarcia-Olmo in the late nineties , 13 and Our experiments confirmed that different types of oncosuppressor mutated cell lines are able to turn malignant when exposed to either EVs collected from cancer cells’ conditioned media or EVs extracted from cancer patients’ sera. Notably the exposed cells displayed features resembling those of the primary tumors with gain of immunohistochemical features similar or identical to the cancer cells that release the EVs .In the present study, for the first time, we shed light on the molecular mechanisms behind this phenomenon. We profiled DNA, mRNA and miRNA expression in colorectal cancer-derived EVs and target fibroblasts prior and following exposure to the cancer EVs. We confirmed that the phenotypical transformation of the fibroblasts is associated with cancer EVs DNA transfer and we showed data that suggest that EVs DNA is actively transcribed in the fibroblasts after the exposure. We further demonstrated that a definite set of miRNA families, transferred from the colon cancer derived EVs to the fibroblasts, activate cell cycle progression and cell survival pathways. Remarkably, we noted that the uptake of cancer miRNAs was concomitant to downregulation of transcription factors that prompted a mesenchymal to epithelial transition of the fibroblasts that acquired epithelial features and immunohistochemistry markers, suggestive of complete colorectal cancer differentiation. Furthermore, we showed that injection of cancer EVs into the blood stream of NOD/SCID mice can prompt malignant transformation at distance, confirming for the first time that horizontal transfer of epithelial malignant traits through EVs might also be applied to in vivo systems.Patients for the current study were recruited from the department of General Surgery at the Royal Victoria Hospital and St-Mary’s Hospital . They underwent an informed consent for blood collection in accordance to a protocol approved by the Ethics Committee of our institution SDR-10-057). Blood samples were collected and categorized in two cohorts: healthy individuals  and patients with colorectal cancer   containing clot-activation additive and a barrier gel to isolate serum. Blood samples were incubated for 60 min at room temperature to allow clotting and subsequently were centrifuged at 1500 g for 15 min. Serum was collected and a second centrifugation was performed on the serum at 2000 g for 10 min, to clear it from any contaminating cells. Serum samples were aliquoted and stored at − 80 °C until use.BRCA1-deficient fibroblasts, previously described . In brief, main differences with the DNA-Seq pipeline are the following: i) for the alignment step, we used STAR aligner instead of the BWA aligner; ii) a tool SplitNCigarReads, specially developed for RNAseq, has been used to splits reads into exon segments and hard-clip any sequences overhanging into the intronic regions; iii) the GATK HaplotypeCaller was used for variant calling with the options dontUseSoftClippedBases and recoverDanglingHeads.The analysis of RNA-Seq reads was performed by following GATK Best Practices workflow for SNP and indel calling on RNAseq data available on the GATK Best Practices website [The variant calling procedure was also performed with the Opossum  and Plat+ and RT− control extracts. 1 μg of cellular or 100 ng of EVs RNA were reverse-transcribed (RT) using the MML-V reverse transcriptase (Invitrogen). Quantitative real-time PCR (qPCR) was performed using the Platinum SYBR Green SuperMix (Invitrogen) and an ABI Prism 7500 Real-Time PCR apparatus (Applied Biosystems). Primer sets used were as shown in Additional file -ΔΔCt formula was used for the calculation of differential gene expression.All primers were designed to flank individual exons and tested by PCR of RTCells or EVs aliquots were lysed in RIPA buffer containing protease inhibitors . Equal amounts of proteins were resolved on 10% SDS-PAGE and transferred to a nitrocellulose membrane (BioRad). Membranes were blocked in TBS containing 5% non-fat dry milk and exposed overnight at 4 °C to mouse-anti-Vimentin , rabbit-anti-CDH1 , Rabbit anti-GM130 , Mouse anti-TSG101 , Mouse anti-Alix , or mouse-anti-β-Actin  antibodies. Membranes were washed in TBST (TBS-0.05% Tween-20) and incubated with either anti-rabbit or anti-mouse peroxidase-conjugated secondary antibody (Sigma) for 1 h at room temperature and the blots were developed using Immobilon Western HRP Substrate (Millipore).Five-week-old female NOD-SCID mice (Jackson Laboratory) were used with approval and in compliance with McGill University Health Centre Animal Compliance Office (Protocols 2012–7280 and 2015–7731). Cells growing in log phase