Fusion genes are structural chromosomal rearrangements resulting in the exchange of DNA sequences between genes. This results in the formation of a new combined gene. They have been implicated in carcinogenesis in a number of different cancers, though they have been understudied in high grade serous ovarian cancer. This study used high throughput tools to compare the transcriptome of high grade serous ovarian cancer and normal fallopian tubes in the interest of identifying unique fusion transcripts within each group. Indeed, we found that there were significantly more fusion transcripts in the cancer samples relative to the normal fallopian tubes. Following this, the role of fusion transcripts in chemo-response and overall survival was investigated. This led to the identification of fusion transcripts significantly associated with overall survival. Validation was performed with different analytical platforms and different algorithms to find fusion transcripts. Though much effort has been invested in defining the biology and natural history of ovarian cancer, standard treatment and prognosis has not changed much since the addition of platinum based chemotherapy . A notabBCR-ABL1 as a recurrent structural chromosomal rearrangement implicated in chronic myeloid leukemia (CML) [TMPRSS2-ERG in prostate cancer [RET-CCDC6 in thyroid carcinoma [EML4-ALK in non-small cell lung cancer (NSCLC) [NTRK fusion genes have also been identified as driver mutations in a variety of different adult and pediatric cancers, and “tumor agnostic” therapies targeting these fusion genes have resulted in impressive treatment responses in phase I and II clinical trials [A fusion gene is a structural chromosomal rearrangement resulting in the exchange of DNA sequences between genes. This results in the formation of a new combined gene. Fusion genes have been implicated in carcinogenesis since the early 1980s. At that time, banding techniques allowed for chromosomal analysis of tumors, which led to the discovery of ia (CML) . This thia (CML) , and conarcinoma , and EML (NSCLC) . Remarkal trials . NRG1 and ABCB1, the latter of which was specifically identified in pre-treated and drug-resistant specimens. This work suggests that fusion genes do play some role in the pathogenesis high grade serous ovarian cancer, recurrence, and/or adaptive drug resistance [Similar work has started with ovarian cancer. Indeed, genetic rearrangement has been identified as a mechanism of tumor suppressor inactivation in ovarian cancer. Genomic rearrangement may result in the formation of fusion genes . Investisistance ,16. BecaOur hypothesis is that fusion genes are part of the genomic rearrangements which occur in ovarian cancer. This study aims to assess differences between fusion transcripts’ presence in primary high-grade serous ovarian cancer (HGSC) and normal fallopian tube samples. We then determine whether these fusion transcripts are associated with clinical outcomes, specifically survival and response to chemotherapy. In this manuscript, “fusion transcript” refers to the sum formation of two partner transcripts.p < 0.001). AL391840.3—SH3BGRL2, AL445985.1—SPATA13, and PFKFB3—LNC02649 , and receipt of neoadjuvant chemotherapy. In the multivariate analysis, three variables were found to be independently significant: age, residual disease after surgery, and receipt of neoadjuvant chemotherapy A,B. No fSTAR-Fusion method to the fastq files. As expected, we did not find any fusion gene on any of the normal samples.As a negative control for the method of detection, we extracted DNA from normal tubes, performed DNA sequencing, and applied the FusionCatcher, a different method with a different algorithm, to detect fusion transcripts and chimeras. Out of the 44 fusion transcripts found to be significant in the univariate analysis , we detected 31 fusion genes with FusionCatcher with accuracy detections over 95% and AUC average of 97% . Two of the transcripts were significant in the validation of the multivariate analysis of survival (p < 0.05): FAM98B--FRMD5 and NRIP1--AJ009632.2. The other two transcripts were close to significance, ARL17A--KANSL1 and CC2D1A--CPNE8, p = 0.054 and p = 0.074, respectively , and other fusion transcripts which were not (AUTS2---INO80C and AC004475.1--PRPF6).Four fusion transcripts identified using  samples . We chosThe TCGA allowed investigators to examine the genomic underpinnings of ovarian cancer, and such analyses have helped identify a molecular subset of patients (HRD) which benefit from targeted therapy (PARP inhibitors). However, this only represents about 50% of patients. For the other 50% of patients, tumor susceptibilities and prognostic biomarkers are still undiscovered. Fusion genes represent one stone which has been left underexplored in the study of ovarian cancer genomics. Fusion genes have provided clarity in the pathogenesis, prognosis, and treatment of other cancers, and technological advances in deep sequencing have allowed researchers to discover the presence of pathogenic fusion genes within a variety of carcinomas—prostate, lung, sarcoma, and even targetable tumor agnostic driver mutations. In ovarian cancer, fusion genes have been linked to drug resistance , potentiSTAR-Fusion and validated these fusion transcripts with FusionInspector. We found that three fusion transcripts were present more frequently in normal fallopian tubes than in HGSC. Possible explanations for these “normal” fusion transcripts are (1) the fusion transcripts are protective against carcinogenesis or (2) the “normal” fusion transcripts are a marker of relative genomic stability compared with HGSC. In terms of functional analysis, the gene SH3BGRL2 has been hypothesized to be a tumor suppressor in renal cell carcinoma [AL391840.3 is not yet known. These fusion transcripts were not found in the analysis of fallopian tube DNA, indicating that these findings represent post-transcriptional modifications. Further analyses are needed to evaluate whether there is a functional meaning to these fusion transcripts, or if they are germline level events rather than occurring at the somatic level.We first sequenced the transcriptome of normal fallopian tubes and primary HGSC. We identified the fusion transcripts present within both tissue types using arcinoma  and servarcinoma . The funabsence of fusion transcripts to differentiate the carcinoma from the normal tissue, which may lack specificity. Lack of specificity, indeed, plagues many non-surgical ovarian cancer diagnostics. We then used these fusion transcripts to construct a prediction model which aims to identify a fusion transcript’s origin—normal fallopian tube versus HGSC. Such a prediction model would potentially be useful for testing serum cell free genomic testing as a non-invasive diagnostic test of ovarian cancer, or so called “liquid biopsy.” The model performs relatively well with an AUC of 95%. Though, such a model would be difficult to apply in the clinical setting because it relies on the ABCB1. Though, in contrast with Patch et al.’s work, our samples were obtained from surgeries as part of primary treatment, rather than recurrent disease [ABCB1 had we analyzed tumor samples obtained at the time of recurrence, rather than primarily evaluating the original tumor. It may be that these fusion transcripts are selected for over the course of multiple treatment lines.We did not identify any specific fusion transcripts associated with chemo-response, suggesting that they may not represent driver mutations which determine response to treatment. This contrasts with Christie et al.’s work , though  disease . PerhapsZBTB8OS—AC090627.1 and ARL17A—KANSL1. Given that these fusion transcripts exist within a large group of fusion transcripts (over 300), it is possible that these were “passenger” mutations which confer some particular survival advantage to the cancer, resulting in a poor prognosis for the patient. These two fusion transcripts may therefore be markers of a particularly aggressive iteration of ovarian cancer. From a functional perspective, ZBTB80S is a component of the tRNA splicing complex required to facilitate the enzymatic turnover of the catalytic subunit for RNA-splicing ligase [AC090627.1 is not known, it has been identified as a partner fusion gene within breast cancer [ARL17A—KANSL1 fusion transcript has been identified in both normal and thymus and T cell lymphoblastic lymphoma tumor samples and is thought to be involved in tumor maintenance, rather than pathogenesis [ARL17A—KANSL1 fusion shows some loss of functional domains, and since the unfused version of these genes are involved with histone acetyltransferase KAT8 and p53, the fused versions are hypothesized to interfere with these functions. This may represent a fusion gene which predisposes patients to carcinogenesis, analogous to mutations in BRCA [ARL17A encodes a GTP-binding protein which is suspected to be involved in protein trafficking and may modulate vesicle budding and uncoating within the Golgi apparatus [KANSL1 is involved with histone acetylation [FusionCatcher were FAM98B--FRMD5 and NRIP1--AJ009632.2. FAM98B is a protein coding gene which counts tRNA processing and gene expression as its related pathways [FRMD5 is also a protein coding gene which is involved in cell migration and has been linked to lung cancer progression [NRIP1 encodes a protein which is involved in transcriptional activation by steroid receptors, including the estrogen receptor [NRIP1 have been linked to breast cancer in genome wide association studies [AJ009632.2 is a long non-coding RNA whose function is not yet known, but a variant of this long non-coding RNA has been linked to Parkinson’s disease in a genome wide association study [Ten fusion transcripts were associated with significantly worse survival. Two, in particular, had striking hazard