Primary bone lymphoma (PBL) is a rare neoplasm of malignant lymphoid cells presenting with one or more bone lesions without nodal or other extranodal involvement. It accounts for approximately 1% of all lymphomas and 7% of malignant primary bone tumors. Diffuse large B-cell lymphoma (DLBCL), not otherwise specified (NOS) represents the predominant histological type and constitutes over 80% of all cases. PBL may occur at all ages with a typical diagnosis age of 45–60 years and a slight male predominance. Local bone pain, soft tissue edema, palpable mass and pathological fracture are the most common clinical features. Diagnosis of the disease, which is frequently delayed due to its non-specific clinical presentation, is based on the combination of clinical examination and imaging studies and confirmed by combined histopathological and immunohistochemical examination. PBL can develop in any part of the skeleton, although it occurs most commonly in the femur, humerus, tibia, spine and pelvis. The imaging appearance of PBL is highly variable and unspecific. In terms of the cell-of-origin, most cases of primary bone DLBCL (PB-DLBCL), NOS belong to the germinal center B-cell-like subtype and specifically originate from germinal center centrocytes. PB-DLBCL, NOS has been considered a distinct clinical entity based on its particular prognosis, histogenesis, gene expression and mutational profile and miRNA signature. PBL carries a favorable prognosis, especially when treated with combined chemoradiotherapy. Primary bone lymphoma (PBL) is currently defined as a neoplasm composed of malignant lymphoid cells that presents with one or more bone lesions without nodal involvement or other extranodal lesions, according to the 2020 World Health Organization (WHO) classification of soft tissue and bone tumors ,2. The mPBL is a rare neoplastic disease and accounts for approximately 1% of all lymphomas, 3–7% of extranodal lymphomas and 7% of malignant primary bone tumors ,10,11,12The clinical features of PBL are generally unspecific, frequently leading to a delay in diagnosis, and, thus, a high index of suspicion is required ,18. The PBL can develop in any part of the skeleton, although is most common in the femur, humerus, tibia, spine and pelvis ,22,24,31Although PBL diagnosis is based on histopathological examination requiring bone biopsy, imaging studies are crucial for the initial depiction, biopsy guidance, lesion extent determination and staging, as well as restaging and treatment response monitoring . In addiPlain X-ray examination is the initial diagnostic modality of choice in patients with suspected PBL . HoweverComputed tomography (CT) plays a significant role in the management of PBL since it can effectively depict soft tissue extension, bone marrow involvement and cortical disruption. It is the primary modality for radiologically guided biopsy, which is essential for the definite diagnosis of PBL . In addiMagnetic resonance imaging (MRI) is vital for the imaging investigation of PBL. It is the modality of choice for the early detection of PBL and the depiction of its soft tissue extension and bone marrow involvement, as it can accurately demonstrate cortical erosion. MRI can also be used to evaluate the outcome of treatment and therapy-related complications, such as epidural lipomatosis, insufficiency fractures and myeloid depletion. However, MRI presents low specificity in the restaging of PBL .MRI findings of PBL can be “protean” and even resemble benign entities . HoweverOf particular interest is the fact that PBL is characterized by bone marrow changes and surrounding soft tissue mass presence withοut significant cortical destruction ,24,33. TThe role of ultrasound examination in cases of PBL is limited. It can often depict a soft tissue mass associated with PBL and also demonstrate the typical imaging pattern of bone cortex preservation despite the extraosseous extension of the lesion . Ultrasomax) [18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) scan is a functional imaging tool capable of detecting viable neoplastic tissue due to its hypermetabolic nature and, thus, its high FDG uptake. PET scans are frequently combined with CT imaging, and recently MRI, allowing for a correlation between functional and anatomical information and, therefore, more accurate localization of metabolic abnormalities . To semimax) ,24.max of 2.5 as a cutoff to distinguish the residual from a metabolically inactive disease achieves a negative prognostic value and sensitivity of 100% to identify residual lymphoma. However, a high false positive rate has been reported, with post-therapeutic osteonecrosis proposed as the main potential cause [FDG PET/CT is the modality of choice for the exclusion of secondary bone lymphoma and for the staging, restaging, follow-up and treatment response assessment of PBL . If PET/al cause . In addial cause . The maial cause . Indeed,al cause  and Wangal cause  reportedMore recently, PET/MRI has become available with encouraging results and offers the opportunity to benefit from the additional information provided by special diagnostic MRI techniques, particularly diffusion-weighted imaging and magnetic resonance spectroscopy . PET/MRISkeletal scintigraphy is one of the standard imaging tools used to investigate tumors involving the musculoskeletal system . HoweverThe radiological appearance of PBL can mimic numerous diseases. In young patients, the differential diagnosis of PBL includes metastatic neuroblastoma, Langerhans’ cell histiocytosis, Ewing’s sarcoma, osteomyelitis and leukemia. In older teenagers and young adults, osteosarcoma should also be considered. In older adults, bone metastases and multiple myeloma are much more frequent than PBL. However, a permeative or moth-eaten pattern of destruction and periosteal reaction in the conditions above are not as common as in PBL and, thus, may raise the suspicion of the latter in this age group . AdditioIn conclusion, the imaging presentation of PBL varies and can be non-specific in some cases ,15. HoweThe histological and immunohistochemical appearance of PBL has been reported in a number of studies ,75,76,77The definite diagnosis of PBL is histological with immunohistochemical examination and, therefore, clinical and radiological suspicion of PBL should be further evaluated by histopathology via bone biopsy ,8,9,22. NHL accounts for the vast majority of PBLs with DLBCL, NOS being the predominant histological type, constituting approximately 80% of cases ,72,75,76The majority of PB-DLBCLs, NOS exhibit a diffuse growth pattern  8,18,72,7218,72.Neoplastic cells in PB-DLBCL, NOS are immunohistochemically positive for B-cell markers, including cluster of differentiation (CD) 19, CD20, leukocyte common antigen (LCA/CD45), CD79a and PAX5, and negative for T-cell markers, such as CD3 and CD5, which can, nevertheless, highlight small T-cells in the background ,26,27,72Lima et al.  utilizedHsieh et al.  describePBL of T-cell origin is extremely rare and more common in Japan and Taiwan than in the West ,16. MatuALCL is an uncommon disease, accounting for less than 5% of all NHL cases. Its presentation with isolated osseous involvement is rare . This neJadidi et al.  reportedPTCL, NOS represents approximately 30–50% of mature T-cell and NK-cell lymphomas . Yu et aMetagenomic next-generation sequencing (mNGS) is a technique that unbiasedly sequences microbial and human nucleic acids from a variety of clinical samples. Therefore, it can detect not only the presence of microorganisms, but also chromosomal copy number variations (CNVs), which can be indicative of a malignancy. Although biopsy and histopathological examination is undoubtedly the gold standard for neoplasm diagnosis, it is an invasive and time-consuming procedure that is not always performed, especially when the presence of a tumor is not primarily considered. mNGS, on the other hand, is a non-invasive and fast technique that can set the suspicion of a malignancy and lead to more focused diagnostic testing for a neoplasm . Indeed,Although the etiology of PBL remains unclear, several factors have been incriminated for its development. It has been reported that PBL might be related to other bone disorders , immunological disorders , viral agents, environmental factors and immunosuppression following transplantation or accompanying acquired immunodeficiency syndrome (AIDS) ,37,80. CDLBCL, NOS has been shown to be morphologically, genetically and clinically heterogenous by numerous studies ,83,84. HGene expression algorithms are used to subdivide DLBCL, NOS into germinal center B-cell-like (GCB), activated B-cell-like (ABC) and an unclassified or type-III group . The GCBImmunohistochemistry algorithms are widely used in routine clinical practice to predict the COO of DLBCL, NOS, although they present concordance issues with gene expression profiling and do not recognize the type-III gene expression category . The HanInterestingly, Dybkær et al.  proposedRegarding PB-DLBCL, NOS specifically, studies have shown that most cases belong to the GCB subtype . In two According to de Groot et al. , anatomiDe Groen et al.  studied As already mentioned, PBL is a heterogenous and uncommon disease, hindering the conduction of randomized clinical trials. Therefore, there is no standard treatment for this condition and the recommended strategies are derived from retrospective studies. Treatment options include chemotherapy or immunochemotherapy, radiotherapy and surgery ,10. PainThe role of surgery in the management of PBL is generally limited. Due to the progress of chemotherapy and radiotherapy, most surgical procedures have been performed for diagnostic purposes, namely biopsy ,9,10,22.Treatment of PBL is based on systemic therapy and the current modalities include chemotherapy or immunochemotherapy with or without radiotherapy, resulting in a 5-year overall survival (OS) of approximately 70% ,20,21,23Radiotherapy was once the standard treatment of PBL with appropriate local disease control, but produced disappointing relapse rates, which led to the introduction of chemotherapy for the management of this neoplasm ,22,23. IPBL presents a generally favorable prognosis ,70,92,99Advanced age is an important, unfavorable predictor of survival in patients with PBL ,101,102.PBL is an uncommon malignancy and DLBCL, NOS represents the predominant histological type. Diagnosis of PBL is frequently delayed and requires combined histopathological and immunohistochemical examination. Although CT is the primary examination for radiologically guided biopsy, MRI is the standard method for early detection, while FDG PET/CT is recognized as the modality of choice for the staging, restaging, follow-up and treatment response assessment of PBL and for the exclusion of secondary bone involvement of systemic lymphomas. PBL generally conveys a favorable prognosis, especially when treated with combined chemoradiotherapy. Most PB-DLBCLs, NOS belong to the GCB subtype and specifically originate from GC centrocytes. Primary bone large B-cell lymphoma is currently included among DLBCLs, NOS; however, it could possibly represent a distinct entity within the category of large B-cell lymphomas due to its unique characteristics, namely its specific prognosis, histogenesis, GEP, mutational profile and miRNA signature.