were harvested by trypsinization and washed twice with HBSS. Mice (2 to 3 mice) were injected subcutaneously in the right flank with 2 million cells in 200 μl HBSS/Matrigel mixture. The resulting xenotransplants were photographed and processed for immunohistochemistry.6 particles in 100 μl HBSS) isolated from MC38 cells conditioned medium. Mice received these injections for 5 weeks. Four weeks later, mice were euthanized and the internal organs were harvested for histology and immunohistochemistry analyses.To explore the possibility that EVs could transfer malignant traits to in vivo models, NOD-SCID mice were injected every other day in the tail vein with EVs  according to standard protocols and processed for immunohistochemistry. Briefly, 5 μm tissue sections were dewaxed in xylene and rehydrated with distilled water. After antigen unmasking, and blocking of endogenous peroxidase in 3% hydrogen peroxide, the slides were incubated with primary antibodies  and from colorectal cancer patients’ sera  . These data suggest that cancer EVs significantly enhanced cell proliferation and decrease apoptosis in BRCA1-KO fibroblasts in vitro.We showed that cancer patient sera increased the proliferation of roblasts . To inveBRCA1-KO fibroblasts were treated for 3 weeks with these EVs isolated from cancer serum and conditioned media from cancer cells, and then were transplanted subcutaneously into NOD/SCID mice. To rule-out any possible contamination or carry-over of cancer cells from media, aliquots of the culture medium were placed in a culture plate and incubated at 37 °C, 5% CO2 for 4 weeks. We did not observe any cell growth during this period. In addition, at the end of the treatment period, an aliquot of BRCA1-KO fibroblasts exposed to EVs isolated from the serum of a female colorectal cancer patient was karyotyped. G-banding analyses revealed a normal male karyotype of 46,XY   did not undergo any malignant transformation after exposure to cancer EVs. These data confirm that cancer EVs transfer their cargo to target BRCA1-KO fibroblasts to induce their malignant transformation and suggest that this transformation is achieved through a mesenchymal to epithelial transition.Mice injected with BRCA1-KO fibroblasts was not acquired during in vitro culture. Immunofluorescence analyses of the 3 weeks exposed cells, showed that while they are Vimentin positive, they were still negative for differentiating markers   and 5 cancer (Cases 14–18) EVs preparation. Cells were analyzed after 1, 2 or 3 weeks of exposure. Cells treated with cancer EVs did not display any difference when compared to cells exposed to control EVs regarding their migratory potential    [BRCA1-KO fibroblasts were exposed to AO-tagged EVs for 24 h and were then analyzed by confocal microscopy. Although AO-tagged DNA positive cells were few (15.6 +/− 3.2%), DNA contained in EVs was still transferred to the target cells nuclei as seen by superimposition with DAPI staining  [BRCA1-KO fibroblasts. Both HT29 cells and BRCA1-KO fibroblasts were analyzed by flow cytometry to analyze the efficiency of BrUTP transfection, and BrUTP positive RNA transfer, respectively. We observed that almost all transfected HT29 cells incorporated BrUTP in their RNA   to verif15.6 +/− .2%, DNA ription) . Conditi15.6 +/− .2%, DNA 15.6 +/− .2%, DNA BRCA1-KO fibroblasts were exposed to these two different media and analyzed for their tumorigenic potential after subcutaneous injection into NOD-SCID mice  of the genetic material involved in the transforming abilities of cancer EVs, we treated HT29 cancer cell EVs and the conditioned medium before EVs isolation with DNase, RNase or both. ice Fig. c. While BRCA1-KO fibroblasts, we sought to further characterize the Evs-contained genetic components and confirm the transfer of colon cancer DNA to the BRCA1-KO fibroblasts. We cleared colon cancer EVs from contaminating external DNA and RNA by using DNase and RNaseA digestions, respectively. BRCA1-KO fibroblasts were then treated or not with colon cancer EVs for 6 weeks. DNA isolated from all cell cultures and from cancer EVs were then processed for high throughout whole-genome sequencing  , 34–37. After completion of the bioinformatics analysis, whole-genome sequencing showed that EVs DNA covered 95–97% of the human genome at a mean depth of 6–9× at high-quality sequencing with 80–86% of bases having greater than or equal to sequencing quality scores of Q20  files of the BRCA1-KO fibroblast sample, we excluded those variants whose reads show more than two non-reference alleles and not confirmed by at least 5 reads. With this quality check, we confirmed that 58 of 268 exonic variants were absent