ratios for worse survival-- g ligase . Though t cancer . The ARLogenesis . Indeed, in BRCA . By itsepparatus . KANSL1 tylation . The twopathways , and it gression ,29. NRIPreceptor , which ppts over 0, it is  studies . AJ00963STAR-Fusion) with an independent method (FusionCatcher) and found that most of significant transcripts associated with survival were also detected with the new method. Some of those poor prognosis transcripts found to be significant in the Star-Fusion analysis trended towards poor prognosis in the FusionCatcher analysis. They remained either significant or close to significant in the repeat multivariate analysis. Then, we selected four fusion transcripts from the multivariate overall survival analysis which were found using STAR-Fusion and FusionInspector: two were identified using FusionCatcher and two were identified using Star-Fusion. RT-PCR was then performed on these four fusion transcripts, and all were identified within the original patient samples, confirming their true presence within the original transcriptome (rather than an artifact). The validation analysis underscores the complexity of fusion transcript detection and analysis. Discovery of fusion transcripts through computational tools is a developing technology that is evolving constantly [BCR-ABL1 [NTRK fusions [We validated our fusion detection analysis  or experienced disease progression during treatment (platinum-refractory). Overall survival was defined as the time from treatment completion to death. Patients who were alive at the end of their follow-up were treated as censored observations.We identified ovarian cancer patients with high grade serous histology from available flash-frozen tumor tissues stored at the University of Iowa Hospitals and Clinics Department of Obstetrics and Gynecology Gynecologic Oncology Bank  that is part of the Women’s Health Tissue Repository . Ovarian cancer patients with clinical and pathological data were included. Patients without RNA of sufficient quality (see below) for RNA-sequencing (RNA-seq) analysis were excluded from the study. Of the 187 patients identified in the original HGSC panel, 112 tumor tissues with sufficient RNA yield and quality were available for analysis.We additionally analyzed 12 fallopian tube samples from women undergoing salpingectomies for contraceptive indications or as part of a hysterectomy for chronic pelvic pain, pelvic organ prolapse, abnormal uterine bleeding, fibroids, or dermoid cyst. The analyzed tissue came from the junction of the ampullary and fimbriated end of fallopian tubes. For the benign fallopian tubes, a separate approval was given by the University of Iowa Institutional Review Board  in coordination with the University of Iowa Tissue Procurement Core Facility. All tissues were obtained from adult patients under informed consent in accordance with the University of Iowa IRB guidelines. Clinical and pathological data were collected from the medical record with IRB approval from the University of Iowa (UI) (IRB ID# 201804817). p-values ≤ 0.05 were considered statistically significant. There were 103 HGSC patient with accompanying clinical data included in the survival analysis  RNA purification kit following the manufacturers’ instructions. Yield and quality of purified cellular RNA was assessed using a Trinean DropSense 16 spectrophotometer and an Agilent Model 2100 bioanalyzer. Samples with an RNA integrity number (RIN)  greater Equal mass total RNA (500 ng) from each qualifying tumor was fragmented, converted to cDNA, and ligated to bar-coded sequencing adaptors using Illumina TriSeq stranded total RNA and DNA library preparation . Molar concentrations of the indexed libraries were confirmed on the Agilent Model 2100 bioanalyzer, and libraries were then combined into equimolar pools for sequencing. The concentration of the pools was confirmed using the Illumina Library Quantification Kit . Sequencing was then carried out on the Illumina HiSeq 4000 genome sequencing platform using 150 bp paired-end Sequencing By Synthesis (SBS) chemistry. All library preparation and sequencing were performed in the Genome Facility of the University of Iowa Institute of Human Genetics (IIHG).For quality control of our RNA-Seq experiments, we looked at the number of reads per sample and number of unmapped transcripts (average read/sample over 27 million). Unmapped transcripts resulted from: (1) transcripts being too short for successful mapping (less than 200 bp) and represented an average 21% of all transcripts; (2) 0% of transcripts had too many mismatches, and (3) 0.2% for other causes. None of the samples had a number of mapped reads below the 10-million threshold.® Methylation Assay alternate incubation instructions. Of the 20 collected fallopian tubes, 12 met the quality standards.Genomic DNA from fresh frozen normal fallopian tubes were purified using the DNeasy Blood and Tissue Kit according to manufacturer recommendations. Yield and purity were assessed using a NanoDrop Model 2000 spectrophotometer with a 260 nm/280 nm absorbance ration of ~1.8 with minimal to no degradation seen using horizontal gel electrophoresis. Following purification, the samples were bisulfite-converted using the EZ-96 Deep-Well Format DNA Methylation Kit following the Illumina InfiniumSTAR-Fusion pipeline to align and map paired-end RNA-seq data from HGSC and normal tube samples. This suite requires several genomic resources that included: the reference genome (human genome version hg38), reference transcript structure annotations, and results from an all-vs-all BLAST+ search of reference transcript sequences [We used the equences . STAR as an extension of the standard mapping procedure. First, the maximum mappable prefix algorithm was used to find the “seeds”, or read sequences, exactly matching to the genome. Then, genomic alignment windows were selected by clustering the anchor seeds. In each genomic window, a local alignment of the read sequence was performed. If the best alignment among all windows did not cover the entire read, chimeric detection was performed by finding the next best scoring window that covered the remainder of the read sequence. The STAR-Fusion mapping parameters are based upon the best practices for STAR [STAR-Fusion determines the most likely correct fusions, filtering out unlikely candidates from the initial predictions. More details can be found in Haas et al.’s publication [FusionInspector, a component of the STAR-Fusion suite, that performs in silico validations of the fusion transcripts discoveries by performing a supervised analysis of fusion predictions [Aligning RNA-Seq reads to the genome to capture split and discordant reads was done with for STAR , as wellfor STAR . Next, Slication . Finallydictions   in a Cox Proportional Hazard ratio multivariate model. A table with all fusion transcripts and their annotation was constructed for all HGSC and tubal samples . Logistiglmnet R package [We additionally built a prediction model with fusion transcript data to determine which patients would have ovarian cancer (HGSC) versus those with normal fallopian tubes. To create this model, we used the lasso regression method, as implemented in the  package . In our  package . We eval package . Bias-co package . FusionCatcher [To validate the detection of fusion transcripts, we used another novel method, nCatcher ,50, whicSTAR-Fusion with FusionInspector validation in DNA from the normal tubal samples. We would expect not to find any fusion genes in normal tissue.As a negative control for our analysis, we performed Whole cell RNA was purified from the appropriate tumors using the mirVana RNA purification kit according to manufacturer’s instructions (Thermo Fisher). Yield and purity were assessed in the University of Iowa Institute of Human Genetics (IIHG) using a Trinean DropSense 16 and an Agilent Model 2100 Bioanalyzer. 2.Fusion TranscriptPCR Primer SequencesTm *CC2D1A-CPNE8Rev: GCAGGTGATGGCTTGATTFor: ATGCACAAGAGGAAAGGAC59.7 °C59.3 °CFAM98B-FRMD5Rev: TGCCGGGAAAGCAACATFor: GTGCTGGACACACTGGAG 61.6 °C61.5 °CAC004475.1-PRPF6Rev: CTTCAGGTTCTTCCAGCTCAAFor: GCAGCAGATGTACGACATGA 61.7 °C61.7 °CAUTS2-INO80CRev: CAGGTTCTTCCCAGGTTCTGTT For: CGGCAGAAGAGGACATCATT61.5 °C63.8 °CThe 500 ng of whole cell RNA from each tumor was reverse transcribed using the SuperScript III kit (Invitrogen). RT-PCR was performed on a BioRad T100 thermal cycler. Tumor cDNA was matched with the appropriate primers as shown below. * Tm is estimated in OligoAnalyzer (IDT) at 1.5 mM MgClPrimers were designed using the appropriate fusion transcript sequence in PrimerQuest at Integrated DNA Technologies (idtdna.com). All primers were also manufactured by IDT. PCR amplifications were carried out for 35 cycles using an annealing temperature of 58.0 °C. In all cases, a negative control RNA was used consisting of whole cell RNA from a benign fallopian tube patient.RT-PCR reactions were run on a 1.6% horizontal agarose gel. The gel was stained with ethidium bromide and visualized under uV irradiation on a Life Technologies E-Gel Imager (Thermo Fisher).RT-PCR amplicons were purified using the QIAGEN QIAquick PCR Purification kit following manufacturer’s instructions (QIAGEN). Each amplicon was then sequenced on an Applied Biosystems Model 3730 × l capillary sequencer in the University of Iowa Institute of Human Genetics (IIHG) using the PCR primers as sequencing primers. Sequence output was then visualized in Finch TV software and validated by BLAST in ENSEMBL . The RT-In summary, we identified novel fusion transcripts that seem to be associated with HGSC and confer poorer survival to some of the patient with the disease. Further investigations will be needed to clarify the mechanisms of action of these transcripts and how they interact with survival. Some of these processes may be even targetable in future research.