The most common in vitro model for absorption, distribution, metabolism, and excretion (ADME) purposes is currently the Caco-2 cell line. However, clear differences in gene and protein expression towards the small intestine and an, at best, fair prediction accuracy of intestinal drug absorption restrict the usefulness of a model for intestinal epithelial cells. To overcome these limitations, we evaluated a panel of low-passaged patient-derived colorectal cancer cell lines of the HROC collection concerning similarities to small intestinal epithelial cells and their potential to predict intestinal drug absorption. After initial screening of a larger panel, ten cell lines with confluent outgrowth and long-lasting barrier-forming potential were further characterized in close detail. Tight junctional complexes and microvilli structures were detected in all lines, anda higher degree of differentiation was observed in 5/10 cell lines. All lines expressed multiple transporter molecules, with the expression levels in three lines being close to those of small intestinal epithelial cells. Compared with the Caco-2 model, three HROC lines demonstrated both higher similarity to jejunal epithelial tissue cells and higher regulatory potential of relevant drug transporters. In summary, these lines would be better-suited human small intestinal epithelium models for basic and translational research, especially for ADME studies. The most common route for drug administration is the oral route . Within The small intestinal epithelium is crucial for the absorption of nutrients and drugs and protects the gut against harmful xenobiotics by forming a tight barrier. Differentiated cell types mediate these functions: absorptive (enterocytes), enteroendocrine (EECs), mucosecreting , and Paneth cells (PCs) , with enIn a previous study, we described a panel of ten 2D colorectal cancer cell lines with long-lasting barrier integrity due to their basal activity and the induction potential of Cytochrome P450 (CYP) 3A4. Of those lines, two candidates with higher CYP3A4 induction potentials than Caco-2 cells were identified . The aimRifampicin (RIF) was obtained from Carl Roth , and vitamin D3 (VD3) was obtained from Hycultec . Stock solutions of RIF (121.5 mM) and VD3 (1 mM) were prepared in DMSO and ethanol, respectively. The final concentration of DMSO was 0.1%.All ten HROC  cell lines were obtained from the HROC collection . The preCaco-2 cells were purchased from CLS . Cell culture was performed as previously described .For functional studies, cells were treated with 20 µM RIF and 100 nM VD3, respectively. Controls were treated with 0.1% DMSO or ethanol. Cells were harvested after incubation for 24 h, 48 h, and 72 h.5 and were grown until reaching 60–70% confluence and ten-day post-confluence, respectively. Cells were scraped and centrifuged at 4 °C at 1200 rpm for 7 min, shock-frozen with liquid nitrogen, and stored at −20 °C for further analysis.For gene expression analysis and protein quantification, cells were seeded in 6-well plates  at a density of 5 × 10−∆∆Ct method. For culture time-dependent gene expression analysis, three normal epithelium biopsies from human small intestine (obtained from the BioBank Rostock) were pooled and included as reference.Total RNA was isolated using Universal RNA Purification Kit  and following the manufacturer’s protocol. RNA quantity was assessed by using NanoDrop 1000 spectrophotometer and ND-1000 spectrophotometer V3.7.1 software . In total, 1000 ng of RNA was reverse transcribed into cDNA using a reverse transcriptase . Quantitative real-time PCR (qRT-PCR) was performed using the Sibir Rox Hot Mastermix (Bioron) and 10 µM each of the sense and antisense primers according to the manufacturer’s protocol . Samples4 cells per well) were seeded and grown until reaching confluence. HROC lines differed in growth rates [® Enhanced Darkfield microscope system  with a 60x oil objective and Ocular Software V2.0  was used. Contrast adjustment and image overlay was performed using Adobe Photoshop CS5 .Thirteen-millimeter-diameter cover slips (Carl Roth) were sterilized with absolute ethanol and placed in 24-well plates (Sarstedt). Cells (10th rates . DespiteProtein abundance of peptide transporter 1 (PEPT1), organic anion-transporting polypeptide 2B1 (OATP2B1), permeability glycoprotein (P-gp), and breast cancer resistance protein (BCRP) were determined using a validated LC-MS/MS-based targeted proteomics method ,15,16.m/v, Promega, Mannheim, Germany) and incubated for 20 min at 60 °C (denaturation). After cooling down, 10 µL of iodoacetamide  was added and the samples were incubated in a darkened water quench for 15 min at 37 °C . For protein digestion, 10 µL of trypsin  was added, and the samples were incubated in a water quench for 16 h at 37 °C. Digestion was stopped following the addition of 20 µL of formic acid . All samples were stored at −80 °C until further processing. The samples were centrifuged one more time for 15 min at 16,000× g and 4 °C. Finally, 50 µL of the digested membrane fraction was mixed with 50 µL of isotope-labeled internal standard (IS) peptide mix (10 nmol/L of each IS). For preparation of the calibration curves, digested human serum albumin (2 mg/mL) was used as a blank matrix and spiked with reference peptides to reach 0.1–25 nmol/L and with IS . The following proteospecific peptides were used: P-gp, AGAVAEEVLAAIR; BCRP, SSLLDVLAAR; OATP2B1, SSPAVEQQLLVSGPGK; and PEPT1, TLPVFPK. All peptides (purity > 97–99%) were from Thermo Fisher Scientific.Cells were lysed, and the membrane protein fraction was extracted using the ProteoExtract Native Membrane Protein Extraction kit  according to the manufacturer’s protocol. The obtained membrane fraction was subjected to determination of the whole protein concentrations using the bicinchoninic acid assay (Thermo Fisher Scientific) and was adjusted to a maximum protein concentration of 2 mg/mL. Subsequently, 100 µL of each membrane fraction were mixed with 10 µL of dithiothreitol , 40 µL of ammonium bicarbonate buffer , and 10 µL of ProteaseMAX™ . Protein quantification was conducted on a 5500 QTRAP triple quadrupole mass spectrometer  coupled to an Agilent Technologies 1260 Infinity system .For each peptide, 3–4 mass transitions were monitored; the final protein abundance data (picomoles/mg protein) were calculated by normalization to the total protein content of the isolated membrane fraction.4 cells per well in 24-well plates (Sarstedt). When reaching confluence, cells were washed with PBS containing Ca2+ and Mg2+ on the cover slips and then were fixed with a solution containing 2% glutaraldehyde and 1% paraformaldehyde in 0.1 M sodium phosphate buffer at pH 7.3 and stored at 4 °C. Further processing for SEM started with two washes in 0,1 M PBS followed by an ascending series of ethanol in distilled water  prior to drying using CO2 in an Emitech K850 critical point dryer . Surface conductivity was established by sputter coating the specimens with a gold layer of approximately 10–15 nm thickness in a Leica SCD 500 sputter coater . Cell surfaces were examined in a field emission scanning electron microscope  using an acceleration voltage of 5 kV at a working distance of 5–6 mm with a high efficiency secondary electron detector for digital image acquisition (image size of 1024 × 756 pixels).SEM was used to examine cell morphology and the extent of villous structures present in confluent HROC and Caco-2 cell monolayers. Cells were seeded on sterilized 13 mm diameter glass cover slips  at a density of 10t-test was used. Spearman’s rank correlation was computed for gene expression data and protein levels. GraphPad Prism software 9.0.0  was used for calculation.For gene expression analysis, QuantStudio™ Real-time PCR software 1.3 (Thermo Fisher Scientific) was used. For statistical analysis between treated and untreated groups and time to confluent growth on alterations of gene expression and protein levels, the paired two tailed Student’s The effects of the ten-day post-confluence (d10) state on intestine-specific gene expression are shown in p = 0.018) and GLUT-2 mRNA , OATP2B1 , and UGT1A6 . Further, a significant correlation was found for VDR and GLUT-2 mRNAs  at d10 post-confluence. In summary, significant correlations between the expression of selected transporters and NRs were more abundant with an increase in culture time.A key aspect of novel human intestinal in vitro models’ characterization was the detection of relevant transporter and nuclear receptor expression. A correlation analysis was performed to examine the simultaneous expression of relevant DTs and NRs to deliver evidence for functional gene–gene interaction networks active in these lines. PXR mRNA expression significantly correlated with VDR mRNA . Referred to human jejunal tissue, higher protein levels of PEPT1 were detected in HROC217 T1 M2 (3.1-fold), HROC60 (2.9-fold), HROC80 T1 M1 (2.9-fold), and HROC32 (2.8-fold) but were lowest in Caco-2 (0.1-fold). PEPT1 protein levels most comparable with human jejunum were found in HROC159 T2 M4 (1-fold), HROC383 (1-fold), HROC126 (0.8-fold), and HROC43 (0.6-fold) in the d10 post-confluence state.Protein levels of PEPT1, OATP2B1, P-gp, and BCRP in the basal and d10 post-confluence state are shown in OATP2B1, P-gp, and BCRP protein levels varied between both groups and were not significantly increased with prolonged confluence. Higher OATP2B1 protein levels compared with that in the jejunum were detected in Caco-2 (9-fold), HROC80 T1 M1 (3-fold), HROC32 (1.9-fold), and HROC159 T2 M4 (1.8-fold), while protein levels most similar to those of jejunum were found for HROC43 (1.2-fold), HROC183 T0 M2 (1.4-fold), and HROC239 T0 M1 (0.5-fold) in the d10 post-confluence state.The P-gp protein had higher abundance in HROC159 T2 M4 (4.2-fold), HROC126 (3.2-fold), HROC183 T0 M2 (1.9-fold), and HROC80 T1 M1 (1.8-fold) compared with the levels in the basal state, with protein levels most similar to those of the jejunum in HROC239 T0 M1 (0.7-fold), HROC217 T1 M2 (0.4-fold), HROC43 (1.8-fold), and HROC80 T1 M1 (1.8-fold). The lowest protein levels were detected in Caco-2 (0.3-fold) and HROC32 (0.2-fold). No expression was found for HROC60 and HROC383 in the d10 post-confluence state.The protein abundance of BCRP was highest in HROC43 (2.1-fold), HROC183 T0 M2 (0.2-fold), and Caco-2 (0.2-fold) and was absent in the remaining lines at d10 post-confluence. However, the BCRP protein levels were closer to the jejunal protein levels in the basal state in HROC217 T1 M2 (0.5-fold), HROC383 (0.5-fold), and HROC239 T0 M1 (0.2-fold) compared with post-confluence.In summary, the protein levels of PEPT1, OATP2B1, P-gp, and BCRP were higher in HROC43, HROC183 T0 M2, HROC217 T1 M2, and HROC239 T0 M1 compared with those in Caco-2, with protein levels closest to those of human jejunal tissue in HROC43. In total, HROC43 was the single cell line demonstrating sufficient human jejunal-like protein levels of PEPT1, OATP2B1, P-gp, and BCRP. The CYP3A4 and PXR proteins were absent in all lines.The effects of RIF and VD3 treatment on transporter expression are shown in RIF and VD3 treatment led to higher regulation of CYP3A4 activity and P-gp mRNA in HROC183 T0 M2 and HROC217 T1 M2 compared with that in Caco-2. HROC60 showed comparable VDR-mediated induction of PXR and UGT1A6 expression and stronger P-gp and OATP2B1 expression than Caco-2. The highest PXR-mediated regulation of CYP3A4 activity as well as the highest OATP2B1, UGT1A6, P-gp, and BCRP expression were found in HROC43. Multiple DTs were downregulated in HROC126 and HROC159 T2 M4. In HROC159 T2 M4, RIF treatment led to downregulation of CYP3A4 activity, as well as UGT1A6, P-gp, and PXR expression, whereas VD3 treatment increased CYP3A4 activity but decreased OATP2B1, UGT1A6, P-gp, BCRP, PXR, and VDR expression.VDR mRNA was increased in HROC32 (2.4-fold change at 48 h), HROC43 (2.1-fold change at 72 h), HROC60 (2.6-fold change at 72 h), and HROC239 T0 M1 (2.1-fold change at 24 h) with VD3 at levels comparable with those of Caco-2 (3.2-fold change at 72 h). VD3-mediated induction of PXR mRNA was highest in HROC43 (3.9-fold change at 72 h) and was further observed for HROC239 T0 M1 (3.5-fold change at 72 h), Caco-2 (3-fold change at 72 h), HROC217 T1 M2 (2.3-fold change at 48 h), and HROC383 (1.7-fold change at 72 h).In HROC383 cells, the only significant change was a decrease in VDR mRNA after VD3 treatment (0.6-fold change at 72 h). HROC383 cells appeared to have low sensitivity to RIF and VD3 and were found to be comparable with Caco-2. However, a strong increase in PXR mRNA with RIF was observed in Caco-2 (16.6-fold change at 48 h). Overall, HROC43, HROC60, HROC80 T1 M1, HROC183 T0 M2, and HROC217 T1 M2 showed clearly stronger responses in gene regulation subsequent to RIF and VDR treatment than Caco-2.ZO-1 is essential for proper apical surface assembly and is typically localized on discrete sides of cell–cell contact and within the cytoplasm . ZO-1 waFurther, SEM revealed well-developed cell–cell boundaries evidenced by protruding membrane ridges and alterations in villi decoration at the apical side of confluent HROC monolayers, indicating tight cell apposition. Representative photographs and resolution details are shown for HROC60, HROC159 T2 M4, and Caco-2 cells as a reference in SEM micrographs demonstrated the presence of microvilli on the apical cell surface of all ten HROC cell lines analyzed after growth to cell monolayers . MicroviIntestinal mucus-producing GCs lubricate luminal contents and are essential in the maintenance of intestinal homeostasis . Mucin 2PCs secrete antimicrobial peptides and immunomodulating proteins. They are essential in modulating the microbiome and in maintaining intestinal homeostasis . ImmunosEECs secrete a variety of hormones and regulate digestion, appetite, gut motility, and metabolism . ImmunofIn summary, cell differentiation to specific cell types was strongly pronounced in HROC32, HROC43, HROC60, HROC80 T1 M1, HROC159 T2 M4, HROC183 T0 M2, and HROC217 T1 M2 cells. In contrast, cell differentiation was restricted in HROC126, HROC239 T0 M1, HROC383, and Caco-2 cells.Intestinal drug transport is mediated by the most prominent transporters PEPT1, OATP2B1, BCRP, and P-gp . In addiDifferentiation in secretory IECs  is of functional relevance in maintaining the digestive and barrier functions of the epithelium . Next toImmunostaining for MUC2 revealed cell line-specific differences in fluorescence patterns. Mucin-stream-like patterns in HROC43 differed considerably from that of the other lines. When taking this observation plus the gene expression and protein quantification data into account, this specific line might indeed model the small intestine well, especially jejunal epithelial cells, whereas in particular for HROC60 and HROC159 T2 M4, a prominent intracellular fluorescence pattern was observed. In another human colonic cell line grown to near confluence, MUC2 was both present within the cell layer and was observed to be secreted into the cell medium . MUC2 exEECs comprise approximately 1% of the intestinal epithelium and secrete hormones and peptides to recruit immune cells . DistincWe identified three lines, HROC43, HROC183 T0 M2, and HROC217 T1 M2, which closely resembled in vivo jejunal properties and would thus be very promising intestinal epithelium in vitro models. As monolayer models, these lines differentiated into all relevant IEC types and expressed a variety of DTs, thereby closely resembling normal human intestinal epithelium . NotablyAnother interesting finding of our study was the identification of small intestinal-like gene expression in the rectal cancer cell line HROC239 T0 M1. There, stable OATP2B1 mRNA and protein expression was also observed, which is a little unexpected since OATP2B1 was not detectable in human rectal tissue . Still, In the present study, we clearly expanded the list of well-differentiating lines by identifying three HROC lines able to differentiate into IEC subtypes even under standard culture conditions; the latter might come handy for many researchers in the field. These lines are immortal but low-passaged, limiting culture-induced genetic alterations and sub-clonal varieties, as described for Caco-2 . FurtherOur study has several limitations. First, we focused on drug transport and regulation mechanisms. Therefore, applications of the models included for nutrient absorption or further functional studies were not tested. Second, the number of transporters quantified on the protein level is smaller than the number of genes included in the expression analyses. Because measuring protein contents with LC-MS/MS is technically challenging and prone to variations, we focused on the detection of PEPT1, OATP2B1, P-gp, and BCRP. Third, the influence of RIF and VD3 on the protein levels of transporters was not analyzed. However, the findings of our study are new and are adequate to characterize the similarity of several HROC lines to human jejunum cell features. Thus, our data provide potentially useful information for new approaches and further investigations of transporters in selected lines. Further, the interpretation of microvilli formation in HROC lines is limited to the fact that microvilli structures and density may strongly be affected by factors like culture conditions or fluid environment . In addiThe initial focus of our study was the investigation of enterocyte differentiation in the preselected HROC cell lines. The objective criteria for such differentiation are expression of certain genes and microvilli formation. Since OATP2B1 is present in enterocytes and PXR expression increases in differentiated IECS, the quantitative data may serve as markers indicating enterocyte differentiation ,52. ExceIn conclusion, the results of our study evidenced that compared with Caco-2, the three human CRC cell lines HROC43, HROC183 T0 M2, and HROC217 T1 M2 displayed higher resemblance to jejunal epithelial tissue and higher regulatory potentials of relevant drug transporters. Therefore, these cell lines may be excellent tools for studying ADME, nutrient absorption, and cell differentiation in the intestinal epithelium.