in the control BRCA1-KO fibroblast sample  were found to be also present as RNA variants following RNA sequencing of BRCA1-KO fibroblasts after exposure to cancer EVs. Three of them  displayed mutations that translated into a change in the amino-acids sequence. These variants were not present in the BRCA1-KO fibroblasts prior to exposure.Five mutated gene variants that were transferred to the BRCA1-KO fibroblasts but not in control BRCA1-KO fibroblasts (Table BRCA1-KO fibroblast RNA). All these findings seem to suggest that EVs DNA might be actively transcribed in exposed BRCA1-KO fibroblasts.Furthermore, by comparing the variant set of the three samples identified from RNA sequencing variant calling, we identified 30 loci that were polymorphic in EVs and exposed ts Table . MoreoveBRCA1-KO fibroblasts prior and following exposure to cancer EVs. We used the Transcriptome Analysis Console to analyze the differential gene expression. Data quality control showed that templates labeling and hybridization were normal and uniform. Also, the PCA mapping and hierarchical clustering showed that samples clustered differently  for miRNA target gene predictions and we found that the differentially expressed miRNAs were already known to be involved in many cellular functions. Following this observation, we performed an interaction network analysis to connect the dysregulated miRNA expression in cancer EVs-treated BRCA1-KO fibroblasts to the regulated transcripts. We focused our analyses on those involved in MET process  and specifically for the extra-membrane portion of some of them. In this in vitro model, the transfer of mutated cancer genes that codify for regions of the HLA system closely associated to areas involved in immunological recognition pave ways to the hypothesis that horizontal transfer of malignant traits might also include the transfer of the ability possessed by cancer cells to escape immune system control, through alteration of the membrane HLA proteins. Further studies addressing the validity of this observation are needed to confirm this assumption that at the moment must be considered and regarded as a pure theoretical possibility.BRCA1-KO fibroblasts following exposure to the same EVs. The observed changes in mRNA levels, in our opinion, might be either secondary to direct transfer of EV cargo


Most cancer driver genes are involved in generic cellular processes such as DNA repair, cell proliferation and cell adhesion, yet their mutations are often confined to specific cancer types. To resolve this paradox, we explained mutation frequencies of selected genes across tumor types with four features in the corresponding normal tissues from cancer-free subjects: mRNA expression and chromatin accessibility of mutated genes, mRNA expressions of their neighbors in curated pathways and the protein-protein interaction network. Encouragingly, these transcriptomic/epigenomic features in normal tissues were closely associated with mutational/functional characteristics in tumors. First, chromatin accessibility was a necessary but not sufficient condition for frequent mutations. Second, variations of mutation frequencies in selected genes across tissue types were significantly associated with all four features. Third, the genes possessing significant associations between mutation frequency variations and pathway gene expression were enriched with documented cancer genes. We further proposed a novel bivariate gene set enrichment analysis and confirmed that the pathway gene expression was the dominant factor in cancer gene enrichment. These findings shed lights on the functional roles of genes in normal tissues in shaping the mutational landscape during tumor genome evolution. Mutations on “driver” genes alter important cellular processes – such as the “hallmarks” like cell proliferation, apoptosis and angiogenesis – and thus drive oncological phenotypes such as tumorigenesis, progression and metastasis3. Mutations on the vast majority of “passenger” genes, in contrast, are likely the byproducts of unstable cancer genomes and bear little or no phenotypical consequences5. Much of the past and recent progress in cancer research has been on charting the mutational landscapes of cancers and deciphering their functional implications. Pre-genomic studies have already mapped recurrent mutations on various driver genes and confirmed their oncological roles9. High-throughput sequencing technologies further advanced the knowledge about the prevalence and functions of the well-known driver mutations13, and mapped rare mutations in large populations16. Today, our knowledge about the mutational landscapes of human cancers is likely near