HER2 amplification and compared our results with a reference breast cancer cohort from The Cancer Genome Atlas to clarify the status for this infrequent but important patient subgroup.Current clinical guidelines suggest that breast cancers with low hormone receptor expression (LowHR) in 1–10% of tumor cells should be regarded as hormone receptor positive. However, clinical data show that these patients have worse outcome compared to patients with hormone receptor expression above 10%. We performed DNA methylation profiling on 23 LowHR breast cancer specimens, including 13 samples with HER2 amplification usually clustered with triple negative breast cancer (TNBC) reference samples . In contrast, most specimens with low hormone receptor expression and HER2 amplification grouped with hormone receptor positive cancers . We observed highly similar DNA methylation patterns of LowHR TNBC-like samples and true TNBCs. Furthermore, the Ki67 proliferation index of LowHR TNBC-like samples and clinical outcome parameters were more similar to TNBCs and differed from LowHR HRpos-like cases.In unsupervised clustering and dimensionality reduction, breast cancers with low hormone receptor expression that lacked We here demonstrate that LowHR breast cancer comprises two epigenetically distinct groups. Our data strongly suggest that LowHR TNBC-like samples are molecularly, histologically and clinically closely related to TNBC, while LowHR HRpos-like specimens are closely related to hormone receptor positive tumors.The online version contains supplementary material available at 10.1186/s13148-021-01176-5. HER2 amplification [HER2 amplification can be treated with different types of anti-HER2-therapy [The classification of breast cancer is based on the expression of the estrogen and progesterone receptor as well as the presence or absence of fication . Patient-therapy .HER2 status is primarily performed by using immunohistochemistry (IHC) and in situ hybridization. In the 2010 revision of the guidelines of the American Society of Clinical Oncology and the College of American Pathologists (ASCO/CAP), the cut-off value for the definition of hormone receptor positive breast cancers was reduced from 10 to 1% [HER2 amplification.The evaluation of hormone receptor expression and the 10 to 1% . Only re10 to 1% . However10 to 1% . Various10 to 1% –13. Thes10 to 1% . This leMore recently, numerous studies showed that DNA methylation profiles are a reliable tool to classify different cancer types, reflecting the tumor’s cell-of-origin –17. WithUnsupervised hierarchical clustering of LowHR and TCGA samples based on DNA methylation profiles revealed four distinct clusters Fig. a, mainlyHER2 amplification status: samples that clustered with hormone receptor positive tumors (LowHR HRpos-like) usually showed HER2 amplification , while this was a rare event in LowHR TNBC-like samples . Of note, the HER2 status in copy number data derived from DNA methylation data was in line with the results from IHC and/or SISH. In particular, we confirmed the presence of HER2 amplification in the two HER2 positive LowHR TNBC-like samples and did not find any evidence for HER2 copy number changes in the two HER2 negative LowHR HRpos-like samples.Specimens with low hormone receptor expression either fell into one of the HR+ clusters or the TNBC cluster. This separation was mainly associated with the Summary copy number plots derived from DNA methylation data revealed additional similarities between LowHR TNBC-like samples and TNBCs, such as frequent losses of chromosome 4q and 5q as well as recurrent gains of chromosome 10p Fig. .Fig. 2SuTo compare DNA methylation profiles between TNBCs and the two LowHR subtypes, we tested our dataset for differentially methylated positions (DMP) and differentially methylated regions (DMR).EN1, TFF3 or IRX1 which have previously been described to be differentially methylated and expressed in TNBCs and hormone receptor positive breast cancers [A correlation analysis of the beta values of all CpGs revealed general hypermethylation in LowHR HRpos-like samples when compared to LowHR TNBC-like and TNBC specimens Fig. a, b. The cancers –28. All To further evaluate potential differences regarding the biological behavior and clinical outcome of the two identified LowHR subtypes and TNBCs, we compared the Ki67 tumor proliferation rate as well as pCR rate and OS between these groups using clinical trial information. DNA methylation classes did not correlate with local tumor stage or nodal status  in TNBCs and 77% (p = 0.0002) in LowHR TNBC-like samples, the proliferation rate (Ki67) of these groups was significantly higher than in LowHR HRpos-like tumors  when compared with LowHR HRpos-like samples  and LowHR TNBC-like samples  was lower than in LowHR HRpos-like cases , although this difference had only borderline significance  shows a DNA methylation pattern of TNBC, has low rates of HER2 amplification, closely resembles the chromosomal pattern of TNBC and has a high proliferation rate and worse clinical outcome.With this study, we demonstrate that breast cancers with low hormone receptor expression can be separated into two highly distinct groups by DNA methylation profiling. One group (“LowHR HRpos-like”) shows a DNA methylation profile similar to hormone receptor positive tumors, is strongly enriched with Since the lowering of the cut-off value for hormone receptor positive breast cancers by the ASCO/CAP in 2010, repeated concerns were raised that the outcome of patients with LowHR tumors is worse, as they do not benefit from endocrine therapy. However, despite from RNA expression profiles, there were no molecular data to support this clinical observation .HER2 amplification predominantly clustered with TNBCs, while specimens with additional HER2 amplification were typically very similar to hormone receptor positive tumors. Four specimens did not fit into this pattern. Differential DNA methylation analysis revealed almost no difference between LowHR TNBC-like and true TNBC samples, strongly supporting the assumption that these subtypes do not only show similar clinical outcome, but are also identical on the molecular level. Our investigation for differentially methylated genes between LowHR HRpos-like and TNBC specimens revealed a considerable overlap with cancer-relevant genes that have previously been described to be differentially methylated and expressed in hormone receptor positive tumors and TNBCs [HER2 amplified tumors are still very similar to hormone receptor positive breast cancers.Using hierarchical clustering as well as t-SNE as a method of dimensionality reduction that has previously been proven to be a valuable tool to identify subgroups in DNA methylation data sets, we observed that normal breast tissue, hormone receptor positive breast cancers and TNBCs can be reliably distinguished based on their epigenetic profile. The small overlap between tumor samples and normal breast tissue was most likely due to low tumor cell content within these specimens, as we have recently demonstrated for other tumor entities . The ovend TNBCs . This inHER2 amplification in LowHR HRpos-like specimens suggests a common origin with hormone receptor positive breast cancer, in line with previous cell-of-origin theories [HER2 amplification that were included in the TCGA reference data set aggregated in one of the HR+ clusters.As DNA methylation signatures are considered to represent the cell-of-origin of a tumor, these findings could indicate that LowHR TNBC-like samples derive from the same cell type as TNBCs. On the other hand, the presence of theories . This isHER2 amplification. Previous studies already showed that there are differences in frequency of numeric alterations between TNBCs and hormone receptor positive tumors and the respective chromosomal locations we observed in our study were in line with these reports [Using genome wide copy number profiles derived from raw DNA methylation data, we were also able to study the differences in frequency of genome copy number alterations between hormone receptor positive tumors, TNBCs and LowHR specimens. In line with our results from DNA methylation profiling, LowHR TNBC-like tumors and TNBCs generally showed comparable numbers of recurrent genome copy abnormalities. On the other hand, copy number profiles of LowHR HRpos-like samples generally resembled hormone receptor positive breast cancers, with the exception of the previously described high rate of  reports , 31. TheBased on our results, DNA methylation-based classification of LowHR tumors could be used to assess the individual prognosis and could guide treatment decisions in further studies. Based on the differences in their epigenetic profiles and clinical outcome data, one might suggest that LowHR TNBC-like samples require a similar treatment as TNBCs, whereas patients with LowHR HRpos-like tumors might still benefit from endocrine therapy. Strikingly, we observed high pCR rates for LowHR HRpos-like samples, which is unexpected due to their molecular similarity with hormone receptor positive breast cancer. However, it remains uncertain if this behavior is characteristic for this specific subgroup or if this observation is biased by other factors. Due to the retrospective design of our study and the small sample size, the clinical results should be interpreted with caution and need to be addressed in larger and prospective clinical trials.In conclusion, using DNA methylation our study provides new molecular support that LowHR breast cancer comprises two molecularly distinct groups that can be separated by DNA methylation profiling, resembling TNBC and hormone receptor positive breast cancer with potential implications for individual patient prognosis and therapy selection.A flowchart which summarizes the composition of the study cohort is available as Additional file HER2 status as part of the central pathology assessment at the Institute of Pathology of the Charité – University Hospital Berlin.For this study, we selected a total of 23 cases from the GeparSixto NCT01426880) and GeparSepto (NCT01583426) trial of the German Breast Group (GBG) and the Arbeitsgemeinschaft Gynäkologische Onkologie – Breast Study Group (AGO-B) with estrogen and progesterone receptor expression between 1 and 10%, including 13 samples with HER2 amplification , 20. Inf426880 anhttps://portal.gdc.cancer.gov/legacy-archive/). In this study, the definition of different breast cancer subtypes is based on the results from IHC. Therefore, we excluded patient samples without documented percentage of estrogen or progesterone receptor positive cells by IHC (n = 271) in the corresponding pathology reports as well specimens with bad quality in DNA methylation analysis (n = 16), as defined below. The resulting reference dataset consisted of 422 samples, including 334 breast cancer and 88 normal breast tissue specimens. The corresponding sample list is available as Additional file Raw DNA methylation data from 709 samples as well as the corresponding pathology reports were obtained from the TCGA BRCA dataset available at the TCGA legacy archive , according to the manufacturer’s instructions. Extracted total DNA quantities were measured using the Qubit™ HS DNA Assay (Thermo Fisher Scientific).We used the Illumina Infinium HD FFPE DNA Restore Kit for DNA restoration of FFPE samples. Following restoration, the EpiTect Bisulfite Kit (Qiagen) was used for bisulfite conversion. DNA methylation analysis was performed using the Illumina Infinium MethylationEPIC BeadChip, according to protocols supplied by the manufacturer.combineArrays function was used to merge data from the 450 K and 850 K array generation into a virtual 450 K dataset. The pfilter function of the wateRmelon package with the parameter perc = 5 was used to filter samples and probes with low quality [pwod function of the wateRmelon package for probe outlier detection. Testing for differentially methylation positions (DMP) and differentially methylation regions (DMR) was performed using the DMPfinder function of the minfi package and the bumphunter function of the bumphunter package, respectively [Statistical analysis was performed using RStudio version 1.1.463 based on the statistical language R version 3.5.1. DNA methylation data were processed using the minfi package. The  quality . Samplesectively . For botRtsne function of the Rtsne package with 2000 iterations while the perplexity was set to 25 [T-distributed stochastic neighbor embedding (t-SNE) plots were generated based on the 5000 most variant CpG sites and using the et to 25 . Heatmapet to 25 . Tumor pThe means of Ki67 proliferation rates were tested for significance using the Wilcoxon–Mann–Whitney-Test. Pathological complete response (pCR) rates were compared using the Fisher’s test. Overall survival (OS) was visualized using Kaplan–Meier curves and tested for significant differences using pairwise logrank test.Additional file 1. Fig. S1: t-distributed stochastic neighbor embedding (t-SNE) plots showing the estimated tumor purity of The Cancer Genome Atlas samples based on manual estimation using light microscopy (a), RNAseq data , copy number variations , DNA methylation  and a combined score .Additional file 2. Fig. S2: Flowchart showing the composition of the study cohort with samples from the publicly available “The Cancer Genome Atlas” (TCGA) dataset as well as specimens from the clinical GeparSixto and GeparSepto trial. Abbreviations: BRCA = breast cancer; ER = estrogen receptor; PR = progesterone receptor; TNBC = triple negative breast cancer; t-SNE = t-distributed stochastic neighbor embedding.Additional file 3. Table S1: Sample annotation list for samples from the TCGA dataset.Additional file 4. Table S2: Table displaying the antibodies used for immunohistochemistry and the corresponding scoring systems.Additional file 5. Table S3: Results from DMR analysis comparing LowHR HRpos-like and TNBC samplesAdditional file 6. Table S4: Results from DMR analysis comparing LowHR TNBC-like and TNBC samplesAdditional file 7. Table S5: Results from DMR analysis comparing LowHR HRpos-like and LowHR TNBC-like samplesAdditional file 8. Table S6: Correlation of clinicopathological metadata with DNA methylation groups