Vipera lebetina obtusa venom, to target the α5β1 integrin, which is a key regulator of GBM cell migration and invasion. This study shows its effect on the adhesion, spreading, migration, and survival of the LBC3, LN18, and LN229 cell lines. The presented results suggest that VLO4 may be a structural platform to design therapeutics for gliomas, which may be used alone or in combination with other integrin blockers such as inhibitors of α9β1 integrin .Integrins play an important role in the development and spread of cancers, including glioblastoma (GBM); therefore, blocking them should be an effective cancer treatment. Our research aims to use the RGD homodimeric disintegrin VLO4, isolated from Vipera lebetina obtusa venom, on the adhesion, spreading, migration, and survival of LBC3, LN18, and LN229 cell lines. This disintegrin, as a potent antagonist for α5β1 integrin, showed pro-adhesive properties for these cell lines, the highest for LN229 and the lowest for LBC3. Glioblastoma cells displayed significant differences in the spreading on the immobilized VLO4 and the natural α5β1 integrin ligand, fibronectin. Solubilized VLO4 showed different cytotoxicity and pro-apoptotic properties among tested cell lines, with the highest against LN18 and none against LN229. Moreover, VLO4 revealed an inhibitory effect on the migration of LBC3 and LN18 cell lines, in contrast to LN229 cells, which were not sensitive to this disintegrin. However, LN229 migration was impaired by VLO5, a disintegrin antagonistic to integrin α9β1, used in combination with VLO4. A possible mechanism of action of VLO4 may be related to the downregulation of α5β1 integrin subunit expression, as revealed by Western blot. VLO4 also inhibited cell proliferation and induced caspase-dependent apoptosis in LBC3 and LN18 cell lines. These results indicate that targeting α5β1 integrin by related VLO4 compounds may be useful in the development of integrin-targeted therapy for glioblastoma.Integrins play a fundamental role in the migration and invasiveness of glioblastoma (GBM) cells, making them suitable targets for innovative cancer therapy. The aim of this study was to evaluate the effect of the RGD homodimeric disintegrin VLO4, isolated from  TERT) promotor, amplification of epidermal growth factor receptor (EGFR) gene, or changes in +7/–10 chromosome copy number  (MTT), gelatin, fibronectin, and type I collagen were provided by Sigma-Aldrich Corp. .Vipera lebetina obtusa  using two steps of reverse-phase high-performance liquid chromatography  × 100, where S is the distance (mm) of the cell edge at 0 h and F is the distance (mm) of the cell edge at 24 h.To measure the effects of disintegrins on cell migration, a wound healing/scratch assay was performed. Cells  with an excitation wavelength of 485 nm and an emission wavelength of 530 nm. The number of adhered cells was calculated from a standard curve prepared from known amounts of cells on the same plate.The Cell adhesion assay was performed according to the procedure described previously . Briefly4/mL) were added in serum-free DMEM. Slides were incubated at 37 °C for 1 h and then fixed with 4% paraformaldehyde. Cells were washed 3× with PBS and permeabilized by incubation with 0.4% Triton X-100 on the ice for 5 min, and mounting buffer containing TRITC-phalloidin and DAPI was added. Slides were analyzed using a fluorescence microscope  with a 40× oil objective.Evaluation of cell spreading area was performed on 8-well glass chamber slides with immobilized fibronectin (10 μg/mL) or VLO4 (10 μg/mL) by overnight incubation at 4 °C in PBS. Slides were blocked with 1% BSA, and cells (5 × 10w/v) in 50 mM Tris-HCl, pH 7.5, 500 mM NaCl, and 0.05% (v/v) Tween 20 (TBS-T) for 1 h at room temperature, membranes were rinsed in TBS-T before incubation with primary antibodies. The membranes were usually cut at a specific height based on the molecular weight marker and separately developed to detect two different proteins in the same samples separated in the same gel or to detect the tested protein and the reference protein (β-actin) simultaneously. After overnight incubation at 4 °C with primary antibodies (1:1000), the secondary antibody (1:2000), conjugated with horseradish peroxidase, was added for 1 h of incubation. The proteins were visualized in the BioSpectrum Imaging System  using Westar Supernova Chemiluminescent Substrate . Densitometric measurement  was used to analyze the expression of proteins relative to β-actin.Cells were lysed in RIPA buffer  with an added cocktail of protease inhibitors (P8340) . The BCA Protein Assay Kit  was used to measure the concentration of total protein in cell lysates. Approximately 20 or 40 µg of protein was resolved in SDS-polyacrylamide gels and transferred onto Immobilon-P Transfer membranes . After blocking with non-fat dried milk (5% Cells were seeded in a glass chamber and cultured in DMEM containing 10% FBS until 80–90% confluency. FITC-VLO4 (1 mg/mL) or FITC-VLO5 (1 mg/mL) was added to the same media, and incubation was performed for 1 h at 37 °C. Cells were fixed with 4% paraformaldehyde by incubation for 1 h at room temperature. Cells were washed 3× with PBS and permeabilized by incubation with 0.4% Triton X-100 on the ice for 5 min. Slides were blocked with 1% BSA in PBS by incubation for 1 h at room temperature, and mounting buffer containing TRITC-phalloidin and DAPI was added. Slides were analyzed using a fluorescence microscope  with a 40× oil objective.2, 5 µM ZnCl2, and 0.02% NaN3 solution and incubated overnight at 37 °C. To develop gels, Coomassie blue R-250 was used. To analyze the activity of MMPs, densitometric measurement was performed .For the determination of MMPs activity, using a sensitive zymography method, cells were incubated in serum-free DMEM because these enzymes also occur in serum and may falsify this assay. The same amount of media proteins were electrophoresed in SDS-polyacrylamide gel (10%) with gelatin (1 mg/mL) as MMPs substrate. Then, the gels were rinsed with 2.5% Triton X-100 in order to remove SDS. To activate MMPs, gels were immersed in 50 mM Tris-HCl, pH 8.0, 5 mM CaClp < 0.05 were considered statistically significant.Statistica 12 software  was used to perform statistical analyses of the data. A one-way ANOVA followed by Tukey’s test was applied, and the results were presented as the mean ± standard deviation (SD). Values of The interaction of VLO4 with three different glioblastoma cell lines was investigated in the cell adhesion assay. Adhesion of LN18, LBC3, and LN229 cell lines to immobilized VLO4 was observed for all three cell lines, although with different potencies a. The moThe next set of cell adhesion assays was devoted to testing the ability of VLO4 to inhibit cell adhesion to immobilized fibronectin, a specific ligand for α5β1 integrin. The weakest interaction with this ECM protein was observed for LBC3 cells b, whereaIn another experiment, we checked whether VLO4 binds specifically to integrin α5β1. The experiment was performed with calcein-labeled LN229 cells, which showed the most efficient binding to disintegrin immobilized on the plate, as shown in The spreading of cells on the surface with immobilized fibronectin and VLO4 is shown in Cell migration was investigated in the wound healing assay. VLO4 at concentrations of 0.05 and 0.1 µM after 24 h of incubation was able to significantly inhibit LBC3 and even more LN18 cell migration, whereas no effect on the LN229 cells was observed a. MoreovThe participation other than α5β1 integrin in the migration of LN229 cells was investigated by targeting α9β1 integrin, expressed only in this line b. This iTreatment of glioblastoma cells with solubilized VLO4 disintegrin resulted in a significant reduction in the expression of the α5 a and β1 In all cell lines, latent and active forms of MMP-2 were detected, while active forms of MMP-9 were detected only in LBC3 and LN18. In LN229, these MMPs formed a high molecular weight complex (130 kDa) a. BlockiThe effect of VLO4 on the cell viability of LN18, LBC3, and LN229 cell lines was assessed in an MTT assay. After 24 h of cell treatment, the inhibitory effect was only observed for LN18 and LBC3 cells and only at higher concentrations . The mosWestern blot analysis revealed a significant increase in the expression of pro-apoptotic Bax in all cell lines treated with VLO4 a. IntereGlioblastoma (CNS WHO grade 4) is still the most difficult tumor to treat ,12,13,14Integrins as cell surface receptors may be an interesting target to repress the development of this pathology, at least as a support for chemo/radiotherapy and surgical intervention. In this study, we focused on blocking α5β1 integrin using RGD homodimeric snake venom disintegrin, VLO4. Using the adhesion assay, we demonstrated the selective binding of VLO4 to α5β1 integrin in three GBM cell lines: LBC3, LN18, and LN229. All these lines interact with VLO4, albeit with different potencies. This interaction was shown in the direct adhesion of the immobilized disintegrin and in competition with the adhesion of the immobilized natural ligand of α5β1 integrin, fibronectin. The results revealed that the LBC3 cell line adheres to the immobilized VLO4 to the lowest extent, and soluble disintegrin is the most effective way to block adhesion of this cell line to immobilized fibronectin. Two other cell lines showed closely similar responses for blocking adhesion to fibronectin, although in direct adhesion to immobilized VLO4, the LN229 cells were more effective. Therefore, the LN229 cell line was used to test the detachment of already adhered cells from the fibronectin matrix. VLO4 appeared to be a potent detachment factor for this cell line, despite its strongest binding to this ECM protein.NTR [The differential interaction of VLO4 with these three cell lines could be explained by the different expression levels of α5β1 integrin on their surfaces. Indeed, Western blots displayed a lower expression of this integrin in LBC3 cells but were similar in LN18 and LN229 cells. However, expression level cannot completely explain such a big difference in activity among these cell lines. It is likely that activation of α5β1 integrin occurs differently in these cells. It may be associated with the presence of other supporting molecules (co-receptors) present on the plasma membrane. Previously, we found the association of α9β1 integrin with the common neurotrophin receptor, p75NTR . AdditioThe wound healing assay revealed that VLO4 inhibited cell migration in LBC3 and LN18 cell lines. However, this disintegrin had no effect on the LN229 cell line in this assay. This event may be explained by the presence of other integrins on the LN229 cell surface, which promote cell migration and reduce or completely diminish the effect of α5β1 integrin. The best candidate was α9β1 integrin, which is expressed on the LN229 cells but not on the two other cell lines investigated in this assay. Therefore, we used a specific snake venom disintegrin, VLO5, to test it in a migration assay. Indeed, VLO5 resulted in the reduced migration of LN229 cells; however, in combination with VLO4. These results clearly indicate that the pro-invasive properties of glioma cells depend on the presence of these two integrins. Previously, we considered α9β1 integrin as a potential therapeutic target for the therapy of glioma . ExpressThere is also evidence that integrins, including α5β1, can induce the expression and modulate the action of different proteases, especially MMP-2 and MMP-9 ,43,44. VIntegrins have been characterized as receptors that, under certain circumstances, may induce pro-apoptotic cell stimuli. It may happen following their binding to some ligands  or by cell detachment (anoikis) ,46. In tOne of the possible mechanisms of the antagonistic effect of disintegrins against integrins is a reduction in the expression of these receptors. The results presented in this paper revealed that treatment of all three glioma cell lines with VLO4 in its soluble form decreased the expression of both subunits α5 and β1. In contrast, the expression of a2 and a9 subunits, which are not involved in the creation of receptors for VLO4, was not affected by this disintegrin. In addition, we tested in this assay another disintegrin, VLO5, which is a ligand for α9β1 integrin. This integrin is only expressed in the LN229 cell line, where VLO5 reduces its expression. Therefore, we can generally conclude that disintegrins are the factors that affect the presence of integrins in the cells. The mechanism of action of disintegrins in this cellular outcome may be related to the endosomal internalization of the integrin/disintegrin complex and lysosomal degradation. The fluorescence imaging using FITC-labeled disintegrins revealed the presence of disintegrins in their granular forms in the cytoplasm. Further studies will explain the possibility of disintegrin-dependent lysosomal degradation of integrins. Interestingly, immobilized VLO4 in its solid form has not shown any toxic effect (no signs of pro-apoptotic effect). However, the spreading of glioma cells on the VLO4 matrix was very limited if compared with the natural ligand, fibronectin. It should be noted that the cytoskeletal actin filaments were not completely developed. These results suggest that VLO4 may be used as a pro-adhesive matrix for selective delivery of chemotherapeutics to cancer cells in the form of, e.g., nanoparticles.Macrovipera lebetina transmediterrannea, antagonizes α5β1, αvβ3, and αvβ6 integrins and inhibits adhesion, migration, and invasion of breast cancer cells (MDA-MB-231 and D3H2LN) [Agkistrodon contortrix contortrix), also has potential in melanoma therapy. It binds to αvβ3 integrin but also recognizes other integrins, including α5β, and showed an inhibiting effect on tumor growth, neovascularization, and metastases [131IVitronectin combined with TMZ therapy resulted in slightly better treatment results than radiation combined with TMZ in GBM mice [Macrovipera lebetina snake venom, inhibited growth and angiogenesis rates of tumor xenografts in colon cancer cells, which was accompanied by significantly reduced expression of α5β1 integrin [Disintegrins isolated from the venom of various snake species show therapeutic potential in many other cancers. Leberagin-C, derived from the venom of  D3H2LN) . In addi D3H2LN) . Previou D3H2LN) . Anothertastases . Recombitastases . 