complete and accessible to the general public, thanks to the efforts of converting decades of studies into structured databases18 and several international consortia generating large-scale cancer genomic data25. There is also tremendous progress in identifying driver genes and deciphering their functions, thanks to various bioinformatics tools28 and interventional technologies such as shRNAs and CRISPR32.Cancer cells harbor a large number of mutations on diverse genes with a wide range of occurrence frequencies34; BRCA1 and BRCA2 in breast and ovarian cancers35; BRAF in melanoma, thyroid and colorectal cancers38; FLT3 and CEBPA in AML40; EGFR in non-small cell lung cancers and brain tumors42. Perhaps the only two exceptions are TP53 and KRAS, which are frequently mutated in most cancer types44. Intriguingly, many frequently mutated genes are primarily involved in fundamental biological processes such as DNA repair45, cell cycle regulation46, cell adhesion47, processes which are important in cancer3 as well as most normal tissues. In the aforementioned examples, APC and beta catenin are members of the Wnt pathway34; EGFR, BRAF and FLT3 are members of the Ras pathway50; BRCA1 and BRCA2 are involved in DNA repair45. Those processes are essential for the development and maintenance of almost all tissue types. It is quite puzzling why their mutations are observed in specific cancer types.Despite the progress in these directions, major puzzles persist in cancer genomics. One of the unresolved paradoxes is the reconciliation between tumor specific mutations and generic functions of the mutated genes. Mutations of most well-known driver genes are confined primarily to certain cancer types, such as APC and beta catenin in colorectal cancers53. Tissue-specific mutations, therefore, likely reflect tissue-specific selective advantages of the mutant cells. It is very difficult, if possible, to directly observe clonal evolution and measure the selective advantages of specific mutations. As a substitute for direct observations of clonal evolution, we suspect that key evidence of selective advantages of mutations exists in the features of normal tissues. For instance, if a gene is not accessible for transcription in a specific tissue, then it may not be transcribed and play any significant role in that tissue. Mutations on the gene thus confer no selective advantage and do not appear frequently in the population.Recurrent mutations are consequences of tumor clonal evolution. Mutations that enhance fitness of cells are fixed in the expanding subclones and thus appear frequently in the patientsThis line of reasoning motivated us to investigate the roles of features in normal tissues in explaining cancer type-specific mutations of genes undergoing selection in cancer genome evolution. In particular, we considered four transcriptomic and epigenomic features in normal tissues: chromatin accessibility and expressions of the targeted genes, expressions of documented pathways and protein binding partners of the targeted genes. Those features were extracted from the data of cancer-free subjects, thus can better reflect pre-cancerous conditions than the data from normal tissues adjacent to tumors. For each candidate feature, we associated variations of mutation frequencies with variations of feature values over multiple tissue types. In addition, we checked whether associations with single or double features revealed the cancer-related functions of genes. Favorably, our analysis outcomes suggest features in normal tissues contain information about tissue-specific mutations and oncological functions of genes, despite the fact that the data of cancers and normal tissues were collected from completely independent sources. The encouraging results serve as an early step toward understanding molecular and clinical characteristics of cancers from an evolutionary perspective.54. Lim et al. concluded that active histone marks were enriched around frameshift indels as compared to missense single nucleotide variation, while repressive histone marks showed the opposite trend55. Yeang et al. showed co-occurring mutations happened at genes in distinct pathways and mutually exclusive mutations happened at genes in the same pathways, hence indirectly validated the clonal evolution hypothesis56. Lawrence et al. showed the spectrum of sequence mutations was closely linked with carcinogenic factors57. Our work distinguishes from those prior studies as it systematically explains cancer type-specific mutations with features in normal tissues. Several recent studies tackled similar problems of explaining cancer type-specific mutations with genetic and epigenetic