Here we report the use of forskolin-modified halloysite nanotubes (HNTs) as a dopant for biopolymer porous hydrogel scaffolds to impart osteoinductive properties. Forskolin is a labdane diterpenoid isolated from the Indian Coleus plant. This small molecule is widely used as a supplement in molecular biology for cell differentiation. It has been reported in some earlier publications that forskolin can activate osteodifferentiation process by cyclic adenosine monophosphate (c-AMP) signalling activation in stem cells. In presented study it was demonstrated that forskolin release from halloysite-doped scaffolds induced the osteodifferentiation of equine mesenchymal stem cells (MSCs) in vitro without addition of any specific growth factors. The reinforcement of mechanical properties of cells and intercellular space during the osteodifferentiation was demonstrated using atomic force microscopy (AFM). These clay-doped scaffolds may find applications to accelerate the regeneration of horse bone defects by inducing the processes of osteodifferentiation of endogenous MSCs. Adiposee marrow ,13. In pe marrow ,15.The cell fate is determined by several factors, including the microenvironment signals during tissue formation. The level of cellular response to a microenvironment is can be restricted firstly by ability to respond and interpret signals and secondly by the variety of them . MechaniNanomaterials have also been used to stimulate MSCs proliferation and assist cellular osteogenic differentiation . NanotecColeus forskohlii. Forskolin known as a cyclic AMP booster and has widely been used to promote cellular responses to neurotrophins in cell physiology and as a component of induction medium for differentiation of adipose-derived stem cells into Schwann cell-like cells [Forskolin is a unique structurally complex labdane-type triterpenoid from a natural source, the plant ke cells . Forskolke cells . Forskolke cells ,25,26.per se as well as nanostructured polymeric materials with halloysite as dopant expressed low level of toxicity for different types of cells and organisms [Halloysite represents a promising nanomaterial for the loading and sustained release of broad range of biological active compounds including small molecules ,28. Hallrganisms . Here weSubcutaneous equine adipose tissue was obtained by excision under local anesthesia with lidocaine solution at the area of the tail base of a 1.5-year-old horse. Permission from the local ethics committee of Kazan Federal University was obtained to carry out procedures for the collection of adipose tissue . Samples were transported to the cell culture laboratory and processed within 4 h. The adipose tissue was minced, centrifuged at 2000 rpm for 10 min to remove excessed lipid fraction, and digested using 0.2% collagenase from hepatopancreas of crab with approximate collagenolytic activity about 750 U/mg  in Hanks’ Balanced Salt Solution (HBSS) with calcium and magnesium  at 37 °C for 1 h with agitation. The homogenate was centrifuged at 1500 rpm for 5 min and the enzymatic solution was decanted. The cell pellet was suspended with a Dulbecco’s phosphate-buffered saline  solution and centrifuged to remove residual enzymes. The obtained cells were cultured in Minimal Essential Medium alfa-modification (α-MEM) supplemented with 10% fetal bovine serum , 100 U/mL penicillin, 100 μg/mL streptomycin, and 2 mM L-glutamine. When the cells had reached 80% of confluence, they were detached from the culture dish using trypsin, suspended in cryopreservation medium containing 10% of dimethyl sulfoxide , frozen and stored in liquid nitrogen for later use. After the monolayer expansion, cells were used for the experiments at passage 3–5. Stemness of resulted cells was confirmed using immunocytochemical staining and flow cytometry analysis of expression of cluster of differentiation  .w/v)) was applied on the cells with subsequent incubation for 1 h under bright illumination. Combination of light and dark field microscopy was used to visualize the oil droplets in the cells, which were incubated in adipogenic differentiation medium. Mucopolysaccharides specific stain was used to detect chondrogenic differentiation the staining for mucopolysaccharides was used. Briefly, the fixed cells were washed with phosphate buffer saline three times for 5 min, stained for 1 h with Alcian Blue (1 g Alcian Blue/100 mL 0.1 M HCl) and then washed with DPBS. During the differentiation process, cultures were monitored intravitally once per 24 h using an inverted AxioObserver A1 microscope  in phase contrast mode. The images were obtained using an AxioImager microscope  and processed with ZEN software 2.0 (blue edition).The equine adipose derived MSCs were analyzed for their capacity to differentiate into the adipogenic, osteogenic and chondrogenic lineage. Differentiation was performed in monolayer cultures. Cells of the third passage were plated on 12-well plates with round plastic cover slips  and incubated in complete α-MEM growth medium until a monolayer culture was obtained (48 h). Then cells were transferred to a specific osteogenesis, adipogenesis, chondrogenesis differentiation kits MesenCult™  to induce differentiation in three directions . The medA total of 10 mg of Stemolecule™ Forskolin  were dissolved in DMSO to obtain a 10× stock solution. Then 10× stock solution was diluted with deionized water to obtain a working solution with a concentration of 10 mM. 200 µL of 10 mM working solution of Forskolin was mixed with dry HNTs (30 mg) in centrifuge tube, suspended on vortex mixer for 10 min and placed into desiccator for loading by vacuum, as described previously . Forskol−1 with a spectral resolution of 2 cm−1. Fourier Transform Infrared Spectroscopy (FT-IR) HNTs, forskolin and forskolin-loaded HNTs were registered using a Frontier FT-IR spectrometer FT-801 . The measurements were recorded at room temperature in the range between 500 and 4000 cm6 cells in 2 mL of α-MEM per well) were applied onto the sliced scaffold and incubated for 14 d with every 48-h medium change. After that, samples were washed with DPBS and fixed with 4% paraformaldehyde for 20 min at room temperature. The efficacy of osteogenic differentiation under influence of forskolin without the differentiation medium was estimated using von Kossa staining  and forskolin-loaded HNTs (6 wt.%) were obtained via a freeze-drying technique as described previously . Brieflystaining  and micr® enhanced dark-field condenser, a fluorite 100× objective and DAGE CCD camera [® Dual Mode Fluorescence system  equipped with a Triple Pass Filter were used to image DAPI-stained cellular nuclei in transmitted fluorescence illumination mode; the exposure time was set at 100 µs.Dark-field microscopy images of MSCs after 14 d differentiation process were obtained using an Olympus BX51 upright microscope equipped with a CytoVivaD camera . Cells w−1). The images were obtained at 512 lines/scan at 0.8–0.9 Hz scan rate and up to 1 nN peak force setpoint. Topography was visualized in height sensor and peak force error channels. The AFM data were processed using Nanoscope Analysis v.1.7 software  [For AFM cells were seeded onto cover slips with 2D scaffold film  and cultMA, USA) . The DerSurface topography imaging and characterization was performed using a VK-X150 3D laser scanning confocal microscope  equipped with a 20× Nikon fluorite objective. The images were obtained in “superfine” mode, collecting simultaneously optical multifocus reconstruction, laser intensity and height data. The specimens were placed onto regular microscopy glass slides and imaged in air under ambient conditions. MultifileAnalyser v. 1.3.0.116 software  was used to process the data. For surface roughness numerical characterization, the flat areas on the images the selected.−1 wavenumbers region. The area of 3600–3700 cm−1 forskolin-HNTs corresponded to HNTs. The wavelength range between 950 cm−1 and 1800 cm−1 can be considered as a fingerprint region for forskolin, which presents numerous characteristic signals many of them detected only for forskolin-HNTs and not detected for HNTs. The typical FT-IR peaks of the forskolin at 1375 and 1383 cm−1 (bending of CH3) were represented by less intense signals in the case of the forskolin-HNTs.The surfaces of 3D polymer scaffolds were visualized and quantitatively characterized using 3D laser scanning microscopy, the typical optical, laser intensity and height (2D and 3D) images are given in In both cases, the surface structure of the scaffolds is sponge-like, with crater-like features, peaks and valleys along with relatively flat areas. The selected surface topography parameters measured are summarized in Specific staining of differentiated cells allowed us to confirm the ability of adipose derived cells develop into multiple specialized cell types. We confirmed that the equine adipose derived MSCs used in this study could be differentiated in three directions: adipogenic, osteogenic and chondrogenic using the commercially available media with specific factors and supplements which induce the morphological and functional changes . OsteogeWe have demonstrated the characteristic changes in cellular morphology and appearance of intracellular and extracellular inclusions under osteogenic, chondrogenic and adipogenic conditions using enhanced dark-field microscopy and AFM as label-free technique. Control cells grown in α-MEM exhibited the fibroblast-like morphology characteristic of MSCs A,E,I. LiNext, we performed the macroscopic qualitative analysis of the possibility of MSCs differentiation into osteogenic lineage on 3D porous scaffolds doped with forskolin . Using sThe samples of cells differentiated on a biopolymer nanostructured matrix were studied using atomic force microscopy . We founCells cultured on matrices doped with forskolin loaded nanotubes exhibited slight enhancement in stiffness in comparison with cells grown on scaffolds without nanotubes as well as on scaffolds with pristine HNTs. The transformed MSCs monolayer showed comparable nonspecific adhesion with MSCs grown on matrices with pristine nanotubes under osteogenic conditions of specific cultural medium as well as on matrices with HNTs in α-MEM .Bone regeneration in vivo proceeds more successfully in the presence of a large pool of MSCs, which are capable of differentiating into a cartilaginous mass in the region of the vessels of damaged tissues with its subsequent ossification . In prevNumerous studies have proven the biosafety of halloysite nanotubes for various organisms ,41,42,43−1 may indicate the appearance of hydrogen bonds between forskolin and the internal surface of HNTs consists of a gibbsite-like array of Al-OH groups [Loading efficacy of forskolin in HNTs was estimated by FT-IR and showed effective binding of nanotubes and forskolin based on the obtained spectra combining the fingerprint region for forskolin and halloysite. The broadening of the absorption band in the region of 3000–3500 cmH groups .Molecular mechanism of bone tissue fabrication could be affected by specific factors, which can initiate and promote the osteogenesis. It has been shown that protein kinase A (PKA) activation forms an important component of the mechanism by which BMP-2 mediates MSC differentiation. After activation PKA phosphorylates its prototypical downstream transcription factor, cAMP-responsive element binding protein (CREB), which is associated with a number of important physiological functions including osteochondrogenic differentiation . Here weWe applied high-resolution microscopy techniques (AFM and enhanced dark-field microscopy) for the label-free visualization of morphological and structural changes of cells which arise in the process of MSC differentiation. Recently, there has been an increase in interest in this technology to distinguish between different morphotypes of cells and their functional state . AFM andIn our study, we demonstrated the stemness of equine adipose derived cells and their multipotency which expressed in possibility of cells to differentiate into discrete cell types and confirms that cells belong to MSCs. Modification of nanotubes with forskolin, a compound belonging to the group of so-called small molecules, made it possible to obtain a new osteoconductive smart polymeric scaffold. This material promoted osteodifferentiation of horse adipose tissue derived mesenchymal stem cells without addition of specific osteogenic media. Label free techniques of visualization have been successfully applied to confirm the changes occurring in cells during osteodifferentiation. We suggest that synergistic action of the mechanical properties of the substrate and the directed chemical signal from forskolin most likely promotes differentiation of MSCs comparable in time and efficiency with the conditions provided by the differentiating media. At the same time, further detailed confirmation of this phenomenon is required, including on other types of cells using methods of enzymatic and analysis of gene induction, which are marker for the process of osteodifferentiation.