131IVitGBM mice . Lebein,integrin , similarintegrin . Instabiintegrin ,54. Howeintegrin .Our results provide new data on the antitumor effects of VLO4 disintegrin, which may have implications for the development of integrin-targeted therapy for glioblastoma. The activity of VLO4 is associated with its interaction with α5β1 integrin, which is expressed on all tested cell lines. The lower effect of this disintegrin on the pro-invasive properties of the LN229 cell line may be related to the expression of α9β1 integrin; theref ore, the combination of antagonists of both integrins, VLO4 (α5β1) and VLO5 (α9β1), may be more effective for the invasion of glioma cells in non-affected tumor areas of the brain.In the presented article, we summarized the initial studies, which will be continued in the future. Additional experiments are required to increase the number of glioma cell lines and possibly primary cells isolated from the patients’ tissue, especially those expressing α9β1 integrin. The next approach will be in vivo experiments in the glioma animal model, such as intracranial implantation of human cell lines in nude rodents. Application of VLO4 in these models will answer the general question about the effectiveness of blocking α5β1 integrin in the development of this tumor.


Glioma is the most common primary brain tumor in dogs and predominantly affects brachycephalic breeds. Diagnosis relies on CT or MRI imaging, and the proposed treatments include surgical resection, chemotherapy, and radiotherapy depending on the tumor’s location. Canine glioma from domestic dogs could be used as a more powerful model to study radiotherapy for human glioma than the murine model. Indeed, (i) contrary to mice, immunocompetent dogs develop spontaneous glioma, (ii) the canine brain structure is closer to human than mice, and (iii) domestic dogs are exposed to the same environmental factors than humans. Moreover, imaging techniques and radiation therapy used in human medicine can be applied to dogs, facilitating the direct transposition of results. The objective of this study is to fully characterize 5 canine glioma cell lines and to evaluate their intrinsic radiosensitivity. Canine cell lines present numerous analogies between the data obtained during this study on different glioma cell lines in dogs. Cell morphology is identical, such as doubling time, clonality test and karyotype. Immunohistochemical study of surface proteins, directly on cell lines and after stereotaxic injection in mice also reveals close similarity. Radiosensitivity profile of canine glial cells present high profile of radioresistance. In fact, in the United States, more than 1 million cases of cancer are diagnosed in dogs each year . BrachycPet dogs with spontaneously arising high-grade gliomas are becoming widely acknowledged as relevant translational models that could be used in addition to murine models. Indeed, the spontaneous oncogenesis of canine glioma is inherently associated with inter-tumor heterogeneity, genetic variability and anti-tumoral immune response that are lacking in immunocompromised xenograft mouse models. Canine high-grade gliomas closely resemble their human counterparts regarding oncogenic mechanisms –10, cancin vitro by performing clonogenic assays which are considered as the gold standard for determining reproductive tumor cell death induced by ionizing radiation (IR). The surviving fraction at 2 Gy (SF2), the fraction of cells surviving a single 2 Gy dose of IR, and the mean inactivation dose (MID) are parameters that can be derived from clonogenic assays. SF2 and MID are useful tools to study the intrinsic radiosensitivity of cell lines and to predict in vivo tumor sensitivity to radiation , supplemented with 10% fetal bovine serum, 1% penicillin/streptomycin (Gibco-BRL). Cells were maintained in an incubator at 37°C with 5% CO2.Canine cell lines were generously provided by Dr. Michael Berens (J3T), Dr. Peter Dickinson  and Dr. Jérôme Abadie (Raffray). The J3T cell line was derived from an anaplastic astrocytoma in a 10 year-old male Boston Terrier . The J3T2.25 cells per well in 6-well plates and maintained in supplemented DMEM  at 37°C for 5 days. Every 24 h, two wells were trypsinized for 5 min at 37°C and the cells were counted using a Thomas cell counting chamber after a dilution 1: 1 with Trypan Blue. The average was carried out between the two wells. Replicates from three 6-well plates were scored each day. The doubling times of each cell line were computed using the “cell calculator++” tool .Cells of each cell line were seeded at a concentration of 1×102.3Metaphase cells were harvested by physical shaking and centrifuged at 500 g for 10 min, then they were resuspended in hypotonic solution of fetal calf serum (1:6) for 20 min, at 37°C. Cells were then centrifuged at 500 g for 10 min and fixed in ethanol: acetic acid  for at least an hour, at 4°C. Cells were spread on a slide and stained with 1% Giemsa solution to generate GTG banding. At least 20 metaphases were evaluated for each cell line to count the total number of chromosomes and the number of metacentric chromosomes per cell.2.45 cells suspended in 5 μL DMEM, using a Hamilton syringe. The injection was precisely located into the right forebrain . The mice were observed for behavior changes suggestive of brain tumor  or general impairment of health . When such changes were detected, they were humanely euthanized by anesthetic overdose by intraperitoneal injection of pentobarbital confirmed by cervical dislocation. Otherwise, mice were followed for behavior changes for a year. After euthanasia, brains were collected for subsequent histological and immunohistochemical analysis. All animals were treated according to the European Communities Council directive (2010/63/EU) regarding the care and use of animals, and all the experimental procedures were approved by the Institution animal ethics committee with authorization number 2018031614514282.Orthotopic canine high-grade xenografts were established in 8 week-old female nude mice (Janvier Labs) as previously described . Briefly2.5Canine high-grade glioma cells cultured in supplemented DMEM were scraped off with a sterile scalpel and transferred into a 15 mL centrifuge tube. They were centrifuged at 400 rpm for 5 min, then resuspended in 10 mL phosphate-buffered saline (PBS) after removal of the supernatant. This operation was repeated one more time. The resulting cell suspension was centrifuged at 400 rpm for 5 min and resuspended in 1 mL of 70% ethanol after removal of the supernatant. The cell suspension was then transferred in a 1.5 mL microcentrifuge tube and centrifuged at 400 rpm for 5 min. Once the supernatant was discarded, the resulting pellet was resuspended in 50 μL of liquefied HistoGel™  and the cell suspension was gently mixed to distribute cells evenly within the gel matrix prior to solidification . HistoGe2.62 flasks  and irradiated at escalating doses  with the Gamma-cell Exactor 40 , as previously described . For statistical analysis, XLSTAT software . Kaplan–Meier estimate is used to evaluate the overall survival of mice with orthotopic xenografts of canine glioma cell lines. Log-rank test was assessed to analyze significant differences between the overall survival of different groups of cell lines. Results with 33.1in vitro clonogenic assay. Karyotyping revealed a number of chromosomes per cell slightly higher than the normal diploid chromosomal count for canines (78) and the presence of 1 to 2 abnormal metacentric chromosomes for all the cell lines but SDT3 which exhibited a chromosome number comprised between 59 and 61 and important chromosomal rearrangements.General characteristics of each cell line were established by assessing doubling time, plating efficiency, karyotype as well as cell morphology and protein expression profile . DoublinCell morphology and protein expression profile were similar for J3T, J3T-Bg, G06A, and Raffray cells: they appeared mainly as spindle- to star-shaped cells with obvious cytonuclear atypia when observed in HE-stained slides and expressed the astrocytic markers GFAP  and Vimentin  and the mean The values of dose response curve derived-parameters  calculated for each cell line are summarized in 3.3All the canine cell lines proved to be tumorigenic after orthotopic implantation in immunocompromised mice, with a tumor take rate varying from 25% for G06A cell line to 100% for J3T and J3T-Bg cell lines . The numThe median survival of canine glioma-derived mouse orthotopic xenografts was significantly different depending on the cell line that was injected in the brain . The medHistological examination of xenografts are displayed in The results of immunohistochemical results obtained for the different xenografts are displayed in 4The aim of the present study was to evaluate the intrinsic radiation sensitivity of five canine high-grade glioma (HGG) cell lines by performing clonogenic assays and examining the SF2 value and MID. To our knowledge, tumorigenicity and intrinsic radiosensitivity of canine HGG has never been assessed.Before assessing their radiosensitivity, the 5 cell lines were confirmed to be neoplastic , astrocytic , canine in origin according to the karyotypic analysis and able to give rise to colonies according to the colony forming efficiency results. Our phenotypic results are in agreement with the literature relative to J3T and Raffray cells. As described in the study of Berens et al., we observed GFAP immunolabeling of more than 90% of J3T cells . In accoin vitro  as a function of radiation dose . Althougin vitro , 48. As in vitro . These din vitro . Indeed,in vitro . The alpin vitro , 51 and in vitro . By contmelanoma , 54. AccIn vitro analysis of the expression profile of the cell line carried out by the laboratory that isolated them revealed a poorly differentiated astrocytic phenotype, a phenotype confirmed by the analysis we carried out on these cells included in HistoGel. The orthotopic implantation of Raffray therefore confirms what had been observed in vitro: the isolated tumor cells reproduce a high-grade astrocytoma and not an oligodendroglioma, which seems to indicate a selection of astrocytic tumor cells during the establishment of the cell line. Finally, the tumors obtained from the G06A do not reproduce the original tumor both in terms of phenotype  and of grade of malignancy . This difference between the original tumor and xenograft for G06A and Raffray could be explained by intrinsic plasticity of glial tumors, leading to slight modifications in tumor phenotype and markers expression between cell culture and xenograft (The intracerebral implantation of the five canine cell lines available to us in the laboratory has confirmed their ability to reproduce high-grade gliomas of malignancy when they are implanted orthotopically: astrocytomas for J3T, SDT3 and Raffray, oligodendroglioma for G06A and poorly differentiated glioma for J3T-Bg. We confirmed that these cells lines were diffuse high-grade glioma as they were all positive for Olig2 except for J3T-Bg cell lines, and astrocytic type for those positive for GFAP and Vimentine . In addienograft . That drThere is growing evidence that pet dogs with spontaneous HGG are relevant models for studying human diffuse gliomas , 10. TheThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The animal study was approved by Institution animal ethics committee with authorization number 2018031614514282. The study was conducted in accordance with the local legislation and institutional requirements.BC and AD: writing – original draft, conceptualization, and methodology. CD: conceptualization and resources. JA: resources. MP: conceptualization, resources, and supervision. EC-J and GM: supervision. All authors contributed to the article and approved the submitted version.