features in normal tissues. Polak et al. showed that variance in mutation rates along cancer genomes was largely explained by cell-of-origin epigenomic states of independent normal samples58. Ha et al. discovered that the somatic mutational landscape in cancer was associated with chromatin features in the corresponding pre-cancerous tissues, which were altered by environmental exposure59. Yamashita et al. demonstrated that genetic and epigenetic alterations in normal tissues had different effects on cancer risk between gastric and esophageal cancers60. Our work shares the common goal with these studies yet adopts different approaches. We included both epigenomic and transcriptomic features, while prior analysis examined exclusively on either epigenomic states or gene expressions alone. Furthermore, we performed association analysis both within each cancer type and across multiple cancer types, while most prior studies targeted within-tissue analysis.Various previous studies aimed for explaining patterns of gene mutations from evolutionary and environmental perspectives. Schaefer and Serrano categorized mutated genes into tissue-general and tissue-specific groups and examined their distinctions in gene expressions, functional annotations, and environmental interactions−5 for all cancer types according to permutation tests). The distribution of EpiOFF genes was sharply peaked around 0, indicating that they were rarely mutated in the corresponding tumor types. In contrast, the distribution of EpiON genes had a much wider tail, indicating diverse mutation frequencies of the members.Chromatin accessibility is a fundamental requisite for transcription. A gene which is not accessible at the chromatin level is in principle not amenable for further downstream regulation and thus confers no functional roles in the corresponding normal tissue. For each tissue type, we divided genes into “EpiON” and “EpiOFF” groups according to their chromatin accessibility states. The EpiON genes possess chromatin states accessible for active transcription, and the EpiOFF genes have inaccessible chromatin states. We compared the distributions of mutation frequencies between the two groups in 11 cancer types  exhibit a similar but weaker pattern as Fig. The distributions of mRNA expressions in normal tissues between EpiON and EpiOFF genes  and checked whether genes possessing strong associations were enriched with known cancer genes. For each feature, we sorted genes ascendingly according to their association p-values and performed a random walk along the sorted list, with increment +1 when hitting a cancer gene, and 0 otherwise. Figure To validate biological relevance of normal tissue features in oncogenesis, we employed gene set enrichment analysis . In contrast, associations with expression, inferred protein-protein interaction partner activity, and chromatin states are moderate or poor indicators for cancer genes . The control feature random walk exhibits random deviations from the straight line as expected .When multiple features are informative indicators for a gene set, their influences on enrichment can exhibit a variety of combinatorial scenarios. The enrichment outcomes of some features may be largely contained in other features, relatively independent, or possess epistatic interactions. To capture these dependencies, we developed a novel statistical method to extend GSEA to two features jointly. In brief, we generated three enrichment random walks in addition to the random walks of single features: a joint random walk by counting the union of gene set members falling within each rank in terms of the two features; a conditional random walk by counting the union of expected gene set enrichment between feature 1 and randomly permuted feature 2; and a reciprocal random walk of permuted feature 1 conditioned on feature 2 enrichment outcomes. Feature 2 possesses additional information of gene set enrichment conditioned on feature 1, if the joint random walk is positively deviated from the expected random walk conditioned on feature 1. Conversely, if either the information of feature 2 is contained in feature 1 or feature 2 is not informative at all, then the joint random walk is not positively deviated from the conditional random walk. The detailed procedures of bivariate GSEA are reported in Materials and Methods.We performed bivariate GSEA for each pair of the four features and reported enrichment outcomes in Fig. Among the four candidate normal tissue features analyzed, pathway activity is the most superior in fitting the differential mutations across cancer types Table  and acco63 and open65 chromatins was previously reported. These