Innate immune cells form a first line of defense against invading pathogens. It comprises a variety of cells including neutrophils, natural killer cells (NK), and macrophages and their precursors monocytes. They are important in host defense for killing of pathogens, regulating immune cell recruitment and activation but also serve trophic functions, maintain tissue homeostasis and control resolution phases of inflammatory responses. In the context of health and disease, innate immune cells display a wide variety of functional phenotypes beyond the old dogma of M1 and M2 macrophages. It is now becoming clear, driven by innovative technologies such as mass cytometry or single cell sequencing, that tissue microenvironments drive very specific phenotypes in relation to disease and this is revolutionizing our understanding of pathology that will provide new opportunity for future personalized interventions.Inflammatory processes and innate immune cells are tightly regulated by epigenetic mechanisms. These processes include genomic DNA methylation and modification of histones by methylation or acetylation, all of which control the epigenetic landscape, chromatin accessibility and thereby the function of immune cells in disease . MoreoveIn this Research Topic on epigenetic regulation of innate immunity, three reviews focus on the involvement of epigenetics in liver resident Kupffer cells, sex hormones and epigenetics in innate immune responses and RNA modifying enzymes on immunity. Four research articles reported on the roles of epigenetic modifying enzymes  in macrophages, epigenetic mark readers BRD2/4 in NK and circular RNA in innate immune cells.Bennett et al. summarize an up-to-date view on epigenetics in development and differentiation of Kupffer cells (KC) and the epigenetic changes accompanying non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). Using transcriptomic and transcription factor (TF) binding analysis, the key regulators of KC differentiation are Notch signaling, SpiC and SMAD in collaboration with lineage determining TFs including PU1 and CEBP. Notably, changes in histone modifications (H3K27) and lncRNAs were observed in KC during the transition to NASH. These changes culminate in the reprograming of liver X receptor (LXR)α that affect its recruitment to target promoters.Shepherd et al. reviews the molecular reprograming of innate immunity by sex hormones, resulting in differential responses to infections between male and female. Sex hormones through binding to nuclear hormone receptors recruits co-activators, co-repressors, and epigenetic modifying enzymes to the promoters of its target genes. The most obvious impacts are discussed during hormonal changes such as pregnancy and menopause in female. The influences of sex hormones on epigenomes of innate immune cells may also involve inflammation in aging population (inflammaging).Epigenetics that are under the influence of sex hormones are proposed to affect sexual dimorphism in innate immunity. 6-methyltransferase (m6A) is reviewed by Tang et al. This modification affects RNA stability, degradation, the ability to be recognized as PAMPs. Writers (METTL14/METTL3), erasers (ALKBH5/FTO) and readers (such as FMRP) of m6A perform the modification in nucleus and cytoplasm which plays a critical role in DC maturation and thus affects adaptive immune responses in autoimmune and cancer. Examples for viruses that may hijack this RNA modifier to camouflage from host recognition are given, and the authors argue that targeting m6A should be exploited to enhance anti-viral and anti-tumor immunity.The importance of RNA NNeele et al. investigate the role of Ezh2, a subunit of polycomb repressive complex (PRC) in myeloid cells, especially macrophages and neutrophils. Ezh2 catalyzes methylation of H3K27 that results in repressive chromatin. Deficiency of Ezh2 in myeloid linage cells reduced atherosclerosis development and inflammatory foam cells while impairing neutrophil migration. In a separate study, Ghiboub et al. reported the involvement of histone deacetylases (HDACs) in human monocytes and macrophages. The authors reported that HDAC3 but not HDAC6 regulates responses of monocytes/macrophages to M1/M2 promoting stimuli and repeated LPS stimulation. Both works highlighted the potential targeting epigenetic modifying enzymes for inflammatory-related conditions.The two research articles investigate histone modifying enzymes in responses of monocytes/macrophages and its potential impact on inflammatory-driven disease. Cribbs et al. focus on the roles of BET bromodomain containing protein (BRD) on NK cell functions. BRDs recognize acetylated lysine residues on histones and acts as scaffold protein for recruitment of TF or protein complex. Using specific inhibitors for BRD2 and 4, they report that BRD2 regulates its inflammatory and cytolytic activity. Using scRNA-seq and ChIP-seq, the authors further reveal the heterogeneity of NK responses to BET bromodomain inhibition that leads to reduced promoter occupancy of BRD2/4. The results from this study warrant further investigation on the potential use NK in cancer immunotherapy.Yang et al. focus on a circular non-coding RNA (circRNA), that acts as sponge against specific miRNA and reduce its availability. Observations of upregulation of Hsa_circ_0060450 in monocytes of type 1 DM patients led the authors to investigate this circRNA. They observed that miR-199a-5p is the target of Hsa_circ_0060450. Because miR-199a-5p promotes type I IFN responses by targeting phosphatase SHP2, increasing Hsa_circ_0060450 may lead to reduced type I IFN responses in type 1 DM. Therapy targeting this cicrRNA may be beneficial for type 1 DM patients in the context of viral infection.Future investigations of components in different cell types under different epigenetic contexts may aid in developing more specific drug(s) that can be used to treat diseases of innate immune dysregulations.MW and TP equally contributed in preparing this Editorial. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Introduction Adult type 2 diabetes (T2DM) threatens public health and most patients manage their diabetic condition while in the community. As it is challenging for patients to properly manage diabetes alone, caregiver involvement in T2DM patient care is encouraged. This study aimed to examine the association between caregiver involvement in T2DM education within a community and the patients’ diabetes care outcomes  level, behavior, or hospitalization). Methods The available scientific literature in PubMed, Cochrane, EMBASE, and CINAHL was searched. The methodological quality of bias was assessed using the Cochrane risk of bias tool. Results A total of 13 out of 741 published studies were synthesized in this review. There is evidence that caregiver involvement in T2DM education is effective in the reduction of HbA1C and BMI, but not necessarily effective in reducing lipids. Study results indicate that caregiver related interventions can significantly improve patient diabetes knowledge, physical activity, and self-efficacy, but results were more mixed regarding medication adherence. Risk of bias analysis classified the majority of studies (77%) to be moderate or high quality. Conclusion This review aimed to explore the association between caregiver involvement in adult T2DM education in the community and patients’ diabetes care outcomes. The findings show an improvement in biological and behavioral self-management outcomes with caregivers involved in T2DM education, though no studies examined the direct association between complications or hospital readmission. Future research focused on tailored interventions and longer follow-up of patient outcomes are recommended.