The single‐cell RNA sequencing (scRNA‐seq) quantifies the gene expression of individual cells, while the bulk RNA sequencing (bulk RNA‐seq) characterizes the mixed transcriptome of cells. The inference of drug sensitivities for individual cells can provide new insights to understand the mechanism of anti‐cancer response heterogeneity and drug resistance at the cellular resolution. However, pharmacogenomic information related to their corresponding scRNA‐Seq is often limited. Therefore, a transfer learning model is proposed to infer the drug sensitivities at single‐cell level. This framework learns bulk transcriptome profiles and pharmacogenomics information from population cell lines in a large public dataset and transfers the knowledge to infer drug efficacy of individual cells. The results suggest that it is suitable to learn knowledge from pre‐clinical cell lines to infer pre‐existing cell subpopulations with different drug sensitivities prior to drug exposure. In addition, the model offers a new perspective on drug combinations. It is observed that drug‐resistant subpopulation can be sensitive to other drugs ; such finding corroborates the previously reported drug combination (Gefitinib + Vorinostat) strategy in several cancer types. The identified drug sensitivity biomarkers reveal insights into the tumor heterogeneity and treatment at cellular resolution. A transfer learning framework for single cell drug response prediction. This framework integrates domain adaptation to learn cell line bulk pharmacogenomics and transfers the knowledge to infer drug sensitivities at single cells before treatment. This ranking‐based framework for drug sensitivity inference provides new strategies to account for intratumoral heterogeneity; providing new perspectives for biomarker discovery and drug combination applications. The intratumoral heterogeneity is caused by many factors, such as genetic heterogeneity of cells and the existence of multiple independent cell subpopulations within a tumor. The intratumor heterogeneity is a well‐known issue leading to treatment failure. Currently, there are past studies which have evaluated the selective pressure after drug treatment using single cell data; for instance, genomics and single‐cell phosphoprotomics analysis on patient‐derived epidermal growth factor receptor (EGFR)‐mutated in vivo mTOR kinase inhibitor (mTORki) resistance glioblastoma (GBM) model have been proposed. Researchers identified promising drug combinations by resolving the related protein signal networks. Moreover, the drug combination efficacy was validated with the complete and lasting tumor suppression on GBM39 implanted mice model. Another research uses single‐cell proteomics to study the drug‐induced (anti‐EGFR therapy) resistance and tumor phenotypic switch  in triple‐negative breast cancer (TNBC). EGFR is highly expressed in TNBC and was suggested as a therapeutic target. However, anti‐EGFR monotherapies for TNBC are normally failed in clinical. Researchers demonstrated that distinct cell subpopulations could independently grow in response to different anti‐cancer treatments. Such kind of anti‐cancer progression may be due to the expansion of previously untargeted (no tailored accurately) or undetected cell subclones. In addition, the treatment that does not target estrogen receptor (ER) may result in incomplete suppression of cancer cells and lead to a phenotype switch from TNBC to ER‐positive enrichment. In contrast, anti‐EGFR therapy may benefit when combined with anti‐ER treatment; it is suggested by the patient‐specific signaling signature (PaSSS) based predictions and was experimentally validated in two TNBC cell lines.The personalized anti‐cancer therapies have achieved successes in several malignancies.1 Howe more studies are mainly based on the tumor data profiled in bulk RNA‐seq. Among them, cancer cell lines are still the most widely applied laboratory models for oncology study and anti‐cancer drug discovery. In contrast to the limited drug sensitivity information in single cells, there are numerous drug efficacy profiling and studies in cancer cell lines. For example, in the Genomics of Drug Sensitivity in Cancer database (GDSC), over 1000 human cancer cell lines were screened with a variety of anti‐cancer compounds including both cytotoxic chemotherapeutics and targeted agents that were clinically approved or undergoing clinical development, allowing the identification of transcriptome‐based (bulk RNA‐seq) drug efficacy predictors and biomarkers. Other related perturbational databases such as Connectivity Map (CMAP), the Library of Integrated Network‐Based Cellular Signatures (LINCS‐L1000), and Cancer Therapeutics Response Portal (CTRP) also include numerous compound sensitivity characteristics of cell lines for drug perturbation analysis. Those large‐scale pharmacogenomic datasets offer researchers an opportunity to better characterize the heterogeneity of perturbational biological features through machine learning technologies. For instance, Peilin et al. developed a deep learning model to impute drug responses in both tumor cell lines and cancer patients, demonstrating the clinical translational potential of the deep learning (DL) method for drug response biomarker discoveries. Hostallero et al. built a tumor tissue‐informed normalization deep learning (TINDL) model to impute the compound efficacy on tumor patients, which integrated transcriptome profiles of both the GDSC database and TCGA primary samples. To alleviate the discrepancies between cell lines and tumor tissues, TINDL integrates tumor type information of test samples to enhance the performance of drug efficacy prediction on patients. Similarly, Hossein et al. proposed an adversarial inductive transfer learning model for the drug efficacy prediction on four anti‐cancer compounds. The adversarial learning is an approach to improve data recognition despite the existence of dataset bias and was applied to bridge the gap between cell lines and clinical datasets  to improve model generalization.However, unlike studies that were currently limited to a few cancer types and a small subset of drugs at the single‐cell level,5 more It was suggested that a multitude of cancer cells with distinct molecular characteristics could coexist in the same tissue. In addition, a small proportion of pre‐existing or acquired resistant cells may evolve and gradually weaken the original medical strategy, and ultimately cause treatment failure. These therapeutic resistances due to tumor heterogeneity suggest that those “one‐size‐fits‐all” evidence‐based medicine approaches, which is informed by bulk RNA‐seq analysis, may provide inappropriate medical solutions for outlier patients.We noticed that the above models were constructed based on bulk RNA‐seq data and many therapeutic strategies of patients are derived from mean recommendations informed by meta‐analysis. The major drawback of bulk RNA‐seq is that it only gives the average and a mixture profile of heterogeneous cells. Its limited capability conceals the uniqueness as well as the actual diversity of individual cells.18 It  metastatic colorectal cancer, and non‐small cell lung cancer. New diagnostics such as molecular imaging and gene expression quantification, as well as novel clinical trial designs, resulting in a reduction of treatment failure and a decreased morbidity rate. Scientists are now calling for more precise methodologies for personalized treatments. The emergence and development of single‐cell sequencing technology offers us an opportunity to decipher previously undiscovered heterogeneity of cancer, facilitating the identification of previously hidden cell subpopulations with distinct characteristics. Recently, a variety of machine learning (ML) and statistical methods have been developed for different single‐cell transcriptomics tasks, such as subtype clustering and cell type distinction. Nevertheless, due to the limited amount of scRNA‐seq data with both sequencing and drug efficacy information , there are few ML models focused on single‐cell drug efficacy prediction, especially those cells sequenced prior to drug treatment. The scRNA‐seq is currently more expensive than bulk sequencing, prolonging its application in large‐scale pharmacogenomic research and clinical studies. Although the latest released version of Single Cell Portal from Broad Institute includes over four hundred single‐cell studies, few of them contain drug sensitivity data. Based on the fact that multiple ML models have elevated the drug efficacy prediction performance on tumor patients by integrating transcriptome information from large cell line pharmacogenomic databases, we hypothesized that the technical shortcoming of scRNA‐seq could be alleviated by integrating the plentiful transcriptome profiles from cell lines of large pharmacogenomic databases while keeping the microscopic resolution features of scRNA‐seq data. In summary, the drug perturbation effect information could be learned and migrated to the scRNA‐seq drug sensitivity prediction task and biomarker discovery.It is noteworthy that this “one‐size‐fits‐all” treatment situation is now shifting to an era of precision medicine in many tumors such as breast cancer,22 metFigure  Therefore, we proposed the single cell drug response prediction framework by integrating adversarial domain adaptation (SCAD) to eliminate the domain shift problem. The model adopts the idea of adversarial discriminative domain adaptation (ADDA) composed of both adversarial learning and domain adaptation. The adversarial learning pits two networks against each other. In our SCAD framework, the feature extractor and domain (datasets) discriminator are trained in an adversarial manner. In the end, the feature extractor can extract domain‐invariant feature representations that the domain discriminator cannot distinguish whether the features come from the source domain or the target domain. Moreover, the adversarial learning is an approach to improve data recognition despite the existence of dataset bias. Domain adaptation is a method to alleviate the unfavorable effects of dataset bias, reducing the differences between the source and target domain distributions to enhance the model generalization. The domain‐invariant feature representations could guarantee the transferability. Based on this benefit, we proposed and adopted ADDA to minimize the domain discrepancy between cell lines (source domain) and scRNA‐seq dataset (target domain). In addition, we considered transfer learning as two independent tasks for the cells with different tolerant origins based on the mechanism