incoherent observations suggest the complex and partially known mechanisms relating chromatin states with subsequent mutations. Specifically, two factors may partially account for the disparity in our analysis. First, a necessary but insufficient condition may yield poor association scores since the model of assessing association strength assumes that an open chromatin state is a necessary and sufficient condition for mutation. Second, tissue-specific and cross-tissue analyses considered very disparate sets of genes. A gene can be uniformly accessible (EpiAllON), uniformly inaccessible (EpiAllOFF), or have varying chromatin states (EpiDIFF) across tissue types. Table The chromatin state of a gene was a strong necessary but insufficient condition for its mutations from tissue-specific study Fig. . HoweverThe actual cause of associations between mutation frequencies and normal tissue features remains unknown. We suspect that it can be understood in light of cancer genome evolution. Recurrent mutations of a specific gene in a particular cancer type likely imply a strong selection for subclones carrying the mutations, thus substantiate the mutated gene’s important role in the genesis and progression of tumor. Two possible transitions from normal tissues to tumors may account for this outcome. The gene and its contextual partners can be active in the corresponding normal tissue (relative to other tissue types). Its mutation enhances or impairs the pathway activity and thus facilitates tumorigenesis and progression. Alternatively, the gene and its contextual partners can be inactive in the corresponding normal tissue. Mutations awake its activity in cancer and confer a selective advantage in tumor genome evolution. The former and the latter lead to positive and negative associations respectively. Our data suggests that for differential features across normal tissues, the former may be more common than the latter Table .Non-parametric tests of equality of two distributions such as Kolmogorov-Smirnov (KS) and Mann-Whitney (MW) tests assume that data points are independently drawn from the distributions. This assumption is violated in our analysis Figs –3 due toBoth Roadmap and Body Map data were derived from a small number of cancer-free subjects  were aggregate statistics over hundreds of cancer patients, and covariates  were derived from subjects totally unrelated to cancer patients. A clinically relevant predictor should target characteristics specific to patients and utilize information from the same patients in prediction. For instance, one may predict the occurrence of a particular gene mutation in a specific patient, based on gene expressions of the patient’s normal tissues prior to tumor onset. This would require prospective investigations to collect pre-cancer normal tissues of the patients. However, it was not our intention to build such predictors, as many efforts have been dedicated to finding biomarkers for diagnosis and prognosis of cancers. Rather, our study can be viewed as an early step toward understanding molecular and clinical characteristics of cancers from an evolutionary perspective. Many recurrent patterns in cancers – such as population-specific mutations, progression stage-specific mutations, and tissue-specific metastasis – are richly documented but remain poorly explained. Investigation from an evolutionary perspective, such as examining the functions and activities of genes in normal tissues, will likely shed light on those problems. Deeper knowledge about the relation between cancer genome evolution and these patterns will facilitate the prevention, diagnosis and treatment of cancers and have tremendous impacts on human health.66 . For protein-coding genes, only nonsynonymous mutations were included in the calculation of mutation frequency, as they altered protein sequences and thus were more likely to change protein functions. Mutation frequency of each protein coding gene in each tissue was counted as the fraction of patients harboring any nonsynonymous mutation on that gene. For non-protein coding genes, such as microRNAs, all sequence mutations were included in the calculation of mutation frequency. Abbreviations for cancer types follows the notations used by TCGA (https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/tcga-study-abbreviations).Somatic mutation data of 19 tumor types called by MuTect algorithm was downloaded from the TCGA data portal67 . The data contained predictions regarding genome-wide chromatin states in individual tissues based on an integrated model of 127 epigenomes with 25 epigenomics marks (such as H3K27me3 and H2A.Z). For a tissue, the data (downloaded