To evaluate the cost-effectiveness of six diagnostic strategies involving magnetic resonance imaging (MRI) targeted biopsy for diagnosing prostate cancer in initial and repeat biopsy settings from the Singapore healthcare system perspective.A combined decision tree and Markov model was developed. The starting model population was men with mean age of 65 years referred for a first prostate biopsy due to clinical suspicion of prostate cancer. The six diagnostic strategies were selected for their relevance to local clinical practice. They comprised MRI targeted biopsy following a positive pre-biopsy multiparametric MRI (mpMRI) [Prostate Imaging – Reporting and Data System (PI-RADS) score ≥ 3], systematic biopsy, or saturation biopsy employed in different testing combinations and sequences. Deterministic base case analyses with sensitivity analyses were performed using costs from the healthcare system perspective and quality-adjusted life years (QALY) gained as the outcome measure to yield incremental cost-effectiveness ratios (ICERs).Deterministic base case analyses showed that Strategy 1 , Strategy 2 (MRI targeted biopsy ➔ systematic biopsy), and Strategy 4 (MRI targeted biopsy ➔ systematic biopsy ➔ saturation biopsy) were cost-effective options at a willingness-to-pay (WTP) threshold of US$20,000, with ICERs ranging from US$18,975 to US$19,458. Strategies involving MRI targeted biopsy in the repeat biopsy setting were dominated. Sensitivity analyses found the ICERs were affected mostly by changes to the annual discounting rate and prevalence of prostate cancer in men referred for first biopsy, ranging between US$15,755 to US$23,022. Probabilistic sensitivity analyses confirmed Strategy 1 to be the least costly, and Strategies 2 and 4 being the preferred strategies when WTP thresholds were US$20,000 and US$30,000, respectively.This study found MRI targeted biopsy to be cost-effective in diagnosing prostate cancer in the biopsy-naïve setting in Singapore.The online version contains supplementary material available at 10.1186/s12913-021-06916-0. Prostate cancer is the second most common cancer diagnosed and the fifth most fatal cancer amongst men globally . In SingDistinguishing clinically significant prostate cancer from clinically insignificant ones is central to the management of prostate cancer. While the definition of clinical significance continues to evolve , the undContemporary non-targeted transrectal or transperineal prostate biopsies rely on real-time ultrasound guidance. Despite good imaging of the prostate gland boundaries and its adjacent organ structures, ultrasound cannot distinguish malignant lesions from benign ones . UltrasoThis study aims to evaluate the cost-effectiveness of six diagnostic strategies involving MRI targeted biopsy for diagnosing prostate cancer in initial and repeat biopsy settings from the Singapore healthcare system perspective.The starting model population was men aged 65 years clinically suspected of having localized prostate cancer based on elevated serum PSA above 4 ng/ml, abnormal digital rectal examination (DRE), or both, and referred for a first prostate biopsy. The starting age of 65 years was used as it corresponded with the estimated age recording a marked increase in age-specific incidence rate of prostate cancer in Singapore . Men preTable A positive prebiopsy mpMRI with a Prostate Imaging – Reporting and Data System (PI-RADS) score of 3 to 5 was subjected to further testing by MRI targeted biopsy. In the initial biopsy setting, patients with negative mpMRI did not proceed to biopsy; in repeat biopsy settings, patients with negative mpMRI received systematic biopsy alone if there was persistent clinical suspicion. Although saturation biopsy is also a form of systematic biopsy, it typically involves extracting 20 or more cores . SaturatA model combining a decision tree and Markov sub-models was developed in consultation with local clinicians to ensure face validity see Fig. . The decThe model was simulated over 20 years using annual cycle length, in line with the estimated life expectancy for men at age 65 years in Singapore . During The outcome measure of the model was quality-adjusted life years (QALYs). Incremental cost-effectiveness ratios (ICERs) were calculated from pairwise comparisons of incremental costs and QALYs of the six diagnostic strategies. An annual discounting rate of 3% was applied to both costs and effectiveness. All analyses were conducted from the Singapore healthcare system perspective, and all costs were in 2020 United States (US) dollars  .The model was built using TreeAge Pro 2018 R2.0 . Analyses were performed using Monte Carlo simulation (seed number set at 16).Model input parameters were obtained from published data where possible and available. To overcome the dearth of real-world local data on downstream management pathways and resources, distribution of care strategies, and distribution of localized prostate cancer across cancer risks, a survey was co-developed and administered to clinician experts from October 2018 to January 2019. Each of the five local public healthcare institutions offering MRI targeted biopsy, and a specialty cancer center, was represented by a clinician expert to provide and coordinate inputs from their practice setting. A total of six responses were received. Clinician experts that provided inputs included urologists, radiologists, and medical oncologist.Based on the number of patients suspected of prostate cancer referred to public healthcare institutions for the first biopsy in the past 5 years, the estimated prevalence of prostate cancer in men referred for the first biopsy was 37.7%. The risk of disease progression from localized prostate cancer to metastasis and the risk of death depends on age, tumor risk, and prostate cancer management strategy . The modConventional prostate cancer management strategies considered in the model included active surveillance which involved active disease monitoring and potentially curative therapy if the cancer progressed ; watchfuUndiagnosed patients and patients on active surveillance were assumed to follow progression rates associated with watchful waiting . PatientA proportion of patients receiving active treatment could experience biochemical recurrence to trigger further treatments such as salvage treatment . When syAll prostate cancer-related deaths were assumed to occur following metastatic disease. In any health state, men could die from causes other than prostate cancer by applying the general population’s all-cause mortality rates for resident males .To ensure face validity, inputs from surveyed clinician experts provided insights to diagnostic and management pathways for prostate cancer, model inputs and assumptions used in the model. For external validity, 15-year overall survival rates from the PREDICT Prostate multivariate model were compared with the current model’s predicted overall survival output  to 1  . Table 3In line with a healthcare system perspective, only direct medical resources were considered. Table One-way sensitivity analyses were conducted over the range of predefined values for specific model parameters using the reported 95% confidence intervals from published literature or ± 10% of point estimate. Except for costs of mpMRI and biopsies in the evaluated diagnostic strategies, costs were considered known parameters and were not evaluated in the one-way sensitivity analyses. If more than two strategies were considered cost-effective, one-way sensitivity analyses were performed for the pair of strategies with the highest ICER in the deterministic base case analyses. The least costly strategy was used as the reference strategy for the pairwise comparison.Multivariate probabilistic sensitivity analyses with 1000 second-order Monte Carlo simulations  were performed. The uncertainty of model inputs was explored by simultaneously and randomly sampling the parameters from assigned distributions. Cost-effectiveness acceptability curves presented the cost-effectiveness probability of each diagnostic strategy over a range of willingness-to-pay (WTP) thresholds.Figure One-way sensitivity analyses were performed for Strategy 4 compared with Strategy 1 as the reference strategy as Strategy 1 had the lowest cost. Figure Probabilistic sensitivity analyses in Fig. This study found that all strategies including MRI targeted biopsy in the initial biopsy setting were cost-effective except when saturation biopsy was used immediately as a second biopsy. Many published cost-effectiveness analyses only evaluated MRI targeted biopsy as a first biopsy compared with systematic transrectal ultrasound-guided (TRUS) biopsy based on a single biopsy protocol –46. WithThis study also incorporated relevant clinical considerations to estimate long-term costs and effectiveness of various diagnostic strategies in single and multiple biopsy settings. Diagnostic accuracy inputs on MRI targeted biopsy for different risk groups were drawn mainly from a recent Cochrane review which comprehensively reviewed the evidence on MRI targeted biopsies based on 17 studies in initial biopsy setting and 8 studies in the repeat biopsy setting . This avThe consistently favorable results of Strategies 1, 2 and 4 showed that early use of MRI targeted biopsy as a first biopsy was potentially cost-effective but could be influenced by when saturation biopsy was used. Strategy 3 where saturation biopsy was the second biopsy was shown to be dominated, whereas Strategy 4 had an ICER of US$19,458 when saturation biopsy was used as the third biopsy, implying later use of saturation biopsy could improve the cost-effectiveness of MRI targeted biopsy in the initial biopsy setting. Despite high sensitivity of 95% in detecting localized prostate cancer, saturation biopsy gave greater utility decrements and costs arising from higher complication rates than systematic biopsy.The discordant results for Strategy 6 in the deterministic base case analyses and probabilistic sensitivity analyses could be explained by the modest difference in the incremental effectiveness between Strategy 4 and Strategy 6, and the multiplicative effect of input parameters in a nonlinear model. In both initial and repeat biopsies, MRI targeted biopsy improved detection of clinically significant cancer and clinically insignificant cancer . CompareDespite a lack of published literature comparing diagnostic strategies that varied position of the MRI targeted biopsy, published literature found greater inconsistency in the cost-effectiveness of MRI targeted biopsy in the repeat biopsy setting. MRI targeted biopsy compared with TRUS systematic biopsy in the initial biopsy setting were consistently cost-effective –47, 50; Successful implementation of any diagnostic strategies in a practice setting requires several considerations. The effectiveness of MRI targeted biopsy depends on the quality assurance of mpMRI. Although PI-RADS scores help standardize the acquisition, interpretation and reporting of prostate mpMRI, the learning curve of interpreting mpMRI remains steep and inter-observer differences persist. Radiology practices performing prostate mpMRI should engage in in-house training and continual quality improvement programs to ensure a minimum competency standard is maintained , 52. WitThis study has several limitations. First, the diagnostic input of MRI targeted biopsy was based upon a review that included all MRI targeted biopsy techniques including cognitive MRI targeted biopsy and in-bore MRI targeting . VariatiSecond, probabilities of metastatic progression and treatment effects were sourced from the SPCG-4 trial which provided up to 18 years’ follow-up data , 53. AltThird, this study did not include a potential diagnostic strategy with two consecutive MRI targeted biopsies due to its low frequency of use in local clinical practice and lack of clarity in the guidelines. This diagnostic strategy could take place if there were concerns that the first MRI targeted biopsy was not optimal due to technical reasons or if the second mpMRI showed changes in the lesion(s) after a negative initial biopsy. Future studies could consider including this strategy when there is more published data or greater clarity in guidelines.Fourth, model inputs such as the prevalence of prostate cancer in men referred for biopsy, the distribution of localized prostate cancer risks, and allocation of care and treatment strategies were informed through surveys of clinician experts working in the local public healthcare institutions. This was due to a lack of published real-world local data. To ensure representativeness of the findings, each public healthcare institution offering prostate biopsy had at least one urologist providing input. Additional inputs from radiologists were also sought. As model inputs and assumptions were developed for a starting age of 65 years, its findings cannot readily apply to other starting ages without a separate undertaking to ensure rigor and relevance of model input and assumptions.Fifth, health utility scores were from published literature derived from Australia, Canada, Netherlands, United Kingdom, and USA due to a lack of published local information , 56, 57.To conclude, this study found Strategy 1, Strategy 2, and Strategy 4 – where MRI targeted biopsy was used in biopsy-naïve patients in the initial biopsy setting – to be cost-effective diagnostic options for prostate cancer. The findings are useful to inform decision making in funding different diagnostic options within the Singapore public healthcare system.Additional file 1: Table S1. Distribution of care strategies for localized prostate cancer of various risks.Additional file 2: Table S2. Distribution of treatments for metastatic cancer and castration-resistant prostate cancer.Additional file 3: Table S3. Percentage of the treatment-related complications.Additional file 4: Table S4. Comparison of overall survival of men from published prostate cancer data and current study’s modelled population.Additional file 5: Table S5. Distribution of patients with localised prostate cancer across risk status.Additional file 6: Table S6. Summary of one-way sensitivity analyses parameters and results - Strategy 4 vs Strategy 1.Additional file 7: Fig. S1. ICER tornado diagram for Strategy 4 vs Strategy 1 (full chart).Additional file 8: Table S7. Probabilistic sensitivity analyses of all strategies for base case.