that drug tolerance may occur due to pre‐existing cell populations before treatment or resistance‐acquired cells after drug exposure. The details of the source domain and target domain datasets with shared compounds, which are proposed to train a model for drug sensitivity prediction at cells after drug exposure, are provided in Table Table In this study, we leveraged transfer learning to learn the pharmacogenomic characteristics of cell lines in the GDSC database to infer the drug sensitivities of samples at the single cell level Figure 1. The s2 In this study, we evaluated the prediction performance of our SCAD model in two scenarios independently. Due to the unbalanced ratio of sensitive and resistant cell lines in the source domain, we conducted both weight sampling and smote sampling in model training processes . In addition, we considered three different strategies to explore the impact of performance in the feature selection step before training, namely “all” , “tp4k” , and “PPI” .It was suggested that the resistance to drug treatment can occur due to the growth of the acquired drug‐tolerant cells or the expansion of pre‐existing subclones.19 In In this study, we relied on average AUC and average AUPR scores as the performance metrics. We also applied a ranking‐based strategy to sort single cells from the inferred most positive (drug‐sensitive) cells to the most negative (drug‐resistant) one for cell stratification and biomarker exploration. Interestingly, we observed that drug‐resistant cell subpopulation to a specific compound may be sensitive to other drugs. This phenomenon offers us an opportunity to explore and identify promising drug pairs for drug‐combination.2.1 Therefore, parental cells (sequenced from drug untreated cell lines) are regarded as sensitive cells, while drug‐tolerant cells that survived after drug exposures are selected and considered as resistant cells. We conducted transfer learning on two GSE datasets with both scRNA‐seq profiles and drug response experiments from three different cell lines . The means and standard deviations (SD) of the AUC and AUPR scores for each drug are summarized in Table Tables  In addition, they clustered the gene expression profiles of 451Lu_BR (BRAFi resistant), 451Lu_Par , A375_BR (BRAFi resistant), and A375_Par  cells to observe the expression patterns among those BRAFi treated and untreated cells. They found that the untreated 451Lu cells are clustered with BRAFi‐resistant 451Lu cells. Similarly, the untreated A375 cells are clustered with BRAFi‐resistant A375 cells. The clustering result suggested that the cell type diversity could be more prominent than the drug perturbational effect  scale. Among them, there are around 1000 single cells prior to drug treatment that sequenced from two different cell lines , based on the expression level of proteins‐coding genes Claudin4 and AXL. By performing the in vitro cell viability assay on the FACS sorted cells with different EpiSen program levels in two head and neck squamous cell carcinoma (HNSCC) model cell lines (JHU006 and SCC47), the authors demonstrated that the EpiSen‐high and EpiSen‐low cells derived from same cell lines show different responses to a dozen of drugs. The authors generated a method to computationally define the EpiSen program status for every single cell, where cells could be classified into EpiSen‐high , EpiSen‐low , and a third neutral reference population . Inspired by the above work, the scRNA‐seq profiles sequenced from the JHU006 cell line and the SCC47 cell line are treated as target domains in our SCAD transfer learning task. GDSC cell lines with both bulk RNA‐seq profiles and drug sensitivity labels from the in vitro experiments were selected to evaluate whether the transcriptome feature and pharmacogenomic knowledge from the source domain could be learned and transferred to help better identify the distinct single cell subpopulations with diverse drug sensitivities prior to treatment. The details of the source domain and the target domain are tabulated in Table In the discovery of above SCP542 study, researchers identified an epithelial senescence‐related (EpiSen) program that can reflect co‐existing distinct subsets of cells observed within cell lines and tumors.20 TheTables As demonstrated in  In this way the drug response predictor trained in the source domain could well predict the drug sensitivities in the target domain. As an example of understanding and visualizing the domain adaption process, we conducted the t‐SNE technique, which is a dimensional reduction method to visualize high dimensional data in 2D or 3D, to visualize the representations learned from feature extractor  in drug AR‐42 with the highest average AUC value under Weight_all mode , considering all genes shared by the source domain and the target domain obtained higher five‐fold average AUC and AUPR scores than merely considering tp4k highly variable genes or PPI genes  while excluding hematopoietic cell lines (blood cancer) derived from leukemia, lymphoma, and myeloma cancer. The sample sizes of GDSC source domain cell lines for each compound with different tumor type origins are listed in Table https://www.cancerrxgene.org/). We conducted a fisher exact test for the frequency distribution of drug‐sensitive cell lines and drug‐resistant cell lines between solid tumor origin and hematopoietic origin in the source domain for each compound. Among all seven drugs, six of them have a significantly different (p <  0.01) frequency distribution between solid tumor origin cell lines and hematopoietic ones . It was supported with clinical evidence that blood cancers are especially responsive to HDAC inhibitors, and Vorinostat is one of the promising options for blood cancers with manageable drug‐related adverse events. Moreover, Vorinostat was approved by US Food and Drug Administration (FDA) to treat advanced cutaneous T‐cell lymphoma (CTCL). Similarly, there is a different proportion of Sorafenib‐sensitive cell lines between solid cancer and blood cancer . Removing the blood cancer cell lines of Sorafenib results in decreased prediction results on single cell data. Sorafenib, a first‐generation type‐II multi‐kinase inhibitor, could interact with multiple cell surface kinases , and VEGFR‐3). It was reported in a recent clinical trial  that Sorafenib could led to a significant prolongation of event‐free survival (EFS) and relapse‐free survival (RFS) in patients with newly diagnosed acute myeloid leukaemia . Based on the above results, the exclusion of hematopoietic cell lines for drugs Vorinostat and Sorafenib might result in an insufficient number of sensitive cell lines for the model to learn, resulting in reduced prediction performances of SCAD in single cell data. Therefore, the tumor cell line origin information can enhance drug efficacy inference, due to the MoA differences between solid cancer and blood cancer.The above results can be attributed to the underlying mechanisms of actions (MoAs) of the drugs  less than 5.6 months (short‐PFS group), and 26 patients obtained long PFS exceeding 12 months (long‐PFS group) after following Cetuximab treatment together with a first line platinum‐based chemotherapy. This GSE65021 dataset can be regarded as an independent clinical cohort to explore the prognostic value of our SCAD model. For a comprehensive comparison with biomarkers identified by bulk RNA‐seq as the evidence‐based support and to evaluate the prognostic prediction value of gene biomarkers that independently identified from scRNA‐seq data, we also selected the drug Cetuximab as our case study.Although we have constructed and evaluated the SCAD model for drug efficacy inference, genes that contribute most to the drug sensitivity prediction are still unknown. However, it is vital to find out those biomarkers to help us better understand the biological mechanism of drug response heterogeneity and the potential for clinical application. Fortunately, a recent functional genomics study includes the pre‐treatment gene expression profiles of 40 head and neck squamous cell cancer (HNSCC) patients (GSE65021).40 Amo method to infer the subset of genes that contributes the most to drug sensitivity prediction . The IntegratedGradients is a method to infer how much an independent variable affects the value of the prediction output from the model, which is one of the most widely used ML model explainer to interpret the association between input data and machine learning model outputs. A total of 70 genes that over‐expressed in predicted Cetuximab‐sensitive cells are identified  results suggested that overexpressed genes in predicted sensitive cells (sc_sens) are enriched in several biological processes , we could not directly compared to bulk RNA‐seq data since there are no EpiSen‐related over‐expression genes that were reported in short‐PFS patients of Cetuximab  or drugs that have not been evaluated in cell viability assay is not available  are presented in Figure To evaluate whether our SCAD model could identify drug‐sensitive cells and drug‐resistant ones within tumors, we selected drug Cetuximab, which obtained the highest AUC and AUPR scores under Weight_all strategy among drugs in the SCC47 cell line Tables  and 6. T20 TheNext, to evaluate the prediction results of the SCAD model in all SCC47 cells  and predicted high resistant (rank_Res) cells are relatively distributed at distinct locations in the PCA plots for most cells Figure , suggestp = 0.0034, Figure To explore the possible gene expression heterogeneity between predicted Cetuximab‐sensitive cells and Cetuximab‐resistant cells (based on our ranking result), we conducted a similar biomarker exploration process that we introduced in Figure 2.5 In addition, the efficacy of this combination is independent on the HPV status, a strong prognosis biomarker in HNC cancer. Gefitinib is an epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) while Vorinostat is a Histone deacetylase inhibitor (HDACi). The synergistic efficacy of dual EGFRi and HDACi in overcoming resistance to monotherapy was also found in many other cancer types, such as non‐small‐cell lung cancer (NSCLC) and mucoepidermoid carcinoma. Interestingly, a similar opposite drug response status was found between drug Gefitinib and drug AR‐42  of cancer cell lines from large pharmacogenomic databases to infer the drug sensitivities of other cell lines or patient samples. However, most of them are based on bulk RNA‐seq data, which only give an average profile of cell mixtures, masking cellular heterogeneity. Fortunately, the growth of scRNA‐seq data offers an opportunity to elucidate the