as genomic coordinates) were converted to gene symbols using biomaRt68. Genes with states “Active Transcription”, “Transcribed – 5’ preferential”, “Strong transcription” and “Transcribed – 3’ preferential” were subsequently labeled as accessible for activate transcription and the remaining were labeled as inaccessible . As with gene expression data, normal tissues were mapped to the corresponding tumor types based on the same guidelines . Data from 15 normal tissues types were mapped to 19 corresponding tumor types studied by the TCGA project  “Primary Site of Disease” and “Histological Subtype” sections. If a tissue has multiple tumor types, all of them are considered individually, for example, gene expression data for kidney tissue was used for all 3 kidney tumor types, KIRC, KICH and KIRP. Raw FPKM values were Z-normalized across before further study.Illumina Body Map, the RNA-Seq gene expression data of normal human body tissues used in this study, was downloaded from EBI Expression Atlasct Table , primari70 (http://www.genome.jp/kegg/kegg2.html). Human protein-protein interaction data was downloaded from Human Protein Research Database (HPRD)71 (http://www.hprd.org/). List of cancer genes was downloaded from COSMIC Cancer Gene Census17 (http://cancer.sanger.ac.uk/census/).Human biological pathway data was downloaded from KEGGFor each tissue type, we subdivided genes into EpiON and EpiOFF groups according to their chromatin states, and displayed their frequency mutation distributions in Fig. We also subdivided genes into highly expressed and lowly expressed groups and visualized their mutation frequency distributions. Unlike chromatin states, the expression level of a gene is a continuous value rather than a discrete state. We thus varied the threshold value separating the two groups to FPKM > 10, 50 percentiles and 75 percentiles and displayed their distributions in Figs We quantified the pathway activity of a gene in a specific tissue with the following procedures. First, we selected all the first-degree neighbors of the designated gene in the pathway and calculated their mean expression value. Second, we randomly drew the same number of genes 100,000 times and calculated the mean expression values in all random trials. Third, the p-value of the pathway neighbors’ mean expression value was the fraction of random trials whose mean expression values exceeded the empirical value. Negative log of the p-value was then used as the pathway activity. Similar calculations were performed for gene expression and the PPI network by considering only the gene’s expression value, and first-degree neighbors in the network, respectively.For chromatin state, we split tissues into the groups with accessible chromatins (“EpiON” group) and inaccessible chromatins (“EpiOFF” group). The test statistic was the difference of median mutation frequencies between EpiON and EpiOFF groups.For other features, we subdivided tissues according to their mutation frequencies instead, as a gene was often highly mutated in only a few tissues. We sorted tissues according to their mutation frequencies ascendingly. Successive data point pairs with the largest gap of mutation frequencies were chosen as the boundary point to split tissues. Data points below the low boundary point (including the low boundary point) were assigned to the lowly mutated group 1. Data points above the high boundary point (including the high boundary point) were assigned to the highly mutated group 2. The test statistic was the difference of median feature values between group 2 and group 1.We randomly permuted mutation frequencies of tissues 100,000 times and evaluated the test statistic in each random trial. The p-value was the fraction of random test statistic values exceeding the empirical test statistic value.When a gene was mapped to multiple pathways, we set the pathway activity p-value as the minimum of p-values over the mapped pathways. This criterion selected the strongest pathway associated with mutational profiles rather than requiring all mapped pathways to be strongly associated with mutational profiles.The false discovery rate of a feature was estimated by comparing the number of significant associations in the empirical data and the number of significant associations in the randomized data. For each gene we permuted its mutation frequency labels 100 times. Steps 1–4 were applied to the permuted data to obtain  significant associations. FDR was calculated asWe devised a permutation test to quantify the association significance between each candidate feature and mutation frequency.nth-degree neighbors of a target gene are the genes in the protein-protein