The above article [1] published with two errors in the Discussion section and one error in the Conclusion section. These errors incorrectly noted an increase in change to DNR status among young Black patients when compared to young White patients.On page 6, right-hand column, the sixth full sentence should read “We also observed a decrease in code status changes to DNR among young Black patients compared to young White patients.”On page 9, left-hand column, the first sentence of the second full paragraph should read “We also noted a decrease in the rates of code status change to DNR for young Black patients compared to young White patients, though no difference was noted in the older age groups.”On page 9, right-hand column, the second sentence in the Conclusion section should read “We confirmed prior observations of increased prevalence of hypertension, younger age, and decreased rates of code status change to DNR among Black stroke patients.”The authors apologize for this error.


Club cell heterogeneity was supported by evaluations of SAE tissue sections, brushed SAE cells, and in vitro air–liquid interface cultures. Three subpopulations included: (1) progenitor; (2) proliferating; and (3) effector club cells. The progenitor club cell population expressed high levels of mitochondrial, ribosomal proteins, and KRT5 relative to other club cell populations and included a differentiation branch point leading to mucous cell production. The small proliferating population expressed high levels of cyclins and proliferation markers. The effector club cell cluster expressed genes related to host defense, xenobiotic metabolism, and barrier functions associated with club cell function. Comparison of smokers vs. nonsmokers demonstrated that smoking limited the extent of differentiation of all three subclusters and altered SAM pointed domain-containing Ets transcription factor (SPDEF)-regulated transcription in the effector cell population leading to a change in the location of the branch point for mucous cell production, a potential explanation for the concomitant reduction in effector club cells and increase in mucous cells in smokers. These observations provide insights into both the makeup of human SAE club cell subpopulations and the smoking-induced changes in club cell biology.The club cell, a small airway epithelial (SAE) cell, plays a central role in human lung host defense. We hypothesized that subpopulations of club cells with distinct functions may exist. The SAE of healthy nonsmokers and healthy cigarette smokers were evaluated by single-cell RNA sequencing, and unsupervised clustering revealed subpopulations of SCGCB1A1 The human SAE is composed of multiple cell types, including basal stem/progenitor cells, ciliated cells, club cells, and mucous-producing goblet cells8. Club cells comprise ~20% of the normal human SAE9 and express high levels of SCGB1A1 11. In contrast to the mouse, where club cells are the stem/progenitor cells, basal cells in the human SAE are the stem/progenitors, with club cells derive from the basal cell population18. Assessment of the club cell transcriptome suggests that club cells play a key role in host defense through the expression of genes involved in immune, anti-protease, anti-bacterial and physical barrier function22.The small airway epithelium (SAE) plays a central role in the early events in the pathogenesis of smoking-related lung disorders and is the first site of smoking-induced alterations in the lung29. Subcluster analysis has revealed both differences within cell types in healthy and disease tissues suggesting that analysis of single cell data at higher resolution can give insight into both normal physiological functions as well as pathophysiological responses. A common environmental challenge in the human airway is exposure to cigarette smoke. Smoking causes changes in SAE basal cell differentiation with increased goblet cell numbers and decreased club and ciliated cells30. In order to determine whether club cells in the small airway constitute a homogenous or heterogenous population and to determine whether cigarette smoking caused changes to one or more club cell subtypes, SAE of normal nonsmoker and healthy smoker lungs were sampled by bronchoscopy and brushing, and single cell transcriptomics was utilized to identify and characterize heterogeneity in the SCGB1A1+ club cell population. Based on the knowledge that club cells have a defense function, this analysis was focused on defining the biology of club cells in the healthy SAE and then understanding how this biology may be altered by the stress of cigarette smoking. Using a previously published dataset31, subclustering of combined nonsmoker and smoker club cell populations led to the identification of three distinct, unique club cell subpopulations . The progenitor population was identified as a branch point leading to further differentiation of both the effector population and mucous cells. Overall differentiation of the club cell lineage was reduced in smokers, leading to a change in the branch point for mucous cell production. Finally, the proportion of the differentiated club cells of the effector subpopulation was decreased and the proportion of mucous cells was increased with cigarette smoking, a finding that was linked to disrupted SAM pointed domain-containing Ets transcription factor (SPDEF) signaling in the effector cell population. Ex vivo and in vitro analyses were consistent with this finding. Furthermore, the number of proliferating club cells was increased in cigarette smokers. The presence of club cell heterogeneity suggests that specific pharmacologic targeting could be possible, leading to direct therapies to increase effector club cell numbers and overall club cell function in cigarette smokers.The advent of single cell transcriptomic analysis has enabled investigators to query populations of cells derived from tissues to identify subclusters of cell populations that were previously thought to be homogenous. For example, recent studies have identified distinct subclusters of airway basal and ciliated cells and alveolar type II cells based on transcriptomic profiling31. Using unsupervised clustering analysis, club cells were identified as cells with high expression of SCGB1A1 (expressed in both intermediate and secretory cells), low expression of KRT5 , and the absence of MUC5AC . However, for all other clusters, the pseudotime analysis indicated that cells in smoker epithelia lagged significantly behind nonsmoker epithelia in terms of differentiation. The most significant differences, based on p values comparing nonsmoker vs. smoker differentiation were observed in club cell subclusters 1 and 3. Of interest, nonsmoker club cell subclusters 1 and 3 clearly occupied distinct differentiation states (p < 10−25) while the difference between smoker club cell subclusters 1 and 3 barely reached significance (p < 10−2), further suggesting that the shift in the Slingshot pseudotime trajectory was due to a failure of club cell progenitors to completely differentiate into effector club cells, potentially leaving them with the stem properties of the club cell subcluster 1 cells and depleting detectable levels of club cells.To gain further insight into the shift in the branch point for mucous cell differentiation, an analysis of pseudotime value driven by slingshot analysis was conducted examining the club cell and mucous cell lineages separately Fig. . When co33 yielded significant smoking-induced changes in six genes nes Fig. . SPDEF-u+ and SCGB1A1− cells. By day 28, while proportions of MUC5B and SCGB1A1 varied, all MUC5B cells had some level of SCGB1A1 expression. In contrast, cells that were SCGB1A1+ and MUC5B− were clearly evident at day 28, suggesting that a population of SCGB1A1+ and MUC5B− cells had emerged  culture over 28 days. During this differentiation period, basal cells developed from a monolayer to a differentiated epithelium consisting of multiple cell types based on morphology and ciliation Fig. . An earlged Fig. . SCGB3A1ged Fig. . Taqman ged Fig. .Fig. 4He35. Pathway analysis identified that club cell subcluster 1 was enriched in metabolic processes and ribosome biosynthesis, with minimal transcripts related to defense signaling and endocytic pathways, suggesting a metabolically active club cell  is an SAE club cell-deficient statezed Fig. , there wzed Fig. . FurtherTo examine the effects of smoking on each club cell subset, differentially expressed genes were analyzed between nonsmokers and smokers Table . This an49 , could be indicative of retained stemness and a potential for multi-potency. Further studies focusing on the club cell subset isolation and in vitro differentiation potential will help to understand the enrichment of metabolic processes on the stem/progenitor function of the precursor club cells.One possible function is as a progenitor cell. In this regard, the club cell subset 1 expressed genes associated with high metabolic activity. The identification of this subcluster as stem-progenitor-related was initially spurred by the relatively high expression of KRT5, a basal cell marker, among the cells of this subcluster. At lower resolution, KRT5 was observed to exhibit a progressively lower expression from basal cells to intermediate cells to club cells to mucous cells, but in the context of the analysis of club cell subclusters, the CC1 progenitor cluster has a higher level of KRT5 expression than the intermediate cell cluster that precedes it in the pseudotime trajectory. The cells in the CC1 progenitor club cell cluster were initially placed into the club cell cluster based on their overall gene expression, making them authentic club cells, but the subcluster analysis was able to find this group of authentic club cells that had distinguishing characteristics such as high KRT5 expression suggesting a stem-progenitor function. Consistent with the role of progenitor club cells having pluripotency, the Slingshot pseudotime trajectory analysis identified this cluster as the branch point for further differentiation of both progenitor club cells and mucous cells. Progenitor club cells have a significant overlap with metabolically active cell signatures with enrichment in genes such as ALDOA, LDHB, ENO1, COX7B and C, COX8A, ATP5G, and ALDH3A266. Ruiz Garcia et al. reported co-localization of KRT5 and KRT7 in the nasal epithelium in cells that clearly had a columnar morphology, consistent with the characteristics of progenitor CC1 cells described here67.An examination of recent literature relating to epithelial cell differentiation concludes that human club cells, in addition to their distinct role in host defense, are also capable of further differentiation along both the secretory and ciliated cell pathways36, subset 3 of club cells likely is focused on fulfilling the well-established role of club cells in host defense. Our analysis found that these effector club cells represent nearly 2/3 of the club cell population, highlighting the insight from single cell sequencing and subclustering analysis in order to separate out unique functions within a single cell type.Effector club cells, unlike the proliferating and progenitor club cells, display marked enrichment in MUC5B, PIGR, CXCL1, CXCL6, LCN2, MUC1, BPIFB1, and CXCL17 anti-inflammatory and immune-related mRNA transcripts. Enriched in the majority of previously shown club cell transcripts68. The data in the present study of club cell subset 2 is consistent with this data and shows that these club cells are a small, distinct portion of the population with unique identifying gene signatures. Furthermore, the data suggests that these cells are not the least differentiated of the club cell subset. The increase of this subtype in smokers leads to a possibility that these cells may be responding to unique environmental cues. This provides more evidence of club cells as a non-terminal cell state and having the potential to maintain homeostasis in the airway.The phenomenon of proliferating club cells was observed by Boers et al.67 examined human airway epithelia differentiated