intratumoral heterogeneity at cellular resolution. There are currently some studies that evaluate the selective pressure after drug treatment using single‐cell proteomics/phosphoprotomics data to study the drug‐induced acquired drug tolerance mechanism and identify possible therapeutic target to address post‐treatment resistance. These results suggest the possibility and necessity of conducting additional explorations of drug response heterogeneity in single‐cell data.It is necessary to infer the drug sensitivities of cells and explore the tumor heterogeneity at single‐cell resolution. Previous studies suggested that the anti‐cancer treatment may fail due to the pre‐existing resistant cells prior to treatment or the acquired tolerant ones after drug exposure.49 To  while the disadvantage is that the resolution is not comparable to single‐cell sequencing. On the contrary, scRNA‐seq drug perturbation data were currently limited, hindering the study of machine learning in single‐cell drug sensitivity prediction. Based on the successful application of transfer learning in many real‐world problems, we proposed a transfer learning framework to learn pharmacogenomics information from cell lines in large public dataset GDSC and transfers the learned model to infer drug efficacy at the single cell resolution.The relatively scarce drug sensitivity information at single cells can be alleviated by transfer learning. Currently, high‐throughput pharmacogenomic databases such as GDSC and CCLE harbor numerous transcriptome profiles and drug response sensitivity data,8, 9 which may not be suitable to infer the drug sensitivities of single cells after the treatment of anti‐cancer agents ideally, since the expression of genes may change a lot before and after drug exposure. As a comparison, we observed stable prediction performance  among a total of seven anti‐cancer drugs when evaluating the drug sensitivities in single cells that were sequenced from two cell lines prior to anti‐cancer therapy. These results suggest that pre‐clinical cell lines may be more suitable for transfer learning analysis in pre‐clinical single cells, where both the source domain and the target domain were sequenced before drug exposures.In this analysis, we explored and demonstrated the feasibility of predicting drug sensitivities of single cells by transfer learning. In the SCP542 project, researchers found that the EpiSen‐high and EpiSen‐low cells sequenced from two independent cell lines are featured with distinct drug response sensitivities to several compounds, this phenomenon was validated by the in vitro cell viability assay experiments. We integrated this information and the corresponding single‐cell sequencing profiles into a dataset to investigate the feasibility of transfer learning on single‐cell expression profiles. We conducted several experiments to infer the drug sensitivities of cells with distinct EpiSen program status by transfer learning. Our SCAD model attempts to integrate and transfer the pharmacogenomic knowledge from cell lines to infer the drug efficacy at scRNA‐seq data. In addition, we formulated our transfer learning work as two independent tasks. Our results showed that the model's drug sensitivity prediction performance was improved with the help of domain adaptation (ADDA) compared to two non‐ADDA baselines. Surprisingly, we also found that the prediction performance can be fluctuating when predicting the drug sensitivities between parental cells and resistant cells after drug exposures. One possible reason is that the source domain GDSC cell lines are sequenced at the pre‐clinical stage (prior to the exposure to different drugs)9 whicDrug resistance ranking identify promising prognostication biomarkers and drug combination. It is vital to uncover the black‐box mechanism of machine learning model for model interpretation. We applied IntegratedGradients to explore the relationships between input and output of SCAD model. For Cetuximab, we observed a high overlap of genes between the overexpressed genes in long PFS patients and the ones with high IntegratedGradient attribution scores in predicted sensitive cells. For the genes identified that may contribute to drug resistance, we also found evidence from the published literature that supported our gene oncology (GO) pathway enrichment analysis results  that contain five‐thousand (5K) single cells (SC). We found that our model obtained slightly increased magnitudes in both AUC and AUPR. Moreover, we also found that the variances of AUC and AUPR in ensemble mode (SCAD (SC‐5K non‐adjusted model plus SCAD SC‐5K adjusted model) are smaller than SCAD (SC‐5K non‐adjusted model), suggesting that the performance of ensemble mode will be more stable than the counterparts  were selected as the target domain for the evaluation of pre‐existing drug‐sensitive and drug‐resistant cells. Drug response sensitivities for each compound in the corresponding cell lines are listed in Table m Etoposide for 3 days) and GSE108383 (scRNA‐seq for A375 cell line and 451Lu cell line) datasets were selected as target domain for the evaluation of acquired resistant after the exposure of drug Etoposide and PLX4720, respectively. The details of drug names, cell lines, and sample size of drug resistant and sensitive cells for source and target domain are shown in Table As previously described, the prediction for pre‐existing drug resistant cells prior to treatment and acquired resistant cells after compound exposure was considered as two independent tasks, due to different drug tolerant mechanisms of tumor heterogeneity were treated.19 As https://ibm.ent.box.com/v/paccmann‐pytoda‐data/folder/91702227426. Genes were standardized into z‐score by scikit‐learn StandardScaler function. Next, cell lines with both RMA normalized bulk RNA‐seq expression profiles and LOBICO binarized drug response IC50 values were kept for further analysis.The GDSC RMA normalized bulk RNA‐seq data was downloaded as the source domain data, which was processed and described in 02227426.34 Gen More specifically, the quality control pre‐processing steps were conducted using the Scanpy package based on the profiling feature of each dataset , considering the diversity of technical and sequencing depth among different target datasets , log‐transformed and standardized as z‐score. In addition, only genes in both the source domain and the target domain were retained for model training and testing.In the SCAD model, scRNA‐seq datasets were regarded as the target domain. Several data quality control (QC) steps described in ref.  were fol36 Morts Table  2. In ad the SCAD model consisted of a feature extractor to learn the representations of the source domain and target domain, a drug response predictor to learn the informative features for drug sensitivity inference, and a domain discriminator to make the feature extractor to learn domain invariant features from both domains . The details of each component of SCAD framework are described as follows and in Figure The source domain data were bulk RNA‐seq of cell lines from the GDSC database with binarized IC50 labels.51 Theh_dim was set to adjust the number of nodes of the feature extractor. The source and target data was denoted as XS, XT, and the source and target representation mapping asThe source domain and target domain shared the same feature extractor, enabling weight sharing and symmetric mapping of data from both domains. Similar to ref. , the feaz_dim was set to customize the number of nodes of the hidden layer. A sigmoid function was appended after the five hidden layers of predictor to obtain the drug sensitivity probability. The sigmoid output was denoted asThe drug response predictor was trained solely based on source domain data, and target domain drug sensitivity labels were unseen until final testing. The drug response predictor was a five‐layer MLP with dropout rate of 0.5. The number of nodes in each layer remained the same. A hyperparameter D) was trained to force the feature extractor to learn domain invariant features from both the source domain and target domain. The domain adaptation could help to reduce the difference between the source domain (GDSC bulk RNA‐seq datasets) and the target domain (scRNA‐seq dataset) distributions to enhance the performance of drug response predictor. In this end of training, the feature extractor might be able to extract feature representations (domain‐invariant features) that the domain discriminator was unable to distinguish whether the coming features were extracted from the source domain or the target domain. The domain discriminator is a five layer MLP with dropout rate of 0.5. The number of nodes in each layer was the same. A hyperparameter h_dim (hyperparameter that is shared with the feature extractor) was set to customize the number of nodes of the hidden layer. A sigmoid function was appended after the five hidden layers. The domain discriminator outputs for source data and target data were denoted asA domain discriminator  to the shared feature space and directly classified by the communal drug response predictor that was learned from the source domain. Solid lines in Figure The performance of the feature extractor and drug predictor were evaluated based on a binary‐cross entropy (BCE) lossThe adversarial loss for discriminator isReferred to ref. , a weighA five‐fold cross‐validation (CV) strategy was conducted to reduce data splitting bias and evaluate the performance of the SCAD model. In each splitting, four‐fold of the source domain sample were selected as the training set to train the source feature extractor, domain discriminator, and drug response predictor, while another fold of the source domain data was treated as the validation set for hyper‐parameter selection (The final selected hyper‐parameters were tabulated in Table rank_genes_groups_df function was then conducted in Scanpy to filter out the inferred statistical significance drug‐resistance and drug‐sensitive related gene.For each fold of the five‐fold cross‐validation, the attribution score for each gene in the testing set of the target domain was calculated and recorded. The attribution scores of each gene were then averaged to get the final mean attribution score, which helped to reduce the bias of single‐fold splitting. Next, genes with the top 5% high or bottom 5% low contribution scores were selected as candidate genes that are related to drug response heterogeneity Figure . Differein Scanpy33 to The authors declare no conflict of interest.Supporting InformationClick here for additional data file.Supplemental Tables S1‐S7Click here for additional data file.