interaction network whose shortest distances from the target gene are n. We counted the numbers of genes possessing at least five neighbors from the first to eighth degrees  as the number of cancer genes above/equal to rank x. y(x)was obtained by a random walk along the sorted genes. Starting with 0, y(x) incremented by 1 if the gene of rank x was a cancer gene, and 0 otherwise. The resulting y(x) was a non-decreasing function.Define If a feature was informative about cancer processes, then the top-ranking genes – genes with strong association p-values – were anticipated to be enriched with cancer genes. Therefore, the random walk would quickly gain a high value and remain stable subsequently.N as the total number of genes, and K as the total number of cancer genes. At step n of a walk, there were n top-ranking genes and k of them were cancer genes. The probability of randomly choosing k cancer genes from the top n genes was given by a hypergeometric distribution:n genes was given byThe null hypothesis is that the feature was uninformative about cancer processes, and whether a cancer gene was included in the top-ranking list was completely determined by random chance. We defined This expected number approximated the expected number y(x) obtained by the empirical data from the y(x) obtained by the null model, which was a straight line for a single feature. Specifically, we normalized random walk curves to 0 ≤ y(x) ≤ 1 and treated them as cumulative distribution functions (CDFs) of random variables. P-values were calculated by comparing empirical Mann-Whitney U test statistics with corresponding 100,000 permutation test statistics.The significance of cancer gene enrichment was quantified by the positive deviation of the To test whether the association between the mutational profile and a candidate feature was informative about the cancer processes, we adopted gene set enrichment analysis as previously proposedy(x) as for univariate GSEA. Two sorted gene lists were generated according to the association p-values of the two features F1 and F2 respectively. yF1F2(x) was defined as the number of cancer genes in the union of top x genes from the two lists, and yF1(x) and yF2(x) were the number of cancer genes in each list respectively. Obviously, yF1F2(x) was not lower than yF1(x) and yF2(x) for any x.We first defined the random walk of enrichment yF2|F1(x). Conditioned on the enrichment yF1(x)of the F1 list at rank x, yF2|F1(x) specified the expected number of cancer genes when F2 was not informative about the gene set. N and K were defined as univariate GSEA. At rank n there were n top-ranking genes and k cancer genes from the F1 list. Suppose by incorporating a randomly sorted F2 list nextra genes and kextra cancer genes were added. The probability that randomly selected n genes added nextra genes to the sorted F1 list of n genes was given by a hypergeometric distributionnextra genes, the probability that kextra of them were cancer genes was given by another hypergeometric distributionn lists then becomesyF2|F1(n).To test whether the two features jointly provided more enrichment information than F1 alone, we constructed a null model curve yF1F1(x) and the expected random walk conditioned on the F1 list yF2|F1(x). If yF1F2(x) was positively deviated from yF2|F1(x), then F2 provided additional information about gene set enrichment after F1 was taken into account. The significance was again quantified by the Mann-Whitney permutation p-value. The additional information of F1 relative to F2 was assessed analogously by comparing yF1F2(x) with yF1|F2(x).Compared the empirical random walk from the joint list yF1F2(x), yF2|F1(x)) and (yF1F2(x), yF1|F2(x)) comparisons:yF1F2(x) is positively deviated from both yF2|F1(x) and yF1|F2(x) – F1 and F2 both provide indispensable enrichment information.yF1F2(x) is positively deviated from yF2|F1(x) but not yF1|F2(x) – F2 is superior to F1 in gene set enrichment.yF1|F2(x) is positively deviated from yF1|F2(x) but not yF2|F1(x) – F1 is superior to F2 in gene set enrichment.yF1|F2(x) is not positively deviated from either yF2|F1(x) or yF1|F2(x) – neither F1 nor F2 is informative in gene set enrichment, or F1 and F2 provide largely overlapped enrichment information.There are four possible outcomes of (Classical GSEA assesses whether a feature contains information about a particular gene set. We extended this framework to multiple features and assessed whether multiple features jointly provided extra gene set information relative to subsets of those features. In this study, we considered two features concurrently, although the framework can be easily extended to more features.Supplementary File 1