on ALI and found evidence for three club cell subsets. One of the three subsets (their CC2) shared properties with the effector club cell cluster in this report. An alignment between the other two subsets and clusters CC1 and CC3 here is not as straightforward. Nevertheless, the principle of heterogeneity among club cells is common to both studies.Other studies have reported heterogeneity in human club cell populations. Ruiz Garcia et al.69.The Slingshot pseudotime trajectory analysis gave important insights into the pathophysiology and targets of cigarette smoking. The Slingshot analysis showed that cigarette smoking imposes a global inhibition on club cell differentiation that is evident in intermediate cells and has a dramatic impact on all three club cell subclusters, essentially halting their differentiation early after club cell differentiation. Interestingly, differentiation proceeded far enough so that representatives of all three club cell subclusters were present in all three smoker subjects. The impact of cigarette smoking was further evident in the trajectory analysis indicating that CC3 effector club cells rather than CC1 progenitor club cells had become the branch point for mucous cell differentiation. Consistent with this observation, signaling pathways related to mucous cell-producing SPDEF were upregulated in CC3 effector cells and competing club cell-producing pathways  were downregulated. These findings are also consistent with a recent report that CSE, acting through Notch 3, induces SPDEF signaling and mucous cell differentiation45. Consistent with this concept, our analysis found that smoking reduces club cell numbers, and, importantly, selectively reduces the proportion of effector club cells to less than half of the total number of club cells. Interestingly, only the effector club cell population with defense transcripts appears to be significantly decreased. Furthermore, direct comparison of the remaining smoker effector club cell transcriptome with nonsmokers shows a marked loss of defense-related transcripts, suggesting a potential decrease in defense function as a result of smoking. This includes the known club cell defense genes MUC5B, CXCL1, SCGB3A1, SLPI, and PIGR. For example, PIGR has been shown to be essential for airway defense function and a decrease in PIGR correlated directly with the severity of disease70. The Slingshot pseudotime trajectory analysis suggested that smoking induced a change in the branch point for mucous cell production from CC1 progenitor club cells to CC3 effector club cells. In addition to explaining the increase in mucous cells, this change would help to explain the depletion in effector club. Analysis of the SPDEF signaling pathway provides one potential set of targets to maintain CC3 effector club cell production in the airway.Identification of club cell clusters is important to our understanding of the response of the airway environmental and/or genetic pathophysiology. Cigarette smoking is the leading cause of COPD, a disease characterized by a decreased proportion of club cells in the SAE36.Subjects  were recruited under a protocol approved by Weill Cornell Medical College Institutional Review Board and provided written informed consent. See Supplementary Table 71. Sequence from cDNA libraries was obtained using Illumina HiSeq 2500 . Quality control data for the dataset are provided in Supplementary Table A single cell suspension of living cells from SAE samples was created by trypsinization, followed by selection of single living cells via flow cytometry with negative selection of dead cells using 4′,6-diamidino-2-phenylindole (DAPI). Live single cells were analyzed in the Weill Cornell Genomics Core Facility by the Drop-seq method72. Raw digital expression matrices containing integer counts of number of transcripts for each gene in each cell were generated separately for each sequence using the McCarroll lab protocol73. Data was filtered based on the following criteria: (1) all genes evaluated were expressed in ≥10 cells and only cells with a minimum of 200 detected genes were retained for analysis; (2) cells expressing >10,000 or <200 unique genes were removed; and (3) cells with >25% mitochondrial genes were removed. Data was further processed and cell clusters identified as described by Zuo et al.36. Briefly, the clustering algorithm, K-nearest neighbor graph based on the Euclidean distance in principal component analysis (PCA) space was applied to group cells together iteratively leading to cells most similar to each other being clustered together. Eleven distinct clusters were identified and signature genes differentially expressed in each cluster were used to identify cluster cell types36.A clustering algorithm for the six SAE samples was performed using Seurat, a developed R package for single cell analysis+KRT5loMUC5AC‾ cells. Dimensional reduction PCA on variably expressed genes was applied. The clustering algorithm, K-nearest neighbor graph based in PCA space approach implemented in “FindClusters” function in Seurat package, was applied to find clusters in club cells yielding three unique club cell populations. To determine the marker genes for these subclusters, cells from each subcluster were compared to all other club cells using the Seurat “FindMarkers” function. Marker genes were required to have an average expression in the subcluster 0.25 log fold higher than the average expression in other club cell subclusters and were required to be expressed in ≥10% of cells in that subcluster. A Bonferroni correction was used to adjust p values. For visualization purposes, club cell subclusters were presented in tSNE plots.Club cells, defined as cells with high SCGB1A1 expression but low levels of KRT5 and MUC5AC expression [<1.2 unique molecular identifiers (UMI)] were analyzed separately from the total SAE. There were 1203 variably expressed genes in the club cell population of SCGCB1A174. Distance moved along the trajectory was determined, and pseudotime values were extracted using the slingPseudotime function in the Slingshot package. The pseudotime values were plotted based on a violin plot in R.Single cell pseudotime trajectories were constructed with Slingshot. Slingshot uses preexisting clustering results to infer lineage tracing based on minimal spanning tree method75. Gene marker criteria were: expression levels differed by at least 0.25 log-fold between the subject cluster and all comparison clusters; and the marker was expressed in ≥10% of cells the subcluster. Genes expressed below an average UMI of 1.2 were removed from analysis. Adjusted p values were calculated using a Bonferroni correction based on the total number of genes in the dataset. The generally applicable gene-set enrichment for pathway analysis (GAGE) package for R Version 2.36.0 was used for gene set enrichment analyses35.To identify signature genes in each subcluster, the expression of genes from each cluster were compared to the expression of genes from all cells of remaining clusters using the Seurat “FindAllMarkers” function, and markers were identified by the Wilcoxon rank sum test for single cell gene expressionp value (Bonferroni correction) was <0.05 and log fold change was >0.25 for genes expressed at least in 10% of cells.To identify differentially expressed genes (DEGs) between smokers and nonsmokers, each cell type was evaluated independently and used Wilcoxon rank sum test as implemented in the Seurat FindMarkers. Genes were considered differentially expressed if the adjusted 36 with the following modifications. Basal cells were expanded in PneumaCult ExPlus Basal medium  on polystyrene T75 flasks  previously coated with a solution of 0.3 mg/mL collagen type IV from human placenta  for 1 h at 37 °C, washed with sterile PBS, and dried. To create a differentiated airway epithelial cell culture grown on ALI, basal cells were isolated using 0.05% trypsin  and seeded at a density of 105 basal cells on collagen IV-coated Costar Transwell inserts  with ExPlus complete growth medium in both the apical (0.1 mL) and basolateral (0.5 mL) chambers (ALI day −2). Cells were expanded for 2 days and then ExPlus complete growth medium was replaced with PneumaCult ALI-S maintenance medium (StemCell Technologies) in the lower chamber only with the apical chamber exposed to air (ALI day 0). Basal cells were allowed to differentiate in ALI culture with media freshly prepared and changed every 2 days. At day 14 through day 28, apical surfaces were washed with PBS  once per week. Samples were collected at days 0, 7, 14, and 28 for analysis of mRNA and protein expression. A limited supply of the primary basal cells used for these studies have been retained and may be made available to qualified scientists by contacting the corresponding author.Basal cells collected from the small airway by brushing as described above were expanded on type IV collagen and frozen as previously describedCells from three ALI transwells per time point were rinsed with PBS and collected in 1 mL of Trizol (ThermoFisher Scientific). Chloroform was utilized to precipitate the RNA and RNeasy kits  were used to elute RNA. Reverse transcriptase reactions were performed using Applied Biosystems TaqMan Reverse Transcription Reagents kit  with 1 μg of RNA in 50 μL reaction. To assess the timing of expression of club cell-specific markers, the following probes (ThermoFisher Scientific) were utilized, including: 18S RNA (4310893E), MUC5B (Hs00861588_m1), SCGB1A1 (Hs00171092_m1), SCGB3A1 (Hs00369360_g1), LYZ (Hs00426232_m1), PIGR (Hs00922561_m1) and SLPI (Hs00268204_m1). All data was normalized to 18S RNA.Paraffin-embedded normal human lung sections were obtained from BioMax  and ALI in vitro sections were embedded and sectioned by Histoserv . Immunofluorescence staining was performed on paraffin-embedded cross-sections and ALI sections. Freshly brushed SAE cells were fixed with 4% paraformaldehyde and permeabilized with 0.01% Triton X-100. All samples were blocked with 10% donkey serum prior to staining. The following primary antibodies were applied to samples overnight at 4 °C: rabbit polyclonal anti-human MUC5B ; rabbit polyclonal anti-human PIGR ; rabbit polycloncal anti-human SLPI ; mouse monoclonal anti-human SCGB3A1 ; and rat monoclonal anti-human SCGB1A1 . Isotype matched non-specific IgG-negative controls included: rat IgG1 (R&D Systems), rabbit IgG, and mouse IgG (ThermoFisher Scientific). To visualize the antibody binding, the following secondary antibodies were used: Alexa Fluor 555 donkey anti-Mouse IgG , Alexa Fluor 488 donkey anti-rat IgG , and Alexa Fluor 647 donkey anti-rabbit IgG , all highly cross-adsorbed by the manufacturer and used at a 1:500 dilution from the stock concentration. The cells were counterstained with DAPI to identify cell nuclei and mounted using ProLong Gold antifade reagent .https://imagej.net/Fiji). For quantitative assessment, images were acquired using fixed exposure settings optimized for each channel in a single data acquisition session. Prior to quantitation, a threshold was applied to images from each channel to create a minimum signal level constituting a positive signal. One or more marker channels were overlaid and combined with the DAPI channel to provide a count of total cells. The incidence of co-localization was scored by a blinded observer according to the presence of a positive pixel value in each channel in the space morphologically associated with a particular nucleus. Five fields from each of the three preparations were scored per condition. A minimum of one hundred cells per phenotype were assayed per immunofluorescence condition.Immunofluorescence microscopy was performed using a Zeiss Axioplan microscope with a ×40 objective and captured with a Zeiss high-resolution monochrome camera. Images of lung cross sections were obtained with a Zeiss 880 confocal microscope using ×40 and ×60 objectives with oil immersion. Images were processed using Zen  and ImageJ software . Two-tailed Student’s Further information on research design is available in the Reporting SummarySupplementary Information